Optimal Pricing and Extended Warranty Strategies for Maximizing Profit in (Re)manufacturing With Customer Returns and Production Recovery by M. Mubashir Unnissa, D. Kalpanapriya, P. Jalapathy

“…The model takes into account convex collection and inspection costs, employs two quality bins for product returns, and recognizes (re)manufacturing losses. The derivation of optimal production and pricing strategies when offering an extended warranty is accomplished through the resolution of a convex programming model. …”
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Optimization Location Routing Problem (LRP) of Humanitarian Aid Distribution Using HGASA Method in Sigi District by Serli Bombang, Bonivasius Prasetya Ichiarto, Humiras Hardi Purba

“…This study aims to determine the location of the distribution centre construction and the optimal route using the Hybrid Genetic Algorithm and Simulated Annealing methods with the objective function of minimizing total costs and minimizing maximum service time for the distribution of humanitarian aid natural disaster 2018 in Sigi Regency.  …”
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Double-layer stacking optimization for electricity theft detection considering data incompleteness and intra-class imbalance by Leijiao Ge, Jingjing Li, Tianshuo Du, Luyang Hou

“…This framework incorporates a time-varying binary transfer function and an external repository to enhance the performance of the optimization algorithm. …”
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An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM by Zhen Chen, ZhenWan Li, Jia Huang, ShengZheng Liu, HaiXia Long

“…Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. …”
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Optimization Strategies and Efficiency Prediction for Silicon Solar Cells with Hybrid Route of PERC and SHJ Passivation Contact by Zixiao Zhou, Qian Kang, Zhaoqing Sun, Yongcai He, Jingjie Li, Lu Wu, Chang Sun, Chaowei Xue, Minghao Qu, Xiaoqing Chen, Zilong Zheng, Bo Wang, Hui Yan, Xixiang Xu, Yongzhe Zhang

“…Additionally, both experimental EQE tests and simulations delve into the composition of JSC gain during the optimization process. This comprehensive investigation not only offers an in‐depth assessment of hybrid solar cell performance, but also outlines promising avenues for future optimization aimed at pushing theoretical efficiency limits further and enhancing suitability for large‐scale production.…”
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A Multi-Objective Optimization Approach for Integrated Allocation of Distributed Generation and Protection Devices in Distribution Systems by Katiani Pereira, Juan M. Home-Ortiz, Edgar M. C. Franco

“…The mathematical model presents three objective functions: investment costs for the acquisition, installation, and maintenance of CPDs and DGs, costs of energy not supplied, and active power losses, considering permanent and temporary faults, as well as load growth over the planning horizon. …”
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OPTIMIZING THE PROCESS OF PICK-UP AND DELIVERY WITH TIME WINDOWS USING ANT COLONY AND TABU SEARCH ALGORITHMS by Imas Saumi Amalia, Toni Bakhtiar, Jaharuddin Jaharuddin

“…The provision of goods shuttle services sometimes faces several constraints, such as the limitation on the number of vehicles, vehicle capacity, and service time, or the vehicle used has single transport access. To avoid losses, a strategy is needed in determining the optimal route and policy for arranging goods in the vehicle especially if there are two types of goods involved. …”
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Performance optimization of electrical equipment in high-altitude photovoltaic power stations based on PSO–MOEAD algorithm by Zhijun Xiao

“…In practical high-altitude PV scenarios, it improves voltage stability by 3% (The average voltage increases by 0.01p.u.), reduces power generation costs by 12.3%, and lowers network losses by 15%. Additionally, it effectively optimizes the thermal performance (e.g., 22.8% reduction in peak surface temperature rise of equipment) and reduces the thermal fault maintenance frequency by 64.3%. …”
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Dynamic Adaptive Environmental Flows (DAE‐Flows) to Reconcile Long‐Term Ecosystem Demands With Hydropower Objectives by Ana Paula Dalcin, Guilherme Fernandes Marques, Amaury Tilmant, Marcelo Olivares

