Abstract A Bayesian solution and its approximations to out-of-sequence measurement problems

Research on Hierarchical Interactive Teaching Model Based on Naive Bayesian ClassificationDongyan FanInformation faculty, Business College of Shanxi University, Taiyuan 030031, ChinaAbstract—The purpose of this research is improving the current inject classroom teaching mode that ignores individual differences and inefficiency of students. By studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we designed and implemented scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to builded a new effective teaching mode. The results show that through scientific classification of students, real-time hierarchical interaction teaching effectively stimulate students' interest in learning, improve cooperation ability, and improve classroom teaching efficiency.Keywords—Naive Bayesian; student classification; hierarchical interactive; teaching modelI.I NTRODUCTIONUnder the background of big data era, the current teaching mode is not adapt to the cultivation of innovative talents, there are many problems, such as low efficiency of classroom, teachers' manipulation of teaching process, ignore the individual differences of students in knowledge transfer ability. Therefore, this study aimed at these problems, by studying classification algorithm in data mining and applying the classification method based on Naive Bayes algorithm, we design and implement scientific classification of students, and draw lessons from stratified and interactive teaching mode, so as to build a new effective teaching mode. The mode enable students to learn efficiently, so as to adapt to the trend of rapid development of new technology and cultivate innovative talents.II.R ESEARCH M ETHODThe research and practice of the hierarchical interactive teaching model based on the Naive Bayesian classification is based on the classification of students' differences. So there are two major tasks need to do: the approaches to the students' difference measurement and grouping and the design of hierarchical interactive teaching framework. Its method flow is shown in Figure I.FIGURE I. RESEARCH METHOD FLOWFirst of all, based on the samples, the naive Bayes algorithm according to the student's attribute value is used to test the students' differences. Then, according to the results to make a scientific difference classification to achieve effective grouping for students. At the same time, the design of the hierarchical interactive teaching framework is carried out by the two subjects (the student is the main body, the teacher is the leading part). Finally, the teaching effect is evaluated and analyzed.III.S TUDENT C LASSIFICATION D ESIGN B ASED ON N AIVEB AYESIANA.Naive Bayesian Theoretical PrincipleAt present, there are many kinds of algorithms in data mining, such as based on Bayes algorithm, decision tree algorithm, neural network algorithm, rough set algorithm, genetic algorithm, support vector machine algorithm and so on. In the practical application of many classification algorithms, the most widely used algorithm is Naive Bayesian algorithm model. Naive Bayes is a simple and effective classification model.From Bayes’ theorem recall that:()()()||P A B P BP B AP A= (1)Equation (1): P(A) and P(B) separate representation the probability of occurrenceof events A andevents B.()|P A B indicates the probability of occurrence of event A under the premise that event B occurs. ()|P A B is a priori probability, and its value is often easily obtained.()|P B A indicates the probability of occurrence of event B under the premise that event A occurs. ()|P B A is a posteriori probability, and its value is the result of the solution of the Bayesian formula.The classifier structure diagram based on the naive Bayes algorithm is shown in Figure II. It’s leaf node Am represents the m attribute, and the root node C represents the category. Suppose {},,D C A S=are training samples, it includes the studentcategory {}12,,iC C C C= and the student attribute {}12,,mA A A A= .Suppose {}12,,nS S S S= represents acollection of classified students, in whichnS represents nthstudent. Suppose {}12,,k mX a a a= is a student to be classified,International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)in which each m a represents an attribute eigenvalue of the pending item k X .FIGURE II. THE CLASSIFIER STRUCTURE DIAGRAMB. Design the Individualized Attributes of StudentsThe student classification method based on the naive Bayes algorithm is used the information of the past students as the sample set , which is used to construct the naive Bayes classifier.Students are classified according to the information of the students' attributes. The students divided into the same category are not simply using the score as criterion of evaluation. Its are classified by comprehensive evaluation after combination of other attributes.The difference classification based on the naive Bayes algorithm is select the individual attributes of the students as shown in Figure III. The students which 8 attribute values similar in the two dimensions (character and learning style) are put into one category, while the 12 attributes values of the three dimensions of personal basic situation, learning interest and cognitive ability are different. The purpose of the classification is to carry out differential teaching to implicit dynamic stratification and heterogeneous cooperation for students'cognitive ability, learning interest and basic information.FIGURE III. INDIVIDUALIZED ATTRIBUTES OF STUDENTSC. Student Classification Design Based on Naive Bayesian The process based on the naive Bayes classification is shown in Figure IV.FIGURE IV. STUDENT CLASSIFICATION CYCLE FLOW CHARTBASED ON NAIVE BAYES ALGORITHM1)()i P C is set to indicate the frequency of the occurrence of the student category i C in the training sample concentration, that is the category probability. For sample data sets, there are different levels of students in each category, which avoids the discrimination of students.()()i i P C Count C n=　(2)The function ()i Count C represents the number of students belonging to category i which is in the entire student sample collection of S .n represents the total number of the entire student sample collection of S .2)()|j j i P A C a = is set to represent the conditional probability of each characteristic attribute value of the student in the category.()()()|i C j j j j i i P A C Count A a a Count C ===(3)j j A a =indicates that the value of the j attribute is j a .Thefunction ()i C j j Count A a =represents the number of students which the attribute name is j A and attribute value is j a in the i student category.3) ()|k i P X C is set to represent the conditional probability of the students k X to be classified in the student category i C , m represents the number of attributes that describe student differences.()()1||mk i j j i j P X C P A C a ===∏ (4)4) ()j j P A a = is set to represent the probability of the student's attribute j A when the value is j a . ()()j j j j P A Count A a a n===　(5)The function ()j j Count A a = indicates the number when the value of attribute j is j a .5) ()k P X is set to indicate the probability that the student k X should be classified in the training sample concentration. ()()1mk j j j P X P A a ===∏ (6)6) ()|i k P C X is set to represent the conditional probability that the student k X should be classified to category i . ()()()()||k i i i k k P X C P C P C X P X =(7)7) ()max |k P C X is set to represent the maximum category probability of the student k X which should be classified to the student category .