Compositional Termination Analysis of Symbolic Forward Analysis

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AbstractIn this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features.1. IntroductionThe new technologies for computer integrated pro-duct development systems are needed to produce technologically advanced products with a wide range of options. Today, industry is facing fierce competi-tion. In order to meet today's global market require-ments, there is a need to introduce advanced production and information technologies to produce higher quality products at less cost with shorter lead times. It has been recognised by both academic and industrial environments that the introduction of new technologies and integration are key factors towards the successful implementation of the integrated production system [1±3]. Although, many research efforts have been made, effectiveness of computer integrated systems in real industrial environment are not satisfactory and shortcomings not solved yet. The major problem is due to lack of automated extraction of information from CAD database for further use in applications following design like assembly, machining, process planning etc.Recently, feature technology is expected to be able to provide an effective tool to automate the integration of design and applications following design. Neural network and feature based approaches with computing technologies have been introduced to overcome the drawbacks for the effective integration and automa-tion of design and applications following design like machining, process planning, analysis, assembly, inspection etc. Various techniques have been proposed to recognize features from solid models. Most of the existing recognition techniques have only limited capability of recognising features in case of non-standard features which are complex in shape and not included in the feature library. The problem of feature recognition is generally due to the limited domain of feature representation in the feature library. In this research, non-standard feature terminology is used for the features which have complex shapes. The terminology of standard and non-standard features is explained with examples in the following sections.Currently, researchers are involved with the problems in the area of feature recognition which are recognition of standard and non-standard features which are not included in the feature library; (ii)feature relations.In this research, an approach based on neural net-work and face scores is developed to extract standard and non-standard features which may not be in the feature library. Feature relations are not subject of this research. Feature relations are crucial for specific feature based applications such as setup planning, fixture design etc. [4].Extensive computational experiments are carried out to determine the optimal network topology over the entire data set. The training and testing process resulted in an optimal neural network structure for the recognition of features. The effectiveness of the pro-posed method has been tested with several examples of standard and non-standard features.2. A review of relevant researchThe two main solid modelling representation are the Boundary representation (B-rep) and Constructive solid geometry (CSG). The CSG stores a tree of low level primitive volumes with their respective Boolean set operators used to construct the solid model. The B-rep stores a solid model with low level entities such as faces, edges and vertices. The CSG and B-rep databases represent the solid model with low level geometric details which do not represent design intent or production functionality. The CSG representation has problem due to non-uniqueness of the CSG tree. The robustness of B-rep justify its usefulness in industrial applications [5±7].The low level part information is not directly usable for the applications following design. Currently, feature technology has been used to convert the low level information into high level information which can be easily used for applications such as machining, process planning etc. Generally, there are two approaches to convert low level information into feature based mod-elling: (1) feature based design, and (2) feature recog-nition. Feature based models can be obtained using one of these approaches. In feature based design, part model is generated by predefined features assigningv attributes to them. Predefined features are stored in feature libraries which are specific to application such as machining, molding, die-casting, fixturing etc. In this approach, the designer intend is immediately included in the part model that can be utilised in subsequent applications. One major drawback of feature based design is due to the number of predefined features. They are finite and usually constructed as simple shapes. This puts limits on designer functions and restricts the designer behaviours to model the parts [8±11].Feature recognition converts CAD product model into feature based model without limitations on designer activities. Although, there are several research works in feature recognition, most of the proposed methods can be classified as rule- , graph-and neural net based approaches. Rule based approaches use the rules which are defined for specific features. Rule based systems suffer since rules are non-unique to a feature and exhaustive search is required to determine features [12,13]. Graph based approaches use sub-graph isomorphism to match the features. In this approach, part is presented in terms of nodes (faces) and arcs (edges) where each arc is associated with convexity or concavity information[14±16]. Graph based approaches was first introduced by Joshi and Chang [17]. In this approach B-rep of a part is presented in terms of nodes and arcs. Joshi and Chang [17] used an attributed adjacency graph (AAG) where each arc is associated with convexity or con-cavity codes. The main disadvantage of graph based techniques is that extensive preprocessing is necessary to construct the graphs and additional computation is necessary to extract the feature sub-graph from the rest of the graph. This approach needs extensive computa-tion search to recognise features.Some of the other techniques used by researchers are syntactic pattern recognition [18,19], geometric reasoning [20,21], volume decomposition [22,23] and the convex hull decomposition [24].The problem of feature recognition is the possibility of wide variety of complex features in any real life parts. It is not possible to generate a feature library to include all kind of features or write rules for features which are not previously defined. Thus, most of the previous methods consist of some con¯icts with either coverage or efficiency because of limitations on the feature recognition domain. Good reviews on feature based design and recognition are given [6,7,25,26].Neural network is one of the most promising approach to overcome the con¯icts exist in feature recognition. Neural net is different from other recog-nition techniques with its two major characteristics, i.e. learning and recall. Learning is the process of adjusting the connection weightsto produce the desired output. Recall is the process of providing an output for a given input in accordance within the neural weight structure. Thus, it can only be possible to recognise the complex features which are not included in feature library by using learning and recall characteristics of neuro-based recognition system. In the following, consideration is given only to neural network based feature recognition literature review as the subject of this study.Prabhakar and Henderson [27] proposed a five layer neural net which is one of the first attempts to use the neural nets in feature recognition. The input to the net is a face adjacency matrix (AM) which consists of relations between faces and each row has information related to one face. Although, it is one of the first successful applications of neural net based feature recognition, it has some con¯icts due to interacting features such as blind-slots such as blind-slots and non-standard type features since face adjacency matrix does not have the all the geometric information to define the features with same topology. In the work of Nezis and V osniakos [28], the topology of the part is transformed into an AAG which is obtained from B-rep data base and converted into a representation pattern, AM, that is taken as input to neural net. The input pattern representation to the network requires conversions of CAD data into a vector form and further computations. This approach does not deal with complex compound features such as in the caseof Prabhakar and Henderson [27].Some researchers presented recognition methods for 2D features. Dagli et al. [29] presented a neural network approach for the recognition of 2D manu-facturing features. The back propagation neural net-work is used to recognise limited number of features. The input to the network is represented by a matrix which consists of binary numbers. Chen and Lee [30] developed a neural network system for 2D feature recognition.Kumara et al. [31], proposed 3D interacting manu-facturing feature recognition system using graph and neural network approach. It has limited domain of features, protrusion and circular type features are not included. Zulki¯i and Meeran [11] developed a system to recognise feature patterns of non-interacting and interacting primitive and circular features using a neural network. This study deals only with primary features.Hwang [32] used a perceptron neural network to recognise 3D features. The face-score is used to present the concavity or convexity of faces. The recognition process is limited to depression features. Chan [33] also used face-score values to recognise the features. Although, Hwang [32], and Chan [33] suc-cessfully used the face-scores to recognise standard type features, non-standard complex shape features and fillets are not considered in their works.Most of the above mentioned methods implemented with only prismatic raw material parts and standard features. In this research, the proposed algorithm is based on similar concepts of Hwang [32] and Chan [33]. However, the domain of recognised features is significantly different being circular and complex features which are often used in real life products.The applicability of the proposed system is tested through various examples. The results show that neuro-based feature recognition is vital to recognise the features which are not included in feature library or not defined previously, since neuro-based systems have the capability of learning and recalling from experiments to match the similarity of patterns.3. Neural network based feature recognitionCAD data base is the main information domain for development of product and life cycle activities. CAD data base represents the part in terms of low level entities which are the geometric and non-geometric information. The low level part definition consists of vertices, edges, loops andfaces. This type of CAD product representation is not suitable to extract theknowledge required for the applications following design. Traditional CAD systems do not provide the information required to automate and integrate the design and applications following design. The con-version of CAD data base to feature based environ-ment provides more better support then other techniques for effective integration of design and applications following design [4,6,34±36] Feature based CAD model can be obtained by using one of feature technologies. The proposed architecture of converting from traditional CAD to feature based environment using neuro-based feature recognition is given in terms of block diagrams in Fig. 1.In this research, feature is defined as a region of a part having form and functional aspects such that they can be used to provide a better approach for theintegration of CAD and CAM. Feature is represented as follows:Feature ˆ f Fwhere w is the weights of neural network interconnec-tions and FGT the information related togeometry and topology. Features are defined as input vectors in terms of vertices, edges and faces and presented to neural network as follows:Featurewhere F is the B-rep based face-scores, t the number of training patterns and y the threshold.Although, there may be any shape of features, there are eight commonly used feature classes which have meaning in manufacturing. Feature classes are defined as the slot, step, pocket, protrusion, blind-slot, corner-pocket, through-hole and blind-hole (classi, i =1,2,. . .,8). In this research, Trajan neural networks commercial simulation package is used to develop a feature recognition system [37]. The proposed struc-ture of neural network with two hidden layers is given in Fig. 2.4. Neural network training algorithmThe type of neural network architecture used in this research is the multilayer perceptron (MLP) which iswidely used for non-linear regression and classifica-tion problems. Among various neural network archi-tectures, back propagation (BP) is a widely used technique for training of multilayer perceptron [29]. Although, neural networks have been applied for a wide range of classification problems, their use in feature recognition is quite recent and the related applications are still a few cases.In literature, there are several cases of classification problems solved successfully by MLP approach. Today, it is estimated that most of all applications utilise BP algorithm in one form or another. In this research, BP with two hidden layers are shown to be capable of providing an accurate approximation to recognise standard and non-standard previously unseen features.Feature classes are defined as the slot, step, pocket, protrusion, blind-slot, corner-pocket, through-hole and blind-hole. The network must be trained with training and validating data sets in such a way that for a given input vector, the output vector must be obtained to classify the pattern. In this research, face-score values are the elements of input vector to the network. Training of neural network is carried out through the presentation of example parts. Verification test is carried out to check the progress of network structure.BP learning is a gradient search process to mini-mize the sum-squared error over the entire training data set. The convergence to the optimal solution is accomplished by adjusting the weight connectionsthrough the partial derivatives of the sum-squared error with respect to the weights [29,38,39].The main disadvantages of BP are its slow learning rate and its ability to converge to local minimum. Several repeated solutions with different initial weights and network parameters are used to converge to the optimal solution. Although, some recent research work has contributed to determining the number of hidden layers, the number of neurons in each layer and selecting the learning rate parameters, the results are still not at satisfactory level to be accepted as general rules for generating optimal neural network architecture [40±44].There is no systematic methodology. In general, parameters of neural network architecture are determined by trial and error approach for the number of hidden layers and number of neurons in hidden layers are found using several repeated runs of the system. Verification is usually used to check the network architecture. Neural network architectures are discussed at lengthin several research works [38,42,45].The performance and convergence of the neural networks in classification problems are determined with the capability of generating correct solutions for input patterns. This depends onseveral issues which are architecture of neural network, type of training algorithm, initial values of weights and number of training epochs. The architecture of the neural net-work depends on the problem to be solved. In this research, features are recognised based on geometry and topology using neural network approach. Some guidelines related to the problem of neural network architecture construction are as follows:the MLP and BP based neural network is well suited approach for classification and pattern recognition problems. the number of hidden layers and the number of neurons in each hidden layer can be determined arbitrarily with trial and error. Conservative approach is the selection of the number between the number of output neurons and the number of input neurons. the use of too many neurons in hidden layer may over-fit the data which causes the loose of general-isation capability of network. if the number of neurons in hidden layer are not enough then the network may not be able to learn.if the interactions between input and output is not simple then the number of hidden neurons and hidden layers should be increased to map this complex relation.In this research, a simple network structure with one hidden layer and a small number of hidden neurons are first considered. The number of neurons are progres-sively increased to map the relation between input and output. The results indicate that one hidden layer is not sufficient to solve the problem. Second, two hidden layers network is constructed with a small number of neurons and then, the number of neurons are increased to obtain the optimal network architecture related to convergence and generalisation performance of the network. Several experiments are carried out using two layer network with different number of hidden neurons to select the optimal network based on recog-nition accuracy and generalisation performance. Two hidden layers with nine hidden neurons of each layer is obtained as best network architecture. The results indicate that if there is not enough hidden neurons then the recognition fails and if too many neurons are used data over-fitting occurs. This reduces the percentage of correct classification and ability to generalise the new features.A network with higher weight models has a more complex function, and is therefore prone to over-fitting. It may invariably achieve lower error even-tually, but this may indicate over-fitting rather than good modeling. In contrast, a network with less weights may not be sufficiently powerful to model the underlying function. In this research, verification is carried out to determine the right complexity of proposed network architecture for feature recognition. Verification set is used to trace the progress of the network. Several experiments are carried out for different number of neurons. Some of them are given in Table 1. Verification test results of one hidden layer network indicates that network architecture is not sufficient for proposed feature recognition algorithm. If neither training nor verification errors drop, this means that network architecture is not sufficient to solve the problem. The number of hidden layers and/or hidden neurons should be increased. The training and verification errors drop naturally during training, but after some progress, if the verification error stops dropping, or indeed starts to rise, this indicates that thenetwork is starting to over-fit the data (Table 2). In this case, the number of hidden layers and/or hidden neurons should be decreased to prevent over-learning. It is verified that two hidden layers with nine hidden neurons of each layer is obtained as best network architecture for non-standard feature recognition. The results also show that conservative approach to select the number of hidden neurons is appropriate for the recognition problem of standard and non-standard features which are previously not defined. In this research, the network architecture related tofeature recognition problem is constructed as a MLP network with two hidden layers and nine neurons in each hidden layer as shown in Fig. 2.In this study, one of N type classification algorithm is applied and a class is selected according to corresponding output unit. Classification confidence limits are set to accept/reject levels as the accept level gives the minimum value which the output must reach to belong to positive class and the reject level gives the maximum value below which it must be belong to negative class. A class is selected if the corresponding output unit is above the accept level and all the other output units are below the reject level. If this condition is not met, then the class is undecided. In this work, a positive classification is indicated as close to 1.0 and negative as close to 0.0. Classification confidence limits for accept and reject levels are selected as 0.85 and 0.15.The train data set which consists of non-standard feature shapes are generated to train the neural net-work. The face codes are computed from step based B-rep representation of CAD solid model. Learning rate and momentum are specified by observing the learn-ing difficulty in converging to the expected minimum error. The network with learning rate Z ˆ 0:4 and momentum m ˆ 0:7 learns fast and converge to accep-table error values. It is often recommended to com-plement a high learning rate with a low momentum, and vice versa, but it may be both high. The algorithm progress iteratively through a number of epochs which is used as stopping condition if there is no more improvements on error term.5. Computational experiments and resultsIn this research, B-rep representation is used to compute the face-scores in terms of vertices and edges of a face. B-rep is most commonly used scheme and preferable to CSG which does not provide unique representation of parts [14,17]. Most of the feature recognition algorithms are developed for B-rep struc-tures. B-rep represent a part in terms of its faces, loops, edges and vertices and relationships of these entities. In object oriented CAD data structures, B-rep infor-mation can be easily reached with interface programs which can be written to reach the information relatedto vertices, edges and faces of the solid model.The input of the network have been coded with numerical values called face-scores which are calcu-lated in function of the geometry and topology of vertices, edges, faces of a part. The face-score values can be calculated to identify the convexity or concavity of part features [32,33].They are calculated from vertex scores. Vertex score value varies accord-ing to edge concavity or convexity. This representa-tion is used to present a feature as a material removal unit. A high concavity score for a face is because of material removal process from the face, and a high concavity score for a face defines material addition to the face.Most of the researchers in the field of feature based design and recognition deal with standard features which are simple in shape like part 3 and 4 as shown in Fig. 5. In this research, non-standard features, which have complex shapes, are also considered. A prismatic part with non-standard type blind-slot and corner-pocket features is given in Fig. 3. Part 1 and part 2 are examples of non-standard step features as shown in Fig. 5.To evaluate the performance of the proposed algo-rithm, several computational experiments have been performed. The algorithm is tested on several exam-ples only a few of which are presented here. The first example is a part with step type feature as shown in Fig. 4. Applying the proposed feature recognition algorithm, the results are obtained as follows: a value of 0.9904 assigned to the second element of output vector, class 2 represents step type feature, the outputneuron with the highest value, which is in confidence limits, represents the class of recognised feature.''In classification problems, the purpose of the net-work is to assign each case to one of a number of classes [46]. The output vector of 8 elements is as follows:OUTPUT ˆ ‰Classi i =1,2,. . .,8] ˆ [slot step pocket protrusion blind-slot corner-pocket through-hole blind-hole].An example for step type feature classification is given below in terms of output vector confidence level values (Fig. 4).The fourth example is a work-piece with non-stan-dard slot and compound type features as shown in Fig. 7. The results of classification are given in Fig. 7.The fifth example is taken from the literature. The results of recognition process are given in Fig. 8. The last example is a work-piece with several through-hole features as shown in Fig. 9.The geometric details of each feature can be extracted from design data base. Each feature has different centre, radius, height values and location. Thus, each feature has a different meaning in terms of machining direction and tool path. For example, the through-hole features of part shown in Fig. 9 have different meaning from the machining point of viewT he experimental results of proposed system for four different step features (part 1 and part 2 are non-standard, part 3 and part 4 are standard) are given in terms of confidence level values as shown in Fig. 5. Acylindrical work-piece which consists of compound and slot features are tested to show the capability of the proposed approach and compared with the results of other researchers. As stated before, most of the examples in literature are raw material prismatic parts with standard features of simple shapes. The results of recognition process are given in Fig. 6 since they have different geometric details related to location of features in sample work-piece.Feature relations can be extracted from design data base. They are crucial for feature based applications such as setup planning which the precedence relation-ships among features are required. Operational sequence of machined features can be presented in terms of relations among features using geometric andnon-geometric constraints [4]. These constraints are due to tolerance specifications, production rules, heur-istic rules etc. Geometric constraints are concerned with dimensional and geometric tolerances. The geo-metrical or form tolerances such as those of perpen-dicular and parallel relations among features are necessary to derive the precedence constraints. For example, features cannot be machined in any arbitrary order due to various types of interactions among thefeatures used to machine a part. These interactions introduce precedence constraints requiring that some features should be machined before or after other features. In Fig. 10, feature 1 should be machined before feature 2 because of feature relations.6. ConclusionsThe aim of this research is to capture the complex relationships that exists in non-standard feature shapes and to develop feature based model by means of learning capability of neural networks. Several examples are tested through the proposed system and it is seen that feature recognition approach using neural networks offers the advantage of automated classification of non-standard features compared to most of the existing traditional methods. The results of experiments can be concluded as features and neural network techniques are the most promising tools for efficient integration of design and manufacturing. The contribution of this paper is in the design of a neural network based system to recognise non-standard com-plex shape features and to develop feature based model and for the integration of design and applica-tions following design.The primary goal in future research directions is to develop neural network based feature recognition approaches that not includes current drawbacks, espe-cially due to industrial curved surfaces and to develop computationally efficient method to capture feature relations for feature based applications.[1] B.O. Nnaji, H.C. Liu, U. Rembold, A product modeller fordiscrete components, International Journal of ProductionResearch 31 (9) (1993) 2017±2044.[2] F. Ozturk, O.B. Alankus, Computer Integrated ManufacturingApplications in an Automotive Factory, Production Planningand Control 6 (6) (1995) 578±583.[3] N.J. Brookes, C.J. Backhouse, Understanding concurrentengineering implementations Ð a case study approach,International Journal of Production Research 36 (11) (1998)3035±3054.[4] F. Ozturk, N. Kaya, O.B. Alankus, S. Sevinc, Machiningfeatures and algorithms for set-up planning and fixturedesign, Computer Integrated Manufacturing Systems 9 (4)(1996) 207±216.。

