Building Consumption Data Systems Driven by AI Plus Expert for Scientific and Technical Literature Information Resources by Guanghui YE, Kai TU, Lina HU, Li HAN, Zhiming FENG

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RT-ring: a small wearable device for tremulous Parkinson’s disease diagnosis in primary care by Jolanda Buonocore, Jolanda Buonocore, Basilio Vescio, Basilio Vescio, Marida De Maria, Marianna Crasà, Rita Nisticò, Pier P. Arcuri, Giuseppe L. Cascini, Giuseppe L. Cascini, Giuseppe L. Cascini, Anna Latorre, Aldo Quattrone, Andrea Quattrone, Andrea Quattrone

“…The RT-ring is a miniaturized mobile device that uses machine learning based on inertial tremor data to estimate the RT pattern. …”
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Leveraging machine learning for analyzing the nexus between land use and land cover change, land surface temperature and biophysical indices in an eco-sensitive region of Brahmani-... by Bhaskar Mandal

“…This present study aims to evaluate land use and land cover changes using five machine-learning algorithms in Google Earth Engine. …”
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A novel method for quantifying the contribution of regional transport to PM_2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory... by K. Hu, H. Liao, D. Liu, J. Jin, L. Chen, S. Li, Y. Wu, C. Wu, S. Zhao, X. Jiang, P. Tian, P. Tian, K. Bi, K. Bi, Y. Wang, Y. Wang, Y. Wang, D. Zhao, D. Zhao

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Psychotherapist remarks’ ML classifier: insights from LLM and topic modeling application by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

“…IntroductionThis paper addresses the growing intersection of machine learning (ML) and psychotherapy by developing a classification model for analyzing topics in therapist remarks. …”
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Optimising Mechanical Performance of Additive Manufactured Composites for Biomedical Applications by Abdul Qadir, Amadi Gabriel Udu, Norman Osa-uwagboe

“…While previous studies have explored the role of process parameters in optimising AM components, the impact of infill geometry on anisotropy and mechanical performance remains underexplored, particularly in the context of machine learning (ML). This study develops an ML-driven framework to predict the tensile and flexural properties of AM SFRP composites with different infill patterns, including triangular, hexagonal, and rectangular. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…The growing availability of wearable devices capable of real-time HRV data collection opens up opportunities for early detection and health status classification using machine learning. ObjectiveThis study aimed to identify HRV patterns capable of distinguishing individuals with active COVID-19 and post–COVID-19 condition and healthy controls using data collected from wearable devices and processed using machine learning models. …”
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Temporal and Spatial Dynamics of Dust Storms in Uzbekistan from Meteorological Station Records (2010–2023) by Natella Rakhmatova, Bakhriddin E. Nishonov, Lyudmila Shardakova, Albina Akhmedova, Alisher Khudoyberdiev, Valeriya Rakhmatova, Dmitry A. Belikov

“…The integration of the ERA5 reanalysis and machine learning techniques offers significant potential for improving SDS monitoring and studies.…”
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IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers by Enes Ozelbas, Tuba Sevimoglu, Tamer Kahveci

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Causal Discovery Analysis Reveals Global Sources of Predictability for Regional Flash Droughts by Sudhanshu Kumar, Di Tian

“…CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high‐pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. …”
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LED-Based Collimating Line-Light Combining Freeform and Fresnel Optics by AnneMarie McCarthy, Javier Romero-Vivas, Ciara O'Hara, Natalia Rebrova, Liam Lewis, Stephen P. Hegarty

“…Illumination for line-scan machine vision systems is required to produce a highly asymmetric elliptical beam pattern, to maximize system speed and accuracy. …”
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ICARus: a pipeline to extract robust gene expression signatures from transcriptome datasets by Zhaorong Li, Juan I. Fuxman Bass, Juan I. Fuxman Bass, Juan I. Fuxman Bass, Juan I. Fuxman Bass

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Integrated Hyperparameter Optimization with Dimensionality Reduction and Clustering for Radiomics: A Bootstrapped Approach by S. J. Pawan, Matthew Muellner, Xiaomeng Lei, Mihir Desai, Bino Varghese, Vinay Duddalwar, Steven Y. Cen

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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

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The AI Paradox: Balancing Free Speech with Online Safety in the Era of Automated Moderation by Cosmin PROȘCANU, Miruna PROȘCANU

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Contrasting spatial variations between above and below-ground net primary productivity in global grasslands by Ying Hu, Yue Yang, Yu Wei, Xiaozhen Li, Yue Jiao, Jiapei Liao, Ruiyu Fu, Lichong Dai, Zhongmin Hu

“…However, the spatial pattern and influencing factors of NPP and its components, especially the belowground net primary productivity (BNPP) remain unclear. …”
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Deep Learning for the Early Diagnosis of Candidemia by Daniele Roberto Giacobbe, Sabrina Guastavino, Anna Razzetta, Cristina Marelli, Sara Mora, Chiara Russo, Giorgia Brucci, Alessandro Limongelli, Antonio Vena, Malgorzata Mikulska, Alessio Signori, Antonio Di Biagio, Anna Marchese, Ylenia Murgia, Marco Muccio, Nicola Rosso, Michele Piana, Mauro Giacomini, Cristina Campi, Matteo Bassetti

“…Conclusions A deep learning model trained on nonspecific laboratory features showed some discriminatory ability to differentiate candidemia from bacteremia, highlighting the ability of deep learning to exploit complex patterns within nonspecific laboratory data. However, the learned patterns did not improve the diagnostic performance of more specific markers. …”
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A Data-Driven Approach for Predicting Remaining Useful Life of Semiconductor Devices Based on Machine Learning and Synthetic Data Generation: A Review and Case Study on SiC MOSFETs by Yarens J. Yarenscruz, Fernando Castano, Alberto Villalonga, Madhav Mishra, Rodolfo E. Haber

“…Data-driven approaches, particularly those methods based on machine learning, are currently being used due to their ability to model complex degradation patterns without the need for explicit physical modeling. …”
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Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training by Joseph A. Kilgore, Jeffrey D. Kopsick, Giorgio A. Ascoli, Gina C. Adam

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