ZONES OF STEADY CAPACITOR EXCITATION IN A MODE OF GENERATION OF TYPICAL ASYNCHRONOUS MACHINES by Postoronca Sv., Barladeanu A., Berzan V., Tirsu V., Ermurachi Iu

“…Borders of zones of steady capacitor excitation of asynchronous electric motors in rated power of 0,25-22,0 kW and generators made on their base, and also character of influence of own losses and active capacity of loading of the equivalent circuit of the asynchronous machine resulted in parameters have been determined. …”
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Development of complex indicators and evaluation of efficiency for dump-type earthmoving and transport machines by N. T. Surashov, D. N. Tolymbek

“…As materials, the well-known 6 specific, generalized parameters and additionally developed by the authors of 20 assessment indicators (specific, generalized, differential and integral) were used to evaluate the competitiveness of the projected new design of the working body of the earth-moving transport machine of the dump type, as well as the operated these machines.Outcomes. …”
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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. Methods We analyzed datasets that combine clinical characteristics, physical function and lifestyle data, gait parameters in motion analysis systems, and wearable sensors collected from persons with PD (n = 102) to perform clustering for subtype classification. …”
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An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code by Yuan-Hang Zhang, Zhian Jia, Yu-Chun Wu, Guang-Can Guo

“…Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. …”
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Emerging generalization advantage of quantum-inspired machine learning in the diagnosis of hepatocellular carcinoma by Domenico Pomarico, Alfonso Monaco, Nicola Amoroso, Loredana Bellantuono, Antonio Lacalamita, Marianna La Rocca, Tommaso Maggipinto, Ester Pantaleo, Sabina Tangaro, Sebastiano Stramaglia, Roberto Bellotti

“…By using previously characterized genetic communities, we minimize the computational complexity associated with the number of qubits, enabling the execution of quantum-inspired algorithms on classical machines. We consider two categories of such algorithms: parameterized quantum circuits (PQC) and tensor networks. …”
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Kinematics Analysis of a Parallel Mixing Machine and Its Scale Optimal Design by Chen Jing, Guo Qian, Zhu Wei

“…According to the material mixing requirements, a three-dimensional model of a multi-dimensional mixing machine based on the 2RRS-S parallel mechanism was designed, and its motion trajectory and motion characteristics of attitude angles were simulated. …”
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Selection of internal design of crystallizer sleeves for continuous casting of 200 mm billets by I. A. Bondarenko, P. V. Volodkin, I. A. Kovaleva

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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…Abstract Background This study aims to develop a machine learning (ML)-based predictive model for evaluating the efficacy of percutaneous pulmonary balloon angioplasty (BPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) by integrating clinical and echocardiographic parameters. …”
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COMPARATIVE ANALYSIS THE PERFORMANCE OF CLIENT-SIDE AND SERVER-SIDE MACHINE LEARNING TECHNOLOGIES by I. Mysiuk, Roman Shuvar

“…The performance analysis of client-side and server-side machine learning technologies is important for understanding the optimal way to model optimization. …”
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Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques by Soni Umangbhai, Jethava Gordhan, Ganatra Amit

“…Previous research has utilized machine learning techniques, including single or multiple classifier systems, ensemble methods, and class-balancing approaches. …”
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Synthesis and Functional Optimization of a Vibratory Machine with a Parallel Mechanism Structure by Mircea-Bogdan Tătaru, Alexandru Rus, Tiberiu Vesselényi, Mariana Raţiu, Ioan Ţarcă

“…Vibratory machines are widely used for the separation of granular materials of various densities and characteristics. …”
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Inferring the equation of state from neutron star observables via machine learning by N.K. Patra, Tuhin Malik, Helena Pais, Kai Zhou, B.K. Agrawal, Constança Providência

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Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process by Shen Yin, Xin Gao, Hamid Reza Karimi, Xiangping Zhu

“…This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. …”
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Predictive sustainability in agriculture: Machine learning analysis of active ingredient restrictions and bans. by Rodrigo Garcia Brunini

“…In this context, the analysis of regulatory lists using advanced machine learning and statistical modeling techniques becomes crucial for identifying the key parameters that influence the restriction and banning of active ingredients. …”
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Detection of opening motion characteristics in DC circuit breakers based on machine vision. by Zhaoyu Ku, Jinjin Li, Dongheng Li, Huajun Dong

“…This method can detect the vibration parameters and bouncing phenomenon of circuit breaker motion machine in millisecond level, and the accuracy is 0.01 mm. …”
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In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing by Robert D. Weeks, Jennifer M. Ruddock, J. Daniel Berrigan, Jennifer A. Lewis, James. O. Hardin

“…Herein, a machine learning (ML) model that estimates ink rheology in‐situ from a simple printed test pattern is reported. …”
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Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes by Anna Burduk, Dagmara Łapczyńska, Joanna Kochańska, Kamil Musiał, Jozef Husár

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Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles by Edgar Amalyan, Shahram Latifi

“…A machine learning framework is developed to interpret vehicle subsystem status from sensor data, providing actionable insights for adaptive control systems. …”
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High accuracy prediction of Thai rice glycemic index using machine learning by Yusuf Durmus

“…This study investigated the effectiveness of machine learning (ML) models in estimating the glycemic index (GI) of Thai rice starches from their physicochemical characteristics. …”
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Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

“…Finally, based on the logistic regression model with the best parameters, the user samples are graded and the final score card is output.…”
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