A novel virtual inductor optimization methodology of virtual synchronous generators for enhanced power decoupling by Xin Zhang, Lijiao Gong, Yanjun Zhang, Xiaolei Ma, Lianshan Han, Santao Jiang, Weiji Zhou

“…The simulation and experimental results verify the correctness and feasibility of the virtual inductor parameter optimization method.…”
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Multi-Objective Optimization of Offshore Wind Farm Configuration for Energy Storage Based on NSGA-II by Xin Lin, Wenchuan Meng, Ming Yu, Zaimin Yang, Qideng Luo, Zhi Rao, Jingkang Peng, Yingquan Chen

“…This paper proposes three different energy storage configuration strategies and adopts the non-dominated sorting genetic algorithm (NSGA-II) to conduct multi-objective optimization of the system. …”
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Programming virulent bacteriophages by developing a multiplex genome engineering method by Hailin Zhang, Ru Zhu, Zhaofei Wang, Ruoting He, Yuran Zhang, Ji Luan, Yaxian Yan, Youming Zhang, Hailong Wang

“…The limitations of naturally isolated phages have promoted the development of genome engineering methods to optimize their functions; however, engineering of virulent phage genomes in bacterial hosts remains challenging. …”
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A QoS-based network planning method for power fiber to the home by Shuai SHAO, Zhijun SHANG, Zhongfeng WANG, Guoliang XIN, Yueyue LI, Xiangyu KONG, Xujing PENG

“…A QoS-based network planning method for power fiber to the home was proposed.Firstly,the distribution structure of different buildings was combined to carry out targeted ODN line path design.Secondly,in the ONU packet cluster planning stage,in order to meet the QoS requirements of multi-service differentiation,the communication cost as the optimization goal was uesd,and the optimization problem model was established with the constraints of access capacity,reliability and delay.At the same time,intelligent algorithms were used to solve the optimization problem model.Finally,the optical path attenuation of the planned network was calculated to meet the actual application needs.The simulation results show that the proposed QoS-based network planning method for power fiber to the home can save network construction cost while ensuring service reliability and delay requirements.…”
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FedOcw: optimized federated learning for cross-lingual speech-based Parkinson’s disease detection by Changqin Quan, Zhonglue Chen, Kang Ren, Zhiwei Luo

“…Conventional federated learning (FL) methods struggle in these heterogeneous, non-independent and identically distributed (non-IID) environments, where differences in data distributions arise from variations in language, speech content, recording conditions, medical measurement techniques, and dataset sizes. …”
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Flood Risks of Cyber‐Physical Attacks in a Smart Storm Water System by Chung‐Yi Lin, Yi‐Chen Ethan Yang, Faegheh Moazeni

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An Improved Fast Mode Decision Method for H.264/AVC Intracoding by Abderrahmane Elyousfi

“…On this basis, only a small number of intraprediction modes are chosen as the best modes for rate-distortion optimization (RDO) calculation. Different video sequences are used to test the performance of the proposed method. …”
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Study of drying kinetics and moisture diffusivity in iron ore briquettes after using different drying techniques by Sharma R., Nimaje D.S.

“…The compressive strength was analyzed after applying the different drying methods, and the highest strength of 4.195 N/mm² was measured at 120°C in the infrared chamber. …”
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Styles energy consumption analysis of lane-changing maneuvers in autonomous vehicles: The role of driving styles by Yu Guo, Guigen Nie, Xiaowei Zou, Dongliang Zhu, Wenliang Gao, Mi Liao

“…However, prior studies have rarely examined energy consumption during lane changes, especially with respect to different driving styles. This study addresses this gap by incorporating driving style variables and employing a quintic polynomial lane-change trajectory model, optimized through Sequential Quadratic Programming (SQP) and the Lagrange multiplier method, to assess energy consumption during lane changes. …”
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Optimization of the Pedagogical Process in Teaching Chemistry to First-Year Students of the Ural State Medical University by E. Yu. Ermishina, N. A. Naronova, N. N. Kataeva, K. O. Golitsyna, N. A. Belokonova

“…The presence of an optimized workshop significantly contributes to the choice of the most effective methods and forms of training for a given lesson, leading to an increase in student performance. …”
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Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm by Muhammad Jawad, Fazli Wahid, Sikandar Ali, Yingliang Ma, Ahmed Alkhyyat, Jawad Khan, Youngmoon Lee

“…The overall efficacy of the fuzzy controllers that switch On/Off the actuators was obtained by minimizing the error between the best estimates of environmental factors and the ABC optimized values. Additionally, the suggested method was contrasted with other effective algorithms, such as Genetic Algorithm (GA), Firefly Algorithm (FA), and Ant Colony Optimization (ACO). …”
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Stochastic job stream scheduling method for cipher chip with multi-cryptography by Li LI, Guo-zhen SHI, Kui GENG, Xiu-ze DONG, Xuan WANG, Feng-hua LI

“…The first level was responsible for distributing tasks to different cipher clusters, and by optimizing the search logic to achieve rapid distribution of data. …”
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Leveraging Success: The Hidden Peak in Debt and Firm Performance by Suzan Dsouza, Krishnamoorthy Kathavarayan, Franklin Mathias, Dharmesh Bhatia, Abdallah AlKhawaja

“…The study identifies the optimal leverage ratio for South African firms and shows how firm size moderates the relationship between debt and profitability, offering tailored insights for firms of different sizes. …”
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Effectiveness of different exercise interventions on depressive symptoms among college students: a network meta-analysis by Yang Xiao, Chaofan Shi, Xiaotian Zhang, Haitao Liu

“…Methods We conducted a systematic search of six databases (PubMed, Web of Science, Cochrane Library, EMBASE, SCOPUS, and ScienceDirect) from their inception to July 1, 2024. …”
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Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography by Anh T. Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell’Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I. Qureshi, Santosh B. Murthy, Shahram Majidi, Guido J. Falcone, Kevin N. Sheth, Jawed Nawabi, Seyedmehdi Payabvash

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Improving agricultural spraying with multi-rotor drones: a technical study on operational parameter optimization by D. Yallappa, R. Kavitha, A. Surendrakumar, B. Suthakar, A. P. Mohan Kumar, Balaji Kannan, M. K. Kalarani

“…Single pass distribution pattern and one-direction application distribution pattern method used for optimizing height of spray, operating pressure and nozzle mounting confirmation from the results of discharge rate, spray angle, effective spray width, spray liquid loss and spray distribution uniformity. …”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Optimizing blood management in burn surgery: a meta-analysis of tranexamic acid vs. placebo by Mohamed Abdo Khalafallah, Jana Ahmed Elsead, Fatima Azzam Aboud, Basmala Mohamed Koraim, Islam Saeed Elhois

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Optimizing precision in ICU arterial access: the impact of ultrasound-guided dynamic needle tip positioning by Xiuqin Chen, Yongrui Wu, Li Zhang, Ying Zhou, Hui Fang

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