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Wear272 (2011) 43–49Contents lists available at ScienceDirectWearj o u r n a l h o m e p a g e:w w w.e l s e v i e r.c o m/l o c a t e/w e arEffect of the addition of carbonaceousﬁbers on the tribological behavior of a phenolic resin sliding against cast ironS.Betancourt a,∗,L.J.Cruz a,A.Toro ba New Materials Research Group,School of Engineering,Pontiﬁcia Bolivariana University,Circular1a N.70–01,Medellín,Colombiab Tribology and Surfaces Group,School of Materials Engineering,National University of Colombia,Cra80N.65-223,Medellín,Colombiaa r t i c l e i n f oArticle history:Received20January2011Received in revised form12July2011 Accepted22July2011Available online 30 July 2011Keywords:CarbonaceousﬁbersPhenolic resinCompositesSliding contactCast iron a b s t r a c tThe tribological behavior of novolac phenolic resin matrix composites reinforced with three kinds of carbonaceousﬁbers was studied in sliding contact against cast iron.Slow pyrolysis was used to obtain carbonaceousﬁbers from Colombian plantainﬁber bundles(crops residues from Urabáregion).After the carbonization process the samples were heated up to either1200or1400◦C ensuring that many morphological aspects of the naturalﬁbers were retained.Then,novolac phenolic resin with HMTA as curing agent and the carbonaceousﬁbers were used to obtain a composite material by compression molding process.Samples with different type and volume fraction of carbonaceousﬁbers were prepared and tested in sliding contact against cast iron in a pin-on-disc wear testing machine.At the end of the tests,the worn surfaces and the debris were analyzed by SEM.A decrease in both friction coefﬁcient and wear of composites was observed with the increase inﬁber volume fraction,which was associated to a beneﬁcial effect of the detachment of carbonaceous material from the worn surface.Under the tested conditions,this material remains at the interface between the composite and the cast iron and helps reduce the shear resistance of the interface.On the other hand, surface fatigue and adhesion wear was identiﬁed as the dominant wear mechanism of the phenolic resin matrix.© 2011 Elsevier B.V. All rights reserved.1.IntroductionPyrolysis process transforms lignocellulosic materials into char-coal and slow heating rate pyrolysis gives rise to a porous carbon frame with the morphology derived from its precursor[1–3],which can be used to ceramic synthesis such as silicon carbide,alu-mina and titania[3].Typical lignocellulosic precursors of charcoal materials that retain morphological aspects of precursor are wood resources.Although non-wood plants are used to get carbon pow-ders and activated carbon,ﬁbrous wastes from agricultural residues of edibles fruits from Musa species such as commercial plantain (Musa AAB,cv“Dominico Harton”)produced in Colombia have been used to get carbonaceousﬁbers[4–7].The sliding friction coefﬁcients of some carbon-based materi-als,either amorphous or crystalline,are among the lowest for any solids.They are important ingredients of brake composite materi-als including different types such as graphite,coke,carbon black, and carbonﬁber[8].Charcoal and carbonaceous substances,for instance,have good frictional characteristics even though they do not exhibit the basal slip properties of graphitic structures.∗Corresponding author.Tel.:+5744488388;fax:+5743544532.E-mail address:santiago.betancourt@.co(S.Betancourt).On the other hand,novolac phenolic resin is a common binder for resin-based friction materials[9].Tribological applications of phenolic resins are usually limited due to their relatively poor stability and wear resistance.Therefore,it is imperative to incorpo-rate various reinforcing andﬁlling constituents such as reinforcing ﬁbers,abrasives,binders,ﬁllers,and friction modiﬁers(solid lubri-cants)into phenolic resin-based friction composites with the purpose of increasing their stability and wear resistance[10–12].The type and relative amount of solid lubricants and abrasives in brake friction materials signiﬁcantly affect the brake performance [13].Solid lubricants are added in relatively small amounts but they strongly affect wear resistance,stopping distance,friction stability and torque variation.Graphite and MoS2are frequently used in commercial brake linings and other chalcogenide compounds such as Sb2S3,ZnS,PbS,and Cu2S are often added for better brake per-formance.However,a few reports focused on the effect of solid lubricants on tribological properties of solidﬁlms coated on the metal substrates are available[14,15].Charcoal in particular has been reported as a potential replace-ment for graphite into composites for antifriction and antiwear applications as aluminum alloys applications[16,17],wood ceram-ics and in brake friction materials using carbonized coconut char powders[18].Nevertheless,it is rarely discussed in the literature the effect of charcoal materials on wear mechanisms acting on the0043-1648/$–see front matter© 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.wear.2011.07.01444S.Betancourt et al./Wear272 (2011) 43–49surfaces in contact.In this work,novolac phenolic resin matrix composites reinforced with three kinds of carbonaceousﬁbers and different volume fraction of reinforcement were prepared.Chem-ical composition and structural characteristics of carbonaceous ﬁbers were studied.Tribological behavior of composites was tested in sliding contact against cast iron in a pin-on-disc wear testing machine.Worn surfaces were studied by SEM and wear mecha-nisms were identiﬁed.2.Materials and experimental methods2.1.MaterialsCarbonaceousﬁbers were obtained by pyrolysis of plantain ﬁbers from leaf sheaths from Urabá,Colombia region.A mechani-cal extraction process for plantainﬁbers was used according to the method described in[19].The samples were further air-dried for at least24h and milled with a RETSCH SM100(Haan,Germany)to obtain a particle size smaller than5mm.Three types of carbonaceousﬁbers(T1000,T1200,T1400)were synthesized by slow pyrolysis performed in an electrical tube fur-nace MTI GSL1600-80X.T1000samples were heated from room temperature up to1000◦C,held at this temperature for90min and then cooled down to room temperature in N2atmosphere with99.9999%purity.T1200and T1400samples were initially treated under the same conditions used for T1000samples,and then submitted to a second thermal process at1200◦C and1400◦C, respectively.The heating(5◦C min−1)and cooling(−5◦C min−1) rates,as well as the nitrogenﬂow rate(200ml min−1),were the same for all the samples.Phenolic resin powder containing hexamethylenetetramine as curing agent was used as the matrix.Resin and carbonaceousﬁbers were molded by compression molding process.Previously,both components were mixed for5min by stirring them in a blender so as to improve the dispersion of the mixture.Contents of car-bonaceousﬁbers were2.5,5.0,7.5,10and12.5%(v/v)and phenolic resin was the balance.Fully mixed raw material was loaded into a 140mm diameter and3.5mm high steel mold and hot pressed at 200bar,165◦C for15min in a thermo hydraulic pressing machine.2.2.ExperimentalThe carbon,hydrogen,nitrogen and sulphur content of the car-bonaceousﬁber bundles were determined using a LECO-CHNS-932 microanalyzer.Oxygen content was determined by means of a LECO-VTF-900furnace coupled to the microanalyzer.Ash content and moisture were determined for each carbon content in accor-dance with UNE32001and32002standards,respectively.For XRD analysis,carbonaceousﬁbers were ground to aﬁne powder and the measurements were carried out in X’Pert Pro MPD,Panalytical, diffractometer operating at25mA and40kV,using Cu-Ka radiation.Table2Pin-on-disc testing conditions.Tribological test conditionsEnvironmental temperature25±2◦C Atmosphere Air–Relative speed1m s−1 Load15.11NTest distance1000m2.3.Tribological behaviorFriction and wear behavior of the phenolic resin and composites were evaluated by using a pin-on-disc tribometer.A cast iron brake disc(220mm in diameter and8mm in thickness)was adapted for the tests and the surfaceﬁnishing wasﬁxed to circa Ra=0.5␮m in all the samples.Table1presents some of the most relevant metallurgical and chemical characteristics of cast iron disc.Phenolic and composite pins were glued to a metallic pin holder with conventional instant glue and machined by lathe turning to get theﬁnal dimensions shown in Fig.1a.The pin wasﬁxed to a rigid arm(Fig.1b)and put in contact with the surface of the disk in movement,while a normal load was applied by dead weights (Fig.1c).A general view of the equipment is shown in Fig.1d.All samples were tested under the same test conditions summarized in Table2and three replicas were obtained for each experimen-tal condition.Friction force was registered10times per second with the aid of a load cell connected to a data acquisition card and software Labview5.1provided by National Instruments under an educational contract.After each friction test,the pins were ultra-sonically cleaned in alcohol for5min,dried in air and weighed in an analytical balance Sartorius CPA225D with resolving power of 0.01mg.2.4.Surface examinationThe worn surfaces of disks and pins were examined by stere-omicroscopy(Nikon SMZ1500)and scanning electron microscopy (JEOL5910LV)in order to identify the main wear mechanisms act-ing during the tests.3.Results and discussionElemental composition ofﬁbers after pyrolysis process and sec-ond thermal treatment are shown in Table3.All samples have high amount of carbon as a consequence of carbonization of ligno-cellulosic compounds.A second thermal treatment increases the carbon content and reduces the amount of oxygen and hydrogen by volatilization.These changes are considered typical in carbon materials due to exposure to high temperature[20].Carbonaceuosﬁbers produced from plantainﬁbers bundles were crystallographically characterized by means of X-ray diffrac-Table1Metallurgical and chemical characteristics of cast iron disc.Nominal chemical composition(wt.%)C 3.65–3.85Sn Max.0.10 Si 2.15–2.795Cu Max.0.60 S Max.0.15Mo Max.0.10 P Max.0.10Ni Max.0.20 Mn0.5–0.9Fe Balance Cr Max.0.25Microstructure Graphite type:1–1A(sheets) Grain size:ASTM3–4 Pearlite:90%min.Ferrite:5%max. Cementite:5%max.Hardness170–217HBS.Betancourt et al./Wear272 (2011) 43–4945Fig.1.Details of positioning of the samples(a–c)and general aspect of pin-on-disc tribometer(d). tion as seen in Fig.2.All samples presented crystalline andamorphous phases associated to carbonaceous material and crys-tals of inorganic substances.Identiﬁcation of diffraction angles andmineral substance is summarized in Table4as a function of car-bonization temperature.Table3Chemical composition of carbonaceousﬁbers measured by elemental analysis.Element(wt.%)SampleT1000T1200T1400Carbon66.6775.9180.12Hydrogen 1.37 1.21 1.29Nitrogen0.520.530.33Sulphur0.120.090.12Oxygen14.059.568.08Table4Crystallographic analysis of carbonaceousﬁbers(refer to Fig.2).Sample Peak2Â(◦)Mineral/chemical formulaT 1000a29.50Calcite/CaCO3b31.95Butschliite/K2Ca(CO3)2 c33.30d43.64T 1200e31.16Butschliite/K2Ca(CO3)2 f33.32g42.92T 1400h26.35Calciumcarbide/CaC2i32.65T1000samples show peaks corresponding to calcium and potas-sium crystals such as Calcite(CaCO3)and Butschliite(K2Ca2(CO3)3),the latter being a non-conventional phase found in wood ashesaccording to Winbo et al.[20].Amorphous region showsdiffuseFig.2.X-ray diffractograms of samples obtained by different processing parameters.Diffuse bands around2Â=26◦and44◦correspond to(002)and(100)planes of agraphite-like carbon structure.Peaks a to i are identiﬁed in Table4.46S.Betancourt et al./Wear 272 (2011) 43–49Fig.3.Friction force vs.testing time curves.Non-reinforced phenolic resin (a)and samples with 7.5%reinforcement content:T 1000(b),T 1200(c)and T 1400(d).bands at 2Â=26.5◦and 42.4◦,which correspond to typical crys-talline planes of non-graphitic carbon materials (002and 100,respectively)[21–23].Structural changes are observed in T 1200and T 1400samples due to the changes in thermal history.Calcite inorganic mineralpeak disappears and a slight shift of Butschliite peak is observed.Also,T 1400samples show a new crystalline phase identiﬁed as cal-cium carbide,which is not observed in T 1000and T 1200samples.Intensity of carbon 002and 100peaks increased for both sam-ples.Packing of basic structural unities (graphenes)along c axisisFig.4.Coefﬁcient of friction (a)and wear rate (b)of phenolic resin and carbonaceous composites in pin-on-disc tests.S.Betancourt et al./Wear272 (2011) 43–4947Fig.5.Aspect of the worn surface of reinforcement-free resin.SEM.The arrows represent the sliding direction.a thermal activated process which occurs at temperatures higher to1000◦C[24].3.1.Friction coefﬁcient and wear resistanceAfter a running-in period that lasted around100s for all the samples,a stable friction force was measured during testing of both phenolic and composite samples,as can be veriﬁed,for instance,in Fig.3where the friction force vs.testing time curves for non-reinforced phenolic resin samples and T1000,T1200and T1400 samples with7.5%of carbonaceousﬁber content are shown.Results of the measurements of stable friction coefﬁcient dur-ing pin-on-disc tests are shown in Fig.4.All composites exhibit an important reduction of friction coefﬁcient as the carbon content is increased.The maximum relative reduction(near to40%)was observed for12.5%carbon content in T1200samples(Fig.4a). Fig.6.Aspect of the worn surface of composite pin.SEM.The arrows represent the sliding direction.48S.Betancourt et al./Wear 272 (2011) 43–49Fig.7.Aspect of the worn surface of grey iron disc SEM.Arrow represents sliding direction.Darker zones correspond to polymer adhered to the disc’s surface.The reduction in wear rate with the addition of carbonaceous ﬁbers was much more dramatic,with relative variations up to 85%with respect to pure phenolic resin,as seen in Fig.4b.The results did not reveal statistical differences among the wear rates of samples T 1000with 2.5,5.0,and 7.5%of carbonaceous ﬁbers.Similar results were obtained for T 1200composites with the same reinforcement content.According to friction coefﬁcient and wear rate results,the addition of carbonaceous ﬁbers to phenolic resin led to an improve-ment in tribological behavior due to signiﬁcant reduction in wear rates while the friction coefﬁcient values remained quite stable.This behavior can be attributed to the presence of graphite-like crystalline layers into the amorphous structure of carbonaceous material,which were detected by means of X-ray diffraction (see diffuse bands in diffractograms of Fig.2),as well as to the beneﬁ-cial effect of the detachment of carbonaceous ﬁbers that remain trapped between the composite and the cast iron during the tests.3.2.Examination of worn surfacesThe typical aspect of the worn surface of ﬁber-free phenolic resin samples is shown in Fig.5.Plastic deformation is observed up to some extent (Fig.5a and b)and evidences of fatigue-related wear mechanisms are clearly identiﬁed in Fig.5c where a parti-cle of material is about to be detached as a result of the joining of cracks that grew parallel to the contact surface.The character-istic pattern consisting of regularly spaced cracks perpendicular to the sliding direction (Fig.5d)has been previously observed in thermoset polymers sliding against smooth surfaces [25,26].These marks were systematically observed at the worn surface of the ﬁber-free samples and effectively denote the occurrence of fatigue-related phenomena.Crazing evidences can also be observed in Fig.5d,which are normally expected in polymers submitted to sliding wear conditions.In regards to composite samples,worn surface examination revealed a random distribution of carbonaceous ﬁbers,with nopreferred orientation with respect to the sliding direction.Fig.6a shows a low magniﬁcation image of the surface of a typical sam-ple,where a number of features such as holes and incomplete carbonaceous ﬁbers can be observed.Typical vascular plant mor-phology can be observed in carbonaceous ﬁbers,Fig.6b and c,in a region where the ﬁbers were broken and the inner part was exposed.Similarly to what was found in ﬁber-free pheno-lic resin samples,crazing and surface fatigue of the polymer are the dominant wear mechanisms,although some cracks were observed along the ﬁbers as well.A fragment of broken ﬁbers clearly shows the morphology of carbonaceous ﬁbers which retain cellular features from precursor and evidences the fracture of carbonaceous material as one of the active wear mechanisms,Fig.6d.Regarding the analysis of the disc’s surface,evidences of adhe-sion of phenolic resin on the disc were found by SEM as shown in Fig.7.In Fig.7a,a slightly darker region that corresponds to the mark left by the fragments of matrix attached to the asperities of the surface of the disc during sliding contact can be observed.Accord-ing to the detailed examination of the worn surfaces,it can be said that initially the resin partially ﬁlls the valleys of the surface rough-ness of the disc and next it continues to accumulate in the whole contact area (Fig.7b–d).Therefore,two main wear mechanisms are active:adhesive wear and surface fatigue.A sequence of events occurring during sliding of composite sam-ples against cast iron is proposed according to the analysis of worn surfaces (Fig.8):once pin and disc surfaces are put in contact,stresses are produced by effect of the normal load and tangential forces during sliding.The main wear mechanisms of phenolic resin are adhesion and surface fatigue.On the other hand,once the car-bonaceous ﬁbers get in contact with cast iron they crack and suffer brittle fracture.Some fragments remain at the interface between pin and disk so the carbonaceous material is crushed and a ﬁne powder is formed.Then,a ﬁne distribution of carbonaceous parti-cles is spread over the contact interface acting as a solid lubricant that provides the conditions for the stable friction coefﬁcient and reduced wear rate observed in the experiments.S.Betancourt et al./Wear272 (2011) 43–4949Fig.8.Schematic of the sequence of events leading to wear of phenolic matrix composites reinforced with carbonaceousﬁbers in contact with cast iron.4.ConclusionsThe tribological behavior of phenolic resin composites rein-forced with carbonaceousﬁbers sliding against cast iron was studied.A reduction in wear rate of composites was observed with the increase in carbonaceousﬁber volume fraction.Friction coef-ﬁcient also showed a trend to reduce with the increase ofﬁber volume fraction,although the measured values were close to those found in pure phenolic resin samples.Adhesion,surface fatigue and crazing were identiﬁed as the dominant wear mechanisms of the phenolic matrix while brittle fracture was the main cause of detachment of carbonaceousﬁbers.The analysis of the worn surfaces revealed that a signiﬁ-cant amount of carbonaceous material removed from the matrix remained at the interface between the composite pins and metallic discs,which was associated to prevention of adhesive wear of the phenolic matrix and a possible function of carbonaceous material as a solid lubricant.References[1]C.Byrne,D.Nagle,Carbonization of wood for advanced materials applications,Carbon35(1997)259–266.[2]P.Greil,Biomorphous ceramics from lignocellulosics,J.Am.Ceram.Soc.21(2001)105–118.[3]H.Sieber,C.Hoffmann,A.Kaindl,P.Greil,Biomorphic cellular ceramics,Adv.Eng.Mater.2(2000)105–109.[4]S.Betancourt,Desarrollo de materiales compuestos de matriz fenólica conmateriales carbonosos sintetizados a partir de lasﬁbras vegetales obtenidas de la vena central de la hoja de plátano.Ph.D.Thesis.Universidad Pontiﬁcia Bolivariana,Medellín,2010.[5]C.R.Rambo,Materiais avanc¸ados inspirados na natureza:estruturas celulares,ﬁbras e compósitos,Exacta9(2006)5–103.[6]C.Rambo,T.Andrade,T.Fey,H.Sieber,A.E.Martinelli,P.Greil,MicrocellularAl2O3ceramics from wood forﬁlter applications,J.Am.Ceram.Soc.91(2008) 852–859.[7]Z.Weiss,J.C.Crelling,G.Simha Martynkova,M.Valaskova,P.Filip,Identiﬁca-tion of carbon forms and other phases in automotive brake composites using multiple analytical techniques,Carbon44(2006)792–798.[8]Y.Gewen,Y.Fengyuan,Mechanical and tribological properties of phenolicresin-based friction compositesﬁlled with several inorganicﬁllers,Wear262 (2007)121–129.[9]S.K.Rhee,M.G.Jacko,P.H.S.Tsang,The role of frictionﬁlm in friction,wear andnoise of automotive brakes,Wear146(1991)89–97.[10]S.J.Kim,H.Jang,Friction and wear of friction materials containing two differentphenolic resins reinforced with aramid pulp,Tribol.Int.146(2000)477–484.[11]P.Gopal,L.R.Dharani,F.D.Blum,Fade and wear characteristics of a glassﬁberreinforced phenolic friction material,Wear174(1994)119–127.[12]M.H.Cho,J.J.S.J.Kim,H.Jang,Tribological properties of solid lubricants(graphite,Sb2S3,MoS2)for automotive brake friction materials,Wear260 (2006)855–860.[13]W.J.Bartz,J.Xu,Wear behavior and failure mechanism of bonded solid lubri-cants,Lub.Eng.43(1987)514–521.[14]M.N.Gardos,Synergistic effects of graphite on the friction and wear of MoS2ﬁlms in air,Tribol.Trans.31(1988)214–227.[15]T.Wang,T.Fan,D.Zhang,G.Zhang,Fabrication and the wear behaviors ofthe carbon/aluminum composites based on wood templates,Carbon44(2006) 900–906.[16]T.P.Murali,S.V.Prasad,M.K.Surappa,P.K.Rohatgi,K.Gopinath,Friction andwear behaviour of aluminium alloy coconut shell char particulate composites, Wear80(1982)149–158.[17]M.Sutikno,P.Marwoto,S.Rustad,The mechanical properties of carbonizedcoconut char powder-based friction materials,Carbon48(2010)3616–3620.[18]D.Joseph,A.Oberlin,Oxidation of carbonaceous Matter I Elemental analysis(C,H,O)and IR spectrometry,Carbon21(1983)559–564.[19]P.Ganán,R.Zuluaga,A.Restrepo,bidi,Mondragon plantainﬁber bun-dles isolated from Colombian agro-industrial residues,Bioresour.Technol.99 (2008)486–491.[20]C.Winbo,E.Rosen,M.Heimb,Thermal analytical study of the decompositionof K2Ca2(CO3)3,Acta Chem.Scand.(1998)431–434.[21]R.Franklin,Crystallite growth in graphitizing and non-graphitizing carbons,Proc.R.Soc.Lond.(1951)196–218.[22]X.Bourrat,in:H.Marsh,F.Rodríguez-Reinoso(Eds.),Science of Carbon Mate-rials,Ed.Universidad de Alicante,Alicante,2000,pp.1–90.[23]F.G.Emmerich,Evolution with heat treatment 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configurational analysis 意思