()()()(){}max 12|max |,|,,|k k k i k P C X P C X P C X P C X = (8) max C indicates the maximum category of conditionalprobability which is obtained by (8).Finally, (8) is used to calculate the maximum category probability of the students to be classified in the students category. That is the category of the students to be classified. At this point, one classification ends.IV. T HE D ESIGN OF THE H IERARCHICAL I NTERACTIVET EACHING F RAMEWORK The hierarchical interactive teaching model is an independent, inquiring and cooperative teaching model based on the classification of the naive Bayes algorithm. This model breaks the original classroom structure, and takes the interaction of teachers and students as the carrier, and also group autonomy, and let the students as the subject of the class. This model is guided by the task of the problem, and it is based on the students' self-study, and it aims at the completion of the task of the group. This model creates an ecological chain class based on group mutual learning to solve problems. It pays attention to the state of learning and the quality of life for every student. The design of the hierarchical interactive teaching model framework is shown in Figure V.FIGURE V. THE HIERARCHICAL INTERACTIVE TEACHING MODELFRAMEWORKThe four layers of the hierarchical interactive teaching model are closely related to each other, and support each other dynamically with the spiral. The five segments drive each other to form a whole, interlace and connect with each other. This teaching mode makes the classroom an active area for teachers and students to resonate with their ideology and to show their personality together.V.A NALYSIS OF T EACHING E FFECTIn this paper, the teaching effect is analyzed from two aspects by using the method of questionnaire and comparative experiment. First, the experimental class's comparative analysis before and after the experiment is carried out. Then, a comparative analysis between the experimental class and the contrast class is carried out.The comparative data of the experimental class before and after the experiment are shown in Figure VI. From Figure VI, it can be seen that 85.72% of the students have An attitude of approval towards the application of the hierarchical interactive teaching model based on the naive Bayes algorithm in the teaching. There are 70.13% of the students satisfied with the improved teaching effect. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have improved obviously.FIGURE VI. THE COMPARATIVE DATA OF THE EXPERIMENTALCLASS BEFORE AND AFTER THE EXPERIMENT The comparison between the experimental class and the contrast class is shown in Figure VII. From Figure VII, we can see that students' satisfaction degree, teaching effect satisfaction and group learning atmosphere based on Naive Bayes algorithm classification are higher than those of the contrast class. At the same time, it can be seen that the students' interest in learning and the ability to communicate and cooperate have also been improved.FIGURE VII. THE COMPARISON BETWEEN THE EXPERIMENTALCLASS AND THE CONTRAST CLASSVI.C ONCLUSIONThe comprehensive analysis shows that, in the implementation of the hierarchical interactive teaching model based on the naive Bayes algorithm, the new teaching mode was accepted by the students , it was welcomed by the students. The new teaching mode can improve the ability of learning interest and collaboration of students. It has a very good teaching effect. Experiments show that the classification algorithm based on Naive Bayes has better feasibility and effectiveness in solving student classification problem.However, due to the limited personal time and ability, there are still some shortcomings in the study. In order to better achieve hierarchical interaction teaching mode based on Naive Bayes algorithm and improve teaching effect, we still need to further improve the limitation of applying naive Bayes algorithm, that is, suppose the attributes of students are independent.A CKNOWLEDGMENTThis work was supported by “Research and construction of the practice teaching system of information specialty(J2016138, The major project of teaching reform research in Shanxi Education Department)” and “The optimization and the platform construction of the practice teaching system of information specialty (SYJ201509, The major project of the research on teaching reform Business College of Shanxi University)”. Our special thanks are due to Prof. Ma Shangcai, for his helpful discussion with preparing the manuscript.R EFERENCES[1]Jonathan Rauh. Problems in Identifying Public and Private Organizations:A Demonstration Using a Simple Naive Bayesian Classification[J]. 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SHORT COMMUNICATIONLoss of genetic diversity in farmed populations of Colossoma macropomum estimated by microsatellitesC.H.A.Santos*†,G.X.Santana ‡,C.S.S a Leit ~a o †,M.N.Paula-Silva †and V.M.F.Almeida-Val*†*Laborat o rio de Gen e tica Aplicada a Aquicultura &Biologia Molecular,Universidade Nilton Lins,Avenida Professor Nilton Lins,Flores,Manaus,69058-030,Brasil.†Laborat o rio de Ecofisiologia e Evoluc ß~a o Molecular,Instituto Nacional de Pesquisas da Amaz ^o nia,Avenida Andr e Ara u jo,Aleixo,Manaus,69060-001,Brasil.‡Centro de Pesquisa e Desenvolvimento Leopoldo Am e rico Miguez de Mello,Rio deJaneiro,20031-000,Brasil.SummaryThe genetic variability of four Colossoma macropomum broodstocks,three from fish farms in different regions and one from the natural environment in Brazil,was analyzed usingmicrosatellite markers.The wild progeny (n =30)were caught in the Solim ~oes –Amazonas River,at the varzea lakes;this location is used to mature the fish from larvae to juveniles.The three fish farms were selected according to the age of their lineages and broodstockavailability:DNOCS (n =21)is located in the Cear aState,representing the oldest lineage of cultivated tambaqui in Brazil;Balbina (n =30)is located in the Amazonas State,representing the youngest stocks of tambaqui farmed in Brazil (approximately 15years);and UFRPE (n =30)is located in the Pernambuco State and is considered to be a mixed stock formed from the DNOCS and Balbina lineages.The analysis of 13microsatellite loci indicated the occurrence of a variability reduction in the farmed populations;the UFRPE stock was the population with the highest diversity level.Low values of molecular coancestry were found in these populations.Additionally,significant differences in the R ST values among the populations were detected,as was the occurrence of genetic structure.The genetic loss found in these populations may have been influenced by the founder effect.Because no breeding programs were during the entire production period and no pedigree records were kept for these broodstocks,we suggest that a wild population might be used as an important genetic resource to increase the genetic diversity of renewal stock lineages.Keywords aquaculture,broodstocks,fish farms,genetic structureBrazilian aquaculture activity increased 51.2%during the period from 2009to 2011,and 86.6%of this growth resulted from continental aquaculture,specifically freshwa-ter fish farms (MPA,2013).Among the farmed species,we identified tilapia (169306.011tonnes)and tambaqui (88718.502t)as the most cultivated species in Brazil,representing 67%of the continental production (IBGE,2014).Tambaqui (Colossoma macropomum )is endemic to the Amazon and Orinoco basins (Araujo-Lima &Goulding,1998)and has been overexploited (Aguiar et al.2013).Thedecline in the natural stock of tambaqui indicates that it is the most promising native species for Brazilian aquaculture.Broodstock formation has been a concern for some fish farms,as reported by Holtsmark et al.