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Supply chains consist of all processes which are needed in order to supply customers with the required products. Traditionally, supply chain management decisions are based on the economic performance of the involved parties. But in recent years, other criteria, such as quality, flexibility or the environment, have become important as well. Especially carbon emissions are high on the political agenda because they are considered to be a major cause of the greenhouse gas effect. In this work it is shown how the performance of supply chains can be evaluated considering both economic and environmental criteria. In particular, the work deals with dual sourcing in the context of the newsvendor model. The impact of environmental regulations (emission taxes and emission trading) on the decisions of companies is analysed.

Business logistics --- Business --- Environmental aspects. --- Trade --- Economics --- Management --- Commerce --- Industrial management --- Supply chain management --- Logistics --- Dual --- Economic --- emission taxes --- emission trading --- Environmental --- Newsvendor model --- offshore supplier --- onshore supplier --- pareto-optimal solution --- Rosic --- Sourcing --- Sustainabilit --- Sustainability

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Electromagnetism plays a crucial role in basic and applied physics research. The discovery of electromagnetism as the unifying theory for electricity and magnetism represents a cornerstone in modern physics. Symmetry was crucial to the concept of unification: electromagnetism was soon formulated as a gauge theory in which local phase symmetry explained its mathematical formulation. This early connection between symmetry and electromagnetism shows that a symmetry-based approach to many electromagnetic phenomena is recurrent, even today. Moreover, many recent technological advances are based on the control of electromagnetic radiation in nearly all its spectra and scales, the manipulation of matter–radiation interactions with unprecedented levels of sophistication, or new generations of electromagnetic materials. This is a fertile field for applications and for basic understanding in which symmetry, as in the past, bridges apparently unrelated phenomena―from condensed matter to high-energy physics. In this book, we present modern contributions in which symmetry proves its value as a key tool. From dual-symmetry electrodynamics to applications to sustainable smart buildings, or magnetocardiography, we can find a plentiful crop, full of exciting examples of modern approaches to electromagnetism. In all cases, symmetry sheds light on the theoretical and applied works presented in this book.

History of engineering & technology --- electromagnetic knots --- helicity --- spin-orbital momentum --- magnetocardiography --- quadratic penalty --- variational mode decomposition --- correlation coefficient --- interval thresholding method --- periodic structures --- dispersion diagram --- high-order coupling --- glide symmetry --- smart building --- harmonics --- geometric algebra --- Poynting Multivector --- electric-magnetic duality symmetry --- quantum anomalies --- optical helicity --- electromagnetic polarization --- particle creation --- Maxwell theory --- constraint equations --- evolutionary equations --- Barium hexaferrite --- titanium --- hysteresis --- X-ray diffraction --- permanent magnet applications --- n/a --- hopfion --- Bateman construction --- null fields --- magnetic levitation --- electrodynamic structure --- ground high speed system --- finite element analysis --- non-local action --- electrodynamics --- electromagnetic duality symmetry --- Aharonov-Bohm effect --- Harvesting --- low-power applications --- vibration --- micro-generator --- optimal solution --- magnetic circuit --- periodical structure --- effective power density --- symmetry

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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

Research & information: general --- water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk

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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk