Can computer models stimulate learning about sustainable land use

合集下载

浅谈Multisim仿真软件在模拟电子技术课程教学中的应用

浅谈Multisim仿真软件在模拟电子技术课程教学中的应用

0 前言《模拟电子技术》这门课程是高等学校里面对于所有强电以及弱电专业课程设置中的一门极其重要的专业基础课程[1],该门课程与《电路分析基础》、《数字电子技术》课程结合起来被人们简称为“三电”课程,其中《模拟电子技术》课程被认为是三者之中最难的课程。

本课程是一门电子技术方面入门性质的技术基础课程,具有很强的工程性和实践性。

《模拟电子技术》课程的先修课程是《高等数学》、《大学物理》、《电路分析基础》等,其中《电路分析基础》中的欧姆定律、基尔霍夫电流定律、基尔霍夫电压定律及一阶RC 电路的暂态分析和受控源电路等是本课程的基础,应在先修课中学好。

本门课程的内容介绍中主要以半导体二极管、晶体三极管和场效应管等电子器件作为基础,分析和设计基本放大电路、集成运放电路等电路,理解并掌握相关电路的结构构成、电路的主要性能指标及参数、电路的工作原理及应用、电路参数的分析计算等。

课程的教学目标是希望学生能够通过对本课程的学习,获得电子技术方面的基本理论、基本知识和基本技能,能够基本掌握各种半导体器件的特性、各类电子线路的模型及分析方法,同时结合实验教学和课程设计使学生能够具有一定的组装和调试基本电子线路的能力以及会分析一些简单的电子线路;培养学生分析问题和解决问题的能力,进一步提高学生在实验研究方面、自学新知识方面、分析计算方面、总结归纳以及实验技能和逻辑推理等诸多方面的能力,为进一步深入学习电子技术的某些领域以及电子技术在专业上的应用打好基础。

同时本课程还注重提高培养学生严谨、规范、理论联系实际的科学态度,为今后处理工程问题,从事工程应用、科研活动和继续深造打下扎实的基础[2]。

但是,由于本门课程工程特点突出、物理概念抽象、分析方法复杂,不管是对于学生的学习还是在实际教学过程中,普遍反映该课程学习起来理解难,掌握难,入门难。

在实际的教学过程中,仅仅使用传统的教学方方式,难以达到教学目标,因此,在课程教学中,可适当引入电路仿真软件如Multi-sim,将抽象的知识形象地显示出来,便于学生理解,在一定程度上激发学生的学习兴趣。

汽车检测维修英语论文

汽车检测维修英语论文

Auto detection and maintenanceAbstract:The modern automobile maintenance technology has become increasingly high tech, from electronic products in automotive applications, automotive diagnostic equipment to the modern use of the Internet in the vehicle maintenance information on the application,and maintenance management software,play in the automotive repair business the role, all reflect the characteristics of modern high-tech vehicle maintenance. Vehicle maintenance is no longer a simple part repair, accurate diagnosis of the fault lies, is the highest level of the modern automobile maintenance.Accession to the WTO under the new situation,their own emphasis on improving the quality of auto repair,auto repair business to change the original way to make our auto repair industry has been rapid development.Keywords: automobile; maintenance features; progressIntroductionAs science and technology of modern automobile industry and the rapid development of ever-changing new technology,new materials,new technology is widely used, especially in electronic technology, the application of hydraulic technology in the car, so that today's car is a culmination of a variety of advanced technologies,innovative chic renovation of the car from time to time . Diagnosis and modern cars is no longer seeing , ears , hands touch, car maintenance is no longer a master train an apprentice in a trade, but the process of using a variety of new technologies. With the rapid development of automotive technology increasingly showing a high-tech features of vehicle maintenance, vehicle maintenance concept with the same time are constantly updated.1, the characteristics of the modern automobile maintenance1.1 Characteristics of Fault DiagnosisHyundai is not a simple mechanical products ,nor is the initial transport,but rather evolved from the original car to a high-tech crystal. Especially electronic technology, computer technology, rapid development, so that the car is improving the degree of technology. Electronic fuel injection system engine (EFIE), ABS anti-lock braking system, SRS airbag system, electronically controlled automatic transmission system (AT), accelerate the slide to adjust the system (ASR), automatic air conditioning (A / C), E suspension system (ECS), power steering system, automatic cruise control, central locking and alarm system,TCS traction power systems and self-diagnosis system,the assembly of pieces by the electronic control unit (ECU) full control, electronic control unit self-diagnostic function that can record failures, and to the code stored in the ECU memory. Through the decoder from the ECU memory to read out the stored trouble codes, to determine the location and failure to provide online troubleshooting help .1.2 Maintenance Tool FeaturesWith the development of automotive technology,maintenance equipment also will produce a qualitative change.Maintenance equipment,automobile production,more use of equipment is no longer a class-based.Since the 90s of the 20th century,groups ofadvanced testing equipment and instruments imported cars into the country. Wheel alignment, decoders, car oscilloscope,automotive electric meter, engine analyzer, exhaust gas tester and computer balancing machine,these old people are very familiar with the testing equipment, has become an indispensable tool for modern service enterprises [4 ]. These testing equipment itself is high-tech products, electronic test technology,computer technology,advanced integration ed to manipulate the testing equipment,technicians need to go through rigorous training,and To master a foreign language and computer technology to master the correct way to use the full functionality testing equipment.This high-tech modern automotive testing equipment,so the technology content of the modern automobile maintenance greatly improved.1.3 Characteristics of service informationWith the information, information and network technology, the industries are in a new period of development. Cars from structure to control the increasingly high-tech, automotive new brand, new equipment, new features abound. Service technician can not repair the thousands of models of information, data, program memory in the brain. Vehicle maintenance technician knowledge, skills, experience and a comprehensive grasp of information, more and show their limitations. The solution is the lack of vehicle maintenance professional network,that is,INTERNET Internet [5].INTER-NET Internet has completely broken the information transmission in space,time limitations,the first time in the most comprehensive information quickly spread rapidly to every corner of the earth.The INTERNET Internet in China has been the modern automotive repair industry emerge from the international automotive repair industry,the service industry technical data query, fault detection and diagnosis, technical training network, has been fully universal.Vehicle repair industry as an example the United States as early as the 20th century,early 90s,in the integrated management of maintenance information,the expert group consultation,on-line query data,online answers to difficult cases,online technical training and on-line purchase of vehicle maintenance information, has become a maintenance the basic characteristics of the industry.Vehicle maintenance professional network,our mid-90s from the 20th century started to Europe and Asia •Di Wei vehicle maintenance professional website, for example, since 1995, namely the establishment of internal use in the member remote communication BBS. In 1996, began to invest heavily in large-scale establishment of car repair INTERNET Internet site [6]. Has developed into the most professional website,covering Europe,the United States and Asia of the cars engine,transmission,air conditioning,suspension,steering,fixed speed,air bags and anti-theft systems and other basic maintenance, repair procedures, various types of data, the class component location diagram,mechanical disassembly diagram and electrical circuit diagram,and realized the answer online,online consulting,online shopping and online training and other functions. 1.4 Characteristics of the maintenance personnel training In our traditional service enterprises,the cultural level of maintenance personnel, theoretical foundations,language levels are low,most of the traditional training methods train an apprentice with the master pattern, it is difficult to achieve mechanical and electrical integration, knowledge of computers, will be a modern foreign language the level of maintenance technicians.With the development of automotive technology,auto repairservices in the technical staff,must have high quality,in addition to automotive expertise with solid theory,but also need to master a variety of automotive testing equipment and instruments,to master a foreign language,Familiar with computer analysis and vehicle maintenance vehicle maintenance professional INTERNET Internet query information on the emerging analysis of a variety of incurable diseases, to accurately judge, skilled excluded,the lowest cost,the shortest working hours,the best quality service,excluding the Class of car trouble, so the owner satisfaction. This, in addition to teaching in schools, the vehicle maintenance and technical personnel but also strengthen their own learning, but also by means of various types of technical training,particularly electronic teaching and online training, and constantly updated service concepts, knowledge, skills and improve their own quality in order to Hyundai repairForeign auto repair education sector has also introduced the operation of a multimedia computer animation and physical education CD-ROM database can be used in distance education and online learning, and the extent of teachers by students and teaching courses, automatic scheduling of teaching video playback, the playback order , play time, at any time to adjust the content and evaluation of different evaluation criteria, to stimulate enthusiasm for learning, active learning will improve the students, and establish a computer teaching of the heuristic and interactive learning environment to enhance learning effectiveness.This way of teaching the computer to form the modern automotive training the new features.1.5 Maintenance Management featuresAs computers and related systems development,and in many countries,computer management in the automotive repair industry has been widely used,and this trend will continue to expand.In China,the use of computerized management has just started,for most auto repair businesses,who have the most comprehensive management system, the most modern management methods,the most accurate management comprehensive data analysis and best service,who can for most customers,in an invincible position in the competition. Computerized management of the business sector can repair department, parts department, workshop, cashier, general manager of various aspects of network operations monitoring, integrated management, the business activities at a glance, manage to overcome the chaotic situation in the past, the management staff from everyday trivial things . Works of liberation, improve efficiency, access to customer support.Top managers can also keep abreast of computer management network system dynamics of vehicle maintenance, easy to co-ordinate arrangements. Can make the maintenance industry to change the traditional mode of manual operation,to achieve a qualitative leap. Can make the tedious affairs director from the freed for more benefits.Computerized management standards, can automatically create complete and accurate customer and vehicle files,for long-term,flexible basis for customer service, improve the maintenance tracking services can add to customer satisfaction.Can eliminate some of the work of errors and improve efficiency. Dynamic tracking of vehicles and customer service department can control vehicles and specific details of each client atany time to remind customers to repair, maintenance and parts replacement, reflecting the integrity of service, timeliness, level.2 modern vehicle repair compared with traditional methodsIn terms of the modern concept of vehicle maintenance and repair system or repair of enterprise management and intelligence aspects of fault diagnosis, compared with traditional repair methods, have a greater leap in quality [8].3, the quality of the modern automobile maintenance enterprises3.1 Characteristics of Enterprise QualityHyundai Motor repair business survival and development to win, we must attach importance to improving the quality of their own, their quality factors include:① business management modernization. ② the construction of enterprise technology management team. ③ enterprise technology business level. ④ maintenance technical data and technical information of use. ⑤ quality level of maintenance of vehicles. ⑥business concepts and sense of service. ⑦ corporate reputation and service reputation.⑧ operational efficiency, employee benefits and competitive price advantage in the market. ⑨ market share of maintenance. 10 corporate social image, reputation and social identity. Enterprise development, the proportion of elements, is a measure of overall quality of the quantitative indicators of business, its mathematical expression isQ = [F1X1 + F2X2 + ... + FnXn] [F1Y1 + F2Y2 + ... + FnYn] = ΣFiXi / FiYi (1)Where Q - the overall quality of business targetsXi - the quality of the enterprises already have elements of the community's percentage of the average statisticsYi - enterprises should have in the quality of the elements, that is, the elements of the average statisticsFi - analysis of factors to determine the importance of each factor, taking a dominant factor, the rest taken from 0 to 13.2 WTO and vehicle maintenanceChina joined the WTO on the impact of the automobile maintenance industry is huge. In order to meet the requirements of after-sales service, foreign car repair industry will have to enter the Chinese market, foreign auto repair industry, vehicle maintenance intervention to the Chinese market offers a more advanced and efficient international technology environment, the promotion of the domestic automobile maintenance industry in innovation, vehicle maintenance trade and technological progress to accelerate the process, will play a role in promoting good [8,9]. The domestic auto repair skills, management ability, management style, production scale, the overall quality of employees and service awareness, and there are still a wide gap between the developed countries, such as auto repair industry in achieving a full range of zero parts delivery and inventory. Mode of operation of vehicle maintenance will gradually with international standards, a variety of mode of operation in full swing, such as special service, agent service, on-site maintenance, special assembly, maintenance, repair chain operations will be achieved, site maintenance, repair and membership means maintenance of the way the club. Fullyreflect the cost to specific quality assurance and service superiority.4 ConclusionThe traditional way of auto repair and maintenance system and the business model must be replaced by modern vehicle repair methods. Previous maintenance often talk about car repair service, Hyundai vehicle maintenance is car sales, parts sales, service information and one of four closely. The new trend of vehicle maintenance is the maintenance of high-tech objects, maintenance equipment modernization, maintenance consulting network, and maintenance of diagnostic experts, computerized maintenance management and clients of social [6, 10].Foreign auto repair auto service companies to enter the domestic market in the form of trade, vehicle repair industry, our country will face a grim situation, and in the car repair business development elements, the dominating factor will be: management, technology, assembly and information. Advocating high-quality vehicle maintenance services industry, brand, modernization, is imperative.译文汽车检测维修摘要:现代汽车维修技术的科技含量已越来越高 ,从电子产品在汽车上的应用 ,到现代汽车诊断设备的使用、互联网在汽车维修资讯上的应用 , 以及维修管理软件在汽车维修企业发挥的作用等 ,处处体现现代汽车维修的高科技特征。

ai时代我们该学什么 英语作文

ai时代我们该学什么 英语作文

Title: Navigating the AI Era: What We Should LearnIn the dawn of the Age of Artificial Intelligence (AI), humanity stands at the cusp of a technological revolution that promises to reshape our lives, industries, and the very fabric of society. As machines become increasingly intelligent, capable of tasks once exclusive to humans, it is imperative for us to adapt and equip ourselves with the skills and knowledge necessary to thrive in this new era. Here are some of the key areas we should focus on learning as we navigate the AI landscape.**1. Coding and Programming:At the heart of AI lies computer science, and mastering programming languages like Python, Java, or C++ is crucial. These languages form the backbone of many AI projects, enabling developers to create algorithms, train models, and build intelligent systems. Learning to code not only empowers individuals to contribute directly to AI advancements but also opens up a vast array of career opportunities in fields ranging from data science to software engineering.**2. Data Analytics and Machine Learning:In the AI era, data is king. Understanding how to collect, analyze, and interpret vast amounts of data is paramount. Machine learning, a subset of AI, enables computers to learn from data without being explicitly programmed, leading to more accurate predictions and decision-making. Learning the basics of statistics, data mining, and machine learning algorithms can help individuals unlock insights that would otherwise remain hidden and drive innovation in various industries.**3. Critical Thinking and Problem-Solving:As AI automates routine tasks, the demand for human creativity, critical thinking, and problem-solving skills increases. These abilities enable us to identify novel challenges, design innovative solutions, and adapt to rapidly changing environments. Encouraging curiosity, fostering a growth mindset, and engaging in activities that stimulate critical thinking are essential for navigating the complexities of the AI era. **4. Ethics and Responsible AI:With the rise of AI, ethical considerations become increasingly important. Learning about the ethical implications of AI technologies, such as privacy, bias, and accountability, is vital to ensure that these advancements benefit society as a whole. Developing a framework for responsible AI involves understanding the legal and social contexts in which these technologies operate, promoting transparency, and advocating for fair and equitable outcomes.**5. Soft Skills:While technical skills are undoubtedly important, soft skills like communication, collaboration, and emotional intelligence are equally crucial in the AI era. As AI systems become more intertwined with our daily lives, the ability to effectively communicate their capabilities, limitations, and potential impacts to non-technicalstakeholders is essential. Moreover, collaborating across disciplines to develop interdisciplinary solutions will be key to unlocking the full potential of AI.**6. Continuous Learning:The field of AI is rapidly evolving, with new technologies and discoveries emerging daily. Embracing a culture of continuous learning, staying up-to-date with the latest research and trends, and seeking out opportunities for professional development is vital for staying relevant and competitive in this dynamic landscape.In conclusion, navigating the AI era requires a blend of technical expertise, critical thinking, ethical considerations, and soft skills. By investing in these areas, we can harness the power of AI to drive progress, innovation, and positive change for all. As we embark on this journey, let us remember that the true potential of AI lies not just in its technological advancements but in how we, as humans, choose to use and shape this remarkable technology.。