(2008).Therefore,it is important to establish the size of the founding population in commercial fish farms because the application of programs to select for specific characteristics has the effect of decreasing genetic variability when applied to the improvements obtained in breeding programs.The main goal of this study was to evaluate the genetic diversity of tambaqui broodstocks,and this information will be essential for recommending the best genetic management practices for breeding programs.Tambaqui fin clips were collected from Brazilian farm broodstocks in Amazonas State (Balbina,Balbina Fish farm;n =30samples),Pernambuco State (UFRPE,Federal Rural University;n =30samples)and Cear a State (DNOCS,Address for correspondenceC.H.A.Santos,Universidade Nilton Lins,Laborat orio de Gen e tica Aplicada a Aquicultura &Biologia Molecular,Avenida Professor Nilton Lins,Flores,69058-030,Manaus,Brasil.E-mails:chenrique@niltonlins.br and carloshenrique@.br Accepted for publication 04January 2016doi:10.1111/age.12422373©2016Stichting International Foundation for Animal Genetics,47,373–376National Department Against Drought;n =21samples).The DNOCS broodstock is the oldest farmed group and was founded 50years ago.The Balbina broodstock is the youngest lineage and was officially created within the last 15years.The UFRPE stock originated from the DNOCS lineage and has received individuals from the Balbina broodstock.The wild population was sampled at the Solim ~o es –Amazonas River (n =30samples),and it was used for comparison of its diversity with the farm stocks.Total genomic DNA was extracted from the fin clips using the protocol described by Sambrook et al.(1989),with some modifications (Appendix S1).The microsatellite loci (13simple sequence repeats)used in this study were described by Santana et al.(2011)and Santos et al.(2009),and the PCR products were sequenced on an ABI 3130xl genetic analyzer (Applied Biosystems Inc.).The determination of the allele sizes was performed using GENEMAPPER v4.0(Applied Biosystems,Inc.)with the standard GeneScan-500LIZ.The inbreeding correction and the detection of null allele occurrence were performed by MICRO -CHECKER (van Oosterhout et al.2004).FSTAT software v.2.9.3.2(Goudet 2001)was used to investigate Hardy –Weinberg equilibrium and linkage disequilibrium with Bonferroni correction to minimize type I error (Rice 1989).The genetic diversity and allelic diversity were estimated using GENETIX v.4.05.2(Belkhir et al.2004)and GDA (Lewis &Zaykin 2001)software packages.MOLKIN v.3.0(Gutie rrez et al.2005)was used to verify the molecular coancestry,autocoancestry and distance of kinship of the populations.A Bayesian cluster approach was applied by STRUCTURE v. 2.3.1(Pritchard et al.2000).The software assumed mixed ancestry between the correlated populations with allelic frequencies.The K value was based on the ancestor groups and the proportion of ancestor members for each individual on these clusters;the algorithm was performed five times for each K value,from 1to 5.The final K value was estimated as the largest value of D K (Evanno et al.2005)with significant probability indicated by P >0.005.Analysis of molecular variance (Excoffier et al.1992)was performed using ARLEQUIN v3.5(Excoffier &Lischer 2010)to characterize the partition of the observed genetic variation inter-and intragenetic groups.The R ST value was estimated for the populations and between each pair of populations using Weir &Cockerham’s (1984)method,and the microsatellite loci followed the stepwise mutation;we opted for R ST instead of F ST .The presence of null alleles and deviations from Hardy –Weinberg equilibrium were detected for two loci in the wildpopulation (Solim ~oes –Amazonas River)and for three loci in the Balbina and UFRPE populations.However,after appli-cation of the estimator to correct the inbreeding for the null alleles at these loci,we concluded that it was not necessary to remove these loci from the data analysis.Furthermore,linkage disequilibrium was not detected in the pairs of loci,suggesting the reliability of all loci used.In fact,it is wellknown that a high level of inbreeding in populations results in a deficit of heterozygotes,which would cause the MICRO -CHECKER program to suspect the presence of null alleles and,thus,indicate the need for the withdrawal of loci from the analysis.We observed that the wild population of the Solim ~oes –Amazonas River had higher genetic variability than did the farmed fish populations (Table 1).Based on these data,we concluded that the current genetic diversity of the wild population is an important genetic resource for future breeding programs,which should be implemented in tambaqui fish farms (Table 2).The founder effect might be the main cause of genetic diversity loss in the populations of the fish farms (a small number of individuals in the base population),which would cause genetic drift in these stocks (Glover et al.2010).In fact,fish farms of tambaqui have a small effective popula-tion size in the broodstock,favoring the reduction in production costs.However,this situation requires increases in genetic variability for the improvement of the breeding programs.Bijlsma &Loeschcke (2011)reported that a small isolated population becomes increasingly subject to genetic drift and inbreeding,resulting in the loss of genetic variation.The apparent solution to neutralize the effects of genetic drift would be to increase the gene flow between the isolated populations,which might restore genetic variation.However,the flow must be enough to restore variation within each population.However,high levels of gene flow can affect the patterns of local adaptation,threatening the resilience of the population (Garant et al.2007;Bridle et al.2010).In the case of the tambaqui analyzed in the present study,the most interesting and viable method would be to increase the gene flow between the wild population and the fish broodstocks.In fact,the use of wild populations to increase genetic variability would also represent an increase in Ne to avoid local adaptation.Increasing the broodstock number to a minimum size of 1000individuals (50%males and 50%females)and establishing a pedigree program is the best way to begin these broodstocks,foreseeing a program of genetic improvement for this species (Ponzoni et al.2010).Analysis of wild tambaqui populations indicated lower values of molecular coancestry compared with the fish farm populations (Table 3),and these values are acceptable forthe planning of a broodstock crossing system ( Alvarez et al.2005;Karhunen &Ovaskainen 2012).The populations presented high levels of structure (Table 4,Fig.1),indicat-ing high levels of inbreeding.According to Amador et al.(2011),the molecular coancestry index is more useful than is pedigree information and it is effective in the recovery of the native genetic material.Holtsmark et al.