Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning by Yong Xu, Xuewei Zhang, Wenlong Xie, Shihong Zhang, Yaqiang Tian, Liansheng Chen

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Chinese Massage (Tuina) Attenuates Knee Osteoarthritis by Modulating Autophagy-Related Cytokines: A Multidisciplinary Methodological Investigation by Wang Z, Zhao C, Li M, Zhang L, Diao J, Wu Y, Yang T, Shi M, Lei Y, Wang Y, Li M, Bian Y, Zhou Y, Xu H

“…Zhen Wang,1,* Chi Zhao,1,2,* Mengmeng Li,1,2,* Lili Zhang,1,* Jieyao Diao,1 Yiming Wu,2 Tao Yang,2 Mingwei Shi,2 Yang Lei,2 Yu Wang,3 Miaoxiu Li,4 Yanqin Bian,5 Yunfeng Zhou,1 Hui Xu1,2 1College of Acupuncture and Massage, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 2Acupuncture and Massage Department, The Third Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 3College of Computer Science, Xidian University, Xian, People’s Republic of China; 4College of Acupuncture and Massage, Shanghai University of Chinese Medicine, Shanghai, People’s Republic of China; 5Orthopaedic Research Laboratory, University of California, Davis, CA, USA*These authors contributed equally to this workCorrespondence: Hui Xu, Email 15036065036@163.comBackground: Tuina therapy has demonstrated its potential in modulating autophagy-related factors in knee osteoarthritis (KOA); however, its core therapeutic targets and specific mechanisms require systematic elucidation through interdisciplinary research.Objective: This study investigated the mechanism by which Tuina alleviates KOA progression using multidimensional approaches, including Mendelian randomization (MR), in vivo experiments, and machine learning.Methods: Genetic data from genome-wide association studies of 60 cytokines and KOA were analyzed using MR analysis to identify autophagy-related factors significantly associated with KOA. …”
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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review by Xiaojian Gai, Chang Xu, Yajia Liu, Qingchun Feng, Shubo Wang

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Streamlining ICI Transformed as a Nonnegative System by David Hyland

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Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters by Daniel F. Zambrano-Gutierrez, Jorge M. Cruz-Duarte, Herman Castañeda, Juan Gabriel Avina-Cervantes

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Missing values imputation using Fuzzy K-Top Matching Value by Azza Ali, Mervat Abu-Elkheir, Ahmed Atwan, Mohammed Elmogy

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GATCGGenerator: New Software for Generation of Quasirandom Nucleotide Sequences by O. Yu. Kiryanova, R. R. Garafutdinov, I. M. Gubaydullin, A. V. Chemeris

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Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration by Zining Song, Chenglong Sun, Yunquan Sun, Lizhe Qi

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Multi-objective Windy Postman Problem in a Fuzzy Transportation Network by Debosree PAL, Haresh Kumar SHARMA, Olegas PRENTKOVSKIS, Falguni CHAKRABORTY, Lijana MASKELIŪNAITĖ

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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data by Wenwu Zhou, Wenwu Zhou, Qingtai Shu, Cuifen Xia, Li Xu, Qin Xiang, Lianjin Fu, Zhengdao Yang, Shuwei Wang

“…Then, the multi-source remote sensing image Sentinel-1/2 and terrain factors were combined to perform regional-scale FCC remote sensing estimation based on the geographically weighted regression (GWR) model. The research results showed that (1) among the 50 extracted ATLAS LiDAR feature indices, the best footprint-scale modeling factors are Landsat_perc, h_dif_canopy, asr, h_min_canopy, toc_roughness, and n_touc_photons after random forest (RF) feature variable optimization; (2) among the BO-RFR, BO-KNN, and BO-GBRT models developed at the footprint scale, the FCC results estimated by the BO-GBRT model were the best (R2 = 0.65, RMSE = 0.10, RS = 0.079, and P = 79.2%), which was used as the FCC estimation model for 74,808 footprints in the study area; (3) taking the FCC value of ATLAS footprint scale in forest land as the training sample data of the regional-scale GWR model, the model accuracy was R2 = 0.70, RMSE = 0.06, and P = 88.27%; and (4) the R² between the FCC estimates from regional-scale remote sensing and the measured values is 0.70, with a correlation coefficient of 0.784, indicating strong agreement. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph by Chris Jennings-Shaffer, David H. Rich, Matthew Macaulay, Michael D. Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A. Suchard, Frederick A. Matsen

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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
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The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance by Taifei Zhao, Hua Wang, Qianwen Ma

“…The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.…”
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Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. …”
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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. …”
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Prediction of Anemia from Multi-Data Attribute Co-Existence by Talal Qadah, Asmaa Munshi

“…Therefore, this study has reevaluated the claims within the domain of detecting and predicting anemia with the best machine learning algorithm. Another research problem, lies with the fact that previous studies on anemia prediction utilized limited machine learning algorithms across a narrow range of datasets, whereas this current study employed numerous machine learning algorithms across a wide range of anemia datasets and tested three hypotheses. …”
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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. …”
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