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The renewable energy sector is one of the fastest growing branches of the economy in the world, including in Poland. Extensive investigation in research centers results in the increased efficiency of obtaining energy from renewable sources, as well as a decrease in the prices of renewable energy installations. The development of renewable energy motivates further research and the development of new technologies. Investments in renewable energy may also benefit the local community by increasing the attractiveness of the region to tourists, creating opportunities for professional activation (especially in areas with high unemployment), increasing the competitiveness of the local economy and its energy efficiency and obtaining raw materials from local producers, mainly farmers, which are an additional source of income for them. Another possible economic advantage is charging lease fees, for instance, for land under wind turbines or fees for ground easement, in order to ensure access to the construction of power lines, e.g., connecting turbines to the grid; lowering heat prices for residents of a given town; building investment plots in or near heat plants and biogas plants, with the provision of heat and electricity at competitive prices directly from these plants; investors covering the costs of modernizing local roads; and creating new transmission, power lines and supply points.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- waste management --- energy recovery --- model of energy recovery --- biogas --- fermentation --- combustion --- mini-grids --- energy access --- energy sustainability --- SDG 7 --- energy affordability --- green growth --- sustainable development --- environmental production --- relationships --- multicriteria taxonomy --- renewable energy sources --- household --- primary solid biofuels --- solar thermal system --- ambient pumps --- : CSR strategy --- financial performance --- energy sector --- : gross electricity production --- renewable sources --- energy transformation --- concentrationanalysis --- cluster analysis --- k-means --- European Union --- renewable energy sources (RES) --- the new EU member states --- Ward’s method: alternative energy sources --- photovoltaic systems --- wind systems --- hydropower systems --- biomass systems ---  

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An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.

Technology: general issues --- History of engineering & technology --- entrained flow coal gasification --- ash melting point --- operation temperature --- Markov process --- stochastic optimization model --- genetic algorithm --- gallium nitride --- magnetic-free converters --- module-level converters --- parallel architecture --- partial shading --- photovoltaic systems --- switched capacitor converters --- hybrid energy storage system --- supercapacitor --- lead–acid battery --- energy management system --- battery degradation --- depth of discharge --- techno-economic analysis --- hybrid power station --- green island --- energy storage --- remote community --- reserves --- k-means --- probabilistic dimensioning --- dynamic dimensioning --- balancing --- wave energy converters --- deep neural networks --- renewable energy sources --- spatial planning --- sentinel satellite imagery --- permanent magnet synchronous machines --- generators --- fault detection --- demagnetization --- artificial intelligence --- data mining --- machine learning --- advanced deep learning --- windspeed forecasting --- solar irradiation forecasting --- increased RES penetration --- smart grid --- scalability --- replicability --- FLEXITRANSTORE --- Angolan economy --- diversification --- strategic alternative --- biofuels

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

Medicine --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a

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Metalliferous minerals play a central role in the global economy. They will continue to provide the raw materials we need for industrial processes. Significant challenges will likely emerge if the climate-driven green and low-carbon development transition of metalliferous mineral exploitation is not managed responsibly and sustainably. Green low-carbon technology is vital to promote the development of metalliferous mineral resources shifting from extensive and destructive mining to clean and energy-saving mining in future decades. Global mining scientists and engineers have conducted a lot of research in related fields, such as green mining, ecological mining, energy-saving mining, and mining solid waste recycling, and have achieved a great deal of innovative progress and achievements. This Special Issue intends to collect the latest developments in the green low-carbon mining field, written by well-known researchers who have contributed to the innovation of new technologies, process optimization methods, or energy-saving techniques in metalliferous minerals development.

Technology: general issues --- History of engineering & technology --- Mining technology & engineering --- metallurgical slag-based binders --- solidification/stabilisation --- As(III) --- As(V) --- calcium hydroxide --- sublevel caving --- numerical simulation --- physical model --- structural parameter --- green mining --- limestone --- high temperature --- confining pressure --- SHPB --- constitutive model --- open-pit mine --- PLAXIS 3D --- dynamic load --- safety factor --- acceleration --- particle sedimentation --- filling mining --- degree of influence --- pipeline transportation --- solid waste utilization --- tailings --- reclamation risk --- hazard identification --- complex network --- hazard management --- digital mine --- mine short-term production planning --- haulage equipment dispatch plan --- ABCA --- NSGA --- settlement velocity measurement --- K-means --- tailings backfill --- unsupervised learning --- cemented paste backfill --- ESEM --- picture processing --- floc networks --- pumping agent --- fractal dimension --- backfill slurry --- strength of cemented backfill --- inhomogeneity of cemented backfill --- cemented tailings backfill --- copper --- zinc --- recovery --- sulfide concentrate --- artificial microbial community --- granular backfill --- bearing characteristics --- numerical model --- particle size --- surface subsidence --- blasting dust movement --- dust concentration --- particle size distribution --- blasting dust reduction --- backfill --- metal mine --- log-sigmoid --- tailings pond --- regional distribution --- dam break --- accident statistics --- causation analysis --- backfilling --- increasing resistance and reducing pressure --- computational fluid dynamics --- spiral pipe --- stowing gradient --- coal-based solid waste --- orthogonal experiment --- strength development --- regression analysis --- engineering performance --- n/a

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The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.

Technology: general issues --- smart grid --- nontechnical losses --- electricity theft detection --- synthetic minority oversampling technique --- K-means cluster --- random forest --- smart grids --- smart energy system --- smart meter --- GDPR --- data privacy --- ethics --- multi-label learning --- Non-intrusive Load Monitoring --- appliance recognition --- fryze power theory --- V-I trajectory --- Convolutional Neural Network --- distance similarity matrix --- activation current --- electric vehicle --- synthetic data --- exponential distribution --- Poisson distribution --- Gaussian mixture models --- mathematical modeling --- machine learning --- simulation --- Non-Intrusive Load Monitoring (NILM) --- NILM datasets --- power signature --- electric load simulation --- data-driven approaches --- smart meters --- text convolutional neural networks (TextCNN) --- time-series classification --- data annotation --- non-intrusive load monitoring --- semi-automatic labeling --- appliance load signatures --- ambient influences --- device classification accuracy --- NILM --- signature --- load disaggregation --- transients --- pulse generator --- smart metering --- smart power grids --- power consumption data --- energy data processing --- user-centric applications of energy data --- convolutional neural network --- energy consumption --- energy data analytics --- energy disaggregation --- real-time --- smart meter data --- transient load signature --- attention mechanism --- deep neural network --- electrical energy --- load scheduling --- satisfaction --- Shapley Value --- solar photovoltaics --- review --- deep learning --- deep neural networks --- n/a