Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data by Arkadiusz Syta, Arkadiusz Podkowiński, Tomasz Chorągiewicz, Robert Karpiński, Jakub Gęca, Dominika Wróbel-Dudzińska, Katarzyna E Jonak, Dariusz Głuchowski, Marcin Maciejewski, Robert Rejdak, Kamil Jonak

“…Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. …”
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Identification of anaerobic bacterial strains by pyrolysis-gas chromatography-ion mobility spectrometry by Tim Kobelt, Jonas Klose, Rumjhum Mukherjee, Rumjhum Mukherjee, Martin Lippmann, Szymon P. Szafranski, Szymon P. Szafranski, Meike Stiesch, Meike Stiesch, Stefan Zimmermann

“…Preliminary experiments have demonstrated that pattern recognition algorithms can predict the genus of isolated bacteria with a precision of up to 97%.…”
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THE PLACE OF THE ‘FRISBEE GAME’ IN THE PHYSICAL CULTURE CURRICULUM IN THE KINDERGARTENS by Ts. Traykova

“…Results: The necessity of including new, attractive, and funny games in the practice of pre-school physical education is argued. an algorithm for initial training of the elements ‘throw’ and ‘catch’ for 6-7-year-olds has been developed. …”
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Carreau’s Rheological Model and A.N. Tikhonov’s Regularization Method: Parametric Identification Based on a CFD model by Anatoly A. Khvostov, Gazibeg O. Magomedov, Victor I. Ryazhskih, Aleksey V. Kovalev, Aleksey A. Zhuravlev, Magomed G. Magomedov

“…The error for the viscosity prediction did not exceed 14.07%. Conclusion. The parametric identification algorithm made it possible to evaluate the rheological parameters of structured liquid media with Carreau's rheological law in cases that lack experimental information on the behavior of the medium at limiting shear rates. …”
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The abundance and sett ecological characteristics of the Asian badger (Meles leucurus) in the Northeast Tiger and Leopard National Park (NE China) by Jiawei Li, Dongrui Li, Yanwen Fu, Yiwen Ma, Jianping Ge, Limin Feng

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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

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Advancing Precision Medicine for Hypertensive Nephropathy: A Novel Prognostic Model Incorporating Pathological Indicators by Yunlong Qin, Jin Zhao, Yan Xing, Zixian Yu, Panpan Liu, Yuwei Wang, Anjing Wang, Yueqing Hui, Wei Zhao, Mei Han, Meng Liu, Xiaoxuan Ning, Shiren Sun

“…RSF and Cox regression were used to establish a renal prognosis prediction model based on the factors screened by the RSF algorithm. …”
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A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment by Zhihong Li, Yang Dong, Yanjie Wen, Han Xu, Jiahao Wu

“…Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. …”
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Cost Optimization of a M/M/1/Wv&Mav Queueing System Using Newton-Raphson and Particle Swarm Optimization Techniques by Ramachandran Remya, Amina Angelika Bouchentouf, Kaliappan Kalidass

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Towards non-virtual inertia control of renewable energy for frequency regulation: Modeling, analysis and new control scheme by Haishu Gao, Feng Zhang, Lei Ding, Gang Zhang, Libin Yang, Athuman Salimu

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UAV-Assisted Superposition Coded Cooperation: Hybrid Relaying and Nakagami- m Channel for UL/DL by Pankaj Kumar, Nikita Goel, Manoj Tolani, Santhosha Kamath

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Investigating the effects of previous injury on subsequent training loads, physical fitness, and injuries in youth female basketball players by Yuanqi Huang, Shaonan Wang, Changfei Li, Yukun Wang, Zhanshuang Bai, Zhanshuang Bai, Binghao Lv, Yuheng Gui, Zhongjian Wei

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Multi-scenario Dynamic Simulation and Optimization of Urban Ventilation Environment: A Case Study of Taiyuan Metropolitan Area by Junda HUANG, Yuncai WANG

