An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang, Jian Xiong

“…The results show that the Improved DCA model achieved superior performance—with an mean absolute error (MAE) of 0.0660, an mean squared error (MSE) of 0.0272, and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> value of 0.9882—and exhibited greater stability across various samples and conditions. This method provides a reliable tool for long-term production forecasting and optimization without extensive geological or engineering information.…”
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Study on Vibration Characteristics and Transmission Path of Mountain Rack Trains Based on the OPTA Method by Liangzhao Qi, Xingqiao Deng, Liyuan Zeng, Chenglong Dong, Yixin Xu, Shisong Wang, Yucheng Liu

“…However, due to the multi-source vibration of gear teeth, wheels, rails, and suspensions, it is difficult to clearly define the vibration characteristics and vibration transmission path of the train, which has a serious impact on its vibration noise suppression and optimization. …”
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Optimization of Microwave-Assisted Extraction Saponins from Sapindus mukorossi Pericarps and an Evaluation of Their Inhibitory Activity on Xanthine Oxidase by Baoqin Deng, Zaizhi Liu, Zhengrong Zou

“…A microwave-assisted extraction (MAE) method was applied to separate saponins from Sapindus mukorossi pericarps. …”
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Application of Taguchi Experimental Design in Optimization of Levulinic Acid Production from Cellulose Derived From Millet Stalk by Shehu Ibrahim, Abdullahi Sokoto Muhammad, Kabiru Jega Umar, Ibrahim Magami Muhammad

“…Cellulose is the primary component of lignocellulosic biomass and the main source of renewable materials in chemical industry. …”
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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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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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GLTDNet: Cross-Domain Road Extraction Through Collaborative Optimization of Global-Local Feature Enhancement and Topological Decoupling by Jie Chen, Changxian He, Hao Wu, Jun Zhang, Siqiang Rao, Songshan Zhou, Jingru Zhu

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Optimization and Simulation of Flat Area Sites Based on Probability Matching and Transfer Intensity Using Remote Sensing Precipitation by Chen Yu, Huaiyong Shao, Xingchuan Yang, Gang Liu, Qi Xin, Jiaming Gu

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The Optimal Scheduling of Integrated Energy System Considering the Incentive and Punishment Mechanism of Electric and Thermal Carbon Emission Factors by Kaiyan Wang, Hengxiang He, Ningning Yang, Xiaowei Wang, Rong Jia

“…To comprehensively explore the potential for carbon reduction through source-load synergy and achieve low-carbon and high-efficiency operation of the system, an IES optimal scheduling method that incorporates a reward and punishment mechanism for dynamic electricity and thermal carbon emission factors is proposed. …”
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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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Optimization Driven Variational Autoencoder GAN for Artifact Reduction in EEG Signals for Improved Neurological Disorder and Disability Assessment by Sikkandar Mohamed Yacin, Sabarunisha Begum S., Alassaf Ahmad, AlMohimeed Ibrahim, Alhussaini Khalid, Aleid Adham, Alhaidar Abdulrahman Khalid

“…This study introduces an innovative method for minimizing artifacts in electroencephalography (EEG) signals by integrating brainstorm optimization (BSO) with a variational autoencoder generative adversarial network (VAE-GAN), resulting in the BrOpt_VAGAN model. …”
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Robust Underwater Vehicle Pose Estimation via Convex Optimization Using Range-Only Remote Sensing Data by Sai Krishna Kanth Hari, Kaarthik Sundar, José Braga, João Teixeira, Swaroop Darbha, João Sousa

“…The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. …”
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Risk–Cost Equilibrium for Grid Reinforcement Under High Renewable Penetration: A Bi-Level Optimization Framework with GAN-Driven Scenario Learning by Feng Liang, Ying Mu, Dashun Guan, Dongliang Zhang, Wenliang Yin

“…Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs.…”
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Calculation of spatial target coordinates in range-difference passive radars by the Levenberg – Marquardt method by A. A. Dmitrenko, S. Y. Sedyshev, Y. У. Kuleshov, A. A. Bogatyrev

“…Upon completing a comparative analysis of obtained characteristics and dependencies, we concluded that it is optimal to include four receiving points in a range-difference passive radar and use the Levenberg – Marquardt method to calculate the spatial coordinates of radio emission sources.…”
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Noise Analysis and Suppression Methods for the Front-End Readout Circuit of a Microelectromechanical Systems Gyroscope by Chunhua He, Yingyu Xu, Xiaoman Wang, Heng Wu, Lianglun Cheng, Guizhen Yan, Qinwen Huang

“…Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. …”
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Optimal dispatching of integrated agricultural energy system considering ladder-type carbon trading mechanism and demand response by Liai Gao, Fan Fei, Yuchen Jia, Peng Wen, Xianlong Zhao, Hua Shao, Tianle Feng, Limin Huo

“…Finally, the model is solved by CPLEX, and the coupling system of a 6-node power distribution network, a 6-node gas network, and a 4-node heat supply network is used to verify the effectiveness of the proposed model and method and optimize the operation of the integrated agricultural energy system.…”
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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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Cross-domain topic transfer learning method based on multiple balance and feature fusion by Zhenshun Xu, Zhenbiao Wang, Wenhao Zhang, Zengjin Tang

“…Additionally, we introduce a topic knowledge distillation method, leveraging topics from the source domain to guide and optimize topic generation in the target domain. …”
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Development of a highly sensitive reporter gene cell line for detecting estrogenic activity (the ER Isjaki assay) by Aline Colonnello Montero, Geeta Mandava, Agneta Oskarsson, Johan Lundqvist

“…Monitoring of estrogens in water sources faces significant challenges, as proposed changes in the European Union regulation for environmental protection of water bodies, compromise the ability of conventional analytical methods to detect low concentrations of estrogens. …”
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Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem by Bikram Parajuli, Xin Feng

“…Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. …”
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