(2008)reported that the implementation of a mating system using molecular coancestry information is the most recommended method in fish farms,especially in populations where the©2016Stichting International Foundation for Animal Genetics,47,373–376Santos et al.374animals suffer a significant inbreeding depression in the short term.However,in the absence of molecular coances-try information for the fish farms,pedigree eventually becomes very important and useful in the management of populations,and in the case of tambaqui,this measure is critical for future breeding programs.In conclusion,the structure of these populations suggests that each stock has diverged from the original population,and the private alleles indicate local adaptation and decreased adaptability to future environmental changes.Conflict of interestThe authors declare no conflicts of interest.Authors’contributionsG.X.S,M.N.P.S.and V.M.F.A.V.designed the study.C.H.A.S,C.S.S.L.and V.M.F.A.V.supervised the study.C.H.A.S and G.X.S.analyzed the data.C.H.A.S.and V.M.F.A.V.wrote the manuscript.G.X.S.and C.S.S.L.assisted in the fish sample collection.All authors have read and edited the manuscript.AcknowledgementsC.H.A.S.is the recipient of a postdoctoral fellowship from CAPES.V.M.F.A.V.is the recipient of research fellowships from CNPq.This work is part of INCT ADAPTA (CNPQ/FAPEAM)coordinated by Adalberto Luis Val and the PRO-AMAZON Tambaqui Project (CAPES)coordinated by Vera M.F.Almeida-Val.Table 1Allelic and genetic diversity indices for wild and captive populations of Colossoma macropomum based on 13microsatellite loci.Populations n Allelic diversityGenetic diversity TNA MNA NEA AR NPA H O H E PIC (%)F IS Loss (À)/gain (+)ID Balbina 3063 4.8 2.7 4.5100.520.6342.030.18À21.30À1.35UFRPE 2144 3.5 1.9 3.210.260.4826.070.46À35.30À15.36DNOCS3056 4.3 2.2 4.130.510.5535.070.07À28.91À8.97Solim ~oes –Amazonas 301128.64.87.6320.530.7973.330.333.0422.98n ,sample size;TNA,total number of alleles;MNA,mean number of alleles;NEA,number of affective alleles;AR,allelic richness;NPA,number of private alleles;H O ,observed heterozygosity;H E ,genetic diversity (expected heterozygosity in Hardy –Weinberg equilibrium);PIC,polymorphism information content (percentage);F IS ,inbreeding coefficient of population;Loss(À)/gain(+),genetic loss and gain;ID,internal diversity.Table 2Value of self-coancestry,molecular coancestry and kinship distance for wild and captive populations of Colossoma macropomum based on 13microsatellite loci.Populations Self-coancestry Molecular coancestry Kinship distance Balbina 0.7330.3540.334UFRPE 0.8390.4630.334DNOCS0.7120.4090.256Solim ~oes –Amazonas 0.7030.2050.489Table 3R ST (above the diagonal)and molecular coancestry (below the diagonal)for Colossoma macropomum subpopulations based on 13microsatellite loci.BalbinaUFRPE DNOCS Solim ~o es –Amazonas Balbina 0.265*0.184*0.230*UFRPE 0.2980.215*0.150*DNOCS0.2740.3540.203*Solim ~oes –Amazonas 0.2100.2700.222*P <0.05(after Bonferroni correction,P =0.008).Table 4Analysis of molecular variance for the four populations of Colossoma macropomum based on 13microsatellite loci.(*P <0.05)Type of change Variance (%)F -value Standard error P -valueWithinindividuals 0.5350.4650.040Between individuals 0.1880.2590.0760.001*Between populations0.2770.2770.0420.001*Figure 1Structural analysis results for the four populations based on 13microsatellite loci.K =4.1,UFRPE;2,wild (Solim ~oes –Amazonas River);3,DNOCS;and 4,Balbina.©2016Stichting International Foundation for Animal Genetics,47,373–376Farmed populations of tambaqui375ReferencesAguiar J.,Schneider H.,Gomes F.,Gomes F.,Carneiro J.,Santos S.,Rodrigues L.R.&Sampaio I.(2013)Genetic variation in native and farmed populations of tambaqui (Colossoma macropomum )in the Brazilian Amazon:regional discrepancies in farming systems.Anais da Acad ^e mia Brasileira de Ci ^e ncia 85,1439–47. Alvarez I.,Guti e rrez J.P.,Royo L.J.,Fern andez I.,G o mez E.,Arranz JJ.&Goyache F.(2005)Testing the usefulness of the molecular coancestry information to assess genetic relationships in livestock using a set of Spanish sheep breeds.Journal of Animal Science 83,737–44.Amador C.,Toro M.A.&Fern andez J.(2011)Removing exogenous information using pedigree data.Conservation Genetics 12,1565–73.Ara ujo-Lima C.A.R.M.&Goulding M.(1998)Os frutos do tambaqui:ecologia,conservac ß~a o e cultivo na Amaz ^o nia ,pp.186.Lithera Maciel Editora Gr afica,S ~a o Paulo.Belkhir K.,Borsa P.,Chikhi L.,Raufaste N.&Bonhomme F.(2004)GENETIX 4.05,logiciel sous Windows TM pour la g e n e tique des populations .Laboratoire G e nome,Populations,Interactions,CNRS UMR 5171,Universit e de Montpellier II,Montpellier,France.Bijlsma R.&Loeschcke V.(2011)Genetic erosion impedes adaptive responses to stressful environments.Evolutionary Applications 5,117–29.Bridle J.R.,Polechova J.,Kawata M.&Butler R.K.(2010)Why is adaptation prevented at ecological margins?New insights from individual-based simulations.Ecology Letters 13,485–94.Evanno G.,Regnaut S.&Goudet J.(2005)Detecting the number of clusters of individuals using the software STRUCTURE :a simulation study.Molecular Ecology 14,2611–20.Excoffier L.&Lischer H.E.L.(2010)ARLEQUIN suite ver 3.5:a new series of programs to perform population genetics analyses under Linux and Windows.Molecular Ecology Resources 10,564–7.Excoffier L,Smouse P.E.&Quattro J.M.(1992)Analysis of molecular variance inferred from metric distance among DNA haplotypes:application to human mitochondrial DNA restriction data.Genetics 131,479–91.Garant D.,Forde S.E.&Hendry A.P.(2007)The multifarious effects of dispersal and gene flow on contemporary adaptation.Func-tional Ecology 21,434–43.Glover K.A.,Hansen M.M.,Lien S.,Als T.D.,Høyheim B.&Skaala Ø.(2010)A comparison of SNP and STR loci for delineating population structure and performing individual genetic assign-ment.BMC Genetics 11,12.Goudet J.(2001)FSTAT ,a program to estimate and test gene diversities and fixation indices (version 2.9.3.2).Updated from Goudet J.(1995).FSTAT ,a computer program to calculate F-statistics.Journal of Heredity 86,485–6.Gutie rrez J.P.,Royo L.J.,Alvarez I.&Goyache F.(2005)MOLKIN v2.0:a computer program for genetic analysis of populationsusing molecular coancestry information.Journal of Heredity 96,718–21.Holtsmark M.,Klemetsdal G.,Sonesson A.K.&Woolliams J.A.(2008)Establishing a base population for a breeding program in aquaculture,from multiple subpopulations,differentiated by genetic drift:I Effects of the number of subpopulations,heritabil-ity and mating strategies using optimum contribution selection.Aquaculture 274,232–40.Instituto Brasileiro de Geogr a fia e Estat ıstica –IBGE.(2014).Produc ß~a o da Agropecu a ria Municipal –2013.IBGE,Rio de Janeiro.Karhunen M.&Ovaskainen O.(2012)Estimating population-level coancestry coefficients by an admixture F model.Genetics 192,609–17.Lewis P.O.&Zaykin D.(2001)GENETIC DATA ANALYSIS :Computer Program for the Analysis of Allelic Data,Version 1.0.(d15).University of Connecticut,Storrs,CT.Minist e rio da Pesca e Aquicultura –MPA.(2013)Boletim Estat ıstica da Pesca e Aquicultura de 2011,pp.60.MPA,Bras ılia.van Oosterhout C.,Hutchinson W.F.,Wills D.P.M.&Shipley P.(2004)MICRO -CHECKER :software for identifying and correcting genotyping errors in microsatellite data.Molecular Ecology Notes 4,535–8.Ponzoni R.W.,Khaw H.L.,Nguyen N.H.&Hamzah A.(2010)Inbreeding and effective population size in the Malaysian nucleus of the GIFT strain of Nile tilapia (Oreochromis niloticus ).Aquacul-ture 302,42–8.Pritchard J.K.,Stephens M.&Donnelly P.(2000)Inference of population structure using multilocus genotype data.Genetics 155,945–59.Rice W.R.(1989)Analyzing tables of statistical tests.Evolution 43,223–5.Sambrook J.,Fritsch E.F.&Maniatis T.(1989)Molecular Cloning:A Laboratory Manual ,2nd ed.Cold Spring,Harbor Laboratory,New York,NY.Santana G.X.,Santos C.H.A.,Sousa C.F.S.,Nascimento P.R.M.,Paula-Silva M.N.,Sousa A.C.B.,Campos T.&Almeida-Val V.M.F.(2011)Isolation of novel microsatellite markers for tambaqui (Colossoma macropomum ,Cuvier 1818),an important freshwater fish of the Amazon.Conservation Genetic Resources 4,197–200.Santos M.C.F.,Hrbek T.&Farias I.P.(2009)Microsatellite markers for the tambaqui (Serrasalmidae,Characiformes),an economi-cally important keystone species of the Amazon river floodplain.Molecular Ecology Resources 9,874–6.Weir B.S.&Cockerham C.C.(1984)Estimating F -statistics for the analysis of population structure.Evolution 38,1358–70.Supporting informationAdditional supporting information may be found in the online version of this article.Appendix S1.Extraction of genomic DNA.©2016Stichting International Foundation for Animal Genetics,47,373–376Santos et al.376。

