The Covariant Picard Groupoid in Differential Geometry

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Plant and Animal Life of the Pacific IslandsOCEANIAThere are both great similarities and considerable diversity in the ecosystems that evolved on the islands of Oceania in and around the Pacific Ocean. The islands, such as New Zealand, that were originally parts of continents still carry some small plant and animal remnants of their earlier biota (animal and plant life), and they also have been extensively modified by evolution. adaptation, and the arrival of new species. By contrast, the other islands, which emerged via geological processes such as volcanism, possessed no terrestrial life, but over long periods, winds, ocean currents, and the feet, feathers, and digestive tracts of birds brought the seeds of plants and a few species of animals. Only those species with ways of spreading to these islands were able to undertake the long journeys, and the various factors at play resulted in diverse ombinations of new colonists on the islands. One estimate is that the distribution of plants was 75 percent by birds, 23 percent by floating, and only 2 percent by windThe migration of Oceanic biota was generally from west to east, with four major factors influencing their distribution and establishment. The first was the size and fertility of the islands on which they landed, with larger islands able to provide hospitality for a wider range of species. Second, the further east the islands, generally the less the species diversity, largely because of the distance that had to be crossed and because the eastern islands tended to be smaller, more scattered, and remote. This easterly decline in species diversity is well demonstrated by birds and coral fish. It is estimated that there were over 550 species of birds in New Guinea, 127 in the15慢跑的石头:Solomon Islands, 54 in Fiji, and 17 in the Society Islands. From the west across the Pacific, the Bismarck Archipelago and the Solomon Islands have more than 90 families of shore fish (witl any species within the families), Fiji has 50 families, and the Society Islands have 30. Third latitude of the islands also influenced the biotic mix, as those islands in relatively cooler latitude notably New Zealand, were unsuited to supporting some of the tropical plants with which Pacific islands are generally associatedFinally, a fourth major factor in species distribution, and indeed in the shaping of Pacific ecosystems, was wind. It takes little experience on Pacific islands to be aware that there prevailing winds To the north of the equator these are called north- easterlies, while to the south hey are called south-easterlies. Further south, from about 30" south, the winds are generally from the west. As a result on nearly every island of significant size there is an ecological difference between its windward and leeward (away from the wind) sides. Apart from the wind action itself on plants and soils, wind has a major effect on rain distribution. The Big Island of Hawaii offers a prime example: one can leave Kona on the leeward side in brilliant sunshine and drive across to the windward side where the city of Hilo is blanketed in mist andWhile such localized plant life and climatic conditions are very noticeable, over Oceania as a whole there is relatively little biodiversity, and the smaller the island and the further east it lies the less there is likely to be. When humans moved beyond the islands of Near Oceania(Australia, New Guinea, and the Solomon Islands), they encountered no indigenous mammals except for flying foxes, fruit bats, and seals on some islands. Other vertebrate species were restricted to flying animals and a few small reptiles. However, local adaptations and evolution over long periods of isolation promoted fascinating species adaptations to local conditions. Perhaps most notable. in the absence of mammals and other predators, are the many species of flightless and ground-nesting birds. Another consequence of evolution was that many small environments boasted their own endemic(native)species, often small in number, unused to serious predation.limited in range, and therefore vulnerable to disruption. In Hawaii, for example, the highly adapted 39 species and subspecies of honeycreepers, several hundred species of fruit flies, and more than 750 species of tree snails are often cited to epitomize the extent of localized Oceanic endemism(species being native to the areParagraph 1There are both great similarities and considerable diversity in the ecosystems that evolved on the islands of Oceania in and around the Pacific Ocean. The islands, such as New Zealand, that were originally parts of continents still carry some small plant and animal remnants of their earlier iota (animal and plant life), and they also have been extensively modified by evolution adaptation, and the arrival of new species. By contrast, the other islands, which emerged via geological processes such as volcanism, possessed no terrestrial life, but over long periods, win ocean currents, and the feet, feathers, and digestive tracts of birds brought the seeds of plants and a few species of animals. Only those species with ways of spreading to these islands were able to undertake the long journeys, and the various factors at play resulted in diverse combinations of new colonists on the islands. One estimate is that the distribution of plants was 75 percent by birds, 23 percent by floating, and only 2 percent by wind慢跑的石头:29. The word"remnants" in the passage is closet in meaning to○remainders○remindereproductions○resemblances30. The passage supports which of the following statements about species on volcanic islands? o Volcanic island species are unlike the species found in other Pacific Ocean locationso Volcanic islands lack the diversity of species found elsewhere in the Pacific.o Volcanic island species are all transplants from distant locations and exist in combinationsnot found elsewhereo Volcanic island species differ from those on other islands in that animal species show greater diversity than plant species do.31. According to paragraph 1, how did the majorityof plant species arrive on islands created by geological processes such as volcanismo They were transported by ocean currentso They were carried to the islands by birdso They were brought to the islands by humanso They were transported by windsThe migration of Oceanic biota was generally from west to east, with four major factors influencing their distribution and establishment. The first was the size and fertility of the islands on which they landed, with larger islands able to provide hospitality for a wider range of species Second, the further east the islands, generally the less the species diversity, largely because of the distance that had to be crossed and because the eastern islands tended to be smaller, more scattered, and remote. This easterly decline in species diversity is well demonstrated by birds and coral fish. It is estimated that there were over 550 species of birds in New Guinea, 127 in the Solomon Islands, 54 in Fiji, and 17 in the Society Islands. From the west across the Pacific, theBismarck Archipelago and the Solomon Islands have more than 90 families of shore fish(with many species within the families), Fiji has 50 families, and the Society Islands have 30. Third, the latitude of the islands also influenced the biotic mix, as those islands in relatively cooler latitudes notably New Zealand, were unsuited to supporting some of the tropical plants with which Pacitic islands are generally associated32. The word "remote" in the passage is closet in meaning toO unknownO isolate33. In paragraph 2, what is the authors purpose in mentioning the estimated numbers of birds17慢跑的石头:nd coral fish species on various Oco To give examples of the wide range of species that can be found on Oceanic islandso To illustrate the decline in species diversity from west to east on Oceanic islandso To identify the influence of latitude upon Oceanic plants and animals34.Which of the sentences below best expresses the essential information in the highlighted sentence in the passage? Incorrect choices change the meaning in important ways or leaveout essential informationo Because of its latitude, New Zealand had a relatively cooler climate than other PacificO New Zealand, like other Pacific islands, showed the effects of latitudes on its rich tropical plants.o Because the latitudinal position of an island also affected its biotic mix, islands in cooler latitudes did not support some tropical species typical of the Pacifo Pacific islands were notable for their impressive biotic mix and association with tropicaplants35. According to paragraph 2, all of the following types of islands are associated with higher species diversity EXCEPTO islands that are large in sizeO islands located in cool latitudesO islands located in the western part of OceaniaO islands located near other landmassesParagraph 3Finally, a fourth major factor in species distribution, and indeed in the shaping of Pacific ecosystems, was wind. It takes little experience on Pacific islands to be aware that there are prevailing winds. To the north of the equator these are called north-easterlies, while to the south they are called south-easterlies. Further south, from about 30 south, the winds are generafrom the west. As a result on nearly every island of significant size there is an ecological difference between its windward and leeward (away from the wind) sides. Apart from the wind action itself on plants and soils, wind has a major effect on rain distribution, The Big Island of Hawaii offers a prime example: one can leave Kona on the leeward side in brilliant sunshine and drive across to the windward side where the city of Hilo is blanketed in mist and rain36. The Big Island of Hawaiis discussed in the passage as an example ofo the relationship between latitude and windo how prevailing winds influence rainfall patternso the relationship between rainfall and species distributiono the effects of wind action upon plants and soils37. What can be inferred from paragraph 3 about Kona and Hiloo The ecosystems of Kona and Hilo differ from each other慢跑的石头:Kona and Hilo have approximately the same rainfall in a given yearta receives northeasterly winds while Hilo receives southeasterly windso Both Aona and Hilo have plants and soils that are often damaged by windsParagraph 4While such localized plant life and climatic conditions are very noticeable, over Oceania as a whole there is relatively little biodiversity, and the smaller the island and the further east it lies the less there is likely to be. When humans moved beyond the islands of Near Oceania (Australia New Guinea, ande Solomon Islandds), they encountered no indigenous mammals except forflying foxes, fruit bats, and seals on some islands. Other vertebrate species were restricted to flying animals and a few small reptiles. However, local adaptations and evolution over long periods of isolation promoted fascinating species adaptations to local conditions. Perhaps most notable, in the absence of mammals and other predators, are the many species of flightless and ground-nesting birds. Another consequence of evolution was that many small environments boasted their own endemic (native) species, often small in number, unused to serious predation, limited in range, and therefore vulnerable to disruption. In Hawaii, for example, the highly adapted 39 species and subspecies of honeycreepers, several hundred species of fruit fiies,and more than 750 species of tree snails are often cited to epitomize the extent of localized Oceanic endemism (species being native to the area8. The word "cited" in the passage is closet in meaning toO expectedO believedO compared39. According to paragraph 4, why have species of flightless and ground-nesting birds become sonumerous on Oceanic islands?o They have no predators on the islandso They were some of the strongest species to arrive on the islandsThey live closer to their food sources than other species doo They are affected less by climatic changes than other animals are40, Which of the following is NOT mentioned in paragraph 4 about the species that live on Oceanic islands?o Certain species are native only to particular islandso Species that are native to Oceanic islands include relatively few mammalsO Populations of most species are small in number.O Some species have evolved over time to become predatorsThere are both great similarities and considerable diversity in the ecosystems that evolved on the Paragraph 1islands of Oceania in and around the Pacific Ocean. The islands, such as New Zealand, thatwere originally parts of continents still carry some small plant and animal remnants of their19慢跑的石头:earlier biota (animal and plant life), and they also have been extensively modified by evolution adaptation, and the arrival of new species. aBy contrast, the other islands, which emerged via geological processes such as volcanism, possessed no terrestrial life, but over long periods, winds. ocean currents, and the feet, feathers, and digestive tracts of birds brought the seeds of plants and a few species of animals. BOnly those species with ways of spreading to these islandsable to undertake the long journeys, and the various factors at play resulted in diverse ombinations of new colonists on the islands. One estimate is that the distribution of plantswas 75 percent by birds, 23 percent by floating, and only 2 percent by wind41. Look at the four squares [] that indicate where the following sentence can be added to the passage.When varied ecosystems are present, they can be explained as resulting in part from the process that formed the islands.Where would the sentence best fit Click on a square [] to add the sentence to the passage.42. Directions: An introductory sentence for a brief summary of the passage is provided below Complete the summary by selecting the THREE answer choices that express the mosmportant ideas in the passage. Some answer choices do not belong in the summary becauseley express ideas that are not presented in the passage or are minor ideas in the passagThis question is worth 2 pointDrag your choices to the spaces where they belong. To review the passag, click view Text. Biodiversity on Oceanic islands is dependent on a number of factors.Answer ChoicesUnlike Oceanic islands that were once part ofcontinental landmasses, islands formed by Species distribution in Oceania is determinedsuch geological processes as volcanism by the location of islands, their size, and thecontain only plants and animals that could be direction of the windtransported thereMost Oceanic islands are similar to oneislands size is less important than its another in latitude and contain plants and latitude in determining species diversityanimals typical of tropical islands.Though biodiversity is low on many Oceanic The absence of natural predators on the islands, many native species have evolved eastern Oceanic islands allowed many species nat are uniquely adapted to their local of large mammals to evolve that we environmentsof inhibiting a wide range of territory。

雅思ogtest8阅读答案passage3

雅思ogtest8阅读答案passage3原文题目及答案解析内容本文为大家带来雅思OGtest8阅读passage3原文题目及答案解析内容。

Left or right?An overview of some research into liberalization: the dominance of one side of the body over the otherACreatures across the animal kingdom have a preference for one foot, eye or even antenna. The cause of this trait, called liberalization, is fairly simple: one side of the brain, which generally controls the opposite side of the body, is more dominant than the other when processing certain tasks. This does, on some occasions, let the animal down: such as when a toad fails to escape from a snake approaching from the right, just because its right eye is worse at spotting danger than its left. So why would animals evolve a characteristic that seems to endanger them?BFor many years it was assumed that liberalization was a uniquely human trait, but this notion rapidly fell apart as researchers started uncovering evidence of liberalization in all sorts of animals. For example, in the 1970s, Lesley Rogers, now at the University of New England in Australia, was studying memory and learning in chicks. She had been injecting a chemical into chicks brains to stop them learning how to spot grains of food among distracting pebbles, and was surprised to observe that the chemical only worked when applied to the left hemisphere of the brain. That strongly suggested that the right side of the chicks brain played little or no role in the learning of such behaviours. Similar evidence appeared in songbirds and rats around the same time, and since then, researchers have built up an impressive catalogue of animal liberalization.CIn some animals, liberalization is simply a preference for a single paw or foot, while in others it appears in more general patterns of behaviour. The left side of most vertebrate brains, for example, seems to process and control feeding. Since the left hemisphere processes input from the right side of the body, that means animals as diverse as fish, toads and birds are more likely to attack prey or food items viewed with their right eye. Even humpback whales prefer to use the right side of their jaws to scrape sand eels from the ocean floor.DGenetics plays a part in determining liberalization, but environmental factors have an impact too. Rogers found that a chick's liberalization depends on whether it is exposed to light before hatching from its egg - if it is kept in the dark during this period, neither hemisphere becomes dominant. In 2004, Rogers used this observation to test the advantages of brain bias in chicks faced with the challenge of multitasking. She hatched chicks with either strong or weak liberalization, then presented the two groups with food hidden among small pebbles and the threatening shape of a fake predator flying overhead. As predicted, the birds incubated in the light looked for foodmainly with their right eye, while using the other to check out the predator. The weakly-lateralized chicks, meanwhile, had difficulty performing these two activities simultaneously.ESimilar results probably hold true for many other animals. In 2006, Angelo Bisazza at the University of Padua set out to observe the differences in feeding behaviour between strongly-lateralized and weakly-lateralized fish. He found that strongly-lateralized individuals were able to feed twice as fast as weakly-lateralized ones when there was a threat of a predator looming above them. Assigning different jobs to different brain halves may be especially advantageous for animals such as birds or fish, whose eyes are placed on the sides of their heads. This enables them to process input from each side separately, with different tasks in mind.FAnd what of those animals who favour a specific side for almost all tasks? In 2009, Maria Magat and Culum Brown at Macquarie University in Australia wanted to see if there was general cognitive advantage in liberalization. To investigate, they turned to parrots, which can be either strongly right- or left-footed, or ambidextrous (without dominance). The parrots were given the intellectually demanding task of pulling a snack on a string up to their beaks, using a co-ordinated combination of claws and beak. The results showed that the parrots with the strongest foot preferences worked out the puzzle far more quickly than their ambidextrous peers.GA further puzzle is why are there always a few exceptions, like left-handed humans, who are wired differently from the majority of the population? Giorgio Vallortigara and Stefano Ghirlanda of Stockholm University seem to have found the answer via mathematical models. These have shown that a group of fish is likely to survive a shark attack with the fewest casualties if the majority turn together in one direction while a very small proportion of the group escape in the direction that the predator is not expecting.HThis imbalance of liberalization within populations may also have advantages for individuals. Whereas most co-operative interactions require participants to react similarly, there are some situations - such as aggressive interactions - where it can benefit an individual to launch an attack from an unexpected quarter. Perhaps this can partly explain the existence of left-handers in human societies. It has been suggested that when it comes tohand-to-hand fighting, left-handers may have the advantage over the right-handed majority. Where survival depends on the element of surprise, it may indeed pay to be different.Questions 27-30Complete each sentence with the correct ending, A-F, below.Write the correct letter, A-F, in boxes 27-30 on your answer sheet.27 In the 1970s, Lesley Rogers discovered that28 Angelo Bisazza’s experiments revealed that29 Magat and Brown’s studies show that30 Vallortigara and Ghirlanda’s research findings suggest thatA liberalization is more common in some species than in others.B it benefits a population if some members have a different liberalization than the majority.C liberalization helps animals do two things at the same time.D liberalization is not confined to human beings.E the greater an animal’s liberalization, the better it is at problem-solving.F strong liberalization may sometimes put groups of animals in danger.Questions 31-35 Complete the summary below.Choose ONE WORD ONLY from the passage for each answer.Write your answers in boxes 31-35 on your answer sheet.Lesley Rogers’ 2004 Experimentliberalization is determined by both genetic and 31__________ influences. Rogers found that chicks whose eggs are given 32__________ during the incubation period tend to have a stronger liberalization. Her 2004 experiment set out to prove that these chicks were better at 33__________ than weakly lateralized chicks. As expected, the strongly lateralized birds in the experiment were more able to locate 34__________ using their right eye, while using their left eye to monitor an imitation 35__________ located above them.Questions 36-40Reading Passage 3 has eight paragraphs, A-H.Which paragraph contains the following information?Write the correct letter, A-H, in boxes 36-40 on your answer sheet.NB You may use any letter more than once.36 description of a study which supports another scientist’s findings37 the suggestion that a person could gain from having an opposing liberalization to most of the population38 reference to the large amount of knowledge of animal liberalization that has accumulated39 research findings that were among the first to contradict a previous belief40 a suggestion that liberalization would seem to disadvantage animalsQuestion 27答案：D关键词：Lesley Rogers定位原文：B段最后两句“That strongly suggested that the right side … animal liberalization.”解题思路：利用人名Lesley Rogers作为关键词，定位到B段，关于Lesley Rogers所做的实验发现，需要具体定位到B段最后2句话，“这有力地表明，在这些行为的学习中，小鸡的大脑右侧发挥了很少或没有作用。

colocalization analysis

colocalization analysisColocalization analysis is a powerful technique used in various fields such as cell biology, immunology, neuroscience, and ecology. It involves the quantification and analysis of the spatial overlap or association between two or more molecules or structures within a biological sample. This can provide insights into the functional relationships and interactions between these molecules, as well as their subcellular localization and distribution patterns.There are several methods and approaches that can be used for colocalization analysis, depending on the nature of the data and the specific research question. One commonly used approach is the calculation of colocalization coefficients, such as the Pearson's correlation coefficient or the Manders' overlap coefficient. These coefficients provide a measure of the degree of colocalization between two molecules, ranging from -1 to 1, where values close to 1 indicate high colocalization and values close to 0 indicate no colocalization.In addition to colocalization coefficients, other statistical methods can be employed to assess the significance of colocalization. These include permutation tests, Monte Carlo simulations, and statistical hypothesis testing using appropriate thresholds. These approaches help determine whether the observed colocalization is statistically significant or if it could occur by chance.There are various software tools available for colocalization analysis, such as ImageJ, Fiji, CellProfiler, and Imaris. These tools provide a range of image processing and analysis functions,including algorithms for colocalization analysis. They allow researchers to analyze and quantify colocalization patterns in their images, and often include visualization options to display colocalization as scatter plots, heatmaps, or intensity overlays.It is important to note that colocalization does not necessarily imply direct interaction or functional association between the molecules being analyzed. It merely indicates that the two molecules are present in the same spatial location within the sample. To determine functional interactions, additional experiments or techniques such as co-immunoprecipitation, co-localization microscopy, or proximity ligation assays may be needed.Some recent studies have utilized colocalization analysis to investigate the localization and interaction of specific molecules within cells or tissues. For example, a study published in the journal Nature Communications used colocalization analysis to examine the spatial relationship between mitochondria and lipid droplets in liver cells. The researchers found that these organelles were closely associated and proposed that this colocalization is essential for lipid metabolism.Another study published in the journal PLOS Genetics used colocalization analysis to study the relationship between gene expression and DNA methylation in human blood cells. The researchers found that certain genomic regions showed high colocalization between these two molecular markers, suggesting a functional association between gene regulation and DNA methylation.In conclusion, colocalization analysis is a valuable technique for studying the spatial relationships and functional associations between molecules in biological samples. By quantifying and analyzing colocalization patterns, researchers can gain insights into the subcellular localization, interactions, and functional relationships of molecules within cells or tissues. These analyses can be performed using various software tools and statistical methods, enabling researchers to extract meaningful information from their imaging data.。

