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Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics. To identify UQ problems, diverse modern techniques for large and complex data analyses have been developed in applied mathematics, computer science, and statistics. This Special Issue of Mathematics (ISSN 2227-7390) includes diverse modern data analysis methods such as skew-reflected-Gompertz information quantifiers with application to sea surface temperature records, the performance of variable selection and classification via a rank-based classifier, two-stage classification with SIS using a new filter ranking method in high throughput data, an estimation of sensitive attribute applying geometric distribution under probability proportional to size sampling, combination of ensembles of regularized regression models with resampling-based lasso feature selection in high dimensional data, robust linear trend test for low-coverage next-generation sequence data controlling for covariates, and comparing groups of decision-making units in efficiency based on semiparametric regression.

Kullback–Leibler divergence --- geometric distribution --- accuracy --- AUROC --- allele read counts --- mixture model --- low-coverage --- entropy --- gene-expression data --- SCAD --- data envelopment analysis --- LASSO --- high-throughput --- sandwich variance estimator --- adaptive lasso --- semiparametric regression --- ?1 lasso --- Laplacian matrix --- elastic net --- feature selection --- sea surface temperature --- gene expression data --- Skew-Reflected-Gompertz distribution --- lasso --- next-generation sequencing --- BH-FDR --- stochastic frontier model --- ?2 ridge --- geometric mean --- resampling --- Gompertz distribution --- adapative lasso --- group efficiency comparison --- sensitive attribute --- MCP --- probability proportional to size (PPS) sampling --- randomization device --- SIS --- Yennum et al.’s model --- ensembles

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The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased.

high-dimensional --- nonlocal prior --- strong selection consistency --- estimation consistency --- generalized linear models --- high dimensional predictors --- model selection --- stepwise regression --- deep learning --- financial time series --- causal and dilated convolutional neural networks --- nuisance --- post-selection inference --- missingness mechanism --- regularization --- asymptotic theory --- unconventional likelihood --- high dimensional time-series --- segmentation --- mixture regression --- sparse PCA --- entropy-based robust EM --- information complexity criteria --- high dimension --- multicategory classification --- DWD --- sparse group lasso --- L2-consistency --- proximal algorithm --- abdominal aortic aneurysm --- emulation --- Medicare data --- ensembling --- high-dimensional data --- Lasso --- elastic net --- penalty methods --- prediction --- random subspaces --- ant colony system --- bayesian spatial mixture model --- inverse problem --- nonparamteric boostrap --- EEG/MEG data --- feature representation --- feature fusion --- trend analysis --- text mining

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This Special Issue contains a series of excellent research works on telecommunications and signal processing, selected from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP) which was held on July 4–6, 2018, in Athens, Greece. The conference was organized in cooperation with the IEEE Region 8 (Europe, Middle East, and Africa), IEEE Greece Section, IEEE Czechoslovakia Section, and IEEE Czechoslovakia Section SP/CAS/COM Joint Chapter by seventeen universities from the Czech Republic, Hungary, Turkey, Taiwan, Japan, Slovak Republic, Spain, Bulgaria, France, Slovenia, Croatia, and Poland, for academics, researchers, and developers, and serves as a premier international forum for the annual exchange and promotion of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors worldwide. This collection of 10 papers is highly recommended for researchers, and believed to be interesting, inspiring, and motivating for readers in their further research.

similarity measure --- dynamic time warping --- n/a --- Least Absolute Shrinkage and Selection Operator (LASSO) --- multispectral information --- transmission convergence layer --- 3D segmentation --- micrographia --- MATLAB --- neural network --- wireless communication --- identification --- interference alignment --- Parkinson’s disease dysgraphia --- NG-PON2 --- timing --- GPON --- semantic segmentation --- fractional-order filters --- maximum likelihood criterion --- kinematic analysis --- multitemporal data --- fractional calculus --- multi-hop relay network --- u-net --- interference leakage --- Richardson iteration --- activation process --- acoustic analysis --- follow-up study --- fractional-order derivative --- electrocardiogram (ECG) --- deep learning --- security --- modulo M quasi-stationary --- cognitive radio --- low-pass filters --- time-interleaved analog-to-digital converter (TIADC) --- sample-and-hold (S/H) mismatch --- authentication --- pattern recognition --- online handwriting --- sparse inference --- Taylor series --- EPON --- open-source --- spine --- machine learning --- brain --- signal representation --- magnitude responses --- Chebyshev filters --- XG-PON --- phonation --- hypokinetic dysarthria --- Parkinson’s disease --- overcomplete multi-scale dictionary construction --- Parkinson's disease dysgraphia --- Parkinson's disease

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity

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This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader.

wind turbine --- electric generator --- spectral analysis --- fault diagnosis --- photovoltaic power forecasting --- data-driven --- deep learning --- variational autoencoders --- RNN --- angle swinging --- grid frequency oscillations --- electromechanical system --- inertial masses --- microgrids --- coordination protection --- distributed generation --- photovoltaic resources --- DigSILENT --- photovoltaic module --- defect detection --- power plant --- efficiency --- thermal image --- photovoltaic aging --- dark I-V curves --- bidirectional power inverter --- online distributed measurement of dark I-V curves --- sustainability --- compressive strength --- Bolomey formula --- sustainable concrete --- glass powder --- solar cell --- solar panel --- parameter extraction --- analytical --- Lambert W-function --- spacecraft solar panels --- I-V curve --- modeling --- wind power --- non-conventional renewable energy --- forecasting --- energy bands --- combinatorial optimization --- deep learning (DL) --- unmanned aerial vehicle (UAV) --- photovoltaic (PV) systems --- image-processing --- image segmentation --- semantic segmentation --- faults diagnostic --- artificial intelligence --- unbalanced datasets --- synthetic data --- artificial neural network based MPPT --- hybrid boost converter --- renewable energies --- solar power system --- microgrid --- control system --- storage system --- primary control --- photovoltaic (PV) plants --- coverage path planning (CPP) --- corrosion monitoring --- FPGA --- offshore wind turbines --- ultrasound --- thickness loss --- SCADA --- visualisation --- software --- wind-turbine --- windfarm --- cross-platform --- HMI --- GUI --- corrosion --- monitoring --- photovoltaic systems --- expected energy models --- fleet-scale --- lasso regression --- performance modeling --- machine learning --- fault location in photovoltaic arrays --- failure modes simulation --- fault detection criterion --- adaptive protection --- distributed power generation --- power distribution --- power system protection