李惠琳，刘昊，李珏，等. 萝卜籽酶解制备萝卜硫素工艺优化及其体外消化研究[J]. 食品工业科技，2024，45（9）：159−167. doi:10.13386/j.issn1002-0306.2023050037LI Huilin, LIU Hao, LI Jue, et al. Process Optimization and in Vitro Digestion Research of Raphanus sativus Seeds Sulforaphane Prepared by Enzymolysis Method[J]. Science and Technology of Food Industry, 2024, 45(9): 159−167. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023050037· 工艺技术 ·萝卜籽酶解制备萝卜硫素工艺优化及其体外消化研究李惠琳1,2，刘　昊1，李　珏1，木耶赛尔•凯代斯1，图尔荪阿依•图尔贡1，赵　雷1，何庆峰1,2,*（1.和田职业技术学院农业科技系，新疆维吾尔自治区和田 848000；2.天津农学院食品科学与生物工程学院，天津 300000）摘　要：为探究酶解法制备萝卜籽萝卜硫素的最佳工艺及其消化特性。

采用红萝卜种子为原料，以萝卜硫素得率为指标，通过单因素实验和响应面优化试验得出最佳酶解工艺条件。

进一步采用体外模拟胃肠消化模型，探究萝卜籽乙酸乙酯提取物萝卜硫素含量及其抗氧化活性的变化规律。

得到萝卜籽制备萝卜硫素的最佳酶解条件为：酶解时间22 min ，酶解温度40 ℃，V C 添加量0.8 mg/g ，在此最佳工艺条件下，萝卜硫素得率为2.11±0.02 mg/g ，与预测值（2.14 mg/g ）接近，相对误差为1.4%，酶解工艺切实可行。

浙江大学2016 –2017 学年春夏学期《Artificial Intelligence》课程期末考试试卷课程号： 21191890 ，开课学院：_计算机科学与技术学院_考试试卷：A卷、B卷（请在选定项上打√）考试形式：闭、开卷（请在选定项上打√），允许带___________入场考试日期： 2017 年 6 月 25 日,考试时间： 120 分钟诚信考试，沉着应考，杜绝违纪。

