Study of the technical parameters dynamics influence on operational performance of hydraulic single-bucket excavators by R. F. Salikhov, V. B. Permiakov

“…The purpose of the proposed article is to improve the method of calculating the performance of a single-bucket hydraulic excavator (EO), taking into account the operating time.Methods and materials. …”
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Sound Quality Prediction Method of Dual-Phase Hy-Vo Chain Transmission System Based on MFCC-CNN and Fuzzy Generation by Jiabao LI, Lichi AN, Yabing CHENG, Haoxiang WANG

“…Noise acquisition tests are conducted under various working conditions, followed by subjective evaluations using the equal interval direct one-dimensional method. Objective evaluations are performed using the Mel-frequency cepstral coefficient (MFCC). …”
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A Review on DTC-SVM Method with Back-to-Back Converter Connected to the Permanent Magnet Generator and Comparison with DTC in the Matrix Converter by Alireza Siadatan, Soroush Sadeghbayan, Ebrahim Afjei

“…The possibility of over-voltage matrix converter there on both sides. In some methods, sampling frequency and high frequency is required in others. …”
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A Rice-Mapping Method with Integrated Automatic Generation of Training Samples and Random Forest Classification Using Google Earth Engine by Yuqing Fan, Debao Yuan, Liuya Zhang, Maochen Zhao, Renxu Yang

“…Therefore, this study proposes a rice mapping method (LR) using Google Earth Engine (GEE), which uses Landsat images and integrates automatic generation of training samples and a machine learning algorithm, with the assistance of phenological methods. …”
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Task-Oriented Structural Health Monitoring of Dynamically Loaded Components by Means of SLDV-Based Full-Field Mobilities and Fatigue Spectral Methods by Alessandro Zanarini

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Efficient model-and-data based channel estimation algorithm by Kai MEI, Haitao ZHAO, Xiaoran LIU, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI

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Predictive Maintenance for Cutter System of Roller Laminator by Ssu-Han Chen, Chen-Wei Wang, Andres Philip Mayol, Chia-Ming Jan, Tzu-Yi Yang

“…Fast Fourier transform (FFT) is used to convert time-domain data into the frequency domain, then key statistical features from critical frequency bands are extracted as independent variables. …”
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Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery by Zhao-Kui Li, Hong-Li Li, Xue-Wei Gong, Heng-Fang Wang, Guang-You Hao

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Explainable AI for Spectral Analysis of Electromagnetic Fields by Dimitris Kalatzis, Agapi Ploussi, Ellas Spyratou, Theodor Panagiotakopoulos, Efstathios P. Efstathopoulos, Yiannis Kiouvrekis

“…A comparative evaluation of six machine learning algorithms was conducted: XGBoost, LightGBM, Random Forests, k-Nearest Neighbors, Neural Networks and Decision Trees to assess prediction performance across each frequency band. …”
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Efficient model-and-data based channel estimation algorithm by Kai MEI, Haitao ZHAO, Xiaoran LIU, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI

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Characteristic Analysis of Bending Fatigue Stress of Asymmetric Gear with Small Teeth Number by Cai Youjie

“…The asymmetric and symmetrical gears with small teeth number are high efficiency machined by numerical control machining method. Their bending fatigue strength is tested by high-frequency fatigue testing machine,and the results for different stress level are analyzed and discussed by using the fatigue cumulative failure hypothesis. …”
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Classification of Artificial Intelligence-Generated Product Reviews on Amazon by Jia-Luen Yang

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Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study by Kyle Masato Ishikawa, Deborah Taira, Joseph Keaweʻaimoku Kaholokula, Matthew Uechi, James Davis, Eunjung Lim

“…In contrast, tree-based models, such as random forest or boosted trees, are often preferred in machine learning (ML) and commercial settings due to their strong predictive performance. …”
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Surface Electromyography Monitoring of Muscle Changes in Male Basketball Players During Isotonic Training by Ziyang Li, Bowen Zhang, Hong Wang, Mohamed Amin Gouda

“…Machine learning methods were employed to perform ten-fold cross-validation and repeated experiments to verify the effectiveness of the features across the different groups. …”
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Reducing Attack Effectiveness in Cognitive Radio Networks by Parastoo Razavi, Reza Berangi

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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks.…”
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Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques by Raddam Sami Mehsen, Majharoddin M. Kazi, Hiren Joshi

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Using actigraphy to assess sleep characteristics by G. A. Trusov, A. V. Korobeinikova, L. V. Getmantseva, S. Yu. Bakoev, A. N. Lomov, A. A. Keskinov, V. S. Yudin

“…This has led to the development of circadian medicine, which focuses on using knowledge of physiological rhythms to optimize treatment and  diagnostic methods. Our article highlights the  role of  actigraphy, a non-invasive method for assessing sleep-wake cycles, in the study and diagnosis of  sleep. …”
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Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…Climate change and water scarcity bring significant challenges to agricultural systems in the Mediterranean region. Novel methods are required to rapidly monitor the water stress of the crop to avoid qualitative losses of agricultural products. …”
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Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks by L. Bardoczi, K. Won, N.J. Richner, A.C. Brown, D. Chow, P. Li, J. Monahan

“…We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. …”
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