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The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies.

cloud computing --- container orchestration --- custom metrics --- Docker --- edge computing --- Horizontal Pod Autoscaling (HPA) --- Kubernetes --- Prometheus --- resource metrics --- fog computing --- task allocation --- multi-objective optimization --- evolutionary genetics --- hyper-angle --- crowding distance --- containers --- leader election --- load balancing --- stateful --- multi-access edge computing --- orchestrator --- task offloading --- fuzzy logic --- 5G --- fog/edge computing --- service provisioning --- service placement --- service offloading --- Internet of Things (IoT) --- task scheduling --- markov decision process (MDP) --- deep reinforcement learning (DRL) --- resource management --- algorithm classification --- evaluation framework --- web --- Web Assembly --- OpenCL --- LWC --- fast implementation --- Internet of things --- IoT actor --- data manager --- GDPR --- computing --- computational offloading --- dynamic offloading threshold --- minimizing delay --- minimizing energy consumption --- maximizing throughputs --- n/a

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Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area.

Technology: general issues --- electricity load forecasting --- smart grid --- feature selection --- Extreme Learning Machine --- Genetic Algorithm --- Support Vector Machine --- Grid Search --- AMI --- TL --- SG --- NB-PLC --- fog computing --- green community --- resource allocation --- processing time --- response time --- green data center --- microgrid --- renewable energy --- energy trade contract --- real time power management --- load forecasting --- optimization techniques --- deep learning --- big data analytics --- electricity theft detection --- smart grids --- electricity consumption --- electricity thefts --- smart meter --- imbalanced data --- data-intensive smart application --- cloud computing --- real-time systems --- multi-objective energy optimization --- renewable energy sources --- wind --- photovoltaic --- demand response programs --- energy management --- battery energy storage systems --- demand response --- scheduling --- automatic generation control --- single/multi-area power system --- intelligent control methods --- virtual inertial control --- soft computing control methods --- n/a

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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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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.

Information technology industries --- 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

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This book, consisting of 21 articles, including three review papers, written by research groups of experts in the field, considers recent research on reinforced polymer composites. Most of them relate to the fiber-reinforced polymer composites, which are a real hot topic in the field. Depending on the reinforcing fiber nature, such composites are divided into synthetic and natural fiber-reinforced ones. Synthetic fibers, such as carbon, glass, or basalt, provide more stiffness, while natural fibers, such as jute, flax, bamboo, kenaf, and others, are inexpensive and biodegradable, making them environmentally friendly. To acquire the benefits of design flexibility and recycling possibilities, natural reinforcers can be hybridized with small amounts of synthetic fibers to make them more desirable for technical applications. Elaborated composites have great potential as structural materials in automotive, marine and aerospace application, as fire resistant concrete, in bridge systems, as mechanical gear pair, as biomedical materials for dentistry and orthopedic application and tissue engineering, as well as functional materials such as proton-exchange membranes, biodegradable superabsorbent resins and polymer electrolytes.

Technology: general issues --- glass fibers --- surface modification --- polyethersulfone --- impregnation --- composite materials --- mechanical properties --- damping properties --- stability --- 3D printing --- composites --- DLP --- lignocellulose --- nanoindentation --- fiber-reinforced polymer --- natural fibers --- synthetic fibers --- PET fiber --- PP --- compatibility --- modification --- co-injection molding --- fiber reinforced plastics (FRP) --- fiber orientation distribution (FOD) --- micro-computerized tomography (μ-CT) scan technology --- bearing --- salt fog aging --- glass-flax hybrid coposites --- pinned joints --- failure modes --- polymer-matrix composites --- carbon fibers --- polysulfone --- rubber --- short jute fibers --- surface treatments --- scanning electron microscopy --- PVA --- CMC --- Na2CO3 --- film --- hydrogel mechanical properties --- nanocomposites --- double-network hydrogels --- polymer–nanoparticle interactions --- bamboo-plastic composites (BPCs) --- waste bamboo fibers --- chemical composition --- physico-mechanical properties --- thermal decomposition kinetics --- PEEK composites --- reinforcements --- self-lubricating bush --- friction and wear --- pin joints --- flat slab --- two-way shear --- carbon fiber reinforced polymers --- glass fiber reinforced polymers --- natural rubber --- maleated natural rubber --- palm stearin --- halloysite nanotubes --- heat treatment --- surface modification of staple carbon fiber --- natural rubber latex --- reinforcement mechanism --- dopamine --- rubber composite --- bifunctionally composite --- sulfonic acid based proton exchange membrane --- silica nanofiber --- mechanical stability --- high temperature fuel cell --- polyetherimide --- polycarbonate --- polyphenylene sulfone --- kenaf fibre --- glass fibre --- hybrid composites --- low velocity impact --- damage progression --- bamboo --- n/a --- poly (lactic acid) (PLA) --- wastes rubber --- recycling --- tensile properties --- polymer-nanoparticle interactions