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This Special Issue collects original research articles discussing cutting-edge work as well as perspectives on future directions in the whole range of theoretical and practical aspects in these research areas: i) Theory of fuzzy systems and soft computing; ii) Learning procedures; iii) Decision-making applications employing fuzzy logic and soft computing.

Research & information: general --- Mathematics & science --- fuzzy partition --- fuzzy transform --- new iterative method --- Cauchy problems --- ANFIS --- basmati rice --- image processing --- grading --- quality assessment --- fuzzy inference system --- causality --- statistics --- concept-mapping --- causal graph --- numerical methods --- systems of ordinary differential equations --- NIM --- fuzzy logics --- linear motion blur --- fuzzy deblurring --- electron beam calibration --- signal and image processing --- propagation modeling --- adaptive-network-based fuzzy system --- LoRa &amp --- LoRaWAN --- radio wave propagation --- artificial neural networks --- subtracting clustering --- dual double fuzzy semi-metric --- double fuzzy semi-metric --- fuzzy semi-metric space --- triangle inequality --- triangular norm --- eye gaze tracking --- interval type-2 fuzzy logic --- vision system --- mobile robots --- intelligent control --- ordered fuzzy number --- fuzzy relation --- preorder --- strict order --- equivalence relation --- variable neighborhood search --- experimental comparison --- statistical analysis --- traveling salesman problem --- soft computing --- modeling --- vector optimization --- methods of solution of vector problems --- optimal decision-making --- numerical realization of decision-making --- n/a

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Amidst impending climate change and enhanced pollution levels around the globe, the need of the hour is to develop bio-based materials that are sustainable and possess comparable performance properties to their synthetic counterparts. In light of the aforementioned, numerous investigations are being conducted to identify, process, and create materials that are concurrently innocuous towards the environment and have superior properties. This book is a collection of such scientific articles that propagate novel ideas for the development of polymeric composite materials, which have application potential in numerous fields such as medicine, automobile, aviation, construction, etc. It also contains a pedagogical article that proposes some strategies to continue experimental research during pandemics. This book will provide readers a quick glance into recent developments regarding polymeric materials and will encourage them to propagate these research ideas further.

solid urban waste --- formaldehyde --- durability --- electrical properties --- mechanical properties --- recycling --- epoxy resin --- flammability --- heat release rate --- microscale combustion calorimetry --- multiple linear regression --- adaptive neuro-fuzzy inference system --- polyvinyl alcohol (PVA) --- bionanocomposites --- nanomechanical behaviour --- thin films --- particle size --- model free --- model fitting --- avrami–eroféev --- DAEM --- superhydrophobic surfaces --- self-healing --- natural hierarchical microstructures --- wood --- bio-composite --- linear low density polyethylene --- performance --- straws --- biocomposites --- nanofibers --- electrospinning --- cell culture --- graphene oxide --- barrier properties --- poly(lactic acid) --- clay --- nanocomposite --- permeability --- bacterial cellulose --- metal organic framework --- adsorption --- chitosan --- composite nanofibers --- silk fibroin --- polycaprolactone --- Taguchi --- rheological properties --- DMA --- injection molding --- thermal properties --- natural fibers --- biochar --- carbon fillers --- nanocomposites --- flame retardants --- fire --- n/a --- PHB --- natural fiber --- compatibilizer --- cellulose --- biocomposite --- avrami-eroféev

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The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications.

sEMG --- deep learning --- neural networks --- gait phase --- classification --- everyday walking --- convolutional neural network --- CRISPR --- leukemia nucleus image --- segmentation --- soft covering rough set --- clustering --- machine learning algorithm --- soft computing --- multistage support vector machine model --- multiple imputation by chained equations --- SVM-based recursive feature elimination --- unipolar depression --- diabetic retinopathy (DR) --- pre-trained deep ConvNet --- uni-modal deep features --- multi-modal deep features --- transfer learning --- 1D pooling --- cross pooling --- IMU --- gait analysis --- long-term monitoring --- multi-unit --- multi-sensor --- time synchronization --- Internet of Medical Things --- body area network --- MIMU --- early detection --- sepsis --- evaluation metrics --- machine learning --- medical informatics --- feature extraction --- physionet challenge --- electrocardiogram --- Premature ventricular contraction --- sparse autoencoder --- unsupervised learning --- Softmax regression --- medical diagnosis --- artificial neural network --- e-health --- Tri-Fog Health System --- fault data elimination --- health status prediction --- health status detection --- health off --- diffusion tensor imaging --- ensemble learning --- decision support systems --- healthcare --- computational intelligence --- Alzheimer’s disease --- fuzzy inference systems --- genetic algorithms --- next-generation sequencing --- ovarian cancer --- interpretable models --- n/a --- Alzheimer's disease