考生姓名：_____________学号：_____________所属院系：_____________1.Fill in the blanks (30 points)1)Two common structures are used, namely first-in-first-out (FIFO queue) and last-in-first-out (LIFO queue). The breadth-first-search uses a __________queue, depth-first search uses a__________ queue.2)In alpha-beta pruning search, the algorithm maintains two values, alpha and beta, whichrepresents the maximum score that the maximizing player is assured of and the minimum score that the minimizing player is assured of respectively. At the beginning of alpha-beta search, alpha is set to __________ and beta is set to __________, i.e. both players start with their lowest possible score.3) A and B are two random variables. P(A) and P(B) are their probabilities. It is knownthat P(A)+P(B)=0.5, and P(A|B)P(B|A)=14. Then the P(A) is_______.4) In your 10-day vacation in Alaska, you kept the following log on the weather andwhether you saw a bear that day:(rain, bear) 1 day (¬rain, bear) 2 days (rain, ¬bear) 6 days (¬rain, ¬bear)1 daya) Compute the marginal probability P(bear ) = _______b) Compute the conditional probability P(¬bear|rain) = _______5) In the figure, the circles show a plot of a trainingdata set of 10 data points, the dash line shows the function f(x) used to generate the data and solid curve shows the higher order polynomial g(x) fitted to given 10 data points. The fitted curve passes exactly through each data point, so the value of RMS error E RMS = 0 and g(x) gives avery poor representation of the function f(x). This behavior is known as___________________ (please select over-fitting or under-fitting to fill in this blank).6) For a given likelihood function p(x n |θ), if we obtain a data set of observations X ={x 1,x 2,x 3} and these data points are independent and identically distributed (i.i.d.), then p (X |θ)=p(x 1,x 2,x 3|θ)= ____________.7) For multivariate Gaussian distribution N (x|μ, Σ) of the D dimensional input space x,we have __________ independent parameters for μ and Σ. If Σ is a diagonal matrix and 2σ∑=I , the number of total parameters reduces to __________.8) The Linear basis function models involve linear combinations of fixed nonlinearfunctions of the input variables. If given basis functions ϕ(x )=(ϕ0(x ),ϕ1(x ),ϕ2(x ))T, where ϕ0(x )=1 and the model parameters w =(w 0,w 1,w 2)T , then the linear basis function y (x , w ) = ____________.9)In general, a deep convolutional neural network consists of convolutional layer,pooling layer, fully-connected layer and classifier layer, the softmax is usuallyemployed at the __________ layer.10)Reinforcement learning mainly consists of policy, value function and model.A__________ maps a state to an action, and a value function is a prediction of future reward. In Q-value function, discount factor γ is usually used, the range of discount factor γ is __________.2. Multiple Choice (36 points, only one of the options is correct.)1)Consider three 2D points a = (0, 0), b = (0, 1), c = (1, 0). Run k-means with twoclusters. Let the initial cluster centers be (-1, 0), (0, 2). What clusters will k-means learn after one iteration? _____(A) {a}, {b, c}(B) {a, b}, {c}(C) {a, c}, {b}(D) none of the above2)The sigmoid function in a neural network is defined as g(x)=e x1+e x. There isanother commonly used activation function called the hyperbolic tangent function,which is defined as tanh(x)=e x−e−xe x+e−x. How are these two functions related?____________(A) tanℎ(x)=g(x)−1(B) tanℎ(x)=2g(x)−1(C) tanℎ(x)=g(2x)−1(D) tanℎ(x)=2g(2x)−13)Which nodes will be pruned along with their branches by alpha-beta pruning? _____(A) I (B) H, I (C) G, H, I (D) C, H, I4)Consider a 3-puzzle where, like in the usual 8-puzzle game, a tile canonly move to an adjacent empty space. Given the initial state which ofthe following state cannot be reached? _____(A) (B) (C) (D)5)Given two Gaussian distribution N(x|−1,1) and N(x|1,1), which of the followingformula is correct? ______________(A) N(0|−1,1)>N(0|1,1)(B) N(−1|−1,1)>N(−1|1,1)(C) N(0|−1,1)<N(0|1,1)(D) N(−1|−1,1)<N(−1|1,1)6)The Fisher’s criterion is defined to be _________(A)the separation of the projected class means.(B)the separation of the projected class variances.(C)the ratio of the between-class variance to the within-class variance.(D)the ratio of the within-class variance to the between-class variance.7)Suppose we have a data set {x1,…,x N} drawn from the mixture of two 2D Gaussians,which can be written as p(x)=0.5N(x|μ1,Σ1)+0.5N(x|μ2,Σ2). If Σ1=Σ2=σ2I in this model, which of the following figures is consistent with the distribution of data points p(x)? _______________(A) (B) (C) (D)8)Consider a polynomial curve fitting problem. If the fitted curve oscillates wildlythrough each point and achieve bad generalization by making accurate predictions for new data, we say this behavior is over-fitting. Which of the following methods cannot be used to control over-fitting? ________________(A)Use fewer training data(B)Add validation set, use Cross-validation(C)Add a regularization term to an error function(D)Use Bayesian approach with suitable prior9)AlexNet (one of popular multi-layer convolutional neural networks for imageclassification) is trained in a_________ setting, K-means clustering is employed in a _________ setting and Boosting for classification is implemented in a_________ setting , linear regression model for classification is realized in a _________ setting.(A) unsupervised, supervised, supervised, unsupervised(B) supervised, supervised, supervised, supervised(C) supervised, supervised, unsupervised, unsupervised(D) supervised, unsupervised, supervised, supervised10)In linear least square regression model, we can add a regularization term to an errorfunction (i.e., sum-of-squares) in order to control _________. The lasso regularizer will introduce____________ solution compared to quadratic regularizer.(A) over-fitting, dense (B) over-fitting, sparse(C) under-fitting, dense (D) under-fitting, sparse11)We can decompose the expected squared loss of one predict model as follows:expected loss = (bias)2 + variance + noiseIn general, one flexible model (i.e., under-fitting) model will introduce high_________ and one rigid model (i.e., overfitting) model will introduce high _______________.There is a tradeoff between a model's ability to minimize bias and variance.