configurational analysis 意思Configurational Analysis is an analytical framework used to examine the relationships between different elements or variables within a system or organization. It seeks to understand how these variables interact with each other and influence the overall performance or outcome. This article will delve into the concept of configurational analysis, its key components, and how it can be applied in various contexts.1. Introduction to Configurational Analysis:Configurational Analysis is based on the assumption that the configuration or arrangement of different elements in a system is crucial in determining the overall performance. It emphasizes the interdependencies and interactions between these elements, rather than focusing solely on individual variables. By examining the patterns and structures that emerge from these relationships, configurational analysis aims to uncover the underlying mechanisms or configurations that lead to specific outcomes.2. Key Components of Configurational Analysis:There are several key components of configurational analysis that need to be considered when conducting this type of analysis:2.1 Variables:Configurational analysis involves the identification and selection of relevant variables. These variables can be quantitative or qualitative in nature, and they should represent different aspects or dimensions of the system or organization under study. It is important to select variables that are meaningful and have a significant impact on the overall performance or outcome.2.2 Configurations:Configurations refer to the patterns or structures that emerge from the relationships between variables. These configurations can be simple or complex in nature, and they can take various forms, such as causal chains, feedback loops, or interaction effects. Configurations may involve both direct and indirect relationships between variables, and they can be represented graphically using causal maps or influence diagrams.2.3 Conditions:Conditions refer to the contextual factors or circumstances that shape the relationships between variables. These conditions can include external factors such as market conditions, regulatory environment, or technological advancements, as well as internal factors such as organizational culture, leadership style, or resource availability. Conditions can have a moderating or mediating effect on the relationships between variables and may influence the emergence of specific configurations.2.4 Outputs:Outputs refer to the desired or intended outcomes of the system or organization under study. These outputs can be tangible or intangible in nature, and they can be measured using various performance indicators or metrics. Outputs can include financial performance, customer satisfaction, employee engagement, or social impact, depending on the context or objectives of the analysis.3. Applying Configurational Analysis:Configurational analysis can be applied in various contexts, including business organizations, public institutions, social systems, or environmental systems. It can be used to understand the factors that contribute to success or failure, to identify critical leverage points or bottlenecks, or to design interventions or strategies for improvement.3.1 Business Organizations:In business organizations, configurational analysis can be used to understand the factors that drive profitability, market share, or customer loyalty. It can help identify the key resources, capabilities, or processes that contribute to competitive advantage and to design effective business models or strategies. Configurational analysis can also be used to analyze organizational culture, leadership style, or decision-making processes and to understand how these factors influence employee motivation, innovation, or performance.3.2 Public Institutions:In public institutions or government agencies, configurational analysis can be used to analyze the factors that contribute to effective policy implementation, service delivery, or citizen engagement. It can help identify the key drivers of public value and to design effective governance structures or accountability mechanisms. Configurational analysis can also be used to analyze the factors that contribute to the success or failure of public-private partnerships or collaborative networks.3.3 Social Systems:In social systems or communities, configurational analysis can beused to analyze the factors that contribute to social cohesion, social capital, or community resilience. It can help identify the key relationships, norms, or values that shape collective action or social change. Configurational analysis can also be used to analyze the factors that contribute to inequality, social exclusion, or conflict and to design interventions or policies for social justice or inclusion.3.4 Environmental Systems:In environmental systems or ecosystems, configurational analysis can be used to analyze the factors that contribute to ecological resilience, biodiversity, or sustainable resource management. It can help identify the key interactions, feedback loops, or thresholds that shape ecosystem dynamics or vulnerability. Configurational analysis can also be used to analyze the factors that contribute to environmental degradation, climate change, or natural disasters and to design interventions or policies for environmental sustainability or adaptation.4. Challenges and Limitations:4.1 Complexity:Configurational analysis involves dealing with complexity and uncertainty, as it seeks to understand and model the relationships between multiple variables and their configurations. This complexity can make it challenging to identify the most relevant variables or to determine the causal relationships between them. It often requires the use of advanced analytical methods, such as fuzzy set analysis or qualitative comparative analysis, to handle the complexity and uncertainty inherent in configurational analysis.4.2 Context-Specific:Configurational analysis is context-specific, meaning that the configurations and relationships identified in one context may not be applicable or transferable to other contexts. This context-specificity can limit the generalizability of findings and may require the replication or validation of results in different settings or conditions. It also makes it challenging to compare or aggregate configurational analysis across different studies or contexts.4.3 Data Availability:Configurational analysis relies on the availability of relevant and accurate data to identify and analyze the relationships between variables. However, data collection and analysis can be time-consuming and resource-intensive, especially when dealing with complex and dynamic systems. It may also be challenging to quantify or measure certain variables or to obtain reliable data for all relevant variables. These data limitations can affect the validity and reliability of configurational analysis findings.5. Conclusion:Configurational analysis is a powerful approach for understanding and analyzing complex systems or organizations. By examining the relationships between variables and their configurations, configurational analysis can provide valuable insights into the underlying mechanisms or configurations that lead to specific outcomes. However, configurational analysis also presents challenges in terms of complexity, context-specificity, and data availability. Despite these limitations, configurational analysis offers a unique and holistic perspective on the interdependenciesand interactions that shape the performance or outcomes of systems or organizations.。

Evaluation of growth performance and whole-body composition of juvenile hybrid striped bass Morone