2025届广东省两校联考高三上学期(10月)一模考试英语试题

2025届广东省两校联考高三上学期（10月）一模考试英语试题一、阅读理解Career Development in Florence: A Journey Through Craftsmanship and LearningThe art of leather craftsmanship in Florence has a rich history, dating back to the 13th century. This exploration into the city’s leather artisans offers insights into the essence of Italian leather craftsmanship.The Leather Career Development Center — PIEROTUCCIEnroll in a complimentary workshop at the PIEROTUCCI Career Development Center and immerse yourself in the intricate process of crafting a leather handbag. Witnessing the meticulous handiwork involved will demystify the premium pricing of PIEROTUCCI products, assuring you that an investment in their bags is an investment in longevity.The Footwear Training Institute — STEFANO BEMERSTEFANO BEMER is renowned for its bespoke footwear, crafted with precision and elegance. The store, which sells luxury shoes ranging from hundreds to thousands of dollars, also serves as a training ground for aspiring shoemakers, with the workshop visible to customers in the front section.The Leather Artisan School — Scuola del CuoioStep into the Scuola del Cuoio, and you’ll feel as though you’ve entered a small college campus. This historic building houses a school dedicated to creating unique leather goods and educating paying students in the art of high-quality leatherworking.The Bookbinding Atelier — Il TorchioRun by Erin Ciulla, Il Torchio is a charming bookbinding workshop. Ciulla might give you a tour of the “guillotine,” an antique-looking machine used for cutting large volumes of paper. In addition to binding books with leather covers, Ciulla also offers services to cover books, journals, and photo albums with hand-made papers.1．What is the primary benefit of attending a workshop at PIEROTUCCI?A．Learning about the history of leather-making.B．Understanding the high cost of luxury shoes.C．Gaining hands-on experience in handbag crafting.D．Observing the antique machinery used in bookbinding.2．Which institution offers a comprehensive education in leather craftsmanship?A．Il Torchio.B．Scuola del Cuoio.C．STEFANO BEMER.D．PIEROTUCCI. 3．What service does Erin Ciulla provide at Il Torchio?A．Selling high-quality leather bags.B．Customizing book covers with leather.C．Teaching courses on leatherworking.D．Manufacturing antique-looking machines.Nicole Latham, a youthful 21-year-old scholar at the University of Leeds, dedicates her time not solely to the pursuit of legal academia, but also to the rigorous domain of weightlifting contests. In parallel, she exhibits proficiency in the martial art of karate. Beyond these physical pursuits, Latham’s health journey is marked by frequent visits to medical practitioners, a consequence of her recent acquisition of a rare affliction: multiple sclerosis (MS). This condition made its insidious debut during her preparation for the A-Level examinations, a period fraught with tension for numerous scholars. Initially, she attributed her symptoms to stress, but it soon became apparent that she was experiencing the onset of MS, specifically vertigo.Despite the onset of this debilitating disease, Nicole persisted in her academic endeavors, even resorting to ocular occlusion in a bid to ameliorate her impaired vision. It was at this juncture that she resolved to revisit her physician, embarking on a regimen of numerous medications, yet to no avail. Sensing a potential misdiagnosis, she promptly sought further diagnostic scrutiny at a hospital.Subsequent to an MRI examination, her condition was confirmed as MS. Following this inaugural episode and her subsequent diagnosis, Latham remained MS-free for several years. However, in August of the year 2021, she encountered another exacerbation, this time manifesting as a persistent tremor in her left hand for a duration of two months. Undeterred by the palpable impediments imposed by her condition in her day-to-day existence, she remained undaunted and resolute in her pursuit. Her aspiration was to inspire her contemporaries with disabilities, demonstrating that a life of vibrancy and fulfillment is attainable despite the adversities posed byMS.In the present day, Nicole leverages her digital platform not only to disseminate awareness regarding MS but also to exhort individuals to heed potential symptoms, a lesson she herself learned the hard way. Moreover, she endeavors to showcase that a life replete with richness and gratification is within reach for those afflicted with MS. Her narrative seeks to illuminate both the exultant peaks and the somber troughs of living with this condition.4．How did Nicole react when the first attack happened?A．She went to the hospital immediately.B．She turned to taking more exercise.C．She took a break from studying.D．She paid no attention to it.5．How did Nicole most probably feel after taking a lot of medicines?A．Relieved.B．Worried.C．Curious.D．Inspired. 6．Which of the following statements shows Nicole’s view on overcoming difficulties?A．Rome was not built in a day.B．Prevention is better than cure.C．Strength comes from a strong will.D．All things are difficult before they are easy.7．What would be the best title for the text?A．Nicole Latham：Always be Ready to HelpB．Meet N icole Latham — a T alented AthleteC．Nicole Latham：Never Let Anyone DownD．Meet Nicole Latham — a Fighter Suffering from MSA radiant grin is a reflection of inner joy. Have you ever been in a public space and received a smile from a stranger? Perhaps you were feeling low, yet their warm and amicable expression could lift your spirits. That person’s smile had the power to shift your gloomy mood. It’s astonishing how such a minor action can influence your emotions so profoundly, and I can attest to this, as I’m sure many of you can.This phenomenon isn’t just a feeling; it’s backed by science. What causes these positive emotions? When you smile at someone, you might feel a fleeting sense of joy. This is because your brain releases endorphins, which are like natural painkillers and can boost your self-esteem.Smiling is a straightforward act of kindness that can also enhance your self-regard.To illustrate, consider someone attending a job interview with their head held high and a smile on their face. They are more likely to be successful. Employers often note that a candidate who avoids eye contact and hides their smile may seem untrustworthy. In contrast, a person with a genuine smile exudes confidence. Regardless of your appearance, a smile can speak volumes and convey sincerity.I find great satisfaction in helping individuals of all ages build their self-esteem. Witnessing the joy and newfound confidence in my clients after our sessions is immensely rewarding. I firmly believe that a genuine, heartfelt smile can bridge gaps between people, even without words. 8．What does the underlined word “low” mean in Paragraph 1?A．Depressed.B．Strengthened.C．Multiplied.D．Returned. 9．What is the role of endorphins? ______A．They induce a sense of happiness.B．They inspire acts of kindness.C．They accelerate brain function.D．They cure certain illnesses.10．Why might someone who doesn’t smile have difficulty getting a job?A．They hold their head too high.B．They look down on others.C．They appear somewhat dishonest.D．They are overly confident.11．What kind of profession might the author have?A．An educator.B．A philanthropist.C．A researcher.D．A counselor.The Renaissance of Creative Thought is burgeoning, perhaps even burgeoning. If you attempted to absorb all the wisdom available today, you would need more than 180 million years to do so. But you are mistaken to assume that all this wisdom would stimulate a surge of innovation to match the abundance of knowledge. Indeed, the last time we found ourselves in a period of significant innovation, pursuing the ideas with the most profound impact, was more than 120 years ago, in a period called the Renaissance of Insight.Innovations, both grand and modest, originate from a new idea. Often, these ideas emerge as a moment of insight — the outcome of a novel connection in our minds made between existing and new knowledge. Studies reveal insights involve quiet signals deep in the brain, just under the surface of awareness. Anything that aids us in noticing quiet signals, such as taking breaksbetween engagements, adopting essential learning approaches, or steering clear of distractions like social media, can enhance the likelihood of insights. However, it is becoming increasingly challenging to find those quiet signals with the escalating use of technology, filling every moment with emergencies and an endless supply of content.Moreover, we also aspire to elevate the quality of them — to be able to sift through grand new ideas and identify the ones that hold genuine value, which can be difficult to measure. Launched in 2015, the Insight Meter (洞察力计量器) permits us to evaluate the potency of our insight experiences on a five-point scale, which is marked by intense emotions, motivation, memory advantage, aftershocks, and subsequent ideas. The Meter consolidates these five variables into a solitary value and enables us to define the significance of a new idea. The level-5 insight, involving the richest emotion, motivation, and lasting impact, holds the utmost significance.Since insights are one of the most effective ways to stimulate engagement, innovation, and behavioral change, the Insight Meter has extensive applications for gauging and enhancing individual and organizational performance. More importantly, it can be employed to measure the impact of different types of work environments and learning approaches on participants’ development — both in the moment or afterward.For organizations to reap the benefits of another age of insight, it is not sufficient to attempt to access more data or augment the number of insights we generate. Instead, it is about creating space for the most significant ideas to emerge from all the knowledge. Utilizing the shared language of the Insight Meter as a means to measure how important ideas are, relative to each other, will enable superior decision-making toward practical and competitive outcomes. And if we are to enter a new age of insight, we must design our environments to allow for the most exceptional insight possible to surface.12．What does the underlined word “burgeoning” in Paragraph 1 probably mean?A．Stabilizing.B．Exploding.C．Shifting.D．Collapsing. 13．According to the passage, how can the likelihood of insights be increased?A．By engaging in ongoing social media interactions.B．By relying on technology to receive regular notifications.C．By stepping away from computers between engagements.D．By participating in additional training and coaching sessions.14．What can be inferred from the passage?A．The Insight Meter dictates the influence of our insights.B．Possessing minimal emotional responses is a level-5 insight.C．Both the quantity and quality of insights are essential to innovation.D．A breakthrough has been made in innovation due to a wealth of information.15．What is the author’s attitude towards the current environment for innovations?A．Uncertain.B．Optimistic.C．Unconcerned.D．Dissatisfied.How to Teach ConfidenceWhile it might seem like some people are just born confident, confidence is largely an acquired skill. 16 Start by building up their self-esteem, independent thought, and positive self—talk. Show them how to achieve goals, and how to deal with failure when it happens. With lessons like these, you can teach the people around you to become more confident.Model confident behavior far people.If you’re trying to improve someone’s confidence, be a model for how they should behave in a confident way. 17 Show them confident interpersonal relations like eye contact, handshakes, and making small talk. This lets them practice in a safe environment.Praise small accomplishments to raise a person’s self-worth.If you’re trying to build someone’s confidence, start small. Each accomplishment they complete is a cause for celebration, even if it seems small. Be happy for your friends, kids, or students. 18Give specific praise so people know what they did well.A specific praise is better than a simple “You did well”. 19 . This makes your praise more genuine and boosts the person self-esteem more by showing them their strengths.Start with a positive statement before correcting something.20 This is especially important if you’re a parent, teacher, or coach. If you do have to make criticisms or corrections, always start by saying something positive first. This raises the person’s spirits and makes it easier for them to take the critical feedback that’s coming up. A．Instead, tell the person exactly what they did well.B．It’s something you can model and teach other people.C．You may have to point out where someone needs to improve.D．Instead of feeling criticized, the person will know you’re sincere.E．Act confident around them and in your interpersonal interactions.F．Your positive energy will teach them to celebrate their achievements.G．You might show someone’s strengths to help them see the bright side.二、完形填空Boo is a 5-year -old rooster. He loves going on road trips, watching TV, and 21 with other house pets: chickens and cats. Boo enjoys many things in his life, but most of all, he 22 to hug with his human mother Mary Bowman.Before he was 23 , Boo’s life wasn’t always that beautiful. He spent the first six months of his life on a farm with many other chickens, where he was treated more like a 24 than a unique being. He was given constant feeding, which is 25 unhealthy because he can not 26 his feed consumption.His now mother adopted him after learning about him from a friend; she was 27 when knowing his unfortunate fate. She decided to help this little guy 28 the meat factory and finally live 29 .In the house, Boo stays close to his humans. When the family goes for a walk in the wild, he wanders free. He, even like a dog, 30 the family when they come home. He likes to spend time with his dad reading comics. Even though Boo can’t read, he likes to look at pictures in the 31 . They play games together as well. Boo spends time with his grandma, too. When she’s playing the piano, he is looking, learning little by little what those 32 do.Boo is an 33 pet. He knows his family and where he lives. He is the soul of the house, the brightest star in the air. Today, Boo is an active part of the local 34 and has a personal account on which he 35 his everyday life with 35K fans.21．A．working out B．hanging out C．figuring out D．carrying out 22．A．benefits B．inspires C．loves D．advocates 23．A．protected B．replaced C．selected D．adopted 24．A．performer B．chief C．product D．species25．A．occasionally B．illegally C．gradually D．extremely 26．A．control B．obey C．predict D．permit 27．A．heartbroken B．patient C．grateful D．disappointed 28．A．complain B．detect C．escape D．resist 29．A．flexibly B．safely C．gently D．regularly 30．A．proves B．assists C．welcomes D．admits 31．A．books B．riddles C．puzzles D．applications 32．A．heels B．legs C．hands D．fingers 33．A．intelligent B．abnormal C．odd D．energetic 34．A．department B．community C．authority D．charity 35．A．drafts B．illustrates C．chats D．shares三、语法填空阅读下面短文，在空白处填入1个适当的单词或括号内单词的正确形式。

英国绘制出青蒿基因组图谱

虑这个发现肯定是一种不可思议的巧合，于是我们试着用其
去了解其他的原子，却发现存在着同样的规律。 ”
美国发现认识元素周期表的新视角
将金属铅转变成黄金或许永远是个神话，不过与其相类似的 “ 丹术 ”不仅可能，而且还相当廉价。美国宾州大学炼３名研究人员近期发表文章说，他们发现某些元素原子的组合所显示的电子特征同其他元素的电子特征相仿。究小组研
膨胀和多铁体系等基础前沿领域的关键科学问题展开研究，同时开展大飞机尾翼、天器整流罩等工程关键部件的无损航检测和寿命评估技术。中国原子能院院长赵志祥认为，该项目的启动，我国在
识元素周期表的新视角。相关研究成果发表在近期的《国美国家科学院院刊》网站上。卡斯尔曼领导的研究小组另外两名成员分别是塞缪尔・泊尼克和达斯萨・古纳偌特恩。泊尼克曾是宾州大培培学的研究生，现为太平洋西北国家实验室的博士后研究员；古纳偌特恩仍是宾州大学的研究生。在研究中，们利用光他电子成像光谱技术，分析研究了一氧化钛和金属镍、一氧化锆和金属钯，以及碳化钨和白金两两之间的相同点。

海马的结构及功能

• （ 2 ）杏仁腹侧传出通路 ventral amygdalofugal projection：进入外侧视前区与下丘脑外侧区、隔区和斜角带核等。
• （ 3 ）杏仁被盖束 amygdalo-tegmental tract：终止于下丘脑外侧区，黑质、蓝斑等。
• （ 4 ）杏仁皮质投射 amygdalo-cortical projection：投射至眶额回、颞上、中、下回。
• 根据细胞形态,不同皮质区发育旳差别以及多种纤维通路旳不同， Lorente do No{1934}把海马又提成CA1、CA2、CA3、 CA4 四个扇形区。 CA4 位于齿状回门内,CA3区内有最大旳锥体细胞,CA3区由大锥体细胞构成, CA1区是邻近下托旳部分,由小锥体细胞构成,CA2区是移行区，由大和小锥体细胞构成。
核等。外侧隔核接受大量从海马经穹窿来旳传
入纤维。
• 1.传入纤维
•
〈1〉发自海马旳纤维经穹隆止于外侧隔
核。
•
〈2〉发自杏仁体旳纤维经斜角带或终纹
止于隔核。
• 〈3〉发自中脑网状构造,黑质、蓝斑、中缝核与下丘脑核旳纤维终止于内侧隔核。
• 〈4〉前穿质发纤维经内侧嗅纹至隔。
• （5）额叶新皮质和扣带回发纤维至隔。
• 3.海马构造旳纤维联络
•
〈1〉传入
•
①丰富旳传入来自内嗅区。
•
②扣带回发纤维经扣带束直接终止
于海马和或经内嗅区中继后发纤维维(胆碱能纤维)经穹
窿、海马伞,终止于海马和齿状回。
•
• ④一侧海马发纤维经同侧海马伞、穹窿脚,经过海马连合至对侧穹窿脚与海马伞, 终止于对侧海马和齿状回。

