Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization
The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mitigating the voltage excursions. This paper proposes a two-timescale hybrid voltage control strategy based on a mixed-integer optimization method and multi-agent reinforcement learning (MARL) to reduce power loss and mitigate voltage violations. In the slow-timescale, the active and reactive power optimization problem involving capacitor banks (CBs), on-load tap changers (OLTC), and ES systems is formulated as a mixed-integer second-order cone programming problem. In the fast-timescale, the reactive power of smart inverters connected to solar photovoltaic systems and active power of EVs are adjusted to mitigate short-term voltage fluctuations with a MARL algorithm. Specifically, we propose an experience augmented multi-agent actor-critic (EA-MAAC) algorithm with an attention mechanism to learn high-quality control policies. The control policies are executed online in a decentralized manner. The proposed hybrid voltage control strategy is validated on an IEEE testing distribution feeder. The numerical results show that our proposed control strategy is not only sample-efficient and robust but also effective in mitigating voltage fluctuations.
Download PDF: https://exemples.eu.org/fkkhFT
Incorporating Scientific Applications Into Engineering Education Through Interactive Simulation Software
Many engineering courses primarily emphasize theoretical instruction, which not only impedes the development of students' practical skills but also diminishes their engagement. Various educational reforms have been introduced to enhance the real‐ world applicability and interactive nature of the classroom. However, these reforms often impose additional time and cognitive burdens on students. This study explores an approach to enhance teaching effectiveness and student engagement by combining extended knowledge with interactive simulation software. Specifically, we incorporate emerging photoacoustic computed tomography (PACT) technology into a university‐level Signals and Systems course as a research case. The potential of this educational approach was ass essed using Likert scale surveys, which indicated a promising improvement in students' understanding of complex concepts when utilizing interactive simulation software. Additionally, positive feedback from students further supports these findings. This study provides a comprehensive account of the implementation and potential advantages of these educational innovations, offering valuable insights for future developments. 1 | Introduction There is a noticeable gap between engineering education and labor market demands, resulting in several social and educational challenges. Engineering education at many universities has not kept pace with the evolving skills required in the workplace, leaving graduates unprepared for empl
Download PDF: https://csurat.eu.org/5uyifT
Sports Analytics for IPL Auctions Machine Learning-Based Player Valuation Using Performance Data
–This research presents a predictive model developed in R to estimate the auction prices of Indian Premier League (IPL) players, based on their performance data and roles. Key performance indicators, such as runs, strike rate, wickets, and economy rate, were analyzed to uncover trends linking player statistics to their market value. The model aims to reduce biases in auction decisions by providing a data-driven approach, offering franchises a more strategic tool for decisionmaking. This study highlights the integration of machine learning techniques in sports analytics to improve the accuracy and fairness of player valuations during IPL auctions.
Download PDF: https://cilasu.eu.org/nnsli8
Antimony sources and mobilization in environmental matrices surrounding the world’s largest Sb mine: evidence from mineralogy and Sb isotope signatures
China produces 48.2% of global antimony (Sb) annually, of which approximately 80% was produced in Southwest China in 2023. The distribution of high-Sb geological background areas in China overlaps with that of karst landforms. The external causes of the exploitation and utilization of Sb resources and the internal causes of the special high geochemical background have led to Sb pollution in the natural ecosystems in this area. This study selected the Xikuangshan (XKS) Sb mine in Hunan Province, a typical Sb deposit in the karst areas of China, as an example to quantitatively identify pollution sources and influencing factors of Sb in the environment around the Sb mining area. Based on the TESCAN integrated mineral analyzer analysis, the major mineralogical compone nts of the representational soil sample were quartz (59.80%), kaolinite (9.93%), calcite (6.08%), albite (3.41%), chlorite-clinochlore (2.89%), clay (1.99%), dolomite (1.81%), and hematite (Fe2O3)/magnetite (Fe3O4) (1.67%). Antimony was found to be mainly rich in Fe2O3/Fe3O4, nepheline (NaAlSiO4), and minerals consisting of O-Al-Si or O-S-Fe in the soil. Based on microscopic X-ray fluorescence analysis, Sb showed similar in situ distribution and enrichment characteristics to those of Al, Fe, S, and Si. The characteristics of the Sb isotope of the environmental samples from the XKS Sb mine were measured for the first time. A large variation in δ123Sb values and chemical species fractions of Sb in soil samples suggests that Sb in soils from different geographical location may have different origins or migration behaviors. The Sb isotope composition of soil can be divided into four endmembers: atmospheric deposition, tailing leaching, river water surface Supplementary Informat ion The online version contains supplementary material available at https://doi. org/10.1007/s10653-025-02711-6. T. Shi (*) · M. Sun · Y. Ji · C. Wu (*) School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China e-mail: shitaoran@126.com C. Wu e-mail: wchf1680@sina.com W. Guo Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China G. Sun State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China J. Li Institute of Eco‑Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China J. Ma State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China Environ Geochem Health (2025) 47:396 396 Page 2 of 18 Vol:. (1234567890) runoff, and rock oxidative weathering. The differences in hydrochemical types and Sb isotopic signatures among river waters indicate multiple sources and factors influencing Sb migration in different locations. The Sb in river water may have been partly influenced by rock leaching. Sweet potato exhibited a similar Sb isotopic signature (δ123Sb = 0.24‰) with surrounding soil (δ123Sb = 0.22‰), which demonstrated that the sweet potato absorbed Sb through contaminated soil to the root. Soil erosion and tailings are the major sources of Sb in street dust. A conceptual model was established to elucidate the pollution sources and the main geochemical processes affecting the mobilization of Sb in environmental matrices in the XKS mining area. This study provides a scientific basis for environmental quality assessment of Sb mining areas and establishment of an effective early warning system for Sb pollution in soil.
Download PDF: https://brasmi.eu.org/nLjzgv< /a>
=?UTF-8?B?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 bnQgZGViYXRlIGFzIHRvIHRoZSBsZXZlbCBvZiB0cnVzdCB3ZSBzaG91bGQgcGxhY2UgaW4gdGhlc2UgbmV3IHRlY2hub2xvZ2llcy4gNiBXaXRoIERPSTogMTAuMTA5Ny9CU0QuMDAwMDAwMDAwMDAwMTcyNA==?=
https://doi.org/10.1038/s41560-026-01965-3 Perspective Irreversible organic cations chemistry limits organic–inorganic halide perovskite stability under illumination or bias
(Abstract not found) Download PDF: https://soald.eu.org/2x2IGR
