Improved Motion Correction in Dynamic Contrast-Enhanced MRI Using Low Rank With Soft Weighting by Jichang Zhang, Faisal Najeeb, Yulin Wang, Xinpei Wang, Pengfei Xu, Hammad Omer, Jianjun Zheng, Jingfeng Zhang, Sue Francis, Paul Glover, Richard Bowtell, Thomas Meersmann, Chengbo Wang

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Optimal Scheduling Strategy of Newly-Built Microgrid in Small Sample Data-Driven Mode by CHEN Shi, YANG Linsen, LIU Yihong, LUO Huan, ZANG Tianlei, ZHOU Buxiang

“…Additionally, the optimal scheduling model is transformed into a Markov decision process and solved using double-delay deep deterministic policy gradient algorithm. …”
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Application of BITCN-BIGRU Neural Network Based on ICPO Optimization in Pit Deformation Prediction by Yong Liu, Cheng Liu, Xianguo Tuo, Xiang He

“…To enhance the prediction of pit deformation and improve accuracy and precision, an Improved Crown Porcupine Optimization Algorithm (ICPO) based on a Bidirectional Time Convolution Network–Bidirectional Gated Recirculation Unit (BITCN-BIGRU) is developed. …”
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Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines by Yuhe Zhou, Yun Bai, Jiajie Li, Baohua Mao, Tang Li

“…This paper developed an integrated optimization model on train control and timetable to minimize the net energy consumption. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…This module is mainly composed of Scale Fusion, CombineFPN and Pixel-Region Attention. To improve the convergence and reduce the complexity of the model, an advanced loss function of super-efficient IOU (SEIOU) and network pruning are applied. …”
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Iterative Optimization and Economic Analysis of Photovoltaic Power Generation Forecasting under Haze Conditions by Wei CHEN, Jing REN, Xinfang WU, Wenying YU, Yongsheng LIU

“…Through the revenue analysis of three photovoltaic economic models, the iterative optimization algorithm can improve the accuracy of photovoltaic revenue forecast.…”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”
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Performance Evaluation and Optimization of 3D Gaussian Splatting in Indoor Scene Generation and Rendering by Xinjian Fang, Yingdan Zhang, Hao Tan, Chao Liu, Xu Yang

“…The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. …”
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Enhanced Artificial Rabbit Optimization with attention-based deep learning for leukemia cancer classification by Sabita Rani Behera, Bibudhendu Pati, Devee Sivaprasad Dulam

“…Then the EARO algorithm is designed by using the Levy flight (LF) model for hyperparameter tuning. …”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…The methodology follows these steps:</p> <p style="text-align: left;">Step 1: Analysing effective dynamic factors of product quality</p> <p style="text-align: left;">Step2: Evaluating Triple Bottom Line (TBL) criteria</p> <p style="text-align: left;">Step 3: Measuring current sustainability state</p> <p style="text-align: left;">Step 4: Implementing ZDM strategies</p> <p style="text-align: left;">Step 5: Measuring improvements in sustainability</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Results</strong></p> <p style="text-align: left;">&nbsp;<strong>Effects</strong> <strong>of Single Unit Defective Product on TBL Sustainability State in Value Stream</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Summary of current sustainability state</p> <table style="float: left;" width="479"> <tbody> <tr> <td width="64"> <p>Product model</p> </td> <td width="56"> <p>Daily schedule (set)</p> </td> <td width="61"> <p>Defective product rate (%)</p> </td> <td width="58"> <p>Number of defective products (set)</p> </td> <td width="85"> <p>Environmental sustainability</p> <p>State</p> </td> <td width="78"> <p>Social sustainability</p> <p>state</p> </td> <td width="78"> <p>Economic sustainability</p> <p>state</p> </td> </tr> <tr> <td width="64"> <p>Refrigerator</p> </td> <td width="56"> <p>480 set</p> </td> <td width="61"> <p>3%</p> </td> <td width="58"> <p>15</p> </td> <td width="85"> <p>Wasted material: 15 set</p> <p>&nbsp;</p> <p>Wasted energy: 239.25 kwh</p> </td> <td width="78"> <p>Waste of manpower: 1650 pmin</p> </td> <td width="78"> <p>Wasted costs:</p> <p>3265.65 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">Future TBL sustainability state</p> <table style="float: left;" width="486"> <tbody> <tr> <td width="67"> <p>Product model</p> </td> <td width="59"> <p>Daily schedule (set)</p> </td> <td width="56"> <p>Defective product rate (%)</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>Number of defective products (set)</p> </td> <td width="83"> <p>Environmental sustainability</p> <p>state</p> </td> <td width="82"> <p>Social sustainability state</p> </td> <td width="62"> <p>Economic sustainability state</p> </td> </tr> <tr> <td width="67"> <p>Refrigerator</p> </td> <td width="59"> <p>480 set</p> </td> <td width="56"> <p>0.2%</p> </td> <td width="16"> <p>&nbsp;</p> </td> <td width="61"> <p>1</p> </td> <td width="83"> <p>Wasted material: 1 set</p> <p>&nbsp;</p> <p>Wasted energy: 15.95 kwh</p> </td> <td width="82"> <p>Waste of manpower: 110 pmin</p> </td> <td width="62"> <p>Wasted costs:</p> <p>217.71 $</p> </td> </tr> </tbody> </table> <p style="text-align: left;"><strong>&nbsp;</strong></p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;">&nbsp;</p> <p style="text-align: left;"><strong>Discussion and conclusion</strong></p> <p style="text-align: left;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Implementing the proposed approach aimed at achieving zero-defect products and enhancing TBL sustainability as its ultimate goal has provided valuable insights for practitioners and tangible improvements in the case study of this research. …”
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Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window by Jin He, Yinzhen Li, Yuhong Chao, Ruhu Gao

