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There are many different theories of intelligence. Although these theories differ in their nuances, nearly all agree that there are multiple cognitive abilities and that they differ in the breadth of content they are typically associated with. There is much less agreement about the relative importance of cognitive abilities of differing generality for predicting important real-world outcomes, such as educational achievement, career success, job performance, and health. Some investigators believe that narrower abilities hold little predictive power once general abilities have been accounted for. Other investigators contend that specific abilities are often as—or even more—effective in forecasting many practical variables as general abilities. These disagreements often turn on differences of theory and methodology that are both subtle and complex. The five cutting-edge contributions in this volume, both empirical and theoretical, advance the conversation in this vigorous, and highly important, scientific debate.

general cognitive ability --- second stratum abilities --- narrow abilities --- cognitive abilities --- ability tilt --- identification --- occupational attainment --- scholastic performance --- longevity --- non-g residuals --- specific abilities --- higher-order factor model --- bifactor model --- intelligence --- general intelligence (g) --- specific factors --- academic achievement --- hierarchical factor model --- educational attainment --- nested-factor models --- ability differentiation --- general abilities --- relative importance --- relative importance analysis --- bifactor(S-1) model --- subscores --- g-factor --- school grades --- non-g factors --- nested-factors model --- general mental ability --- cognitive tests --- specific cognitive abilities --- curvilinear relations --- specific ability --- situational specificity --- predictor-criterion bandwidth alignment --- job performance --- health --- machine learning --- academic performance --- general factor --- Intelligence. --- Forecasting. --- Forecasts --- Futurology --- Prediction --- Human intelligence --- Intelligence --- Mind --- Ability --- Psychology --- Thought and thinking

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This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine.

Medicine --- artificial intelligence --- computer-aided diagnosis --- facial phenotypes --- machine learning --- complexity theory --- dementia --- cognitive dysfunction --- neuropsychological tests --- mental status and dementia tests --- spontaneous intracerebral hemorrhage (SICH) --- 90-day function outcome --- mortality --- osteoarthritis --- venous thrombosis --- VTE risk prediction --- machine learning algorithm --- population-based cohort study --- pituitary adenoma --- craniopharyngioma --- optic chiasm --- multicenter --- treatment outcome --- liver neoplasms --- deep learning --- diabetic complication --- gene-gene interaction --- AGER --- IL6R --- multiple sclerosis --- DNA methylation --- entropy --- atherosclerosis --- plaque characterization --- physical activity --- osteoporosis --- osteoporotic fracture --- vertebral fracture --- hip fracture --- distal radius fracture --- small for gestational age --- exposure to radiation --- prediction --- coronary plaque --- major adverse cardiovascular events --- coronary artery disease --- coronary computed tomographic angiography --- acute pancreatitis --- predictor --- interventions --- type 2 diabetes mellitus (T2DM) --- prediction model --- Chinese elderly --- prediabetes --- incident diabetes --- predictive models --- artificial intelligence --- computer-aided diagnosis --- facial phenotypes --- machine learning --- complexity theory --- dementia --- cognitive dysfunction --- neuropsychological tests --- mental status and dementia tests --- spontaneous intracerebral hemorrhage (SICH) --- 90-day function outcome --- mortality --- osteoarthritis --- venous thrombosis --- VTE risk prediction --- machine learning algorithm --- population-based cohort study --- pituitary adenoma --- craniopharyngioma --- optic chiasm --- multicenter --- treatment outcome --- liver neoplasms --- deep learning --- diabetic complication --- gene-gene interaction --- AGER --- IL6R --- multiple sclerosis --- DNA methylation --- entropy --- atherosclerosis --- plaque characterization --- physical activity --- osteoporosis --- osteoporotic fracture --- vertebral fracture --- hip fracture --- distal radius fracture --- small for gestational age --- exposure to radiation --- prediction --- coronary plaque --- major adverse cardiovascular events --- coronary artery disease --- coronary computed tomographic angiography --- acute pancreatitis --- predictor --- interventions --- type 2 diabetes mellitus (T2DM) --- prediction model --- Chinese elderly --- prediabetes --- incident diabetes --- predictive models

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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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The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased.

Information technology industries --- Computer science --- bandwidth selection --- correlation --- edge-preserving image denoising --- image sequence --- jump regression analysis --- local smoothing --- nonparametric regression --- spatio-temporal data --- linear mixed model --- ridge estimation --- pretest and shrinkage estimation --- multicollinearity --- asymptotic bias and risk --- LASSO estimation --- high-dimensional data --- big data adaptation --- dividend estimation --- options markets --- weighted least squares --- online health community --- social support --- network analysis --- cancer --- functional principal component analysis --- functional predictor --- linear mixed-effects model --- mobile device --- sparse group regularization --- wearable device data --- Bayesian modeling --- functional regression --- gestational weight --- infant birth weight --- joint modeling --- longitudinal data --- maternal weight gain --- transfer learning --- deep learning --- pretrained neural networks --- chest X-ray images --- lung diseases --- causal structure learning --- consistency --- FCI algorithm --- high dimensionality --- nonparametric testing --- PC algorithm --- fMRI --- functional connectivity --- brain network --- Human Connectome Project --- statistics

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Heart failure prevalence continues to rise globally. Regardless of the underlying etiology, heart failure remains a progressive disease, largely irreversible and end-stage heart failure requires transplantation. Book focuses on the challenges and recent advances of diagnosis, treatment and prevention of heart failure with or without associated comorbidities. We hope that readers will appreciate the wide breadth of topics and clinical utility of the articles and reviews included to this book collection.

Medicine --- Cardiovascular medicine --- biomarkers --- ST-segment elevation myocardial infarction --- preserved left ventricular ejection fraction --- reduced left ventricular ejection fraction --- heart failure --- NT-proBNP --- soluble ST2 --- galectin-3 --- matrix metalloproteinases --- tissue inhibitors of metalloproteinases --- mortality --- ejection fraction --- cardiopulmonary exercise test --- ventilatory inefficiency --- angiotensin receptor–neprilysin inhibitor --- echocardiography --- HFrEF --- risk stratification --- left ventricle end-diastolic diameter --- E/e’ ratio --- left ventricle outflow tract velocity-time integral --- hospitalization predictor --- short-term prognosis --- heart failure readmission --- acute myocardial infarction --- blood biomarkers --- diagnosis --- congestion --- clinical assessment --- preserved ejection fraction --- type 2 diabetes mellitus --- myocardial infarction --- chronic heart failure --- heart rate variability --- 2D echocardiography --- 24-h ECG monitoring --- chronic kidney disease --- pulmonary hypertension --- arteriovenous fistulas --- overhydration --- heart transplant --- cardiac allograft vasculopathy --- heart transplant rejection --- transthoracic echocardiography --- longitudinal strain --- sensing parameters --- pacing parameters --- adverse left ventricular remodeling --- left ventricular-arterial coupling --- fatty liver --- cardiovascular disease --- fibrosis --- epicardial fat --- left atrial strain --- edema --- dilated cardiomyopathy --- fluid management --- endothelial dysfunction --- n/a --- angiotensin receptor-neprilysin inhibitor --- E/e' ratio