From Data Mining to Knowledge Discovery in Databases

s Data mining and knowledge discovery in databases have been attracting a signiﬁcant amount of research, industry, and media atten-tion of late. What is all the excitement about?This article provides an overview of this emerging ﬁeld, clarifying how data mining and knowledge discovery in databases are related both to each other and to related ﬁelds, such as machine learning, statistics, and databases. The article mentions particular real-world applications, speciﬁc data-mining techniques, challenges in-volved in real-world applications of knowledge discovery, and current and future research direc-tions in the ﬁeld.A cross a wide variety of ﬁelds, data arebeing collected and accumulated at adramatic pace. There is an urgent need for a new generation of computational theo-ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging ﬁeld of knowledge discovery in databases (KDD).At an abstract level, the KDD ﬁeld is con-cerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easi-ly) into other forms that might be more com-pact (for example, a short report), more ab-stract (for example, a descriptive approximation or model of the process that generated the data), or more useful (for exam-ple, a predictive model for estimating the val-ue of future cases). At the core of the process is the application of speciﬁc data-mining meth-ods for pattern discovery and extraction.1This article begins by discussing the histori-cal context of KDD and data mining and theirintersection with other related ﬁelds. A briefsummary of recent KDD real-world applica-tions is provided. Deﬁnitions of KDD and da-ta mining are provided, and the general mul-tistep KDD process is outlined. This multistepprocess has the application of data-mining al-gorithms as one particular step in the process.The data-mining step is discussed in more de-tail in the context of speciﬁc data-mining al-gorithms and their application. Real-worldpractical application issues are also outlined.Finally, the article enumerates challenges forfuture research and development and in par-ticular discusses potential opportunities for AItechnology in KDD systems.Why Do We Need KDD?The traditional method of turning data intoknowledge relies on manual analysis and in-terpretation. For example, in the health-careindustry, it is common for specialists to peri-odically analyze current trends and changesin health-care data, say, on a quarterly basis.The specialists then provide a report detailingthe analysis to the sponsoring health-care or-ganization; this report becomes the basis forfuture decision making and planning forhealth-care management. In a totally differ-ent type of application, planetary geologistssift through remotely sensed images of plan-ets and asteroids, carefully locating and cata-loging such geologic objects of interest as im-pact craters. Be it science, marketing, ﬁnance,health care, retail, or any other ﬁeld, the clas-sical approach to data analysis relies funda-mentally on one or more analysts becomingArticlesFALL 1996 37From Data Mining to Knowledge Discovery inDatabasesUsama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00areas is astronomy. Here, a notable success was achieved by SKICAT ,a system used by as-tronomers to perform image analysis,classiﬁcation, and cataloging of sky objects from sky-survey images (Fayyad, Djorgovski,and Weir 1996). In its ﬁrst application, the system was used to process the 3 terabytes (1012bytes) of image data resulting from the Second Palomar Observatory Sky Survey,where it is estimated that on the order of 109sky objects are detectable. SKICAT can outper-form humans and traditional computational techniques in classifying faint sky objects. See Fayyad, Haussler, and Stolorz (1996) for a sur-vey of scientiﬁc applications.In business, main KDD application areas includes marketing, ﬁnance (especially in-vestment), fraud detection, manufacturing,telecommunications, and Internet agents.Marketing:In marketing, the primary ap-plication is database marketing systems,which analyze customer databases to identify different customer groups and forecast their behavior. Business Week (Berry 1994) estimat-ed that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for ex-ample, American Express reports a 10- to 15-percent increase in credit-card use. Another notable marketing application is market-bas-ket analysis (Agrawal et al. 1996) systems,which ﬁnd patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.Investment: Numerous companies use da-ta mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million;since its start in 1993, the system has outper-formed the broad stock market (Hall, Mani,and Barr 1996).Fraud detection: HNC Falcon and Nestor PRISM systems are used for monitoring credit-card fraud, watching over millions of ac-counts. The FAIS system (Senator et al. 1995),from the U.S. Treasury Financial Crimes En-forcement Network, is used to identify ﬁnan-cial transactions that might indicate money-laundering activity.Manufacturing: The CASSIOPEE trou-bleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major Euro-pean airlines to diagnose and predict prob-lems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European ﬁrst prize for innova-intimately familiar with the data and serving as an interface between the data and the users and products.For these (and many other) applications,this form of manual probing of a data set is slow, expensive, and highly subjective. In fact, as data volumes grow dramatically, this type of manual data analysis is becoming completely impractical in many domains.Databases are increasing in size in two ways:(1) the number N of records or objects in the database and (2) the number d of ﬁelds or at-tributes to an object. Databases containing on the order of N = 109objects are becoming in-creasingly common, for example, in the as-tronomical sciences. Similarly, the number of ﬁelds d can easily be on the order of 102or even 103, for example, in medical diagnostic applications. Who could be expected to di-gest millions of records, each having tens or hundreds of ﬁelds? We believe that this job is certainly not one for humans; hence, analysis work needs to be automated, at least partially.The need to scale up human analysis capa-bilities to handling the large number of bytes that we can collect is both economic and sci-entiﬁc. Businesses use data to gain competi-tive advantage, increase efﬁciency, and pro-vide more valuable services to customers.Data we capture about our environment are the basic evidence we use to build theories and models of the universe we live in. Be-cause computers have enabled humans to gather more data than we can digest, it is on-ly natural to turn to computational tech-niques to help us unearth meaningful pat-terns and structures from the massive volumes of data. Hence, KDD is an attempt to address a problem that the digital informa-tion era made a fact of life for all of us: data overload.Data Mining and Knowledge Discovery in the Real WorldA large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week , Newsweek , Byte , PC Week , and other large-circulation periodicals. Unfortu-nately, it is not always easy to separate fact from media hype. Nonetheless, several well-documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.In science, one of the primary applicationThere is an urgent need for a new generation of computation-al theories and tools toassist humans in extractinguseful information (knowledge)from the rapidly growing volumes ofdigital data.Articles38AI MAGAZINEtive applications (Manago and Auriol 1996).Telecommunications: The telecommuni-cations alarm-sequence analyzer (TASA) wasbuilt in cooperation with a manufacturer oftelecommunications equipment and threetelephone networks (Mannila, Toivonen, andVerkamo 1995). The system uses a novelframework for locating frequently occurringalarm episodes from the alarm stream andpresenting them as rules. Large sets of discov-ered rules can be explored with ﬂexible infor-mation-retrieval tools supporting interactivityand iteration. In this way, TASA offers pruning,grouping, and ordering tools to reﬁne the re-sults of a basic brute-force search for rules.Data cleaning: The MERGE-PURGE systemwas applied to the identiﬁcation of duplicatewelfare claims (Hernandez and Stolfo 1995).It was used successfully on data from the Wel-fare Department of the State of Washington.In other areas, a well-publicized system isIBM’s ADVANCED SCOUT,a specialized data-min-ing system that helps National Basketball As-sociation (NBA) coaches organize and inter-pret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Su-personics, which reached the NBA ﬁnals.Finally, a novel and increasingly importanttype of discovery is one based on the use of in-telligent agents to navigate through an infor-mation-rich environment. Although the ideaof active triggers has long been analyzed in thedatabase ﬁeld, really successful applications ofthis idea appeared only with the advent of theInternet. These systems ask the user to specifya proﬁle of interest and search for related in-formation among a wide variety of public-do-main and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like (<http:// www.fﬂ/>). CRAYON(/>) allows users to create their own free newspaper (supported by ads); NEWSHOUND(<http://www. /hound/>) from the San Jose Mercury News and FARCAST(</> automatically search information from a wide variety of sources, including newspapers and wire services, and e-mail rele-vant documents directly to the user.These are just a few of the numerous suchsystems that use KDD techniques to automat-ically produce useful information from largemasses of raw data. See Piatetsky-Shapiro etal. (1996) for an overview of issues in devel-oping industrial KDD applications.Data Mining and KDDHistorically, the notion of ﬁnding useful pat-terns in data has been given a variety ofnames, including data mining, knowledge ex-traction, information discovery, informationharvesting, data archaeology, and data patternprocessing. The term data mining has mostlybeen used by statisticians, data analysts, andthe management information systems (MIS)communities. It has also gained popularity inthe database ﬁeld. The phrase knowledge dis-covery in databases was coined at the ﬁrst KDDworkshop in 1989 (Piatetsky-Shapiro 1991) toemphasize that knowledge is the end productof a data-driven discovery. It has been popular-ized in the AI and machine-learning ﬁelds.In our view, KDD refers to the overall pro-cess of discovering useful knowledge from da-ta, and data mining refers to a particular stepin this process. Data mining is the applicationof speciﬁc algorithms for extracting patternsfrom data. The distinction between the KDDprocess and the data-mining step (within theprocess) is a central point of this article. Theadditional steps in the KDD process, such asdata preparation, data selection, data cleaning,incorporation of appropriate prior knowledge,and proper interpretation of the results ofmining, are essential to ensure that usefulknowledge is derived from the data. Blind ap-plication of data-mining methods (rightly crit-icized as data dredging in the statistical litera-ture) can be a dangerous activity, easilyleading to the discovery of meaningless andinvalid patterns.The Interdisciplinary Nature of KDDKDD has evolved, and continues to evolve,from the intersection of research ﬁelds such asmachine learning, pattern recognition,databases, statistics, AI, knowledge acquisitionfor expert systems, data visualization, andhigh-performance computing. The unifyinggoal is extracting high-level knowledge fromlow-level data in the context of large data sets.The data-mining component of KDD cur-rently relies heavily on known techniquesfrom machine learning, pattern recognition,and statistics to ﬁnd patterns from data in thedata-mining step of the KDD process. A natu-ral question is, How is KDD different from pat-tern recognition or machine learning (and re-lated ﬁelds)? The answer is that these ﬁeldsprovide some of the data-mining methodsthat are used in the data-mining step of theKDD process. KDD focuses on the overall pro-cess of knowledge discovery from data, includ-ing how the data are stored and accessed, howalgorithms can be scaled to massive data setsThe basicproblemaddressed bythe KDDprocess isone ofmappinglow-leveldata intoother formsthat might bemorecompact,moreabstract,or moreuseful.ArticlesFALL 1996 39A driving force behind KDD is the database ﬁeld (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot ﬁt in the main memory is of fun-damental importance to KDD. Database tech-niques for gaining efﬁcient data access,grouping and ordering operations when ac-cessing data, and optimizing queries consti-tute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memo-ry and pay no attention to how the algorithm breaks down if only limited views of the data are possible.A related ﬁeld evolving from databases is data warehousing,which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways: (1) data cleaning and (2)data access.Data cleaning: As organizations are forced to think about a uniﬁed logical view of the wide variety of data and databases they pos-sess, they have to address the issues of map-ping data to a single naming convention,uniformly representing and handling missing data, and handling noise and errors when possible.Data access: Uniform and well-deﬁned methods must be created for accessing the da-ta and providing access paths to data that were historically difﬁcult to get to (for exam-ple, stored ofﬂine).Once organizations and individuals have solved the problem of how to store and ac-cess their data, the natural next step is the question, What else do we do with all the da-ta? This is where opportunities for KDD natu-rally arise.A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles pro-posed by Codd (1993). OLAP tools focus on providing multidimensional data analysis,which is superior to SQL in computing sum-maries and breakdowns along many dimen-sions. OLAP tools are targeted toward simpli-fying and supporting interactive data analysis,but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.Basic DeﬁnitionsKDD is the nontrivial process of identifying valid, novel, potentially useful, and ultimate-and still run efﬁciently, how results can be in-terpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported. The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other ﬁelds of AI (be-sides machine learning) to contribute to KDD. KDD places a special emphasis on ﬁnd-ing understandable patterns that can be inter-preted as useful or interesting knowledge.Thus, for example, neural networks, although a powerful modeling tool, are relatively difﬁcult to understand compared to decision trees. KDD also emphasizes scaling and ro-bustness properties of modeling algorithms for large noisy data sets.Related AI research ﬁelds include machine discovery, which targets the discovery of em-pirical laws from observation and experimen-tation (Shrager and Langley 1990) (see Kloes-gen and Zytkow [1996] for a glossary of terms common to KDD and machine discovery),and causal modeling for the inference of causal models from data (Spirtes, Glymour,and Scheines 1993). Statistics in particular has much in common with KDD (see Elder and Pregibon [1996] and Glymour et al.[1996] for a more detailed discussion of this synergy). Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quan-tifying the uncertainty that results when one tries to infer general patterns from a particu-lar sample of an overall population. As men-tioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were ﬁrst introduced. The concern arose because if one searches long enough in any data set (even randomly generated data),one can ﬁnd patterns that appear to be statis-tically signiﬁcant but, in fact, are not. Clearly,this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct rele-vance to KDD. Thus, data mining is a legiti-mate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical as-pects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree pos-sible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.Data mining is a step in the KDD process that consists of ap-plying data analysis and discovery al-gorithms that produce a par-ticular enu-meration ofpatterns (or models)over the data.Articles40AI MAGAZINEly understandable patterns in data (Fayyad, Piatetsky-Shapiro, and Smyth 1996).Here, data are a set of facts (for example, cases in a database), and pattern is an expres-sion in some language describing a subset of the data or a model applicable to the subset. Hence, in our usage here, extracting a pattern also designates ﬁtting a model to data; ﬁnd-ing structure from data; or, in general, mak-ing any high-level description of a set of data. The term process implies that KDD comprises many steps, which involve data preparation, search for patterns, knowledge evaluation, and reﬁnement, all repeated in multiple itera-tions. By nontrivial, we mean that some search or inference is involved; that is, it is not a straightforward computation of predeﬁned quantities like computing the av-erage value of a set of numbers.The discovered patterns should be valid on new data with some degree of certainty. We also want patterns to be novel (at least to the system and preferably to the user) and poten-tially useful, that is, lead to some beneﬁt to the user or task. Finally, the patterns should be understandable, if not immediately then after some postprocessing.The previous discussion implies that we can deﬁne quantitative measures for evaluating extracted patterns. In many cases, it is possi-ble to deﬁne measures of certainty (for exam-ple, estimated prediction accuracy on new data) or utility (for example, gain, perhaps indollars saved because of better predictions orspeedup in response time of a system). No-tions such as novelty and understandabilityare much more subjective. In certain contexts,understandability can be estimated by sim-plicity (for example, the number of bits to de-scribe a pattern). An important notion, calledinterestingness(for example, see Silberschatzand Tuzhilin [1995] and Piatetsky-Shapiro andMatheus [1994]), is usually taken as an overallmeasure of pattern value, combining validity,novelty, usefulness, and simplicity. Interest-ingness functions can be deﬁned explicitly orcan be manifested implicitly through an or-dering placed by the KDD system on the dis-covered patterns or models.Given these notions, we can consider apattern to be knowledge if it exceeds some in-terestingness threshold, which is by nomeans an attempt to deﬁne knowledge in thephilosophical or even the popular view. As amatter of fact, knowledge in this deﬁnition ispurely user oriented and domain speciﬁc andis determined by whatever functions andthresholds the user chooses.Data mining is a step in the KDD processthat consists of applying data analysis anddiscovery algorithms that, under acceptablecomputational efﬁciency limitations, pro-duce a particular enumeration of patterns (ormodels) over the data. Note that the space ofArticlesFALL 1996 41Figure 1. An Overview of the Steps That Compose the KDD Process.methods, the effective number of variables under consideration can be reduced, or in-variant representations for the data can be found.Fifth is matching the goals of the KDD pro-cess (step 1) to a particular data-mining method. For example, summarization, clas-siﬁcation, regression, clustering, and so on,are described later as well as in Fayyad, Piatet-sky-Shapiro, and Smyth (1996).Sixth is exploratory analysis and model and hypothesis selection: choosing the data-mining algorithm(s) and selecting method(s)to be used for searching for data patterns.This process includes deciding which models and parameters might be appropriate (for ex-ample, models of categorical data are differ-ent than models of vectors over the reals) and matching a particular data-mining method with the overall criteria of the KDD process (for example, the end user might be more in-terested in understanding the model than its predictive capabilities).Seventh is data mining: searching for pat-terns of interest in a particular representa-tional form or a set of such representations,including classiﬁcation rules or trees, regres-sion, and clustering. The user can signiﬁcant-ly aid the data-mining method by correctly performing the preceding steps.Eighth is interpreting mined patterns, pos-sibly returning to any of steps 1 through 7 for further iteration. This step can also involve visualization of the extracted patterns and models or visualization of the data given the extracted models.Ninth is acting on the discovered knowl-edge: using the knowledge directly, incorpo-rating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. This process also includes checking for and resolving po-tential conﬂicts with previously believed (or extracted) knowledge.The KDD process can involve signiﬁcant iteration and can contain loops between any two steps. The basic ﬂow of steps (al-though not the potential multitude of itera-tions and loops) is illustrated in ﬁgure 1.Most previous work on KDD has focused on step 7, the data mining. However, the other steps are as important (and probably more so) for the successful application of KDD in practice. Having deﬁned the basic notions and introduced the KDD process, we now focus on the data-mining component,which has, by far, received the most atten-tion in the literature.patterns is often inﬁnite, and the enumera-tion of patterns involves some form of search in this space. Practical computational constraints place severe limits on the sub-space that can be explored by a data-mining algorithm.The KDD process involves using the database along with any required selection,preprocessing, subsampling, and transforma-tions of it; applying data-mining methods (algorithms) to enumerate patterns from it;and evaluating the products of data mining to identify the subset of the enumerated pat-terns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which pat-terns are extracted and enumerated from da-ta. The overall KDD process (ﬁgure 1) in-cludes the evaluation and possible interpretation of the mined patterns to de-termine which patterns can be considered new knowledge. The KDD process also in-cludes all the additional steps described in the next section.The notion of an overall user-driven pro-cess is not unique to KDD: analogous propos-als have been put forward both in statistics (Hand 1994) and in machine learning (Brod-ley and Smyth 1996).The KDD ProcessThe KDD process is interactive and iterative,involving numerous steps with many deci-sions made by the user. Brachman and Anand (1996) give a practical view of the KDD pro-cess, emphasizing the interactive nature of the process. Here, we broadly outline some of its basic steps:First is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint.Second is creating a target data set: select-ing a data set, or focusing on a subset of vari-ables or data samples, on which discovery is to be performed.Third is data cleaning and preprocessing.Basic operations include removing noise if appropriate, collecting the necessary informa-tion to model or account for noise, deciding on strategies for handling missing data ﬁelds,and accounting for time-sequence informa-tion and known changes.Fourth is data reduction and projection:ﬁnding useful features to represent the data depending on the goal of the task. With di-mensionality reduction or transformationArticles42AI MAGAZINEThe Data-Mining Stepof the KDD ProcessThe data-mining component of the KDD pro-cess often involves repeated iterative applica-tion of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algo-rithms that incorporate these methods.The knowledge discovery goals are deﬁned by the intended use of the system. We can distinguish two types of goals: (1) veriﬁcation and (2) discovery. With veriﬁcation,the sys-tem is limited to verifying the user’s hypothe-sis. With discovery,the system autonomously ﬁnds new patterns. We further subdivide the discovery goal into prediction,where the sys-tem ﬁnds patterns for predicting the future behavior of some entities, and description, where the system ﬁnds patterns for presenta-tion to a user in a human-understandableform. In this article, we are primarily con-cerned with discovery-oriented data mining.Data mining involves ﬁtting models to, or determining patterns from, observed data. The ﬁtted models play the role of inferred knowledge: Whether the models reﬂect useful or interesting knowledge is part of the over-all, interactive KDD process where subjective human judgment is typically required. Two primary mathematical formalisms are used in model ﬁtting: (1) statistical and (2) logical. The statistical approach allows for nondeter-ministic effects in the model, whereas a logi-cal model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applica-tions given the typical presence of uncertain-ty in real-world data-generating processes.Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classiﬁcation, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewilder-ing to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fun-damental techniques. The actual underlying model representation being used by a particu-lar method typically comes from a composi-tion of a small number of well-known op-tions: polynomials, splines, kernel and basis functions, threshold-Boolean functions, and so on. Thus, algorithms tend to differ primar-ily in the goodness-of-ﬁt criterion used toevaluate model ﬁt or in the search methodused to ﬁnd a good ﬁt.In our brief overview of data-mining meth-ods, we try in particular to convey the notionthat most (if not all) methods can be viewedas extensions or hybrids of a few basic tech-niques and principles. We ﬁrst discuss the pri-mary methods of data mining and then showthat the data- mining methods can be viewedas consisting of three primary algorithmiccomponents: (1) model representation, (2)model evaluation, and (3) search. In the dis-cussion of KDD and data-mining methods,we use a simple example to make some of thenotions more concrete. Figure 2 shows a sim-ple two-dimensional artiﬁcial data set consist-ing of 23 cases. Each point on the graph rep-resents a person who has been given a loanby a particular bank at some time in the past.The horizontal axis represents the income ofthe person; the vertical axis represents the to-tal personal debt of the person (mortgage, carpayments, and so on). The data have beenclassiﬁed into two classes: (1) the x’s repre-sent persons who have defaulted on theirloans and (2) the o’s represent persons whoseloans are in good status with the bank. Thus,this simple artiﬁcial data set could represent ahistorical data set that can contain usefulknowledge from the point of view of thebank making the loans. Note that in actualKDD applications, there are typically manymore dimensions (as many as several hun-dreds) and many more data points (manythousands or even millions).ArticlesFALL 1996 43Figure 2. A Simple Data Set with Two Classes Used for Illustrative Purposes.。

鹦鹉英语作文

Parrots are known for their vibrant colors and remarkable ability to mimic human speech.Here are some points to consider when writing an essay about parrots:1.Introduction to Parrots:Begin your essay by introducing parrots as a group of birds known for their intelligence and social nature.Mention their wide variety of species, ranging from the small lovebirds to the large macaws.2.Physical Characteristics:Describe the physical features of parrots,such as their beaks, which are adapted for cracking seeds and nuts,and their strong feet for perching and climbing.3.Coloration and Species:Discuss the diversity in coloration among parrots,from the green of the budgerigar to the vibrant hues of the scarlet macaw.Mention the different species and their unique characteristics.4.Habitat and Distribution:Explain where parrots are typically found,such as tropical and subtropical regions,and how their distribution is affected by factors like climate and habitat loss.5.Diet and Feeding Habits:Describe the diet of parrots,which includes seeds,nuts,fruits, and sometimes insects.Highlight their feeding habits,such as their use of their beaks to manipulate food.6.Social Behavior:Parrots are known for their social behavior.Discuss how they live in flocks and communicate with each other using a variety of calls and sounds.7.Mimicry and Communication:One of the most fascinating aspects of parrots is their ability to mimic sounds,including human speech.Explain how this ability is used for communication within their social groups and how it has made them popular pets.8.Conservation Status:Discuss the conservation status of parrots,highlighting the threats they face such as habitat destruction,poaching for the pet trade,and climate change.9.Captive Care and Ethical Considerations:If youre writing about parrots as pets,its important to address the ethical considerations of keeping such intelligent and social creatures in captivity.Discuss the importance of providing them with a suitable environment,social interaction,and mental stimulation.10.Conclusion:Conclude your essay by summarizing the key points and emphasizing the importance of protecting parrots in the wild and providing proper care for those kept aspets.Remember to use descriptive language and,if possible,include anecdotes or personal experiences to make your essay more engaging.Additionally,ensure that your essay is wellstructured,with a clear introduction,body,and conclusion.。

基于网络药理学探讨丹参-葛根药对治疗心肌纤维化的作用机制

基于网络药理学探讨丹参-葛根药对治疗心肌纤维化的作用机制张力立1，马瑞松1，张曦1，陈娇2，秦贞苗21.海南省人民医院&海南医学院附属海南医院心血管内科，海南海口570311；2.海南医学院药学院，海南海口571199【摘要】目的采用网络药理学探讨丹参-葛根药对治疗心肌纤维化的活性成分及作用机制。

方法从中药系统药理学分析平台获取丹参-葛根药对活性成分和作用靶点，通过GeneCards 数据库获取心肌纤维化的相关靶点，使用Venny 2.1软件获取两者共同靶点；运用STRING 数据库和Cytoscape 3.7.1软件构建共同靶点的蛋白-蛋白互作(PPI)网络并进行拓扑学分析；采用ClusterProfiler R 功能包对共同靶点进行基因本体(GO)功能和KEGG 通路富集分析；最后使用Cytoscape 3.7.1软件构建“活性成分-靶点-通路”网络并分析。

结果筛选得到丹参-葛根药对候选活性成分30个，活性成分和心肌纤维化共同靶点41个。

共同靶点PPI 网络的平均点度值为19.7，平均介数为19.1，度值和介数均超过平均值的靶点共有14个。

KEGG 显著富集到73条通路，其中与心肌纤维化相关的通路有6条。

“活性成分-靶点-通路”网络显示，丹参中的木犀草素、丹参酮IIA 和葛根中的葛根素、β-谷甾醇等活性成分通过共同调控脂质与动脉粥样硬化、糖尿病并发症中的AGE-RAGE 、血流剪切力与动脉粥样硬化、缺氧诱导因子-1(HIF -1)、肿瘤坏死因子(TNF)、白细胞介数-17(IL -17)等信号通路起到抗心肌纤维化的功效。

