Multi-clustering algorithm based on improved tensor chain decomposition by ZHANG Hongjun, ZHANG Zeyu, ZHANG Yingjiao, YE Hao, PAN Gaojun

“…Based on this, the application of tensor decomposition technology in multi-clustering algorithms was focused on especially for the processing of large multi-source heterogeneous datasets. The tensor train (TT) method was studied and improved in depth, which had significantly improved its performance in multi-clustering tasks by introducing a new optimization strategy. …”
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The Synergy of Renewable Energy and Desalination: An Overview of Current Practices and Future Directions by Levon Gevorkov, José Luis Domínguez-García, Lluis Trilla

“…A comprehensive review of major desalination methods has been conducted, with a particular focus on the application of solar and wind energy. …”
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Immunological AI Optimizer Deployment in a 330 MW Lignite-Fired Unit for NO_x Abatement by Konrad Świrski, Łukasz Śladewski, Konrad Wojdan, Xianyong Peng

“…Unlike reactive secondary methods, the combustion optimizer reshapes the combustion process to reduce emissions while improving efficiency. …”
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A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications by Ghulam Abbas, Arshad Ali, Mohamed Tahar Ben Othman, Muhammad Wasim Nawaz, Ateeq Ur Rehman, Habib Hamam

“…In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Optimizing Location of Car-Sharing Stations Based on Potential Travel Demand and Present Operation Characteristics: The Case of Chengdu by Yu Cheng, Xu Chen, Xiaohua Ding, Linting Zeng

“…This study aims to use different data source with statistical models and machine learning algorithm to help car-sharing operator to choose the optimal location of new stations and adjust the location of existing stations. …”
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Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review by Walid Bensalmi, Ahmed Belhani, Abdellatif Bouzid-Daho

“…This paper explores various design and sizing methods for HES, focusing on combining clean sources, including wind and solar, with conventional energy options. …”
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Distributionally robust optimal scheduling of flexible distribution networks considering dynamic spatio-temporal correlation of renewable energy by Shunxiang Yu, Xiaoming Dong, Chengfu Wang, Tianguang Lu

“…Existing methods often treat spatio-temporal correlations among RESs in a static manner, neglecting their dynamic variations and resulting in suboptimal dispatch strategies. …”
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Optimizing oral 3-hydroxybutyrate dosage using pharmacokinetic model to improve cognitive function and mood in healthy subjects by Kentaro Nishioka, Kentaro Nishioka, Kentaro Nishioka, Takahiro Ishimoto, Mariko Sato, Ruki Yasuda, Yumi Nakamura, Hiroshi Watanabe, Toshihide Suzuki, Yudai Araragi, Yukio Kato, Ken-ichi Yoshida, Norihito Murayama

“…Previous studies indicated that achieving a maximum concentration (Cmax) of 3-HB in plasma at 0.28 mM could initiate ketone metabolism in the brain; we hypothesized that attaining this Cmax would improve brain health.MethodsWe aimed to demonstrate the efficacy of an optimized single oral dose of 3-HB on cognitive function and mood through two clinical studies: a pharmacokinetic study and an efficacy study. …”
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Thermal performance analysis of quaternary hybrid nanofluids with radiative and Joule heating effects in magnetohydrodynamic flow over a stretched surface by Faisal Mumtaz, A. Al-Zubaidi, Tasawar Abbas, S. Saleem

“…The solution procedure is carried out using numerical simulations, specifically the shooting method and the 4th-order Runge-Kutta (Rk-4) technique. …”
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Fast Frequency Control Strategy Based on Worst-Case Network Attack Perception by Wentao Xu, Zhenghang Song, Peiyuan Guan

“…A fast frequency control (FFC) strategy using the proximal policy optimization based on worst-case network attack perception (worst-case PPO) algorithm is proposed to address the complexity of fast frequency control in power systems and the risks posed by network attacks. …”
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Research on the Variable Factors Influencing the Vitality of Commercial Districts Based on the SOR Theory Model by Qinghua Zhou, Yubo Wang

“…Based on the SOR (stimulus–organism–response) theoretical model, this study integrates methods including space syntax analysis, POI diversity measurement, streetscape semantic segmentation, and kernel density estimation. …”
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Optimizing E-Commerce Selection Under Uncertainty: A New Framework for Decision-Making With Extended Fuzzy TOPSIS by Jency Leona Edward, Palanivel Kaliyaperumal

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Short-Term Electric Load Forecasting for an Industrial Plant Using Machine Learning-Based Algorithms by Oğuzhan Timur, Halil Yaşar Üstünel

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Green-chemistry of by-products from Sai-Nam-Phueng orange in Thailand (1): Recovering a valuable source of bioactive compounds using response surface methodology, antioxidant, and... by Kanchanok Kodchakorn, Prachya Kongtawelert

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Multi-Time Scale Optimal Dispatch of Distribution Network with Pumped Storage Station Based on Model Predictive Control by Pengyu Pan, Zhen Wang, Gang Chen, Huabo Shi, Xiaoming Zha

“…To address this, a multi-time scale optimal dispatch method based on model predictive control is proposed, including a day-ahead stage and an intra-day rolling stage. …”
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Adaptive Multi-Objective Firefly Optimization for Energy-Efficient and QoS-Aware Scheduling in Distributed Green Data Centers by Ahmed Chiheb Ammari, Wael Labidi, Rami Al-Hmouz

“…The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. …”
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Production chain and COVID-19 disruption: Sustainability via flexibility framework a case of the beverage industry by Adedugba Adebayo, Adeyemo Felicia, Oreagba Oluwakemi

“…The study also evaluated a contextual analysis of an organization, the impact of the corona-virus outbreak, and the challenges the organization encountered. A purposive method was utilized as an information-gathering source from 200 participants. …”
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Kneeliverse: A universal knee-detection library for performance curves by Mário Antunes, Tyler Estro, Pranav Bhandari, Anshul Gandhi, Geoff Kuenning, Yifei Liu, Carl Waldspurger, Avani Wildani, Erez Zadok

“…Kneeliverse further includes Z-Method, a recently developed algorithm specifically designed for multi-knee detection.…”
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Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads by Keyong Hu, Qingqing Yang, Lei Lu, Yu Zhang, Shuifa Sun, Ben Wang

“…To further manage uncertainties, a distributionally robust optimization (DRO) approach is introduced. This method uses 1-norm and ∞-norm constraints to define an ambiguity set of probability distributions, thereby restricting the fluctuation range of probability distributions, mitigating the impact of deviations on optimization results, and achieving a balance between robustness and economic efficiency in the optimization process. …”
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