Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach by Mostafa Pasandideh, Jason Kurz, Mark Apperley

“…Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. …”
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A Review of Condition Monitoring of Permanent Magnet Synchronous Machines: Techniques, Challenges and Future Directions by Alexandros Sergakis, Marios Salinas, Nikolaos Gkiolekas, Konstantinos N. Gyftakis

“…This work examines widely applied methods like Motor Current Signature Analysis (MCSA) and flux monitoring, alongside more recent approaches such as time-frequency analysis, observer-based techniques and machine learning strategies. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…The proposed approach performs spectrogram analysis and utilizes several machine learning methods, including SVM (Support Vector Machine), Random Forest, MLP (Multilayer Perceptron), and DepAudioNet. …”
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Continuous and Unconstrained Tremor Monitoring in Parkinson’s Disease Using Supervised Machine Learning and Wearable Sensors by Fernando Rodriguez, Philipp Krauss, Jonas Kluckert, Franziska Ryser, Lennart Stieglitz, Christian Baumann, Roger Gassert, Lukas Imbach, Oliver Bichsel

“…While wearables-based clinical assessments during standardised and scripted tasks have been successfully implemented, assessments during unconstrained activity remain a challenge. Methods. We developed and implemented a supervised machine learning algorithm, trained and tested on tremor scores. …”
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ACCOUNTING OF INSTRUMENTAL ERRORS IN THE CONTROL OF WINDINGS OF ELECTRICAL MACHINES WITH THE USE OF QUASI-PERIODIC TEST SIGNALS by A. A. Sheinikov, Yu. V. Suchodolov, V. V. Zelenko

“…The solution of problems of diagnostics of windings of electric machines is associated with the necessity of selection of quasi-periodic test signals against the background noise. …”
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Leveraging IoT-Enabled Sensor Networks and Machine Learning for Early Detection and Management of Wheat Rust by Adnan Myasar M., Almoussawi Zainab Abed, Anuradha Kodali

“…This education discovers the incorporation of IoT-enabled sensor systems and machine learning methods to statement the encounters accompanying with wheat corrosion management. …”
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Machine learning for early detection of plant viruses: Analyzing post-infection electrical signal patterns by Elham Ghasemi, Esmaeil Ebrahimie, Ali Niazi

“…This represents superior performance with minimal input data compared to existing methods. These results demonstrate the potential of electrical signal analysis combined with machine learning as a practical, rapid, non-invasive, and affordable tool for early virus detection in plants that is easy to use by non-specialists. …”
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A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines by S. Milani, J. Leoni, S. Cacciola, A. Croce, M. Tanelli

“…Traditional inspection methods are resource-intensive, time-consuming, and also struggle to identify the specific misaligned blades. …”
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Predicting main behaviors of beef bulls from accelerometer data: A machine learning framework by Vinicius A. Camargo, Edmond A. Pajor, Sayeh Bayat, Jennifer M. Pearson

“…Traditional methods to monitor free-range cattle, such as breeding beef bulls, are time-consuming. …”
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Experimental Identification of Milling Process Damping and Its Application in Stability Lobe Diagrams by Cvijetin Mladjenovic, Katarina Monkova, Aleksandar Zivkovic, Milos Knezev, Dejan Marinkovic, Vojin Ilic

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Intelligent Fault Severity Detection of Rotating Machines Based on VMD-WVD and Parameter-Optimized DBN by Ning Jia, Yao Cheng, Youyuan Tian, Feiyu Yang

“…Compared with BPNN, the traditional DBN, VMD-DBN, VMD-PSO-DBN, and other methods, the proposed algorithm has strong adaptive feature extraction ability and generalization of application.…”
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Reduction of structural noise inside crane cage by piezoelectric actuators - FEM simulation by Marek S. KOZIEŃ, Jerzy WICIAK

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Screw Conveyor Rotor Multi-objective Optimization Design by ZHANG Yuan, JIANG Xiao-han, FAN Hua-ping, LI Xi-han

“…Aiming at the optimization problem of the rotor of spiral conveyor structure of Pill making machine of Chinese Medicine when it worked in a complex environment easily lead to deformation which was caused by the low frequency coupling vibration from other parts′ vibration interference, using the finite element method for modal analysis. …”
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Adaptive Determination of Optimum Switching Frequency in SiC-PWM-Based Motor Drives: A Speed-Dependent Core Loss Correction Approach by Sepideh Amirpour, Sima Soltanipour, Torbjorn Thiringer, Pranav Katta

“…The approach involves conducting a comprehensive real-time finite element method (FEM) analysis of losses induced by pulsewidth modulation (PWM) voltages in an interior permanent magnet synchronous machine, compared to conventional sinusoidal current excitation feeding. …”
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A comprehensive framework for predicting electric vehicle's participation in ancillary service markets by Saeed Naghdizadegan Jahromi, Amir Abdollahi, Ehsan Heydarian‐Forushani, Mehdi Shafiee

“…An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. …”
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Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction by Samir Shamma, Mohamed Ali Hussein, Eslam M. A. El-Nahrery, Ahmed Shahat, Tamer Shoeib, Anwar Abdelnaser

“…This study investigates the association between OCP exposure and thyroid disturbances using machine learning (ML) models. Blood samples were analyzed for the concentration of 16 OCPs and thyroid hormones (T3, T4, TSH) using traditional methods such as Logistic Regression and least absolute shrinkage and selection operator (LASSO) and more advanced ML models such as Random Forest (RF), Support Vector Machine (SVM), XGBoost, and Gradient Boosting Machine (GBM). …”
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Changes in white matter predict efficacy of repetitive transcranial magnetic stimulation in Parkinson's disease by Jinying Han, Lingling Lv, Xin Chen, Mengqi Wang, Lili Hu, Fengbo Xing, Pingping Liu, Liuzhenxiong Yu, Ziye Zhao, Jinmei Sun, Tingting Liu, Jingjing Cheng, Minhao Zhu, Manman Gao, Gongjun Ji, Rong Ye, Kai Wang, Panpan Hu

“…A support vector machine (SVM) model was developed to predict cTBS efficacy and validated in an independent cohort (n = 22). …”
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A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation by Mumtaz Ali, Ramendra Prasad, Salman Alamery, Aitazaz Ahsan Farooque

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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays by Sina Hasibi Taheri, Javad Mohammadpour, Ali Lalbakhsh, Slawomir Koziel, Stanislaw Szczepanski

“…Results demonstrate that the designed WAIMs effectively enhance the scanning range of both microstrip and waveguide arrays within the desired frequency range. The method achieves a calculation time of 0.3 s per angle, significantly faster than previous approaches, with a total runtime under an hour and minimal RAM usage (9.7 MB). …”
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Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning by Seung Hyun Kim, Jun Byung Park, Jae Yoon Na, Shahzad Ahmed, Jihyun Keum, Hyun-Kyung Park, Sung Ho Cho

“…This study presents a novel frequency modulated continuous wave (FMCW) radar-based machine learning (ML) system designed for early screening to predict and identify infants at high risk for poor neurodevelopmental outcomes. …”
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