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In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Research & information: general --- Mathematics & science --- multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid-environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq

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This Special Issue addresses a topic that is of great relevance as, nowadays, in developed countries, individuals spend most of their time indoors and, depending on each person, the presence at home ranges between 60% and 90% of the day, with 30% of that time spent sleeping. Considering these data, indoor residential environments have a direct influence on human health, especially considering that, in developing countries, significant levels of indoor pollution make housing unsafe, having an impact on the health of inhabitants. Therefore, housing is a key health factor for people all over the world, and various parameters, such as air quality, ventilation, hygrothermal comfort, lighting, physical environment, and building efficiency, can contribute to healthy architecture, as well as to the conditions that can result from the poor application of these parameters. The articles in this Special Issue thus address issues concerning indoor environmental quality (IEQ), which is described, more simply, as the conditions inside a building. This includes air quality, but also access to daylight and views, pleasant acoustic conditions, and occupant control over lighting and thermal comfort. IEQ also includes the functional aspects of the space, such as whether the layout provides easy access to tools and people when needed and whether there is sufficient space for the occupants. Building managers and operators can increase building occupant satisfaction by considering all aspects of IEQ rather than focusing on temperature or air quality alone.

indoor air quality --- thermal comfort --- airtightness --- natural ventilation --- educational buildings --- thermal insulation --- sustainable materials --- fique --- thermal conductivity --- thermogravimetry --- green architecture --- urban heat island --- microclimate --- feed-forward neural networks --- air temperature measurements --- in-situ measurements --- urban models --- urban environment --- climate change --- COVID-19 --- MgO-based cement --- sustainability --- energy efficiency --- architecture --- building evaluation --- functional adequacy --- human-centered --- IEQ --- learning space --- place attachment --- social interaction --- social participation --- sustainable building --- quality air --- epidemiology --- data analysis --- statistics --- nursing homes --- geopolymer --- fly ash --- basalt fiber --- basalt waste aggregate --- mechanical properties --- lean manufacturing --- modular construction --- sustainability architecture --- efficient buildings --- lean construction

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This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators.

History of engineering & technology --- sensor network --- data fusion --- complex network analysis --- fault prognosis --- photovoltaic plants --- ANFIS --- statistical method --- gradient descent --- photovoltaic system --- sustainable development --- PV power prediction --- artificial neural network --- renewable energy --- environmental parameters --- multiple regression model --- moth-flame optimization --- parameter extraction --- photovoltaic model --- double flames generation (DFG) strategy --- Solar cell parameters --- single-diode model --- two-diode model --- COA --- photovoltaic systems --- maximum power point tracking --- single stage grid connected systems --- solar concentrator --- spectral beam splitting --- diffractive optical element --- diffractive grating --- PVs power output forecasting --- adaptive neuro-fuzzy inference systems --- particle swarm optimization-artificial neural networks --- solar irradiation --- photovoltaic power prediction --- publicly available weather reports --- machine learning --- long short-term memory --- integrated energy systems --- smart energy management --- PV fleet --- clustering-based PV fault detection --- unsupervised learning --- self-imputation --- implicit model solution --- photovoltaic array --- series–parallel --- global optimization --- partial shading --- deterministic optimization algorithm --- metaheuristic optimization algorithm --- genetic algorithm --- solar cell optimization --- finite difference time domain --- optical modelling --- thermal image --- photovoltaic module --- hot spot --- image processing --- deterioration --- linear approximation --- MPPT algorithm --- duty cycle --- global horizontal irradiance --- mathematical modeling --- feed-forward neural networks --- recurrent neural networks --- LSTM cell --- performances evaluation --- clear sky irradiance --- persistent predictor --- photovoltaics --- artificial neural networks --- national power system

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This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators.

History of engineering & technology --- sensor network --- data fusion --- complex network analysis --- fault prognosis --- photovoltaic plants --- ANFIS --- statistical method --- gradient descent --- photovoltaic system --- sustainable development --- PV power prediction --- artificial neural network --- renewable energy --- environmental parameters --- multiple regression model --- moth-flame optimization --- parameter extraction --- photovoltaic model --- double flames generation (DFG) strategy --- Solar cell parameters --- single-diode model --- two-diode model --- COA --- photovoltaic systems --- maximum power point tracking --- single stage grid connected systems --- solar concentrator --- spectral beam splitting --- diffractive optical element --- diffractive grating --- PVs power output forecasting --- adaptive neuro-fuzzy inference systems --- particle swarm optimization-artificial neural networks --- solar irradiation --- photovoltaic power prediction --- publicly available weather reports --- machine learning --- long short-term memory --- integrated energy systems --- smart energy management --- PV fleet --- clustering-based PV fault detection --- unsupervised learning --- self-imputation --- implicit model solution --- photovoltaic array --- series–parallel --- global optimization --- partial shading --- deterministic optimization algorithm --- metaheuristic optimization algorithm --- genetic algorithm --- solar cell optimization --- finite difference time domain --- optical modelling --- thermal image --- photovoltaic module --- hot spot --- image processing --- deterioration --- linear approximation --- MPPT algorithm --- duty cycle --- global horizontal irradiance --- mathematical modeling --- feed-forward neural networks --- recurrent neural networks --- LSTM cell --- performances evaluation --- clear sky irradiance --- persistent predictor --- photovoltaics --- artificial neural networks --- national power system