结论揭示了丹参-葛根药对多成分、多靶点、多通路治疗心肌纤维化的作用特点，为进一步研究丹参-葛根药对治疗心肌纤维化的作用机制提供理论依据和新的思路。

【关键词】网络药理学；丹参；葛根；心肌纤维化；作用机制【中图分类号】R542.2+3【文献标识码】A【文章编号】1003—6350（2024）06—0862—08Mechanisms of the herbal pair of Salvia miltiorrhiza and Pueraria lobata in treating myocardial fibrosis based on network pharmacology.ZHANG Li-li 1,MA Rui-song 1,ZHANG Xi 1,CHEN Jiao 2,QIN Zhen-miao 2.1.Department of Cardiovasology,Hainan General Hospital,Hainan Hospital Affiliated to Hainan Medical University,Haikou 570311,Hainan,CHINA;2.School of Pharmacy,Hainan Medical University,Haikou 571199,Hainan,CHINA【Abstract 】ObjectiveTo investigate the active ingredients and mechanism of Salvia miltiorrhiza and Puerarialobata in treating myocardial fibrosis by network pharmacology.MethodsThe pharmacologic analysis platform of tra-ditional Chinese medicine system was used to search the active ingredients and the action targets of herbal pair of Salvia miltiorrhiza and Pueraria lobata.The related target of myocardial fibrosis was obtained by GeneCards database.The com-mon targets of the above two were obtained by Venny 2.1software.The protein-protein interaction (PPI)network of common targets was constructed and topological analysis was carried out by using STRING database and Cytoscape 3.7.1software.Gene ontology (GO)function and KEGG pathway enrichment of common targets were analyzed using ClusterProfiler R function package.Finally,Cytoscape 3.7.1was used to construct and analyze the network diagram of “active ingredients-targets-pathways ”.ResultsThirty candidate active ingredients and 41common targets of active in-gredients and myocardial fibrosis were obtained.The average point degree value and median number of common target PPI network were 19.7and 19.1,and there were 14targets with both degree value and median number exceeding the av-erage.KEGG was significantly enriched to 73pathways,of which 6pathways were associated with myocardial fibrosis.The active ingredient-target-pathway network showed that luteolin and tanshinone IIA in salvia miltiorrhiza,puerarin and β-sitosterol in Pueraria lobata jointly regulated the signaling pathways of lipid and atherosclerosis,AGE-RAGE in diabetic complications,atherosclerosis,hypoxia inducible factor (HIF -1),tumor necrosis factor (TNF),interleukin (IL -17)to play an anti-myocardial fibrosis effect.ConclusionSalvia miltiorrhiza and Pueraria lobata treated myocardi-al fibrosis through multi-ingredient,multi-target,and multi-path ways,which provides theoretical basis and new thought for further research on the anti-myocardial fibrosis mechanism of Salvia miltiorrhiza and Pueraria lobata.【Key words 】Network pharmacology;Salvia miltiorrhiza Bge.;Pueraria lobata (Willd.)Ohwi;Myocardial fibro-sis;Mechanism ·论著·doi:10.3969/j.issn.1003-6350.2024.06.020基金项目：2021年海南省自然科学基金(编号：821RC679、821RC581)。

Entropy changes in the clustering of galaxies in a

Vol.3, No.1, 65-68 (2011)doi:10.4236/ns.2011.31009Natural ScienceEntropy changes in the clustering of galaxies in an expanding universeNaseer Iqbal1,2*, Mohammad Shafi Khan1, Tabasum Masood11Department of Physics, University of Kashmir, Srinagar, India; *Corresponding Author:2Interuniversity Centre for Astronomy and Astrophysics, Pune, India.Received 19 October 2010; revised 23 November 2010; accepted 26 November 2010.ABSTRACTIn the present work the approach-thermody- namics and statistical mechanics of gravitating systems is applied to study the entropy change in gravitational clustering of galaxies in an ex-panding universe. We derive analytically the expressions for gravitational entropy in terms of temperature T and average density n of the par-ticles (galaxies) in the given phase space cell. It is found that during the initial stage of cluster-ing of galaxies, the entropy decreases and fi-nally seems to be increasing when the system attains virial equilibrium. The entropy changes are studied for different range of measuring correlation parameter b. We attempt to provide a clearer account of this phenomena. The entropy results for a system consisting of extended mass (non-point mass) particles show a similar behaviour with that of point mass particles clustering gravitationally in an expanding uni-verse.Keywords:Gravitational Clustering; Thermodynamics; Entropy; Cosmology1. INTRODUCTIONGalaxy groups and clusters are the largest known gravitationally bound objects to have arisen thus far in the process of cosmic structure formation [1]. They form the densest part of the large scale structure of the uni-verse. In models for the gravitational formation of struc-ture with cold dark matter, the smallest structures col-lapse first and eventually build the largest structures; clusters of galaxies are then formed relatively. The clus-ters themselves are often associated with larger groups called super-clusters. Clusters of galaxies are the most recent and most massive objects to have arisen in the hiearchical structure formation of the universe and the study of clusters tells one about the way galaxies form and evolve. The average density n and the temperature T of a gravitating system discuss some thermal history of cluster formation. For a better larger understanding of this thermal history it is important to study the entropy change resulting during the clustering phenomena be-cause the entropy is the quantity most directly changed by increasing or decreasing thermal energy of intraclus-ter gas. The purpose of the present paper is to show how entropy of the universe changes with time in a system of galaxies clustering under the influence of gravitational interaction.Entropy is a measure of how disorganised a system is. It forms an important part of second law of thermody-namics [2,3]. The concept of entropy is generally not well understood. For erupting stars, colloiding galaxies, collapsing black holes - the cosmos is a surprisingly or-derly place. Supermassive black holes, dark matter and stars are some of the contributors to the overall entropy of the universe. The microscopic explanation of entropy has been challenged both from the experimental and theoretical point of view [11,12]. Entropy is a mathe-matical formula. Standard calculations have shown that the entropy of our universe is dominated by black holes, whose entropy is of the order of their area in planck units [13]. An analysis by Chas Egan of the Australian National University in Canberra indicates that the col-lective entropy of all the supermassive black holes at the centers of galaxies is about 100 times higher than previ-ously calculated. Statistical entropy is logrithmic of the number of microstates consistent with the observed macroscopic properties of a system hence a measure of uncertainty about its precise state. Statistical mechanics explains entropy as the amount of uncertainty which remains about a system after its observable macroscopic properties have been taken into account. For a given set of macroscopic quantities like temperature and volume, the entropy is a function of the probability that the sys-tem is in various quantumn states. The more states avail-able to the system with higher probability, the greater theAll Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-6866 disorder and thus greater the entropy [2]. In real experi-ments, it is quite difficult to measure the entropy of a system. The technique for doing so is based on the thermodynamic definition of entropy. We discuss the applicability of statistical mechanics and thermodynam-ics for gravitating systems and explain in what sense the entropy change S – S 0 shows a changing behaviour with respect to the measuring correlation parameter b = 0 – 1.2. THERMODYNAMIC DESCRIPTION OF GALAXY CLUSTERSA system of many point particles which interacts by Newtonian gravity is always unstable. The basic insta-bilities which may occur involve the overall contraction (or expansion) of the system, and the formation of clus-ters within the system. The rates and forms of these in-stabilities are governed by the distribution of kinetic and potential energy and the momentum among the particles. For example, a finite spherical system which approxi-mately satisfies the viral theorem, contracts slowlycompared to the crossing time ~ ()12G ρ- due to the evaporation of high energy particles [3] and the lack of equipartition among particles of different masses [4]. We consider here a thermodynamic description for the sys-tem (universe). The universe is considered to be an infi-nite gas in which each gas molecule is treated to be agalaxy. The gravitational force is a binary interaction and as a result a number of particles cluster together. We use the same approximation of binary interaction for our universe (system) consisting of large number of galaxies clustering together under the influence of gravitational force. It is important to mention here that the characteri-zation of this clustering is a problem of current interest. The physical validity of the application of thermody-namics in the clustering of galaxies and galaxy clusters has been discussed on the basis of N-body computer simulation results [5]. Equations of state for internal energy U and pressure P are of the form [6]:(3122NTU =-)b (1) (1NTP V=-)b (2) b defines the measuring correlation parameter and is dimensionless, given by [8]()202,23W nb Gm n T r K Tτξ∞=-=⎰,rdr (3)W is the potential energy and K the kinetic energy ofthe particles in a system. n N V = is the average num-ber density of the system of particles each of mass m, T is the temperature, V the volume, G is the universalgravitational constant. (),,n T r ξ is the two particle correlation function and r is the inter-particle distance. An overall study of (),n T r ξ has already been dis-cussed by [7]. For an ideal gas behaviour b = 0 and for non-ideal gas system b varies between 0 and 1. Previ-ously some workers [7,8] have derived b in the form of:331nT b nT ββ--=+ (4) Eq.4 indicates that b has a specific dependence on the combination 3nT -.3. ENTROPY CALCULATIONSThermodynamics and statistical mechanics have been found to be equal tools in describing entropy of a system. Thermodynamic entropy is a non-conserved state func-tion that is of great importance in science. Historically the concept of entropy evolved in order to explain why some processes are spontaneous and others are not; sys-tems tend to progress in the direction of increasing en-tropy [9]. Following statistical mechanics and the work carried out by [10], the grand canonical partition func-tion is given by()3213212,1!N N N N mkT Z T V V nT N πβ--⎛⎫⎡=+ ⎪⎣Λ⎝⎭⎤⎦(5)where N! is due to the distinguishability of particles. Λrepresents the volume of a phase space cell. N is the number of paricles (galaxies) with point mass approxi-mation. The Helmholtz free energy is given by:ln N A T Z =- (6)Thermodynamic description of entropy can be calcu-lated as:,N VA S T ∂⎛⎫=- ⎪∂⎝⎭ (7)The use of Eq.5 and Eq.6 in Eq.7 gives()3120ln ln 13S S n T b b -⎛⎫-=-- ⎪ ⎪⎝⎭- (8) where S 0 is an arbitary constant. From Eq.4 we write()31bn b T β-=- (9)Using Eq.9, Eq.8 becomes as3203ln S S b bT ⎡⎤-=-+⎢⎣⎦⎥ (10)Again from Eq.4All Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-68 6767()13221n b T b β-⎡⎤=⎢⎣⎦⎥ (11)with the help of Eq.11, Eq.10 becomes as()011ln ln 1322S S n b b b ⎡-=-+-+⎡⎤⎣⎦⎢⎥⎣⎦⎤ (12) This is the expression for entropy of a system consist-ing of point mass particles, but actually galaxies have extended structures, therefore the point mass concept is only an approximation. For extended mass structures we make use of softening parameter ε whose value is taken between 0.01 and 0.05 (in the units of total radius). Following the same procedure, Eq.8 becomes as()320ln ln 13N S S N T N b Nb V εε⎡⎤-=---⎢⎥⎣⎦(13)For extended structures of galaxies, Eq.4 gets modi-fied to()()331nT R b nT R εβαεβαε--=+ (14)where α is a constant, R is the radius of a cell in a phase space in which number of particles (galaxies) is N and volume is V . The relation between b and b ε is given by: ()11b b b εαα=+- (15) b ε represents the correlation energy for extended mass particles clustering gravitationally in an expanding uni-verse. The above Eq.10 and Eq.12 take the form respec-tively as;()()3203ln 111bT b S S b b ααα⎡⎤⎢⎥-=-+⎢⎥+-+-⎢⎥⎣⎦1 (16) ()()()120113ln ln 2111b b b S S n b b ααα⎡⎤-⎡⎤⎢⎥⎣⎦-=-++⎢⎥+-+-⎢⎥⎣⎦1 (17)where2R R εεεα⎛⎫⎛⎫=⎪ ⎪⎝⎭⎝⎭(18)If ε = 0, α = 1 the entropy equations for extended mass galaxies are exactly same with that of a system of point mass galaxies approximation. Eq.10, Eq.12, Eq.16and Eq.17 are used here to study the entropy changes inthe cosmological many body problem. Various entropy change results S – S 0 for both the point mass approxima-tion and of extended mass approximation of particles (galaxies) are shown in (Figures 1and2). The resultshave been calculated analytically for different values ofFigure 1. (Color online) Comparison of isothermal entropy changes for non-point and point mass particles (galaxies) for an infinite gravitating system as a function of average relative temperature T and the parameter b . For non-point mass ε = 0.03 and R = 0.06 (left panel), ε = 0.04 and R = 0.04 (right panel).All Rights Reserved.N. Iqbal et al. / Natural Science 3 (2011) 65-68 68Figure 2. (Color online) Comparison of equi-density entropy changes for non-point and point mass particles (galaxies) for an infinite gravitating system as a function of average relative density n and the parameter b. For non-point mass ε= 0.03 and R = 0.04.R (cell size) corresponding to different values of soften-ing parameter ε. We study the variations of entropy changes S – S0with the changing parameter b for differ-ent values of n and T. Some graphical variations for S – S0with b for different values of n = 0, 1, 100 and aver-age temperature T = 1, 10 and 100 and by fixing value of cell size R = 0.04 and 0.06 are shown. The graphical analysis can be repeated for different values of R and by fixing values of εfor different sets like 0.04 and 0.05. From both the figures shown in 1 and 2, the dashed line represents variation for point mass particles and the solid line represents variation for extended (non-point mass) particles (galaxies) clustering together. It has been ob-served that the nature of the variation remains more or less same except with some minor difference.4. RESULTSThe formula for entropy calculated in this paper has provided a convenient way to study the entropy changes in gravitational galaxy clusters in an expanding universe. Gravity changes things that we have witnessed in this research. Clustering of galaxies in an expanding universe, which is like that of a self gravitating gas increases the gases volume which increases the entropy, but it also increases the potential energy and thus decreases the kinetic energy as particles must work against the attrac-tive gravitational field. So we expect expanding gases to cool down, and therefore there is a probability that the entropy has to decrease which gets confirmed from our theoretical calculations as shown in Figures 1 and 2. Entropy has remained an important contributor to our understanding in cosmology. Everything from gravita-tional clustering to supernova are contributors to entropy budget of the universe. A new calculation and study of entropy results given by Eqs.10, 12, 16 and 17 shows that the entropy of the universe decreases first with the clustering rate of the particles and then gradually in-creases as the system attains viral equilibrium. The gravitational entropy in this paper furthermore suggests that the universe is different than scientists had thought.5. ACKNOWLEDGEMENTSWe are thankful to Interuniversity centre for Astronomy and Astro-physics Pune India for providing a warm hospitality and facilities during the course of this work.REFERENCES[1]Voit, G.M. (2005) Tracing cosmic evolution with clus-ters of galaxies. Reviews of Modern Physics, 77, 207- 248.[2]Rief, F. (1965)Fundamentals of statistical and thermalphysics. McGraw-Hill, Tokyo.[3]Spitzer, L. and Saslaw, W.C. (1966) On the evolution ofgalactic nuclei. Astrophysical Journal, 143, 400-420.doi:10.1086/148523[4]Saslaw, W.C. and De Youngs, D.S. (1971) On the equi-partition in galactic nuclei and gravitating systems. As-trophysical Journal, 170, 423-429.doi:10.1086/151229[5]Itoh, M., Inagaki, S. and Saslaw, W.C. (1993) Gravita-tional clustering of galaxies. Astrophysical Journal, 403,476-496.doi:10.1086/172219[6]Hill, T.L. (1956) Statistical mechanics: Principles andstatistical applications. McGraw-Hill, New York.[7]Iqbal, N., Ahmad, F. and Khan, M.S. (2006) Gravita-tional clustering of galaxies in an expanding universe.Journal of Astronomy and Astrophysics, 27, 373-379.doi:10.1007/BF02709363[8]Saslaw, W.C. and Hamilton, A.J.S. (1984) Thermody-namics and galaxy clustering. Astrophysical Journal, 276, 13-25.doi:10.1086/161589[9]Mcquarrie, D.A. and Simon, J.D. (1997) Physical chem-istry: A molecular approach. University Science Books,Sausalito.[10]Ahmad, F, Saslaw, W.C. and Bhat, N.I. (2002) Statisticalmechanics of cosmological many body problem. Astro-physical Journal, 571, 576-584.doi:10.1086/340095[11]Freud, P.G. (1970) Physics: A Contemporary Perspective.Taylor and Francis Group.[12]Khinchin, A.I. (1949) Mathamatical Foundation of statis-tical mechanics. Dover Publications, New York.[13]Frampton, P., Stephen, D.H., Kephar, T.W. and Reeb, D.(2009) Classical Quantum Gravity. 26, 145005.doi:10.1088/0264-9381/26/14/145005All Rights Reserved.。

初中趣味英语基础知识抢答100题

初中趣味英语基础知识抢答100题1. What is the capital of Australia?The capital of Australia is Canberra.2. Who wrote the famous play, Romeo and Juliet? Romeo and Juliet was written by William Shakespeare.3. How many planets are there in our solar system? There are eight planets in our solar system.4. What is the largest ocean in the world?The Pacific Ocean is the largest ocean in the world.5. Who painted the Mona Lisa?The Mona Lisa was painted by Leonardo da Vinci.6. What is the chemical symbol for gold?The chemical symbol for gold is Au.7. What is the largest organ in the human body?The skin is the largest organ in the human body.8. Who is the author of the Harry Potter series?J.K. Rowling is the author of the Harry Potter series. 9. What is the tallest mountain in the world?Mount Everest is the tallest mountain in the world.10. Which country is known as the land of the rising sun? Japan is known as the land of the rising sun.11. Who invented the telephone?Alexander Graham Bell invented the telephone.12. What is the largest desert in the world?The Sahara Desert is the largest desert in the world. 13. What is the chemical symbol for oxygen?The chemical symbol for oxygen is O.14. Who was the first person to walk on the moon?Neil Armstrong was the first person to walk on the moon.15. What is the longest river in the world?The Nile River is the longest river in the world.16. What is the national bird of the United States?The national bird of the United States is the Bald Eagle.17. Which country is famous for the Taj Mahal?India is famous for the Taj Mahal.18. Who wrote the famous novel, To Kill a Mockingbird?To Kill a Mockingbird was written by Harper Lee.19. What is the chemical symbol for water?The chemical symbol for water is H2O.20. Who painted the famous artwork, The Starry Night? The Starry Night was painted by Vincent van Gogh.21. What is the largest species of penguin?The Emperor Penguin is the largest species of penguin.22. Who was the first president of the United States?George Washington was the first president of the United States.23. What is the longest bone in the human body?The femur is the longest bone in the human body.24. Who wrote the famous novel, Pride and Prejudice?Pride and Prejudice was written by Jane Austen.25. What is the chemical symbol for carbon?The chemical symbol for carbon is C.26. Who is the famous scientist who proposed the theory of relativity?Albert Einstein proposed the theory of relativity.27. What is the largest continent in the world?Asia is the largest continent in the world.28. Who painted the famous artwork, The Last Supper? The Last Supper was painted by Leonardo da Vinci. 29. What is the national flower of Japan?The national flower of Japan is the cherry blossom.30. Who invented the lightbulb?Thomas Edison invented the lightbulb.31. What is the largest mammal in the world?The blue whale is the largest mammal in the world.32. Who wrote the famous novel, The Great Gatsby? The Great Gatsby was written by F. Scott Fitzgerald.33. What is the chemical symbol for iron?The chemical symbol for iron is Fe.34. Who is the famous scientist who developed the theory of evolution?Charles Darwin developed the theory of evolution.35. What is the largest bird in the world?The ostrich is the largest bird in the world.36. Who painted the famous artwork, The Scream?The Scream was painted by Edvard Munch.37. What is the national flower of the United States?The national flower of the United States is the rose.38. Who invented the theory of gravity?Isaac Newton invented the theory of gravity.39. What is the largest waterfall in the world?Angel Falls is the largest waterfall in the world.40. Who wrote the famous novel, Moby-Dick?Moby-Dick was written by Herman Melville.41. What is the chemical symbol for hydrogen?The chemical symbol for hydrogen is H.42. Who is the famous scientist who discovered penicillin? Alexander Fleming discovered penicillin.43. What is the largest big cat in the world?The tiger is the largest big cat in the world.44. Who painted the famous artwork, Guernica?Guernica was painted by Pablo Picasso.45. What is the national flower of England?The national flower of England is the rose.46. Who invented the theory of general relativity?Albert Einstein invented the theory of general relativity.47. What is the largest fish in the world?The whale shark is the largest fish in the world.48. Who wrote the famous novel, 1984?1984 was written by George Orwell.49. What is the chemical symbol for silver?The chemical symbol for silver is Ag.50. Who is the famous scientist who developed the laws of motion?Isaac Newton developed the laws of motion.51. What is the largest reptile in the world?The saltwater crocodile is the largest reptile in the world.52. Who painted the famous artwork, The Persistence of Memory?The Persistence of Memory was painted by Salvador Dalí.53. What is the national flower of France?The national flower of France is the iris.54. Who invented the theory of quantum mechanics?Max Planck and Albert Einstein contributed to the development of the theory of quantum mechanics.55. What is the largest cat species in the world?The Siberian tiger is the largest cat species in the world.56. Who wrote the famous novel, The Catcher in the Rye?The Catcher in the Rye was written by J.D. Salinger.57. What is the chemical symbol for calcium?The chemical symbol for calcium is Ca.58. Who is the famous scientist who discovered the theory of relativity?Albert Einstein formulated the theory of relativity.59. What is the largest amphibian in the world?The Chinese giant salamander is the largest amphibian in the world.60. Who painted the famous artwork, The Birth of Venus?The Birth of Venus was painted by Sandro Botticelli.61. What is the national flower of China?The national flower of China is the peony.62. Who invented the theory of special relativity?Albert Einstein formulated the theory of special relativity.63. What is the largest land-dwelling arthropod in the world?The coconut crab is the largest land-dwelling arthropod in the world.64. Who wrote the famous novel, Animal Farm?Animal Farm was written by George Orwell.65. What is the chemical symbol for helium?The chemical symbol for helium is He.66. Who is the famous scientist who discovered the lawsof motion?Sir Isaac Newton discovered the laws of motion.67. What is the largest butterfly species in the world?The Queen Alexandra's birdwing is the largest butterfly species in the world.68. Who painted the famous artwork, Girl with a Pearl Earring?Girl with a Pearl Earring was painted by Johannes Vermeer.69. What is the national flower of Germany?The national flower of Germany is the cornflower.70. Who invented the theory of general relativity?Albert Einstein developed the theory of general relativity.71. What is the largest living reptile in the world?The saltwater crocodile is the largest living reptile in the world.72. Who wrote the famous novel, The Lord of the Rings?The Lord of the Rings was written by J.R.R. Tolkien.73. What is the chemical symbol for sodium?The chemical symbol for sodium is Na.74. Who is the famous scientist who developed the theory of evolution by natural selection?Charles Darwin is the famous scientist who developed the theory of evolution by natural selection.75. What is the largest bird of prey in the world?The Andean condor is the largest bird of prey in the world.76. Who painted the famous artwork, The Night Watch?The Night Watch was painted by Rembrandt.77. What is the national flower of Italy?The national flower of Italy is the lily.78. Who invented the theory of quantum mechanics?Max Planck and Albert Einstein played key roles in the development of the theory of quantum mechanics.79. What is the largest species of bear in the world?The polar bear is the largest species of bear in the world.80. Who wrote the famous novel, The Hobbit?The Hobbit was written by J.R.R. Tolkien.81. What is the chemical symbol for potassium?The chemical symbol for potassium is K.82. Who is the famous scientist who discovered the concept of gravity?Sir Isaac Newton discovered the concept of gravity.83. What is the largest land animal in the world?The African elephant is the largest land animal in the world.84. Who painted the famous artwork, The Creation of Adam?The Creation of Adam was painted by Michelangelo.85. What is the national flower of Russia?The national flower of Russia is the chamomile.86. Who invented the theory of special relativity?Albert Einstein formulated the theory of special relativity.87. What is the largest snake species in the world?The reticulated python is the largest snake species in the world.88. Who wrote the famous novel, The Chronicles of Narnia?The Chronicles of Narnia was written by C.S. Lewis.89. What is the chemical symbol for nitrogen?The chemical symbol for nitrogen is N.90. Who is the famous scientist who discovered the laws of motion and gravity?Sir Isaac Newton discovered the laws of motion and gravity.91. What is the largest rodent in the world?The capybara is the largest rodent in the world.92. Who painted the famous artwork, The Starry Night Over the Rhône?The Starry Night Over the Rhône was painted by Vincent van Gogh.93. What is the national flower of Spain?The national flower of Spain is the carnation.94. Who invented the theory of relativity?Albert Einstein is credited with formulating the theory of relativity.95. What is the largest land-dwelling arachnid in the world?The Goliath birdeater spider is the largest land-dwelling arachnid in the world.96. Who wrote the famous novel, War and Peace?War and Peace was written by Leo Tolstoy.97. What is the chemical symbol for phosphorus?The chemical symbol for phosphorus is P.98. Who is the famous scientist who proposed the laws of motion?Sir Isaac Newton proposed the laws of motion.99. What is the largest fish species in the world?The whale shark is the largest fish species in the world.100. Who painted the famous artwork, The Creation of the Sun, Moon and Planets?The Creation of the Sun, Moon and Planets was painted by Michelangelo.。