(A) variance, bias (B) bias, bias(C) variance, variance (D) bias, variance12)Which description is not correct in terms of supervised learning, semi-supervisedlearning, unsupervised learning and reinforcement learning?__________(A) Reinforcement learning is one of specific supervised learning methods.(B) Semi-supervised learning falls between unsupervised learning (without anylabeled training data) and supervised learning (with completely labeled training data).(C) Reinforcement learning is neither supervised learning nor unsupervised.(D) Deep reinforcement learning is a combination of deep learning and reinforcementlearning.13)In reinforcement learning, Q-learning defines a function Q(s, a) representing the_______________ when we perform action a in state s, and continue optimally from that point on. The Q-function can be learned iteratively by ______________.(A) The immediate reward plus discounted maximum future reward, Bellman equation(B) Discounted maximum future reward, Markov decision process(C) The immediate reward plus discounted maximum future reward, Markov decisionprocess(D) Discounted maximum future reward, Bellman equation14)When we use one deep convolutional neural network model to classify 101 concepts,which option is not correct in the following description?_________(A)The output of the last fully connected layer can be used as the learning features ofeach concept.(B)The dimension of the classification layer can be 101.(C)The convolutional kernels are pre-defined (i.e., data-independent).(D) Dropout is used to boost the performance.15)Which description is not correct in terms of deep learning?__________(A)Deep learning is essentially a method to learn the features of raw data.(B)Backpropagation is conducted to optimize the weights of deep neural networks sothat the neural network can learn how to correctly map arbitrary inputs to outputs.(C)The achieved performance of deep learning is due to its powerful representationability via many of non-linear mappings.(D)Deep convolutional neural network for classification is employed in an end-to-endmechanism via unsupervised learning.16) Which description is not correct about K-Means clustering?___________(A)K-means clustering can be used for image segmentation and image compression.(B)K is the number of clusters and is generally pre-defined.(C)Each data point can be assigned to more than one cluster.(D)If the dimension of each data points is D, the dimension of cluster centers is D.17)The number of pruned successors in alpha-beta pruning is highly dependent on _____.(A)The moving order(B)The initialized values of alpha and beta(C)The number of terminal nodes(D)Whether breadth-first search or depth-first search is employed18)What description is not correct in terms of AI?(A)Deep learning is one kind of machine learning methods.(B)Machine learning is deep learning.(C)Search is one kind of methods used in AI.(D)In general, LeNet-5 (one of deep convolutional neural networks) maps eachhandwriting images into 0-9 digital character concept space.3.Calculus and Analysis (34 points)1)(Game Playing, 8 points) As shown in the following figure, there is a MINMAXsearch tree with three layers. The utility values for the leaf nodes are respectively displayed at the bottom of the figure.(a) Fill in the blanks for the utility values associated with the tree nodes (i.e., B, C, D) as well as the root node A. (4 points)(b) Draw mark ‘//’ on some branches in the figure to show that they are pruned by the alpha-beta pruning algorithm. (4 points)2) (Boosting, 8 points ) Boosting is a powerful technique for combining multiple “base” classifiers to produce a form of committee whose performance can be significantly better than that of any of the base classifiers. Consider a two-class classification problem, in which the training data comprises 2D input vectors 1,...,N x x along with corresponding binary target variables 1,...,N t t where {1,1}n t ∈+-. Assume that we have trained three base classifiers 12(),()f f x x ,3()f x and the corresponding weighting coefficients 123,,ααα. Please answer:(a) The final classifier learned by boosting can be given by: (4 points)F final (x )=(b) If three base classifiers 12(),()f f x x ,3()f x are shown in the figure and1230.3,0.5,0.7ααα===. Each base classifier partitions the input space into tworegions separated by a linear decision boundary Ωi . The dark regio n with ‘+’ means the target value is +1 and the bright region with ‘-’ means the target value is -1. Three decision boundaries have been put together in the last right figure and separate the space into six sub-regions, please mark the final decision result in the figure with ‘+’ or ‘-’ for each sub -region.(4 points)3) (Image Restoration, 6 points ) Please share several key tricks that effectively improve the performance of your Image Restoration Algorithm in Project 2. (About 100~150 words).4)(Deep learning, 12 points)(a) Convolution is very important in deep convolutional neural network. Please calculate the convolved value of the center pixel in Figure (1) with the given convolutional kernel in Figure (2). (3 points)(b) Given a single depth slice in Figure (3), please give out the average-pooling value of this slice with 2×2 filters and stride 2. (3 points)(c) If we trained a deep convolutional neural network as follows in Figue (4). The sofmax is used to classify five concepts (e.g., car, airplane, truck, ship and person). If we input a car image into the trained deep model, please write out one of likely 5-dimensional outputs by the deep model. (3 points)Figure (4)(d) Please write down the trainable parameters in this model. (3 points)《Artificial Intelligence》Final Examination Answer SheetName： Student ID： Dept.： _1.Fill in the blanks (30 points, 2pt/per)1).______________________, _______________________2). _____________________, _______________________3)._____________________4). _____________________, _______________________5)._____________________6). _____________________7)._____________________, _______________________8)._____________________9)._____________________10).____________________ , _______________________2.Multiple Choice (36 points, 2pt/per)3. Calculus and Analysis (34 points)1) (Game Playing, 8 points)(a) (4 points)(b)(4 points)2) (Boosting, 8 points)(a) (4 points)F final(x)=(b) (4 points)3) (Image Restoration, 6 points)4) (Deep Learning, 12 points)(a)(3 points)(b)(3 points)(c)(3 points)(d)(3 points)。

