LEADER 03359nam 22006614a 450 001 9910143966903321 005 20230721030239.0 010 $a1-282-11236-8 010 $a9786612112362 010 $a0-470-27780-7 010 $a0-470-27652-5 035 $a(CKB)1000000000376162 035 $a(EBL)469115 035 $a(OCoLC)609847848 035 $a(SSID)ssj0000353825 035 $a(PQKBManifestationID)11263225 035 $a(PQKBTitleCode)TC0000353825 035 $a(PQKBWorkID)10302016 035 $a(PQKB)10771296 035 $a(MiAaPQ)EBC469115 035 $a(Au-PeEL)EBL469115 035 $a(CaPaEBR)ebr10296462 035 $a(CaONFJC)MIL211236 035 $a(EXLCZ)991000000000376162 100 $a20061030d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in food diagnostics$b[electronic resource] /$feditors, Leo M.L. Nollet, Fidel Toldra? ; administrative editor, Y.H. Hui 205 $a1st ed. 210 $aAmes, Iowa $cBlackwell Pub.$d2007 215 $a1 online resource (386 p.) 300 $aDescription based upon print version of record. 311 $a0-8138-2221-1 320 $aIncludes bibliographical references and index. 327 $aAssuring safety and quality along the food chain -- Methodologies for improved quality control assessment of food products -- Application of microwaves for on-line quality assessment -- Ultrasounds for quality assurance -- NMR for food quality and traceability -- Electronic nose for quality and safety control -- Rapid microbiological methods in food diagnostics -- Molecular technologies for detecting and characterizing pathogens -- DNA-based detection of GM ingredients -- Protein-based detection of GM ingredients -- Immunodiagnostic technology and its applications -- Rapid liquid chromatographic techniques for detection of key (bio)chemical markers -- Sampling procedures with special focus on automatization -- Data processing -- Data handling -- The market for diagnostic devices in the food industry. 330 $aFood diagnostics is a relatively new and emerging area fuelled in large part by the ever-increasing demand for food safety. Advances in Food Diagnostics provides the most updated, comprehensive professional reference source available, covering sophisticated diagnostic technology for the food industry. Editors Nollet, Toldra?, and Hui and their broad team of international contributors address the most recent advances in food diagnostics through multiple approaches: reviewing novel technologies to evaluate fresh products; describing and analyzing in depth several specific modern diagnostic 606 $aFood$xAnalysis 606 $aFood adulteration and inspection 606 $aFood$xQuality 606 $aFood$xSafety measures 615 0$aFood$xAnalysis. 615 0$aFood adulteration and inspection. 615 0$aFood$xQuality. 615 0$aFood$xSafety measures. 676 $a664/.07 701 $aNollet$b Leo M. L.$f1948-$0308584 701 $aToldra?$b Fidel$0430057 701 $aHui$b Y. H$g(Yiu H.)$0858835 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143966903321 996 $aAdvances in food diagnostics$92041380 997 $aUNINA LEADER 03411nam 22005055 450 001 9910682571203321 005 20230502090707.0 010 $a9783839464854 024 7 $a10.1515/9783839464854 035 $a(CKB)26399270200041 035 $a(DE-B1597)635215 035 $a(DE-B1597)9783839464854 035 $a(NjHacI)9926399270200041 035 $a(OCoLC)1378176472 035 $a(Perlego)3740965 035 $a(EXLCZ)9926399270200041 100 $a20230502h20232023 fg 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmotional Imprints of War $eA Computer-Assisted Analysis of Emotions in Dutch Parliamentary Debates, 1945-1989 /$fMilan van Lange 205 $a1st ed. 210 1$aBielefeld : $cBielefeld University Press, $d[2023] 210 4$dİ2023 215 $a1 online resource (330 p.) 225 0 $aDigital Humanities Research ,$x2749-1986 ;$v6 327 $tFrontmatter -- $tContents -- $t1. Introduction . On War, Emotions, and Computers in History -- $t2. Emotions -- $t3. Materials and Data . Digitised Sources and a Lexicon -- $t4. Methods and Operationalisation . A Computer-assisted Approach to the Analysis of Digitised Historical Texts -- $t5. Peering Through the Macroscope . Baseline and Background -- $tCase Study 1 . ?The Resistance? -- $tIntroduction -- $tA History of Resistance Legislation (1947?1985) -- $t6. Erratic Emotions . Mining the Underground in the Dutch Parliament -- $t7. A