加拿大披碱草×老芒麦杂种加倍一代群体的聚类分析

ＫｅｒｓＥｃｎｄｎｉ；．ｉｉｃｓＣ Ⅱ ＭｏｅｕａｒｅｓＣｕｔｒａａｙｉｙｗｏｄ：．ａａｅｓＥｓｒｕ；，；ｌｃｌｒｍａｋｒ；ｌｓｎｌｓｓｓｂｉｅ
ＡｂｔａｔＣｕｔｒａａｙｉｏ０ｉｄｖｄａｓｉＩｏｃｒｄｎｉａｄａｄＥ．ｉｉｃｓｗｓｄｎｈｏｇＳｓｒｃ：ｌｓｅｎｌｓｓｆｎｉｉｕｌｎＣｌｆＥ．ａｔｅｓｉｎｎｓｂｒｕａｏｅｔｉｉｌｒｔｏｆｉｉｎａｉｄｆｏ０２６０ｔ．９ｖＲＡＰＡｃｏｉｇＲＡＰＤｎＴｖＥＳａｄｔｅｇｎｔｃｓｍｉａｙｃｅｆｃｅｔｖｒｅｒｍ．８ｏ０９８ｂｉ１Ｄ．ｃｄｎａｄＥＳ
ａｄｍｏｅｕａｒｅｓ．ｈｅｕｔｈｗｅｈｔｔｅｇｎｔｉｌｒｔｏｆｉｉｎｆ７ｎｉｉｕｌａｉｄｆｏ０４ｔｎｌｃｌｒｍａｋｒＴｅｒｓｌｓｓｏｄｔａｈｅｅｉｓｍｉａｉｙｃｅｃｅｔｏ０ｉｄｖｄａｓｖｒｅｒｍ．ｏｃ
中图分类号：５３９￥４．文献标识码：Ａ文章编号：００— ０１２ｌ）２— １６— ６１０７９（００００００
ＣｌｓｅａｙｉｆＣｌｐｌｔｏｆＥ．ａａｅｓｓａｓｂｒｃｓｕｔｒＡｎｌｓｓｏＨＰｏｕａｉｎｏｃｎｄｎｉｎｄＥ．ｉｉｉｕ
摘要：过ＥＴ同工酶和ＲＰ通ＳＡＤ标记，加拿大披碱草与老芒麦加倍一代的７对０个单株进行了聚类分析．果表结明，ＥＴ标记的单株之间的相似系数在０４和１０之问；ＡＤ标记，株之间的相似系数在０２６０和０９１用Ｓ．．ＲＰ单．８．９８之间；倍一代群体分为三类：别为偏向母本加拿大披碱草的、向父本老芒麦的和变异较大的类群；加倍一代群加分偏对体的７０个单株进行抽样研究时，从这三个类群中按比例抽取样本，便科学全面地对其进行研究。应以关键词：拿大披碱草；芒麦；倍一代；子标记；类分析加老加分聚

风景园林专业英语试题

1 Why Pagodas Don't Fall DownIn a land swept by typhoons and shaken by earthquakes, how have Japan's tallest and seemingly flimsiest old buildings - 500 or so wooden pagodas - remained standing for centuries? Records show that only two have collapsed during the past 1400 years. Those that have disappeared were destroyed by fire as a result of lightning or civil war. The disastrous Hanshin earthquake in 1995 killed 6,400 people, toppled elevated highways, flattened office blocks and devastated the port area of Kobe. Yet it left the magnificent five-storey pagoda at the Toji temple in nearby Kyoto unscathed, though it levelled a number of buildings in the neighbourhood.Japanese scholars have been mystified for ages about why these tall, slender buildings are so stable. It was only thirty years ago that the building industry felt confident enough to erect office blocks of steel and reinforced concrete that had more than a dozen floors. With its special shock absorbers to dampen the effect of sudden sideways movements from an earthquake, the thirty-six-storey Kasumigaseki building in central Tokyo - Japan's first skyscraper - was considered a masterpiece of modern engineering when it was built in 1968.Yet in 826, with only pegs and wedges to keep his wooden structure upright, the master builder Kobodaishi had no hesitation in sending his majestic Toji pagoda soaring fifty-five metres into the sky - nearly half as high as the Kasumigaseki skyscraper built some eleven centuries later. Clearly, Japanese carpenters of the day knew a few tricks about allowing a building to sway and settle itself rather than fight nature's forces. But what sort of tricks?The multi-storey pagoda came to Japan from China in the sixth century. As in China, they were first introduced with Buddhism and were attached to important temples. The Chinese built their pagodas in brick or stone, with inner staircases, and used them in later centuries mainly as watchtowers. When the pagoda reached Japan, however, its architecture was freely adapted to local conditions - they were built less high, typically five rather than nine storeys, made mainly of wood and the staircase was dispensed with because the Japanese pagoda did not have any practical use but became more of an art object. Because of the typhoons that batter Japan in the summer, Japanese builders learned to extend the eaves of buildings further beyond the walls. This prevents rainwater gushing down the walls. Pagodas in China and Korea have nothing like the overhang that is found on pagodas in Japan.为什么宝塔不会倒塌在这片被台风席卷、地震撼动的土地上，日本最高、看起来最脆弱的老建筑——500多座木制宝塔——是如何屹立了几个世纪的?记录显示，在过去的1400年里，只有两处坍塌。

latent class model

Latent Class ModelsbyJay Magidson, Ph.D.Statistical Innovations Inc.Jeroen K. Vermunt, Ph.D.Tilburg University, the NetherlandsOver the past several years more significant books have been published on latent class and other types of finite mixture models than any other class of statistical models. The recent increase in interest in latent class models is due to the development of extended computer algorithms, which allow today's computers to perform latent class analysis on data containing more than just a few variables. In addition, researchers are realizing that the use of latent class models can yield powerful improvements over traditional approaches to cluster, factor, regression/segmentation, as well as to multivariable biplots and related graphical displays.What are Latent Class Models?Traditional models used in regression, discriminant and log-linear analysis contain parameters that describe only relationships between the observed variables. Latent class (LC) models (also known as finite mixture models) differ from these by including one or more discrete unobserved variables. In the context of marketing research, one will typically interpret the categories of these latent variables, the latent classes, as clusters or segments (Dillon and Kumar 1994; Wedel and Kamakura 1998). In fact, LC analysis provides a powerful new tool to identify important market segments in target marketing. LC models do not rely on the traditional modeling assumptions which are often violated in practice (linear relationship, normal distribution, homogeneity). Hence, they are less subject to biases associated with data not conforming to model assumptions. In addition, LC models have recently been extended (Vermunt and Magidson, 2000a, 2000b) to include variables of mixed scale types (nominal, ordinal, continuous and/or count variables) in the same analysis. Also, for improved cluster or segment description the relationship between the latent classes and external variables (covariates) can be assessed simultaneously with the identification of the clusters. This eliminates the need for the usual second stage of analysis where a discriminant analysis is performed to relate the cluster results to demographic and other variables.Kinds of Latent Class ModelsThree common statistical application areas of LC analysis are those that involve1)clustering of cases,2)variable reduction and scale construction, and3)prediction.This paper introduces the three major kinds of LC models:•LC Cluster Models,•LC Factor Models,•LC Regression Models.Our illustrative examples make use of the new computer program (Vermunt and Magidson, 2000b) called Latent GOLD®.LC Cluster ModelsThe LC Cluster model:•identifies clusters which group together persons (cases) who share similar interests/values/characteristics/behavior,•includes a K-category latent variable, each category representing a cluster. Advantages over traditional types of cluster analysis include:•probability-based classification: Cases are classified into clusters based upon membership probabilities estimated directly from the model,•variables may be continuous, categorical (nominal or ordinal), or counts or any combination of these,•demographics and other covariates can be used for cluster description.Typical marketing applications include:•exploratory data analysis,•development of a behavioral based and other segmentations of customers and prospects.Traditional clustering approaches utilize unsupervised classification algorithms that group cases together that are "near" each other according to some ad hoc definition of "distance". In the last decade interest has shifted towards model-based approaches which use estimated membership probabilities to classify cases into the appropriate cluster. The most popular model-based approach is known as mixture-model clustering, where each latent class represents a hidden cluster (McLachlan and Basford, 1988). Within the marketing research field, this method is sometimes referred to as “latent discriminant analysis” (Dillon and Mulani, 1999). Today's high-speed computers make these computationally intensive methods practical.For the general finite mixture model, not only continuous variables, but also variables that are ordinal, nominal or counts, or any combination of these can be included. Also, covariates can be included for improved cluster description.As an example, we used the LC cluster model to develop a segmentation of current bank customers based upon the types of accounts they have. Separate models were developed specifying different numbers of clusters and the model selected was the one that had the lowest BIC statistic.This criteria resulted in 4 segments which were named:1)Value Seekers (15% of customers),2)Conservative Savers (35% of customers),3)Mainstreamers (40% of customers),4)Investors (10% of customers).For each customer, the model gave estimated membership probabilities for each segment based on their account mix. The resulting segments were verified to be very homogeneous and to differ substantially from each other not only with respect to their mix of accounts, but also with respect to demographics, and profitability. In addition, examination of survey data among the sample of customers for which customer satisfaction data were obtained found some important attitudinal and satisfaction differences between the segments as well. Value seekers were youngest and a high percentage were new customers. Basic savers were oldest.Investors were the most profitable customer segment by far. Although only 10% of all customers, they accounted for over 30% of the bank’s deposits. Survey data pinpointed the areas of the bank with which this segment was least satisfied and a LC regression model (see below) on follow-up data related their dissatisfaction to attrition. The primary uses of the survey data was to identify reasons for low satisfaction and to develop strategies of improving satisfaction in the manner that increased retention.This methodology of segmenting based on behavioral information available on all customers offers many advantages over the common practice of developing segments from survey data and then attempting to allocate all customers to the different clusters. Advantages of developing a segmentation based on behavioral data include:•past behavior is known to be the best predictor of future behavior,•all customers can be assigned to a segment directly, not just the sample for which survey data is available,•improved reliability over segmentations based on attitudes, demographics, purchase intent and other survey variables (when segment membership is based on survey data,a large amount of classification error is almost always present for non-surveyedcustomers) .LC Factor ModelsThe LC Factor model:•identifies factors which group together variables sharing a common source of variation,•can include several ordinal latent variables, each of which contains 2 or more levels,•is similar to maximum likelihood factor analysis in that its use may be exploratory or confirmatory and factors may be assumed to be correlated or uncorrelated(orthogonal).Advantages over traditional factor analysis are:•factors need not be rotated to be interpretable,•factor scores are obtained directly from the model without imposing additional assumptions,•variables may be continuous, categorical (nominal or ordinal), or counts or any combination of these,•extended factor models can be estimated that include covariates and correlated residuals.Typical marketing applications include:•development of composite variables from attitudinal survey items,•development of perceptual maps and other kinds of biplots which relate product and brand usage to behavioral and attitudinal measures and to demographics,•estimation of factor scores,•direct conversion from factors to segments.The conversion of ordinal factors to segments is straightforward. For example, consider a model containing 2 dichotomous factors. In this case, the LC factor model provides membership classification probabilities directly for 4 clusters (segments) based on the classification of cases as high vs. low on each factor: segment 1 = (low, low); segment 2 = (low, high); segment 3 = (high, low) and segment 4 = (high, high). Magidson and Vermunt (2000) found that LC factor models specifying uncorrelated factors often fit data better than comparable cluster models (i.e., cluster models containing the same number of parameters).Figure 1 provides a bi-plot in 2-factor space of lifestyle interests where the horizontal axis represents the probability of being high on factor 1 and the vertical axis the probability of being high on factor 2. The variable AGE was included directly in the LC Factor model as a covariate and therefore shows up in the bi-plot to assist in understanding the meaning of the factors. For example, we see that persons aged 65+ are most likely to be in the (low, high) segment, as are persons expressing an interest in sewing. As a group, their (mean) factor scores are (Factor 1, Factor 2) = (.06, .67).Since these factor scores have a distinct probabilistic interpretation, this bi-plot represents an improvement over traditional biplots and perceptual maps (see Magidson and Vermunt 2000). Individual cases can also be plotted based on their factor scores.Figure 1: Bi-plot for life-style dataThe factor model can also be used to deal with measurement and classification errors in categorical variables. It is actually equivalent to a latent trait (IRT) model without the requirement that the traits be normally distributed.LC Regression ModelsThe LC Regression model, also known as the LC Segmentation model:• is used to predict a dependent variable as a function of predictors,• includes an R-category latent variable, each category representing a homogeneous population (class, segment),• different regressions are estimated for each population (for each latent segment),• classifies cases into segments and develops regression models simultaneously.Advantages over traditional regression models include:• relaxing the traditional assumption that the same model holds for all cases (R=1)allows the development of separate regressions to be used to target each segment,• diagnostic statistics are available to determine the value for R,Factor10.00.20.40.60.8 1.0Factor21.00.80.60.40.20.0AGECAMPINGHUNTINGFISHINGWINESKNITTINGSEWINGFITNESSTENNISGOLFSKIBIKINGBOATINGGARDENTRAVEL 18-2425-3435-4445-5455-6465+camp huntfish wines knit sew fitness tennis golf ski bikeboat gardentravel•for R > 1, covariates can be included in the model to improve classification of each case into the most likely segment.Typical marketing applications include:•customer satisfaction studies: identify particular determinants of customer satisfaction that are appropriate for each customer segment,•conjoint studies: identify the mix of product attributes that appeal to different market segments,•more generally: identify segments that differ from each other with respect to some dependent variable criterion.Like traditional regression modeling, LC regression requires a computer program. As LC regression modeling is relatively new, very few programs currently exist. Our comparisons between LC regression and traditional linear regression are based on the particular forms of LC regression that are implemented in the Latent GOLD® program. For other software see Wedel and DeSarbo (1994) and Wedel and Kamakura (1998). Typical regression programs utilize ordinary least squares estimation in conjunction with a linear model. In particular, such programs are based on two restrictive assumptions about data that are often violated in practice:1)the dependent variable is continuous with prediction error normally distributed,2)the population is homogeneous - one model holds for all cases.LC regression as implemented in the Latent GOLD® program relaxes these assumptions: 1) it accommodates dependent variables that are continuous, categorical (binary, polytomous nominal or ordinal), binomial counts, or Poisson counts,2) the population needs not be homogeneous (i.e., there may be multiple populations as determined by the BIC statistic).One potential drawback for LC models is that there is no guarantee that the solution will be the maximum likelihood solution. LC computer programs typically employ the EM or Newton Raphson algorithm which may converge to a local as opposed to a global maximum. Some programs provide randomized starting values to allow users to increase the likelihood of converging to a global solution by starting the algorithm at different randomly generated starting places. An additional approach is to use Bayesian prior information in conjunction with randomized starting values which eliminates the possibility of obtaining boundary (extreme) solutions and reduces the chance of obtaining local solutions. Generally speaking, we have achieved good results using 10 randomized starting values and small Bayes constants (the default option in the Latent GOLD program).In addition to using predictors to estimate separate regression model for each class, covariates can be specified to refine class descriptions and improve classification of cases into the appropriate latent classes. In this case, LC regression analysis consists of 3 simultaneous steps:1)identify latent classes or hidden segments2)use demographic and other covariates to predict class membership, and3)classify cases into the appropriate classes/segmentsDependent variables may also include repeated/correlated observations of the kind often collected in conjoint marketing studies where persons are asked to rate different product profiles. Below is an example of a full factorial conjoint study designed to assist in the determination of the mix of product attributes for a new product.Conjoint Case StudyIn this example, 400 persons were asked to rate each of 8 different attribute combinations regarding their likelihood to purchase. Hence, there are 8 records per case; one record for each cell in this 2x2x2 conjoint design based on the following attributes:•FASHION (1 = Traditional; 2 = Modern),•QUALITY (1 = Low; 2 = High),•PRICE (1 = Lower; 2 = Higher) .The dependent variable (RATING) is the rating of purchase intent on a five-point scale. The three attributes listed above are used as predictor variables in the model and the following demographic variables are used as covariates:•SEX (1 = Male; 2 = Female),•AGE (1 = 16-24; 2 = 25-39; 3 = 40+).The goal of a traditional conjoint study of this kind is to determine the relative effects of each attribute in influencing one’s purchase decision; a goal attained by estimating regression (or logit) coefficients for these attributes. When the LC regression model is used with the same data, a more general goal is attained. First, it is determined whether the population is homogeneous or whether there exists two or more distinct populations (latent segments) which differ with respect to the relative importance placed on each of the three attributes. If multiple segments are found, separate regression models are estimated simultaneously for each. For example, for one segment, price may be found to influence the purchase decision, while a second segment may be price insensitive, but influenced by quality and modern appearance.We will treat RATING as an ordinal dependent variable and estimate several different models to determine the number of segments (latent classes). We will then show how this methodology can be used to describe the demographic differences between these segments and to classify each respondent into the segment which is most appropriate. We estimated one- to four-class models with and without covariates. Table 1 reports the obtained test results. The BIC values indicate that the three-class model is the best model (BIC is lowest for this model) and that the inclusion of covariates significantly improves the model.Table 1: Test results for regression models for conjoint dataModelLog-likelihood BIC-valueNumber ofparametersWithout covariatesOne segment-440288467Two segments-4141831915Three segments-4087831223Four segments-4080834631With covariatesTwo segments-4088828418Three segments-4036824629Four segments-4026829340The parameter estimates of the three-class model with covariates are reported in Tables 2 and 3 and 4. As can be seen from the first row of Table 2, segment 1 contains about 50% of the subjects, segment 2 contains about 25% and segment 3 contains the remaining 25%. Examination of class-specific probabilities shows that overall, segment 1 is least likely to buy (only 5% are Very Likely to buy) and segment 3 is most likely (21% are Very Likely to buy).♦Table 2: Profile outputClass 1Class 2Class 3Segment Size0.490.260.25RatingVery Unlikely0.210.100.05Not Very Likely0.430.200.12Neutral0.200.370.20Somewhat Likely0.100.210.43Very Likely0.050.110.21♦Table 3: Beta's or parameters of model for dependent variableClass 1Class 2Class 3Wald p-value Wald(=)p-value Fashion 1.97 1.140.04440.19 4.4e-95191.21 3.0e-42Quality0.040.85 2.06176.00 6.5e-38132.33 1.8e-29Price-1.04-0.99-0.94496.38 2.9e-1070.760.68The beta parameter for each predictor is a measure of the influence of that predictor on RATING. The beta effect estimates under the column labeled Class 1 suggest that segment 1 is influenced in a positive way by products for which FASHION = Modern (beta = 1.97) and in negative way by PRICE = Higher (beta = -1.04), but not by QUALITY (beta is approximately 0). We also see that segment 2 is influenced by all 3 attributes, having a preference for those product choices that are modern (beta = 1.14), high quality (beta = .85) and lower priced (beta = -0.99). Members of segment 3 preferhigh quality (beta = 2.06) and the lower (beta = -.94) product choices, but are not influenced by FASHION.Note that PRICE has more or less the same influence on all three segments. The Wald (=) statistic indicates that the differences in these beta effects across classes are not significant (the p-value = .68 which is much higher than .05, the standard level for assessing statistical significance). This means that all 3 segments exhibit price sensitivity to the same degree. This is confirmed when we estimate a model in which this effect is specified to be class-independent. The p-value for the Wald statistic for PRICE is2.9x10-107 indicating that the amount of price sensitivity is highly significant.With respect to the effect of the other two attributes we find large between-segment differences. The predictor FASHION has a strong influence on segment 1, a less strong effect on segment 2, and virtually no effect on segment 3. QUALITY has a strong effect on segment 3, a less strong effect on segment 2, and virtually no effect on segment 1. The fact that the influence of FASHION and QUALITY differs significantly between the 3 segments is confirmed by the significant p-values associated with the Wald(=) statistics for these attributes. For example, for FASHION, the p-value = 3.0x10-42.The beta parameters of the regression model can be used to name the latent segments. Segment 1 could be named the “Fashion-Oriented” segment, segment 3 the “Quality-Oriented” segment, and segment 2 is the segment that takes into account all 3 attributes in their purchase decision.♦Table 4: Gamma's: parameters of model for latent distributionClass 1Class 2Class 3Wald p-valueSexMale-0.560.71-0.1524.47 4.9e-6Female0.56-0.710.15Age16-250.84-0.59-0.2453.098.1e-1126-40-0.320.59-0.2740+-0.520.010.51The parameters of the (multinomial logit) model for the latent distribution appear in Table 4. These show that females have a higher probability of belonging to the “Fashion-oriented” segment (segment 1), while males more often belong to segment 2. The Age effects show that the youngest age group is over-represented in the “Fashion-oriented”segment, while the oldest age group is over-represented in the “Quality oriented”Segment.ConclusionsWe introduced three kinds of LC models and described applications of each that are of interest in marketing research, survey analysis and related fields. It was shown that LC analysis can be used as a replacement for traditional cluster analysis techniques, as a factor analytic tool for reducing dimensionality, and as a tool for estimating separate regression models for each segment. In particular, these models offer powerful new approaches for identifying market segments.BIOSJay Magidson is founder and president of Statistical Innovations, a Boston based consulting, training and software development firm specializing in segmentation modeling. His clients have included A.C. Nielsen, Household Finance, and National Geographic Society. He is widely published on the theory and applications of multivariate statistical methods, and was awarded a patent for a new innovative graphical approach for analysis of categorical data. He taught statistics at Tufts and Boston University, and is chair of the Statistical Modeling Week workshop series. Dr. Magidson designed the SPSS CHAID™ and GOLDMineR® programs, and is the co-developer (with Jeroen Vermunt) of Latent GOLD®.Jeroen Vermunt is Assistant Professor in the Methodology Department of the Faculty of Social and Behavioral Sciences, and Research Associate at the Work and Organization Research Center at Tilburg University in the Netherlands. He has taught a variety of courses and seminars on log-linear analysis, latent class analysis, item response models, models for non-response, and event history analysisall over the world, as well as published extensively on these subjects. Professor Vermunt is developer of the LEM program and co-developer (with Jay Magidson) of Latent GOLD® .ReferencesDillon, W.R., and Kumar, A. (1994). Latent structure and other mixture models in marketing: An integrative survey and overview. R.P. Bagozzi (ed.), Advanced methods of Marketing Research, 352-388,Cambridge: Blackwell Publishers.Dillon, W.R.. and Mulani, N. (1989) LADI: A latent discriminant model for analyzing marketing research data. Journal of Marketing Research, 26, 15-29.Magidson J., and Vermunt, J.K. (2000), Latent Class Factor and Cluster Models, Bi-plots and Related Graphical Displays. Submitted for publication.McLachlan, G.J., and Basford, K.E. (1988). Mixture models: inference and application to clustering. New York: Marcel Dekker.Vermunt, J.K. & Magidson, J. (2000a). “Latent Class Cluster Analysis”, chapter 3 in J.A. Hagenaars and A.L. McCutcheon (eds.), Advances in Latent Class Analysis. Cambridge University Press.Vermunt, J.K. & Magidson, J. (2000b). Latent GOLD 2.0 User's Guide. Belmont, MA: Statistical Innovations Inc.Wedel, M., and DeSarbo, W.S (1994). A review of recent developments in latent class regression models. R.P. Bagozzi (ed.), Advanced methods of Marketing Research, 352-388, Cambridge: Blackwell Publishers. Wedel, M., and Kamakura, W.A. (1998). Market segmentation: Concepts and methodological foundations. Boston: Kluwer Academic Publishers.。

