Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures by Shahram Lotfi, Shahin Ahmadi, Alla P. Toropova, Andrey A. Toropov

“…This study aimed to develop a QSPR model for predicting the impact sensitivity of 404 nitro compounds using the Monte Carlo algorithm implemented in CORAL-2023 software. The Simplified Molecular Input Line Entry System (SMILES) was employed to represent the molecular structures, while correlation weight descriptors were computed. …”
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A lightweight weed detection model for cotton fields based on an improved YOLOv8n by Jun Wang, Zhengyuan Qi, Yanlong Wang, Yanyang Liu

“…Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.…”
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Detection and Optimization of Photovoltaic Arrays’ Tilt Angles Using Remote Sensing Data by Niko Lukač, Sebastijan Seme, Klemen Sredenšek, Gorazd Štumberger, Domen Mongus, Borut Žalik, Marko Bizjak

“…The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. …”
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Deep Reinforcement Learning-Based Active Disturbance Rejection Control for Trajectory Tracking of Autonomous Ground Electric Vehicles by Xianjian Jin, Huaizhen Lv, Yinchen Tao, Jianning Lu, Jianbo Lv, Nonsly Valerienne Opinat Ikiela

“…And the approach also addresses the nonlinear vehicle dynamic characteristics of AGEVs while mitigating internal and external disturbances by leveraging the inherent decoupling capability of ADRC and the data-driven parameter adaptation capability of DDPG. …”
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Rotor Dynamic Behaviour of Electro-Rheological Lubricant Operated Multi-Lobe Journal Bearings by Kuldeep Narwat, Vivek Kumar, Simran Jeet Singh, Abhishek Kumar

“…The lubricant flow within the journal-bearing clearance space is described by Reynold’s equation, while the non-linear behavior of the lubricant is characterized using the Bingham model. …”
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Automated 3D defect inspection in shield tunnel linings through integration of image and point cloud data by Hongwei Huang, Shuyi Liu, Mingliang Zhou, Hua Shao, Qingtong Li, Phromphat Thansirichaisree

“…However, the inability of cameras to accurately capture 3D spatial coordinates complicates tasks such as 3D visualization, while the relatively low resolution of laser scanners makes it difficult to detect small defects such as microcracks. …”
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Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models. by Yubo Zhao, Mo Chen

“…Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, while too high a concentration may lead to eutrophication of the water body and threaten the health of the aquatic environment. …”
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Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification by YoungJae Son, Tiejun Chen, Sung-June Baek

“…To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. …”
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A two-stage approach to enhancing biofuel supply chains through predictive and optimization analytics by Mehdi Soltani Tehrani, Siamak Noori, Ehsan Dehghani

“…Additionally, the Lagrangian relaxation technique is implemented to achieve precise solutions while preserving computational efficiency. For large-scale scenarios, the study leverages the non-dominated sorting genetic algorithm and multi-objective simulated annealing to generate near-optimal solutions. …”
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Enhanced Imaging in Scanning Transmission X-Ray Microscopy Assisted by Ptychography by Shuhan Wu, Zijian Xu, Ruoru Li, Sheng Chen, Yingling Zhang, Xiangzhi Zhang, Zhenhua Chen, Renzhong Tai

“…In this study, we designed an accurate reconstruction strategy to obtain the probe spot with the vibration effects being eliminated, and developed an image enhancement technique for STXM by combining the reconstructed probe with the deconvolution algorithm. This approach significantly improves the resolution of STXM imaging and can break the limitation of the focal spot on STXM resolution when the scanning step size is near or below the spot size, while the data processing time is much shorter than that of ptychography. …”
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Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity.…”
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A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles by Chenghao Lyu, Nuo Lei, Chaoyi Chen, Hao Zhang

“…The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. …”
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A linear oscillator model predicts dynamic temporal attention and pupillary entrainment to rhythmic patterns by Lauren K. Fink, Brian K. Hurley, Joy J. Geng, Petr Janata

“…An adaptive thresholding algorithm adjusted deviant in- tensity at multiple probed temporal locations throughout each rhythmic stimulus. …”
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Consensus-Based Intelligent Distributed Secondary Control for Multiagent Islanded Microgrid by Ali M. Jasim, Basil H. Jasim, Flah Aymen, Hossam Kotb, Ahmed Althobaiti

“…For the estimation sublayer, discrete dynamic consensus algorithm-based state estimators are presented to collect average information of frequency, voltage, and reactive power. …”
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A Stackelberg Trust-Based Human–Robot Collaboration Framework for Warehouse Picking by Yang Liu, Fuqiang Guo, Yan Ma

“…An iterative Stackelberg trust strategy generation (ISTSG) algorithm is designed to achieve the optimal long-term collaboration benefits between humans and robots, which is solved by the Bellman equation. …”
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Energy Simulation and Optimal Scheduling of Integrated Energy System Considering Hybrid Energy Storage by Yongwei LUO, Chengren LI, Ling ZHANG, Jianmin ZHANG, Xinhong WU, Jinbu LIAN, Jin FAN

“…An energy flow matrix model is established for the IES equipment with the minimal operating cost of the IES as the objective function, and a hybrid algorithm combining dynamic programming and the improved PSO is proposed. …”
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A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks by Benedetta Picano, Dinh Thai Hoang, Diep N. Nguyen

“…This framework is based on a two-sided game, enabling each layer to express its individual preferences for node allocation while allowing nodes to prioritize their preferred layers. …”
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Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations by Oluwole John Famoriji, Thokozani Shongwe

“…Because a machine learning algorithm is trained by utilizing data obtained from numerous 5G base stations, it exhibits the capability to estimate the strength of the electric field effectively at every point of arbitrary radiation, while the base station generates a network and serves various numbers of 5G terminals running in different modes of service. …”
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Adaptive PPO With Multi-Armed Bandit Clipping and Meta-Control for Robust Power Grid Operation Under Adversarial Attacks by Mohamed Massaoudi, Katherine R. Davis

“…This paper proposes a novel composite enhanced proximal policy optimization (CePPO) algorithm to improve power grid operation under adversarial conditions. …”
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Integrated Systemic Modeling of Production Scheduling, Maintenance, and Quality Control in Closed-Loop Supply Chains by Javad Rahim, Ali Morovati Sharifabadi, Davood Andalib Ardakani

“…A Genetic Algorithm (GA) was utilized to optimize decision variables, including sample size, sampling intervals, control limits, and maintenance schedules, ensuring robust solutions under real-world constraints. …”
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