Experimental verification of position sensing for a magnetic coil via Fano resonance by Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Mikiko Suzuki, Michio Yasunishi, Hiroshi Yoshimoto, Xiaopeng Li, Ziqi Yu, Taehwa Lee, Hideo Iizuka

“…The characteristics of the system are investigated by using a method-of-moment based electromagnetic simulator, with sufficiently large volume of scattering parameters for different positions of the transmitting coil. …”
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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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Predictive Maintenance for Cutter System of Roller Laminator by Ssu-Han Chen, Chen-Wei Wang, Andres Philip Mayol, Chia-Ming Jan, Tzu-Yi Yang

“…In the era of Industry 4.0, equipment maintenance is shifting toward data-driven strategies. Traditional methods rely on usage time or cycle counts to estimate component lifespan. …”
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Acoustic signatures of bulbar ALS: Predictive modeling with sustained vowels and LightGBM by Zahra Farrokhi, Seyed Amirali Zakavi, Arian Sarafraz, Maryam Valifard, Salar Yousefzadeh, Zahra Mashhadi Tafreshi, Omid Anbiyaee, Navid Rostami, Mahsa Asadi Anar, Niloofar Deravi

“…This paper discusses the use of acoustic analysis of sustained vowel phonations (SVP) and machine learning in ALS detection. Methods: An SVP corpus of 128 (64 /a/ and 64 /i/) from 31 patients with ALS and 33 healthy controls (HC) was employed. 131 acoustic features, including jitter, shimmer, Mel-Frequency Cepstral Coefficients (MFCCs), and Pathological Vibrato Index (PVI), were extracted. …”
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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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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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Diagnostics of Rotor Damages of Three-Phase Induction Motors Using Acoustic Signals and SMOFS-20-EXPANDED by Adam GLOWACZ

“…A feature extraction step was performed using SMOFS-20-EXPANDED (shortened method of frequencies selection-20-Expanded). A classification step was performed using 3 classifiers: LDA (Linear Discriminant Analysis), NBC (Naive Bayes Classifier), CT (Classification Tree). …”
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Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities by Mahdieh Shokrollahi Barough, Mohammad Darzi, Masoud Yunesian, Danesh Amini Panah, Yekta Ghane, Sam Mottahedan, Sohrab Sakinehpour, Tahereh Kowsarirad, Zahra Hosseini-Farjam, Mohammad Reza Amirzargar, Samaneh Dehghani, Fahimeh Shahriyary, Mohammad Mahdi Kabiri, Marzieh Nojomi, Neda Saraygord-Afshari, Seyedeh Ghazal Mostofi, Zeynab Yassin, Nazanin Mojtabavi

“…This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model. Method: A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. …”
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BattyCoda: A novel open-source software for bat call annotation and classification by Gabriela C. Nunez-Mir, Kevin M. Boergens, Jessica C. Montoya, Hannah ter Hofstede, Angeles Salles

“…However, because these classification methods require large training datasets, they have not yet been generalized to classify types of bat communication calls. …”
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A 3D Free Vibration Analysis of the Horn-Gear System through Chebyshev–Ritz Method in Ultrasonic Gear Honing by Yi Li, Ming Lv, Shiying Wang, Huibin Qin

“…Through a comparative analysis of the frequencies obtained by these three methods, it showed that the results achieved by the Chebyshev–Ritz method were close to those obtained from the experiment and finite element method. …”
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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

“…Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. …”
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Identification of a Novel Subtype of Ocular Hypertensive Eyes with the Lowest Risk of Progression Under Treatment by Asma Poursoroush, Vida Abedi, Xiaoqin Huang, Jiang Li, Alireza Vafaei Sadr, Amy L. de Jongh Curry, Louis R. Pasquale, Michael V. Boland, Chris A. Johnson, Siamak Yousefi

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A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft by Dongsheng Yuan, Feng Liu, Zhonggang Yin, Yanqing Zhang, Yanping Zhang, Peien Luo

“…Abstract The fault signal characteristics of motor rolling bearings for more electric aircraft are easily masked by strong background noise. Directly using machine learning, deep learning, or other methods results in a lower accuracy in fault recognition. …”
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Comparative Study of the Excavation Damage and Rockburst of the Deeply Buried Jinping II Diversion Tunnels Using a TBM and the Drilling-Blasting Method by Jing Yang, Xing-Guo Yang, Jia-Wen Zhou, Yong Liu, Bao-Shun Dong, Hai-Bo Li

“…Based on the results of acoustic wave tests and rockburst statistical analysis conducted, this study focuses on the excavation damaged zone (EDZ) and rockburst events in the Jinping II diversion tunnels excavated using the tunnel boring machine (TBM) method and the drilling-blasting method. …”
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Electric Motor Vibration Signal Classification Using Wigner–Ville Distribution for Fault Diagnosis by Jian-Da Wu, Wen-Jun Luo, Kai-Chao Yao

“…Through the WVD method, the brushless motor data is converted, and the YOLO (you only look once) deep coiling machine neural method is used to identify and classify the brushless motor WVD images. …”
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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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Compound ultrasonic vibration systems with a complex disk tool: Local resonance characteristics, FEM-based modal analysis, and design method by Cunying Zhang, Wenbo Bie, Yu Liu, Fan Chen

“…This vibration system conforms to the local resonance theory of slender rods. A theoretical method for calculating the nodes of the ultrasonic vibration system with a discoid non-resonant frequency was developed. …”
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Marine Mammals Classification using Acoustic Binary Patterns by Maheen NADIR, Syed Muhammad ADNAN, Sumair AZIZ, Muhammad Umar KHAN

“…Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. …”
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Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value... by Jie Zhou

“…We developed Support Vector Machine (SVM) and Logistic Regression (LR) models, with the LR model demonstrating a more robust predictive capability. …”
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