雅思G类阅读答案机经文档

第一篇：收音机Radio AutomationToday they are everywhere. Production lines controlled by computers and operated by robots. There's no chatter of assembly workers, just the whirr and click of machines. In the mid-1940s, the workerless factory was still the stuff of science fiction. There were no computers to speak of and electronics was primitive. Yet hidden away in the English countryside was a highly automated production line called ECME, which could turn out 1500 radio receivers a day with almost no help from human hands.AJohn Sargrove, the visionary engineer who developed the technology, was way ahead of his time. For more than a decade, Sargrove had been trying to figure out how to make cheaper radios. Automating the manufacturing process would help. But radios didn't lend themselves to such methods: there were too many parts to fit together and too many wires to solder. Even a simple receiver might have 30 separate components and 80 hand-soldered connections. At every stage, things had to be tested and inspected. Making radios required highly skilled labor-and lots of it.BIn 1944, Sargrove came up with the answer. His solution was to dispense with most of the fiddly bits by inventing a primitive chip-a slab of Bakelite with all the receiver's electrical components and connections embedded in it. This was something that could be made by machines, and he designed those too. At the end of the war, Sargrove built an automatic production line, which he called ECME (electronic circuit-making equipment), in a small factory in Effingham, Surrey.ECME LineCAn operator sat at one end of each ECME line, feeding in the plates. She didn't need much skill, only quick hands. From now on, everything was controlled by electronic switches and relays. First stop was the sandbluster, which roughened the surface of the plastic so that molten metal would stick to it. The plates were then cleaned to remove any traces of grit. The machine automatically checked that the surface was rough enough before sending the plate to the spraying section. There, eight nozzles rotated into position and sprayed molten zinc over both sides of the plate. Again, the nozzles only began to spray when a plate was in place. The plate whizzed on. The next stop was the milling machine, which ground away the surface layer of metal to leave the circuit and other components in the grooves and recesses. Now the plate was a composite of metal and plastic. It sped on to be lacquered and have its circuits tested. By the time it emerged from the end of the line, robot hands had fitted it with sockets to attach components such as valves and loudspeakers. When ECME was working flat out, the whole process took 20 seconds.DECME was astonishingly advanced. Electronic eyes, photocells that generated a small current when a panel arrived, triggered each step in the operation, so avoiding excessive wear and tear on the machinery The plates were automatically tested at each stage as they moved along the conveyor. And if more than two plates in successionwere duds, the machines were automatically adjusted-or If necessary halted. In a conventional factory, workers would test faulty- circuits and repair them. But Sargrove's assembly line produced circuits so cheaply they Just threw away the faulty ones. Sargrove's circuit board was even more astonishing for the time. It predated the more familiar printed circuit, with wiring printed on aboard, yet was more sophisticated. Its built-in components made it more like a modem chip.EWhen Sargrove unveiled his invention at a meeting of the British Institution of Radio Engineers in February 1947, the assembled engineers were impressed. So was the man from The Times. ECME, he reported the following day, "produces almost without human labour, a complete radio receiving set. This new method of production can be equally well applied to television and other forms of electronic apparatus."FThe receivers had many advantages over their predecessors. Wit components they were more robust. Robots didn't make the sorts of mistakes human assembly workers sometimes did. "Wiring mistakes just cannot happen/ wrote Sargrove. No wires also meant the radios were lighter and cheaper to ship abroad. And with no soldered wires to come unstuck, the radios were more reliable. Sargrove pointed out that the circuit boards didn't have to be flat. They could be curved, opening up the prospect of building the electronics into the cabinet of Bakelite radios.GSargrove was all for introducing this type of automation to other products. It could be used to make more complex electronic equipment than radios, he argued. And even if only part of a manufacturing process were automated, the savings would be substantial. But while his invention was brilliant, his timing was bad. ECME was too advanced for its own good. It was only competitive on huge production runs because each new job meant retooling the machines. But disruption was frequent. Sophisticated as it was, ECME still depended on old-fashioned electromechanical relays and valves-which failed with monotonous regularity. The state of Britain's economy added to Sargrove's troubles. Production was dogged by power cuts and post-war shortages of materials. Sargrove's financial backers began to get cold feet.HThere was another problem Sargrove hadn't foreseen. One of ECME's biggest advantages-the savings on the cost of labour-also accelerated its downfall. Sargrove's factory had two ECME production lines to produce the two circuits needed for each radio. Between them these did what a thousand assembly workers would otherwise have done. Human hands were needed only to feed the raw material in at one end and plug the valves into their sockets and fit the loudspeakers at the other. After that, the only job left was to fit the pair of Bakelite panels into a radio cabinet and check that it worked.ISargrove saw automation as the way to solve post-war labor shortages. With somewhat Utopian idealism, he imagined his new technology would free people from boring, repetitive jobs on the production line and allow them to do more interestingwork. "Don't get the idea that we are out to rob people of their jobs,' he told the Daily Mirror. “Our task is to liberate men and women from being slaves of machines."JThe workers saw things differently. They viewed automation in the same light as the everlasting light bulb or the suit that never wears out-as a threat to people's livelihoods. If automation spread, they wouldn't be released to do more exciting jobs. They'd be released to join the dole queue. Financial backing for ECME fizzled out. The money dried up. And Britain lost its lead in a technology that would transform industry just a few years later.Question 1-7SummaryThe following diagram explains the process of ECME:Complete the following chart of the paragraphs of Reading Passage, using no more than two words from the Reading Passage for each answer. Write your answers in boxes 1-7 on your answer sheet.Question 8-11SummaryComplete the following summary of the paragraphs of Reading Passage, using no more than two words from the Reading Passage for each answer. Write your answers in boxes 8-11 on your answer sheet.Sargrove had been dedicated to create a___8___radio by automation of manufacture. The old version of radio had a large number of independent___9___After this innovation made, wireless-style radios became___10___and inexpensive to export oversea. As the Sargrove saw it, the real benefit of ECME's radio was that it reduced___11___of manual work .which can be easily copied to other industries of manufacturing electronic devices.Cheaper、components、lighter、costQuestion 12-13Choose the correct letter, A, B, C or D.Write your answers in boxes 12-13 on your answer sheet.12 What were workers attitude towards ECME Model initially? AA anxiousB welcomingC boringD inspiring13 What is the main idea of this passage? CA approach to reduce the price of radioB a new generation of fully popular products and successful businessC an application of the automation in the early stageD ECME technology can be applied in many product fields篇章结构:体裁说明文题目收音机自动化---集成电路的先驱结构Paragraph 1: 在电子技术极其落后的1940s, 英国出现了收音机自动化生产线---ECME.A段：收音机并不特别适合用自动化流程生产。

