Research on Model Reconstruction Methods for Partial Point Cloud Information Loss by Zhengping CHANG, Guoshuai YUAN, Jiuhua LI, Yang ZHAO, Zhongqi WANG

“…Therefore, the overall surface point cloud of the part cannot be fully obtained, preventing the refactored model from accurately representing real physical information. Therefore, research on model reconstruction methods without local point clouds is essential. …”
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Research on Cooperative Search and Dynamic Target Encirclement of Unmanned Surface Vehicle Swarms Based on Improved Pheromone Algorithm by Antong Zhang, Yunfan Yang, Zhuo Chen, Tao Bao, Yu Guo, Xu Liu, Wenhui Yin

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Research on Adaptive Planning of Three-Dimensional Trajectory for Uncrewed Aerial Vehicle Inspection Based on Nonlinear Weibull Algorithm by Zhuang Liu, Ning Yang, Jiaxing Fu, Huanqing Cai, Xuebei Wei

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Research on Wafer CMP Temperature Online Detection Compensation Algorithm Based on GA-BP Improved Neural Network by Binjie Li, Kuan Shen, Zhilong Song, Binghai Lyu, Wenhong Zhao

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Research on UAV aerial imagery detection algorithm for Mining-Induced surface cracks based on improved YOLOv10 by Jiayong An, Siyuan Dong, Xuanli Wang, Chenlei Li, Wenpei Zhao

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Fault Diagnosis Research on Bearingof Motor Based on LMD And Support Vector Machine by YIN Zhao-jie, XU Tong-le, ZHENG Dian-kun

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Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) by Minal Patil, Manish Chawhan, Roshan Umate, Abhishek Madankar, Bhumika Neole

“…To meet these challenges, the algorithm of the gray wolf enhancement (GWOCA) focuses on the use of energy and choosing the best cluster (CHS). …”
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Research on Multi-objective Shielding Intelligent Optimization Method Based on Non-dominated Sorting Genetic Algorithm NSGA-Ⅲ by WANG Mengqi, ZHENG Zheng, MEI Qiliang, PENG Chao, GAO Jing, ZHOU Yan

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine by Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du

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Wireless Sensor Networks Focusing on Predicting Average Localization Error through Machine Learning Applications by Ioanna Gounari, Mattheos Kanzilieris

“…These findings highlight the competitive advantage of the DTRT model in this context. This research offers valuable insights into the effectiveness of different ML algorithms for predicting ALE in WSNs. …”
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Research on the Virtual Coupling Technology for Urban Rail Transit Trains by YANG Weifeng, LEI Chengjian, HAN Chen, LI Qihe, XIE Xing

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Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams by Samira Khodabandehlou, Alireza Hashemi Golpayegani

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Sidelobe Level Suppression in Linear and Planar Phased Arrays Using Grey Wolf Optimization With Local Search Refinements by Muhammad Athallah Adriansyah, Aditya Inzani Wahdiyat, Ismi Rosyiana Fitri, Noor Asniza Murad, Catur Apriono

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Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Lei Wang, Jinghang Li, Wenbo Zhang, Yu Li

“…Secondly, the chaotic sequence is introduced into the artificial bee colony algorithm to find a better initial honey source, which increases the diversity of the population, and combines the Levy flight to update the search step to avoid falling into the trap of local optimum. …”
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Additional Treatments to the Local tumour for metastatic prostate cancer-Assessment of Novel Treatment Algorithms (IP2-ATLANTA): protocol for a multicentre, phase II randomised con... by Francesca Fiorentino, Iqbal Shergill, John McGrath, Manal Kumar, Martin John Connor, Taimur Tariq Shah, Katarzyna Smigielska, Emily Day, Johanna Sukumar, Naveed Sarwar, Michael Gonzalez, Alison Falconer, Natalia Klimowska-Nassar, Martin Evans, Olivia Frances Naismith, Kamalram Thippu Jayaprakash, Derek Price, Shiva Gayadeen, Dolan Basak, Gail Horan, Denise Sheehan, Azman Ibrahim, Cathryn Brock, Rachel A. Pearson, Nicola Anyamene, Catherine Heath, Bhavan Rai, Giles Hellawell, Stuart McCracken, Bijan Khoubehi, Stephen Mangar, Vincent Khoo, Tim Dudderidge, John Nicholas Staffurth, Mathias Winkler, Hashim Uddin Ahmed

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Radiofrequency Technologies of Local Positioning in Healthcare by D. S. Bragin, I. V. Pospelova, I. V. Cherepanova, V. N. Serebryakova

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Adaptation of local work organization to modern conditions by D. Yu. Levin

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FEATURES OF LOCAL IMMUNITY IN PATIENTS WITH OROPHARYNGEAL CANCER by O. I. Keith, M. N. Tilliashaihov, P. V. Svetitsky, E. Yu. Zlatnik, G. I. Zakora, A. A. Ganiev, G. P. Nistratov

“…We explored the experience of the Rostov Research Institute of Oncology and the Republican Specialized Scientific and Practical Medical Center of Oncology and Radiology of the Ministry of Health of the Republic of Uzbekistan.Materials and methods. …”
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An effective algorithm for measuring diastolic artery diameters by A.P.G. HOEKS, Xu DI, P.J. BRANDS, R.S. RENEMAN

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Improved Firefly Algorithm Based on Heuristic Information by CUI Jia-rui, LI Qing, YANG Liu-yi, WANG Heng, ZHANG Bo-yu

“…Firefly Algorithm (FA) is an optimization algorithm based on swarm intelligence which mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies With the aim to address the disadvantages of the firefly algorithm of slow convergence speed and ease of falling into the local optimum in the later period of the evolution process, the firefly algorithm is improved herein Two kinds of heuristic information are proposed into the algorithm to guide the convergence of the algorithm The first one takes the current global best as the heuristic information referencing the “global optimal” idea in particle swarm optimization, therefore, an algorithm called FAGO (Firefly Algorithm based on Global Optimization) is formed The second one is called FABE (Firefly Algorithm based on Bayesian Estimation) using the optimal moving direction calculated by Bayesian estimation as heuristic information The improved algorithms in this study are applied to numerical simulations of several classical test functions and compared with traditional FA and some other′s research are carried out The simulation results show that the proposed algorithms can well accelerate the convergence speed and improve the convergence accuracy…”
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