The potential habitat of Phlomoides rotata in Tibet was based on an optimized MaxEnt model by Jun-Wei Wang, Jun-Wei Wang, Min Xu, Norbu Ngawang, Yonghao Chen, Ngawang Bonjor, Xiaoyan Jia, Zhefei Zeng, Zhefei Zeng, La Qiong, La Qiong

“…However, overharvesting and climate change have progressively reduced its distribution range, threatening its survival.MethodsThis study employed an optimized MaxEnt model, integrating field survey data and multiple environmental variables, to predict and analyze the potential suitable distribution of P. rotata in Tibet.ResultsThe model achieved high predictive accuracy, with Ture skill statistic (TSS) = 0.87 and Cohen’s Kappa Coefficient (Kappa) = 0.81. …”
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Conservative or surgical treatment? Optimizing management strategies in medial hypertensive intracerebral hemorrhages by M.Ye. Polishchuk, A.A. Kaminskyi, A.A. Oblyvach, D.V. Shchehlov, O.M. Honcharuk, A.V. Muravskyi

“…Objective ‒ to determine the optimal treatment strategy for patients with medial hypertensive intracerebral hemorrhages (ICH) by comparing the effectiveness of conservative and surgical approaches. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…This paper studied the user association strategy that could improve the scalability of cell-free networks. Methods:The network scalable degree was designed as a measure of scalability,and then a user association strategy to improve network scalable degree was studied by using optimization theory. 1) For modelling the optimization problem, firstly, the network coupling degree, representing the degree of association among nodes, was constructed to establish the mathematical relationship between the network scalable degree and AP group (APG).Thus,the problem of improving the network scalable degree was modeled as the problem of minimizing the network coupling degree.Then,a multi-objective optimization problem of minimum network coupling degree and maximum user rate was established to find the balance between network scalable degree and network service quality. 2) For solving the optimization problem,to avoid the high computational complexity,a heuristic user association strategy based on APG mergence and topological potential optimization was proposed.With the proposed algorithm,the number of APG could be reduced by APG mergence,and the number of APG that AP belongs to could be reduced by AP exiting APG. …”
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Optimizing therapeutic outcomes of immune checkpoint blockade by a microbial tryptophan metabolite by Giorgia Renga, Emilia Nunzi, Marilena Pariano, Matteo Puccetti, Marina Maria Bellet, Giuseppe Pieraccini, Fiorella D’Onofrio, Ilaria Santarelli, Claudia Stincardini, Franco Aversa, Francesca Riuzzi, Cinzia Antognelli, Marco Gargaro, Oxana Bereshchenko, Maurizio Ricci, Stefano Giovagnoli, Luigina Romani, Claudio Costantini

“…Here we have assessed whether microbial metabolites working at the interface between microbes and the host immune system may optimize ICI therapy.Methods To this purpose, we have tested indole-3-carboxaldehyde (3-IAld), a microbial tryptophan catabolite known to contribute to epithelial barrier function and immune homeostasis in the gut via the aryl hydrocarbon receptor (AhR), in different murine models of ICI-induced colitis. …”
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Optimizing Cardiac Monitoring Utilization in the Emergency Department for Patients Awaiting In-Hospital Beds by Renee Bradley, Edmund SH Kwok, Marie-Joe Nemnom, Venkatesh Thiruganasambandamoorthy

“…This study aims to assess the use of cardiac monitoring among patients waiting in ED for inpatient bed, interventions due to monitoring, and identify risk factors for serious adverse events (SAE) to optimize the use of monitors. Methods: We performed a 2-week health records review of hospitalized/boarded patients in the ED on cardiac monitors. …”
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Ray Tracing Methods for Correcting Chromatic Aberrations in Imaging Systems by Dmitry Reshidko, Masatsugu Nakanato, José Sasián

“…In the first method we use forward and reverse ray tracing and fictitious nondispersive glasses, to cancel the monochromatic aberration content and allow the ray tracing optimization to focus mainly on the color correction. …”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…To address the issue of low accuracy in existing remaining useful life (RUL) prediction algorithms for rolling bearings, this paper proposes a novel RUL prediction method based on the Beluga Whale Optimization (BWO) algorithm, Variational Mode Decomposition (VMD), an improved Convolutional Block Attention Module (CBAM*), and a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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Motion Planning Methods for Automated Parking: A Comprehensive Review by CHEN Xiaoming, LI Bai, FAN Lili, ZHANG Youmin