新媒体社交媒体营销外文翻译文献

文献信息：文献标题：Elements of strategic social media marketing: A holisticframework（战略性社交媒体营销要素：整体框架）国外作者：Reto Felix, Philipp A. Rauschnabel, Chris Hinsch文献出处：《Journal of Business Research》,2017,70:118-126字数统计：英文2632单词，15772字符；中文5082汉字外文文献：Elements of strategic social media marketing:A holistic frameworkAbstract Social media marketing is an integral element of 21st-centurybusiness. However, the literature on social media marketing remains fragmented and is focused on isolated issues, such as tactics for effective communication. The curr research a pplies a qualitative,theory-building a pproach to develop a strategicfour generic dimensions of strategic social m ediaframework that articulatesmarketing. Social m edia marketing scope represents a range from defenders t osocial media marketing c ulture includes the poles o f conservatism andexplorers,modernism, social media marketing structures fall between hierarchies and networks, and social m edia marketing governance ranges from autocracy t o anarchy. B yproviding a comprehensive conceptualization and definition of strategic social mediaframework that e xpands beyondmarketing, this r esearch proposes a n integrativeextant marketing theory. Furthermore, managers can apply the framework to position their organizations on these four dimensions in a manner consistent with their overa corporate mission and objectives.Key Words: Strategic social media marketing; Holistic framework; New media; Definition of social media marketing; Social media strategy; Digital marketing1.IntroductionUnderstanding the role of social media in the context of marketing is critical f both researchers and managers (e.g. Fong & Burton, 2008; Kumar, Bezawada,2013). Most existingRishika, Janakiraman, &Kannan, 2016; Schultz &Peltier,studies focus on particular issues, such as purchase behavior (Chang, Yu, & Lu, 2015 Kumar et al., 2016; Relling, S chnittka, Sattler, & Johnen, 2016), customerrelationshipmanagement (Trainor, Andzulis, R app, & Agnihotri, 2014), brandmanagement (Asmussen, Harridge-March, Occhiocupo, & Farquhar, 2013),innovation management (Gebauer, Füller, & Pezzei, 2013), and employee recruitment (Sivertzen,Nilsen, & Olafsen, 2013). W hile these s tudies detail advancements inspecialized areas of social media knowledge in a marketing and management context, extant literature does not provide a holistic framework for social media marketing the strategic level. This deficiency is surprising because both academics (Labrecqu vor dem Esche, Mathwick, Novak, & Hofacker, 2013; Schultz & Peltier, 2013; Yadav& Pavlou, 2014) and practitioners (Divol, Edelman, & Sarrazin, 2012) acknowledge new complexities accompanying these m edia and agree that r esearch into s ocialsocial mediastrategicmedia marketing n eeds to be reconceptualized.In a nutshell,marketing remains an untested user interaction paradigm (Naylor, Lamberton, & West, 2012) with little published academic research.The current article aims to address this theoretically and managerially importan research gap by exploring the following two research questions: How is strategicsocial m edia marketing defined a nd conceptualized? and What factors demandsocial media marketing s trategy?an organization'swhen constructingconsiderationSpecifically, this research attempts to define the continua on which critical strat social media marketing decisions lie and to integrate them into a holistic framewor2.MethodologyThe study e mployed a two-stage r esearch design. The first stage c onsisted ofin-depth interviews (Fontana & Frey, 1998) with seven European social m ediaexperience in socialmarketing e xperts who possess b oth national and internationalmedia marketing. Following a purposive sampling strategy (Lincoln & Guba, 1985), experts were recruited according to their job position, experience, and direct expos to social media marketing practices in real industry settings. Seeking depth rather tthe qualitative research breadth, the sample size instage 1 was commensurate with-richparadigm in which relatively small sample sizes are used to generate informationdata (Patton, 1990). A ll informants agreed t o audiotape the i nterviews (between 25and 60 min), which resulted in 117 pages of double-spaced, verbatim transcripts.procedure c onsisted of a qualitative The second stage o f the data collectionsurvey of social media marketing experts (Miles & Huberman, 1994). The survey data were used not for confirmation but as a new and independent qualitative data source with a focus on triangulating the information obtained through the depth interview and online surveys (Jack & Raturi, 2006). Respondents came from a list of 265 social media marketing experts identified through managerially focused magazines, through interviews in business magazines, or because they were mentioned as knowledgeable and experienced experts in personal communication. E-mail requests were sent to allexperts along with two reminders, which resulted in 50 returned surveys (responserate = 18.9%). Seven data s ets w ere eliminated because o f incomplete a nswers orbecause social media marketing plays a minor role in the respondents' daily work. The final sample consisted of 43 respondents (age: m = 37; SD = 9 years; 74% male; 88% European) with various backgrounds in their position and/or industry.Table 1 Summary of stage 2 informantsCompany size (employees)< 50 14 (32.6%)50–99 5 (11.6%)100–499 3 (7.0%)500–9998 (18.6%)1000–4999 6 (14.0%)5000–10,000 3 (7.0%)>10,000 4 (9.3%)Social media marketing experience (in years)Average: 6.4 years1–2 5 (11.6%)3–519 (44.2%)6–812 (27.9%)9 and more 7 (16.3%)Percentage of working time associated with social mediaAverage: 52.8%<20%7 (16.3%)20–3910 (23.3%)40–59 6 (14.0%)60–79 3 (7%)80–10015 (34.9%)n/a 2 (4.7%)Age (in years)Average: 37< 30 9 (20.9%)30–3919 (44.2%)40–4910 (23.3%)50–59 5 (11.6%)Self-reported experience in…Means aSocial Media 6.2Social Media Marketing 5.9Marketing 5.7Customer Management 4.7Advertising 5.2Communications/Public Relations 5.6How much experience do you have in the following areas? (1 = no experience at aall; 7 = highly experienced)Respondents were asked to (1) define s ocial m edia marketing, (2) discussself-selected best and worst practice examples of social media marketing, (3) discusssuccess factors and success m etrics, and (4) d escribe their ideal implementation of-selected organization. The survey employed commonsocial media marketing in a self& Mcbride, 2009), s uch as addingdesign t echniques (Smyth, Dillman, C hristian,large answer fields and asking respondents to be as specific as possible, to increrespondents' motivation to provide detail.3.FindingsFig. 1 shows the strategic social media marketing framework with its four central dimensions.Fig. 1. Strategic social media marketing framework.social media marketing s cope addresses the question whether companies First,use social media marketing p redominantly for communication with one or a fewstakeholders or comprehensively (both externally and internally) as a genuine tool f Defenders use social m edia marketing primarily a s a one-waycollaboration.rather than communication tool t o entertainconsumers or to inform stakeholders,groups. Conversely, explorers are interested in integrating employees or communityan authentic social media marketing c ollaborationinteractionsbased on reciprocalwith many different stakeholders such as clients, employees, suppliers, andgovernment agents.between conservatism, Second, social media marketing c ulture distinguishesapproach t owhich is represented by an encapsulated,mass advertisingtraditional,social media marketing, and modernism, which is characterized by a more permeable,open, and flexible social media marketing culture.Third, social media marketing structure addresses the organization and departmentalization of the social media marketing assignment in the firm. Hierarchieapproach with a clearly defined s ocial media marketingstand for a centralizedassignee. Networks represent an organizational structure in which all employees are responsible for social media marketing, and thus a dedicated social media marketing director is no longer necessary.Fourth, social m edia marketing governance refers t o how the companyestablishes rules and guidelines and how social media marketing responsibilities ar controlledin the company. The extreme position of autocracy describes a situationwith precise regulations on who in the company is allowed to interact on social medi platforms. Conversely, anarchy represents a situation without any such rules o rguidelines.The current research focuses on the extremes of each continuum, but, in general, firms likely choose (intentionally or unintentionally) a position somewhere between the poles o n each dimension. For example, c ompanies need to find a position onsocial media marketing g overnance t hat neither regulates everything employees areallowed to say nor leaves t hem without any guidance on which to base their responsibilitiesor behaviors. Fig. 1 also suggests that d ecisions on social mediageneral(e.g.,marketing s hould i ndeed b e guided by the firm's internalinfluencersvision, mission, corporate goals, corporate culture, available resources), which in should be in line with external i nfluencers (e.g., communities, competition,government regulation).4.Discussion and implicationsThis research addresses the absence of a holistic framework for strategicsocialreveals several approachesmedia marketing. A review of the marketing l iteraturemanagementregarding aspects of strategicmarketing s uch as customer r elationship(e.g., Payne & Frow, 2005) or marketing organization (Workman, Homburg, &Gruner, 1998). However, few articles address the strategic marketing of social mediaand none put forth a holistic social media marketing framework.While extant research related to social media marketing investigates social media mostly through the lens of a particular marketing problem (e.g. Fong & Burton, 2008; Kim & Ko, 2012; Kumar et al., 2016) or witha focus on customers and communication (e.g., Chang et al., 2015), t he findings of this s tudy reveal f ourgeneral social m edia marketing dimensions that firms should address when conceptualizing or managing their strategic social media marketing approach. As the findings indicate, these dimensions are interdependent, and companies should strive position themselves on the four dimensions in an integrated way, rather than treati them as isolated, independent decisions.4.1.A new definition of social media marketingThis research suggests a new definition of social media marketing: Social media-functional concept that uses social media marketing is an interdisciplinary and cross(often in combination with other communications channels) to achieve organizational goals by creating value for stakeholders. On a strategic level, social media marketi covers an organization's decisions about social media marketing scope (ranging from defenders to explorers), culture (ranging from conservatism to modernism), structurto networks), and governance (ranging from autocracy to(ranging from hierarchiesanarchy).4.2.Implications for social media marketing scopeSocial media marketing provides firms with an opportunity to use social media to with customers, employees, communities, and other stakeholdersbuild relationships(i.e., when they act as explorers). At the same time, firms may choose to view soci media as simply another c ommunications channel through which they can pushwhen they act as defenders). Though potentiallyinformation to customers (i.e.,approach does nottake advantage of thefor c ustomers, the defendercreating valuewithin the network of customers,opportunitiesfor building r eal relationshipsemployees, interest groups, the government, and other stakeholders, as propagated by modern relationshipmarketing (Payne & Frow, 2005). However, the explorerstakeholders in theapproach may require firms to redefine the role o f differentorganization.4.3.Implications for social media marketing cultureManagement and organizational behavior researchers (Zheng et al., 2010) as well as marketing academics and practitioners (Deshpandé & Farley, 2004) recognize the-financial firmimportance of culture and organizational climate for financial and non performance. The current research emphasizes t he importance o f culture for s ocialmedia marketing. Companies engaging in social media marketing must acknowledgethat stakeholders can take control of and manipulate social media content (Labrecque et al., 2013). Thus, companies should contemplate t he trade-offs between anwhich provides m oreencapsulated social media marketing c ulture (conservatism),connect a nd engageand consumers' desire tobrand constructs,control of importantwith firms displaying a more progressive, permeable culture (i.e., modernism).4.4.Implications for social media marketing structureExtant marketing research investigates how the elements of marketing should besuch as formalization,characteristics,organized a ccording t o a firm's structural(e.g.,Olson, S later, & Hult, 2005). C onsequently,and specializationcentralization,and organizationalsocial m edia marketing structure focuses on responsibilitiesemployed to configure social media marketing. Whereas social mediahierarchiesmarketing governance pertains to who can or should say what in social media, social media marketing structure focuses on who has the responsibility to post and interac in these m edia. A s the informants emphasized, firms s hould i ntegratesocial mediaThe informantsmarketing in a way that fits with their o verarching strategies.or asets o f benefits that c an emerge from either a hierarchicalidentifieddifferentnetworked structure. However, they recommended that specific decisions about who has the responsibility to interact online with customers, activists, and pundits sh be formally discussed in the organization.4.5.Implications for social media marketing governancerights, and Research on governance usually investigates the structures,responsibilities among different employees in organizations (Freeman & Reed, 1983).-discipline of corporate governance, Information technology (IT) governance, as a subfocuses on specifying which individuals have the responsibility for making decisions on the use of IT (Brown & Grant, 2005). Whereas IT governance traditionally focuses-related purposes, social media can potentially be used byon the use of IT for work(company-granted a ccess) or unofficial(personalany employee in either officialaccount access) capacities. Therefore, the applicability of IT governance research is limited u se when extended to social m edia marketing. S ome companies havedeveloped the idea of educating e mployees about the personal a nd firm-related consequences of “undesirable” social media use through social mediamarketingguidelines andguidelines(Linke &Zerfass, 2013). However, building social mediagovernance into a holistic framework for social media marketing is novel. The role o employees in promoting brands in other contexts (and thus increasing firm value) is(Morhart, Herzog, & Tomczak, 2009).well represented in the academic literatureWeber Shandwick's (2014) recent s tudy reveals a n emerging movement termed“employee activism” in which one-third of the surveyed respondentswere socialwho defended their employers and advocated f or the firm online.media activistsEmployees may be better able to understand the needs of consumers and products that can meet those needs, and they can effectively advocate and promote the firm online. These technologiesall employees to champion the firm. Forhave allowed v irtuallyNordstrom has policies to provide e mployees withexample, the fashion r etailerof social media marketingThis applicationand expectations.knowledge, d irection,governance can increase the overall social media marketing s uccess o f the firm (Nordstrom, 2015; Ross, Beath, & Sebastian, 2015).5.Limitations and future researchavenues forfruitfulSeveral limitationsto the current study s uggest p otentiallyapproach r eveals four d imensions o f strategicfuture research. First, the qualitativesocial media marketing and identifies the extreme points of each dimension. However,of differentto identify the impactfuture researchcould u se quantitative approachespositions on each of these dimensions. Research could also investigate the influenc of each dimension on firm or social media marketing p erformance. For example,studies could try to isolate the effect of each dimension on outcome variables such2013) or, m ore specifically,newconsumer–brand engagement (Schultz & Peltier,is themedia brand engagement (Hennig-Thurau et al., 2010). A second limitationover-representation of European (especially German) informants in the analyses. Prior research d iscusses cross-cultural differences in consumers' u se of social m edia(Bernoff &Li, 2008; World Newsmedia Network, 2015). Furthermore, extantresearch advocates for the adaptation of social media content to the targeted cult(Tsai &Men, 2012). T hus, caution should b ebased on differingconsumer profilestaken in extrapolating the framework to other cultural contexts. Future research mig determine w hether aspects of cultural or economic context add dimensions t o theapproaches r egardingproposed f ramework or whether they simply require differentthe four dimensions.Future research should also investigate how other characteristics, such as cultur the type of firm (e.g., B2B vs. B2C), the industry (e.g., financial services vs.“idealadvertising agency), company size, or available resources, influence a firm'ssocial media marketing. Finally,position” on each of the dimensions o f strategic-regulatory bodies (e.g.,future research could investigate the role of regulatory or selfon social media marketing g overnance a ndWord of Mouth Marketing A ssociation)how firms can create v alue and form core competencies b y superseding t hese requirements.6.ConclusionThis study sheds light on the complex nature of strategic social media marketingis too complex to be managed and executedSocial m edia marketing, in practice,-functional collaborations exclusively by a single individual or even department. Crossalong the four d imensions o f social media marketing a re necessary to successfullynavigate in this dynamic arena.中文译文：战略性社交媒体营销要素：整体框架摘要社交媒体营销是21世纪商业的一个组成部分。

高二英语世界历史中的伟大人物与事件阅读理解25题

高二英语世界历史中的伟大人物与事件阅读理解25题1<背景文章>Alexander the Great is one of the most renowned figures in history. His conquests spanned a vast territory, leaving a lasting impact on the world. Born in Pella, Macedonia, Alexander was the son of King Philip II. From a young age, he showed great courage and leadership.Alexander's army was highly disciplined and skilled. They defeated many powerful kingdoms and empires. His conquests included Persia, Egypt, and parts of India. Along the way, he established many cities and spread Greek culture.One of Alexander's greatest achievements was his ability to blend different cultures. He encouraged his soldiers to marry local women and promoted the exchange of ideas and traditions. This led to a rich cultural synthesis that influenced future generations.Alexander's leadership style was also remarkable. He was known for his bravery in battle and his ability to inspire his troops. He led from the front and was always willing to take risks. His strategic thinking and military genius allowed him to overcome seemingly insurmountable obstacles.The impact of Alexander's conquests was far-reaching. He opened uptrade routes and promoted cultural exchange. His empire became a center of learning and innovation. Many of the ideas and institutions that emerged during his reign had a profound influence on the development of Western civilization.1. Alexander the Great was born in ___.A. AthensB. SpartaC. PellaD. Rome答案：C。

刺突蛋白三聚体是Alpha-冠状病毒亚单位疫苗的潜在候选者

刺突蛋白三聚体是Alpha-冠状病毒亚单位疫苗的潜在候选者冠状病毒（Coronavirus ，CoV ）是单股、正链RNA 病毒，按照系统发育分析，可分为4个属：Alpha-、Beta-、Gamma-和Delta-冠状病毒属。

目前，Alpha-CoVs （HCoV-229E 、HCoV-NL63）和Beta-CoVs （HCoV-HKU1、HCoV-OC43）广泛流行于人群中，感染症状较轻。

SARS-CoV 、SARS-CoV-2和MERS-CoV 属于Beta-CoVs ，具有高致病性。

已有研究表明Alpha-CoVs 刺突（Spike ）蛋白的受体结合功能域（RBD ）处于“lying ”构象，而Beta-CoVs 的RBD 处于“standing ”构象。

当前，已经针对SARS-CoV 、SARS-CoV-2和MERS-CoV 开发了多种靶向S 蛋白或RBD 的亚单位疫苗。

与Beta-CoVs 相比，Alpha-CoVs 的S1亚基结构和受体识别模式，特别是RBD 的结构及其在S-trimer 中的构象状态存在显著差异，S 蛋白诱导产生中和抗体的能力及机制需要深入研究。

近期发表于《Journal of Virology 》的研究论文，系统分析了Alpha-CoVs 和Beta-CoVs 的S-trimer 和RBD 的构象状态和免疫原性，为疫苗设计提供新思路。

该研究指出Alpha-CoVs （HCoV-229E ）中，亚单位疫苗应优先使用S-trimer 而不是RBD 。

在SARS-CoV 和SARS-CoV-2等Beta-CoVs 中，S-trimer 和RBD 均是亚单位疫苗的理想候选者。

该研究通过结构和B 细胞表位分析发现，Al-pha-CoV （HCoV-229E ）RBD 处于“lying ”构象，其B 细胞表位数目显著少于Beta-CoVs （SARS-CoV 和SARS-CoV-2）。

绿野仙踪英语阅读题目

绿野仙踪英语阅读题目【原创版】目录1.绿野仙踪英语阅读题目概述2.绿野仙踪的作者及背景介绍3.绿野仙踪的主要内容4.绿野仙踪的阅读价值5.绿野仙踪英语阅读题目的答案与解析正文绿野仙踪英语阅读题目《绿野仙踪》是一部美国经典童话故事，原名《The Wonderful Wizard of Oz》，作者是 L.Frank Baum。

这部作品自 1900 年问世以来，一直备受读者喜爱，并被翻译成多种语言。

它也被改编成电影、舞台剧等形式，在世界范围内产生了广泛的影响。

故事讲述了一个名叫多萝西的小女孩被龙卷风带到了一个神奇的国度——奥兹国。

在那里，她遇到了许多有趣的角色，包括稻草人、铁皮人、胆小鬼和黄砖路。

多萝西和她的朋友们在奥兹国经历了一系列冒险，最终找到了神奇的巫师，希望他能帮助她们实现各自的愿望。

在这个过程中，多萝西和她的朋友们学到了许多重要的道理，例如勇气、智慧、爱心和友谊。

这些价值观也正是《绿野仙踪》的阅读价值所在。

通过这个故事，读者可以学到，真正的力量来自于内心的勇气、智慧和爱心，而非外在的物质。

针对《绿野仙踪》的英语阅读题目，主要包括对故事情节的理解、角色分析、以及价值观的探讨。

通过回答这些问题，可以帮助读者更深入地理解这部作品，提高英语阅读能力。

以下是一些《绿野仙踪》英语阅读题目的答案与解析：1.Who is the main character in "The Wonderful Wizard of Oz"?Answer: DorothyAnalysis: Dorothy is the protagonist of the story, who embarks on an adventure in the Land of Oz with her friends.2.What does the Tin Woodman lack and what does he want from the Wizard?Answer: The Tin Woodman lacks a heart, and he wants the Wizard to give him one.Analysis: The Tin Woodman"s desire for a heart represents his longing for emotions and empathy.3.What lesson does Dorothy learn at the end of the story?Answer: Dorothy learns that there"s no place like home.Analysis: This lesson highlights the importance of appreciating what we have and the value of home and family.通过回答这些问题，读者可以更好地理解《绿野仙踪》的故事情节，深入分析角色特点，以及领会作品所传达的价值观。