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Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.

hybrid energy storage system --- plug-in hybrid electric vehicle --- Li-ion battery --- emerging electric machines --- lithium-ion capacitor --- electric vehicles (EVs) --- efficient energy management strategies for hybrid propulsion systems --- plug-in hybrid --- attributional --- electric vehicle --- energy system --- energy efficiency --- modified one-state hysteresis model --- air quality --- adaptive neuron-fuzzy inference system (ANFIS) --- Markov decision process (MDP) --- simulated annealing --- Paris Agreement --- mobility needs --- interleaved multiport converte --- dynamic programming --- state of health estimation --- strong track filter --- LCA --- modelling --- consequential --- losses model --- voltage vector distribution --- parallel hybrid electric vehicle --- electricity mix --- time-delay input --- convex optimization --- lifetime model --- artificial neural network (ANN) --- Li(Ni1/3Co1/3Mn1/3)O2 battery --- battery power --- CO2 --- capacity degradation --- regenerative braking --- open-end winding --- novel propulsion systems --- group method of data handling (GMDH) --- state of charge --- Well-to-Wheel --- energy storage systems --- including wide bandgap (WBG) technology --- wide bandgap (WBG) technologies --- marginal --- lithium polymer battery --- life-cycle assessment (LCA) --- energy management --- dual inverter --- lithium-ion battery --- measurements --- plug-in hybrid electric vehicles (PHEVs) --- emerging power electronics --- Q-learning (QL) --- fuel consumption characteristics --- Plugin Hybrid electric vehicle --- Energy Storage systems --- meta-analysis --- range-extender --- engine-on power --- reinforcement learning (RL) --- multi-objective genetic algorithm --- power sharing --- energy management strategy --- power distribution --- hybrid electric vehicles --- system modelling

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The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems.

Technology: general issues --- History of engineering & technology --- gait recognition --- biometrics --- regularized discriminant analysis --- particle swarm optimization --- grey wolf optimization --- whale optimization algorithm --- FMCW --- vital sign --- XGBoost --- MFCC --- COVID-19 --- 3D human pose estimation --- deep learning --- generalization --- optical sensing principle --- modular sensing unit --- plantar pressure measurement --- gait parameters --- 3D human mesh reconstruction --- deep neural network --- motion capture --- neural networks --- reconstruction --- gap filling --- FFNN --- LSTM --- BILSTM --- GRU --- pose estimation --- movement tracking --- computer vision --- artificial intelligence --- markerless motion capture --- assessment --- kinematics --- development --- machine learning --- human action recognition --- features fusion --- features selection --- recognition --- fall risk detection --- balance --- Berg Balance Scale --- human tracking --- elderly --- telemedicine --- diagnosis --- precedence indicator --- knowledge measure --- fuzzy inference --- rule induction --- posture detection --- aggregation function --- markerless --- human motion analysis --- gait analysis --- data augmentation --- skeletal data --- time series classification --- EMG --- pattern recognition --- robot --- cyber-physical systems --- RGB-D sensors --- human motion modelling --- F-Formation --- Kinect v2 --- Azure Kinect --- Zed 2i --- socially occupied space --- facial expression recognition --- facial landmarks --- action units --- convolutional neural networks --- graph convolutional networks --- artifact classification --- artifact detection --- anomaly detection --- 3D multi-person pose estimation --- absolute poses --- camera-centric coordinates --- deep-learning --- n/a