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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti–Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure–property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- n/a --- Ti-Mn alloys --- structure-property relationship

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This reprint is a continuation of our previous reprint “Sustainable agricultural, biological, and environmental engineering applications” [ISBN 978-3-0365-2921-9], which was published in January 2022. The reprint contains research and review works focused on agricultural engineering technologies and applications. In this regard, the reprint covers topics including agricultural storage, quality evaluation of fruits, evaporative cooling and desiccant systems, solar coffee roasting, solar yogurt processing, greenhouse environment, greenhouse ventilation, greenhouse thermal requirements, aquaculture production system, bioreactor landfill, waste management, fertilization reduction in agriculture, sustainable porous surfaces, simulation and modeling, artificial intelligence, and machine learning. Such agricultural engineering studies are urgently needed in the 21st century, particularly from the perspective of the agriculture–energy–food security nexus.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- greenhouse --- microclimate --- Bayesian optimization --- deep neural network --- roses yield --- Gaussian process --- gradient boosting --- pool boiling heat transfer coefficient --- sintered coated porous surfaces --- gaussian process --- gradient boosting regression trees --- response surface --- renovation index --- CFD simulation --- airflow --- evaporative cooling --- desiccant dehumidification --- agricultural storage --- air conditioning --- system performance --- lemon cordial --- microwave --- preservation --- green processing --- antioxidant potential --- renewable energy --- Scheffler concentrator reflector --- batch-type solar roaster --- response surface methodology --- coffee roasting --- municipal solid waste --- sanitary landfill --- open dumps --- waste to energy --- climate change --- yogurt processing --- solar energy --- solar-based heating and cooling --- thermal analysis --- vegetable yield --- nitrogen use efficiency --- nutrient leaching --- leaching-to-input ratio --- nitrogen fertilizer economic benefit --- environment --- eutrophication --- particulate fraction --- effluent --- treatment --- thermal screens --- heating demand --- TRNSYS --- greenhouse internal temperature --- building energy simulation --- longwave radiation --- soil total nitrogen --- BP neural network --- support vector machines --- spatial distribution --- remote sensing

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The intense development of novel data-driven and hybrid methods for structural health monitoring (SHM) has been demonstrated by field deployments on large-scale systems, including transport, wind energy, and building infrastructure. The actionability of SHM as an essential resource for life-cycle and resilience management is heavily dependent on the advent of low-cost and easily deployable sensors Nonetheless, in optimizing these deployments, a number of open issues remain with respect to the sensing side. These are associated with the type, configuration, and eventual processing of the information acquired from these sensors to deliver continuous behavioral signatures of the monitored structures. This book discusses the latest advances in the field of sensor networks for SHM. The focus lies both in active research on the theoretical foundations of optimally deploying and operating sensor networks and in those technological developments that might designate the next generation of sensing solutions targeted for SHM. The included contributions span the complete SHM information chain, from sensor design to configuration, data interpretation, and triggering of reactive action. The featured papers published in this Special Issue offer an overview of the state of the art and further proceed to introduce novel methods and tools. Particular attention is given to the treatment of uncertainty, which inherently describes the sensed information and the behavior of monitored systems.

Technology: general issues --- probabilistic data-interpretation --- Bayesian model updating --- error-domain model falsification --- iterative asset-management --- practical applicability --- computation time --- swarm-based parallel control (SPC) --- Internet of Things (IoT) --- soil–structure interaction (SSI) --- semi-active control --- adjacent buildings --- Bayesian inference --- model updating --- modal identification --- structural dynamics --- bridges --- sensor placement optimisation --- structural health monitoring --- damage identification --- mutual information --- evolutionary optimisation --- inertial sensor fusion --- instrumented particle --- MEMS --- sediment entrainment --- sensor calibration --- frequency of entrainment --- varying environmental and operational conditions --- damage detection and localization --- Gaussian process regression --- autoregressive with exogenous inputs --- distributed sensor network --- mode shape curvatures --- n/a --- soil-structure interaction (SSI)

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The intense development of novel data-driven and hybrid methods for structural health monitoring (SHM) has been demonstrated by field deployments on large-scale systems, including transport, wind energy, and building infrastructure. The actionability of SHM as an essential resource for life-cycle and resilience management is heavily dependent on the advent of low-cost and easily deployable sensors Nonetheless, in optimizing these deployments, a number of open issues remain with respect to the sensing side. These are associated with the type, configuration, and eventual processing of the information acquired from these sensors to deliver continuous behavioral signatures of the monitored structures. This book discusses the latest advances in the field of sensor networks for SHM. The focus lies both in active research on the theoretical foundations of optimally deploying and operating sensor networks and in those technological developments that might designate the next generation of sensing solutions targeted for SHM. The included contributions span the complete SHM information chain, from sensor design to configuration, data interpretation, and triggering of reactive action. The featured papers published in this Special Issue offer an overview of the state of the art and further proceed to introduce novel methods and tools. Particular attention is given to the treatment of uncertainty, which inherently describes the sensed information and the behavior of monitored systems.

probabilistic data-interpretation --- Bayesian model updating --- error-domain model falsification --- iterative asset-management --- practical applicability --- computation time --- swarm-based parallel control (SPC) --- Internet of Things (IoT) --- soil–structure interaction (SSI) --- semi-active control --- adjacent buildings --- Bayesian inference --- model updating --- modal identification --- structural dynamics --- bridges --- sensor placement optimisation --- structural health monitoring --- damage identification --- mutual information --- evolutionary optimisation --- inertial sensor fusion --- instrumented particle --- MEMS --- sediment entrainment --- sensor calibration --- frequency of entrainment --- varying environmental and operational conditions --- damage detection and localization --- Gaussian process regression --- autoregressive with exogenous inputs --- distributed sensor network --- mode shape curvatures --- n/a --- soil-structure interaction (SSI)

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The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories—regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges.

Technology: general issues --- History of engineering & technology --- distribution system --- microgrids --- power quality --- power system management --- power system reliability --- smart grids --- distribution networks --- Monte Carlo simulations --- PV hosting capacity --- photovoltaics --- green communities --- energy independence --- HOMER --- wind turbines --- power losses --- power system optimization --- PV curves --- DG --- TSA/SCA --- solar-powered electric vehicle parking lots --- different PV technologies --- PLO's profit --- uncertainties --- smart grid paradigm --- distributed generation --- model-based predictive control --- robustness --- worst-case scenario --- min-max optimisation --- intraday forecasting --- Gaussian process regression --- machine learning --- off-grid system --- composite control strategy --- solar photovoltaic panel --- wind turbine --- diesel generator --- energy storage system (ESS) --- synchronous machine (SM) --- permanent magnet brushless DC machine (PMBLDCM) --- power quality improvement