Evaluation of Growth Performance and Whole-body Composition of Juvenile Hybrid Striped Bass Morone chrysops 3Morone saxatilis and Red Drum Sciaenops ocellatus Fed High-protein and High-lipid DietsG ARY S.B URR 1ANDP ENG L I 1Department of Wildlife and Fisheries Sciences,Texas A&M University System,College Station,Texas 77843-2258USA,and Aquaculture Protein Center,CoE,NorwayJ ONATHAN B.G OFFDepartment of Wildlife and Fisheries Sciences,Texas A&M University System,College Station,Texas 77843-2258USAD ELBERT M.G ATLIN III 2Department of Wildlife and Fisheries Sciences,Texas A&M University System,College Station,Texas 77843-2258USA,and Aquaculture Protein Center,CoE,NorwayB ARBARA G RISDALE -H ELLAND and S TA˚LE J.H ELLAND AKVAFORSK (Institute of Aquaculture Research),SunndalsøraN-6600Norway,and Aquaculture Protein Center,CoE,NorwayAbstractTo investigate potential use of increasing nutritional density of diets for rapid growth of warm-water ﬁshes,a feeding trial was conducted in which growth performance,body indexes,and whole-body composition of juvenile hybrid striped bass fed diets comprising protein (49,54,and 59%),lipid (16,20,23,and 28%),and energy (22.0–25.1kJ/g)concentrations beyond established minimum levels were compared to those of ﬁsh fed a more typical commercial reference diet (37.5%crude protein,10.5%crude lipid,and 19.6kJ/g energy on a dry matter basis).A subset of the experimental diets and the commercial reference diet also were fed to juvenile red drum.After 6wk of feeding,hybrid striped bass fed the high-protein and high-lipid diets showed much greater growth performance compared to ﬁsh fed the commercial diet.Increasing dietary protein level,but not lipid level,tended (P #0.1)to enhance weight gain and feed efﬁciency of hybrid striped bass.Hepatosomatic index (HSI),intraperitoneal fat (IPF)ratio,and whole-body protein were signiﬁcantly (P ,0.01)inﬂuenced by dietary protein level.The dietary lipid and associated energy level had signiﬁcant negative linear effects on daily feed intake.Linear regression analysis showed that dietary energy :protein ratio,largely inﬂuenced by dietary protein level,moderately but signiﬁcantly inﬂuenced weight gain,HSI,IPF ratio,and whole-body protein of hybrid striped bass and red drum.Red drum grew very similar to hybrid striped bass in response to the experimental diets.However,signiﬁcant differences in HSI,IPF ratio,whole-body protein,lipid,moisture,and ash between hybrid striped bass and red drum were observed,indicating species differences in protein and energy partitioning.In particular,the excessive lipid in the diet increased HSI and whole-body lipid of red drum but not of hybrid striped bass.Expensive labor and utility infrastructure costs are the primary constraints to expansion of the aquaculture industry in developed coun-tries such as the USA.Because industrializedﬁsh culture requires much higher maintenance than the poultry and swine industries,develop-ment of growth-enhancing strategies is one of the prioritized goals for aquaculture research,not only to increase proﬁtability but also to reduce the risk of disease or culture system fail-ure.Although hybrid striped bass culture is the fastest growing segment of aquaculture in the1Equally contributed to this work.2Corresponding author.JOURNAL OF THEWORLD AQUACULTURE SOCIETY Vol.37,No.4December,2006ÓCopyright by the World Aquaculture Society 2006421USA,it has been recognized that high produc-tion cost is the greatest limitation to this industry (Carlberg et al.2000).It has been estimated that hybrid striped bass production costs can be reduced by12%if growth rates can be increased by20%(Sullivan2006).Compared to transge-netic techniques and use of anabolic agents and hormones that may involve biosafety issues, dietary fortiﬁcation of macronutrients,espe-cially protein and energy,is a potentially prom-ising way to enhance growth and protein accretion and compensate for labor and other expenses by shortening production cycles.The optimal levels of macronutrients and most criti-cal trace nutrients have been established for hybrid striped bass(reviewed by Gatlin1997; Webster2002).Brown et al.(1992)investigated incremental protein levels(ranging from25to 55%)in the diet of hybrid striped bass and found that hybrid striped bass performed optimally when fed a diet containing40%protein(dry matter basis)when the lipid(10%)and energy levels(17kJ/g)were kept constant.Nematipour et al.(1992)subsequently established the opti-mal dietary energy:protein(E:P)ratio based on the various dietary protein levels.This model was widely used to establish requirements for macronutrients and most critical trace nutrients for hybrid striped bass(reviewed by Gatlin 1997;Webster2002).However,growth perfor-mance and body composition traits of hybrid striped bass fed the high-protein and high-lipid diets that have become standard in production of cold-water species such as salmonids have not been investigated.This study was designed to evaluate nutrient-dense diets with three pro-tein levels(49,54,and59%)and four lipid lev-els(16,20,23,and28%).In addition,a separate feeding trial with a subset of these experimental diets was conducted with red drum,a marine sciaenid for seafood production and stock enhancement.Growth performance and body composition indexes were compared to explore the species differences in protein and energy partitioning.Materials and MethodsExperimental DietsThe experimental diets were formulated from ﬁsh meal,ﬁsh oil,and cornstarch(Table1)and were produced as3.5-mm extruded pellets by the Norwegian Institute of Fisheries and Aqua-culture Research(Fyllingsdalen,Norway).TheT ABLE1.Formulation and proximate analysis of experimental diets.ConstituentDiet code(%protein/%lipid)49/2049/2349/2854/1654/2054/2354/2859/2059/23Reference aIngredient(%)Fish meal b62.5962.9963.4968.5969.0969.5970.0375.7376.29—Norseaﬁsh oil12.3017.1021.907.0011.7016.5021.4111.1015.90—PregeﬂoÒM c23.7018.5013.2023.0017.8012.507.1511.76 6.40—Vitamin premix d 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00—Mineral premix d0.400.400.400.400.400.400.400.400.40—Y2O30.010.010.010.010.010.010.010.010.01—Analyzed proximatecomposition(%dry matter)eMoisture7.3 6.5 6.77.77.1 6.8 6.4 6.7 6.38.3 Crude protein48.949.149.054.053.754.552.959.359.037.5 Crude lipid19.622.727.615.620.523.028.520.423.610.5 Ash8.48.78.49.09.59.09.39.710.08.3 Total energy(kJ/g diet)22.723.925.122.022.923.825.023.324.419.6 E:P ratio(kJ/g protein)46.448.751.140.842.643.747.239.241.448.1a Reference diet was manufactured by Rangen Inc.,Angelton,Texas,USA.b Norse LT-94,65%herring and35%herring processing waste,Norsildmel,Fyllingsdalen,Norway.c Pregelatinized native corn starch,Roquette Freres,Lestrem Cedex,France.d Same as Mundheim et al.(2004).e Values of proximate composition of experimental diets represent means of two analyses.422BURR ET AL.experimental diets contained one of three crude protein levels(49,54,and59%)and one of four crude lipid levels(16,20,23,and28%).These diets were compared to a typical production diet of37.5%protein and10.5%lipid(dry matter basis)purchased from Rangen Inc.(Angleton, TX,USA).These different diets allowed for an incomplete factorial analysis of the effects of different protein levels at the same lipid level and vice versa.The chemical analysis of exper-imental diets was performed following estab-lished procedures as described by Hatlen et al. (2005).Feeding TrialsFeeding trials with juvenile hybrid striped bass and red drum were conducted at the Texas A&M University Aquacultural Research and Teaching Facility.Prior to initiation of these feeding trials,juvenile hybrid striped bass, Morone chrysops3Morone saxatilis,obtained from Keo Fish Farms(Keo,AR,USA),and juvenile red drum,Sciaenops ocellatus,from the Texas Parks and Wildlife Department Marine Development Center(Flour Bluff,TX, USA),were subjected to2-wk conditioning pe-riods to adjust to standardized regimes.Theﬁsh were maintained in a recirculating culture sys-tem consisting of thirty110-L aquariums con-nected to a sandﬁlter,a settling chamber,and a bioﬁlter.For hybrid striped bass,salinity was maintained at2.060.5ppt by adding stock salt and an artiﬁcial sea salt mixture to well water.The temperature was maintained at 2561C by conditioning the ambient air,and ﬁsh were subjected to a12:12light:dark pho-toperiod maintained by artiﬁcial lighting.Dis-solved oxygen was maintained close to saturation by blowing compressed air through air stones into each tank.Water quality was monitored periodically to ensure conditions were maintained within acceptable levels.The reference diet was fed to all theﬁsh during the 2-wk conditioning period after which hybrid striped bass juveniles averaging approximately 11g were then graded by size and stocked as groups of12individuals having a total weight of132.363.8g(mean6SD).Triplicate groups ofﬁsh were randomly assigned1of10diets.Theﬁsh were fed to visual satiety twice daily.The duration of this feeding trial was 6wk.In the red drum feeding trial,the same refer-ence diet was fed to all theﬁsh during the2-wk conditioning period after whichﬁsh averaging approximately18.0g were then graded by size and stocked as groups of11individuals having a total weight of197.964.0g(mean6SD). All the environmental conditions were main-tained closely to those used for hybrid striped bass except that salinity was maintained at 8ppt.Subsets of the experimental diets from the hybrid striped bass feeding trial as well as the commercial reference diet were fed to red drum.Four replicate groups ofﬁsh were ran-domly assigned one of six diets.Theﬁsh were fed to visual satiety twice daily.The duration of this feeding trial also was6wk.Performance Responses,BodyIndexes,and Compositional AnalysesAt the end of both feeding trials,threeﬁsh from each tank were euthanized with MS-222 and weighed.Their livers and intraperitoneal fat(IPF)were removed and weighed separately to compute hepatosomatic index(HSI)and IPF ratio.The livers and IPF were returned to the sample and the threeﬁsh from each tank were homogenized in a blender after being frozen in liquid nitrogen.The homogenate samples were then analyzed for protein,lipid,moisture,and ash according to established procedures(Webb and Gatlin2003).Daily feed intake(%body weight/d)was calculated based on geometric average body weight,while other performance responses included weight gain,speciﬁc growth rate,feed efﬁciency ratio,protein efﬁciency ratio(PER),protein conversion efﬁciency (PCE),and survival(Webb and Gatlin2003).Statistical AnalysisAll the data from the hybrid striped bass feed-ing trial were subjected to Levene’s test of equal-ity of error variances and one-way ANOV A followed by Duncan’s multiple range test as well as two-way ANOV A using SPSSÒ(Chicago,IL, USA).Response parameters of both species also were subjected to linear regression analysisHIGH-PROTEIN DIET EV ALUATION WITH HYBRID STRIPED BASS AND RED DRUM423against dietary E:P ratio,total dietary protein, lipid,and energy.Separate linear regression anal-ysis was performed on both species to determine the inﬂuence ofﬁnal weight on whole-body protein content.In addition,correlation tests between daily feed intake and IPF ratio and whole-body lipid were performed.The treatment effects were considered signiﬁcant if the P value was at or below0.05.Because ingredient infor-mation was not available for the commercial ref-erence diet and its formulation likely differed considerably from the experimental diets, this treatment was excluded from the two-way ANOV A and linear regression analysis.ResultsHybrid striped bass fed the experimental diets generally had signiﬁcantly higher weight gain and feed efﬁciency compared to those fed the commercial reference diet,although the differ-ences in survival,PER,and PCE amongﬁsh fed the experimental diets and the reference diet were not statistically signiﬁcant(Table2).The signiﬁcant improvement in growth performance of hybrid striped bass was most likely attribut-able to the dietary fortiﬁcation of protein and lipid,although the difference in ingredients between the experimental and reference diets may have contributed to this result.A rather strong tendency(P#0.1)that the increasing dietary protein level in the experimental diets (49,54,and59%)enhanced weight gain and feed efﬁciency was observed in this trial,whereas the linear regression analysis showed that the dietary E:P ratio had a signiﬁcant(P50.01)inverse inﬂuence on weight gain and accounted for 23.2%of the variation in this variable. Dietary lipid level tended to inﬂuence the feed intake based on two-way ANOV A(Table2). This observation was further conﬁrmed by linear regression analysis,which showed that the increasing dietary lipid level(15,20,23,andT ABLE2.Growth performance of hybrid striped bass fed the experimental diets.1Dietary factorsWeight gain(%increase)Survival(%)Feedintake(g/100gbody weight/d)2Feedefﬁciencyratio(g gain/g feed)Proteinefﬁciencyratio(g gain/g protein fed)Proteinconversionefﬁciency(g protein gain/g protein fed)3100%Crude protein %Crude lipid4920261.5±8.0bc80.6±2.8 2.68±0.07 1.1±0.0ab 2.2±0.034.7±1.2 23206.3±32.4c69.4±12.1 2.53±0.080.9±0.1bc 1.9±0.328.5±4.228227.1±22.1bc80.6±15.5 2.46±0.09 1.1±0.1ab 2.2±0.333.5±3.5 5416242.6±15.6bc88.9±2.8 2.69±0.22 1.1±0.1ab 2.0±0.131.3±1.7 20265.6±16.0bc91.7±4.8 2.67±0.09 1.2±0.0ab 2.0±0.133.1±1.723301.8±22.1a66.7±4.8 2.64±0.13 1.1±0.0ab 2.0±0.031.7±1.128230.8±2.7bc83.3±4.8 2.31±0.04 1.2±0.0ab 2.2±0.132.6±1.5 5920294.2±24.3ab69.4±2.8 2.32±0.08 1.2±0.0a 2.1±0.136.1±1.4 23255.0±14.7abc80.6±2.8 2.56±0.08 1.1±0.0ab 1.8±0.131.3±1.4 Reference(37/10)3142.6±27.1d58.3±9.6 2.60±0.080.6±0.3c 1.5±0.323.3±4.2One-way ANOV A4P0.0010.1060.1210.0000.1450.084Two-way ANOV A5Dietary protein0.0880.7860.1490.1030.8490.677 Dietary lipid0.1640.3530.0640.1710.2490.169 Dietary protein3dietary lipid0.0750.1430.2350.9750.8530.6961Values represent means6SEM of three replicate groups.Initialﬁsh weight is11.060.1g/ﬁsh(mean6SEM).2Feed intake51003dry feed consumption(g)/([W t3W0]1/2)3t).3This treatment was excluded from two-way ANOV A because of unknown ingredient composition.4Signiﬁcance probability associated with the F statistic.Values in a column that do not have the same superscript are signiﬁcantly different at P#0.05based on Duncan’s multiple range test.5Signiﬁcance probability associated with the F statistic.424BURR ET AL.28%)signiﬁcantly(P50.035)reduced feed intake of hybrid striped bass,although only 16.7%of the variation in feed intake was attribut-able to dietary lipid.Similarly,the increase in the total dietary energy,largely dependent on dietary lipid level,signiﬁcantly(P50.025)reduced feed intake in this study but dietary E:P ratio did not.Body indexes including HSI and IPF ratio were inﬂuenced by dietary treatments.Increas-ing the dietary protein level signiﬁcantly (P,0.01)reduced HSI and IPF ratio(Table3). In addition,the linear regression analysis showed that increasing dietary E:P ratio slightly increased HSI(P50.025,R250.06)and IPF ratio(P50.001,R250.13).However,neither dietary lipid level nor total dietary energy showed signiﬁcant inﬂuences on HSI and IPF ratio of hybrid striped bass.Dietary protein pos-itively affected whole-body protein concentra-tion as determined by both factorial ANOV A (P,0.001)and linear regression analysis(P ,0.001,R250.44)(Table3).Similarly,lin-ear regression analysis also showed that dietary E:P ratio negatively affected whole-body pro-tein concentration(P,0.01,R250.67). However,whole-body moisture,lipid,and ash were not signiﬁcantly affected by various levels of dietary protein and lipid.Linear regression analysis showed that theﬁnal weight of hybrid striped bass signiﬁcantly(P50.02)inﬂuenced whole-body protein and accounted for20.8%of the variation.The feeding trial with juvenile red drum also demonstrated the superiorﬁsh growth conferred by the high-protein and high-lipid diets (Table4),which was signiﬁcantly better than that ofﬁsh fed the reference diet.IPF ratio and whole-body lipid were signiﬁcantly affected by dietary treatment.Linear regression analysis showed that increasing dietary E:P ratio(rang-ing from39.2to51.1kJ/g protein)signif-icantly decreased growth rate(P50.01, R250.33)and whole-body protein(P50.01, R250.32),as well as increased HSI (P50.03,R250.24),IPF ratio(P50.001,T ABLE3.Body indexes and whole-body composition of hybrid striped bass fed the experimental diets.1 Dietary factorsHSI (%)IPF ratio(%)Whole-bodymoisture(%)Whole-bodyprotein(%)Whole-bodylipid(%)Whole-bodyash(%)%Crude protein %Crude lipid4920 2.9±0.2ab7.2±0.3abc67.4±0.415.5±0.4cde9.8±1.0 3.5±0.123 2.9±0.1a7.6±0.7ab67.6±0.715.0±0.2de12.9±0.8 3.4±0.228 2.4±0.1bdc8.2±0.8a67.5±0.715.3±0.3cde10.5±1.1 3.6±0.0 5416 2.8±0.2ab 6.7±0.6abc69.0±0.615.6±0.3cde10.5±0.5 3.6±0.120 2.6±0.1abc 5.8±0.2c69.2±0.315.9±0.2abc9.7±1.2 3.5±0.323 2.3±0.1dc7.1±0.8abc68.5±0.115.8±0.1bcd10.2±1.4 3.2±0.328 2.7±0.0abc 6.1±0.1bc67.9±0.114.9±0.1e10.4±0.9 3.5±0.2 5920 2.0±0.2d 5.6±0.8c67.4±2.116.6±0.1a10.6±0.7 3.7±0.223 2.1±0.2d 6.3±0.7bc68.0±0.416.4±0.2ab8.3±0.5 3.6±0.2 Reference(37/10)2 2.8±0.2abc 5.5±0.3c69.5±0.515.6±0.2cde10.5±0.8 3.4±0.1 One-way ANOV AP.F30.0000.0060.4690.0010.2110.747Two-way ANOV A P.F4Dietary protein0.0000.0020.2770.0010.1690.209 Dietary lipid0.2450.3330.8460.0790.9100.458 Dietary protein3dietary lipid0.0690.5070.7890.1500.0630.937HSI5hepatosomatic index;IPF5intraperitoneal fat.1Values represent means6SEM of three replicate groups(n53);initial whole-body composition was as follows: 72.4%moisture,15.2%protein,6.7%lipid,and4.2%ash.2This treatment was excluded from two-way ANOV A because of unknown dietary composition.3Signiﬁcance probability associated with the F statistic.Values in a column that do not have the same superscript are signiﬁcantly different at P#0.05based on Duncan’s multiple range test based on one-way ANOV A.4Signiﬁcance probability associated with the F statistic.HIGH-PROTEIN DIET EV ALUATION WITH HYBRID STRIPED BASS AND RED DRUM425T A B L E 4.C o m p a r i s o n o f g r o w t h ,s u r v i v a l ,b o d y i n d e x e s ,a n d w h o l e -b o d y c o m p o s i t i o n b e t w e e n h y b r i d s t r i p e d b a s s a n d r e d d r u m i n r e s p o n s e t o d i e t a r y p r o t e i n a n d l i p i d l e v e l s .S p e c i e s%C r u d e p r o t e i n %C r u d e l i p i dS p e c i ﬁc g r o w t h r a t e (%b o d y w e i g h t /d )S u r v i v a l (%)H S I (%)I P F r a t i o (%)W h o l e -b o d y m o i s t u r e (%)W h o l e -b o d y p r o t e i n (%)W h o l e -b o d y l i p i d (%)W h o l e -b o d y a s h (%)R e d d r u m 149203.03±0.11a b84.1±7.82.8±0.12.9±0.3a b71.6±0.917.1±0.57.3±0.8a b3.7±0.1232.97±0.09a b75.0±10.13.1±0.13.7±0.2a72.2±0.317.5±0.56.4±0.7a b c4.3±0.3282.87±0.13b65.9±15.53.1±0.13.6±0.4a71.6±0.615.8±0.27.9±0.8a3.8±0.154203.15±0.17a b65.9±13.12.8±0.12.3±0.4b c71.7±0.518.1±0.25.7±0.5b c4.2±0.159203.31±0.10a81.8±6.42.7±0.32.3±0.3b c72.3±1.318.3±1.15.4±0.3b c4.0±0.2R e f e r e n c e (37/10)22.03±0.05c77.3±7.93.5±0.61.6±0.2c 74.1±0.417.2±0.15.0±0.5c 3.9±0.1O n e -w a y A N O V A P .F 30.0000.5700.3990.0010.4460.0680.0270.233H y b r i d s t r i p e d b a s s 449203.06±0.0580.6±2.82.9±0.27.2±0.367.4±0.415.5±0.49.8±1.03.5±0.1232.64±0.2469.4±12.12.9±0.17.6±0.767.6±0.715.0±0.212.9±0.83.4±0.2282.81±0.0680.6±15.52.4±0.18.2±0.867.5±0.715.3±0.310.5±1.13.6±0.054203.08±0.1091.7±4.82.6±0.15.8±0.269.2±0.315.9±0.29.7±1.23.5±0.359203.26±0.1569.4±2.82.0±0.25.6±0.867.4±2.116.6±0.110.6±0.73.7±0.2A N O V A P .F 5S p e c i e s 0.2120.3960.0000.0000.0000.0000.0000.000D i e t a r y p r o t e i n 0.2420.7700.0080.0090.6680.1200.5230.422D i e t a r y l i p i d 0.1890.4920.1460.1050.9160.2710.3780.502S p e c i e s 3d i e t a r y p r o t e i n 0.9400.1160.0510.4400.5370.8480.2510.350S p e c i e s 3d i e t a r y l i p i d0.4110.4910.0410.6930.9600.2080.0280.126H S I 5h e p a t o s o m a t i c i n d e x ;I P F 5i n t r a p e r i t o n e a l f a t .1V a l u e s r e p r e s e n t m e a n s 6S E M o f f o u r r e p l i c a t e g r o u p s (n 54).I n i t i a l ﬁs h w e i g h t i s 18.060.1g /ﬁs h (m e a n 6S E M ).2T h i s t r e a t m e n t w a s e x c l u d e d f r o m t w o -w a y A N O V A b e c a u s e o f u n k n o w n d i e t a r y c o m p o s i t i o n .3S i g n i ﬁc a n c e p r o b a b i l i t y a s s o c i a t e d w i t h t h e F s t a t i s t i c .V a l u e s i n a c o l u m n t h a t d o n o t h a v e t h e s a m e s u p e r s c r i p t a r e s i g n i ﬁc a n t l y d i f f e r e n t a t P #0.05b a s e d o n D u n c a n ’s m u l t i p l e r a n g e t e s t .4V a l u e s r e p r e s e n t m e a n s 6S E M o f t h r e e r e p l i c a t e g r o u p s (n 53).I n i t i a l ﬁs h w e i g h t i s 11.060.1g /ﬁs h (m e a n 6S E M ).5S i g n i ﬁc a n c e p r o b a b i l i t y a s s o c i a t e d w i t h t h e F s t a t i s t i c .426BURR ET AL.R250.49),and whole-body lipid(P50.01, R250.33).Dietary lipid level and total dietary energy had similar effects on red drum.In par-ticular,increasing dietary lipid increased HSI and whole-body lipid of red drum based on the linear regression analysis,which was different from that of hybrid striped bass.Linear regres-sion analysis showed that theﬁnal weight of red drum signiﬁcantly(P50.02)inﬂuenced whole-body protein and accounted for25.5% of the variation.Both hybrid striped bass and red drum fed the experimental diets showed indistinguishable superior growth compared toﬁsh fed the refer-ence diet.Survival ofﬁsh in the two trials was not signiﬁcantly inﬂuenced by dietary treat-ment;however,the body indexes and whole-body composition did show signiﬁcant species differences under optimal culture environments (Table4).Dietary protein inﬂuenced HSI (P,0.01)and IPF ratio(P,0.01)with a ten-dency(P50.12)to affect whole-body protein of both species.However,HSI and whole-body lipid of the twoﬁsh species responded differ-ently to dietary lipid level.DiscussionThe experimental diets generally conferred faster growth and superior feed efﬁciency to juvenile hybrid striped bass and red drum,com-pared toﬁsh fed the commercial reference diet, which was formulated based on minimum warm-waterﬁsh nutritional requirements and least-cost feed formulation principles.The increasing dietary protein level in the experi-mental diets showed a strong tendency to increase weight gain;however,this growth improvement could at least partially be inﬂu-enced by an optimized E:P ratio.An appropriate E:P ratio has been reported by various research groups for hybrid striped bass(Brown et al. 1992;Nematipour et al.1992;Keembiyehetty and Wilson1998)and striped bass(Woods et al. 1995).However,the optimal E:P ratio could be inﬂuenced by protein quality and energy digestibility,as well as environmental factors such as temperature(Keembiyehetty and Wilson1998).A dietary E:P ratio established using puriﬁed diets might underestimate the optimal E:P ratio when high-quality protein in-gredients are used.In addition,most of the pre-vious studies in which E:P ratio was studied did not use isonitrogenous diets.Therefore,dietary protein level might contribute to the growth per-formance differences.In this study,only high E:P ratio values were tested.The linear regres-sion analysis showed that the increase in the dietary E:P ratio from39to49kJ/g protein signiﬁcantly reduced weight gain and feed efﬁciency of hybrid striped bass,supporting the optimal dietary E:P ratio of38kJ/g protein for hybrid striped bass fedﬁsh-meal-based diets reported by Keembiyehetty and Wilson(1998). In the present study,the differences in protein utilization(PCE and PER)were rather high among hybrid striped bass fed the experimental diets, althoughﬁsh fed the commercial reference diet showed noticeably inferior protein utilization. This is possibly because of reduced digestibility of protein ingredients in the commercial formu-lation.Increased dietary lipid within a relatively low level has been shown to enhance feed efﬁ-ciency of warm-water species(Daniels and Robinson1986;Serrano et al.1992;Thoman et al.1999);however,growth performance indexes for the two species were not responsive to changes in dietary lipid level over15%, which is in agreement with the results of Craig et al.(1999)and Gaylord and Gatlin(2000). No signiﬁcant protein-sparing effect by exces-sive dietary lipid was observed with the hybrid striped bass,which is in agreement with the ob-servations of Gallagher(1999)and also with those of Gaylord et al.(2003)who studied sum-mer founder(Paralichthys dentatus).Dietary energy(from protein,fat,and carbo-hydrate)has been postulated to be one of the main factors inﬂuencing feed intake inﬁsh(re-viewed by de la Higuera2001).The daily feed intake of hybrid striped bass in this study was signiﬁcantly inhibited by the total dietary energy as well as lipid,which supported this the-ory.It also was the only test response in this study that was inﬂuenced by total dietary energy.It is not known whether this phenome-non results from lipid itself or the energy pro-vided by dietary lipid.Jobling and Miglavs (1993)found that body fat accumulation andHIGH-PROTEIN DIET EV ALUATION WITH HYBRID STRIPED BASS AND RED DRUM427energy depots inﬂuenced appetite ofﬁsh.The key adipokine leptin,which is involved in food intake control,has been characterized with terrestrial animals.However,our study failed to show a noticeable correlation between daily feed intake and IPF ratio or whole-body lipid in hybrid striped bass.This phenomenon needs further investigation.Body condition indexes including HSI and IPF ratio were signiﬁcantly inversely inﬂuenced by dietary protein level in the present study.This could partially be explained by improved die-tary E:P ratio as well.The linear regression anal-ysis showed a moderate but signiﬁcant inﬂuence of dietary E:P ratio on HSI and IPF ratio of both species.In addition,whole-body protein of both hybrid striped bass and red drum was signiﬁ-cantly inﬂuenced by dietary protein level and E:P ratio in this study,which is in agreement with responses of striped bass(Woods et al.1995), hybrid striped bass(Keembiyehetty and Wilson 1998),red drum(Webb and Gatlin2003),and otherﬁsh species such as rainbow trout,Onco-rhynchus mykiss(Azevedo et al.2004);chinook salmon,Oncorhynchus tshawytscha(Azevedo et al.2004);Asian sea bass,Lates calcarifer (Williams et al.2003);and Japanese seabass, Lateolabrax japonicus(Ai et al.2004).Lipid in the diet did not appear to inﬂuence whole-body lipid,HSI,or IPF ratio of hybrid striped bass in this study.This indicates that once a threshold of dietary lipid content is reached, any further increase did not contribute to addi-tional depot lipid in tissues of this species. However,red drum responded differently to excessive dietary lipids.Whole-body lipid, HSI,and IPF of red drum juveniles increased linearly with dietary lipids,which is in agree-ment with reported observations in juveniles (,5g initial weight;Daniels and Robinson 1986;Serrano et al.1992;Gatlin2002)and sub-adults(;150g initial weight;Turano et al. 2002).The substantial differences in body condition indexes and whole-body composition of juve-nile hybrid striped bass and red drum have been indirectly observed from the published literature on these two species over the past two decades. However,because of the tremendous inﬂuences of nutrition on body indexes and composition, these species differences were not fully substan-tiated until the direct comparison was made in this study.Except for the difference in salinity of the culture system used for the two feeding trials,all other environmental factors were con-trolled indistinguishably.The differences in HSI,IPF ratio,whole-body protein,lipid,mois-ture,and ash between the two species were sub-stantial and signiﬁcant,although both species exhibited similar rapid growth on the experi-mental diets.Hybrid striped bass can store2–2.5times more IPF and greater whole-body lipid than red drum,which is undesirable because of production waste and accelerated lipid rancidity. According to Ramseyer(2002),whole-body nitrogen content or crude protein ofﬁsh includ-ing hybrid striped bass and red drum is strongly dependent on theﬁsh wet weight.The observa-tion in the present study was in agreement with this analysis.However,only20.8and25.5% of the variation in whole-body protein were attributable toﬁnal weight of hybrid striped bass and red drum,respectively,in the present study. By contrast,dietary protein contributed44.1% of variation in whole-body protein of hybrid striped bass,compared to22.4%for red drum. Considering the variousﬁnal weights of hybrid striped bass and red drum fed the experimental diets,changes in whole-body protein were attributable to bothﬁsh and dietary protein levels.Our studies with juvenile hybrid striped bass and red drum showed that both species fed high-protein and high-lipid diets showed over-whelming growth performance compared toﬁsh fed a commercial reference diet,which may indicate manipulating nutrient density of the diet is a strategy to increaseﬁsh growth and pos-sibly production efﬁciency.The IPF ratios of both hybrid striped bass and red drum fed the high-protein and high-lipid diets were elevated compared to published values for these two species,which might cause reduced dress-out percentage and oxidative stability of product.A thorough investigation on the economics of production using these types of diets is still needed.Of the combinations of various protein and lipid levels in the present study,the diet428BURR ET AL.。

功能性便秘-罗马IV新看点

长期效应
微生物通路可用于预防因双歧杆菌菌群减少而引起的气体产生及肠道运行缓慢
药理学说明：
• 乳果糖原型到达结肠，在结肠中被细菌降解为短链脂肪酸；
• 结肠PH值降低，同时渗透压和充盈量增加；
• 由于PH值降低的直接刺激和粪便体积增加的间接刺激，最终导致肠道蠕动增快和预期结肠通过时间缩短，加速排便。
罗马Ⅳ：功能性便秘的诊断步骤
FC的诊断需要进行以下5个循序渐进的步骤：
临床病史体格检查尽量少的实验室检查结肠镜或其他检查* 特殊的检查用以评估便秘的病理生理机制**
*有条件时可在特定病例中进行；**有必要且有条件时进行。
Douglas A. Drossman, 等. 罗马IV: 功能性胃肠病[M]. 科学出版社, 2016: 645.
Data on file
乳果糖加速升结肠排空快于聚乙二醇
肠道排空运行方向
横结肠
升结肠逆重排空
降结肠
乙状结肠
一项随机、双盲、交叉研究，纳入10名健康志愿者，接受乳果糖10g bid po或聚乙二醇10g bid po，治疗第4天予111铟标记的阳离子交换树脂颗粒胶囊，在第5天，给予99锝标记的试验餐以观察胃、小肠、结肠传输时间。结果显示：与PEG（聚乙二醇）相比，常规剂量的乳果糖显著加快升结肠排空。
关键点：
评估便秘症状时，最理想的是停用缓泻剂以及其他可引起便秘的药物和补充制剂
1. 必须包括下列2项或2项以上** a. ¼（25%）以上的排便感到费力 b. ¼（25%）以上的排便为干粪球或硬粪（Bristol粪便性状量表 1~2型） c. ¼（25%）以后的排便有不尽感 d. ¼（25%）以上的排便有肛门直肠梗阻/堵塞感 e. ¼（25%）以上的排便需要手法辅助（如用手指协助排便、盆底支持） f. 每周自发排便（SBM）少于3次