Lecture1:Machine Learning Intro

Lecture1:Machine Learning Intro

Supervised Learning Problems
there are two classes of problems Classification and regression. goal is to learn a mapping from inputs x to outputs y, given a labeled set of input-output pairs D = {(xi, yi)}Ni =1. D is called the training set, and N is the number of training examples.. For a classification problem, y∈{1,2,…,C}
If there are just two classes, it is sufficient to return the single number p(y = 1|x,D), p(y = 1|x,D) + p(y =0|x,D) = 1. Given a probabilistic output, we can always compute our “best guess” as to the “true label”:
with experience E
2. we define machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty

高级小众英文句子

高级小众英文句子

In an epoch marked by relentless technological progress, where artificial intelligence (AI), big data analytics, and the Internet of Things (IoT) have become integral to our daily lives, a nuanced understanding of the intricate interplay between technology, ethics, and human flourishing is paramount. This discourse delves into the multifaceted implications of this confluence, examining the transformative power of technology, its ethical quandaries, and the potential it holds for fostering or impeding human well-being. It is through this comprehensive analysis that we can discern the contours of a digital future that aligns innovation with moral rectitude and promotes holistic human development.I. The Transformative Power of TechnologyA. Augmenting Human CapabilitiesThe advent of advanced technologies has undeniably augmented human capabilities across myriad domains. AI, for instance, has revolutionized healthcare by enabling precision medicine, expediting disease diagnosis, and facilitating drug discovery. Similarly, virtual and augmented reality technologies have transcended geographical boundaries, democratizing education and enabling immersive learning experiences. Moreover, smart homes and cities, fueled by IoT, have enhanced energy efficiency, safety, and convenience, reshaping urban living.B. Democratization of Knowledge and CommunicationThe digital age has also democratized access to information and communication, fostering global connectivity and collaboration. Social media platforms, search engines, and open-source repositories have made knowledge more accessible than ever before, empowering individuals to learn, create, and share ideas on a scale previously unimaginable. Furthermore, these tools have facilitated citizen journalism, amplifying marginalized voices and contributing to a more inclusive public discourse.C. Economic Disruption and Opportunity CreationTechnology-driven disruption has upended traditional industries,engendering new business models and job roles. The rise of e-commerce, digital finance, and the gig economy, for example, has reconfigured consumer behavior and labor markets, presenting both challenges and opportunities. While automation and AI-driven job displacement raise concerns about income inequality and structural unemployment, they also stimulate innovation, entrepreneurship, and the creation of high-skilled jobs in emerging sectors like cybersecurity and data science.II. Ethical Quandaries in the Technological LandscapeA. Privacy and Data SecurityThe exponential growth of data generation and harvesting has precipitated significant privacy concerns. Ubiquitous surveillance, unauthorized data breaches, and the commodification of personal information challenge the fundamental right to informational self-determination. Moreover, the opacity surrounding data collection, storage, and usage practices by tech giants exacerbates these issues, necessitating robust regulatory frameworks and transparent data governance mechanisms.B. Algorithmic Bias and DiscriminationAI systems, trained on historical data, can perpetuate and amplify existing societal biases, leading to discriminatory outcomes in areas such as hiring, lending, and criminal justice. Ensuring fairness, accountability, and transparency in algorithmic decision-making thus becomes crucial to prevent the entrenchment of systemic inequalities. This calls for diverse and representative training datasets, explainable AI models, and regular audits to identify and mitigate bias.C. Artificial Intelligence and the Future of WorkThe increasing automation of tasks and the advent of autonomous systems raise profound ethical questions about the nature of work, human dignity, and social cohesion. As machines assume roles once performed by humans, there is a need to redefine the value of labor, explore alternative economic models, and invest in reskilling and upskilling programs to safeguard against technologicalunemployment and foster a more equitable distribution of the benefits of automation.III. Human Flourishing in the Digital AgeA. Promoting Well-being and Mental HealthWhile technology can enhance productivity and convenience, it also bears the potential to disrupt sleep patterns, foster addictive behaviors, and contribute to feelings of loneliness and disconnection. To ensure technology serves human flourishing, designers must adopt a 'human-centered' approach, prioritizing user well-being by incorporating features that promote digital mindfulness, healthy screen-time habits, and meaningful social interactions.B. Fostering Creativity and Intellectual PursuitsTechnological advancements can stimulate creativity and intellectual curiosity by providing access to vast repositories of knowledge, powerful creative tools, and collaborative platforms. Encouraging the use of technology for self-expression, lifelong learning, and interdisciplinary exploration can nurture a society that values critical thinking, artistic innovation, and cultural diversity.C. Strengthening Social Connections and Community EngagementDespite concerns about the isolating effects of digital communication, technology can also facilitate meaningful connections and civic engagement. Virtual communities, online support groups, and digital volunteering platforms demonstrate the potential of technology to transcend geographical barriers, unite individuals around shared interests and causes, and foster a sense of belonging and purpose.IV. Charting a Path Forward: Aligning Technology, Ethics, and Human FlourishingAchieving a harmonious convergence of technology, ethics, and human flourishing requires concerted efforts from various stakeholders, including policymakers, technologists, educators, and citizens. Key strategies include:1. Developing comprehensive and adaptable ethical frameworks that guide thedevelopment, deployment, and regulation of emerging technologies.2. Investing in digital literacy initiatives to empower individuals to navigate the digital landscape responsibly, critically assess information, and protect their privacy.3. Encouraging multi-stakeholder dialogue and collaboration to address the complex ethical challenges posed by technology and foster consensus on shared values and norms.4. Incentivizing responsible innovation by promoting research and development focused on human-centered design, equitable outcomes, and environmental sustainability.5. Implementing policies that mitigate the negative consequences of technological disruption, such as income support programs, job retraining initiatives, and progressive taxation schemes.In conclusion, the confluence of technology, ethics, and human flourishing in the digital age presents a rich tapestry of opportunities and challenges. By embracing a nuanced understanding of this dynamic relationship and proactively shaping a future that aligns innovation with ethical principles and human well-being, we can harness the transformative power of technology to create a more equitable, resilient, and fulfilling society.。

人工智能教育在中小学生英语学习中的应用

人工智能教育在中小学生英语学习中的应用

The Development Trends of Artistic Intelligence Education
Personalized learning
AI powered education systems can analyze student data to provide personalized learning experiences and improve learning outcomes
AI can create interactive learning experiences that engage students and promote critical thinking and problem-solving skills
03
The Application Status of Artistic Intelligence Education in English
Intelligent speech synthesis technology
Speech synthesis application
Intelligent speech synthesis technology can convert text information into natural speech, simulate language communication in real contexts, and provide students with a more authentic language learning experience.
1960s
The first generation of AI systems emerged with the development of symbolic AI, including expert systems and theoretical provers

电化学认识模型及其在高三原电池复习教学中的应用

电化学认识模型及其在高三原电池复习教学中的应用

电化学认识模型及其在高三原电池复习教学中的应用一、本文概述Overview of this article电化学作为化学学科的一个重要分支,主要研究电与化学反应之间的相互作用和转化关系。

电化学认识模型是对电化学现象进行理解和分析的理论工具,它帮助学生和教师从微观层面理解原电池的工作原理、电极反应以及能量转化等核心知识。

在高三原电池复习教学中,电化学认识模型的应用具有非常重要的意义。

本文旨在探讨电化学认识模型的基本概念和构建方法,并分析其在高三原电池复习教学中的应用策略及效果。

通过深入研究和实践,我们期望能够帮助学生更好地理解和掌握原电池的工作原理,提高复习效率,为未来的学习和实践打下坚实的基础。

Electrochemistry, as an important branch of chemistry, mainly studies the interaction and transformation relationship between electricity and chemical reactions. The electrochemical understanding model is a theoretical tool for understanding and analyzing electrochemical phenomena, whichhelps students and teachers understand the core knowledge of the working principle, electrode reactions, and energy conversion of primary batteries from a micro level. The application of electrochemical understanding models is of great significance in the review teaching of primary batteries in the third year of high school. This article aims to explore the basic concepts and construction methods of the electrochemical understanding model, and analyze its application strategies and effects in the review teaching of primary batteries in the third year of high school. Through in-depth research and practice, we hope to help students better understand and master the working principle of primary batteries, improve review efficiency, and lay a solid foundation for future learning and practice.二、电化学认识模型的构建Construction of Electrochemical Understanding Model电化学认识模型的构建是理解和应用电化学原理的关键。

简述幼儿教师素质对幼儿发展的影响

简述幼儿教师素质对幼儿发展的影响

简述幼儿教师素质对幼儿发展的影响幼儿教师素质是指幼儿教师所具备的知识、技能、态度和素养等方面的综合能力。

幼儿教师的素质对幼儿的发展有着重要的影响。

优秀的幼儿教师能够提供良好的教育环境和教育方法,促进幼儿的全面发展,塑造他们的人格和品质。

幼儿教师的知识水平对幼儿的发展起着决定性的作用。

幼儿教师需要具备扎实的学科知识,包括语言、数学、科学、音乐、美术等多个领域。

只有掌握了丰富的知识,幼儿教师才能够在教学中灵活运用,满足幼儿的学习需求。

例如,在语言教育中,幼儿教师需要掌握儿童语言发展规律和语言教学方法,通过丰富多样的语言活动,激发幼儿的语言表达能力,促进其语言发展。

幼儿教师的教育技能对幼儿的发展也有着重要的影响。

教育技能包括教学设计、教学方法、教学评价等方面的能力。

优秀的幼儿教师能够根据幼儿的特点和需求,设计富有趣味性和挑战性的教学活动,激发幼儿的学习兴趣和积极性。

同时,幼儿教师还需要善于运用多种教学方法,如故事讲解、游戏引导、情景模拟等,以满足幼儿的多元化学习需求。

通过合理的教学评价,幼儿教师可以了解幼儿的学习情况,及时调整教学策略,促进幼儿的个体发展。

幼儿教师的态度和素养对幼儿的发展具有重要的影响。

幼儿教师需要具备耐心、细心、爱心等良好的教育态度。

他们应该关心每个幼儿的成长,尊重幼儿的个性差异,注重培养幼儿的独立性和自信心。

同时,幼儿教师还需要具备良好的职业道德和职业素养,以身作则,成为幼儿的良师益友。

优秀的幼儿教师能够与幼儿建立良好的师生关系,为幼儿提供稳定、温暖的成长环境,促进幼儿的身心健康发展。

总结起来,幼儿教师素质对幼儿的发展具有重要的影响。

优秀的幼儿教师具备丰富的知识、灵活的教育技能,以及良好的教育态度和素养。

他们能够为幼儿提供良好的教育环境和教育方法,促进幼儿的全面发展。

因此,提升幼儿教师的素质是促进幼儿教育质量的关键,也是保障幼儿健康成长的重要保证。

【参考译文】Title: The Impact of Preschool Teacher's Qualities on Children's DevelopmentThe qualities of preschool teachers refer to their comprehensive abilities in knowledge, skills, attitudes, and qualities. The qualities of preschool teachers have a significant impact on children's development. Excellent preschool teachers can provide a good educational environment and methods to promote the overall development of children and shape their personality and character.Firstly, the knowledge level of preschool teachers plays a decisive role in children's development. Preschool teachers need to have a solid knowledge of various subjects, including language, mathematics, science, music, art, etc. Only with a wealth of knowledge can preschool teachers flexibly apply it in teaching and meet the learning needs of children. For example, in language education, preschool teachers need to grasp the developmental rules of children's language and language teaching methods, stimulate children's language expression ability through various language activities, and promote their language development.Secondly, the teaching skills of preschool teachers also have an important impact on children's development. Teaching skills include the abilities in teaching design, teaching methods, and teaching evaluation. Excellent preschool teachers can design interesting and challenging teaching activities according to the characteristics and needs of children, stimulate their interest and motivation to learn. At the same time, preschool teachers need to be proficient in using various teaching methods, such as storytelling, guided games, and simulated situations, tomeet the diverse learning needs of children. Through reasonable teaching evaluation, preschool teachers can understand children's learning situations and adjust teaching strategies in a timely manner to promote their individual development.Thirdly, the attitudes and qualities of preschool teachers have a significant impact on children's development. Preschool teachers need to have good attitudes such as patience, attentiveness, and love in education. They should care about the growth of each child, respect their individual differences, and focus on cultivating their independence and self-confidence. At the same time, preschool teachers should also possess good professional ethics and professional qualities, serve as role models, and become good teachers and friends for children. Excellent preschool teachers can establish good teacher-student relationships with children, provide a stable and warm growth environment, and promote their physical and mental health development.In conclusion, the qualities of preschool teachers have a significant impact on children's development. Excellentpreschool teachers have rich knowledge, flexible teaching skills, as well as good educational attitudes and qualities. They can provide a good educational environment and methods for children, promote their overall development. Therefore, improving the qualities of preschool teachers is the key to promoting the quality of preschool education and an important guarantee for the healthy growth of children.。