Strong Disposition . Discussing the Pension Act for Extraordinary Government Employees -- $tCase Study 2 . ?War Victims? -- $tIntroduction -- $tA History of Alleviating War Victims? Suffering in Parliament (1945?1989 -- $t8. Emotional Consistency . A Macroscopic View on War Victim Debates -- $t9. Emotional Scaffolding . The Construction of War Victim Legislation in Parliament -- $t10. Conclusion . On the Role of Emotions and Computers -- $tSupplements -- $tBibliography -- $tAuthor Information 330 $aHistorical research can be enhanced by methods and resources from various disciplines, ranging from psychology to computer linguistics. With a creative and innovative perspective on ?things we think we know?, Milan van Lange presents a computer-assisted historical investigation into the role of emotions in dealing with consequences of World War II in the Netherlands. By ?emotion mining? digitised sources, van Lange shows where emotions were present and how they were expressed and discussed in the political engagement with people who experienced long-term effects of the war, such as former collaborators and war criminals, the resistance, and war victims. 410 0$aDigital humanities research. 606 $aPolitical oratory$zNetherlands$xData processing 606 $aWorld War, 1939-1945$zNetherlands$xInfluence 606 $aHISTORY / Social History$2bisacsh 607 $aNetherlands$xPolitics and government$y1945- 615 0$aPolitical oratory$xData processing. 615 0$aWorld War, 1939-1945$xInfluence. 615 7$aHISTORY / Social History. 676 $a322.4309492 700 $avan Lange$b Milan, $4aut$4http://id.loc.gov/vocabulary/relators/aut$01357997 801 0$bDE-B1597 801 1$bDE-B1597 912 $a9910682571203321 996 $aEmotional Imprints of War$93365479 997 $aUNINA LEADER 10921oam 22005293 450 001 9910969817803321 005 20251117113019.0 010 $a1-315-15250-9 010 $a1-351-64854-3 035 $a(CKB)4100000007506915 035 $a(MiAaPQ)EBC5630530 035 $a(EXLCZ)994100000007506915 100 $a20240223d2019 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHandbook of Environmental and Ecological Statistics 205 $a1st ed. 210 1$aMilton :$cCRC Press LLC,$d2019. 210 4$dİ2019. 215 $a1 online resource (882 pages) 311 08$a1-4987-5202-0 327 $aCover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- 1: Introduction -- I: Methodology for Statistical Analysis of Environmental Processes -- 2: Modeling for environmental and ecological processes -- 2.1 Introduction -- 2.2 Stochastic modeling -- 2.3 Basics of Bayesian inference -- 2.3.1 Priors -- 2.3.2 Posterior inference -- 2.3.3 Bayesian computation -- 2.4 Hierarchical modeling -- 2.4.1 Introducing uncertainty -- 2.4.2 Random effects and missing data -- 2.5 Latent variables -- 2.6 Mixture models -- 2.7 Random effects -- 2.8 Dynamic models -- 2.9 Model adequacy -- 2.10 Model comparison -- 2.10.1 Bayesian model comparison -- 2.10.2 Model comparison in predictive space -- 2.11 Summary -- 3: Time series methodology -- 3.1 Introduction -- 3.2 Time series processes -- 3.3 Stationary processes -- 3.3.1 Filtering preserves stationarity -- 3.3.2 Classes of stationary processes -- 3.3.2.1 IID noise and white noise -- 3.3.2.2 Linear processes -- 3.3.2.3 Autoregressive moving average processes -- 3.4 Statistical inference for stationary series -- 3.4.1 Estimating the process mean -- 3.4.2 Estimating the ACVF and ACF -- 3.4.3 Prediction and forecasting -- 3.4.4 Using measures of correlation for ARMA model identification -- 3.4.5 Parameter estimation -- 3.4.6 Model assessment and comparison -- 3.4.7 Statistical inference for the Canadian lynx series -- 3.5 Nonstationary time series -- 3.5.1 A classical decomposition for nonstationary processes -- 3.5.2 Stochastic representations of nonstationarity -- 3.6 Long memory processes -- 3.7 Changepoint methods -- 3.8 Discussion and conclusions -- 4: Dynamic models -- 4.1 Introduction -- 4.2 Univariate Normal Dynamic Linear Models (NDLM) -- 4.2.1 Forward learning: the Kalman filter -- 4.2.2 Backward learning: the Kalman smoother -- 4.2.3 Integrated likelihood. 