Temporal changes of phytoplankton community at different

Temporal changes of phytoplankton community at different depths of a shallow hypertrophic reservoir in relation to environmental variablesYongSu Kwon 1$,SoonJin Hwang 2$,KuSung Park 2,HoSeob Kim 3,BaikHo Kim 2,KyungHoon Shin 4,KwangGuk An 5,YoungHee Song 6and YoungSeuk Park 1*1Department of Biology and The Korea Institute of Ornithology,Kyung Hee University,Seoul 130701,Korea 2Department of Environmental Science,Konkuk University,Seoul 143701,Korea3Watershed Management Research Divisions,National Institute of Environmental Research,Incheon 404170,Korea 4Department of Environmental Marine Science,Hanyang University,Ansan 425791,South Korea5School of Bioscience and Biotechnology,Chungnam National University,Daejeon 305764,South Korea 6Rural Research Institute,Ansan 426908,South KoreaReceived 15January 2009;Accepted 8April 2009Abstract –We characterized phytoplankton community succession at diﬀerent depths of a shallow hypertrophic reservoir in relation to physical and chemical environmental variables.The phytoplankton community was sampled biweekly at three diﬀerent water depths (surface,middle and bottom)in the reservoir from November 2002to February 2004.A range of 18environmental variables including temperature,elec-trical conductivity (EC),total phosphorus (TP)and total nitrogen (TN)were measured to assess their inﬂu-ence on phytoplankton community succession.As well,combined multivariate analyses with a cluster analysis and a nonmetric multidimensional scale (NMDS)were conducted.Microcystis aeruginosa was the dominant species in all seasons except spring.Thus,Cyanophyceae was a dominant taxonomic group.In spring,Bacillariophyceae dominated,followed by Cryptophyceae and Chlorophyceae.The succession was relatively delayed at the middle and bottom layers compared with at the surface layer.Abundance and species richness of phytoplankton were also higher in the surface layer than in the bottom layer.Cluster analysis classiﬁed the phytoplankton community into four clusters at each depth,and the changes were also well reﬂected in the NMDS ordination.Each cluster showed seasonal patterns characterized by indicator species,as well as environmental variables such as temperature,conductivity,and nutrients including N and P.Seasonal dynamics of the phytoplankton community was the strongest at the surface layer and weakest at the bottom layer.These depth-variable environmental variables are likely to be the key factors driving changes in the phytoplankton community composition.Key words:Algae /classiﬁcation /lakes /succession /vertical diﬀerencesIntroductionChanges in phytoplankton communities in lakes occur in space and time,and are related to the physical,chemical,and biological conditions of the water bodies(Reynolds,1984;Wetzel,2001;Valerio et al.,2008).Temporal variability in the structure and function of the phytoplankton community in a lake is of fundamental importance to lake metabolism (Calijuri et al.,2002),studies of which are important to aid understanding of lake ecosystems as well as for eﬀective management of lake water quality.Many studies have investigated temporal changes in phytoplankton communities in lakes,including long-term changes (Reynolds,1984;Romo and Miracle,1994;Chen et al.,2003;Winder and Hunter,2008)and seasonal dynamics (Salmaso,1996,2002;Wang et al.,2007).Typical seasonal succession models of the phyto-plankton community in temperate lakes shows that Bacillariophyceae dominate during early spring,Chloro-phyceae dominate in late spring,and Cyanophyceae dominate during summer (Reynolds,1984).However,phytoplankton species composition and succession can*Corresponding author.E-mails:parkys@khu.ac.kr ,sjhwang@konkuk.ac.kr $These two authors contributed equally.Article published by EDP SciencesAnn.Limnol.-Int.J.Lim.45(2009)93–105Available online at:ÓEDP Sciences,2009DOI:10.1051/limn/2009014have diverse patterns depending on the environmental conditions (Abdul-Hussein and Mason,1988;Wetzel,2001;Reynolds,2006).Additionally,phytoplankton com-munities can undergo signiﬁcant changes within a single year (Padisak,1992).Variation in phytoplankton communities also occurs with depth in response to environmental conditions (Huovinen et al.,1999;Ptacnik et al.,2003;Reynolds,2006).The vertical distribution of phytoplankton in lakes appears to be aﬀected by factors that include light,temperature,nutrients,predation,and mixing patterns within the water column,and,thus,their composition and biomass varies with depth (Huisman et al.,1999;Gervais et al.,2003;Ptacnik et al.,2003;Pinilla,2006).In par-ticular,the inﬂuences of the gradient of incident light and mixing patterns in the water column have been studied as niches for diﬀerent groups of species related to their mo-tility,buoyancy,and size (Huisman et al.,1999;Ptacnik et al.,2003).The combination of nutrient availability and temperature is also a key factor in the spatial and temporal dynamics of phytoplankton,and aﬀects their productivity and growth period (Reynolds,1984,1988;Wetzel,2001).As such,phytoplankton succession of lentic systems is generally understood as the outcome of the complicated interactions in the water column.The succession,however,does not necessarily indicate the ‘mean’or ‘integrated’interaction through the whole water column.Vertical dis-tribution of phytoplankton in deep oligotrophic lakes seems to be distinct depending primarily on light avail-ability (Brook and Torke,1977;Tilzer et al.,1977)and algal adaptation to low light intensities (Priscu and Goldman,1983),and it is likely separated by stratiﬁcation.Distinct vertical distribution may not be evident in shallowsystems,due to more probable mixing and resuspension.Despite this rationale,little information is available on spatial phytoplankton succession in shallow lentic sys-tems.Thus,it is worth investigating the succession of phytoplankton at diﬀerent depths of a shallow system,because the results provide basic information on how shallow lentic systems drive phytoplankton succession,whether phytoplankton succession is characteristic along the depth proﬁle,and to clarify the major variables at diﬀerent depths.The results will contribute to an increased understanding of the integrated phytoplankton dynamics in shallow eutrophic reservoirs.The objective of this study was to assess phytoplankton community succession at diﬀerent water depths of a hypertrophic reservoir.Various approaches were used to analyze temporal changes in the community in relation to environmental variables,including community indices,hierarchical cluster analysis,nonmetric multidimensional scale,and indicator species analysis.Materials and methodsStudy areaThe study was conducted at the Shingu Reservoir,a shallow hypertrophic agricultural reservoir located in Chungcheongnamdo,Korea (36x 10'31.37''N,126x 37'02.24''E)(Fig.1).The reservoir has a surface area of 0.1km 2,mean depth of 3.9m,catchment area of 2.55km 2,and water storage capacity of 411r 103m 3.There are two inﬂowing streams with a 1.3km and 3.4km channel length,respectively.WaterqualityFig.1.Location of our sampling site (’)in the Shingu Reservoir (Chungcheongnamdo,Korea).Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10594in the reservoir is aﬀected by livestock waste from farms near the reservoir,and the water level is inﬂuenced by precipitation during the summer monsoon as well as from irrigated paddyﬁelds(KARICO,2001).Oxygen depletion has been recorded in the reservoir during summer,while the water surface freezes during winter(Kim,2004). Sample collectionPhytoplankton samples were collected biweekly from the bank near the deepest point in the reservoir from November2002to February2004.Samples were not col-lected between January and February2003due to thick ice cover on the reservoir.Samples were collected from three diﬀerent depths(surface,middle and bottom layers)at2m intervals.Water samples(100mL)for the identiﬁcation of phytoplankton species and measurement of cell densities were stored in Whirl-Pak bags andﬁxed with Lugol’s sol-ution(2%ﬁnal concentration).Cell densities were meas-ured by microscopy(Axiostar plus;Zeiss,Germany)using a Sedgwick-Rafter counting chamber at200–400r mag-niﬁcation.Phytoplankton were identiﬁed to species as de-scribed previously(Prescott,1962;Hirose and Yamagishi, 1977;John et al.,2003).Temperature,dissolved oxygen(DO),pH,and electric conductivity were measured using an in situ RE232/SDI12 data logger(Hydrolab,USA)at each depth.Secchi depth was measured using20cm diameter Secchi discs.For nu-trient analyses,5L water samples collected at each depth using a Van Dorn sampler(WildCo.,USA)were placed in sterilized polyethylene bottles and were transported to the laboratory on ice.Total phosphorus(TP),total dissolved phosphorus(TDP),and dissolved inorganic phosphorus (DIP)were analyzed in triplicate using the ascorbic acid method(APHA,1995).Particulate organic phosphorus (POP)was determined by subtraction of the TDP value from the TP value.Total nitrogen(TN)was measured using the cadmium reduction method following persulfate digestion(APHA,1995).Ammonia nitrogen(NH3N)was measured using an indolphenol reagent,and nitrite nitrogen(NO2N)and nitrate nitrogen(NO3N)were meas-ured using the cadmium reduction method(APHA,1995). Dissolved inorganic nitrogen(DIN)was calculated as the sum of NO3N,NO2N and NH3N.Suspended solids(SS) and the chemical oxygen demand(COD)were measured using standard methods(APHA,1995).The total dataset comprised63samples(22surface layer,20middle layer,and21bottom layer samples)of the phytoplankton community and18environmental factors. Data analysisCommunity indices including species richness,abun-dance,and species diversity index were estimated at each water depth and sampling time.For each sample,species richness and abundance were expressed as the number of species and cell density,respectively.Species diversity was estimated according to the Shannon index(Shannon, 1948).Pearson correlation coeﬃcients were calculated among environmental variables.The Kruskal-Wallis (KW)test was used to assess diﬀerences in community indices at the diﬀerent depths,and the nonparametric Dunn’s multiple comparisons test was done for post hoc comparisons.The analyses were made using STATISTICA software(StatSoft,2004).Temporal changes in phytoplankton communities in relation to environmental variables were analyzed using multivariate statistical analyses,hierarchical cluster ana-lysis,and non-metric multidimensional scaling(NMDS). Prior to the multivariate statistical analyses,phytoplank-ton species densities were log transformed to reduce variation.To avoid the problem of log(0)being undeﬁned, a value of1was added to all data points.The data were rescaled in the range of0and1based on the min-max transformation,giving the same level of importance to all species in the analysis.The analyses were conducted in two steps:cluster analysis for classiﬁcation and NMDS for ordination of temporal changes in the community.Cluster analysis was conducted to classify temporal changes in the phytoplankton community in relation to species density at diﬀerent water depths using Ward’s linkage method with Euclidean distance measure.Samples for each water depth were classiﬁed into clusters based on the similarities of their community composition.A multi-response permutation procedure(MRPP;Mielke et al., 1976),which is a nonparametric procedure for testing the hypothesis of no diﬀerence between two or more groups of entities,was conducted to evaluate the signiﬁcance of the clusters.Cluster analysis and MRPP were conducted using PCORD(McCune and Meﬀord,1999).Diﬀerences in environmental variables among clusters or depths were evaluated using the KW test and Dunn’s nonparametric multiple comparison test,using STATISTICA software (StatSoft,2004).Indicator species analysis(IndVal;Dufre ne and Legendre,1997)was used to evaluate indicator species in each cluster deﬁned in the cluster analysis.The indicator value for each species in a group is the product of its relative abundance and its relative frequency(r100),and ranges from0(no indication)to100(perfect indication) (Peterson and Keister,2003).A perfect indicator of a particular group should be faithful and exclusive to that group,never occurring in another group(McCune and Grace,2002).Species with an indicator valueﬁve times higher than in any other cluster are deﬁned as good indicators(Keister and Peterson,2003).To determine the signiﬁcance of species indicator values a Monte Carlo simulation was performed.The analysis was carried out using PCORD(McCune and Meﬀord,1999).NMDS was used to characterize temporal changes in the phytoplankton community at the three depths in the study reservoir.The NMDS technique appears to be superior to other ordination techniques when applied to ecological data(Kenkel and Orloci,1986;Bettinetti et al., 2000).NMDS was performed using PCORD(McCune and Meﬀord,1999),based on the Bray-Curtis distanceY.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10595measure and 100runs with a maximum of 400iterations per run.A Monte Carlo test with 99randomizations was used to determine the probability that the observed stress value of the ﬁnal solution would occur by chance alone (Laughlin and Abella,2007).To assist interpretation of the changes in community proﬁles,Pearson correlation coeﬃcients between dimension scores,and the environ-mental variables were calculated and plotted using PCORD (McCune and Meﬀord,1999).ResultsChanges in environmental variablesMost environmental variables showed clear seasonal dynamics at each of the three diﬀerent depths (Fig.2).Concentrations of both inorganic and total N were highest in spring and early summer,lowest in autumn,and increased during winter.The concentrations of all forms of P were high in summer and autumn,and low in winter and spring.Signiﬁcant oxygen depletion and a decrease in pH were observed in the bottom layer in summer,and coincided with high concentrations of NH 3N (mean ¡SD;surface:0.15¡0.28mg.L x 1;middle:0.39¡0.34mg.L x 1;bottom:1.03¡0.28mg.L x 1)and NO 2N (mean ¡SD;surface:0.06¡0.03mg.L x 1;middle:0.15¡0.05mg.L x 1;bottom:0.19¡0.04mg.L x 1),although they were not signiﬁcantly diﬀerent (KW test,P =0.186and 0.149,respectively)due to their vari-ability.The NO 3N was greater in the middle layer (mean ¡SD;surface: 1.14¡0.19mg.L x 1;middle:1.50¡0.23mg.L x 1;bottom: 1.27¡0.19mg.L x 1)(KW test,P =0.566).NH 3N (r =x 0.47,P <0.05),DIN (r =x 0.45,P <0.05)and TN (r =x 0.35,P <0.05)were negatively correlated with pH,whereas DO (r =0.27,P <0.05)was positively correlated with pH.DO was most strongly correlated with temperature (r =x 0.59,P <0.05),followed by NO 2N,TP,NH 3N,TDP,SRP,TN,POP,conductivity,pH and SS (r values =x 0.58,x 0.42,x 0.41,x 0.40,x 0.39,x 0.31,x 0.30,0.29,0.27and x 0.27,respectively;P <0.05).NH 3N showed a signiﬁcant correlation with DIN (r =0.79,P <0.001)and TN (r =0.61,P <0.001).Temperature (r =0.63,P <0.001)and DO (r =x 0.58,P <0.01)were highly correlated with NO 2N,but DIN (r =0.21,P >0.05)was not signiﬁcantly correlated with temperature.TDP and SRP values were more than three times higher in the bottom layer in summer compared to other periods.Electric conductivity was negatively correlated with tem-perature (r =x 0.64,P <0.001),with the highest values (251.4m S.cm x 1)occurring in the bottom layer in May,and the lowest value (96.0m S.cm x 1)occurring in the middle layer in August.TP showed asigniﬁcantFig.2.Temporal variation in environmental variables at three water depths in the Shingu Reservoir from November 2003to February 2004(dark bars delineate periods where the reservoir is covered with ice).SS:suspended solids,TN:total nitrogen,DIN:dissolved inorganic nitrogen (the sum of NH 3N,NO 2N,and NO 3N),SRP:soluble reactive phosphorus,TDP:total dissolved phosphorus,DOP:dissolved organic phosphorus,POP:particulate organic phosphorus,TP:total phosphorus.Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10596correlation with POP (r =0.90,P <0.001)and the TN:TP ratio (r =x 0.62,P <0.01),and TN showed a signiﬁcant correlation with DIN (r =0.77,P <0.001)and the TN:TP ratio (r =0.70,P <0.001).SD was highest in May (1.2m)and showed a negative correlation with SS,which peaked during October and November (r =x 0.81,P <0.001).POP showed a signiﬁcant correlation with SS (r =0.62,P <0.01).Changes in phytoplankton community compositionA total of 66phytoplankton species were recorded from the three diﬀerent depths from November 2002to February 2004;52in the surface layer,50in the middle layer,and 56in the bottom layer.Over the whole water column the Chlorophyceae dominated total species rich-ness (36species),followed by Cyanophyceae (15species),Bacillariophyceae (12species),Cryptophyceae (2species),and Dinophyceae (1species).However,Cyanophyceae dominated the cell abundance at all depths during the study period,with peak abundance occurring in summer and autumn (Fig.3a).Other taxonomic groups dominated only during spring,when Cyanophyceae did not occur.Bacillariophyceae dominated only in March and April,and Cryptophyceae dominated only in April and May (Fig.3b);both showed similar patterns at all three diﬀerent depths.Chlorophyceae displayed a slightly diﬀer-ent pattern,being dominant only in the bottom layer in June.The peak time of Chlorophyceae abundancechanged as a function of depth,occurring in the surface layer in March,in the middle layer in April,and in the bottom layer in May.Overall phytoplankton abundance was highest in the surface layer (Dunn’s test,P <0.05)and lowest in the bottom layer (Dunn’s test,P <0.05),and showed a gradient of abundance as a function of depth (Fig.3b).Bacillariophyceae and Chlorophyceae increased in relative abundance in August and November,especially in the bottom layer.The abundance of each taxonomic group was a reﬂec-tion of their dominant species.For example,the abun-dance of Cyanophyceae was dominated by Microcystis spp.including Microcystis aeruginosa ,Bacillariophyceae was dominated by Aulacoseira varians ,Cryptophyceae was dominated by Rhodomonas sp.,and Chlorophyceae was dominated by Dictyosphaerium pulchellum (Fig.4).However,slightly diﬀerent patterns occurred at diﬀerent depths,particularly in the bottom layer.For the cyano-phytes,Oscillatoria sp.(May 2003)and Aphanizomenon sp.(June 2003)dominated before a M.aeruginosa bloom occurred in summer;the chlorophyte Monoraphidium contortum was dominant in March,and the bacillariophyte A.ambigua was dominant in November 2003.Species richness was higher in the surface layer (mean 16.8)than in the bottom layer (mean 14.0)(KW test,P =0.003),and the Shannon diversity index was higher in the bottom layer (mean 1.4)than in the surface layer (mean 1.2),although the diﬀerence was not statistically signiﬁcant (KW test,P =0.231)(Fig.5).However,the Shannon diversity index in the surface layerwasFig.3.Temporal changes in the phytoplankton community for cell density (a)and relative abundance (b)in the Shingu Reservoir from November 2002to February 2004(no data are available for January and February in 2003because of ice cover).Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10597signiﬁcantly lower in summer (mean 0.60)than in other seasons (spring:1.52,autumn:1.21,winter:1.22)(Dunn’s test,P <0.05),indicating a predominance of cyanophytes during munity analyses Surface layerThe phytoplankton community in the surface layer was classiﬁed into four clusters (I–IV)based on their simi-larities (Fig.6a),and displayed clear seasonality.The MRPP showed signiﬁcant diﬀerences among clusters (A =0.137,P <0.001).Samples in November 2002were classiﬁed into cluster III,samples in spring 2003were in cluster I,samples from June to October 2003were in cluster II,and samples in winter (from 2003to 2004)were in cluster IV.Sample names in Figure 6were identiﬁed according to the sampling year (the ﬁrst two numbers,e.g.03for 2003),the sampling month (the next three charac-ters,e.g.AUG for August),and the order of sampling time in the corresponding month at two week intervals (the last number).Some environmental variables including tem-perature and TP showed signiﬁcant diﬀerences among clusters (KW test,P <0.05)(Table 1),but others including SD,DO,COD,NH 3N,TN,and SRP did not.Temperature was signiﬁcantly higher in cluster II than in other clusters,TP was signiﬁcantly higher in cluster II thanin clusters I and IV,TDP diﬀered signiﬁcantly between clusters I and III,and the TN:TP ratio diﬀered signiﬁ-cantly between clusters I and II (Dunn’s test,P <0.05).A two-dimensional ordination of NMDS explained 69%of the variance (0.34and 0.35of the determination coeﬃcients,r 2,for axes 1and 2,respectively)in the phyto-plankton distance matrix of the surface layer (Fig.6b).The axes explained signiﬁcantly more variance than would be expected by chance,based on Monte Carlo permutation tests (P =0.01).Overall,the ordination showed clear seasonal changes in the phytoplankton community.Samples in spring were on the right part of the ordination,samples in summer were on the upper left part,samples in autumn were on the left part,and samples in winter were on the lower part.The arrows in the NMDS ordination show temporal changes in the community from November 2002to February 2004.Each sample was characterized on the basis of the clusters deﬁned by the cluster analysis (Fig.6a).Using the NMDS analysis,species could be ordinated on the biplot based on their contribution to the commu-nity ordination (Fig.6c),reﬂecting their occurrence at diﬀerent times.The results were consistent with the IndVal analysis conducted to evaluate indicative species for each cluster;all species presented in the plot had an indicatorvalue >25%(Dufrene and Legendre,1997).Cluster I was represented with three indicator species including Selenastrum minutum and Aphanocapsa sp.,cluster II with two species (Crucigenia rectangularis and Staurastrum astroideum ),cluster III with seven species including Ankistrodesmus bibraianus and Nitzschia palea ,and cluster IV was with three species including Chlamydomonas sp.(Monte Carlo test,P <0.05)(Fig.6c).Good indicators were deﬁned as having an indicator value at least ﬁve times higher than in any other cluster (shown in bold).The eﬀects of environmental variables were charac-terized by calculating correlation coeﬃcients between en-vironmental factors and NMDS axis scores.The variables having signiﬁcant correlation coeﬃcients (Pearson cor-relation coeﬃcient,P <0.05)are shown as arrows on the NMDS ordination (Fig.6c),where the arrow length indi-cates the magnitude of the correlation value and the arrow direction implies a correlation with each axis.Axis 1was most highly correlated with conductivity (r =0.80,P <0.05),followed by NH 3N (r =0.75,P <0.05),NO 3N (r =0.72,P <0.05),TP (r =x 0.68,P <0.05),TN (r =0.48,P <0.05),and pH (r =x 0.46,P <0.05).Axis 2was positively correlated with temperature (r =0.70,P <0.05),SS (r =0.56,P <0.05),and TN (r =0.45,P <0.05),but negatively correlated with DO (r =x 0.62,P <0.05).N and P showed diﬀerent relationships with the phytoplankton community.High values of conductivity and N-related variables characterized samples in spring (cluster I),whereas high values of P-related variables characterized samples in summer (cluster II).Middle layerThe phytoplankton community in the middle layer was classiﬁed into four clusters (1–4),and mostlyshowedFig.4.Changes in the dominant species at three diﬀerent depths (a:surface,b:middle,c:bottom).Abundance was rescaled between 0and 1with min-max transformation.Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10598Fig.6.a)Dendrogram of a hierarchical cluster analysis of the phytoplankton community in the surface layer using the Ward linkage method with Euclidean distance measure.b)NMDS ordination of the phytoplankton community (axis 1:r 2=0.34,axis 2:r 2=0.35).c)Ordination of species selected using the IndVal analysis with signiﬁcant environmental variables in the surface layer.Good indicators in each group are bolded,identifying that their indicator values were more than ﬁve times higher for that group than for any othergroups.Fig.5.Changes in species richness (a)and the Shannon diversity index (b)at the three diﬀerent depths.Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–10599seasonal variation (MRPP,A =0.123,P <0.0001)(Fig.7a).Although the classiﬁcation was similar to that of the surface layer,the seasonality in the middle layer was weaker than in the surface layer,showing that samples in diﬀerent seasons were grouped together in diﬀerent clusters,particularly in cluster 3.Samples in cluster 1ranged from March to May,and samples in cluster 2ranged from June to October.Samples in cluster 3were mixed for November to December 2002,October 2003,and March 2003,and cluster 4represented samples for autumn and winter (from November 2003to February,2004).Environmental variables including temperature,NO 2N,TDP,and POP showed signiﬁcant diﬀerences among clusters (KW test,P <0.05)(Table 1).Temperature was signiﬁcantly diﬀerent between cluster 2(highest value)and cluster 4(lowest value)(Dunn’s test,P <0.05).TDP was highest in cluster 3and signiﬁcantly diﬀerent from cluster 4(Dunn’s test,P <0.05).A two-dimensional NMDS ordination explained around 65%of the variance in phytoplankton community structure in the middle layer (axis 1,r 2=0.27;axis 2,r 2=0.48)(Monte Carlo test,P =0.01)(Fig.7b).Samples in cluster 3(Fig.7a)were more highly scattered than others,and the ordination of samples in time order was slightly mixed compared with that of the surface layer,although they showed seasonality.Based on the IndVal analysis,cluster 1was character-ized by ﬁve indicator species including D.pulchellum and Tetrahedron incus ,cluster 3was characterized by seven species including Coelastrum longissima and N.palea ,and cluster four was characterized by three species including Chlamydomonas sp.(Monte Carlo test,P <0.05)(Fig.7c).However,cluster 2did not have any indicator species.These characteristics were reﬂected in the biplot ordin-ation,with the species indicated in bold (D.pulchellum in cluster 1,N.palea in cluster 3,and Chlamydomonas sp.in cluster 4)having indicator values for those clusters that were more than 5times the values in other clusters.Based on the correlation coeﬃcients between environ-mental variables and NMDS axis scores,the NMDS axes were signiﬁcantly correlated with some environment vari-ables,and were visualized on the NMDS ordination map (Fig.7c)with vector length and direction.Conductivity (r =x 0.77,P <0.05),NH 3N (r =x 0.56,P <0.05),and DIN (r =x 0.47,P <0.05)were negatively correlated to axis 1,whereas NO 2N (r =0.54,P <0.05)and temperature (r =0.45,P <0.05)were positively correlated to axis 1.Axis 2was most highly correlated with TDP (r =x 0.60,P <0.05),followed by TP,NH 3N,and TN (r =x 0.49,r =x 0.49and r =x 0.46,respectively,P <0.05).Bottom layerCluster analysis classiﬁed the phytoplankton com-munities in the bottom layer into four clusters (i–iv)(MRPP,A =0.109,P <0.0001)(Fig.8a).The classiﬁ-cation showed weak seasonality compared with those of the surface and middle layers.Environmental variables including temperature,conductivity,DO,NH 3N,DIN,T a b l e 1.E n v i r o n m e n t a l v a r i a b l e s a t d i ﬀe r e n t c l u s t e r s a t e a c h d e p t h o f t h e S h i n g u R e s e r v o i r .E n v i r o n m e n t a l v a r i a b l e s S u r f a c e l a y e r M i d d l e l a y e r B o t t o m l a y e r I I I I I I I V1234i i i i i i i vS e c c h i d e p t h (m )0.880.650.590.81T e m p e r a t u r e (x C )14.42a b23.66a 7.58b7.53b15.45a b22.38a9.28a b6.23b11.34a b17.90a7.95a b6.77bp H 8.22a b9.38a 7.35b8.52a b8.037.867.448.557.307.247.438.49C o n d u c t i v i t y (m S .c m x 1)207.88a134.02b 185.38a b185.63a b210.03134.08178.66193.33221.10a146.06b186.30a b190.80a bD O (m g .L x 1)11.5010.7011.8512.059.458.0311.4711.876.04a b3.33a10.95a b11.17bS S (m g .L x 1)9.68a14.62a b 20.55b10.8a b9.859.5816.8411.1313.8419.7116.914.99C O D (m g .L x 1)9.7610.3411.839.2110.659.3511.449.4410.3010.109.858.69N H 3N (m g .L x 1)0.5380.0940.3620.230.5630.2120.4070.2411.319a0.583a b0.303a b0.305bN O 2N (m g .L x 1)0.036a b0.057a 0.017b0.026a b0.044a b0.105a0.023b0.022a b0.0270.1260.0160.022N O 3N (m g .L x 1)1.67a0.86a b 0.81b0.86b1.661.050.930.911.280.970.800.89D I N (m g .L x 1)2.25a1.01b 1.19a b1.12b2.261.361.361.172.63a1.68a b1.12a b1.22bT N (m g .L x 1)2.842.422.482.112.952.702.422.043.063.812.101.96S R P (m g .L x 1)3.464.484.184.462.732.383.943.912.802.754.654.54D O P (m g .L x 1)10.97a b16.84a 12.29a b7.41b10.7511.0811.236.7611.15a10.89a11.10a b5.77bT D P (m g .L x 1)14.43a b21.32a 15.64a b11.88b13.48a b13.45a b15.17a10.67b13.95a b24.70a15.75a b10.31bP O P (m g .L x 1)44.99a77.22b 63.67a b51.86a b46.45a57.08a b65.66b53.84a b60.2767.1060.0760.99T P (m g .L x 1)59.42a98.54b 79.31a b63.74a59.9370.5380.8364.5174.2291.8075.8271.30T N :T P r a t i o48.75a25.99b 32.15a b35.01a b49.4738.1930.6630.8142.2229.1127.1629.37T h e l e t t e r s i n e a c h r o w f o r e a c h l a y e r i n d i c a t e s i g n i ﬁc a n t d i ﬀe r e n c e s f o r t h a t v a r i a b l e a m o n g t h e c l u s t e r s ,b a s e d o n t h e D u n n ’s m u l t i p l e c o m p a r i s o n t e s t s (P <0.05).Y.Kwon et al.:Ann.Limnol.-Int.J.Lim.45(2009)93–105100。