专四人工智能不会使人变懒的英语作文

专四人工智能不会使人变懒的英语作文

专四人工智能不会使人变懒的英语作文全文共3篇示例,供读者参考篇1AI Will Not Make People LazyWith the rapid advancement of artificial intelligence (AI) technology, concerns have been raised about its potential impact on human laziness. Some argue that as machines become increasingly capable of performing various tasks, people may become overly reliant on them and lose motivation to engage in productive activities. However, I firmly believe that AI will not cultivate laziness among humans; rather, it will serve as a catalyst for innovation, efficiency, and personal growth.First and foremost, it is crucial to understand that AI is not a sentient being capable of independent thought ordecision-making. It is a tool designed to assist and augment human capabilities, not replace them entirely. Just as the invention of calculators did not make people lazy in mathematics, AI will not foster laziness but will instead enhance our ability to tackle complex problems and streamline tedious processes.One of the primary advantages of AI is its potential to automate repetitive and mundane tasks, freeing up human time and cognitive resources for more creative and intellectually stimulating endeavors. By delegating routine tasks to AI systems, individuals can focus their efforts on higher-level thinking, problem-solving, and innovation. This newfound freedom from monotonous work could actually inspire people to pursue their passions, explore new ideas, and engage in activities that contribute to personal growth and societal advancement.Moreover, the integration of AI into various industries and fields will likely increase efficiency and productivity, enabling individuals to accomplish more with less effort. For example, in the healthcare sector, AI-powered diagnostic tools can assist physicians in making more accurate and timely diagnoses, reducing the likelihood of misdiagnosis and improving patient outcomes. In the field of education, AI-driven tutoring systems can provide personalized learning experiences tailored to each student's unique needs and learning styles, enhancing their understanding and retention of material. These applications of AI not only streamline processes but also empower humans to achieve better results with their efforts.Additionally, AI has the potential to spark curiosity and foster a desire for continuous learning. As AI systems become more advanced and capable of tackling increasingly complex tasks, humans will be motivated to expand their knowledge and develop new skills to remain relevant and competitive. Instead of succumbing to laziness, individuals may be driven to engage in lifelong learning, embrace new challenges, and adapt to the ever-evolving technological landscape.Furthermore, the development and implementation of AI require human expertise, creativity, and ethical oversight. While AI systems can perform specific tasks efficiently, they lack the ability to set goals, make value judgments, or navigate the nuances of human interactions. Humans will continue to play a vital role in defining the objectives, parameters, and ethical frameworks within which AI operates. This necessity for human input and governance will prevent individuals from becoming complacent or lazy, as they must remain actively engaged in shaping the trajectory of AI development.It is important to acknowledge that the transition to anAI-driven society may present challenges, such as job displacement and the need for reskilling. However, these challenges should not be viewed as obstacles to overcome butrather as opportunities for growth and adaptation. Throughout history, technological advancements have always been accompanied by societal transformations, and it is our responsibility as humans to embrace these changes, acquire new skills, and adapt to the evolving landscape.In conclusion, AI will not cultivate laziness among humans; instead, it will serve as a powerful tool to augment our capabilities, increase efficiency, and foster innovation. By automating routine tasks, AI will free up human time and cognitive resources for more creative and intellectually stimulating endeavors. Furthermore, the integration of AI into various industries and fields will increase productivity and enable individuals to achieve better results with their efforts. Additionally, the development and implementation of AI will require human expertise, creativity, and ethical oversight, preventing complacency and promoting continuous learning and adaptation. Rather than succumbing to laziness, individuals will be motivated to embrace new challenges, expand their knowledge, and actively shape the trajectory of AI development. Ultimately, AI will be a catalyst for human growth and progress, not a enabler of laziness.篇2Will AI Make Us Lazy? A Student's PerspectiveAs a student in the rapidly evolving world of technology, I can't help but be in awe of the remarkable advancements in artificial intelligence (AI). From virtual assistants like Siri and Alexa to self-driving cars and highly sophisticated language models, AI is infiltrating every aspect of our lives. However, amidst this technological revolution, a persistent concern has emerged: will AI make us lazy?To address this question, we must first understand the fundamental nature of AI and its intended purpose. AI is designed to augment and enhance human capabilities, not to replace them entirely. It is a tool, much like a calculator or a word processor, that aims to streamline tasks and increase efficiency. Just as calculators didn't make mathematicians lazy, AI will not breed laziness in us; rather, it will free us from mundane and repetitive tasks, allowing us to focus on more creative and intellectually stimulating endeavors.One of the primary advantages of AI is its ability to process vast amounts of data and perform complex calculations at lightning-fast speeds. This capability is particularly valuable in fields such as medical research, where AI can analyze vast datasets and identify patterns that would be nearly impossiblefor human researchers to discern. By offloading thesedata-intensive tasks to AI systems, researchers can dedicate more time and energy to interpreting the results, formulating hypotheses, and designing experiments – tasks that require human ingenuity and creativity.In the realm of education, AI holds immense potential for personalized learning experiences. Intelligent tutoring systems can adapt to individual students' learning styles, strengths, and weaknesses, providing customized instruction and feedback. This tailored approach can enhance engagement, motivation, and overall learning outcomes. Far from promoting laziness, AI in education empowers students to take an active role in their learning journey, fostering a growth mindset and a love for knowledge.Moreover, AI can serve as a powerful tool for automating tedious and time-consuming tasks, freeing us from the shackles of monotony. For instance, in the world of writing, AI language models can assist with tasks such as grammar checking, plagiarism detection, and even generating rough drafts based on outlines or prompts. However, these AI tools are not intended to replace human creativity and critical thinking; rather, they are designed to streamline the writing process, allowing writers tofocus on refining their ideas, developing compelling narratives, and crafting impactful compositions.Critics may argue that the convenience and efficiency offered by AI could lead to complacency and a reliance on technology to do the heavy lifting. However, this argument overlooks the inherent human drive for growth, exploration, and self-actualization. Throughout history, technological advancements have not diminished human ambition or curiosity; instead, they have fueled new frontiers of discovery and innovation.As students, we are the architects of the future, and AI is a powerful tool in our arsenal. By embracing AI responsibly and harnessing its potential, we can unlock new realms of knowledge, push the boundaries of human achievement, and tackle the most pressing challenges facing our world. From developing sustainable energy solutions to advancing medical treatments, AI can amplify our collective efforts and accelerate progress in ways we have yet to imagine.Ultimately, the fear that AI will make us lazy is unfounded. Rather than promoting indolence, AI promises to be a catalyst for human ingenuity, freeing us from the shackles of monotony and empowering us to pursue our passions, ask bold questions,and explore the depths of human potential. As students, it is our responsibility to embrace AI as a tool for personal and collective growth, while retaining our critical thinking skills, ethical principles, and unwavering commitment to lifelong learning.篇3The Rise of AI: Will It Make Us Lazier or Smarter?With the rapid advancements in artificial intelligence (AI) technology, concerns have been raised about whether AI will foster laziness in humans by doing all the work for us. However, as a university student, I believe that AI has the potential to enhance our intellectual capabilities rather than make us complacent. In this essay, I will explore the ways in which AI can stimulate our curiosity, facilitate learning, and encourage creative thinking, ultimately leading to a more engaged and intellectually active society.First and foremost, it is essential to understand the nature of AI and its limitations. AI systems, no matter how advanced, are not sentient beings capable of abstract reasoning or independent thought. They are designed to process vast amounts of data and perform specific tasks efficiently, but they lack the depth of understanding and creativity that humanspossess. AI is a tool, much like a calculator or a search engine, that can assist us in our endeavors but cannot replace the human mind's ability to think critically, ask questions, and generate novel ideas.One of the primary ways in which AI can foster intellectual engagement is by sparking our curiosity and encouraging us to delve deeper into various subjects. AI-powered virtual assistants, for instance, can provide us with a wealth of information on any topic we desire, but they cannot comprehend the nuances or make meaningful connections between disparate concepts. It is up to us, as humans, to analyze the information provided, identify gaps in our understanding, and formulate new questions to explore. This process of inquiry and discovery is fundamental to intellectual growth and can be facilitated by AI's ability to quickly retrieve and synthesize data from numerous sources.Moreover, AI can revolutionize the way we learn by providing personalized and adaptive educational experiences. AI-driven learning platforms can analyze a student's strengths, weaknesses, and learning styles, and tailor the content and delivery methods accordingly. This personalized approach can make learning more engaging and effective, as students are presented with material that aligns with their interests andabilities. Additionally, AI-powered virtual tutors can provide real-time feedback, identify areas of struggle, and offer targeted support, fostering a more active and immersive learning experience.Furthermore, AI can be a powerful tool for nurturing creativity and innovative thinking. By automating repetitive tasks and handling complex data processing, AI can free up our cognitive resources, allowing us to focus on higher-order thinking and creative problem-solving. For instance, in the field of design, AI can assist in generating initial concepts and iterations, enabling designers to explore a broader range of ideas and refine their creative visions. Similarly, in scientific research, AI can analyze vast amounts of data, identify patterns, and generate hypotheses, allowing researchers to concentrate on interpreting the results and formulating new theories.However, it is crucial to acknowledge that the integration of AI into our lives requires a responsible and ethical approach. As AI systems become more sophisticated, we must remain vigilant about potential biases, privacy concerns, and the implications of delegating decision-making to algorithms. Education and awareness are key to ensuring that AI is used as a tool to augment human intelligence rather than replace it.In conclusion, while the rise of AI may seem daunting to some, it presents an opportunity for us to become more intellectually engaged and creative. By leveraging AI's capabilities to assist us in retrieving information, personalizing learning experiences, and automating tedious tasks, we can free up our cognitive resources to focus on higher-order thinking, curiosity-driven exploration, and innovative problem-solving. AI should not be viewed as a threat to human intelligence but rather as a powerful tool that can enhance our intellectual capabilities if used responsibly and ethically. As students and lifelong learners, it is our responsibility to embrace AI's potential while maintaining our critical thinking skills, intellectual curiosity, and drive for continuous learning and growth.。

双语教师教研活动(3篇)

双语教师教研活动(3篇)

第1篇一、活动背景随着全球化进程的加快,双语教育在我国逐渐受到重视。

为了提高双语教师的教学水平和教学质量,促进教师之间的交流与合作,我校于近日组织了一次双语教师教研活动。

本次活动旨在通过研讨、分享和实践,提升教师的专业素养,为学生的全面发展奠定坚实基础。

二、活动目标1. 提升双语教师的专业素养,增强教学能力。

2. 促进教师之间的交流与合作,形成良好的教研氛围。

3. 探索适合我校学生的双语教学模式,提高教学质量。

三、活动内容(一)主题讲座本次教研活动首先邀请了资深双语教育专家进行主题讲座。

专家从双语教育的理念、实践和挑战等方面进行了深入浅出的讲解,为老师们提供了宝贵的理论指导。

1. 讲座主题:双语教育的理念与实践专家指出,双语教育不仅仅是语言教学,更是一种跨文化教育。

在教学中,教师应注重培养学生的语言能力、文化意识和思维能力。

同时,专家还分享了在双语教学中如何处理母语与外语的关系、如何设计有效的教学活动等问题。

2. 讲座主题:双语教学中的挑战与应对策略专家强调,双语教学中面临着诸多挑战,如语言障碍、文化差异、教学方法等。

针对这些问题,专家提出了一系列应对策略,如加强教师培训、丰富教学资源、开展跨文化交流活动等。

(二)教学研讨在讲座之后,教师们分组进行了教学研讨。

各组围绕以下主题展开讨论:1. 如何在双语教学中融入跨文化元素?2. 如何提高学生的语言运用能力?3. 如何设计有效的教学活动,激发学生的学习兴趣?在研讨过程中,教师们积极发言,分享了自己的教学经验和心得。