“…Three scenarios of natural development, farmland protection and ecological priority are set to simulate the land use types. Then, a prediction model is constructed based on the random forest algorithm. …”
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MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE by Gangsheng Cao, Shen Jia, Qing Wu, Chunming Xia

“…Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. …”
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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…However, while combining multiple trees may provide higher prediction quality than a single one, it sacrifices the interpretability property resulting in “black-box” models. …”
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Research on the Molding Design and Optimization of the Molding Process Parameters of the Automobile Trunk Trim Panel by Youmin Wang, Zhaozhe Zhu, Lingfeng Tang, Qinshuai Jiang

“…In order to put forward the theoretical calculation formula for the compression force of the compression mold of the trunk trim panel, obtain the influence trend of the process parameters on the molding quality of the trunk trim panel, and obtain the optimal process parameters combination for the compression molding of the trunk trim panel, four process parameters, the heating temperature, time, compression pressure, and holding time, which affected the compression molding, were selected as the level factors; the maximum thinning rate, maximum thickening rate, and shrinkage rate of the trunk trim panel were selected as evaluation indicators and orthogonal experiments were designed and completed; the comprehensive weighted scoring method was used to obtain the comprehensive score results and obtain the comprehensive evaluation indicators of the best combination of process parameters of trunk trim panel; BP neural network and genetic algorithm were used to study the change trend of the evaluation indicators of trunk trim panel with the changes of process parameters; based on the optimal process parameter combination and the established neural network’s prediction function, the maximum thinning rate, maximum thickening rate, and shrinkage rate under a single process parameter change could be predicted, and the influence of a single process parameter on the maximum thinning rate, maximum thickening rate, and shrinkage rate could be obtained; the process parameters were optimized, and a maximum thinning rate of 28%, a maximum thickening rate of 4.3%, and a shrinkage rate of 0.8% were obtained; the optimal molding process parameters of the trunk trim panel were heating temperature of 209°C, heating time of 62 s, molding pressure of 14 kPa, and holding pressure time of 49 s; after optimization, the maximum shrinkage rate was 28.0880%, the maximum thickening rate was 44.3264%, and the shrinkage rate was 0.8901%; according to the optimal process parameters, the quality of the trunk trim panel was very good, which met the production quality requirements.…”
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Comparative analysis of lumped and semi-distributed hydrological models for an upland watershed in Ethiopia by Gebiaw T. Ayele, Bofu Yu

“…The study aimed at evaluating model performance and sensitivity of parameters in predicting streamflow for tropical watersheds. Calibration and uncertainty analysis (UA) for SWAT was performed using four UA techniques available in the SWAT (SWAT-CUP) and the genetic algorithm was used for parameter estimation for conceptual models. …”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives that rely on discrepancies between predicted and observed concentrations cannot be directly applied to determine the permissible loadings, limiting the application of the adjoint equation method to this issue. …”
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Numerical analysis method of stress wave transmission attenuation of coal and rock structural plane by Wenlong SHEN, Renren ZHU, Ziqiang CHEN, Guocang SHI

“…This study demonstrates that the machine learning prediction model based on BP artificial neural network technology has well-applicability, which can quickly determine the model parameters under the current inclination angle and axial static load of the coal rock structural plane, provide an efficient data-driven correction method for the parameters of the Barton-Bandis intrinsic model of the coal rock structural plane and also predict the parameters of numerical simulation of the coal rock structural plane under the larger inclination angle and axial static load ranges other than the given training samples.…”
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Inversion Method for Permitting Loadings of Pollutant from Lateral Effluents Based on Adjoint Equations by SHI Xiaoyan, ZHANG Hong, TAO Chunhua, LU Lingjiang, WAN Xin, LIU Zhaowei

“…However, optimization objectives based on the discrepancies between predicted and observed concentrations cannot be straightforwardly employed for determining the permissible loadings, thus restricting the application of the adjoint equation method to this issue. …”
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