Research on the optimal operation of a prosumer micro energy network centred on data centres by Yuanshi Zhang, Yiwu Ge, Shunjiang Wang, Weiqi Pan, Yiwen Feng, Peng Qiu

“…By considering data interaction and power sharing among multiple micro‐energy networks, the alternating direction method of multipliers algorithm is used to solve the system‐distributed optimization problem in two stages. …”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…In manufacturing enterprises, the variety of heat treatment processes, insufficient data in the early stages of production, and the continuous increase of samples over time make it difficult to predict and optimize carbon emissions dynamically. Therefore, this paper proposes a low-carbon optimization method for heat treatment processes based on incremental data-driven approaches. …”
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Optimization Of Shell And Tube Cycle Recuperator Design In Organic Rankine Geothermal Power Plant by Muhammad Rifqi Dwi Septian

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Optimization planning of new rural multi-energy distribution network based on fuzzy algorithm by Huanhuan Ye, Qing Wang, Yongsheng Xian, Bo Wen, Yuange Li, Siwei Hou

“…Existing methods are difficult to cope with the volatility and uncertainty of energy sources, resulting in uneven load distribution, high energy loss and low system efficiency. …”
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A new approach for monitoring spatial and temporal changes in forest types in subtropical regions with sample migration and multi-source remote sensing data by Pengfei Zheng, Dongyang Han, Jiang Liu, Bin Xu, Panfei Fang, Shaodong Huang, Wendou Liu, Shaozhi Chen

“…We then conducted a change detection analysis using a post-classification comparison method. We propose a novel workflow for processing ground survey data to generate stable reference samples, which are then used with a transfer learning approach to construct a multi-year sample library. …”
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Multi-Time Scale Coordinated Optimization of Energy Systems Under Flexible Load Response by Jinfeng Gao, Daifeng Gao, Chun Xiao

“…To address these challenges, this study investigates multi-time scale collaborative optimization of energy systems based on flexible load response, utilizing a combination of qualitative and quantitative methods. …”
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Optimizing integration techniques for UAS and satellite image data in precision agriculture — a review by Aliasghar Bazrafkan, C. Igathinathane, Nonoy Bandillo, Paulo Flores

“…This review explores the significance of integrating high-resolution UAS and satellite imagery via pixel-based, feature-based, and decision-based fusion methods. The study investigates optimization techniques, spectral synergy, temporal strategies, and challenges in data fusion, presenting transformative insights such as enhanced biomass estimation through UAS-satellite synergy, improved nitrogen stress detection in maize, and refined crop type mapping using multi-temporal fusion. …”
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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Hyperparameter optimization using Bayesian methods enhances predictive performance. …”
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Sunflower-based butterfly optimization algorithm with enhanced RNN for the harmonics elimination in multilevel inverter by V. Mohan, G. Krithiga, M. Thamil Alagan, V. Sathya

“…In this paper, the Sunflower based– Butterfly Optimization Algorithm (SF-BOA) is presented as a method for evaluating transcendental nonlinear equations using an MLI in a SHE approaches. …”
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Search direction optimization of power flow analysis based on physics-informed deep learning by Baoliang Li, Qiuwei Wu, Yongji Cao, Changgang Li

“…Power flow analysis is crucial for obtaining power system operation states and optimizing control measures. The increasing integration of renewable energy sources has resulted in a more complex power system, posing challenges to the computational efficiency and convergence of conventional power analysis methods. …”
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A GD-PSO Algorithm for Smart Transportation Supply Chain ABS Portfolio Optimization by Yingjia Sun, Hongfeng Ren

“…Different from forward selection or linear optimization, which could have low efficiency for complicated problems with large sample size and multiple objectives, new methods and algorithms for NP-hard problems would be necessary to be investigated. …”
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Research on optimization technology of new pipeline design for regional natural gas pipeline network by Jingyi CUI, Kunfeng ZHU, Cuixian GAO, Li GU, Jing REN, Yuxing LI, Wuchang WANG

“…To meet future gas demand and enhance the efficiency of pipeline design, it is essential to review and analyze existing pipeline planning and design methods, as well as optimization algorithms. There is an urgent need to develop a set of design optimization methods suitable for complex pipeline network systems with multiple gas sources, numerous users, and aging pipeline infrastructures. …”
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Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations by Panagiotis Michailidis, Iakovos Michailidis, Elias Kosmatopoulos

“…The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. …”
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Chance-constrained optimal schedule of battery energy storage considering the uncertainties of renewable generation by Zhi Li, Dawei Xie, Haifeng Ye, Yujun Li, Jinzhong Li, Yichi Chen, Yue Yang

“…Since renewable energy generation has strong uncertainties and pure conventional unit dispatch schemes are limited by the unit-operating capacities, such scheduling is inapplicable for power systems with high proportions of renewable energy sources (RESs). We propose an optimal scheduling model for battery energy storage systems (BESSs) by considering the uncertainties of RESs. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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Strategies for Construction and Optimization of the Ecological Security Pattern for Human Settlements in the Wuding River Basin by Xiaomeng WANG, Anrong DANG, Biao TONG, Xinyi LIU

“…The goal is to balance urban development with ecological protection, thereby optimizing the security and resilience of human settlement ecosystems.MethodsThis research follows a structured approach based on the formulation of strategy for construction and optimization of ecological security pattern. …”
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Proposals for the development of the system of taxation of transport tax (passenger car transport) by A. M. Terekhov

“…The article is devoted to the identification of ways to improve the system of taxation of transport tax on road transport in the Russian Federation, including the optimization of the current method of calculating the tax.Materials and methods. …”
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Optimal Dual RNA-Seq Mapping for Accurate Pathogen Detection in Complex Eukaryotic Hosts by Infanta Eden M., Umashankar Vetrivel

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Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel by Fatemeh Mobasheri, Mostafa Khajeh, Mansour Ghaffari-Moghaddam, Jamshid Piri, Mousa Bohlooli

“…However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of traditional methods. Microwave-assisted extraction (MAE) has shown promise, but optimizing it for maximum efficiency and yield remains a challenge. …”
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Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol by Nicole Minckas, Alim Swarray-Deen, Sue Fawcus, Rosa Chemwey Ndiema, Annie McDougall, Jennifer Scott, Samuel Antwi Oppong, Ayesha Osman, Alfred Onyango Osoti, Katherine Eddy, Mushi Matjila, George Nyakundi Gwako, Joshua P. Vogel, A. Metin A. Gülmezoglu, Adanna Uloaku Nwameme, Meghan A. Bohren, the PEARLS Trial collaborative group

“…Data will be analyzed using thematic analysis and triangulated across sources and participant groups. The findings will inform trial design and help optimize implementation. …”
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