通过讨论,大家达成了以下共识:1. 在教学中,应注重培养学生的跨文化意识,帮助他们更好地理解和尊重不同文化。

2. 通过多样化的教学活动,如角色扮演、小组讨论等,提高学生的语言运用能力。

3. 教师应关注学生的学习兴趣,设计富有创意的教学活动,激发他们的学习热情。

(三)教学实践为了将教研成果转化为实际教学效果,教师们进行了教学实践。

各组根据研讨成果,设计了相应的教学活动,并在课堂上进行实践。

线上教学和传统教学的优缺点英语作文

线上教学和传统教学的优缺点英语作文

线上教学和传统教学的优缺点英语作文全文共3篇示例,供读者参考篇1The Pros and Cons of Online vs Traditional LearningAs a student living through the COVID-19 pandemic, I've experienced both online and traditional in-person learning. Each has its own unique advantages and disadvantages that are worth exploring.The Rise of Online EducationWhen the pandemic first hit and schools shut down, we were thrust into the world of online learning almost overnight. Zoom became our virtual classroom, textbooks were replaced by PDFs, and we attended lectures from the comfort of our homes. It was a jarring transition, but one that allowed education to continue despite the circumstances.The Pros of Online LearningConvenience - One of the biggest perks of online learning is the convenience factor. There's no commute to class, no packing a bag in the morning. You can attend your virtual lectures in yourpajamas if you really want to. For those with long commutes or personal obligations like childcare, this flexibility is invaluable.Cost Savings - Online education also tends to be more affordable than traditional in-person programs. You save money on things like transportation, parking fees, meal plans, and often tuition itself is reduced for online degree or course options.Flexibility - In addition to the locational flexibility, online classes give you more control over your schedule. Recorded lectures let you view material on your own time instead of having to adhere to a fixed weekly calendar. This flexibility makes it easier for students with full-time jobs or other commitments to pursue their education.The Cons of Online LearningIsolation - One of the biggest downsides I experienced with online learning was the isolation and lack of human interaction. Staring at a screen for hours on end with little peer engagement can feel extremely lonely and demotivating at times. An active classroom environment with in-person discussions is hard to replicate online.Technical Issues - Let's face it - online learning is highly dependent on technology behaving properly. Wifi issues,computer glitches, or failure to understand the learning platforms can all derail your virtual classroom experience. These technical difficulties are disruptive and take time away from actual learning.Distractions - When you're taking classes from home, you're surrounded by more potential distractions. Family members, chores, TV - it's easy to get sidetracked and lose focus without the academic environment of a physical classroom. Developing self-discipline becomes crucial.The Traditional Classroom ExperienceAfter over a year of Zoom University, I was elated to return to in-person classes on campus. There's something special about the traditional classroom learning experience.The Pros of In-Person LearningClassroom Environment - Being in an actual classroom surrounded by other students is incredibly conducive to learning. The focused academic environment, ability to turn to your neighbor for help, and hearing different perspectives during discussions creates deeper engagement with the material.Face-to-Face Interaction - In addition to a dedicated learning space, in-person classes facilitate face-to-facecollaboration and relationship-building between students and professors. Having real human interaction makes a huge difference, both academically and socially. Ideas flow more naturally in person.Hands-On Learning - Certain subjects simply can't be taught as effectively through a screen. Courses like labs, art classes, trades, and performance arts require physical practice with specialized equipment or spaces. These hands-on experiences are extremely valuable.The Cons of Traditional LearningScheduling Conflicts - One downside of traditional education is less flexibility with scheduling. You have to adhere to a fixed weekly schedule of classes which can be difficult to manage if you have work, family obligations, or other commitments. Recorded lectures don't allow you to learn at your own pace.Commute - Getting yourself to campus for every class can become a major hassle, especially if you don't live locally. Traffic, parking costs, and the physical act of commuting multiple times a week is a significant investment of time and money that online learning eliminates.No Geographic Freedom - In-person classes at a specific school limit where you can live while pursuing your education. With online programs, you could technically live anywhere with an internet connection. The ability to learn from any location is a plus for those wanting more geographic flexibility.The Best of Both WorldsUltimately, both online and traditional classroom learning have their share of advantages and disadvantages. The ideal educational experience likely incorporates elements of both. Having the flexibility to view some lectures online while also attending in-person classes and discussions creates a nice middle ground. Universities offering mixed delivery models may be the future.As students, we have to weigh the pros and cons based on our individual needs, learning styles, and life situations. For me personally, I thrive in an engaging classroom setting and prefer the traditional approach for a richer academic experience. But I'm extremely grateful for online options that have allowed learning to continue through difficult circumstances. Both formats serve an important purpose in facilitating accessible, quality education.篇2The Ups and Downs of Online vs Traditional EducationAs a student navigating the modern educational landscape, I've experienced both online learning and traditional in-person classes. Each approach has its unique advantages and disadvantages, and after weighing them carefully, I've developed my own perspective on their relative merits. Let me break it down for you.The Rise of Online EducationWhen the COVID-19 pandemic hit, online learning transitioned from a niche option to a widespread necessity. Suddenly, students worldwide found themselves adapting to virtual classrooms, video lectures, and online assessments. While the shift was jarring for many, myself included, I soon discovered some surprising benefits to this new educational format.Flexibility: The Beauty of ConvenienceOne of the most significant advantages of online learning is the flexibility it affords. No more rushing across campus to make it to class on time or dealing with the hassle of scheduling conflicts. With online courses, I could attend lectures from the comfort of my own home or wherever I had an internetconnection. This flexibility was a game-changer, especially for those of us juggling studies with work, family commitments, or other responsibilities.Moreover, the recorded lectures allowed me to learn at my own pace. If I missed something or needed to revisit a concept, I could simply rewind the video or refer back to the materials. This self-paced approach catered to different learning styles and helped me better grasp complex topics.Cost-Effective and AccessibleOnline education also proved to be a more cost-effective option for many students. Without the need for commuting or relocating near a physical campus, we could save on transportation and housing expenses. Additionally, online programs often have lower tuition fees compared to traditional universities, making higher education more accessible to a broader range of students, regardless of their financial circumstances.The Drawbacks: Social Isolation and Technical ChallengesHowever, as liberating as online learning can be, it also presents some significant drawbacks. One of the most glaring challenges is the lack of face-to-face interaction and the socialisolation that can come with it. As humans, we thrive on personal connections and the energy of a shared learning environment. Online classes, while convenient, can feel impersonal and detached, making it harder to build meaningful relationships with peers and instructors.Furthermore, the reliance on technology can be adouble-edged sword. Technical issues, such as internet connectivity problems or software glitches, can disrupt the learning experience and cause frustration. Not everyone has equal access to reliable technology or a conducive study environment at home, which can exacerbate existing educational disparities.The Traditional Classroom ExperienceWhile online learning has gained significant traction, the traditional in-person classroom experience remains thetried-and-true method for many students and educators. Let's explore the advantages and disadvantages of this approach.The Richness of Face-to-Face InteractionsOne of the most significant advantages of traditional classrooms is the opportunity for direct, face-to-face interactions with instructors and peers. This personal connection fosters asense of community and facilitates dynamic discussions, where ideas can be exchanged, debated, and refined in real-time. The energy and spontaneity of a shared physical space can stimulate intellectual curiosity and enhance the learning experience.Additionally, in-person classes often provide hands-on learning opportunities, such as laboratory experiments, field trips, or practical demonstrations, which can be challenging to replicate in a virtual environment.Structured Learning EnvironmentTraditional classrooms offer a dedicated and structured learning environment, free from the distractions that can arise when studying at home or online. The physical separation of the classroom from other aspects of life can help students focus and stay engaged with the material being taught.Moreover, the structured schedule and routine of attending classes at set times can instill discipline and time management skills, preparing students for the demands of professional life after graduation.The Downsides: Rigid Schedules and Limited AccessHowever, traditional in-person education is not without its drawbacks. One of the most significant challenges is the lack offlexibility in scheduling. Students often have to plan their lives around rigid class schedules, which can be particularly difficult for those with work or family obligations.Furthermore, access to traditional education can be limited by geographical constraints. Students may need to relocate or commute long distances to attend their preferred institutions, which can be costly and impractical for some.Striking the Right BalanceAs with many aspects of life, the ideal educational approach may lie in finding a balance between online and traditional learning methods. Hybrid models, which combine elements of both, can offer the best of both worlds – the flexibility and accessibility of online learning, coupled with the personal interactions and hands-on experiences of traditional classrooms.Furthermore, as technology continues to evolve, online learning platforms are becoming more sophisticated, offering features like virtual reality simulations and interactive multimedia tools that can enhance the learning experience and bridge the gap between virtual and in-person instruction.In conclusion, both online learning and traditional in-person education have their unique advantages and disadvantages.While online education offers unparalleled flexibility and accessibility, it can lack the personal connections and hands-on experiences of traditional classrooms. Conversely, in-person education fosters a rich learning environment but may be limited by rigid schedules and geographical constraints.As a student, I believe that the ideal approach lies in finding the right balance between these two modes of learning, tailored to individual needs and preferences. By embracing the strengths of both online and traditional education, we can create a more inclusive, engaging, and effective learning experience for students of all backgrounds and circumstances.篇3The Pros and Cons of Online vs Traditional LearningAs a student who has experienced both online and traditional classroom learning, I can say that each mode of education has its own set of advantages and disadvantages. The ongoing COVID-19 pandemic has forced many educational institutions to shift towards online teaching, and this has sparked a debate about the efficacy and feasibility of virtual learning environments. In this essay, I will critically analyze the pros and cons of online learning and traditional classroom-based learning.Online Learning: AdvantagesFlexibility and Convenience: One of the most significant advantages of online learning is the flexibility it offers. Students can attend classes from the comfort of their homes or any location with an internet connection. This eliminates the need for commuting, saving time and money. Furthermore, online courses often provide recorded lectures, allowing students to learn at their own pace and revisit the material as needed.Access to a Wider Range of Courses: Online learning platforms offer a vast array of courses from various institutions around the world. This diversity enables students to explore subjects and enroll in programs that might not be available locally, broadening their educational horizons.Cost-Effectiveness: In many cases, online courses are more cost-effective than traditional on-campus programs. Students can save money on transportation, accommodation, and other associated expenses, making education more accessible to individuals with financial constraints.Self-Paced Learning: Online courses typically allow students to work through the material at their own pace, accommodating different learning styles and schedules. This self-paced approach can be particularly beneficial for individuals who prefer to taketheir time understanding concepts or those juggling multiple responsibilities.Online Learning: DisadvantagesLack of Face-to-Face Interaction: One of the biggest drawbacks of online learning is the absence of face-to-face interaction with instructors and peers. This lack of personal connection can make it difficult to build meaningful relationships, ask questions in real-time, and engage in discussions that foster deeper learning.Technical Issues: Online learning heavily relies on technology, and technical issues such as internet connectivity problems, software compatibility issues, or device malfunctions can disrupt the learning process and cause frustration.Self-Discipline and Time Management Challenges: Online learning requires a high level of self-discipline and time management skills. Without the structured environment of a traditional classroom, some students may struggle to stay motivated, organized, and on track with their studies.Lack of Hands-On Learning Opportunities: Certain subjects, such as science laboratories or technical workshops, may bechallenging to replicate effectively in an online setting, limiting the practical and hands-on learning experiences for students.Traditional Classroom Learning: AdvantagesFace-to-Face Interaction and Collaboration: Traditional classroom settings foster face-to-face interaction between students and instructors, enabling real-time discussions, immediate feedback, and collaborative learning opportunities. This personal interaction can enhance understanding, promote critical thinking, and facilitate the development of interpersonal skills.Structured Learning Environment: Traditional classrooms provide a structured learning environment with set schedules and routines. This structure can help students stay focused, motivated, and accountable, as well as facilitate better time management and study habits.Access。

优秀教师板书设计要求

优秀教师板书设计要求

优秀教师板书设计要求优秀教师板书设计要求教师板书是课堂教学中重要的辅助工具之一,它能够直观地展示教师的教学内容和思路,帮助学生理解知识点,提高教学效果。

因此,设计一份优秀的教师板书尤为重要。

下面是一些关于优秀教师板书设计的要求。

一、内容明确优秀教师板书的设计应该以学科知识为核心,准确传达教学内容。

在设计中,教师应该思考以下几个问题:这堂课的重点知识点是什么?哪些概念、定理或公式是学生难以理解的重点?如何用简练的文字和图表来表达这些内容?教师需要对教学内容进行归纳总结和提炼,确保板书的内容简练明确,易于理解。