211251927_化学修饰多糖的方法及生物活性研究进展

杨艺，赵媛，孙纪录，等. 化学修饰多糖的方法及生物活性研究进展[J]. 食品工业科技，2023，44（11）：468−479. doi:10.13386/j.issn1002-0306.2022070383YANG Yi, ZHAO Yuan, SUN Jilu, et al. Research Progress on Chemical Modification Methods of Polysaccharides and Their Biological Activity[J]. Science and Technology of Food Industry, 2023, 44(11): 468−479. (in Chinese with English abstract). doi:10.13386/j.issn1002-0306.2022070383· 专题综述 ·化学修饰多糖的方法及生物活性研究进展杨　艺1，赵　媛2，孙纪录3，邵娟娟1,*（1.河北农业大学理工学院，河北沧州 061000；2.江南大学化工学院，江苏无锡 214122；3.河北农业大学食品科技学院，河北保定 071000）摘　要：多糖属于生物大分子，其生物活性取决于结构及理化性质。

研究表明，多糖的化学修饰可以使其结构多样性显著增加，提高生物活性，甚至增加新的生物活性。

本文系统综述了近年来化学修饰多糖的研究进展，包括常用的化学修饰方法、各类化学修饰对多糖分子量、理化特性或空间结构的影响、化学修饰多糖的生物活性以及化学修饰多糖在医药和食品工业中的应用前景及挑战，以期为化学修饰多糖的深入研究提供参考建议，同时为未来基于人类健康的食品医药开发提供重要的依据。

关键词：多糖，化学修饰，生物活性，结构，理化性质本文网刊:中图分类号：O629.12 文献标识码：A 文章编号：1002−0306（2023）11−0468−12DOI: 10.13386/j.issn1002-0306.2022070383Research Progress on Chemical Modification Methods ofPolysaccharides and Their Biological ActivityYANG Yi 1，ZHAO Yuan 2，SUN Jilu 3，SHAO Juanjuan 1, *（1.College of Science and Technology, Hebei Agricultural University, Cangzhou 061000, China ；2.School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, China ；3.College of Food Science and Technology, Hebei Agricultural University, Baoding 071000, China ）Abstract ：Polysaccharides are biological macromolecules and their biological activities depend on their structure and physicochemical properties. Studies have shown that chemical modification of polysaccharides can significantly increase their structural diversity, improve their biological activities, and even add new biological activities. This article reviews systematacially the research progress of chemical modification of polysaccharides in recent years, including frequently-used methods of chemical modification, the influence of various chemical modification on molecular weight of polysaccharides,physical and chemical properties and spatial structure, the biological activity of chemically modified polysaccharides as well as their pharmaceutical and food industrial application prospect and challenges. It is expected to offer a reference for the further research chemically modified polysaccharides and provide an important basis for the future development of food and medicine based on human health.Key words ：polysaccharide ；chemical modification ；biological activity ；structure ；physicochemical property近年来，多糖在食品、医药等领域的发展一直是人们关注的热点。

基于点云数据的主成分分析重构表面算法

第24 卷第1 期2010 年3 月黑龙江工程学院学报(自然科学版)J o u r n al of Heilo n gjia n g In s tit u t e of Tec h n olo g yVo l . 24 №. 1Ma r.,2010基于点云数据的主成分分析重构表面算法张贺,杨金玲,曹先革(黑龙江工程学院测绘工程学院,黑龙江哈尔滨150050)摘要:提出一种基于三维点云数据的主成分分析重建三维表面模型的方法,该方法利用基于主成分分析的动态聚类方法对三维扫描数据进行聚类,进而对点云数据重构—点片,研究在局部利用二维三角网构网技术构建三角网, 然后在考虑局部三角网边缘一致性的基础上组合成整体三维表面模型的算法。

应用实例表明,该算法能有效地完成重建物体三维表面模型。

关键词:点云数据;主成分分析;三维表面重构;Dela u nay中图分类号: P208文献标识码: A文章编号:167124679 (2010) 0120039204Princip al component analysis reconstru ction surfacealgorithm ba s ed on points cloud d ataZ H A N G He , YA N G J i n2li ng , CA O Xia n2ge(Dep t . of Sur veying and Map ping Engineering , Heilo n gjia n g In stit u t e of Tech n o lo g y , Ha r b in 150050 ,China)Abstract :A met h o d of u s i n g p r i n cip a l co m po n e n t a n al y si s to reco n s t r u ct t h ree2di me n s io n al s urf a ce mo d el ba se d o n t h ree2di me n sio nal poi nt s clo ud dat a i s p u t fo rwa r d . A nd it u s e s t h e dyna mic cl u st e ri n g m et h o d of p ri ncip al co mpo ne nt a nal ysi s to cl u st e r t he t h ree2di me n sio nal sca nni ng dat a , so t hat it reco n st r u ct s t h e poi nt s clo ud dat a —poi nt s slice ; st udie s co n s t r uct t he t ria ngle net wo r k by t he t wo2di me n sio nal t r i a n gle t e c h n olo g y i n local a r ea , a n d t h e n it co m po s e s t h e al g o rit h m of t h e w h ole t h ree2di me n s io n al s urf a ce m o d el ba s e d o n co n s i d e r i n g t h e local a r ea t ria n gle cur v ed surf a ce net w o r k e d ge co n s i s t e n ce .The app l icatio n e x2 a m p l e ma n if e s t s t h at t h e al g o r it h m ca n reco n s t r u ct t h e o b ject t h r ee2di me n s io n al s urf a ce m o d el eff icie n tl y. K ey w ords : p oi n t s clo u d dat a ; p r i n cip a l co mpo n e n tDela u na ya n al y si s ; t h ree2di me n s io n al s urf a ce reco n s t r u ctio n ;三维激光扫描是近年来发展起来的一种新型空间数据获取手段和工具,能快速、准确地获取物体的三维几何模型,在文物保护、工业零件检测、医学、测绘等诸多领域具有重要的意义。

Productivity and Undesirable Outputs

230
A directional distance function approach
or Fa ¨ re et al. (1993) one could estimate a shadow price. One possible solution is to use a productivity index that does not require information on prices of eﬄuents, for example, the Malmquist index; see Fa ¨ re and Grosskopf (1996). However, in the presence of undesirable outputs, this index may not be computable. Here we propose a new index, which we call the Malmquist–Luenberger productivity index which overcomes the shortcomings of the original Malmquist index. This index readily allows for inclusion of undesirable outputs without requiring information on shadow prices. It also explicitly credits ﬁrms or industries for reductions in undesirable outputs, providing a measure of productivity which will tell managers whether their “true” productivity has improved over time. This index also tells managers if there has been technical progress (a shift in the best practice frontier) and whether they are catching up to the frontier. Since the index is computed using a data envelopment analysis type approach, information concerning benchmark ﬁrms and technical eﬃciency is also generated for individual ﬁrms. In order to illustrate the applicability of this index, we compute productivity for data from the Swedish paper and pulp industry. We begin with a discussion of the way in which we model technology, and then turn to our measure of productivity based on this model. Section 4 includes a discussion of our data and results. Section 5 provides a brief conclusion. 2. Modelling technology with good and bad outputs The basic pollution problem is that production of “good” outputs, such as paper or electricity, is typically accompanied by the joint production of undesirable by-products, such as suspended solids or SO2. The fact that goods and bads are jointly produced means that reduction of bads will be “costly”: either resources must be diverted to “clean-up” (e.g. scrubbers), production must be cut back, or ﬁnes must be paid. M I More formally, if we denote good outputs by yvR+ , bad outputs by bvR+ , and inputs N by xvR+, then we can describe technology in a very general way via the output sets P(x)={( y, b): x can produce ( y, b)}. (2.1)

近红外光谱结合回归预测法判别木材的生物腐朽

第48卷第10期2012年10月林业科学SCIENTIASILVAESINICAEVol.48，No.10Oct．，2012收稿日期：2011－11－27；修回日期：2012－03－29。

基金项目：国家自然科学基金（30800889）；国家林业局“948”项目（2003-4-27）。

*本研究得到了美国农业部林务局的首席科学家Chung-Yun Hse 博士的帮助，在此表示感谢。

近红外光谱结合回归分析预测法判别木材的生物腐朽*杨忠1黄安民1江泽慧2（1．中国林业科学研究院木材工业研究所北京100091；2．国际竹藤中心北京100102）摘要：利用近红外光谱结合多变量回归分析中常用的主成分回归（PCR ）和偏最小二乘法回归（PLSR ）分析预测法来判别木材的生物腐朽，并与前期采用的SIMCA 和PLS-DA 2种判别方法进行对比分析。

结果表明：1）应用近红外光谱结合多变量回归分析方法对校正集样本建立的判别模型，其校正及验证结果与标准值的相关性很高，相关系数均大于0.95，SEC 和SEP 都很低（0.07 0.20），利用模型对未参与建模的样本进行检测，发现2个模型对未腐朽、白腐和褐腐3种类型样本的判别准确率均为100%（偏差都小于0.27）；2）对于相同样本集的判别效果，PLSR 法比PCR 法的判别效果好，且二者都比采用SIMCA 法的效果好，并都与PLS-DA 法的判别结果相近，说明利用近红外光谱结合回归分析预测法能有效地检测木材的生物腐朽，并对生物腐朽的类型进行准确判别。