“…This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time-dependent passenger flow and provide better prompt train service according to passengers’ demand. …”
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Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization by Guozhu Song, Jian Wang, Rongting Ma, Yan Shi, Yaqi Wang

“…Initially, the Fasternet bottleneck in YOLOv8 is replaced with the c2f bottleneck, and the MLCA attention mechanism is added after the backbone network to construct the FastMLCA-YOLOv8 model for fruit stalk recognition. Subsequently, the optimized K-means algorithm, utilizing K-means++ for clustering centre initialization and determining the optimal number of clusters via Silhouette coefficients, is employed to segment the fruit stalk region. …”
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Optimal design of frame structures equipped with viscous dampers using machine learning techniques by Yi Wen, Jianze Wang, Jun Xu, Kaoshan Dai, Yuanfeng Shi, Reza Sharbati

“…It is widely accepted that the use of supplemental damping system is an effective measure to improve seismic performance of buildings. This study aims to develop an automated optimization design method for structures with damping systems to rapidly determine the optimal parameters and placement of viscous dampers in such structures. …”
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Research and Application of a Multi-Agent-Based Intelligent Mine Gas State Decision-Making System by Yi Sun, Xinke Liu

“…The system integrates the reasoning capabilities of LLMs and optimizes task allocation and execution efficiency of agents through the study of the hybrid multi-agent orchestration algorithm. …”
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Coordinated Optimal Dispatch of Distribution Grids and P2P Energy Trading Markets by Jing Deng, Fawu He, Qingbin Zeng, Jie Yan, Rangxiong Liu, Dongsheng He, Song Zhou

“…To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. …”
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Optimization of Central Pattern Generator-Based Torque-Stiffness-Controlled Dynamic Bipedal Walking by William Suliman, Chadi Albitar, Lama Hassan

“…This reduction enables the employment of the particle swarm algorithm to find the optimal values of these parameters which lead to different solutions with different performance criteria. …”
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Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine by Hong Yang, Lipeng Gao, Guohui Li

“…Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. …”
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Multi-objective optimization placement strategy for SDN security controller considering Byzantine attributes by Tao WANG, Hongchang CHEN

“…By giving the software defined network distributed control plane Byzantine attributes, its security can be effectively improved.In the process of realizing Byzantine attributes, the number and location of controllers, and the connection relationship between switches and controllers can directly affect the key network performance.Therefore, a controller multi-objective optimization placement strategy for SDN security controllers considering Byzantine attributes was proposed.Firstly, a Byzantine controller placement problem (MOSBCPP) model that comprehensively considered interaction delay, synchronization delay, load difference and the number of controllers was constructed.Then, a solution algorithm based on NASG-II was designed for this model, which included the initialization function, the mutation function, the fast non-dominated sorting function and the elite strategy selection function.Simulation results show that this strategy can effectively reduce interaction delay, synchronization delay, load difference and the number of controllers, while improving control plane security.…”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

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