二、排版整齐教师板书的排版要整齐、有序,遵循一定的布局原则。

教师应该合理安排文字和图表的位置,确保内容逻辑清晰,视觉效果好。

字体的大小和颜色应该适宜,不宜过小或过大,颜色不宜过于花哨,以免影响学生的阅读和理解。

同时,教师还应该注意板书的字迹工整,避免错别字和涂改痕迹。

三、图文并茂优秀教师板书应该是图文并茂的,注重用图表来展示和说明知识点。

教师可以通过插入图片、绘制图表等方式,帮助学生更好地理解抽象的概念和模型。

同时,教师还可以使用颜色、形状等视觉元素,增强图像的表现力,吸引学生的注意力。

但是,在使用图表时,教师要注意图表的简洁性和准确性,避免信息过载和误导学生。

四、注重互动优秀教师板书设计应注重互动性,鼓励学生参与。

教师可以设置一些问题或挑战,让学生通过板书来思考和回答。

在板书设计中,教师可以留出一些空白的位置,供学生写下自己的思考、问题或答案。

教师还可以通过板书设计,引导学生进行小组讨论或展示,增强学生的合作与交流。

通过互动,教师能够更好地了解学生的学习情况,帮助学生解决问题和掌握知识。

总之,优秀教师板书设计应该以学科知识为核心,内容明确,排版整齐,图文并茂,注重互动。

教师需要根据学生的学习情况和自身的教学需求,设计出具有针对性和引导性的板书,提高教学效果,培养学生的学习兴趣和能力。

优秀的教师板书设计不仅能够帮助学生更好地理解知识,还能够激发学生的学习热情,促进他们主动参与课堂活动,进一步提高教学质量。

人工智能在农业方面应用英语作文

人工智能在农业方面应用英语作文

人工智能在农业方面应用英语作文Artificial Intelligence Is Helping FarmsHi there! Today I want to tell you all about how artificial intelligence (AI) is being used on farms to help grow our food. AI is really cool and amazing technology that is making farming easier and better for the environment. Let me explain what I mean.First off, what is artificial intelligence? Well, it's kind of like having a super smart computer brain that can learn things, recognize patterns, and make decisions without a human telling it exactly what to do every step of the way. Instead of just following instructions, AI can figure stuff out on its own by looking at lots of data and examples.AI is being used in agriculture in some really neat ways. One way is with drones – those little flying robots that can go up in the air and take pictures from above the fields. Farmers put cameras and sensors on drones that use AI to automatically check on the crops and look for problems. The AI can spot signs of disease, drought, nutrient deficiencies, or pest infestations way earlier than a human walking through the fields could.That's important because treating crop problems quickly helps prevent bigger damage and wasted food. Plus the drones cover the whole field way faster than a person could. Using AI drones lets farmers take better care of their crops with less labor and chemicals needed. How cool is that?Another awesome use of AI is in robotics for things like automated harvesting, weed removal, and pruning. The robots have cameras and sensors that use AI vision to identify the crop versus a weed, or the ripe fruit versus unripe ones. Then the robot's arms can just grab the right things while leaving the rest untouched.Those AI robots are way more precise and efficient than having human workers do all that by hand. And harvesting by robot prevents as much bruising and damage to the produce too. AI robots that spray pesticides precisely where needed can really cut down on chemical use as well. Using AI automation like this makes farms more productive while being gentler on the environment.AI is even being used for soil and water management on farms now. Scientists have these dense sensor networks in fields that constantly monitor the soil moisture levels, nutrient concentrations, salinity, and all sorts of other data. The AI takesin all those real-time readings and uses machine learning algorithms to optimize irrigation and fertilizer schedules. That way, every single plant gets exactly the right amount of water and nutrients it needs, when it needs it.Not only does this AI precision farming grow more food, it also prevents over-watering and excessive fertilizer runoff. So it's better for the environment by cutting down on freshwater waste and nutrient pollution in rivers and streams from the farms. I think that's really important for protecting nature!Weather prediction is another area where AI helps farmers a ton. You know how weather forecasts these days use computer models with tons of data inputs from meteorological sensors across the globe? Well those models use machine learning, which is a kind of AI. The models can analyze patterns in gigantic climate datasets to predict temperature, precipitation, wind patterns and more.Having accurate predictions of rain, frost, heat waves and other conditions allows farmers to take steps to protect their crops at the right times. AI weather forecasting means higher yields and less crop losses from unexpected freak events.Something else cool is using AI for monitoring livestock like cows and chickens. There are systems with cameras and audiosensors that use AI to detect unusual animal behaviors that could signal illnesses, distress, or other issues. The AI learns the normal sounds and movements, so it can alert the farmer if anything seems off with an animal's activity or vocalizations. That helps keep farm animals healthy and comfortable.There are even AI systems that use facial recognition on cows and pigs to monitor each individual animal's health and needs! Can you imagine a computer knowing every single cow in a whole herd just by their face? Mind-blowing!Overall, AI is revolutionizing agriculture and food production in so many ways. From drones and robots to precision environment monitoring, predictive analytics and more, AI makes farming more efficient and sustainable. Using these really smart technologies helps farmers grow more food while using less water, land, fertilizers and pesticides.I think AI in agriculture is such an important advancement for our world's future food supply. With the human population continuing to grow, we need to be able to grow more food in better ways that don't damage the planet's ecosystems and resources. AI is part of farming's evolution to feed humanity while being environmentally conscious and sustainable.What do you think about artificial intelligence being used on farms? I find it all totally fascinating! Of course, AI is just a tool and farmers' knowledge and hard work are still absolutely essential. But having the assistance of AI to make farms more productive, efficient and eco-friendly is an amazing thing in my book!。

针对英语教学存在的问题

针对英语教学存在的问题

One size fits all approach
Traditional methods tend to teach all students in the same way, emphasizing individual differences in learning styles, abilities, and needs
Some English teachers have a negative attitude towards teaching, stacking enthusiasm and responsibility
Teachers' lake of respect for students' subjectivity and creativity can stimulate students' learning motivation
Independent professional level of teachers
Teachers' failure to establish a positive and harmonious relationship with students can lead to poor communication and cooperation between teachers and students
Inflexible current
02
Failure to adapt current to reflect changes in language usage and cultural contexts can make learning feel updated and immediate to students