“…This paper offers a thorough review of the motion planning methods applied within the automated parking domain, categorizing them into geometric methods, sampling methods, search methods, numerical optimization methods, and machine learning methods. …”
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Design of Hybrid Renewable Energy Systems: Integrating Multi‐Objective Optimization Into a Multi‐Criteria Decision‐Making Framework by Tebello Ntsiki Don Mathaba, Khaled Abo‐Al‐Ez

“…A combination of the MO particle swarm optimization and a newly developed MO leaders‐and‐follower algorithms (MO‐LaF/PSO) is used to generate optimal configurations based on minimal levelized cost of energy (LCOE), renewable energy (RE) power abandonment, and CO2 emissions, while maintaining an acceptable level of reliability. …”
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Viewpoint Selection for 3D Scenes in Map Narratives by Shichuan Liu, Yong Wang, Qing Tang, Yaoyao Han

“…The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. …”
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Methods for quantitative determination of microalgal lipid and fatty acids content by I. V. Morshchinin

“…Accurate and reproducible lipid quantification is essential for strain selection, process optimization, and industrial scaling. This review presents a comprehensive and critical evaluation of contemporary lipid quantification methods applied to microalgae. …”
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Convex Optimization of Markov Decision Processes Based on Z Transform: A Theoretical Framework for Two-Space Decomposition and Linear Programming Reconstruction by Shiqing Qiu, Haoyu Wang, Yuxin Zhang, Zong Ke, Zichao Li

“…The proposed approach introduces three mathematical innovations: (i) a spectral clustering mechanism that reduces state-space dimensionality while preserving Markovian properties, (ii) a Lagrangian dual formulation with adaptive penalty functions to handle operational constraints, and (iii) a warm start algorithm accelerating convergence in high-dimensional convex optimization. …”
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Implementing a dung beetle optimization algorithm enhanced with multi-strategy fusion techniques by xiaojie zhou, Majid Khan Majahar Ali, Farah Aini Abdullah, Lili Wu, Ying Tian, Tao Li, Kaihui Li

“…To address these limitations, this study introduces a multi-strategy improved dung beetle optimization algorithm (CDBO). Initially, the golden sine method is implemented during the rolling phase to improve the algorithm’s capacity for both late area mining and early broad exploration; Then, in order to move the population closer to the ideal location during the foraging phase, the self-spiral method of the whale optimization algorithm is adopted. …”
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Transformer fault diagnose intelligent system based on DGA methods by Saad A. Mohamed Abdelwahab, Ibrahim B. M. Taha, Rizk Fahim, Sherif S. M. Ghoneim

“…Dissolved Gas Analysis (DGA) is widely adopted to identify transformer faults. While traditional DGA methods such as the IEC Code, Rogers Ratio, and Duval triangle exist, their diagnostic accuracies are often lacking. …”
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Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

“…The framework systematically reduced observation periods and increased censoring rates while measuring performance through multiple metrics following Bayesian hyperparameter optimization. (3) Results: Model performance showed greater sensitivity to increased censoring rates than to reduced observation periods. …”
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Combination of Feature Selection and Learning Methods for IoT Data Fusion by V. Sattari-Naeini, Zahra Parizi-Nejad

“…The results derived from five compound schemes are investigated andcompared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and StorageCapacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable,Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. …”
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Hybrid Optimization-Based Sequential Placement of DES in Unbalanced Active Distribution Networks Considering Multi-Scenario Operation by Ruihua Si, Xintong Yan, Wanxun Liu, Ping Zhang, Mengdi Wang, Fengyong Li, Jiajia Yang, Xiangjing Su

“…Subsequently, the sizing and scheduling of DES for the selected location are conducted through coordinated optimization across the inter and inner layers, which can be solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP). …”
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RRT-Based Optimizer: A Novel Metaheuristic Algorithm Based on Rapidly-Exploring Random Trees Algorithm by Guang-Jin Lai, Tao Li, Bao-Jun Shi

“…Consequently, new optimization methods are required. Inspired by the search mechanism of the Rapidly-exploring Random Trees (RRT) algorithm commonly used in robot path planning, we propose a novel metaheuristic algorithm called RRT-based Optimizer (RRTO). …”
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A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems by Minling Pan, Bingqi Jia, Lei Zhang, Haihong Pan, Lin Chen

“…To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. …”
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Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness by Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu

“…Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. …”
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