Crystal structure prediction based on diffusion model and graph network optimization by Tao Hong, Jiong Yang, Guixin Cao

“…However, stable crystal structures only exist at the global minimum of formation energy, making optimization based on density functional theory calculations or graph neural networks (GNNs) surrogate models time-consuming due to the extensive search space. In this work, we propose a crystal structure prediction method called DiffOA, which combines a diffusion model with an optimization algorithm based on GNNs. …”
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A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem by Gaochao Xu, Yan Ding, Jia Zhao, Liang Hu, Xiaodong Fu

“…We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. …”
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An Improved Machine Learning-Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable-Integrated Microgrids by Irfan Ali Channa, Dazi Li, Mohsin Ali Koondhar, Fida Hussain Dahri, Ibrahim Mahariq

“…By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. …”
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The Place of Local Field Potentials in Deep Brain Stimulation Programming for Parkinson’s Disease: A Review by Chun Him Shelton Leung, Hugh D. Simpson, Dominic Thyagarajan

“…Conclusions: More research is required to compare different algorithms that utilize LFPs in DBS programming to identify a simple, practical and time-saving algorithm incorporating reliable LFP biomarkers that will enhance the DBS programming experience for both patients and clinicians.…”
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Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures by Min-Ying Li, Kang-Di Lu, Yu-Xing Dai, Guo-Qiang Zeng

“…The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.…”
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SUDDEN CARDIAC DEATH IN PATIENTS WITH CORONARY HEART DISEASE: RESULTS OF THE RUSSIAN MULTI-CENTRE EPIDEMIOLOGICAL STUDY OF MORTALITY, MORBIDITY, AND DIAGNOSTICS AND TREATMENT QUALI... by S. A. Boytsov, N. N. Nikulina, S. S. Yakushin, S. A. Akinina, G. I. Furmenko

“…In the population of 285736 patients with CHD (76,4% aged 18 years or older; 46% men), the incidence of SCD cases registered in MIs was compared to the study algorithm-based incidence of SCD. …”
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Dual-objective optimization of prefabricated component logistics based on JIT strategy by Chunli Zhang, Jianbo Jiang, Chaoming Xia, Yan Fu, Jun Liu, Peng Duan

“…An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the model, offering enhanced solution diversity and local search capabilities. …”
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Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework by Mohammad Javad Aliabadi, Masoud Radmehr

“…The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. …”
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The electric vehicle routing problem with drones: An energy minimization approach for aerial deliveries by Nikolaos A. Kyriakakis, Themistoklis Stamadianos, Magdalene Marinaki, Yannis Marinakis

“…A Variable Neighborhood Descent algorithm is utilized in all variants as a local search procedure. …”
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A Novel Chaotic Particle Swarm Optimized Backpropagation Neural Network PID Controller for Four-Switch Buck–Boost Converters by Luoyao Ren, Dazhi Wang, Xin Yan, Yupeng Zhang, Jiaxing Wang

“…To address these challenges, chaotic optimization algorithms are integrated with BPNN. The chaotic particle swarm optimization (CPSO) algorithm enhances the global search capability, enabling a faster system response and minimizing overvoltage. …”
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Optimal Coverage Path Planning for Unmanned Surface Vehicles Using Flexible Formation Tracking Control by Minsung Park, Bo Woo Nam

“…The proposed method first divided the irregular target area into convex sub-regions using a convex decomposition, and the optimal visiting sequence was determined using a traveling salesman problem (TSP) approach with a genetic algorithm. Traditional back-and-forth search paths were then used for local coverage. …”
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Optimum Design of Fibre Orientation in Composite Laminate Plates for Out-Plane Stresses by Rasoul Khandan, Siamak Noroozi, Philip Sewell, John Vinney, Mehran Koohgilani

“…Simulated annealing (SA) is used to search for the optimal design. This optimisation algorithm has been shown to be reliable as it is not based on the starting point, and it can escape from the local optimum points. …”
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Modern fixation techniques versus traditional tension band wiring for olecranon fractures: a systematic review and meta-analysis of functional outcomes, healing time, and complicat... by Chengjing Wang, Changqing Li

“…Methods A comprehensive systematic search was conducted across PubMed, Embase, Cochrane Library, and Web of Science databases from January 2013 to February 2025. …”
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Unilateral versus bilateral pedicle screw fixation in lumbar fusion: A systematic review of overlapping meta-analyses. by Yachao Zhao, Sidong Yang, Wenyuan Ding

“…<h4>Methods</h4>A comprehensive literature search in PubMed, Embase, and the Cochrane Library databases was conducted to identify meta-analyses that compare unilateral with bilateral PSF in lumbar fusion. …”
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A novel nonparametric adaptive kernel density estimation method for removing nonrandom noise from ICESat-2 photon-counting LiDAR data by Zijia Wang, Sheng Nie, Cheng Wang, Xiaohuan Xi, Xiaoxiao Zhu, Jieying Lao, Bisheng Yang

“…Elevation profiles fitted by our method closely align with manually labeled results, exhibiting an average bias of 0.003 m, an average MAE of 0.006 m, and an average RMSE of 0.010 m, outperforming the compared methods. Specifically, our method reduces the mean bias by approximately 86.96% compared to the specular return removal method, 57.14% compared to the multiple scattering removal method, and 95.00% compared to ICESat-2 data products. …”
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Optimization study of dynamic emergency feeder bus paths with the sudden interruption of urban railway traffic by Dejie Xu, Huilin Geng, Changwu Hui, Liang Gong, Hang Yuan, Qian Liu

“…The algorithm combines the Max-Min Ant System (MMAS) and Simulated Annealing (SA) to enhance search performance. …”
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Effectively Detecting Communities by Adjusting Initial Structure via Cores by Mei Chen, Zhichong Yang, Xiaofang Wen, Mingwei Leng, Mei Zhang, Ming Li

“…In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. …”
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Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework by Gregorius Airlangga, Ronald Sukwadi, Widodo Widjaja Basuki, Lai Ferry Sugianto, Oskar Ika Adi Nugroho, Yoel Kristian, Radyan Rahmananta

“…This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. …”
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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine by Jichang Peng, Ya Gao, Lei Cai, Ming Zhang, Chenghao Sun, Haitao Liu

“…A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. …”
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Research progress on test scenario of ship autonomous navigation by Lijia CHEN, Kai WANG, Liwen HUANG, Shengwei LI, Xinwei ZHOU, Yanzhi LIU

“…Based on the characteristics in test object and scenario requirements, the scenario construction methods are divided into real data extraction, mechanism modeling and reconstruction, data-driven derivation, and optimization search methods. The performance and characteristics of different scenario construction methods are compared and described, and the problems of scenario construction methods are analyzed in combination with different testing requirements such as algorithm verification optimization, performance evaluation, and safety verification. …”
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