Higher performance enhancement of direct torque control by using Space Vector Modulation for doubly fed induction machine by Gouaamar Radouan, El Fadl Adiba, Bri Seddik, Mekrini Zineb

“…For highly perturbed systems, traditional Proportional-Integral (PI) speed controllers fail as their gain values are a function of system parameters that inherently change such as engine properties. …”
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Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission by Jakub Gęca, Dariusz Czerwiński, Bartosz Drzymała, Krzysztof Kolano

“…The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. …”
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Study on the Morphology, Wear Resistance, and Corrosion Resistance of CuSn12 Alloys Subjected to Machine Hammer Peening by Ning Nie, Lu Yu, Shouwei Xu, Qiyuan Tian, Chenchen Ding, Lihong Su, Hui Wang

“…In this study, machine hammer peening (MHP) was employed to enhance the surface properties of CuSn12 alloys, and the effects of different impact energies on the surface morphology, mechanical properties, and electrochemical properties were systematically investigated. …”
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Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms by M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami

“…This paper investigates the application of machine learning algorithms for LULC mapping in Al Ain city, UAE. …”
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Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective by Marianna Weaver, Dylan A. Goodin, Hunter A. Miller, Dipan Karmali, Apurv A. Agarwal, Hermann B. Frieboes, Sally A. Suliman

“…While several factors are known to cause PMV, including acid-base, weakness, and delirium, lesser-utilized but routinely measured parameters such as platelet count, glucose levels and fevers may also be relevant. …”
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Optimization of Hybrid Machining of Nomex Honeycomb Structures: Effect of the CZ10 Tool and Ultrasonic Vibrations on the Cutting Process by Oussama Beldi, Tarik Zarrouk, Ahmed Abbadi, Mohammed Nouari, Jamal-Eddine Salhi, Mohammed Abbadi, Mohamed Barboucha

“…To this end, a 3D numerical model based on the finite element method, developed using Abaqus/Explicit 2017 software, allows us to simulate the interaction between the cutting tool and the thin walls of the structure to be machined. The objective of this study was to validate a numerical model through experimental tests while quantifying the impact of critical machining parameters, including the rotation speed and tilt angle, on process performance, in terms of surface finish, tool wear, cutting force components and chip size. …”
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Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study by María-José Muñoz-Martínez, Manuel Casal-Guisande, María Torres-Durán, Bernardo Sopeña, Alberto Fernández-Villar

“…This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. …”
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Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties by Amir Hossein Sheikhshoaei, Ali Khoshsima

“…The critical properties including critical temperature (Tc), critical pressure (Pc), critical volume (Vc), and acentric factor (ω), together with boiling point (Tb), and molecular weight (Mw) were used as input parameters. Models employed include Decision Tree (DT), Adaptive Boosting Decision Tree (AdaBoost-DT), Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GBoost), TabNet, and Deep Neural Network (DNN). …”
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Study on the Dynamic Evolution of Transverse Collusive Bidding Behavior and Regulation Countermeasures Under the “Machine-Managed Bidding” System by Zongyuan Zhang, Jincan Liu, Zhitian Zhang, Bin Chen

“…The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. …”
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Predictive modeling of Cbr and compressibility in lime stabilized lateritic soil using machine learning and Pchip data augmentation by Hyginus Obinna Ozioko, Emmanuel Ebube Eze

“…Integrating laboratory experiments with machine learning (ML) predictive modeling, the research aimed to support field-scale applications. …”
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Effects of an orthogonal magnetic field on discharge craters created during the single-spark electrical discharge machining process by JIA Jian-yu, WANG Yan-qing, YANG Sheng-qiang, LI Wen-hui

“…In this paper, the morphology and characteristics of craters created during the process of magnetic field assisted electrical discharge machining (MF-EDM) were studied. The results of this study may be applied to production practice, and it is expected that the machining of tapered holes can be realized using magnetic field assisted EDM. …”
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Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning by Xiangyong Li, Zeyang Zhou, Xiaoyang Zhang, Xinmeng Cheng, Chungen Xing, Yong Wu

“…Preoperative computed tomography (CT) image parameters and clinical characteristics were collected to establish seven ML models for predicting 3-year survival post-LaTME. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. …”
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Application of Machine Learning Algorithms for Predicting the Dynamic Stiffness of Rail Pads Based on Static Stiffness and Operating Conditions by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Víctor Calzada, Isidro Carrascal, Jose Casado, Soraya Diego

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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performances and material properties correlate. …”
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3D Printing of chewable oral tablets using drug nanosuspension inks: an experimental and machine learning study by Hang Truong, Nikki Rodriguez, Alperen Abaci, Gulenay Guner, Joshua Young, Ecevit A. Bilgili, Murat Guvendiren

“…Analytical models predicted strut diameter (D) based on printing parameters—pressure (P), speed (v), and nozzle height (h)—but showed reduced accuracy under nonideal conditions. …”
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A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach by Paola Argiento, Anna D'Agostino, Rossana Castaldo, Monica Franzese, Matteo Mazzola, Ekkehard Grünig, Lavinia Saldamarco, Valeria Valente, Alessandra Schiavo, Erica Maffei, Davide Lepre, Antonio Cittadini, Eduardo Bossone, Michele D'Alto, Luna Gargani, Alberto Maria Marra

“…All patients underwent a protocol of diagnostic techniques for PH according to the recommended guidelines. Machine learning (ML) approaches were considered to develop classifiers aiming to automatically detect patients affected by PH, based on the patient’s characteristics, anamnestic data, and non-invasive parameters, transthoracic echocardiography (TTE) results and spirometry outcomes. …”
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Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning by Giulia Corniani, Stefano Sapienza, Gloria Vergara-Diaz, Andrea Valerio, Ashkan Vaziri, Paolo Bonato, Peter M. Wayne

“…This study introduces a framework using wearable sensors and machine learning to monitor Tai Chi training adherence and proficiency. …”
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Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms by Dongmei Yu, Zitao Wang, Chao Yue, Jianping Wang

“…This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. …”
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Two-stage prediction of drift ratio limits of corroded RC columns based on interpretable machine learning methods by Yan Zhou, Yizhi Qiu, Liuzhuo Chen

“…To address this gap, this paper introduces a two-stage machine learning (ML) approach for the simultaneous prediction of DRLs in CRCCs, utilizing quasi-static test data from 290 corroded column specimens. …”
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