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a rXiv:mat h-ph/05954v123Sep25The Covariant Picard Groupoid in Diﬀerential Geometry ∗Stefan Waldmann ∗∗Fakult¨a t f¨u r Mathematik und Physik Albert-Ludwigs-Universit¨a t Freiburg Physikalisches Institut Hermann Herder Straße 3D 79104Freiburg Germany September 2005FR-THEP 2005/10Abstract In this article we discuss some general results on the covariant Picard groupoid in the context of diﬀerential geometry and interpret the problem of lifting Lie algebra actions to line bundles in the Picard groupoid approach.Keywords:Morita equivalence,∗-Algebras,Picard groupoid,Hopf algebra actions.MSC (2000):16D90,16W30,16W10,53D55Contents 1Introduction 12From Geometry to Algebra 23Morita Equivalence in Diﬀerent Flavours 44The Covariant Situation 75The case of a Lie algebra 81IntroductionIn this work we would like to illustrate and exemplify some general results from [16]where the general framework of a Morita theory which is covariant under a given Hopf algebra was studied.One main motivation to do so is coming from (deformation)quantization theory [3],see e.g [12,14]for recent reviews.Here Morita equivalence provides an important notion of equivalence ofobservable algebras[6,31].In particular,on cotangent bundles the condition for star products to be Morita equivalent is shown to coincide with Dirac’s integrality condition for magnetic charges of a background magneticﬁeld[6]leading to a natural interpretation of Morita equivalence also in more general situations.From the diﬀerential geometric point of view,it is a natural question whether all these techniques as developed in[5,7–9,31]can be made compatible with a certain given symmetry of the underlying manifold.On the purely algebraic level,a fairly general notion of‘symmetry’is that of a Hopf algebra action of a given Hopf algebra.In[16]we studied this type of symmetry in the general situation.From this general framework we shall specialize now into two directions:on one hand,the algebras on which the symmetry acts and whose Morita theory shall be studied will now be com-mutative:we are interested in the algebra of functions C∞(M)on a manifold.On the other hand, the symmetry in question will either be coming from a Lie group action on M or from a Lie algebra action as its inﬁnitesimal counterpart.It is well-known that two commutative algebras are Morita equivalent if and only if they are isomorphic,see e.g.the textbook[19],whence for commutative algebras Morita equivalence seems to be a useless notion.However,this is not true as things become interesting if one asks in addition in how many ways two algebras can be Morita equivalent compared to the ways in which they can be isomorphic.It turns out that in general there are new possibilities which makes Morita theory interesting even in the commutative framework.This phenomenon is precisely encoded in the so-called Picard groupoid which we shall compute for the case of function algebras.This way,weﬁnd an interesting and non-trivial class of examples illustrating the general ideas of[16].Moreover,it will also be of independent interest as we are now able to re-interpret several well-known problems and results in diﬀerential geometry from a Morita theoretic point of view.Finally,the commutative situation with the algebras being function algebras C∞(M)is expected to be the starting point for a discussion of Morita equivalence of star products as in[6]but now being compatible with a symmetry of the classical phase space[15].The article is organized as follows:In Section2we recall some well-known arguments why one should and how one can pass from a geometric to a more algebraic description of diﬀerential geometry.The next section is devoted to a general discussion on Morita theory in diﬀerentﬂavours, taking into account speciﬁc structures of the algebras in question.Here we are mainly interested in∗-involutions and notions of positivity.In Section4we add one more structure to be preserved by Morita theory,namely a symmetry which we model by a Hopf algebra action.This can be specialized to group actions and Lie algebra actions.The last section contains some new material, namely the explicit computation of a certain part of the Lie algebra covariant Picard group. Acknowledgments:It is a pleasure for me to thank the organizers and in particular Michel Cahen and Willy Sarlet for their kind invitation to the20th International Workshop on Diﬀerential Geo-metric Methods in Theoretical Mechanics in Ghent where the content of this work was presented. Moreover,I would like to thank Stefan Jansen and Nikolai Neumaier for valuable discussions and comments on the manuscript.2From Geometry to AlgebraIn some sense,diﬀerential geometric methods in mathematical physics correspond mainly to clas-sical theories:Hamiltonian mechanics on a symplectic or Poisson manifold M is one prominent example.On the other hand,quantum theories require a more algebraic approach:here the un-certainty relations in physics are modelled mathematically by non-trivial commutation relationsbetween observables in some noncommutative algebra,the observable algebra.Thus quantization in a very broad sense can be understood as the passage from geometric to noncommutative algebraic structures.An intermediate step is of course to encode the geometric structures on M in algebraic terms based on the commutative algebra of functions C∞(M)where,in view of applications to quantization,it is convenient to consider complex-valued functions.Then it is a folklore statement(Milnor’s exercise)that one can recover the smooth manifold M from the∗-algebra C∞(M).More speciﬁcally:every∗-homomorphismΦ:C∞(M)−→C∞(N) between function algebras is actually of the formΦ=φ∗with some smooth mapφ:N−→M between the underlying manifolds,see e.g.[13,24]for a recent discussion.Thus the category of ∗-algebras with∗-homomorphisms as morphisms becomes relevant to diﬀerential geometry.The‘dictionary’to translate geometric to algebraic terms,which is also one of the cornerstones of Connes’noncommutative geometry[11],can be extended in various directions.We mention just one further example also relevant to Morita theory:by the well-known Serre-Swan theorem, see e.g.[29],the(complex)vector bundles E−→M correspond toﬁnitely generated projective modules over the algebra C∞(M)via E↔Γ∞(E).In more geometric terms this means that for any vector bundle there exists another vector bundle F−→M such that E⊕F is a trivial vector bundle.This is of course the key to relate the algebraic K0-theory of C∞(M)to the topological K0-theory of M.Let us now turn to Morita theory.Itsﬁrst motivation came from the question what one can say about two algebras A and B provided one knows that their categories of(left)modules are equivalent,see[19,23].Clearly,in view of applications to quantum mechanics a good understanding of the more speciﬁc modules given by∗-representations of the observable algebras on(pre)Hilbert spaces is crucial for any physical interpretation.As we shall not start to deﬁne what a reasonable category of modules over the∗-algebra C∞(M)should be—though this can perfectly be done,see e.g.[7,27,31]and references therein—we take a diﬀerent motivation which will lead essentially to the same structures.The idea is to take the category of∗-algebras,keep the objects and enhance the notion of morphisms.This can be expected to be interesting as for function algebras we already know what the‘ordinary’morphisms are:pull-backs by smooth maps.Thus a generalization would lead to a generalization of smooth maps between manifolds,when we translate things back using our‘dictionary’.In particular,it might happen that algebras become isomorphic in this new, enhanced category(which will turn out to be not the case for function algebras)and one might have more‘automorphisms’of a given algebra(which will indeed be the case for function algebras). In general,the invertible morphisms in a category form a(large)groupoid in the obvious sense which is called the Picard groupoid of the category.Thus a major step in understanding the whole category is to consider its Picard groupoid of invertible arrowsﬁrst.In principle,the whole idea should be familiar from geometric mechanics as one example of en-hancing a category by allowing more general morphisms is given by the symplectic‘category’:ﬁrst one considers symplectic manifolds as objects and symplectomorphisms as morphisms.Though this is a reasonable choice to look at,it turns out to be rather boring as the choice for the morphisms is too restrictive.More interesting is the‘category’where one considers morphisms to be canonical relations,see e.g.[2].However,this is no longer an honest category since the composition of mor-phisms is only deﬁned when certain technical requirements like clean intesections of the canonical relations are fulﬁlled.Nevertheless this‘symplectic category’is by far more interesting now.Other examples are the Morita theory for(integrable)Poisson manifolds by Xu[32]as well as the Morita theory of Lie groupoids,see e.g.[22].3Morita Equivalence in Diﬀerent FlavoursAfter having outlined the general ideas in the previous section we should start being more concrete now.As warming up we discuss the‘enhancing of the category’for the category of unital algebras with usual algebra morphismsﬁrst,see e.g.[4,19]for this classical approach.Here the generalized morphisms are the bimodules:For two algebras A and B a(B,A)-bimoduleE,which we shall frequently denote byB EAto indicate that B acts from the left while A acts fromthe right,is considered as an arrow A−→B.Why does this give a reasonable notion of morphisms?In particular,we have to deﬁne the com-position of morphisms.Thus letB EAandCFBbe bimodules then their tensor productCFB⊗BBEAover B is a(C,A)-bimodule and hence an arrow A−→C.However,this is not yet an associativecomposition law as for three bimodulesD GC,CFB,BEAwe have a canonical isomorphismD GC⊗C(CFB⊗BBEA)∼=(DGC⊗CCFB)⊗BBEA(3.1)as(D,A)-bimodules but not equality.The way out is to use isomorphism classes of bimodules as arrows instead of bimodules themselves.Then the tensor product becomes indeed associative andthe isomorphism class of the canonical bimoduleA AAserves as the identity morphism of the objectA since we use unital algebras for simplicity.Theﬁnal restriction we have to impose is that in a category the morphism space between two objects has to be a set,which is a priori not clear in our enhanced category.Therefor one should pose additional constraints on the bimodules likeﬁnitely generatedness.However,we shall ignore these subtleties in the following as the new notion of isomorphisms in this category will be unaﬀected anyway.However,we still have to show that we really get an extension of our previous notion of mor-phisms.Thus letΦ:A−→B be an algebra homomorphism.Then on B we deﬁne a right A-modulestructure by b·Φa=bΦ(a)and obtain a bimoduleB BΦA.Its isomorphism class is denoted byℓ(Φ).It is easy to see thatℓ(Φ◦Ψ)=ℓ(Φ)◦ℓ(Ψ)andℓ(id A)is the class ofA AAwhence our previousnotion of morphisms is indeed contained in the new one.If we denote this new category by ALG then two unital algebras A and B are called Morita equivalent iﬀthey are isomorphic in ALG.Without going into the details this is equivalent to the existence of a certain bimodule which is‘invertible’with respect to the composition⊗.In fact, such bimodules can be characterized rather explicitly,see e.g.[19].The isomorphism classes of these invertible bimodules constitute now the Picard groupoid of this category ALG which we shall denote by Pic.The invertible arrows from A to B are denoted by Pic(B,A)while the isotropy group of this groupoid at the local unit A is denoted by Pic(A),the Picard group of A.The mapℓinduces now a group homomorphism such that1−→InnAut(A)−→Aut(A)ℓ−→Pic(A)(3.2) is exact,whence in the commutative case,the automorphism group of A is a subgroup of the Picard group Pic(A).Finally,it can be shown that for commutative A,the exact sequence(3.2)is split whencePic(A)=Aut(A)⋉Pic A(A),(3.3) where the subgroup Pic A(A)consists of the symmetric invertible bimodules,i.e.those where a·x=x·a for all x∈E and a∈A.Then Pic A(A)is called the commutative or static Picard group, see e.g.[8,10]for a discussion and further references.It is a well-known theorem in Morita theory that for unital algebras A,B the equivalencebimodulesB EAare certainﬁnitely generated projective right A-modules such that Hom A(EA)∼=B.Coming back to our example A=C∞(M)we see,using the Serre-Swan theorem,that the only candidates for the symmetric self-equivalence bimodules are the sectionsΓ∞(L)of a complex line bundle.In fact,it turns out thatΓ∞(L)is indeed invertible with inverse given by the class of Γ∞(L∗)sinceΓ∞(L)⊗C∞(M)Γ∞(L)∼=Γ∞(L∗⊗L)∼=C∞(M)as C∞(M)-bimodules.This shows that the static Picard group of C∞(M)is just the‘geometric’Picard group,i.e.the group of isomorphism classes of complex line bundles with the tensor product as ing the Chern class to classify complex line bundles then gives according to(3.3)Pic(C∞(M))=Diﬀeo(M)⋉ˇH2(M,),(3.4) where the semidirect product structure comes from the usual action of diﬀeomorphisms onˇH2(M,).In general,all Morita equivalence bimodulesB EAfor A=C∞(M)are isomorphic to someΓ∞(E)with a vector bundle E−→M of non-zeroﬁbre dimension.Moreover,B has to be isomorphic toΓ∞(End(E)).Thus for function algebras C∞(M)we have a complete description of the Picard groupoid.We shall now specialize our notion of Morita equivalence:we have already argued that the ∗-involution of C∞(M)should be taken into account when having applications to quantization in mind.Moreover,one can include notions of positivity into Morita theory.One deﬁnes a linear functionalω:A−→to be positive ifω(a∗a)≥0for all a∈A.Then an element a∈A is called positive ifω(a)≥0for all positive linear functionalsωof A,see[7,27,30,31]for a detailed discussion.It is clear that for applications to quantum theories such notions of positive functionals are crucial as they encode expectation value functionals and hence the physical states for the observable algebra.In particular,for A=C∞(M)oneﬁnds that positive linear functionals are precisely the inte-grations with respect to compactly supported positive Borel measures.This follows essentially from Riesz’representation theorem,see[5,App.B].From this it immediately follows that f∈C∞(M) is positive iﬀf(x)≥0for all x∈M,whence the above,purely algebraic deﬁnition reproduces the usual notion.We can now state the deﬁnition of∗-Morita equivalence[1]and strong Morita equivalence bimodules,see[26]as well as[20]for Rieﬀel’s original formulation in the context of C∗-algebras and[5,7]for the general case of∗-algebras.Instead of describing the‘enhanced category’way,we directly give the deﬁnition in terms of bimodules which is entirely equivalent,see[7].Deﬁnition3.1A∗-Morita equivalence bimoduleB EAis a(B,A)-bimodule together with innerproducts·,·A:E×E−→A(3.5) andB ·,· :E×E−→B(3.6) such that for all x,y,z∈E,a∈A and b∈B we have:1. ·,·A(resp.B ·,· )is linear in the right(resp.left)argument.2. x,y·aA = x,yAa and B b·x,y =b B x,y .3. x,yA = y,xA∗andB x,y =B y,x∗.4. ·,·Aand B ·,· are non-degenerate.5. ·,· A and B ·,· are full.6. x,b ·y A = b ∗·x,y A andB x,y ·a =B x ·a ∗,y .7.B x,y ·z =x · y,z A .If in addition the inner products are completely positive thenB E A is called a strong Morita equiv-alence bimodule.Here ·,· A is called full if the -span of all elements x,y A is the whole algebra A ;in generalthese elements constitute a ∗-ideal.Moreover, ·,· A is called completely positive if for all n ∈and all x 1,...,x n ∈E the matrix ( x i ,x j A )∈M n (A )is positive in the ∗-algebra M n (A ).The composition of bimodules is again the tensor product where on C F B ⊗B B E A the A -valuedinner product is now deﬁned by Rieﬀel’s formulax ⊗φ,y ⊗ψ F ⊗E A = φ, x,y F B ·ψ E A,(3.7)and analogously for the C -valued inner product.It is then a non-trivial theorem that this is indeed completely positive again,if the inner products on E and F have been completely positive [7].Passing to isometric isomorphism classes one can show that this gives a groupoid:the ∗-Picard groupoid Pic ∗and the strong Picard groupoid Pic str ,respectively.In particular,the local unit at A is given by the isometric isomorphism class of A A A equipped with the inner productsa,b A =a ∗b and A a,b =ab ∗.(3.8)More generally,A n ,viewed as (M n (A ),A )-bimodule equipped with the canonical inner productsM n (A ) x,y =ni =1x · y,· A and x,y A =ni =1x ∗i y i (3.9)implements the strong Morita equivalence between A and M n (A ).Since we simply can forget the additional structures we obtain canonical groupoid morphismsPic strPic ∗Pic ,(3.10)which have been studied in [7]:in general,none of them is surjective nor injective,even on the level of the Picard groups.The geometric interpretation of the inner products is that they correspond to Hermitian ﬁber metrics on the corresponding line bundles or vector bundles,respectively:Indeed,this can be seen easily from the very deﬁnitions.Since up to isometry there is only one positive Hermitian ﬁber metric on a given line bundle we have in the case of A =C ∞(M )Pic str (C ∞(M ))=Diﬀeo(M )⋉ˇH 2(M,)=Pic (C ∞(M )).(3.11)Remark 3.2In the approach of [5,7–9]one main point was to replace the real numbers by an arbitrary ordered ring R and by the ring extension C =R (i)with i 2=−1.This allows to include also the formal star product algebras from deformation quantization into the game.They are deﬁned as algebras over the formal power series [[λ]].Surprisingly,essentially all of the constructions involving positivity go through without problems.4The Covariant SituationLet us now pass to the covariant situation:we want to incorporate some given symmetry of the∗-algebras in question.Here we have two main motivations and examples from diﬀerential geometry: First,a smooth actionΦ:M×G−→M of a Lie group G on M,where by convention we choose a right action in order to have a left action g→Φ∗g on C∞(M)by∗-automorphisms.Second,as inﬁnitesimal version ofΦ,a Lie algebra action,i.e.a Lie algebra homomorphismϕ:g−→X(M) from a realﬁnite dimensional Lie algebra g into the Lie algebra of real vectorﬁelds,which correspond to the∗-derivations of C∞(M).In order to formalize and unify both situations it is advantageous to consider Hopf∗-algebras and their actions on algebras.Thus let H be a Hopf∗-algebra,i.e.a unital∗-algebra with a coassociative coproduct∆,a counitǫand an antipode S such that∆:H−→H⊗H as well as ǫ:H−→are∗-homomorphisms and S(S(g∗)∗)=g for all g∈H,see e.g.[17,Sect.IV.8].For the coproduct we shall use Sweedler’s notation∆(g)=g(1)⊗g(2).The two geometric examples we want to discuss are now encoded in the following Hopf∗-algebras:First,recall that any group G deﬁnes its group algebra[G]which becomes a Hopf∗-algebra by setting∆(g)=g⊗g,ǫ(g)=1and S(g)=g−1=g∗for g∈G⊆[G].In this case H=[G]is even cocommutative,i.e.∆=∆opp where the opposite coproduct is deﬁned by∆opp(g)=g(2)⊗g(1).Second,for any real Lie algebra the complexiﬁed universal enveloping algebra U(g)⊗= U(g)becomes a Hopf∗-algebra by setting∆(ξ)=ξ⊗+⊗ξ,ǫ(ξ)=0and S(ξ)=−ξ=ξ∗together with the resulting extensions to all of U(g).Again,U(g)is cocommutative.The situation that a group G acts by∗-automorphisms on a∗-algebra as well as a Lie algebra representation by∗-derivations can be uniﬁed in terms of Hopf∗-algebras as follows:A∗-action⊲of H on A is a bilinear map⊲:H×A−→A such that g⊲(h⊲a)=(gh)⊲a and H⊲a=a,i.e.A is a left H-module,and g⊲(ab)=(g(1)⊲a)(g(2)⊲b),g⊲A=ǫ(g)A,and(g⊲a)∗=S(g)∗⊲a∗for all g,h∈H and a,b∈A.Then it is well-known and easy to see that for our two examples[G] and U(g)this indeed generalizes and uniﬁes the action by∗-automorphisms and∗-derivations, respectively.The interesting relations are all encoded in the diﬀerent coproducts.In principle and probably even more naturally,one should consider coactions instead of actions of H,see e.g.[17,Sect.III.6].Nevertheless,we stick to the more intuitive point of view where H ‘acts’.Now suppose we have∗-algebras A,B with a∗-action of H.Let furthermoreB EAbe a strongor∗-Morita equivalence bimodule.Then we call the bimodule H-covariant if there is an H-module structure on E denoted by⊲,too,such that we have the following compatibilitiesg⊲(b·x)=(g(1)⊲b)·(g(2)⊲x)(4.1)g⊲(x·a)=(g(1)⊲x)·(g(2)⊲a)(4.2)g⊲B x,y =B g(1)⊲x,S(g(2))∗⊲y (4.3)g⊲ x,yA = S(g(1))∗⊲x,g(2)⊲yA(4.4)for all x,y∈E,a∈A,b∈B and g,h∈H.Of course,in the case of ring-theoretic Morita theory one only requires(4.1)and(4.2).Taking isometric isomorphism classes also respecting the action of H gives the H-covariantﬂavours of the Picard groupoids,denoted by Pic H,Pic∗H,andPic str H ,respectively.Since we can successively forget the additional structures we get the following commuting diagram of canonical groupoid morphisms:Pic str HPic ∗HPic HPic Pic strPic ∗,(4.5)Now let us interpret the diagram on the level of Picard groups and in our geometric situation:Let e.g.H =U (g )and A =C ∞(M )be as before,equipped with an action of g by ∗-derivations.Then the kernel and the image of the group morphismPic H (A )−→Pic (A )(4.6)encodes on which line bundles we can lift the g -action and if so,in how many diﬀerent ways up to isomorphism.Analogously,in the strong situation one requires in addition compatibility with the Hermitian ﬁber metric.The case of H =[G ]leads to the question of existence and uniqueness of liftings of the group action on M to a group action on L by vector bundle automorphisms.In the strong case one requires the lift in addition to be unitary with respect to the ﬁber metric.All this can easily be seen from the compatibility requirements (4.1),(4.2),(4.3),and (4.4)applied to our situation.Clearly,all these lifting problems are very natural questions in diﬀerential geometry and have been discussed by various authors,see in particular [18,21,25,28],whence one can rely on the tech-niques developed there.Even though our approach does not give essential new techniques to attack the (in general quite diﬃcult)lifting problem,it shines some new light on it and unreveals some additional structure of the problem,namely the groupoid structures together with the canonical groupoid morphisms (4.5).Moreover,this point of view embeds the lifting problem in some larger and completely algebraic context since neither the ∗-algebras have to be commutative nor has the Hopf ∗-algebra to be cocommutative.As remarked already,we can even replace and by R and C ,respectively,and incorporate in particular the formal star product algebras from deformation quantization as well.5The case of a Lie algebraIn this last section we consider the case of H =U (g )more closely and develop some general results from [16]slightly further.Assume that B E A is a ∗-Morita equivalence bimodule which allows for a lift of the actions ofH on A and B .Then it was shown in [16,Thm 4.14]in full generality that the possible lifts are parametrized by the following group U (H,A ):We consider linear maps Hom (H,A )with the usual convolution product given by (a ∗b )(g )=a (g (1))b (g (2)).This makes Hom (H,A )an associative algebra with unit e (g )=ǫ(g )A ,see e.g.[17,Sect.III.3].Then we consider the following conditions for a ∈Hom (H,A )a (H )=A ,(5.1)a (gh )=a (g (1))(g (2)⊲a (h ))for all g,h ∈H,(5.2)(g(1)⊲b)a(g(2))=a(g(1))(g(2)⊲b)for all b∈A,g∈H,(5.3))a S(g∗(2)) ∗=ǫ(g)A for all g∈H.(5.4)a(g(1)Then U(H,A)is deﬁned to be the subset of those a∈Hom(H,A)which satisfy(5.1)–(5.4)and it turns out that U(H,A)is a group with respect to the convolution product[16,App.A].Moreover, for unitary central elements c∈U(Z(A))one deﬁnesˆc∈U(H,A)byˆc(g)=c(g⊲c−1).Then one obtains the exact sequence1−→U(Z(A))H−→U(Z(A)) −→U(H,A)(5.5) U(Z(A))⊆U(H,A)is a central and hence normal subgroup.Thus we can deﬁne and the imageU(Z(A)).the group U0(H,A)=U(H,A)The parametrization of all possible lifts is obtained by a free and transitive group action⊲→⊲b of U(H,B)on the set of lifts given by)·(g(2)⊲x),(5.6)g⊲b x=b(g(1)where b∈U(H,B),g∈H and x∈E.In fact,for H-covariantly∗-Morita equivalent algebras A and B we have U(H,A)∼=U(H,B).Moreover,⊲b and⊲give isomorphic actions iﬀb=ˆc for some c∈U(Z(B))whence the isomorphism classes of lifts are parametrized by the group U0(H,B)∼=U0(H,A)which acts freely and transitively via(5.6)on the isomorphism classes of lifts.While the above characterization works in full generality we want to specialize now to Lie algebra actions where H=U(g).First,it follows from[16,Prop.A.7]that U(U(g),A)= U(U(g),Z(A))and hence U0(U(g),A)=U0(U(g),Z(A))since U(g)is cocommutative.Thus the values of a∈U(U(g),A)are automatically central,essentially by(5.3).Moreover,the groups U(U(g),A)and U0(U(g),A)are abelian.Since the center Z(A)is invariant under the g-action (by derivations!)we can restrict a∈U(U(g),A)to g⊆U(g)and obtain a Chevalley-Eilenberg cochainα=a g∈C1CE(g,Z(A)).Evaluating the conditions(5.2),(5.3)and(5.4)on elements ξ,η∈g weﬁnd by a simple computation the following lemma:Lemma5.1The restriction gives an injective group homomorphismU(U(g),A)∋a→α=a g∈Z1CE(g,Z(A)antiHermitian)(5.7) into the Chevalley-Eilenberg one-cocycles with values in the anti Hermitian central elements of A. The injectivity easily follows from successively applying(5.2).Conversely,givenα∈Z1CE(g,Z(A)antiHermitian)we can construct an element a∈U(U(g),A) with a g=α:Let T k(g)denote the k-th complexiﬁed tensor power of g and deﬁne a(k):T k(g)−→A inductively bya(0)=A and a(k)(ξ⊗Y)=α(ξ)a(k−1)(Y)+ξ⊲a(k−1)(Y)for k≥1,(5.8) where Y∈T k−1(g).Then a lenghty but straightforward computation usingδCEα=0shows that a= ∞k=0a(k)passes to the universal enveloping algebra U(g),viewed as a quotient of T•(g)in the usual way,and fulﬁlls(5.1)to(5.4).Thus we have:Theorem5.2The map(5.7)is an isomorphism of abelian groups.Note that this is in some sense surprising as the condition for αto be a cocycle is linear while the condition (5.2)for a is highly non-linear .It only becomes linear when evaluated on ξ,η∈g ⊆U (g )thanks to the fact that these elements are primitive,i.e.satisfy ∆(ξ)=ξ⊗+⊗ξ.Thus a simpliﬁcation like in Theorem 5.2cannot be expected for more non-trivial Hopf ∗-algebras.Moreover,under the identiﬁcation (5.7)the elements ˆc give just the cocycles ˆc (ξ)=c (ξ⊲c −1)asusual.Note that in general U (Z (A ))⊆Z 1CE (g ,Z (A )antiHermitian )are not CE-coboundaries.Thus,if we want to relate U 0(U (g ),A )to Lie algebra cohomology we have to assume an additional structure for A :Deﬁnition 5.3Let A be a unital ∗-algebra.Then an exponential function exp is a map exp :Z (A )−→Z (A )such that1.exp(a +b )=exp(a )exp(b ),2.exp(0)=A ,3.D exp(a )=exp(a )Da ,4.exp(a ∗)=exp(a )∗,for all a,b ∈Z (A )and D ∈Der (A ).Note that D ∈Der (A )induces an outer derivation D Z (A )of the center.We shall now assume that A has an exponential function where our motivating example is of course A =C ∞(M )with the usual exponential.The ﬁrst trivial observation is that for a ∈Z (A )we haveexp(a )(ξ)=−(δCE a )(ξ),(5.9)and for a =−a ∗∈Z (A )antiHermitian we clearly have exp(a )∈U (Z (A )).Thus in this case U (Z (A ))contains all anti Hermitian CE-coboundaries in Z 1CE (g ,Z (A )antiHermitian ).Note however,that ingeneral, U (Z (A ))is strictly larger.To measure this we consider those elements in U (Z (A ))which are not in the image of exp:we deﬁne the abelian groupH 1dR (Z (A ),2πi )=U (Z (A ))H 1dR (Z (A ),2πi ).(5.12)In particular,this applies to A =C ∞(M )whence we obtain the full classiﬁcation of the in-equivalent lifts of the Lie algebra action to line bundles.Note that in general,U 0(U (g ),A )does not depend on the line bundle itself but is universal for all line bundles.。