关键词：近红外光谱；回归分析预测法；木材；生物腐朽；判别中图分类号：O657.3；S781文献标识码：A文章编号：1001－7488（2012）10－0120－05Discrimination of Wood Biological Decay by NIR Coupled with RegressionAnalysis Prediction MethodYang Zhong1Huang Anmin 1Jiang Zehui2（1．Research Institute of Wood Industry ，CAFBeijing 100091；2．International Center for Bamboo and Rattan Beijing 100102）Abstract ：The use of near infrared （NIR ）spectroscopy coupled with regression analysis prediction method to detectwood biological decay was investigated in this paper．Principal component regression （PCR ）analysis and partial least squares regression （PLSR ）analysis were compared with the results of extensive research on SIMCA and PLS-DA methods by analysis of the correlation coefficients and the model residuals．The results shown that correlation between the predicted variable of calibration and validation and the measured variable is significant with correlation coefficient （r ）over 0.95with low SEC and SEP （0.07－0.20）；the discriminant accuracy for the non-decay ，white-rot and brown-rot decay samples are 100%（deviation ＜0.27）by the PCR and PLSR models based on test set samples ；the discriminant accuracy by PLSR is better than that by PCR due to the lower deviation ，while both of PCR and PLSR have better discriminant accuracy than that by SIMCA pattern recognition ，and has the same discriminant accuracy as PLS-DA method．It ’s suggested that NIR spectroscopy coupled with PCR and PLSR analysis prediction methods could be used to rapidly detect wood biological decay．Key words ：near infrared （NIR ）spectroscopy ；regression analysis prediction method ；wood ；biological decay ；discrimination近红外光谱（NIR ）结合化学计量学中的定性分析方法在食品、医药和农林产品等的检测或识别方面得到了许多应用（李庆波等，2004；王丽等，2004；杨忠等，2005；2007；2008），在近红外光谱的判别分析中SIMCA 和PLS-DA 判别分析方法是2种比较常用的方法。

heterogeneous analysis

heterogeneous analysisHeterogeneous analysis refers to the process of examining diverse and varied data sources to gain insights, identify patterns, and make informed decisions. It involves analyzing data that come from different types, formats, structures, or sources.Heterogeneous analysis can be applied to various fields, including business, science, social sciences, and technology. It often requires combining and integrating data from multiple sources, such as structured databases, unstructured text documents, images, audio, and video files.The goal of heterogeneous analysis is to uncover hidden patterns, relationships, and trends that may not be evident by analyzing each data source separately. By bringing together different types of data, researchers or analysts can gain a more comprehensive understanding of the phenomenon under study and make more accurate predictions or conclusions.The methods used in heterogeneous analysis can vary depending on the characteristics of the data being analyzed. They may include statistical techniques, data mining, machine learning, natural language processing, image or signal processing, and other advanced analytical tools.Overall, heterogeneous analysis allows for a more holistic and comprehensive approach to data analysis, enabling organizations and researchers to extract valuable insights and make data-driven decisions across a wide range of domains.。

翻译——精选推荐

翻译AbstractSeismic Performance Assessment and Probabilistic Repair Cost Analysis of Precast Concrete Cladding Systems for Multistory BuildingsbyJeffrey Patrick HuntDoctor of Philosophy in Engineering –Civil and Environmental EngineeringUniversity of California, BerkeleyProfessor Bozidar Stojadinovic, ChairAnalytical and experimental tests have shown that the seismic response of multistorymoment-frame structures with precast concrete cladding in moderate to severe earthquakes is significantly influenced by the cladding system. Moreover, considerable damage to the cladding system components from recent earthquakes has been reported. The cladding system can account for a significant portion of the initial cost of a building, often as much as 20%. However, inseismic analysis and design, engineers typically ignore the additional stiffness and damping thatthe cladding system may provide, which could prove to be beneficial or detrimental to the building’s seismic performance. Most of the efforts in nonlinear dynamic modeling focus on representing the behavior of structural elements and do not include the effects of non-structural elements such as cladding systems. The purpose of the research discussed in this dissertation isto study the effect that the cladding system has on the structural response of multistory buildings,to develop analytical equations to estimate the seismic demands in the cladding connections, to calculate the probability of failure of typical cladding connections, and to determine the postearthquake repair costs and repair times of typical cladding systems.The nine-story LA SAC steel moment-frame building is selected as the study building,and a two-dimensional, nonlinear model is developed of the bare-frame structure in OpenSees.The steel moment-resisting frame of the bare-frame structure is modeled using nonlinear forcebeam-column line elements capable of representing distributedplasticity along their length. Theframe connections are reduced-beam section (RBS) moment connections, and their modeledcyclic moment-rotation behavior is based on experimental test results of the connection.Analytical models of three different precast cladding designs are applied to the bare-framestructure to study their effect on the building’s seismic response. The three cladding designs represent common systems used in regular multistory buildings in modern construction. The first cladding design, cladding type C1, consists of alternating horizontal bands of spandrel panels (covering the exterior floor beams) and glazing. The spandrel panels extend the full width of thebay. The second cladding design, cladding type C2, consists of spandrel panels that extend thefull height of the story with rectangular window openings “punched” into their surface. The thirdcladding design, cladding type C3, consists of the same spandrel panels as in type C1 withcolumn cover panels spanning between adjacent spandrel panels.The force-deformation curvesof the connections used in the model are obtained from experimental tests of push-pullconnections and column cover connections. The total seismic mass of the models with the cladding systems is the same as the total seismic mass of the bare-frame model. However; in themodels with cladding, the seismic mass is distributed between the beam-column nodes and thenodes of the cladding system according to their respective tributary weights.The effects of the cladding on the seismic response of the bare-frame structure are studiedby performing modal analyses, nonlinear static pushover analyses, and nonlinear dynamic timehistoryanalyses of the analytical models. The inclusion of cladding decreases the fundamentalperiod of the building by only 4%; however, the effects of the cladding on the maximuminterstory drifts, floor accelerations, and plastic hinge rotations are significant. Time-historyanalyses of each model are performed using 140 ground motions. The ground motions in eachbin are scaled by a common factor (cloud method with constant scaling) to ensure nonlinearresponse was captured. The time-history results are plotted in log-log space, and a linear trendline is fitted to the data to represent the mean maximum response values. The time-history resultsreveal that the addition of cladding reduces the mean maximum interstory drift ratios in the bareframemodel by up to 22%, 28%, and 33% for the 50%-, 10%-, and 2%-in-50 year probability ofexceedance levels, respectively. The reductions in interstory drift are the largest for claddingtype C3 and smallest for cladding type C1. The mean residual interstory drifts are small for alllevels of intensity and were not significantly affected by the cladding. The mean maximum flooraccelerations are not significantly affected by cladding types C1 and C2: the mean values ofmaximum floor accelerations in the bare frame structure are reduced by only 8% for these twocladding types. On the other hand, the mean values of the maximum acceleration at the roof levelin the model with cladding type C3 are up to 35%, 63%, and 97% larger than the values in thebare frame structure for the 50%-, 10%-, and 2%-in-50 year probability of exceedance level,respectively.The finite-element models of structures with cladding are time-consuming to create andcomputationally demanding to analyze. Thus, analytical equations are derived to describe themechanisms for deformation in the cladding connectors. The equations are used to estimate themaximum deformations in the push-pull and column cover connectors. The maximumdeformations estimated from the equations are compared to the maximum deformations recordedfrom the time-history analyses. The comparisons of the median values of maximum deformationbetween the two approaches show that the analytical equations provide good estimates of the maximum deformations up the height of the building. The analytical equations can be used as conservative estimates of deformation for the seismic design ofsimilar cladding connectors.The time-history analysis results show that significant deformations develop in thecolumn cover connections in moderate earthquakes. The deformations exceed the life-safety, and in some cases, the collapse prevention performance criteria. Thus, the failure probabilities of the column cover connections subject to multiple hazard levels are investigated using structural reliability theory. The analytical equations for estimating the deformations in the column cover connectors are used to construct the limit-state function describing the structural reliability of the connectors. The random variables consist of the maximum interstory drift, the gap width in the slotted connections, and the failure shear deformation in the connectors. The deterministic parameters in the limit-state functions are the panel dimensions and the story height. The correlation coefficients are calculated for the maximum interstory drifts between different stories. The components of the column covers consist of fourconnectors (one in each corner).The component failure probabilities (calculated using FORM) are as high as 44.2%, 70.0%, and 100% for the 50%-, 10%-, and 2%-in-50 year probability of exceedance levels, respectively. The。

asymptotic analysis缩写

asymptotic analysis缩写Asymptotic analysis is a mathematical method used to analyze the behavior of an algorithm as the input size approaches infinity. It is widely used in computer science and engineering to compare different algorithms and design efficient algorithms. In this article, we will discuss the basics of asymptotic analysis and its important concepts.Firstly, we need to understand the importance of asymptotic analysis. In computer science and engineering, we often deal with huge datasets and complex algorithms. Therefore, it is important to know how the algorithm will behave as the input size becomes large. Asymptotic analysis helps us to estimate the computational time and space complexity of an algorithm for large inputs. This estimation can help us to choose the best algorithm for a given problem.Asymptotic analysis is based on the concept of limits. A limit is the value a function approaches as the input value approaches a certain point. We use big O, big Omega, and big Theta notations to express the growth rate of a function. These notations give us a rough idea about the behavior of the function.Big O notation: The big O notation gives the upper bound of the running time of an algorithm. We say that algorithm A has a time complexity of O(f(n)) if the running time of the algorithm does not exceed a constant multiple of f(n) for large n. For example, if the running time of the algorithm A is less than or equal to 2n^2+3n+4, we can say that the time complexity of the algorithm A is O(n^2).Big Omega notation: The big Omega notation gives the lower bound of the running time of an algorithm. We say that algorithm A has a time complexity of Omega(f(n)) if the running time of the algorithm is not less than a constant multiple of f(n) for large n. For example, if the running time of the algorithm A is greater than or equal to n^2/2, we can say that the time complexity of the algorithm A is Omega(n^2).Big Theta notation: The big Theta notation gives the tight bounds of the running time of an algorithm. We say that algorithm A has a time complexity of Theta(f(n)) if the running time of the algorithm is between a constant multiple of f(n) and another constant multiple of f(n) for large n. For example, if the running time of the algorithm A is between 5n^2+3n+4 and 7n^2+5n+6, we can say that the time complexity of the algorithm A is Theta(n^2).Asymptotic analysis also covers the space complexity of an algorithm. We use the same notations to express the growth rate of the space usage of an algorithm. For example, if the space used by the algorithm A is less than or equal to 3n+4, we can say that the space complexity of the algorithm A is O(n).In conclusion, asymptotic analysis is an important concept in computer science and engineering. It helps us to estimate the computational time and space complexity of an algorithm for large inputs. By using the big O, big Omega, and big Theta notations, we can compare different algorithms and choose the best algorithm for a given problem.。

