A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations by Xiaomei Zhang, Xinchen Dai, Ping Lou, Jianmin Hu

“…In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. …”
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Maximum Energy Absorbed from the Persian Gulf Waves Considering Uncertainty in Power Take off Parameters by Mohammad Jalali, Reihaneh Kardehi Moghaddam, Naser Pariz

“…Compared to particle swarm optimization and conventional black hole algorithm, the results of the proposed method indicate enhancements in reference velocity tracking and absorbed power. …”
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Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach by Zhen Yang, Titi Liu, Guobin Chen

“…The simulation results show that the proposed game algorithm improves network performance compared with other existing schemes.…”
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Corrective soft bus-bar splitting for reliable operation of hybrid AC/DC grids by Basel Morsy, Matthew Deakin, Adolfo Anta, Jochen Cremer

“…As the original problem is NP-hard, this paper utilizes a column-and-constraint generation algorithm. Case studies on IEEE 5, 24, 39, and 67 hybrid AC/DC systems show superiority of the proposed method, manifested as significant improvement in operating costs, security, and converter redispatch needs, under different loading conditions.…”
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Application of Markov processes for analysis and control of aircraft maintainability by Yu. M. Chinyuchin, A. S. Solov'ev

“…In order to reduce the number of the mathematical operations for the analysis of aeronautical engineering maintainability by using non-stationary Markov processes an algorithm for their optimization is presented. The suggested methods of the analysis by means of Markov chains allow to execute comparative assessments of expected maintenance and repair costs for one or several one-type objects taking into account their original conditions and operation time. …”
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High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation by Rui Li, Jianchao Yang, Zheng Dai, Xingyu Lu, Ke Tan, Weimin Su

“…In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. …”
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Integrated Cloud-Twin Synchronization for Supply Chain 5.0 by Divya Sasi Latha, Tartat Mokkhamakkul

“…The model determines optimal weights to balance objectives, achieving an optimal objective function value that reflects trade-offs among operational efficiency, cost, and sustainability. …”
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A review on agrowaste based activated carbons for pollutant removal in wastewater systems by Karinate Valentine Okiy, Joseph Nwabanne Tagbo, Walter Peter Echeng

“…The deployment of mathematical and machine learning approaches (ANN and novel GMDH algorithms) in optimization of batch and continuous adsorption processes are also highlighted. …”
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Technology for risk assessment at product lifecycle stages using fuzzy logic by A. N. Chesalin, S. Ya. Grodzenskiy, Pham Van Tu, M. Yu. Nilov, A. N. Agafonov

“…It is suggested that if there is a priori information about previously occurred events that can be used for risk analysis and fore casting, the fuzzy conclusion should be refined using widely known methods of mathematical statistics, optimization algorithms, for example, gradient descent, simplex method or genetic algorithms. …”
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A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments by Junchi Zhang, Yanning Xian, Xun Zhu, Hongtao Deng

“…However, the commonly used Rapidly-exploring Random Tree (RRT) algorithm for UAV path planning often generates suboptimal paths that require extensive post-processing to improve. …”
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Nondestructive freshness recognition of chicken breast meat based on deep learning by Rui Jian, Guangbo Li, Xie Jun, Guolong Shi

“…Experimental results show that in the improved algorithm, YOLOv8n_CA_DSC3 achieves suboptimal recall rate but optimal precision, average precision at IoU = 0.5, and average precision at IoU = 0.5:0.95. …”
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Spatiotemporal pattern analysis of land use in Jiangsu Province based on long-term time series remote sensing images by Zhendong Ji, Lingzhi Yin, Jinhong Wang

“…Principal Component Analysis (PCA) was applied to reduce feature dimensionality, and the Random Forest classification algorithm was optimized with Bayesian Optimization and Tree-structured Parzen Estimators (TPE) for improved performance. …”
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Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer by Dawei Wang, Xuanrui Li, Menghan Wu, Yixin He, Yi Lou, Yu Pang, Yi Lu

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Energy-Efficient User Association Strategy for Hyperdense Heterogeneous Networking in the Fifth Generation Systems by Lei Li, Hao Jin, Zhipeng Yan, Changqing Yang, Yong Wu

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Sonar-Based Simultaneous Localization and Mapping Using the Semi-Direct Method by Xu Han, Jinghao Sun, Shu Zhang, Junyu Dong, Hui Yu

“…To obtain better feature extraction results in specific directions, we propose a method that accelerates the computation of the two-dimensional SO-CFAR algorithm, with the time cost being only a very slight increase compared to the one-dimensional SO-CFAR. …”
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Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…Finally, the solution is optimized through a local search strategy. In this study, three dispatch centers were selected within the maize growing area of Hebei Province, and comparative analyses were conducted for 20, 40, 50, 100 and 120 farmlands, respectively.The results indicate that the MCMPP-DRL algorithm achieves a reduction in total scheduling costs of at least 9.66%, 14.34% and 24.41% compared to Ant Colony Optimization (ACO), Simulated Annealing (SA) and Genetic Algorithms(GA), respectively.The significant optimization in scheduling costs demonstrates that the MCMPP-DRL algorithm establishes a robust theoretical foundation and offers technical support for addressing complex scheduling problems involving multiple dispatch centers and multiple.…”
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HR Management Big Data Mining Based on Computational Intelligence and Deep Learning by Genliang Zhao, Zhe Xue

“…Decomposing the structure of a large number of existing posts through data mining will greatly improve the effect of enterprise human resource structure optimization. …”
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Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning by A. Ahajjam, M. Allgaier, R. Chance, E. Chukwuemeka, J. Putkonen, T. Pasch

“…A sequential Genetic Algorithm (GA) is employed for cost-effective feature selection from 49 geospatial features, including remote sensing and reanalysis data. …”
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An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems by Jiahao Zhang, Kaiqiang Feng, Jie Li, Chunxing Zhang, Xiaokai Wei

“…On this basis, fully considering the high-order moments of estimation errors, the maximum versoria criterion is introduced as the optimization criterion to construct a novel cost function, further effectively suppressing deviations caused by non-Gaussian disturbances and improving system navigation accuracy. …”
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Enhancing Crowd Safety at Hajj: Real-Time Detection of Abnormal Behavior Using YOLOv9 by Amani A. Alsabei, Tahani M. Alsubait, Hosam H. Alhakami

“…Leveraging deep learning, this research accurately identifies features of abnormal behavior from the HAJJv2 dataset, specifically curated and annotated for the Hajj context. Optimization of the YOLOv9 algorithm for this scenario demonstrated superior performance metrics (mean Average Precision (mAP@0.5), Recall, and Precision) when compared with its predecessors (YOLOv4, YOLOv5, YOLOv7, and YOLOv8), highlighting significant improvements in detection accuracy and real-time applicability. …”
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