“…Results show that the approach can adjust e‐flows (coordinated with other hydro‐system releases) over the time horizon, sacrificing them at certain times at the expense of some ecosystem loss, but improving long‐term ecosystem functioning. …”
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A Day-Ahead Two-Stage Distributed Optimal Scheduling Method for Active Distribution Network Based on ADMM by Jin ZHANG, Cungang HU, Tao RUI

“…In order to solve this problem, this paper proposes a two-stage distributed optimal dispatching method considering the operation costs and network loss of the active distribution network. …”
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Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention by Jianjianxian Liu, Tao Xing, Xiangyu Wang

“…The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. …”
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Advances in the Clinical Use of Clopidogrel: A Review of Individualized Treatment Strategies and Monitoring Optimization Based on Genetic Polymorphisms by Dou N, Ma H, Zhang P, Lu R, Hang J, Sun J

“…This article summarizes the latest research progress and monitoring methods of clopidogrel, and suggests that clinics should combine genotyping and platelet function testing with monitoring of blood levels to optimize treatment and provide data reference for clinical administration of clopidogrel. …”
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Optimizing Screening Performance for the Risk of Hyperoxaluria and Urolithiasis Using the Urinary Oxalate/Creatinine Ratio: A Retrospective Analysis by Pierre Letourneau, Lara Cabezas, Aurélie De Mul, Nadia Abid, Christelle Machon, Cécile Poussineau, Cécile Acquaviva, Justine Bacchetta, Laurence Derain-Dubourg, Sandrine Lemoine

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SDKU-Net: A Novel Architecture with Dynamic Kernels and Optimizer Switching for Enhanced Shadow Detection in Remote Sensing by Gilberto Alvarado-Robles, Isac Andres Espinosa-Vizcaino, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes

“…SDKU-Net integrates dynamic kernel adjustment, a combined loss function incorporating Focal and Tversky Loss, and optimizer switching to effectively tackle class imbalance and improve segmentation quality. …”
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Reliability Analysis and Optimality for a New Extended Topp‐Leone Distribution Based on Progressive Censoring With Binomial Removal by Mohammed Elgarhy, Gaber Sallam Salem Abdalla, Ehab M. Almetwally, Mustapha Jobarteh, Amaal Elsayed Mubarak

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Hybrid design of optimal shunt capacitor allocation and reconfiguration of radial distribution systems considering techno-economic benefits by Ifeoluwa Olajide Fajinmi, Gafari Abiola Adepoju, Sunday Adeleke Salimon, Kareem M. AboRas, Oluwaseyi Wasiu Adebiyi, Olubunmi Onatoyinbo, Mohammad Khishe, Pradeep Jangir, Wulfran Fendzi Mbasso

“…Hence, this study provides an innovative method for the performance enhancement of radial distribution networks (RDNs) through simultaneous reconfiguration and Shunt Capacitor (SC) allotment utilizing a newly developed Dandelion Optimizer (DO) and a novel objective function. The main aim of the optimization process is to maximize economic benefits while also assessing technical advantages. …”
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Using the proximal policy optimization and prospect theory to train a decision-making model for managing personal finances by Vladyslav Didkivskyi, Dmytro Antoniuk, Tetiana Vakaliuk, Yevhen Ohinskyi

“…The tasks can be formulated as follows: 1) design a reinforcement learning environment featuring different investment options with varying average returns and volatility levels; 2) train the reinforcement learning agent using the Proximal Policy Optimization algorithm to learn recommended investment allocations; 3) implement a reward function based on Prospect Theory, incorporating parameters that reflect different investor risk profiles, such as loss aversion and diminishing sensitivity to gains and losses. …”
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Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding by Sakhile Twala, Xianming Ye, Xiaohua Xia, Lijun Zhang

“…Based on an accurate forecast of the future load shedding patterns, we formulate the residents’ inconvenience and the loss of power supply probability during load shedding as the objective function. …”
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Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization by Marek Ružička, Marcel Vološin, Juraj Gazda, Taras Maksymyuk, Longzhe Han, MisCha Dohler

“…Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. …”
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