1. THE NEED FOR EMPIRICAL STUDIES OF DR IN USE

Buckingham Shum, S. (1996). Analyzing the Usability of a Design Rationale Notation. In T. P. Moran and J. M. Carroll, (Eds.) Design Rationale: Concepts, Techniques, and Use, 185-215. Hillsdale, NJ: Lawrence Erlbaum Associates.Analysing the Usability of a Design Rationale NotationSimon Buckingham ShumABSTRACTSemiformal, argumentation-based notations are one of the main classes of formalism currently being used to represent design rationale (DR). However, our understanding of the demands on designers of using such representations has to date been drawn largely from informal and anecdotal evidence. One way to tackle the fundamental challenge of reducing DR’s representational overheads, is to understand the relationship between designing, and the idea structuring tasks introduced by a semiformal DR notation. Empirically based analyses of DR in use can therefore inform the design of the notations in order to turn the structuring effort to the designers’ advantage. This is the approach taken in this chapter, which examines how designers use a DR notation during design problem solving.Two empirical studies of DR-use are reported, in which designers used the QOC notation (MacLean et al., this volume) to express rationale for their designs. In the first study, a substantial and consistent body of evidence was gathered, describing the demands of the core representational tasks in using QOC, and the variety of strategies which designers adopt in externalising ideas. The second study suggests that an argumentation-based design model based around laying out discrete, competing Options is inappropriate during a depth-first,‘evolutionary’ mode of working, centered around developing a single, complex Option. In addition, the data provide motivation for several extensions to the basic QOC notation. The chapter concludes by comparing the account of the QOC–design relationship which emerges from these studies, with reports of other DR approaches in use.Simon Buckingham Shum is a Research Fellow at the Knowledge Media Institute, The Open University, UK, studying the implications and applications of the internet and interactive media for learning anddesign.Knowledge Media Institute, The Open University, Milton Keynes, MK7 6AA, U.K.Email: S.Buckingham.Shum@CONTENTS1.THE NEED FOR EMPIRICAL STUDIES OF DR IN USE2.THE STUDIES: DESIGNERS, TRAINING, AND TASKS3.CORE REPRESENTATIONAL TASKS IN QOC-AUTHORING3.1.QOC authoring as an opportunistic activity3.2.Classifying ideas3.3.Naming and renaming3.4.Structuring and restructuring3.5.Summary4.PROBLEMS USING QOC IN ‘EVOLUTIONARY’ DESIGN:TRYING TO ARGUE ABOUT ONE OPTION?4.1.Difficulties encountered with QOC constructsQuestionsOptionsCriteria4.2.Characterising the relationship between QOCand the two modes of designing5.QOC’S EXPRESSIVENESS5.1.Representing evolution within QOC structures5.2.Expressing constraints and dependencies5.3.The subtleties of expressing Options, Criteria, and Assessments6.RELATIONSHIP TO REPORTS OF OTHER DR APPROACHES IN USE7.CONCLUSIONS8.REFERENCES1.THE NEED FOR EMPIRICAL STUDIES OF DESIGN RATIONALE IN USESemiformal, argumentation-based notations are one of the main classes of formalism currently being used to represent design rationale (DR). Whilst from a notational perspective, graphical formalisms are well suited for recording design arguments as they arise, doing so also introduces representational overheads for the designer. This chapter is concerned with understanding the nature of the extra cognitive work introduced by argumentation-based DR. This is obviously important in the context of a fast-flowing, time-pressured activity such as software design, in which ‘documentation’ is already a bad word.Usable, effective DR tools can only be developed once we have an understanding of the cognitive, group, and organizational factors implicated in the introduction of explicit DR to the design process. The present work focusses on the cognitive factors which determine the usability of argumentation-based DR notations, taking as an example the QOC notation and Design Space Analysis perspective (MacLean, Young, Bellotti, & Moran, this volume, Section 2). Whilst MacLean et al. focus on the properties of QOC as a representation for DR, and its relationship to other approaches, attention in this chapter shifts to the process of authoring QOC, and its relationship to different modes of software design activity. The analyses of the data gathered in these studies address issues relating to QOC’s usability and scope, with broader implications for other DR approaches and their associated representational schemes.2.THE STUDIES: DESIGNERS, TRAINING, AND TASKSAnalyses of two empirical studies are presented in this chapter. All of the data reported are drawn from video-based observational analyses of design problem solving. In Study A, 12 pairs of software designers (16 professionals/8 students) spent an hour using QOC to redesign and rationalize the user interface to a bank’s automated teller machine (ATM). The task was based on that employed by MacLean et al. (this volume, Figure 4).1 In Study B, in which different modes of design are considered, an electronics research student engaged in doctoral research described his work (designing Smalltalk-80 data structures) and use of QOC over three 141hour sessions.In Studies A and B, the designers underwent a training procedure which introduced DR as a general concept, and QOC specifically. Emphasis was placed on the importance of developing coherent rationales which would communicate clearly to an outsider the key reasons behind the designs, reflecting the more retrospective Design Space Analysis approach. The QOC tutorial-tasks were intended to give the designers practice in structuring natural discourse semiformally. Designers were required to translate into QOC the key aspects of several fictional design discussions, such that a third party could understand what had been discussed. By varying the length of these design discussions, and the medium in which they were presented (as transcripts plus sketches, or as a video-recording of a discussion) the representational task became steadily more demanding. Details can be found in Shum (1991).1Two tasks were in fact used, in a between-subjects experimental design. The first described user-steps fora Standard-ATM (SATM) interface, and requested a new design and DR. The second additionallydescribed the Fast-ATM interface, and several SATM usability problems. Designers were required to evaluate the FATM, and if necessary, devise and rationalize changes. The data from both tasks are combined for the analysis presented here.1In all of the studies, designers used pens and large sheets of paper as opposed to a software tool. Under these conditions, the authoring process could be studied with minimal interference from extraneous factors, whilst preserving or even enhancing properties of the online medium such as display space, resolution, and ease of local editing. Whilst computational tools can alleviate some of the mechanical overheads of the task, the core tasks of deciding how to express reasoning as structured argumentation remain essentially unchanged.Throughout this paper, extracts from the design transcripts are used to illustrate points. In longer transcript extracts, the key points are shown in bold, and ideas recorded as QOC are shown like this. Most of the examples in this section are from Study A’s ATM design problem, which centered around reducing customer queues without sacrificing the number of services offered. Other extracts are from Study A tutorial exercises: one task was to design the remote control for a video-recorder intended for the elderly, and the other was to design an airport public information symbol to indicate a “one hour left-luggage office.”3.CORE REPRESENTATIONAL TASKS IN QOC-AUTHORINGIn order to translate ideas into QOC, the designer is faced with three basic cognitive tasks: deciding what kind of an idea one has (classification), how to label it meaningfully (naming), and how it relates to other ideas (structuring). Before these tasks are illustrated, however, it is necessary to emphasize their non-linear relationship, that is, the exploratory, opportunistic nature of the process.3.1.QOC authoring as an opportunistic activityWhen studying designers using QOC, it soon becomes clear that externalising ideas as structured argumentation is not a smooth, top-down process. Continual revision and switching from one task to another characterize QOC authoring as an opportunistic mode of working (Guindon, 1990). The QOC evolves through multiple, sometimes embedded, represent-and-evaluate cycles, switching between different parts of the structure.Various approaches to representing QOC were adopted, demonstrating that the process of developing QOC analyses is quite different from the orderly structure of the final product. For instance, in the following extract in which the designers discuss how the ATM should dispense different kinds of output, it was most natural to generate Options, then Criteria, and the Question last of all.[Study A: Pair 2]P:Halifax machines drop everything into a little drawer... the Question here is... wellthe ideas [i.e. Options] are as is, and everything from one place. The Criteriaare...D:what are you going to call the Question though?P:Hmm, I get caught on the Questions....the Criteria are natural feel to it - getting itfrom different holes doesn’t feel naturalD:actually, it’s more like a teller, more humanP:what? If you get it all from the same hole?D:the same kind of thing like when you go the counter, and the guy gives you itthrough the little slotP:[writes] security in mind (everything from a draw – feels secure). [LinkingOptions to Criteria] - as is – it doesn’t have a natural feel to it, everything fromdifferent slots.2What’s the Question here? (frustrated tone).D:erm... I suppose physical layout of...P:layout of holes [starts to write]D:physical layout of input/output stuff [P. writes layout of I/O for cash/card/receipt]Figure 1 shows the order in which QOC was constructed in another situation, illustrating switching between Questions to capture new ideas as they suggest themselves.2prog. info?minimise keys on controlno. of keystrokes1510Figure 1: [Study A tutorial exercise: Pair 2] Moving opportunistically to a new Question and Options as they arise, and then back to complete the original (numbers added to show sequence of ideas). In QOC, boxed Options indicate a decision, or at least a working commitment.In some cases, subjects explicitly adopted ‘strategies’ to representing the QOC, as ways of imposing some structure on their task. For example, in order to control the tendency to pursue new ideas as they arose opportunistically, one pair declared:[Pair 12: Study A]G:let’s try to write down some of the Questions of concern here, and then someCriteria, and then I think some of the Options will come from that - possibly... ’cosQuestions and Criteria are related a lot aren’t theyJ:so if we have one heading Questions and another here for Criteria, which might notbe relatedG:right - I’m going to concentrate in terms of Questions, and then the Criteria mightcome from the Questions. length of queue is a Question...About a minute later, they started to discuss the details of a possible Option but stopped themselves intentionally, aware that at this early stage it might be a tangent:A:so could you have a machine that behaves like a regular ATM or a fast ATMdepending on where you put the card in?2Apart from ‘cleaned-up’ graphic appearance, all QOC examples used in this chapter are copied directly from designers' QOC representations, unless otherwise stated.3A:you could... but would that be...? – right, soG:yeah - so (writes) variety of machines ... (no further discussion on that Option untillater)It is important to note, however, that strategies of this sort seemed to be short-term, flexible modes of working, that is, they did not govern the structure of the whole session, and within them there was flexibility to attend to different parts of the QOC and to switch strategies, such as:• listing Questions in advance, before elaborating them;• generating Options and Criteria, and then the Question;• generating Questions and Options first, and then evaluating with Criteria.QOC authoring is not only opportunistic when used during conceptual problem solving of the sort required by the ATM task. Even when the decisions have been made and all the arguments are known (i.e. retrospective DR), working out how best to represent them is a separate task. Having recognized that the authoring process is far from a tidily sequenced activity, let us now turn to its constituent tasks.3.2.Classifying ideasThis section focusses on the normal process of classifying ideas. When QOC is being used, the vocabulary and orientation of discussion inevitably changes, with regular references to the new constructs, e.g. that’s an Option; could that be a Question?; this Criterion keeps coming up, and so forth. As with any language, fluency increases with use, such that arguments are more smoothly and accurately translated over time. In Study A, it was found that normally subjects classified ideas without spending much time discussing what type they should be. However, as the examples of restructuring QOC show [Section 3.4], the first translation which springs to mind is not necessarily the optimal representation. Even relative ‘experts’with QOC (e.g. QOC’s developers) still engage in restructuring, reclassification, and renaming, revision activities which are dealt with below. The following examples illustrate classification difficulties characteristic of early use of QOC.(i) Figure 2 shows a typical error in classifying an idea (natural order is initially recorded as an Option, but then corrected to be a Criterion):fast atm-order of eventsfastFigure 2: [Study A: Pair 2] A typical error in classifying an idea (a Criterion as an Option).(ii) In the following extract, an Option is first represented as a Question: [Study A: Pair 2]4D:what does this do then? – the Fast ATM do, if you press the cash amount and not(put in) the card?P:I guess it just goes ‘Ha Ha,’ and clears itself. It’ll have a time out, or clear key.D: yeah, but that’s an Option though – a Clear key.P:on where?D:well on the normal ATM. You could change the order of the events, but you have aclear key and a timeoutP:fast ATM order of events [writes this as a new Question]... that’s an Option?D:yeah that’s an OptionP:oh, it’s an Option which addresses this security issue isn’t it [deletes Question:clear key and timeout on ATM, and writes clear key + timeout as an Option].What’s the Question? It’s sort of a vandalism issue...(iii) This extract shows the pair generating ideas in discussion, and then striving to represent those ideas as QOC:[Study A: Pair 5]R:that’s a design decision [pointing to the keypad sketch]. Deciding that Cancel isgoing to be the emergency get-out, and that we should stick with that as its mode -the mode of that key is ‘get out of this, now’.J:well it certainly is... it goes back to this question of buttons - is that what theCancel... I mean we’ve added the Cancel key, and consider that to be a designconclusion – I like that because it’s intuitive. I suppose that could be a Criterion –in fact it’s a very important user-Criterion. [sigh] How do we record that?(...)J:We’ve not got much time left. We could do with identifying this Cancel key morespecifically. But I don’t know what the Question is.R:in fact the Question is ‘How does the card get returned?’J:you can put it under the question of buttons......I think the Enter key and Cancel key are part of the question of buttons. That’sthe Question [indicates Q. of buttons] – now there are probably other Questionswhich feed into this, and I don’t know that you can actually... Hah! [laughs] Whenis something a Question, and when is it a Criterion?In the above extract, the decisions and arguments are clear in J.’s mind, but neither he nor his partner are sufficiently fluent with QOC to translate them. J. notes in particular the informality inherent in QOC (e.g. how to classify ideas).3.3.Naming and renamingNaming entities is often a process of renaming. The renaming of nodes was a prevalent activity for every pair of designers in Study A. Renaming reflects the problem solving process of developing ideas; if a QOC is constructed as the problem is explored, it is inevitable that node-names which do not reflect current understanding of the problem must be updated.Naming in QOC takes up a significant amount of time for several reasons. Firstly, a node’s name must be succinct, and convey the idea it represents. Secondly, to aid interpretation, a further constraint on Criteria is that they be expressed positively, e.g. easy to learn,low error5rate,low cost,high speed.3 Thirdly, a particularly important characteristic of names is focus. Focus refers to the level of generality at which the idea is expressed: a Question may address several issues; an Option may embody several key features which differentiate it from others, but not along the dimension which is addressed by the Question; a Criterion might be expressed so generally (e.g. intuitive; simple) that it is hard to see how it relates to an Option.A fourth property of a name is its relationship to others of its type: it should be distinctive. An Option may really be an example of another, or two Criteria might really be re-expressions of each other (e.g. each side of a trade-off—depending on the context, this might be useful or redundant). Both distinctiveness and focus in naming are characteristics of ‘well-formed’ QOC.4Although these requirements were not made explicit to designers in Study A, they still appreciated the importance of finding good names for ideas. The extracts below illustrate the cooperative process of refining names.[Study A tutorial exercise: Pair 5]R:so how are we going to...T:“keys to what kind of functions...”R:that’s not a very good way of putting it...T:it’s like the “classes of functions...”R:classes! That’s the way to put it.T:What classes of function keys?R:...you see teletext is the only thing you read – you don’t read other things – youdon’t read the picture.T:what we have are two negative reasonsR:we have to make them positive though...ok, so, “easy to read?”T:well, that wasn’t the point was it? um... it was like that they couldn’t actually...R: they can’t see it, so they don’t need it.T:[laughs] yeah -- it’s like the Criterion is that you’re providing a function which theycan actually make use of, and they can’t make use of the teletext because it’s toosmall to read.R:ok – useful function?T:yeah, ok.The following comment summarizes the experience of many subjects in having to name Criteria positively:[Study A: Pair 5]R:... I mean, I really struggled on that first exercise, and found that very awkward andvery difficult. In fact the thing I found most difficult was negating everything, sothat the attribute was a positive attributeJ:yesR:I just couldn’t get my brain to pick out the right word to describe that attribute.3With this constraint, supports Assessment links to Options can always be interpreted as ‘pros’, and objects-to links as ‘cons’; because Criteria have different weights [Section 5.3], decisions clearly cannot be made on the basis of how many supports links Options have, but they provide an initial visual indication.4Principles for well-formed structures were collated as a ‘QOC styleguide.’63.4.Structuring and restructuringThe primary organization which presents itself to someone browsing a QOC diagram is the Question structure, and it is under Questions which design ideas must be eventually placed. For this reason, the problems which designers choose to address through the Questions are important: the Questions addressed define the space which the team sees their design occupying, and guides the direction of future deliberation. Many examples of Question structuring and restructuring were collated in this and other studies, three of which are reproduced below. Note that renaming of Questions is covered here (rather than in the previous section) because of their importance in shaping the macro-structure of the QOC. (i) Working out the Question together:[Study A: Pair 12]G:(new sheet) Our first Question - do you want different kinds of ATMs for thedifferent – you know, a fast ATM and a fully functional one – or do you want to doit all at once?J:ok, so what’s the Question - ’cos those are the Options aren’t they?G:do you want..um.. well the Question is...em...J:single machineG:yeah, kinds of... do you want to have just one machine or do you want to have...J:well those are OptionsG:yeah, well I know those are Options [laughs], but the Question can kind of beg thequestionJ:well it could be a Question – “do you want a variety of machines?” Yes or NoG:well [Question] number of ATM designs: one and more than one - typically two:[Options] fast and fully functional(ii) The subjects return to their first Question, and realize that it no longer expresses what they have now identified as the real problem (the design of the first screen):[Study A: Pair 10]T:oh no. [pause - returns to Question] Except that this isn’t really how to developuser interface - it’s [really to do with] the first screen isn’t it?A:what do we show initially?T:yeahA:[changes first Question] what do we display on 1st screen?(iii) Similar to the last example, Figure 3 shows how a Question is refocussed to express the problem which the generated Options now seem to be addressing (user interface education).reduces queueseffectivetake noticeFigure 3: [Study A: Pair 2] Refocussing a general Question to capture the issue actually addressed by the Options.7As Bellotti, MacLean and Moran (1991) emphasize, asking the right Questions is critical to developing a useful design space representation, and avoiding particular mental sets or design fixations (Jansson & Smith, 1991). The data collected in these studies in fact demonstrate that Question revision is the natural process which designers follow when using QOC, even though the Study A designers (from whom all of the above examples are taken) were not explicitly told to work on refining Questions. Design Space Analysis, with its particular emphasis on asking good Questions, attempts to build on and support this activity.The above examples showed that the reformulation of Questions often takes place in response to the Options generated. The relationship is reciprocal, however, since an insight into the nature of a Question can lead to restructuring of those Options, by moving them to new or existing Questions; another example would be making an implicit criterion embedded in a Question explicit as a Criterion. Whilst this distinction is useful for analytic purposes, the two are often tightly interwoven during authoring. Several examples of restructuring are presented below.(i) In Figure 4, Options to a Question are moved when it is realized that the Question breaks down into two separate Questions (the Options are separated to indicate that stopwatch and subsequent Options now respond to a second Question).how to represent only for one hour hourglassunderstandability stopwatchhow to showtime passingFigure 4: [Study A tutorial exercise: Pair 8] Dividing a Question into two as it is realized that the Options serve two different roles.(ii) In Figure 5, the designers make the Criterion increased queueing explicit, rather than leaving it embedded in the Question. It can then be used to differentiate between the two Options.how to increase no. of services available, but not increase queueingreduce no. of screensneed extra'complete key' increased queueingFigure 5: [Study A: Pair 8] Extracting an important, but implicit requirement in a Question, and making it explicit as a Criterion to evaluate the Options.8(iii) In another incident [Study A: Pair 10], two designers initially recorded several ideas as Options (reduce response time, minimize key depressions, minimize no. screens) in response to a high level Question, How to reduce queues?They then realized that they really wanted to choose all of them, which was a clue that they could serve as Criteria. The Question was restructured accordingly, and the Criteria reused in subsequent Questions. This pattern has been observed on other occasions, and reflects the process of defining goals (or requirements) as the first step to formulating and evaluating solutions. Recognizing regularities such as these is valuable to user communities as they seek to build and share knowledge and expertise in a particular formalism.(iv) In Study B, a design session involved the gradual identification of hierarchical relationships between Options. The designer redrew his QOC structure in order to make this explicit and went on to develop the hierarchy further, as shown schematically in Figure 6.910low memory requirementslow data transfer rate requiredlow processorcalculation requiredlow data throughput requirement during playlow memory requirementwhen to download data to new instrument allocation?decide whether there is time to downloadlow (high level) computation timecompute time to loadpreload data patterns if Option 1allocate instruments1. one inst. inst. for each eventQuestion Option 1.2.1Option 1.1Option 1.2Figure 6: [Study B] Structural overview of QOC structures to show how they were restructured in order to workon the Option hierarchy (Option numbers added to show the transition). Note that the process of making the Option-structure explicit prompted the designer to change Option 1.3 to Option 1.2.3, and to decompose Option 1.2.2 one level further. (As the designer was using pen and paper and renamed and restructured extensively, the original QOC was littered with changes which have been omitted for clarity). Reproduced from Buckingham Shum and Hammond (1994), with permission, Academic Press, Ltd.3.5.Study A: ConclusionsOpen-ended, ill-structured, ‘wicked’ problems (Rittel & Webber, 1973) are rendered manageable only through the exploratory process of framing and reframing views in order to better understand constraints on the solution space. For the designers studied, designing the ATM user interface engendered such a mode of working, which led to extensive revision of QOC names and structure as ideas developed.Whilst the amount of effort devoted to classifying, naming, and structuring was not documented quantitatively, it is likely that relative amounts would depend at least in part on the users, task and familiarity of domain. Ongoing experiences in using QOC does, however, suggest that these tasks persist as features of ‘expert’ QOC-use, although experts are able to draw on strategies for advancing the QOC in situations which might ‘stall’ a less experienced user (cf. Section 3.4, example (iii), and MacLean et al.’s heuristics (this volume, Appendix)). One would not in fact expect such features of the task to disappear since a claim made explicitly by proponents of semiformal notations is that the discipline of expressing ideas within a constrained vocabulary encourages a dialogue with the representation, which can ‘talk back’ to the designer and expose weaknesses in thinking.The above analysis of authoring behaviour was based on data from a task intentionally selected to allow QOC to be studied—the problem domain was novel, with many issues left open, and the competing ATM designs (presented to half the designers) focussed attention on tradeoffs between Options. In contrast, the evidence described next, from Study B, points to a possible boundary to QOC’s scope of application. Specifically, the study suggests that QOC’s focus on arguing about design spaces defined by multiple Options is poorly suited to work dominated by the evolution of a single Option.4.PROBLEMS USING QOC IN ‘EVOLUTIONARY’ DESIGN:TRYING TO ARGUE ABOUT ONE OPTION?Three sessions were spent with a designer who was working on developing a music composition system in the Smalltalk environment. In session 1, it became clear that many of his ideas were already quite well developed, as a lot of thinking had been invested in the problem beforehand; the main task to which QOC was put was therefore rationalization and decision making. The designer was very positive about QOC’s role in this context, and it was clear from the data (Shum, 1991, Case Study 1) that QOC had assisted in drawing out existing but vague ideas, and clarified relationships between Options and Criteria which would have otherwise remained unarticulated (see Figure 6 for an extract from session 1). In sessions 2 and 3, however, serious difficulties were encountered in using QOC, and no explicit DR was constructed.Let us begin by characterising what will be termed the ‘evolutionary’ mode of working, that is, the iterative development of what the designer conceptualized as one, complex design Option. The designer spent sessions 2 and 3 developing two representations of two Smalltalk data structures, respectively, a hierarchy of data types, and a table of data types such that each column progressively refined the previous one.The designer described the method of developing the hierarchy in session 2 as follows: [Study B]what I’m doing is a sort of consistency check – thinking through the implications of whatI’m doing – this draft suggestion here. And I’ll incrementally alter things [i.e. the datastructure] – I mean I’ve already done that many times to get to this stage...11。

相变混凝土能量桩热-力学特性的数值模拟与试验验证

第37卷第2期农业工程学报 V ol.37 No.2268 2021年1月Transactions of the Chinese Society of Agricultural Engineering Jan. 2021 相变混凝土能量桩热-力学特性的数值模拟与试验验证杨卫波1,2，杨彬彬1，汪峰1（1. 扬州大学电气与能源动力工程学院，扬州 225127；2. 热流科学与工程教育部重点实验室（西安交通大学），西安 710049）摘要：为了获得热力耦合作用下相变混凝土能量桩的热-力学特性，建立了其三维数值模型，比较了传统和相变混凝土能量桩热-力学特性的差异，分析了埋管管腿间距及桩体长径比对相变混凝土能量桩热-力学特性的影响规律。

结果表明，相变材料（Phase Change Material，PCM）的固液相变可使单位桩深换热量提高10.3%，且可降低桩身温度变化幅度，由温度变化所引起的桩身位移、轴力及侧摩阻力变化量也相应减小。

随桩基埋管管腿间距增加，能量桩的换热量和土壤热影响范围增大，桩身轴力减小，桩身位移呈现先增大后减小趋势；加大桩体长径比会增加总换热量，但会导致单位桩深换热量降低及桩顶位移的增加，不利于桩基结构的稳定性。

试验验证表明：所建能量桩数值模型可用于模拟相变混凝土能量桩的热-力学特性，其桩壁中点温度与桩顶位移的预测最大相对误差分别在5.1%与12%以内，平均相对误差分别为4.2%、9.9%。

研究结论对于相变混凝土能量桩的优化设计与运行具有重要指导意义。

关键词：能量桩；相变；热力学特性；数值模拟；试验验证doi：10.11975/j.issn.1002-6819.2021.2.031中图分类号：TU473，TU83 文献标志码：A 文章编号：1002-6819(2021)-2-0268-10杨卫波，杨彬彬，汪峰. 相变混凝土能量桩热-力学特性的数值模拟与试验验证[J]. 农业工程学报，2021，37(2)：268-277. doi：10.11975/j.issn.1002-6819.2021.2.031 Yang Weibo, Yang Binbin, Wang Feng. Numerical simulation and experimental validation of the thermo-mechanical characteristics of phase change concrete energy pile[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(2): 268-277. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2021.2.031 0 引言地源热泵作为浅层地热能利用技术之一，因其节能、高效和环保等优势而在建筑节能中得到广泛推广[1]。

非线性系统自适应观测器的结构参数化设计方法(IJISA-V10-N2-1)

l Design Method of Adaptive Observers for Nonlinear Systems
Nikolay Karabutov
Moscow Technological University (MIREA), Moscow, Russia E-mail: kn22@yandex.ru, nik.karabutov@ Received: 02 July 2017; Accepted: 11 September 2017; Published: 08 February 2018 Abstract—The structural-parametrical method for design of adaptive observers (AO) for nonlinear dynamic systems under uncertainty is proposed. The design of AO is consisting of two stages. The structural stage allowed identifying a class of nonlinearity and its structural parameters. The solution of this task is based on an estimation of the system structural identifiability (SI). The method and criteria of the system structural identifiability are proposed. Effect of an input on the SI is showed. We believe that the excitation constancy condit

计量经济学中英文词汇对照

Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covaห้องสมุดไป่ตู้iance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Asymmetric distribution Asymptotic bias Asymptotic efficiency Asymptotic variance Attributable risk Attribute data Attribution Autocorrelation Autocorrelation of residuals Average Average confidence interval length Average growth rate BBB Bar chart Bar graph Base period Bayes' theorem Bell-shaped curve Bernoulli distribution Best-trim estimator Bias Binary logistic regression Binomial distribution Bisquare Bivariate Correlate Bivariate normal distribution Bivariate normal population Biweight interval Biweight M-estimator Block BMDP(Biomedical computer programs) Boxplots Breakdown bound CCC Canonical correlation Caption Case-control study Categorical variable Catenary Cauchy distribution Cause-and-effect relationship Cell Censoring

componential analysis

componential analysisComponential analysis is a method of studying and understanding the meaning of words, phrases, and sentences. It is an approach used inlinguistics to analyze language structure by analyzing its component parts. This method of analysis is based on the idea that all linguistic units can be broken down into smaller elements which can then be studied in isolation.The term ‘componential analysis’ was first coined in the 1950s by American linguist Edward Sapir. He defined it as “the analysis of a language unit into its component elements and the description of their interrelationships”. Componential analysis is based on the concept of semiotics, which is the study of signs and symbols and their relationship to communication. As such, componential analysis is used to understand how different symbols are related to one another and how they contribute to the overall meaning of a given text.In componential analysis, a linguistic unit is examined for its semantic components, or “semantic features”. These features are the individual elements that make up the meaning of a particular word, phrase, or sentence. For example, the word “dog” could be broken down into the following semantic features: mammal, animal, four-legged, pet, canine. By examining these semantic features, we can gain an understanding of the overall meaning of the word.Componential analysis is commonly used in linguistics to analyze the structure of languages. By breaking down language into its component parts, linguists can better understand how various elements of language interact with one another. This can help them to develop theories about the underlying structures of language and how they influence the way in which people use language to communicate.Componential analysis can also be used to analyze the meaning of words and phrases. By looking at the individual elements that make up aword or phrase, we can gain a better understanding of what the word or phrase means. For example, if we were to look at the phrase “a big dog”, we could break it down into the following components: adjective (big), noun (dog). By looking at these two components separately, we can gain a better understanding of the overall meaning of the phrase.Finally, componential analysis can also be used to study the relationship between words and their meanings. By looking at the different components of a word or phrase, linguists can gain insight into how a particular word or phrase is interpreted by speakers of a given language. This can help them to develop theories about how language is used and understood by different people.Componential analysis is an important tool for linguists and other language researchers. By breaking down language into its component parts, we can gain a better understanding of how language works and how it is used to communicate. Componential analysis can also help us to gain insight into the relationship between words andtheir meanings, allowing us to better understand how language is interpreted by different people.。