Benchmarking the Non-Paraxial Beam Propagation Method for Deep-Tissue Microscopy by Praveen Kalarickel Ramakrishnan, Qi Hu, Peter R. T. Munro

“…This study evaluates the accuracy and computational efficiency of the Beam Propagation Method (BPM) for modeling light propagation in microscopy of biological tissue. …”
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Deep Reinforcement Learning for RIS-Assisted Multi-UAV MU-MISO Communication Networks: Sum-Rate and Energy Efficiency Maximization by Alif Rahmatullah Umar, Hasan Albinsaid, Chia-Po Wei, Chih-Peng Li

“…We formulate separate optimization problems for sum-rate and energy efficiency, and address them using deep reinforcement learning (DRL) algorithms, namely proximal policy optimization (PPO) and deep deterministic policy gradient (DDPG). …”
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An Automatic Damage Detection Method Based on Adaptive Theory-Assisted Reinforcement Learning by Chengwen Zhang, Qing Chun, Yijie Lin

“…Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations. …”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

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An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP by Yiquan Du, Xiuguo Zhang, Zhiying Cao, Shaobo Wang, Jiacheng Liang, Fengge Zhang, Jiawei Tang

“…In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. …”
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Efficient IP address retrieval using a novel octet based encoding technique for high speed lookup to improve network performance by Veeramani Sonai, Indira Bharathi, Sajjad Shaukat Jamal, Zaid Bassfar, Sameer Abdullah Nooh

“…This paper proposes an efficient deterministic approach to encoding IP prefixes that reduces storage complexity. …”
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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

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Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu

“…This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. …”
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Uncertainty modeling of excavator’s variable displacement pump based on EFAST and MH-Cuckoo method by Su Deying, Lian Xiaozhen, Xie Meina

“…The uncertainty method yielded average MARE values of 1.24 %, 1.92 %, 2.57 %, and 3.02 %, outperforming the deterministic method (2.74 %, 3.41 %, 5.30 %, and 7.20 %). …”
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Research on Random Discrete Ordinates Method Based on Quasi-Monte Carlo Integration Technique by DAI Tao, XU Longfei, LI Baiwen, SHEN Huayun, HU Yuan, MA Ruiyao

“…Owing to its superior computational accuracy and efficiency compared to alternative numerical approaches, the SN method has been extensively adopted in neutron transport applications, particularly in nuclear reactor design and radiation shielding optimization. …”

Distributionally Robust Policy and Lyapunov-Certificate Learning by Kehan Long, Jorge Cortes, Nikolay Atanasov

“…This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. …”
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Research on Parallel Computing Method for Neutron Transport on Tetrahedral Unstructured Meshes Based on COME by ZHONG Yangyu, ZHANG Tiancheng, ZHOU Xiafeng

“…Owing to the inhomogeneous distribution of materials in complex geometric reactor cores, traditional source iteration methods face significant challenges in solving neutron transport equations efficiently. …”

Electromagnetic Scattering of Electrically Large Ship above Sea Surface with SBR-SDFM Method by Lixin Guo, Tiantian Feng

“…Accuracy and performance of this method are validated and evaluated by comparing with multilevel fast multipole method of FEKO for electrically small objects. …”
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A Novel Method Based on Particle Flow Filters for Stellar Gyroscope Parameter Estimations by Erol Duymaz

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Development of Hybrid Neutron Dynamics Algorithm Based on Transient Fission Matrix Combined Method by LI Yuehang, HE Donghao, LIU Xiaojing, PAN Qingquan

“…The deterministic method and the Monte Carlo method are used in the transient analysis of nuclear reaction systems, but high-fidelity methods are inefficient in computation, and the accuracy of the low order method is not sufficient. …”

A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations by Dong Sui, Kai Zhang

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Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method by Ali Rizehvandi, Shahram Azadi, Arno Eichberger

“…Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. …”
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Quantitative operation risk assessment method for power grid with large-scale distributed new energy by Shuai ZHAO, Xiaolin ZHENG, Tao LU, Xiaojing YANG, Ning JIANG, Haipeng LIU

“…Finally, the application case results of actual power grid show that the risk assessment method proposed in this paper not only improves the risk assessment accuracy and calculation efficiency of the power grid with distributed new energy, but also can more comprehensively reflect the real-time operation risk characteristics of the system.…”
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Pharmacoeconomic study of fluorescent lymphography and radionuclide diagnostics methods for sentinel lymph node detection in breast cancer by E. P. Kulikov, M. V. Shomova, D. S. Titov, A. N. Demko, M. A. Maistrenko

“…Sentinel lymph node (SLN) biopsy is a reliable diagnostic method used to assess the spread of the malignant process in regional lymph nodes. …”
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FedDDPG: A reinforcement learning method for federated learning-based vehicle trajectory prediction by Jinlong Li, Ruonan Li, Guojie Ma, Weihong Yang, Hongye Wang, Zhaoquan Gu

“…However, trajectory data collected from roadside units often contains varying levels of noise, which poses unique challenges for traditional FL methods. To address these challenges, this paper proposes a personalized optimization solution called FedDDPG (Federated Learning with Deep Deterministic Policy Gradient) for VTP with FL paradigm. …”
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