SemiBoost Boosting for Semi-supervised Learning

SemiBoost Boosting for Semi-supervised Learning

SemiBoost:Boosting for Semi-supervisedLearningPavan Kumar Mallapragada,Rong Jin,Anil K.Jain,and Yi LiuDepartment of Computer Science and Engineering,Michigan State University,East Lansing,MI-48823{pavanm,rongjin,jain,liuyi3}@AbstractSemi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning.Most previous studies have focused on designing special algorithms to effectivelyexploit the unlabeled data in conjunction with labeled data.Our goal is to improve the classificationaccuracy of any given supervised learning algorithm by using the available unlabeled examples.We callthis as the Semi-supervised improvement problem,to distinguish the proposed approach from the existingapproaches.This problem is particularly important when we need to train a supervised learning algorithmwith a limited number of labeled examples and a multitude of unlabeled examples.We present a boostingframework for semi-supervised learning,termed as SemiBoost.The key advantages of the proposedsemi-supervised learning approach are:(a)any supervised learning algorithm can be improved with amultitude of unlabeled data,(b)efficient computational algorithm by the iterative boosting algorithm, November21,2007DRAFTand(c)exploiting both manifold and cluster assumption in training classification models.Our empiricalstudy on16different datasets demonstrates that the proposed framework improves the performance ofseveral commonly used supervised learning algorithms,given a large number of unlabeled examples.We also show that the performance of the proposed algorithm,SemiBoost is comparable to the state-of-the-art semi-supervised learning algorithms.Index TermsMachine learning,Semi-supervised learning,Semi-supervised improvement,Manifold assumption, Cluster assumption,BoostingI.I NTRODUCTIONSemi-supervised learning has received a significant interest in pattern recognition and machine learning.The key idea of semi-supervised learning,specifically semi-supervised classification,is to exploit both labeled and unlabeled data to learn a classification model.Enormous amount of data is being generated everyday in the form of news articles,documents,images and email to name a few.Most of the generated data is uncategorized or unlabeled,thereby making it difficult to use supervised approaches to automate applications like personal newsfiltering,email spam filtering,and document and image classification.Typically,there is only a small amount of labeled data available,for example,based on which articles a user marks interesting,or which email he marks as spam,but there is a huge amount of data that has not been marked.As a result,there is an immense need for algorithms that can utilize the small amount of labeled data, combined with the large amount of unlabeled data to build efficient classification systems. While semi-supervised classification is a relatively newfield,the idea of using unlabeled samples for prediction was conceived several decades ago[1],in the form of transduction. Given a set of labeled samples(labeled set),and a set of unlabeled samples(transduction set), November21,2007DRAFTthe goal of a transductive learner is to predict the labels of the samples in the transduction set.On the other hand,inductive algorithms build a general decision rule over the input feature space, using the samples from both labeled and transduction sets.This rule can then be applied later on unlabeled data points unseen during the training phase.To differentiate from the unlabeled data used for training,we refer to this set of unlabeled data as the induction set.Existing semi-supervised classification algorithms may be classified into two categories based on their underlying assumptions.An algorithm is said to satisfy the manifold assumption if it utilizes the fact that the data lie on a low-dimensional manifold in the input ually,the underlying geometry of the data is captured by representing the data as a graph,with samples as the vertices,and the pairwise similarities between the samples as edge-weights.Several graph based algorithms such as Label propagation[2],[3],Markov random walks[4],Graph cuts[5], Spectral graph transducer[6],and Low density separation[7]proposed in the literature are based on this assumption.Most of these algorithms tend to be inherently transductive.While transductive learning has some useful applications(e.g.,Content Based Image Retrieval[8]), many pattern recognition and machine learning tasks involve prediction of unseen data,which requires inductive algorithms.Some approaches have been developed to extend the transductive algorithm to be inductive[9].These are called the out-of-sample extensions,which are applicable only to a few graph based algorithms.An intuitive way to convert a transductive algorithm to an inductive algorithm is by training a suitable classifier on the data,and then assuming the predicted labels on the transduction set as true labels.However,the predictions made by the transductive classifier are independent of the supervised classifier that will be trained later on this data,which may lead to a suboptimal performance.Several algorithms have been proposed for semi-supervised learning which are naturally November21,2007DRAFTually,they are based on an assumption,called the cluster assumption[10].It states that the data samples with high similarity between them,must share the same label.This may be equivalently expressed as a condition that the decision boundary between the classes must pass through low density regions.This assumption allows the unlabeled data to regularize the decision boundary,which in turn influences the choice of classification models.Many successful semi-supervised algorithms like TSVM[8]and Semi-supervised SVM[11]follow this approach. These algorithms assume a model for the decision boundary,resulting in an inductive classifier. Manifold regularization[12]is another inductive approach,that is built on the manifold assumption.It attempts to build a maximum-margin classifier on the data,while minimizing the corresponding inconsistency with the similarity matrix.This is achieved by adding a graph-based regularization term to an SVM based objective function.Most semi-supervised learning approaches design specialized learning algorithms to effectively utilize both labeled and unlabeled data.However,it is often the case that a user already has a favorite(well-suited)supervised learning algorithm for his application,and would like to improve its performance by utilizing the available unlabeled data.In this light,a more practical approach is to design a technique to utilize the unlabeled samples,regardless of the underlying learning algorithm.Such an approach would accommodate for the task-based selection of a classifier, while providing it with an ability to utilize unlabeled data effectively.We refer to this problem of improving the performance of any supervised learning algorithm using unlabeled data as Semi-supervised Improvement,to distinguish our work from the standard semi-supervised learning problems.To address the semi-supervised improvement,we propose a boosting framework,termed SemiBoost,for improving a given supervised learning algorithm with unlabeled data.SimilarNovember21,2007DRAFTFig.1.Block diagram of the proposed algorithm,SemiBoost.The inputs to SemiBoost are:labeled data,unlabeled data and the similarity matrix.to most boosting algorithms[13],SemiBoost improves the classification accuracy iteratively. At each iteration,a number of unlabeled examples will be selected and used to train a new classification model using the given supervised learning algorithm.The trained classification models from each iteration are combined linearly to form afinal classification model.An overview of the SemiBoost is presented in Fig1.The key difficulties in designing SemiBoost are:(1)how to sample the unlabeled examples for training a new classification model at each iteration?,and(2)what class labels should be assigned to the selected unlabeled examples?It is important to note that unlike supervised boosting algorithms where we select labeled examples that are difficult to classify,SemiBoost needs to select unlabeled examples,at each iteration. One way to address the above questions is to exploit the clustering assumption and the large margin criterion.One can improve the classification margin by selecting the unlabeled examples with the highest classification confidence,and assign them the class labels that are predicted by the current classifier.The assigned labels are hereafter referred to as the pseudo-labels. The labeled data,along with the selected pseudo-labeled data are utilized in the next iteration for training a second classifier.This is broadly the strategy adopted by approaches like Self-November21,2007DRAFTtraining[14],ASSEMBLE[15]and Semi-supervised MarginBoost[16].However,a problem with this strategy is that the introduction of examples with predicted class labels may only help to increase the classification margin,without actually providing any novel information to the classifier.Since the selected unlabeled examples are the ones that can be classified confidently, they are far away from the decision boundary.As a result,the classifier trained by the selected unlabeled examples is very likely to share the same decision boundary with the original classifier that was trained only by the labeled examples.This is because by adjusting the decision boundary, the examples with high classification confidence will gain even higher confidence.This implies that we may need additional guidance for improving the base classifier,along with the maximum margin criterion.It is to be noted that margin maximization by itself does not provide any additional information to the semi-supervised learner.To overcome the above problem,we propose to use the pairwise similarity measurements to guide the selection of unlabeled examples at each iteration,as well as for assigning class labels to them.For each unlabeled example x i,we compute the confidence of assigning the example x i to the positive class as well as the confidence of assigning it to the negative class.These two confidences are computed based on the prediction made by the boosted classifier and the similarity among different examples.We then select the examples with the highest classification confidence together with the labeled examples to train a new classification model in each iteration. The new classification model will be combined linearly with the existing classification models to make improved predictions.The following section discusses the existing semi-supervised learning methods,and their relationship with SemiBoost.November21,2007DRAFTTABLE IA SUMMARY OF SEMI-SUPERVISED CLASSIFICATION ALGORITHMS.T AND I IN THE LAST COLUMN DENOTET RANSDUCTIVE AND I NDUCTIVE PROPERTY OF THE ALGORITHM,RESPECTIVELY.Group SummaryTLabel Propagation[2],[3]Min-cuts[5]TMRFs[4],GRFs[9]TLDS[17]TSGT[6]TLapSVM[12]ICo-training[18]ISelf-training[14]ISSMB[16]IASSEMBLE[15]IMixture of Experts[19]IEM-Naive Bayes[20]ITSVM[8],S3VM[11]IGaussian processes[21]ISemiBoost(Proposed)III.R ELATED WORKTable I presents a brief summary of the existing semi-supervised learning methods and the underlying assumptions.Thefirst column shows the assumptions on the data used in the algorithm.The second column gives the name of the approach with its reference,followed by a brief description of the method in column3.Column4specifies if the algorithm is naturally inductive(I)or transductive(T).From Table I,one can see that almost all the algorithms based on manifold assumption are usually transductive,Laplacian SVM being an exception.On the November21,2007DRAFTother hand,almost all the approaches that are based on cluster assumption tend to be inductive. Graph-based approaches represent both the labeled and the unlabeled examples by a connected graph,in which each example is represented by a vertex,and pairs of vertices are connected by an edge if the corresponding examples have large similarity.The well known approaches in this category include Harmonic Function based approach[9],Spectral Graph Transducer(SGT)[6], Gaussian process based approach[21],Manifold Regularization[12]and Label Propagation approach[2],[3].The optimal class labels for the unlabeled examples are found by minimizing their inconsistency with both the supervised class labels and the graph structure.A popular way to define the inconsistency between the labels y={y i}ni=1of the samples {x i}n i=1,and the pairwise similarities S i,j is the quadratic criterion,F(y)=ni=1n j=1S i,j(y i−y j)2=y T L ywhere L is the combinatorial graph Laplacian.Given a semi-supervised setting,only a few labels in the above consistency measure are assumed to be known,and the rest are considered unknown.The task is to assign values to the unknown labels in such a way that the overall inconsistency is minimized.The approach presented in[5]considers the case when y i∈{±1}, thereby formulating it as a discrete optimization problem and solve it using a min-cut approach. Min-cuts are however prone to degenerate solutions,and hence the objective was minimized using a mixed integer programming approach in[22],which is computationally prohibitive[11].A continuous relaxation of this objective function,where y i∈[0,1]has been considered in several approaches,which is solved using Markov randomfields[4],Gaussian randomfields and harmonic functions[9].The label propagation and harmonic function approach assume that the given labels are exact,and split the objective function in such a way that only the labels of unknown samples are estimated.Spectral graph transducer on the other hand,predicts the labels November21,2007DRAFTof even the labeled samples,however,with a penalty for violating the given labels.The proposed framework is closely related to the graph-based approaches in the sense that it utilizes the pairwise similarities for semi-supervised learning.The inconsistency measure used in the proposed approach follows a similar definition,except that an exponential cost function is used instead of a quadratic cost for violating the labels.Unlike most graph-based approaches, we create a specific classification model by learning from both the labeled and the unlabeled examples.This is particularly important for semi-supervised improvement,whose goal is to improve a given supervised learning algorithm with massive amounts of unlabeled data.The approaches built on cluster assumption utilize the unlabeled data to regularize the decision boundary.In particular,the decision boundary that passes through the region with low density of unlabeled examples is preferred to the one that is densely surrounded with unlabeled examples. These methods specifically extend SVM or related maximum margin classifiers,and are not easily extensible to non-margin based classifiers like decision trees.Approaches in this category include transductive support vector machine(TSVM)[8],Semi-supervised Support Vector Machine (S3VM)[11],and Gaussian processes with null category noise model[21].The proposed algorithm,on the other hand,is a general approach which allows the choice of a base classifier well-suited to the specific task.Kernel learning methods compute the optimal kernel matrices using both the labeled and the unlabeled examples.Cluster kernel[23],kernel alignment method[24],semi-definite program-ming approach[25],and graph kernel approach[26]take this approach.SemiBoost is built on the assumption that the kernel matrix is known.The kernels obtained using the kernel learning methods can be directly incorporated into SemiBoost,thereby deriving the advantages specific to optimal kernel matrices.November21,2007DRAFTEnsemble methods have gained significant popularity under the realm of supervised classifi-cation,with the availability of algorithms such as AdaBoost[27].The semi-supervised counter parts of ensemble algorithms rely on the cluster assumption,and prime examples include AS-SEMBLE[15]and Semi-supervised MarginBoost(SSMB)[16].Both these algorithms work by assigning a pseudo-label to the unlabeled samples,and then sampling them for training a new supervised classifier.SSMB and ASSEMBLE are margin-based boosting algorithms which minimize a cost function of the formJ(H)=C(y i H(x i))+C(|H(x i)|),where H is the ensemble classifier under construction,and C is a monotonically decreasing cost function.The term y i H(x i)corresponds to the margin definition for labeled samples.A margin definition involves the true label y i,which is not available for the unlabeled samples.A pseudo-margin definition is used such as|H(x i)|in ASSEMBLE,or H(x i)2in SSMB,thereby getting rid of the y i term in the objective function using the fact that y i∈{±1}.However, the algorithm relies on the prediction of pseudo-labels using the existing ensemble classifier at each iteration.In contrast,the proposed algorithm combines the similarity information along with the classifier predictions to obtain more reliable pseudo-labels,which is notably different from the existing approaches.SSMB on the other hand requires the base learner to be a semi-supervised algorithm in itself[16],[15].Therefore,it is solving a different problem of boosting semi-supervised algorithms,in contrast with the proposed algorithm.Self-training[14]is an intuitive wrapper-based approach for improving a supervised algorithm using unlabeled samples.Itfirst labels the unlabeled samples by training a supervised classifier on labeled samples.Those unlabeled samples which have high confidence of prediction are included in the training set along with the labeled samples,and the classifier is retrained.Because November21,2007DRAFTof its capability to utilize existing specialized supervised algorithms,self-training is popular in several applications,e.g text categorization,when compared to graph based approaches which usually are computationally complex(matrix inversion[9],eigen value computation[6]).Also graph based approaches tend to replace the existing supervised algorithms,thereby ignoring any special advantages supervised learners may present.However,self-training solely relies on the labels predicted by a classifier on the unlabeled data for training a new supervised classifier.Given the small size of the available labeled training sample in many applications,there is a high probability of making an error on labeling the unlabeled samples.Any such error in the learning of base classifier gets magnified over the self-training iterations.Similar observations were made in[28].The proposed algorithm on the other hand,gives more reliable label predictions as it combines the similarity information effectively with classifier predictions.While retaining the advantage of being wrapper based,the proposed algorithm SemiBoost has the advantages of graph based classifiers embedded into the objective function by enforcing the consistency between the similarity measure and the predicted labels. In essence,the SemiBoost algorithm combines the advantages of graph based and ensemble methods,resulting in a more general and powerful approach for semi-supervised learning.III.S EMI-SUPERVISED BOOSTINGWefirst describe the semi-supervised improvement problem formally,and then present the SemiBoost algorithm.A.Semi-supervised improvementLet D={x1,x2,...,x n}denote the entire dataset,including both the labeled and the unla-beled examples.Suppose that thefirst n l examples are labeled,given by y l=(y l1,y l2,...,y l nl),November21,2007DRAFTwhere each class label y li is either+1or−1.We denote by y u=(y u1,y u2,...,y u nu),the imputedclass labels of unlabeled examples,where n u=n−n l.Let the labels for the entire dataset be denoted as y=[y l;y u].Let S=[S i,j]n×n denote the symmetric similarity matrix,where S i,j≥0represents the similarity between x i and x j.Let A denote the given supervised learning algorithm.The goal of semi-supervised improvement is to improve the performance of A using the unlabeled examples and the pairwise similarity S.It is important to distinguish the problem of semi-supervised improvement from the exist-ing semi-supervised classification approaches.As discussed in section2,any ensemble based algorithm must rely on the pseudo-labels for building the next classifier in the ensemble.On the other hand,Graph based algorithms use the pairwise similarities between the samples,and assign the labels to unlabeled samples such that they are consistent with the similarity.In the semi-supervised improvement problem,we aim to build an ensemble classifier which utilizes the unlabeled samples in the way a graph based approach would utilize.B.SemiBoostTo improve the given learning algorithm A,we follow the idea of boosting by running the algorithm A iteratively.A new classification model will be learned at each iteration using the algorithm A,and the learned classification models at different iterations will be linearly combined to form thefinal classification model.1)Objective function:The unlabeled samples must be assigned labels following two main criteria:(a)the points with high similarity among unlabeled samples must share the same label, (b)those unlabeled samples which are highly similar to a labeled sample must share its label. Our objective function F(y,S)is a combination of two terms,one measuring the inconsistencyNovember21,2007DRAFTbetween labeled and unlabeled examples F l (y ,S ),and the other measuring the inconsistency among the unlabeled examples F u (y u ,S ).Inspired by the harmonic function approach,we define F u (y ,S ),the inconsistency between class labels y and the similarity measurement S ,asF u (y u ,S )=n u i,j =1S i,j exp(y u i −y u j ).(1)Many objective functions using similarity or kernel matrices,require the kernel to be positive semi-definite to maintain the convexity of the objective function (e.g.,SVM).However,since exp(x )is a convex function,and we assume that S i,j is non-negative ∀i,j ,the function F u (y u ,S )is convex irrespective of the positive definiteness of the similarity matrix.This allows similarity matrices which are asymmetric (e.g.,similarity computed using KL-divergence)without chang-ing the convexity of the objective function.Asymmetric similarity matrices arise when using directed graphs for modeling classification problems,and are shown to perform better in certain applications related to text categorization [29].Though our approach can work for general similarity matrices,we assume that the similarity matrix provided is symmetric.Note that Eq (1)can be expanded as F u (y u ,S )=12 S i,j exp(y u i −y u j ),and due to the symmetry of S ,we haveF u (y u ,S )=n u i,j =1S i,j cosh(y u i −y u j ),(2)where cosh(y i −y j )=(exp(−y i +y j )+exp(y i −y j ))/2is the hyperbolic cosine function.Note that cosh(x )is a convex function with its minimum at x =0.Rewriting Eq (1)using the cosh(.)function reveals the connection between the quadratic penalty used in the graph Laplacian based approaches,and the exponential penalty used in the current ing a cosh(.)penalty not only facilitates the derivation of boosting based algorithms but also increases the classification November 21,2007DRAFTmargin.The utility of an exponential cost for boosting algorithms is well known[30]. The inconsistency between labeled and unlabeled examples F l(y,S)is defined asF l(y,S)=n li=1n u j=1S i,j exp(−2y l i y u j).(3)Combining Eqs(1)and(3)leads to the objective function,F(y,S)=F l(y,S)+CF u(y u,S).(4) The constant C is introduced to weight the importance between the labeled and the unlabeled data.Given the objective function in(4),the optimal class label y u is found by minimizing F. Letˆy li,i=1,···,n l denote the labels predicted by the learning algorithm over the labeled examples in the training data.Note that in Eq(4),there is no term corresponding to the inconsistency between predicted labels of the labeled samples and their true labels,which would be F ll= n l i=1exp(y l i,ˆy l i).Adding this term would make the algorithm specialize to AdaBoost when no unlabeled samples are present.Since in practice,there is limited amount of labeled data available,selecting an even smaller subset of samples to train the classifier may not be effective.The current approach therefore,includes the prediction on the labeled data in the form of constraints,thereby utilizing all the available labeled data at each iteration of training a classifier for the ensemble.The problem can now be formally expressed as,min F(y,S)s.t.ˆy li=y l i,i=1,···,n l.(5) This is a convex optimization problem,and therefore can be solved effectively by numerical methods.However,since our goal is to improve the given learning algorithm A by the unlabeled data and the similarity matrix S,we present a boosting algorithm that can efficiently minimize the objective function F.The following procedure is adopted to derive the boosting algorithm. November21,2007DRAFT•The labels for the unlabeled samples y u iare replaced by the ensemble predictions over the corresponding data sample.•A bound optimization based approach is then used tofind the ensemble classifier minimizing the objective function.•The bounds are simplified further to obtain the sampling scheme,and other required pa-rameters.The above objective function is strongly related to several graph based approaches,manifold regularization and ensemble methods.A discussion on the relationship between SemiBoost and several commonly used semi-supervised algorithms is presented in the Appendices F and G.C.AlgorithmWe derive the boosting algorithm using the approach presented in[30].An alternate,conven-tional way to derive the boosting algorithm to use the Function Gradient approach presented in[31].This approach may also be viewed as a relaxation approach to approximate the original objective function by a linear function.Such an approach however,involves specification of a parametric step size.In our derivation,the step size is automatically determined thus overcoming the difficulty in determining the step-size.Let h t(x):X→{−1,+1}denote the2-class classification model that is learned at the t-th iteration by the algorithm A.Let H(x):X→R denote the combined classification model learned after thefirst T iterations.It is computed as a linear combination of thefirst T classification models,i.e.,H(x)=T t=1αt h t(x),November21,2007DRAFTwhereαt is the combination weight.At the(T+1)-st iteration,our goal is tofind a new classifier h(x)and the combination weightαthat can efficiently minimize the objective function F. This leads to the following optimization problem:arg min h(x),αn li=1n u j=1S i,j exp(−2y l i(H j+αh j))+C n u i,j=1S i,j exp(H i−H j)exp(α(h i−h j))(6)s.t.h(x i)=y li,i=1,···,n l,(7) where H i≡H(x i)and h i≡h(x i).This expression involves products of variablesαand h i,making it non-linear and hence difficult to optimize.The constraints,however,can be easily satisfied by including all the labeled samples in the training set of each component classifier.To simplify the computation, we construct the upper bound of the objective function,described in Proposition1. Proposition1:Minimizing Eq(7)is equivalent to minimizing the function2n uj=1S i,j e H j−H i(9)q i=n lj=1S i,j e2H iδ(y j,−1)+CThe quantities p i and q i can be interpreted as the confidence in classifying the unlabeled example x i into the positive class and the negative class,respectively.The upper bound in Eq(8)is not very useful for a boosting algorithm because it is difficult to compute the weighting scheme of examples directly from Eq(8).The expression in Eq(8) is further simplified in the following proposition.November21,2007DRAFT。

有的人认为现代技术和传统课程英语作文

有的人认为现代技术和传统课程英语作文

有的人认为现代技术和传统课程英语作文全文共5篇示例,供读者参考篇1大家想想看,我们平时在家里或者和朋友们在一起的时候,都是用中文在交流吧?如果突然要求我们用英语来写作文,是不是会感到很吃力呢?就算我们学了很多英语单词和语法规则,但要运用这些知识熟练地写出一篇文章还是很困难的。

不过,我倒是觉得,偶尔用英语写一写作文也是件非常有趣的事情!就好像我们在玩一种新游戏一样,用陌生的语言来表达自己的想法,这对于锻炼我们的英语能力可是大有裨益的。

而且,如果我们能熟练运用英语写作的话,那不是很酷吗?不过,我们毕竟还是小朋友,现在最重要的是打好语言的基础。

所以老师觉得,我们可以先用中文来写作文,但同时也要努力学习英语,将来说不定就能用纯正的英语来写出精彩的文章了呢!你们觉得老师说得对吗?无论用什么语言写作,最重要的是能够清楚地表达出自己的想法。

只要我们努力学习,相信总有一天我们会成为写作小高手的!那么,现在就让我们动笔写一写自己的小故事吧!篇2有些人说,现在科技发达了,网上有很多学习资源,我们完全可以自己在网上学习新知识。

