Magnetic particle grinding and finishing test of mixed particle size abrasives by Bingyang LIU, Yunlong DING, Wenjie SHAO, Bing HAN, Yan CHEN

“…Taking the spindle speed of the machine tool (A), the abrasive mass ratio (B) and the abrasive particle size ratio (C) as the research objects, the experimental parameters are analyzed and optimized using response surface methodology. …”
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Development of MHz X-ray phase contrast imaging at the European XFEL by Jayanath C. P. Koliyadu, Daniel Moško, Eleni Myrto Asimakopoulou, Valerio Bellucci, Šarlota Birnšteinová, Richard Bean, Romain Letrun, Chan Kim, Henry Kirkwood, Gabriele Giovanetti, Nerea Jardon, Janusz Szuba, Trey Guest, Andreas Koch, Jan Grünert, Peter Szeles, Pablo Villanueva-Perez, Fabian Reuter, Claus-Dieter Ohl, Mike Andreas Noack, Francisco Garcia-Moreno, Zuzana Kuglerová-Valdová, Libor Juha, Martin Nikl, Wataru Yashiro, Hitoshi Soyama, Daniel Eakins, Alexander M. Korsunsky, Jozef Uličný, Alke Meents, Henry N. Chapman, Adrian P. Mancuso, Tokushi Sato, Patrik Vagovič

“…Together with the diagnostic camera upstream of the instrument and the MHz XPCI setup at the SPB/SFX instrument, simultaneous two-plane measurements for future beam studies and feedback for machine parameter tuning are now possible.…”
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Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study by Yi Xie, Yi Xie, Xinmeng Wang, Huiwen Yang, Huiwen Yang, Jiayao Zhang, Honglin Wang, Zineng Yan, Jiaming Yang, Zhiyuan Yan, Zhiwei Hao, Pengran Liu, Yijie Kuang, Zhewei Ye, Zhewei Ye

“…Swarm learning (SL), a decentralized machine learning framework, addresses these limitations by enabling collaborative model training through secure parameter aggregation while preserving data locality. …”
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Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images by Jiejie Fan, Jiejie Fan, Yang Liu, Yang Liu, Yiguang Fan, Yihan Yao, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Huifang Wang, Haikuan Feng, Haikuan Feng, Haikuan Feng

“…The Leaf Area Index (LAI) is a crucial parameter for evaluating crop growth and informing fertilization management in agricultural fields. …”
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An investigation into certain Persian Suffixes extracted from Tǎrix-ol-vozarǎ: natural morphology by shiva ahmadi, jalal Rahimian

“…There are two sub-criteria for this parameter: Qualitative criterion and quantitative criterion. …”
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An ensemble learning approach for predicting nanoparticle dispersion properties in cutting fluids using a small sample dataset by Abhishek Shrotriya, Vinay Vakharia, Himanshu Borade, Rakesh Chaudhari, Jay Vora

“…This study demonstrates machine learning's effectiveness in capturing non-linear correlations in small sample datasets using grid search optimization and accurate predictions. …”
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Sharpening of Plunge Shaving Cutters by 刘俐平, 李华敏

“…The tooth curve of a plunge shaving cutter for an external gear is a recess transcendental curve,so it is difficult to grind it.This paper presents the method for sharpening flanks of the plunge shaving cutter by modifying a grinding wheel in a general grinding machine,optimizes the sharpening parameters and lays the foundation for the production and application of the plunge shaving cutter.…”
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Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. …”
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Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Sani I. Abba, Jamil Hussain, Soo-Mi Choi

“…In this research, FSM was conducted by using 13 parameters that affect floods and flood occurrence points as inputs. …”
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VarProDMD: Solving Variable Projection for the Dynamic Mode Decomposition with SciPy’s optimization suite by Gerhard Reinerth, David Messmann, Jean Elsner, Ulrich Walter

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Online chicken carcass volume estimation using depth imaging and 3-D reconstruction by Innocent Nyalala, Zhang Jiayu, Chen Zixuan, Chen Junlong, Kunjie Chen

“…Volume is calculated from the reconstructed models using the surface integration method, and additional 2-D and 3-D features are extracted as input parameters for machine learning models. Multiple regression models were evaluated, with the bagged tree model demonstrating superior performance, achieving an R² value of 0.9988, RMSE of 5.335, and ARE of 2.125%. …”
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A New Approach for Online Transient Instability Assessment and Prediction via Tracing the Variation Trend of Synchronous Generator Energy by Parva Soori, Alireza Sobbouhi, Mohamma Reza Aghamohammadi, Mohamma Sadegh Sepasian

“…This novel criterion is implemented locally on each generator within the network, enabling effective prediction of transient instability through the sole measurement of the machine’s electrical power. The proposed approach is online, conceptual, and independent of fault data, network configuration, and system parameters. …”
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A Hybrid Analytical Framework for Cracking and Some Fruit Quality Features in Sweet Cherries by Erol Aydın, Mehmet Ali Cengiz, Leyla Demirsoy, Hüsnü Demirsoy

“…Key fruit characteristics were measured, and novel machine learning algorithms were applied to identify associations between variables. …”
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Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images by Mahsa Raisi-Nafchi, Mahnoosh Tajmirriahi, Hossein Rabbani, Zahra Amini

“…Three classification algorithms were evaluated: support vector machine, K-nearest neighbor, and random forest. In the three-class classification task (Grades II–IV), the support vector machine exhibited superior performance, achieving accuracy, sensitivity, and specificity of 98.49%, 98.42%, and 99.23% in 2D mode, and 93.52%, 93.23%, and 96.72% in 1D mode. …”
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Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment by Hyeon Cheol Kim, Si Young Ha, Jae-Kyung Yang

“…Machine learning is emerging as a promising solution to address these challenges, offering a viable alternative for predictive modeling and process control. …”
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Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (<i>Salvia officinalis</i> L.) Components in... by Dajana Vukić, Biljana Lončar, Lato Pezo, Vladimir Vukić

“…To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models—including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)—were developed and evaluated. …”
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Error effect of executive elements movement of the lathe tool on forming motion paths by Vilor L. Zakovorotny, Valery E. Gvindzhiliya

“…Introduction. Any metal-cutting machine has errors of the executive elements movement depending on its geometric accuracy and state. …”
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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation by J. Prabin Jose, G. Jaffino, Mohammed Al Awadh, Koppula Srinivas Rao, Yan Yafang, Krishna Moorthy Sivalingam

“…The performance of the proposed method is compared with other five machine learning algorithms, including Support Vector Machine, Random Forest, Gradient Boosting, Naive Bayes, and Multilayer Perceptron neural networks. …”
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Seed Size and Shape Analysis of Registered Common Bean (Phaseolus vulgaris L.) Cultivars in Turkey Using Digital Photography by Bahadır Sayıncı, Erdal Elkoca, İsmail Öztürk, Mazhar Kara, Talha Özmen

“…So, the size and shape data of bean cultivars are of mostly importance to engineers, machine manufacturers and machine designers.…”
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Archetype Identification and Energy Consumption Prediction for Old Residential Buildings Based on Multi-Source Datasets by Chengliang Fan, Rude Liu, Yundan Liao

“…Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. …”
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