热带作物学报2021, 42(9): 2542 2548 Chinese Journal of Tropical Crops收稿日期 2021-02-23；修回日期 2021-03-20基金项目 国家自然科学基金项目（No. 31770657，No. 31570544，No. 31900016）。

作者简介 陈 彬（1990—），男，博士研究生，研究方向：森林微生物资源遗传多样性。

*通信作者（Corresponding author ）：梁俊峰（Liang Junfeng ），E-mail ：*******************。

Russula indocatillus , a New Record Species in ChinaCHEN Bin 1, 2, SONG Jie 1, WANG Qian 1, LIANG Junfeng 1*1. Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, Guangdong 510520, China;2. Nanjing For-estry University, Nanjing, Jiangsu 210037, ChinaAbstract: Russula indocatillus was reported as new species to China. A detailed morphological description, illustrations and phylogeny are provided, and comparisons with related species are made. It is morphologically characterized by a brownish orange to yellow ochre pileus center with butter yellow to pale yellow margin, white to cream spore print, subglobose to broadly ellipsoid to ellipsoid basidiospores with bluntly conical to subcylindrical isolated warts, always one-celled pileocystidia, and short, slender, furcated and septated terminal elements of pileipellis. The combination of detailed morphological features and phylogenetic analysis based on ITS-nrLSU-RPB2 sequences dataset indicated that the species belonged to Russula subg. Heterphyllidia sect. Ingratae . Keywords: Russulaceae; new record species; phylogeny; taxonomy DOI 10.3969/j.issn.1000-2561.2021.09.014印度碗状红菇——一个中国新纪录种陈 彬1,2，宋 杰1，王 倩1，梁俊峰1*1. 中国林业科学研究院热带林业研究所，广东广州 510520；2. 南京林业大学，江苏南京 210037摘 要：本研究报道一个中国红菇属新记录种——印度碗状红菇（Russula indocatillus ）。

考博英语摘要写作真题模拟练习-中英对照论文题目：数据挖掘的建模及在生物信息学中的应用研究中文摘要随着科学技术的飞速发展，经济和社会都取得了极大的进步，与此同时，在各个领域产生了大量的数据，如何从这些数据中发现有价值的知识及规律，成为目前理论与实践研究的热点与难点。

与此同时，生命科学技术的快速发展也产生了大量的生物数据，单纯地利用传统的生物实验方法将很难快速且全面的处理如此多生物数据，从而必然制约了生命科学及制药工程的快速发展。

在这种情况下，生物信息学应运而生。

生物信息学是一门生物学与信息科学交叉而形成的年轻学科，旨在运用信息学、物理学、化学、数学、计算机科学、系统科学的理论和方法来研究生物系统和生物过程的信息量和信息流，在已有数据的基础之上发现相应的规律和知识并进而用来进一步指导与解释生物实验与生命现象，加速对生命本质特征的认识。

本论文在数据挖掘及生物信息学理论与方法上进行了深入的研究与探索。

聚类分析是数据挖掘研究中的重要内容，成为各学科研究中的重要工具。

但在现实生活中，常常遇到高维数据集的处理且在大多数情况下，这些数据集对于各个聚类存在属性不平衡的现象。

根据这一点，本文创新性提出了在核特征空间中的属性加权核聚类算法，实验表明新聚类算法能很好地反映各属性对于各个聚类的重要性，因而取得了比传统聚类算法更好的结果。

传统聚类算法的应用对象往往局限于单一独立的数据集，但在很多情况下一个数据集要和其他数据集相互发生关联。

基于信息理论，本文首先提出了一合作聚类算法，反映了数据集间的相互作用关系，结果表明聚类结果将受到其他数据集的影响。

我们同时也从理论上证明了这两个算法的收敛性。

蛋白折叠是比蛋白的三维结构更深层次的知识信息，因而是更加困难的研究课题，同时，从蛋白序列预测蛋白折叠类型能够进一步为预测该蛋白的三维结构提供极有价值的信息。

本文从生物系统的复杂性角度出发，创新性地提出了基于集成分类器框架的蛋白折叠预测系统，从多个生物特征角度对序列信息源及特征进行融合决策预测，结果证明所得到的集成预测系统是非常有效的，把蛋白折叠的预测精度提高了6-21%。