可是我并不完全同意这种说法。

虽然网上确实有很多资源,但是如果没有老师的指导和同学们的互相交流,单凭看视频或者阅读文字还是很难真正掌握知识的。

比如说英语课,我们每周都要学习新单词、语法知识,并且做各种各样的练习题。

如果只是自己在家里看视频或者读课本,遇到不懂的地方没人可以解答疑惑,那学习效果肯定会大打折扣。

再比如写作文,同学们互相检查意见,老师批改指出错误,让我们对于文章结构、语法运用、词汇搭配等有进一步的提高。

如果每个人只是写写就完了,而没有后续反馈和指导,那写出来的作文水平就很难有进步了。

当然,我并不是说网上的学习资源就完全不好。

在课堂上学习知识后,我们还是可以通过观看视频、阅读辅助教材等方式来加深理解和印象。

网上确实有很多好的资源可以利用。

不过,网上学习和传统的课堂学习并不矛盾,它们可以相互补充,相得益彰。

人工智能会使大脑变懒惰吗英语作文

人工智能会使大脑变懒惰吗英语作文

人工智能会使大脑变懒惰吗英语作文Will Artificial Intelligence Make Our Brains Lazy?In the age of rapidly advancing technology, the question of whether artificial intelligence (AI) will make our brains lazy is a pertinent one. AI, with its ability to perform complex tasks with remarkable efficiency, has the potential to revolutionize various aspects of human life. However, this also raises concerns about its potential impact on our cognitive abilities and whether it will lead to a decrease in mental activity.First and foremost, it is important to understand that AI is a tool designed to augment human capabilities, not replace them. AI can perform calculations, analyze vast amounts of data, and make decisions based on predictive models, but it lacks the creativity, intuition, and emotional intelligence that humans possess. These human attributes are essential for innovation, problem-solving, and understanding the nuances of human interaction.Moreover, the development of AI has led to an increase in demand for skilled workers who can effectively utilize this technology. This means that individuals need to adapt to the changing landscape and acquire new skills to remain competitive in the job market. This process of continuous learning and adaptation requires cognitive effort and stimulates the brain.Additionally, AI can be used to enhance cognitive functions. For example, AI-powered tools can assist in memory recall, improve decision-making, and enhance problem-solving abilities. These tools can provide support, rather than replacement, for human cognitive functions.However, there are concerns that the widespread use of AI may lead to a decrease in mental activity. Some argue that as AI becomes more capable, individuals may rely on it to perform tasks that require cognitive effort, thereby reducing their own mental engagement. This couldpotentially lead to a decrease in cognitive function and an increase in cognitive laziness.To mitigate these concerns, it is important to strike a balance between utilizing AI and maintaining cognitive engagement. Individuals should make an effort to perform tasks that require critical thinking, creativity, and problem-solving, as these activities stimulate the brain and help maintain cognitive health. Additionally, educators and employers should focus on fostering environments that promote critical thinking and creativity, rather than simply relying on AI to perform tasks.In conclusion, while AI has the potential to enhance human capabilities, it does not inherently make our brains lazy. The key lies in how we utilize AI and maintain a balance between relying on it and engaging our cognitive abilities. By fostering environments that promote critical thinking, creativity, and continuous learning, we can ensure that AI serves as a tool for augmentation, rather than a cause for cognitive decline.Furthermore, it's important to recognize that AI is not a monolithic entity; it consists of a range of technologiesand applications, each with its own impact on human cognition. Some AI systems, such as those used in automation and robotics, may replace human tasks, leading to a decrease in mental engagement. However, other AI systems, such as those used in education and healthcare, can enhance cognitive function and help individuals learn and develop new skills.Additionally, the impact of AI on cognition may vary depending on the individual. Some people may find that AI tools help them work more efficiently, while others may feel that they are losing cognitive abilities as they rely more on technology. This variation highlights the need for individuals to be aware of the potential impact of AI on their cognition and to take active steps to maintain and develop their cognitive abilities.In summary, while there are concerns that AI may have an impact on human cognition, the overall impact remains complex and variable. It is crucial for us to approach AI with a balanced perspective, recognizing its potential to augment human capabilities while also taking activemeasures to maintain and develop our own cognitive abilities. By doing so, we can harness the power of AI to create a future where technology and human cognition coexist harmoniously.。

Scientists Laboring to Make Computers Learn to Think 科学家为使计算机学会思考而绞尽脑汁

Scientists Laboring to Make Computers Learn to Think 科学家为使计算机学会思考而绞尽脑汁

Scientists Laboring to Make Computers Learn to Think 科学家为使计算机学会思考而绞尽脑汁1. There is little doubt that human beings and computers are getting friendlier. There days bank customers have learned to accept an automated teller wishing Them good day or "explaining 'that a transaction can't go through right now. Smiling patiently after losing at computer chess, people gamely give it another try. And even a computerized robot on an assembly line seems less of a threat Than it used to be.人和计算机正越益变得友善起来,已是没有什么疑问的了。

目前,银行的客户已学会接受自动取款机(自动出纳机)向他们问好,?quot;解释"现在木能立即办完业务。

人们在与计算机下棋输了以后一耐心地笑笑,他们还会兴致勃勃地再试一次。

现在甚至在装配线上由计算机控制的机器人似乎也不会有"过去那样的威胁。

2.But computer scientists aren't satisfied with keeping people's relationships With machines on such a superficial level .A branch of their research called artificial intelligence is trying to teach computers to simulate the human thinking process. Along the way, researchers believe they are unraveling more secrets of how The mind works--with the computer's help. And that new information, in turn, Is being used to push toward a break through in developing machines with qualities that resemble basic reasoning skill--the power first to see and hear then to infer, argue or answer queries presented to them in simple language.也计算机科学家们并不满足于人与机器的关系保持在这样肤浅的水平上。

Computational Models of Human Learning

Computational Models of Human Learning

Computational Models of Human LearningMichael PazzaniUniversity of California, IrvineSince the inception of the field of machine learning, some researchers have been involved with building computational models of human learning. From the psychologists perspective, there are a variety of reasons to be interested in machine learning:•Implementing a working computer program that learns forces researchers to precisely specify their theory of human learning. Attempting to implement a computationalmodel can raise new issues that might otherwise have been overlooked. Testing acomputational model and comparing its performance to that of human learners can help to identifying shortcomings of existing theories and suggest areas for future research.•Models of machine learning proposed by either the experimental or theoretical machine learning communities may provide useful insights or starting points for models ofhuman learning.In my opinion, there are good reasons that every machine learning researcher should also be aware of psychologists findings on human learning:•Experimental findings on human subjects can call attention to inadequacies of current computational models and suggest areas for possible research. For example, Thau(1992) provides experimental evidence that suggests that human learners, unlike somecomputational models of unsupervised learning, selectively allocate attention todifferent dimensions during learning.•Human learners are the closest approximizations to general purpose learning machines that are available for study. There are many open problems and current research topicsin machine learning that are related to learning tasks that people solve every day. Evenif one is not interesting in modeling human performance, insights into how peopleperform these task can provide useful starting points for machine learning. People canacquire new skills without interfering with existing skills. People can learn fromincomplete and contradictory information. People can use existing knowledge to aid the learning process, yet can learn in the absence of relevant background knowledge.People can learn from complex, high dimensional visual and auditory data.This special issue presents a diverse group of papers. Each paper explores a different learning task. A variety of models are proposed to account for human learning behaviors, including neural networks, case-based reasoning, probabilistic models and statistical induction. The paper by Jones and VanLehn investigates a model that accounts for data on how young children learn to add. The paper by Kazman proposes a model of the child acquiring lexical and syntactic knowledge by being exposed to samples of adult’s language usage. Martin and Billman investigate how a learner may acquire overlapping concept from unsupervised data. Seifert, Hammond, Johnson, Converse, McDougal and Vanderstoep propose a model of how experiences are stored in memory so that they may be retrieved in appropriate situations. Shultz, Mareschal and Schmidt explore how children learn to predict what will occur when weights are placed on a balance scale.I have to admit that although the subfield of computational modeling has been around as long as experimental approaches to machine learning and computational learning theory, it hasn’t grown as rapidly. One factor that may be responsible for the growth of experimental machine learning is a common set of benchmark problems that researchers can use to compare alternative theories.I believe that much progress has been made due to friendly competition between researchers trying to improve the performance of algorithms on these databases.It is through comparing and contrasting alternative theories of the same phenomenon that scientific understanding of the phenomenon progresses. Unfortunately, this does not occur often enough in computational modeling of human learning. Notable exceptions include several papers in this issue, and a series of papers on learning the past tense of verbs (Rumelhart & McClelland, 1986; Pinker & Prince, 1988; MacWhinney & Leinbach, 1991; and Ling & Marinov, 1993). I’d like to propose that the UCI Repository of Machine Learning Databases and Domain theories be extended to include databases that have been used to evaluate computational models of human learning.1 I’ve stored four databases used in Pazzani (1992) in:pub/machine-learning-databases/cognitive-modeling and I encourage others involved in modeling human learning to contactml-repository@ to archive other databases here. I hope that other researchers will make use of these databases to replicate or improve upon existing models.I’d like to thank the reviewers of the papers to this special issues, and all authors who submitted 1.By the way, I also believe that much may be gained by studying animal learning, so the archive may also beextended to include such databases.papers. I have learned much from being involved in this special issue and I look forward to following progress on computational models of human learning.ReferencesLing. X.C. & Marinov, M. (1993). Answering the connectionist challenge: A symbolicmodel of learning the past tense of English verbs. Cognition, 49, 235-290.MacWhinney, B. & Leinbach, J. (1991). Implementations are not conceptualizations: Revising the verb learning model. Cognition, 39, .235-290.Pazzani, M. (1991). The influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory & Cognition, 17, 3, 416-432.Pinker, S., & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193.Rumelhart, D. & McClelland, J. (1986) On learning the past tenses of English verbs. D. Rumelhart, & J. McClelland (Eds.). Parallel distributed processing: Explorations in the microstructure of cognition. (pp. 216-271). Cambridge, MA: MIT Press.Thau, D. (1992). Primacy effects and selective attention in incremental clustering. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. (pp. 219-223). Hillsdale, NJ: Lawrence Erlbaum Associates.。