327 $a4.2.4 Some properties of NDLMs -- 4.2.5 Dynamic generalized linear models (DGLM) -- 4.3 Multivariate Dynamic Linear Models -- 4.3.1 Multivariate NDLMs -- 4.3.2 Multivariate common-component NDLMs -- 4.3.3 Matrix-variate NDLMs -- 4.3.4 Hierarchical dynamic linear models (HDLM) -- 4.3.5 Spatio-temporal models -- 4.4 Further aspects of spatio-temporal modeling -- 4.4.1 Process convolution based approaches -- 4.4.2 Models based on stochastic partial differential equations -- 4.4.3 Models based on integro-difference equations -- 5: Geostatistical Modeling for Environmental Processes -- 5.1 Introduction -- 5.2 Elements of point-referenced modeling -- 5.2.1 Spatial processes, covariance functions, stationarity and isotropy -- 5.2.2 Anisotropy and nonstationarity -- 5.2.3 Variograms -- 5.3 Spatial interpolation and kriging -- 5.4 Summary -- 6: Spatial and spatio-temporal point processes in ecological applications -- 6.1 Introduction - relevance of spatial point processes to ecology -- 6.2 Point processes as mathematical objects -- 6.3 Basic definitions -- 6.4 Exploratory analysis - summary characteristics -- 6.4.1 The Poisson process-a null model -- 6.4.2 Descriptive methods -- 6.4.3 Usage in ecology -- 6.5 Point process models -- 6.5.1 Modelling environmental heterogeneity - inhomogeneous Poisson processes and Cox processes -- 6.5.2 Modelling clustering - Neyman Scott processes -- 6.5.3 Modelling inter-individual interaction - Gibbs processes -- 6.5.4 Model fitting - approaches and software -- 6.5.4.1 Approaches -- 6.5.4.2 Relevant software packages -- 6.6 Point processes in ecological applications -- 6.7 Marked point processes - complex data structures -- 6.7.1 Different roles of marks in point patterns -- 6.7.2 Complex models - dependence between marks and patterns -- 6.7.3 Marked point pattern models reflecting the sampling process. 327 $a6.8 Modelling partially observed point patterns -- 6.8.1 Point patterns observed in small subareas -- 6.8.2 Distance sampling -- 6.9 Discussion -- 6.9.1 Spatial point processes and geo-referenced data -- 6.9.2 Spatial point process modeling and statistical ecology -- 6.9.3 Other data structures -- 6.9.3.1 Telemetry data -- 6.9.3.2 Spatio-temporal patterns -- 6.9.4 Conclusion -- 6.10 Acknowledgments -- 7: Data assimilation -- 7.1 Introduction -- 7.2 Algorithms for data assimilation -- 7.2.1 Optimal interpolation -- 7.2.2 Variational approaches -- 7.2.3 Sequential approaches: the Kalman filter -- 7.3 Statistical approaches to data assimilation -- 7.3.1 Joint modeling approaches -- 7.3.2 Regression-based approaches -- 8: Univariate and Multivariate Extremes for the Environmental Sciences -- 8.1 Extremes and Environmental Studies -- 8.2 Univariate Extremes -- 8.2.1 Theoretical underpinnings -- 8.2.2 Modeling Block Maxima -- 8.2.3 Threshold exceedances -- 8.2.4 Regression models for extremes -- 8.2.5 Application: Fitting a time-varying GEV model to climate model output -- 8.2.5.1 Analysis of individual ensembles and all data -- 8.2.5.2 Borrowing strength across locations -- 8.3 Multivariate Extremes -- 8.3.1 Multivariate EVDs and componentwise block maxima -- 8.3.2 Multivariate threshold exceedances -- 8.3.3 Application: Santa Ana winds and dryness -- 8.3.3.1 Assessing tail dependence -- 8.3.3.2 Risk region occurrence probability estimation -- 8.4 Conclusions -- 9: Environmental Sampling Design -- 9.1 Introduction -- 9.2 Sampling Design for Environmental Monitoring -- 9.2.1 Design framework -- 9.2.2 Model-based design -- 9.2.2.1 Covariance estimation-based criteria -- 9.2.2.2 Prediction-based criteria -- 9.2.2.3 Mean estimation-based criteria -- 9.2.2.4 Multi-objective and entropy-based criteria -- 9.2.3 Probability-based spatial design. 