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Environmental disasters are becoming more frequent. These disasters not only include the most common natural disasters, but also include man-made disasters, such as public health, accident disasters, etc., which have caused greater damage to human society and cities. Because of the limitations of a single government-led model in emergency response, the emergency preparedness of communities, families and individuals are more important. In particular, the emergency preparedness psychology and behavior of individuals directly determine whether or not they can effectively protect themselves and their families in the first time of disaster. This Special Issue focuses on environmental disasters and individuals’ emergency preparedness in the perspective of psychology and behavior.

Psychology --- social networks --- trust --- risk perception --- multiple disasters --- China --- volunteering --- disaster preparedness --- accidental life insurance --- training --- organizational identification --- pandemic --- public sentiment --- system dynamics --- cross-validation --- simulation and control --- place attachment --- self-efficacy --- disaster experience --- water resources carrying risk --- vulnerability of disaster-bearers --- hazard of disaster-causing factors --- coping behaviors --- psychological capital --- theory of planned behavior --- structural equation model --- MHO staff --- emergency preparedness behavior --- COVID-19 --- campus signal --- disaster awareness --- structural regression model

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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.

History of engineering & technology --- deposit insurance --- implied volatility --- static arbitrage --- parameterization --- machine learning --- calibration --- dichotomous response --- predictive model --- tree boosting --- GLM --- validation --- generalised linear modelling --- zero-inflated poisson model --- telematics --- benchmark --- cross-validation --- prediction --- stock return volatility --- long-term forecasts --- overlapping returns --- autocorrelation --- chain ladder --- Bornhuetter–Ferguson --- maximum likelihood --- exponential families --- canonical parameters --- prior knowledge --- accelerated failure time model --- chain-ladder method --- local linear kernel estimation --- non-life reserving --- operational time --- zero-inflation --- overdispersion --- automobile insurance --- risk classification --- risk selection --- least-squares monte carlo method --- proxy modeling --- life insurance --- Solvency II --- claims prediction --- export credit insurance --- semiparametric modeling --- VaR estimation --- analyzing financial data --- n/a --- Bornhuetter-Ferguson

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Over the past several years, notions of developmental trajectories-particularly criminal trajectories-have taken hold as important areas of investigation for researchers interested in the longitudinal study of crime. This accessible volume presents the first full-length overview of criminal trajectories as a concept and methodology and makes the case for a developmental approach to the topic. The volume shows how a developmental perspective is important from a practical standpoint, helping to inform the design of prevention and early intervention programs to forestall the onset of antisocial and criminal activity, particularly when it begins in childhood. Crime in this view does not suit a one-size-fits-all model. There are different types of criminals who develop as the result of different types of developmental factors and experiences. By considering what risk factors may set the stage for later crimes in certain circumstances, the authors argue that we may be able to intervene at any point along the life course and, if addressed early enough, prevent criminal behavior from taking root. 'Criminal Trajectories' offers a comprehensive synthesis of the findings from numerous criminal trajectory studies, presented through a multi-disciplinary lens. It addresses the policy and practice implications of these findings for the criminal justice system-including a critique of current sentencing and incarceration practices-and presents twelve recommendations informed by developmental frameworks for future work.

Criminal psychology. --- Crime --- Criminal behavior, Prediction of. --- Criminal behavior. --- Sociological aspects. --- Biological processes. --- Bootstrapping. --- Chronic. --- Class enumeration. --- Controversies. --- Correlates. --- Criminal career. --- Criminology. --- Cross-validation. --- Desistance. --- Developmental and life-course theories of crime. --- Developmental cascades. --- Dual taxonomy. --- Dynamic transaction. --- Heterogeneity. --- Historical background. --- Human agency. --- Intervention. --- Joint trajectories. --- Latent growth mixture modeling. --- Later life outcomes. --- Life course. --- Life span. --- Longitudinal data. --- Longitudinal. --- Machine learning. --- Monetary cost of crime. --- Nonnormality. --- Overextraction. --- Predictors. --- Prevention. --- Programming. --- Rehabilitation. --- Reification. --- Relational developmental systems theory. --- Reporting standards. --- Risk factor. --- Self-regulation. --- Semiparametric group-based trajectory modeling. --- Statistical technique. --- Trajectory. --- Turning points.

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Information technology industries --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering

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This reprintshows recent advances in dam safety related to overtopping and the prevention, detection, and risk assessment of geostructural risks. Related to overtopping, the issues treated are: the throughflow and failure process of rockfill dams; the protection of embankment dams against overtopping by means of a rockfill toe or wedge-shaped blocks; and the protection of concrete dams with highly convergent chutes. In the area of geostructural threats, the detection of anomalies in dam behavior from monitoring data using a combination of machine learning techniques, the numerical modeling of seismic behavior of concrete dams, and the determination of the impact area downstream of ski-jump spillways are also studied and discussed. In relation to risk assessment, three chapters deal with the development of fragility curves for dikes and dams in relation to various failure mechanisms.

Technology: general issues --- History of engineering & technology --- hydraulic structure --- sky-jump --- spillway --- flip bucket --- chute --- basin --- erosion --- flow rate --- jet flow --- wave overtopping --- levee --- cover --- probabilistic framework --- slope stability --- piping --- overtopping --- fragility curves --- Monte Carlo simulation --- dam --- stilling basin --- bucket --- flood --- weir --- safety --- protection --- dam protection --- wedge-shaped block --- WSB --- dam spillway --- dam safety --- ACUÑA --- rockfill dams --- throughflow --- numerical modeling --- non-Darcy flow --- porous media --- Forchheimer equation --- high velocity --- crushed rock --- rounded materials --- hydraulic mean radius --- intrinsic permeability --- shape of particles --- angularity of particles --- surface roughness of particles --- river levees --- geogrid reinforcement --- First Order Reliability Method (FORM) --- Surface Response Method (SRM) --- high gravity dams --- dam-foundation-reservoir dynamic interaction --- earthquake input mechanisms --- hydrodynamic pressure --- foundation size --- reservoir length --- stacking --- blending --- combination --- meta-learner --- experts --- machine learning --- Cross Validation --- radial displacement --- rockfill dam --- dam failure --- overflow --- floods --- dam breach --- n/a --- ACUÑA