Premier Dynamic Bandwidth Management and Tensile Wavelength Selection Ensuring QoS for NG-EPONs by Purushottam Singh, Pushpendra Kumar, Harshita Patel, Kanojia Sindhuben Babulal, Gayathri Ananthakrishnan

“…Comparative analysis reveals that TW-DBA outperforms existing algorithms like Flexible Wavelength (FW), First-Fit, and Water Filled, both in computational efficiency and resource allocation. …”
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Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network by Xinhua He, Wenfa Hu

“…This emergency system is modeled as a minimal queuing response time model of location and allocation. A solution to this complex mathematical problem is developed based on genetic algorithm. …”
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Optimizing Locations of Primary Schools in Rural Areas of China by Yulong Chen

“…Scientific location selection of schools is an important way to optimize the allocation of educational resources, improve the efficiency of operating schools, and realize the balanced development of education, especially in rural areas. …”
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Trade-off of cognition and semantic communication for UAV-IRS enhanced intelligent networks by Yuwei Zhang, Pengshan Ren

“…To improve the efficiency of semantic transmission and optimize the resource allocation between cognition and communication, this paper proposes a joint optimization framework tailored for semantic-aware communication. …”
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Toward Efficient Hierarchical Federated Learning Design Over Multi-Hop Wireless Communications Networks by Tu Viet Nguyen, Nhan Duc Ho, Hieu Thien Hoang, Cuong Danh Do, Kok-Seng Wong

“…This paper proposes a two-hop communication protocol with a dynamic resource allocation strategy to investigate the possibility of bandwidth allocation from a limited network resource to the maximum number of clients participating in FL. …”
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ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses by Yangyan Li, Tingting Chen

“…Additionally, ISCCO optimized goods allocation using a genetic algorithm enhanced integer linear programming model. …”
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Dynamic Adaptation for Independent Task Scheduling Using Dynamic Programming in Multiprocessor Systems by Lotfi BENDIAF, Ahmed HARBOUCHE, Mohammed Amin TAHRAOUI

“…Efficient scheduling requires optimizing competing objectives, such as minimizing makespan and maximizing processor utilization, to ensure that resources are used effectively. In this work, we propose DYnamic Task Allocation using dynamic programminG (DyTAg), a task scheduling algorithm based on dynamic programming, designed to support dynamic adaptation in HCS. …”
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Study on Optimal Decision of Unit Commitment with Flexible Reserve Participation in Power Grid by ZHOU Yi, LI Yichen, FU Youshu, QIN Kangping, ZHU Wen, FAN Dongqi

“…Secondly, a unit combination model considering the flexible reserve is established with the objective function of minimizing the total operating cost of the system, and the optimization algorithm program is written in the Lingo environment to optimize a variety of reserve resources. …”
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An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems by A. Al-Wattar, S. Areibi, G. Grewal

“…An efficient reconfigurable platform is implemented along with novel placement/scheduling algorithms. The proposed algorithms tend to reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software and hardware to efficiently utilize resources, and reduce computation time. …”
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Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning by Xiaojin DING, Yehui XU, Wen BAO, Gengxin ZHANG

“…To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.…”
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Secure communication approach for Internet of things based on C-RAN fronthaul compression by Yong WANG, Mu ZHOU, Zengshan TIAN, Jinjun WU

“…A resource allocation algorithm was proposed for downlink C-RAN secure communication system.This algorithm jointly optimized the parameters of base station (BS) mode,quantized noise and beamforming under the constraints of SINR requirements of Internet of things information receiver,eavesdropped receiver and fronthaul link capacity.Such a design was a non-convex optimization problem,and a step-by step optimization algorithm was proposed to decompose the original optimization problem efficiently.Semi-definite technique and function smoothing approach were adopted,and the local optimization solution of the original problem was obtained by the iterative different of convex method.Bi-section approach was further proposed to determine BS mode,and the transmitting power of the BSs were finally optimized.Simulation results indicate that the proposed joint optimization algorithm achieves better performance than group sparse optimization algorithm and baseline algorithm,and it has approximate optimal performance as the exhaustive search algorithm.…”
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The moderating role of professional grief in the relationship between job stress dimensions and turnover intention among nurses by Xue Liang, Jue Wang

“…Methods This study utilized linear regression models and the Extreme Gradient Boosting machine (XGBoost) learning algorithm to analyze the impact of the total score of job stress and their dimensions on turnover intention. …”
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Gemini: A Cascaded Dual-Agent DRL Framework for Task Chain Planning in UAV-UGV Collaborative Disaster Rescue by Mengxuan Wen, Yunxiao Guo, Changhao Qiu, Bangbang Ren, Mengmeng Zhang, Xueshan Luo

“…Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. …”
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A multi objective optimization framework for smart parking using digital twin pareto front MDP and PSO for smart cities by Dinesh Sahu, Priyanshu Sinha, Shiv Prakash, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang

“…Evaluation outcomes also show that the proposed algorithm is better than Round Robin, Random Allocation, and Threshold Based algorithms in terms of 25% improvement in the search time, 18% better energy usage, and 30% less traffic congestion. …”
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Multihop cost awareness task migration with networking load balance technology for vehicular edge computing by Shih-Yang Lin, Jie-Qin Wang, Shi-Ming Peng, Miao-Hui Yang, Shuo Jia

“…This paper investigates dynamic resource allocation and task migration mechanisms for cooperative vehicular edge computing (VEC) servers to expand computing capabilities of local server. …”
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Discrete Phase Shift IRS-Assisted Energy Harvesting in Cognitive Radio Networks With Spectrum Sensing by Lilian Chiru Kawala, Guoquan Li, Mihertie Habtamu Demeke, Junzhou Xiong, Hao Xiong, Hang Hu

“…Simulation results demonstrate the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. …”
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Dynamic Subchannel Assignment-Based Cross-Layer MAC and Network Protocol for Multihop Ad Hoc Networks by Khanh Nguyen Quang, Van Duc Nguyen, Hyunseung Choo

“…The proposed dynamic sub-channel assignment algorithm provides a new interference avoidance mechanism which solves several drawbacks of existing radio resource allocation techniques in wireless networks using OFDMA/TDD, such as the hidden node and exposed node problems, mobility, and cochannels interference in frequency (CCI). …”
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Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. by Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer, Michael Götz

“…This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. …”
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Application of 5G + edge computing technology in intelligent monitoring construction of electricity business office by Wei Cui, Junwei Li, Wei Ge, Bo Zhang, Tianwei Liu

“…This paper proposes a heterogeneous node deployment algorithm based on regional density, which divides the network regionally by raster and heterogeneous node communication range, and determines the optimal location of heterogeneous nodes by regional density, realizing the reasonable allocation and utilization of network resources. …”
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Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings by Stelios Prekas, Panagiotis A. Karkazis, Panagiotis Trakadas

“…The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. …”
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