  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

Experiences in End-of-Life Care: A Literature Review Original Research Article Asian Nursing ResearchVirtual study abroad and exchange studioAutomation in Constructionenvironmental impact assessments on science and policy: An analysis of the Three Gorges Project Original Research ArticleJournal of Environmental Managementvisualisation for participatory landscape planning—a study from Denmark Original Research ArticleLandscape and Urban Planningrelationships between design and use of urban park spaces Original Research ArticleLandscape and Urban PlanningResearch Highlights►BIM allows using holistic real-world cases that better simulate project conditions. ► BIM allowed learning three project planning methods in an integrated way. ► BIM allows the incorporation of change management in assignments. ► BIM allows learning project plan local optimizations. ► We recommend construction engineer programs adopt BIM to develop practical skills.Shaken, shrinking, hot, impoverished and informal: Emerging research agendas in planning Original Research ArticleProgress in PlanningThe digital design studio has an area of application where conventional media are incapable of being used; collaboration in learning, design and dialogue with people in places other than where one lives. This distinctive opportunity has lead the authors to explore a form of design brief and virtual design studio (VDS) format not well addressed in the literature. Instead of sharing the same design brief, students in this alternative format design a project in the other students’ city and do not collaborate on the samedesign. Collaboration with other students takes the form of teaching each other about the city and culture served by the design. The authors discovered these studios produce a focus on site context that serves our pedagogical objectives – a blend of architectural, landscape architectural and urban design knowledge. Their students use a range of commercial CAD and computer supported collaborative work (CSCW) software common to that used in many VDS experiments reported on in the literature. However, this conventional use of technology is contrasted with a second distinctive characteristic of these studios, the use of custom software tools specifically designed to support synchronous and asynchronous three-dimensional model exchange and linked attribute knowledge. The paper analyzes some of the virtual design studio (VDS) work between the Swiss Federal Institute of Technology, the University of Toronto, and the University of Melbourne. The authors articulate a framework of VDS dimensions that structures their teaching and research.Etudes virtuelles outre-mer et studio d’échangeLe studio de design digital comprend des domaines d’application ou les média conventionnelles ne sont pas suffisantes; la collaboration lors de l’apprentissage, et la conception et le dialogue avec des gens vivant ailleurs. Cette opportunité distincte a mené les auteurs à explorer une forme de description du design et de studio de design virtuel qui n’est pas bien addressée dans la litérature. Au lieu de se partager la même description, dans ce format alternatif les étudiants travaillent sur un projet situé dans la ville des autres étudiants, mais ne collaborent pas sur le même design. La collaboration avec les autres étudiants prend la forme de se renseigner les uns les autres au sujet de la ville et de la culture desservies par le projet. Les auteurs ont découvert que ces studios produisent un focus sur le contexte du site qui correspond à leurs objectifs pédagogiques – un mélange de paysage architectural et de connaissances en planification urbaine. Les étudiants utilisent une gamme d’outils pour le DAO commercial et travail collaboratif aidé par ordinateur (TCAO), analogues à ceux utilisés lors de plusieurs expériences sur les SDV décrites dans la litérature.Cependant, cette utilisation conventionnelle de la technologie contraste avec une deuxièmecaractéristique distinctive de ces studios, l’utilisation d’outils informatiqu es conçus pour permettrel’échange synchrone et asynchrone de modèles trois-dimensionnels et la connaissance de leurs attributs.Ce papier examine la collaboration en studio de design virtuel qui a eu lieu entre l’Institut Fédéral de Technologie Suisse, l’Université de Toronto, et l’Université de Melbourne. Les auteurs articulent un cadre de dimensions SDV qui guide leur enseignement et leurs recherches.Deep-sea benthic community and environmental impact assessment at the Atlantic Frontier Original Research ArticleContinental Shelf ResearchA rticle Outline1. Introduction2. Virtual design studio paradigms2.1. Motivations2.2. Distinguishing dimensions3. Our approach3.1. Zurich/Ottawa and Melbourne/Toronto4. Discussion4.1. Design process4.2. Design outcomes4.3. Presentations and assessments5. Organizational and technical issues for VDS5.1. Co-ordinators5.2. Collaboration projects5.3. Collaboration tools5.4. Infrastructure5.5. Training and help resources5.6. Review of end-products6. SummaryAcknowledgementsReferencesSociety demands solutions to design problems in urban and rural planning that are based on logic and trains of thought that can be put into words. This is in order to support discussion, debate and criticism. The central purpose of this study was to develop an instrument for design critique in landscape architecture. To judge contemporary professional practice, a ―triptych‖ of three types of landscape architecture has been constructed. These are traditional, modern and post-modern.Traditional landscape architecture is an art-and-craft-based approach. In modern landscape architecture, landscape is seen as an object to be analysed scientifically. Post-modern landscape architecture deals with the participation of the population. The task of the post-modern designer is to get the user emotionally involved in the product through the design process.With this instrument, it is possible to become aware of what motivates the individual designer in landscape architecture.This paper describes patterns of use in public open space such as parks that indicate relationships between the design of parks and the detailed ways that users inhabit (or not) such places. It focuses particularly on the use of comparatively level and regularly mown grassed areas. It draws on a combination of behaviour-mapping and GIS supported techniques of spatial annotation and visualization, as applied to urban parks in two European cities, to reveal common patterns of behaviour that appear to be correlated with particular layouts and details. It demonstrates the value of the methodology in revealing relationships between design and use that are based on empirical evidence, and supporting the kind of detailed design guidance that can be of benefit for future design practitioners. It shows how guidance can be arrived at, based on the particulars of the case study sites and cities, and provides a starting point for further studies using the same methods. The value of the research is in helpingdesigners be confident that layouts proposed for intended uses will, in practice serve those uses (and users) well and be likely to be used as predicted.A rticle Outline1. Introduction2. Background literature3. Methodology3.1. Data collection3.2. Database creation and analysis techniques4. Key findings4.1. Occupation of grassed areas: ―landmarks‖ and the ―edge effect‖4.2. Active use of lawns/grassed areas4.3. Buffer zones5. Discussion5.1. Sitting in the grass: dimensions of the ―edge effect‖ (see Fig. 14)5.2. Personal space/public distance5.3. Size and shape of activity spaces and compactness of groups using them5.4. Buffer zones for activity spaces6. Implications and limitations7. ConclusionReferencesVitaeCritique and theory in Dutch landscape architecture Original Research Article Landscape and Urban PlanningReverse logistics network design for the collection of End-of-Life Vehicles in Mexico Original Research ArticleEuropean Journal of Operational ResearchSummaryBackgroundIdentification of people who most frequently engage in sexual risk behaviour while tra velling abroad would be useful for the design and targeting of health education and promotion campaigns.MethodsEligible participants were people living in the UK aged 18–34 years who had travelled abroad without a partner in the previous 2 years. Respondents were first screened for eligibility as part of representative face-to-face and telephone surveys by a market research company. Eligible individuals who agreed to take part then underwent a computer-assisted telephone interview. Reinterviewing continued until 400 eligible people had been contacted. We also interviewed a control group of 568 young people who had travelled abroad without a partner in the previous 2 years but who did not report a new sexual relationship during their travels.FlndingsOne in ten of the eligible participants reported sexual intercourse with a new partner. Travellers who reported a new sexual relationship abroad were also likely to report large numbers of sexual partners at home. Of the 400 people who had a new sexual partner abroad, 300 (75%) used condoms on all occasions with the new partner. Logistic regression modelling showed differences between men and women in those factors linked to the practice of unsafe or safer sex while travelling. For men, patterns of condom use abroad with casual partners (p<0·001) reflected patterns of use at home (p<0·001), whereas for women, patterns of condom use varied according to their partners' backgrounds (p<0·001).InterpretationCondoms are widely used among young travellers, but patterns of use vary by sex. Campaigns about sexual health targeted at international travellers should continue, not least because young people who meet new sexual partners abroad may be a convenient proxy group for that minority of the populationwho report most sexual partners at home. Such campaigns should be designed differently for men and women.A rticle OutlineIntroductionMethodsRespondentsStudy designStatistical analysisResultsDiscussionAcknowledgementsReferencesResponsibilities of theorists: The case of communicative planning theory Original Research ArticleProgress in PlanningInvestigating the relationship between schedules and knowledge transfer in software testing Original Research ArticleInformation and Software Technology环境艺术风景园林专业硕士设计管理经验方案设计能力熟悉规划详细初步施工图现场施工方法AutoCAD "Sketch-up" Photoshop 相关软件国外学习生活背景Scenarios for the Austrian food chain in 2020 and its landscape impacts Original Research ArticleLandscape and Urban PlanningThis paper seeks to describe several features of establishing a closed-loop supply chain for the collection of End-of-Life Vehicles in Mexico. To address this task, the problem is handled through Reverse Logistics and is modelled through an Uncapacitated Facility Location Problem. The solution of this model is obtained using software SITATION©. Furthermore, this work also presents a brief description of the current Mexican ELV management system and the future trends in ELV generation in Mexico. The main result is the configuration of three collection networks within Mexico, which correspond to three possible scenarios that consider 100%, 90% and 75%, respectively, of collection coverage. Regions with high ELV generation are identified as well as relevant factors affecting total costs in the reverse supply chain.A rticle Outline1. Introduction2. Reverse logistics and EoL vehicle management3. ELV management in Mexico4. Strategic network design for ELV collection in Mexico4.1. Definition of the problem4.2. Model formulation4.3. Scenarios5. SITATION© data requirements5.1. Demand and candidate nodes5.2. Demand of ELV collection5.3. Fixed and transport costs6. Results and discussion7. ConclusionsAcknowledgementsAppendix A. Main results of UFCFLP implementation and its solution through software SITATION©A.1. Scenario-1A.2. Scenario-2A.3. Scenario-3References264 articles found for: pub-date > 1984 and tak(((Environmental Art) or Master or construction or Landscape or Architecture or design or management or experience) and (Design or capacity or more or familiar or with or the or (preliminary planning)) and (method or construction or drawings or "on-site" or planning or AutoCAD or "Sketch-up" or Photoshop or software)) Background study and life abroadEntrepreneurial heuristics: A comparison between high PL (pioneering-innovative) and low PI ventures Original Research ArticleJournal of Business VenturingEntrepreneurship researchers in recent years have been experiencing growing disillusionment about the traditional lines of research in the field with its focus on the conducive environment, background and early experiences of the entrepreneur, and the traits and motives of the entrepreneur. It has been rightly pointed out that such research has had very little success either in predicting entrepreneurial behaviour or in producing useful inputs for entrepreneurship training and development. The present paper reports on a study of a new variable, namely entrepreneurial heuristics, which has tremendous potential for predicting entrepreneurial behaviour and providing training and counseling inputs for entrepreneurs.―Entrepreneurial heuristics‖ were defined as the thumb-rules guiding the management decisions involved in the start-up and management of a new venture. The objective of the study was to identify such decision-rules and compare them for the more innovative and less innovative ventures. Data on innovativeness and use of heuristics were collected from 138 published undisguised cases on entrepreneurs, using the ―case-survey method‖ that involved the content analysis of these cases and quantification of the above variables. Case data thus collected were verified against the field data collected from a comparable group of 26 ventures, which raised the size of the final sample to 164.The sample was then divided into three groups based on the distribution of innovativeness scores; the top third was called the high PI group and the bottom third the low PI group. Heuristics that were significantly different for the two groups were called the PI heuristics, and the others were called the general entrepreneurial heuristics. The two groups of heuristics were separately factor-analyzed to find the PI orientations and the general entrepreneurial orientations respectively.A regression analysis showed that entrepreneurial orientations especially the PI ones could explain as much as 50% of the variance in innovativeness and provided some support for the hypothesis that the causal relationship between PI orientation and innovativeness is likely to be stronger in that direction (heuristics causing innovativenss) than in the opposite direction. Finally, a discriminant analysis has shown that PI orientations could fairly well discriminate between the high PI and the low PI groups with a probability of misclassification of 0.12.An analysis of the PI heuristics juxtaposed with the case facts from which they were originally derived has shown that the high PI ventures could be described using a hypothesised model characterized by the following five orien tations: • intrinsic orientation as opposed to extrinsic orientation • organic growth orientation as opposed to transplantation orientation • entity orientation as opposed to property orientation • people orientation as opposed to self-orientation • vision orientation as opposed to opportunity orientationUnderstanding the heuristics of innovative ventures (more importantly those of successful ventures, which have not been investigated here) would be of use to entrepreneurs as well as academics, especially because it is much easier for a person to change policies than to change traits/motives and/or worry about background and the environmental conditions. Moreover, in entrepreneurial heuristics we have a researchable variable that is much more closely related to entrepreneurial action than are traits and motives. An added advantage is that there is no need for a paradigm shift because the study of heuristics is well within the strategic choice paradigm, as opposed to the population ecology paradigm.construction project management with BIM support: Experience and lessons learned Original Research ArticleAutomation in ConstructionHow the future landscape will look depends particularly on the outcome of the socio-economically motivated decisions of farmers, food processors, retailers and consumers, all members of the food supply chain. However, a long-term perspective on the food supply chain and its landscape effects is confronted with a great deal of uncertainty and data constraints. These difficulties can be partly avoided by using the personal judgements of agents whose decisions control the structure of present and future food supply chains. A well-established agent-based method for dealing with and describing variation in the future is the method of scenario planning. The aim of this paper is to present the application of the scenario approach to the Austrian food supply chain in 2020 and its landscape impacts. A critical discussion of the scenarios should reflect their explanatory power regarding future development options for landscapes. The first section of the paper outlines the interactions between society, the food supply chain and the landscape in a conceptual model. It describes the applied scenario technique and the research setting involving agents from agriculture, the food industry, retailing, gastronomy, and consumer organisations. Four scenarios for the food chain in 2020 are presented (Liberal Market Scenario, Protective Policy Scenario, Fast World Scenario, Slow World Scenario) and their respective consequences and strategies are discussed. The scenario technique used is found to be a useful means of gathering and structuring disperse expert knowledge. The paper concludes that—despite some methodological limitations—scenarios can deal with uncertainty concerning the socio-economic driving forces of landscape change and therefore can be used as a preliminary step in formulating robust strategies for landscape management.A rticle Outline1. Introduction2. Model of the interactions between society, the food chain and the landscape3. Methods for medium- to long-term perspectives3.1. Scenario technique—a methodology to imagine possible and plausible futures3.2. The two scenario processes and the applied methods4. Results4.1. The practitioners’ scenarios4.2. The scientists’ scenarios5. Discussion5.1. Comparison of the practitioners’ and the scientists’ scenarios5.2. Potential strategies to be deduced from the scenarios5.3. Critical reflection on the scenario approach6. ConclusionAcknowledgementsReferencesVitaeuncertainty in life after stroke: A qualitative study of the experiences of established and new informal carers in the first 3 months after discharge International Journal of Nursing StudiesDespite its remarkable achievements, the field of international business (IB) is under attack; its legitimacy and importance are challenged. Structural weaknesses, in particular the existence of two subfields – one drawing on economics and strategy, the other on cross-cultural studies – have contributed to IB, but have failed to build the micro-process bridges that would have united and distinguished the field. The sociology of the field with its dominant positivist research paradigm also has not helped. We propose amulti-method, paradigmatic interplay approach to IB research for building intellectual bridges that would draw on the unique demographics of IB researchers and allow the field to be more united and hopefully produce stronger, more relevant research.A rticle OutlineA critical summary of the IB field from the strategy and culture perspectivesThe early economics perspective: surfing the crestsCross-cultural perspective: exploring the troughsLost opportunities: missing the full force of the waveLimitations of the economics perspectiveLimitations of the cross-cultural perspectiveFurther limitations from the sociology of the field itself: damned if you do…An opportunity for integrating and defining the field: riding the full waveToward multi-methods paradigmatic interplay in IB researchReferencesEnvironmental Art Master of Landscape Architecture design and management experience Design capacity of more familiar with the preliminary planning method of construction drawings on-site constructionAutoCAD "Sketch-up" Photoshop softwareBackground study and life abroadexperience in design education as a resource for innovative thinking: The case of Bruno Munari Original Research ArticleProcedia - Social and Behavioral SciencesDesign education in general includes various design fields such as product design, graphic design, communication design and design in engineering. Designing as an activity captures all these various fields. Design refers basically to a problem solving method, a creative problem solving approach and relevant processes. Design as an activity has always been considered as a creative tool. Design education mainly focuses on enhancing creative approaches with various 2D and 3D project based basic design studies. As the tools of designing developed in parallel with technology, the core structure of the education is based on a model with creative and analytical aspects: Designerly way of thinking aims at originality and uniqueness. Today the need for innovation has become more evident than ever. The main purpose of the paper is to explore and to identify the relationship between creativity, innovation and design related to design education. Bruno Munari(Milano, 1907-1998) as a designer and a design educator, is one of the prominent names reflecting innovation and creativity in the history of Italian Design. His innovative contribution to Italian Design is reinforced by his experimental design educator background in research for creativity. The paper aims at exploring the educational structures through history of design and design education that leads to creative thinking and nurture sustainable innovation through the case study of Bruno Munari's works as a designer and as an educator.Landscape architecture at the Wageningen Agricultural University Original Research ArticleLandscape and Urban PlanningThis paper presents experiences and lessons learned during the introduction of Building Information Models (BIM) in construction engineering project management courses. We illustratively show that the introduction of BIM-based project management tools helped the teachers of two courses to develop more realistic project-based class assignments that supported students with learning how to apply different formal project management methods to real-world project management problems. In particular, we show that the introduction of BIM allows educators to design a class project that allowed the use of more realistic cases that better simulate real-world project conditions, helped students to learn how different project management methods integrate with each other, integrate change management tasks in a class assignment, and learn how to optimize project plans.A rticle Outline1. Introduction2. BIM to support project management education3. Research methodology4. Case description4.1. Managing fabrication and construction class — Stanford University4.1.1. Class background4.1.2. Case analysis4.2. Integrated project management — Twente University4.2.1. Class background4.2.2. Case analysis5. Case analysis, findings, and implications5.1. Comparison of the Stanford class exams5.2. Cross case analysis5.2.1. Findings6. Limitations and suggestion for future research7. ConclusionAcknowledgementsReferences。

相关文档
最新文档