Routine Laboratory Markers-Based Machine Learning Model for Predicting Severe Kawasaki Disease in Pediatric Patients by Wu M, Chen J, Gao Y, Chen H, Li W

“…This study introduces a novel machine learning approach for the early prediction of SKD in pediatric populations, utilizing routinely collected laboratory parameters.Methods: We extracted patients’ age, sex, and 67 standard laboratory markers from the clinical records of 1,466 patients diagnosed with KD at the Children’s Hospital of Nanjing Medical University. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Microcirculatory parameters as risk factors for predicting progression of posterior staphyloma in highly myopic eyes: a case–control study by Haoru Li, Nan Gao, Ruixin Li, Luobu Luodian, Jinyuan Sui, Yang Bai, Di Wu, Qing He, Yuxin Wang, Zhiqing Li, Ruihua Wei

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Therapy response prediction of focal cortex stimulation based on clinical parameters: a multicentre, non-interventional study protocol by Andreas Schulze-Bonhage, Ann Mertens, Felix von Podewils, Martin Hirsch, Yaroslav Winter, Susanne Knake, Ekaterina Pataraia, Thomas Mayer, Yvonne G Weber, Rainer Surges, Jan Wagner, Hajo M Hamer, Tim Wehner, Christoph Baumgartner, Lukas Imbach, Bernhard J Steinhoff, Sotirios Kalousios, Jürgen Hesser, Matthias Dümpelmann, Elisabeth Kaufmann, Josua Kegele, Georg Leonhardt, Carlos M Quesada, Berthold R Voges

“…Multivariate statistical and machine learning analyses are employed to identify key predictive biomarkers associated with patient outcomes (responders vs non-responders). …”
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Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M-type Stars Using a Spectral Emulator by Jun-Chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, Xiao Kong

“…Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within 70 hr. …”
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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

“…In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.…”
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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

“…In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.…”
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Optimizing turning parameters of duralumin nano Cr₂C₃–MoS₂ using ANN and MOORA: A multi-objective approach by Ramesh Vellaichamy, Pugazhenthi Rajagopal, Ajith Arul Daniel Selsam Chandradoss, A. Geetha Selvarani

“…The composite material was synthesized via liquid metallurgy stir casting methodology, incorporating 5 wt% nano-Cr₂C₃ as the primary reinforcement phase and 2 wt% MoS₂ as a solid lubricant additive to enhance tribological characteristics and machinability. The experimental framework employed a Taguchi L₂₇ orthogonal array design to systematically investigate the influence of critical machining parameters like cutting velocity, feed rate, and depth of cut on multiple response characteristics including surface roughness (Ra), tool vibration amplitude, and acoustic emission (AE) intensity. …”
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Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm by Jiongkang Ren, Shisong Wang, Keqi Ren

“…The optimized grinding process parameters reduce the grinding time by 17.41%, improve the grinding surface quality by 4.46%, and reduce the grinding cost by 1.12% compared with the conventional machining scheme. …”
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Evaluation and Multi-Objective Optimisation of Cutting Parameters in Turning of AISI 1020 Mild Steel using Formulated Cutting Fluid by Osayamen Gregory Ehibor, Mathew Sunday Abolarin, Mohammed Baba Ndaliman, Aliyu Alhaji Abdullahi

“… Input parameter like the cutting fluid is one of the requirements for minimal surface roughness, cutting temperature, tool wear and optimal material removal rate coupled with improved machinability and productivity. …”
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Concept of K-300 Series Steam Turbine Flow Part Modernization for Transition to Operation with Ultra-Supercritical Steam Parameters by Roman A. Rusanov

Breakage Monitoring of Executive Body Cutters in Continuous Miner According to Mechanical Vibration Parameters. Part 1. Research Methodology by V. K. Sheleg, A. S. Romanovich, I. A. Konoplianik

“…An algorithm for recording vibration parameters has been developed, which makes it possible to separate its sources.…”
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Determination of the Optimum Combination of Parameters of Specialized Blades for the Shantui SD 32 Bulldozer When Moving Low-Density Materials by Tavbay Khankelov, Maloxat Abdukadirova, Otabek Ochildiev, Zebo Alimova, Gulxayo Niyazova, Anvar Kadirov

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Unit-level Digital Twin Model Construction Technology for Part Manufacturing Processes by LI Dong, LIN Zhiwen, CHEN Chuanhai, ZHAO Yongsheng, LIU Zhifeng

“…This paper reviews the construction technology of unit-level digital twins for part machining, focusing on three key aspects: virtual entity model construction, process strategy design, and process parameter decision making. …”
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Improving kernel ridge regression for medical data classification based on meta-heuristic algorithms

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A face gear grinding method and its experimental verification using cylindrical internal gear grinding machine by ZHANG Congcong, CAO Xuemei, DENG Xiaozhong, HAN Zhengyang, AN Xiaotao, XU Hao

“…The face gear tooth surface equation that contains the machine tool parameter errors is derived, and the influence of the parameter errors on the topological deviation of the tooth surface is analyzed. …”
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Design and Experiment of a Laser Scoring Device for <i>Camellia oleifera</i> Fruits by Xinhan Luo, Yujia Cui, Xiwen Yang, Guangfa Hu, Zhili Wu

“…Experimental studies were conducted to optimize the key parameters of the custom-built laser scoring machine, aiming to improve scoring qualification rates. …”
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Enhancing electric vehicle powertrain energy efficiency using robust nonlinear control approaches by Ilyass El Myasse, Mohamed Lmouradi, Abdelmounime El Magri, Rachid Lajouad, Pankaj Kumar

“…The significant dynamics of these disturbances and uncertainties in vehicle parameters have a substantial impact on vehicle performance. …”
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Technological Features of Forming Flat Tool for Processing Axicons by A. S. Kozeruk, R. O. Dias Gonzalez, A. A. Sukhotzkiy, M. I. Filonova, V. O. Kuznechik

“…It has been substantiated that in order to control the process of forming flat surfaces while using a free grinding method it is advisable to choose such machine setup parameters as a tool diameter and its rotation frequency, an amplitude of the reciprocating movement of the part along the tool (or vice versa) and frequency of this movement, as well as the value of  the working  force.  …”
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Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations by Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund

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