327 $a9.2.3.1 Simple random sampling -- 9.2.3.2 Systematic random sampling -- 9.2.3.3 Stratified random sampling -- 9.2.3.4 Variable probability sampling -- 9.2.4 Space-filling designs -- 9.2.5 Design for multivariate data and stream networks -- 9.2.6 Space-time designs -- 9.2.7 Discussion -- 9.3 Sampling for Estimation of Abundance -- 9.3.1 Distance sampling -- 9.3.1.1 Standard probability-based designs -- 9.3.1.2 Adaptive distance sampling designs -- 9.3.1.3 Designed distance sampling experiments -- 9.3.2 Capture-recapture -- 9.3.2.1 Standard capture-recapture -- 9.3.2.2 Spatial capture-recapture -- 9.3.3 Discussion -- 10: Accommodating so many zeros: univariate and multivariate data -- 10.1 Introduction -- 10.2 Basic univariate modeling ideas -- 10.2.1 Zeros and ones -- 10.2.2 Zero-inflated count data -- 10.2.2.1 The k-ZIG -- 10.2.2.2 Properties of the k-ZIG model -- 10.2.2.3 Incorporating the covariates -- 10.2.2.4 Model fitting and inference -- 10.2.2.5 Hurdle models -- 10.2.3 Zeros with continuous density G(y) -- 10.3 Multinomial trials -- 10.3.1 Ordinal categorical data -- 10.3.2 Nominal categorical data -- 10.4 Spatial and spatio-temporal versions -- 10.5 Multivariate models with zeros -- 10.5.1 Multivariate Gaussian models -- 10.5.2 Joint species distribution models -- 10.5.3 A general framework for zero-dominated multivariate data -- 10.5.3.1 Model elements -- 10.5.3.2 Specific data types -- 10.6 Joint Attribute Modeling Application -- 10.6.1 Host state and its microbiome composition -- 10.6.2 Forest traits -- 10.7 Summary and Challenges -- 11: Gradient Analysis of Ecological Communities (Ordination) -- 11.1 Introduction -- 11.2 History of ordination methods -- 11.3 Theory and background -- 11.3.1 Properties of community data -- 11.3.2 Coenospace -- 11.3.3 Alpha, beta, gamma diversity -- 11.3.4 Ecological similarity and distance. 327 $a11.4 Why ordination? -- 11.5 Exploratory analysis and hypothesis testing -- 11.6 Ordination vs. Factor Analysis -- 11.7 A classification of ordination -- 11.8 Informal techniques -- 11.9 Distance-based techniques -- 11.9.1 Polar ordination -- 11.9.1.1 Interpretation of ordination scatter plots -- 11.9.2 Principal coordinates analysis -- 11.9.3 Nonmetric Multidimensional Scaling -- 11.10 Eigenanalysis-based indirect gradient analysis -- 11.10.1 Principal Components Analysis -- 11.10.2 Correspondence Analysis -- 11.10.3 Detrended Correspondence Analysis -- 11.10.4 Contrast between DCA and NMDS -- 11.11 Direct gradient analysis -- 11.11.1 Canonical Correspondence Analysis -- 11.11.2 Environmental variables in CCA -- 11.11.3 Hypothesis testing -- 11.11.4 Redundancy Analysis -- 11.12 Extensions of direct ordination -- 11.13 Conclusions -- II: Topics in Ecological Processes -- 12: Species distribution models -- 12.1 Aims of species distribution modelling -- 12.2 Example data used in this chapter -- 12.3 Single species distribution models -- 12.4 Joint species distribution models -- 12.4.1 Shared responses to environmental covariates -- 12.4.2 Statistical co-occurrence -- 12.5 Prior distributions -- 12.6 Acknowledgments -- 13: Capture-Recapture and distance sampling to estimate population sizes -- 13.1 Basic ideas -- 13.2 Inference for closed populations -- 13.2.1 Censuses and finite population sampling -- 13.2.2 The problem of imperfect detection -- 13.2.3 Capture-recapture on closed populations -- 13.2.4 Distance sampling methods on closed populations -- 13.2.5 N-mixture models for closed populations -- 13.2.6 Count regression -- 13.3 Inference for open populations -- 13.3.1 Crosbie-Manly-Schwarz-Arnason model -- 13.3.2 Cormack-Jolly-Seber model and tag-recovery models -- 13.3.3 Pollock's robust design. 327 $a13.3.4 Capture recapture models for population growth rate. 330 $aThis handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. 606 $aEnvironmental sciences$xStatistical methods 606 $aEcology$xStatistical methods 615 0$aEnvironmental sciences$xStatistical methods. 615 0$aEcology$xStatistical methods. 676 $a557.072/7 700 $aGelfand$b Alan E$0460540 701 $aFuentes$b Montserrat$01853858 701 $aHoeting$b Jennifer A$0614464 701 $aSmith$b Richard Lyttleton$01853859 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910969817803321 996 $aHandbook of Environmental and Ecological Statistics$94450623 997 $aUNINA