Revisiting universal extra-dimension model with gravity mediated decays by Kirtiman Ghosh, Katri Huitu, Rameswar Sahu

“…These methods improve sensitivity to fat-brane mUED signatures and offer promising prospects for probing this model in future LHC runs.…”
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Two-Stage Optimization of Mobile Energy Storage Sizing, Pre-Positioning, and Re-Allocation for Resilient Networked Microgrids with Dynamic Boundaries by Hongtao Lei, Bo Jiang, Yajie Liu, Cheng Zhu, Tao Zhang

“…The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. …”
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RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA–protein binding sites prediction by Jiangbo Zhang, Yunhui Peng, Feifei Cui, Zilong Zhang, Shankai Yan, Qingchen Zhang

“…The graphs are processed using a graph neural network with DiffPool. To optimize feature integration, we incorporate an improved dung beetle optimization algorithm, which adaptively assigns fusion weights during inference. …”
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Road Feel Simulation Strategy for Steer-by-Wire System in Electric Vehicles Based on an Improved Nonlinear Second-Order Sliding Mode Observer by Leiyan Yu, Zihua Hu, Hao Zhang, Xinyue Wu, Haijie Huang, Xiaobin Liu

“…Addressing the shortcoming that steer-by-wire (SBW) system cannot directly transmit road feel, this study investigates a SBW system dynamics model, steering angle tracking control, and road feel simulation algorithm design. …”
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A Model for Bus Crew Scheduling Problem with Multiple Duty Types by Mingming Chen, Huimin Niu

“…., day duty) has also been considered. An optimization model is formulated as a 0-1 integer programming problem to improve the efficiency of crew scheduling at the minimum expense of total idle time of crew for a circle bus line. …”
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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorith... by S. Ajith, S. Vijayakumar, N. Elakkiya

“…Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. …”
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IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing by Li-Nan Zhu, Peng-Hang Li, Xiao-Long Zhou

“…In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. …”
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A hybrid AI-CFD framework for optimizing heat transfer of a premixed methane-air flame jet on inclined surfaces by Panit Kamma, Kittipos Loksupapaiboon, Juthanee Phromjan, Chakrit Suvanjumrat

“…To reduce the computational expense of these simulations, a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model was developed. The ANN accurately predicted thermal efficiency based on operational parameters, while the GA optimized these inputs to achieve maximum thermal efficiency of 76.9955 %, closely matching the CFD-predicted value of 70.86 % (discrepancy:6.1355 %). …”
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Optimization of Energy Management Strategy for Series Hybrid Electric Vehicle Equipped with Dual-Mode Combustion Engine Under NVH Constraints by Shupeng Zhang, Hongnan Wang, Chengkai Yang, Zeping Ouyang, Xiaoxin Wen

“…An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. …”
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Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity by Zhi Gao, Zhi Gao, Zhi Gao, Yongfang Jiang, Yongfang Jiang, Yongfang Jiang, Mengxuan Chen, Mengxuan Chen, Mengxuan Chen, Weihang Wang, Weihang Wang, Weihang Wang, Qiyao Liu, Qiyao Liu, Qiyao Liu, Jing Ma, Jing Ma, Jing Ma

“…Using the SHAP method, the five most important factors for predicting mNGS-positive results were albumin, procalcitonin, blood culture, disease type, and sample type.ConclusionThe validated LightGBM-based predictive model could have practical clinical value in enhancing the application of mNGS in the etiological diagnosis of FUO, representing a powerful tool to optimize the timing of mNGS.…”
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Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization. by Huazan Liu, Yukang He, Qichao Hu, Jianfei Guo, Lan Luo

“…This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. …”
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Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand by Peerapon Vateekul, Panyawut Sri-iesaranusorn, Pawit Aiemvaravutigul, Adsadawut Chanakitkarnchok, Kultida Rojviboonchai

“…In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. …”
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Enhancing Campus Mobility: Simulated Multi-Objective Optimization of Electric Vehicle Sharing Systems Within an Intelligent Transportation System Frameworks by Omar S. Aba Hussen, Shaiful J. Hashim, Nasri Sulaiman Member, S.A.R. Alhaddad, Bassam Y. Ribbfors, Masanobu Umeda, Keiichi Katamine

“…The main objectives are to reduce the number of unserved demands and operational costs. A simulation model was developed in MATLAB, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), a powerful multi-objective optimization technique that balances conflicting objectives to achieve the best trade-offs for operational efficiency. …”
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Intelligent Network-Level Energy Saving Strategy With STGNN-Driven Traffic Prediction and Path Optimization in Transport Networks and Field Trial by Xinyu Chen, Liuyan Han, Minxue Wang, Jiang Sun, Yong Gao, Xuegang Ou, Dechao Zhang, Han Li

“…Combined with K-shortest-path optimization algorithm, the system dynamically reallocates traffic to minimize active switch fabrics while maintaining strict service quality guarantees. …”
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A Ship Heading Estimation Method Based on DeepLabV3+ and Contrastive Learning-Optimized Multi-Scale Similarity by Weihao Tao, Yasong Luo, Jijin Tong, Qingtao Xia, Jianjing Qu

“…The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. …”
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Fire Hawk Optimizer Adjusted Tri-Stage (1+PI)-PI-TID Cascade Controller for Automatic Generation Control of PSI by Moses Awal, Michael Robson Atim, Jimmy Nabende Wanzala, Johnes Obungoloch, Mohamed Barakat

“…This study aims to enhance the performance of automatic generation control (AGC) in power systems interconnection (PSI) by employing the fire hawk optimizer (FHO), a green algorithm, to optimize a novel tri-stage cascade controllers (CCs) configuration: (1+PI)-PI-PID and (1+PI)-PI-TID CCs. …”
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A novel dilated weighted recurrent neural network (RNN)-based smart contract for secure sharing of big data in Ethereum blockchain using hybrid encryption schemes by Swetha S, Joe Prathap P M

“…This algorithm manages keys efficiently and securely, which improves data security during blockchain operations. …”
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Identification model of mine water inrush source based on XGBoost and SHAP by Bencong Kou, Tingxin Wen

“…For water inrush source identification and feature analysis, a novel method combining XGBoost and SHAP is suggested. The model uses Ca2+, Mg2+, K+ + Na+, HCO3 -, Cl-, SO4 2-, Hardness, and pH as discriminators, and the key parameters in the XGBoost model are optimized by introducing the improved sparrow search algorithm. …”
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Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis by Sujata Dash, Ajith Abraham, Ashish Kr Luhach, Jolanta Mizera-Pietraszko, Joel JPC Rodrigues

“…In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. …”
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Application of PSO-Optimized Twin Support Vector Machine in Medium and Long-Term Load Forecasting under the Background of New Normal Economy by Xiang He, Yan Chen, Kai Hu, Lin Wan

“…In order to improve the accuracy of medium and long-term load forecasting in the new normal economy, this paper combines the PSO-optimized twin support vector machine to build a medium and long-term load forecasting model under the background of the new normal economy. …”
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