The state of the biogeometric profile of the posture of young athletes specializing in hand-to-hand combat as a prerequisite for the development of corrective and preventive measur... by Vitalii Kashuba, Аndrii Radchenko, Yuri Radchenko, Ilia Vako, Vitaly Usychenko

“…In the course of the study, the features of the biomechanics of posture of young athletes specializing in hand-to-hand combat were determined. …”
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Feasibility study of Learning Together for Mental Health: fidelity, reach and acceptability of a whole-school intervention aiming to promote health and wellbeing in secondary schoo... by Neisha Sundaram, Oliver Lloyd-Houldey, Joanna Sturgess, Elizabeth Allen, Semina Michalopoulou, Steven Hope, Rosa Legood, Stephen Scott, Lee D Hudson, Dasha Nicholls, Deborah Christie, Russell M Viner, Chris Bonell

“…Interventions Whole-school intervention featuring student needs assessment, action groups involving staff and students which selected actions from an evidence-based menu, restorative practice to improve relationships and address student behaviour and a social and emotional skills curriculum. …”
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Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis by Fahim Faisal, Antonios Danelakis, Marte-Helene Bjørk, Bendik Winsvold, Manjit Matharu, Parashkev Nachev, Knut Hagen, International Headache Genetics Consortium, Erling Tronvik, Anker Stubberud

“…The best model during training and validation was used on unseen test sets. …”
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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations (PDEs) that is based on online/adaptive learning. It is applied in the context of multiphase flow in porous media. …”
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Radiomics with Clinical Data and [¹⁸F]FDG-PET for Differentiating Between Infected and Non-Infected Intracavitary Vascular (Endo)Grafts: A Proof-of-Concept Study by Gijs D. van Praagh, Francine Vos, Stijn Legtenberg, Marjan Wouthuyzen-Bakker, Ilse J. E. Kouijzer, Erik H. J. G. Aarntzen, Jean-Paul P. M. de Vries, Riemer H. J. A. Slart, Lejla Alic, Bhanu Sinha, Ben R. Saleem

“…<b>Objective:</b> We evaluated the feasibility of a machine-learning (ML) model based on clinical features and radiomics from [¹⁸F]FDG PET/CT images to differentiate between infected and non-infected intracavitary vascular grafts and endografts (iVGEI). …”
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Evaluation of Hand-Scaling Skills of Dental Hygienist Students: Identification of Contact Between Hand-Scaler Blade Tip and Tooth Surface by Tomoko Yui, Sung-Gwi Cho, Yuki Sato, Yasuaki Orita, Ming Ding, Jun Takamatsu, Takahiro Wada, Tsukasa Ogasawara

“…The accuracy was 88.8&#x0025; with six features from the force sensor alone. These results indicate that the IMU alone can identify the correct contact state, highlighting the possibility of creating a realistic simulator for training dental hygienists in evaluating the blade-contact state.…”
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USING E-LEARNING TO PREVENT INFORMATIONAL STRESS OF EMPLOYEES WORKING REMOTELY by Oleksandra Pinchuk, Nataliia I. Pinchuk, Olena I. Bondarchuk, Valentyna V. Balakhtar, Kateryna S. Pavlenok

“…The following specific features of informational stress, by their information impact, are highlighted: 1) features associated with information overload, 2) features associated with human interaction with information technology, and 3) features associated with professional activities. …”
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Digital Repeat Photography Application for Flowering Stage Classification of Selected Woody Plants by Monika A. Różańska, Kamila M. Harenda, Damian Józefczyk, Tomasz Wojciechowski, Bogdan H. Chojnicki

“…All the image RGB parameters, designated for each plant separately, were used as plant features for the models’ parametrization. The training data were subjected to various transformations to achieve the best classifications using the weighted <i>k</i>-nearest neighbors algorithm. …”
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Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review by Adam C. Szekely-Kohn, Marco Castellani, Daniel M. Espino, Luca Baronti, Zubair Ahmed, William G. K. Manifold, Michael Douglas

“…Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. …”
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Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks by Haoyu Wang, Sheng Cui, Changjian Ke, Chenglong Yu, Zi Liang, Deming Liu

“…We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. …”
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Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study by Lei Yuan, Qi Wang, Fei Sun, Hongcan Shi

“…To address this, we developed a CT-based deep learning model integrated with the systemic immune-inflammatory-nutritional index (SIINI) for early prediction of ICI response. …”
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A compliant metastructure design with reconfigurability up to six degrees of freedom by Humphrey Yang, Dinesh K. Patel, Tate Johnson, Ke Zhong, Gina Olson, Carmel Majidi, Mohammad F. Islam, Teng Zhang, Lining Yao

“…To address this gap, we propose a metastructure concept featuring reconfigurable motional freedom and tunable stiffness, adaptable to various form factors and applications. …”
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A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points by Suyu Wang, Quan Yue, Zhenlei Xu, Peihong Qiao, Zhentao Lyu, Feng Gao

“…We propose a novel cooperative Multi-Agent Reinforcement Learning (MARL) algorithm based on MADDPG, termed MACPH, which innovatively incorporates three mechanisms: a Composite Experience Replay Buffer (CERB) mechanism that balances recent and important historical experiences through a dual-buffer structure and mixed sampling; an Adaptive Parameter Space Noise (APSN) mechanism that perturbs actor network parameters and dynamically adjusts the perturbation intensity to achieve coherent and state-dependent exploration; and a Huber loss function mechanism to mitigate the impact of outliers in Temporal Difference errors and enhance training stability. …”
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MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging by Sibtain Syed, Rehan Ahmed, Arshad Iqbal, Naveed Ahmad, Mohammed Ali Alshara

“…The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. …”
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DTASUnet: a local and global dual transformer with the attention supervision U-network for brain tumor segmentation by Bo Ma, Qian Sun, Ze Ma, Baosheng Li, Qiang Cao, Yungang Wang, Gang Yu

“…First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. …”
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InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem by Seungchan An, Yeonjin Lee, Junpyo Gong, Seokyoung Hwang, In Guk Park, Jayhyun Cho, Min Ju Lee, Minkyu Kim, Yun Pyo Kang, Minsoo Noh

“…To further expand the chemical space, InertDB also features 64,368 generated inactive compounds (GICs) produced using a deep generative AI model trained on the CIC dataset. …”
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Combining Transfer Learning and Ensemble Algorithms for Improved Citrus Leaf Disease Classification by Hongyan Zhu, Dani Wang, Yuzhen Wei, Xuran Zhang, Lin Li

“…By constructing efficient deep learning models and training and optimizing them with a large dataset of citrus leaf images, we ensured the broad applicability and accuracy of citrus leaf disease detection, achieving high-precision classification. …”
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Collaborative AI Dysarthric Speech Recognition System With Data Augmentation Using Generative Adversarial Neural Network by Yibo He, Kah Phooi Seng, Li-Minn Ang

“…The third stage integrates the Inception-ResNet module with a temporal masking strategy using an enhanced CycleGAN-based conversion model to efficiently map conformal and non-conformal phonological features while preserving the overall speech structure and resolving temporal irregularities. …”
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GPE-DNeRF:Neural radiance field method for surface geological bodies reconstruction by Xinyi Wang, Weihua Hua, Xiuguo Liu, Peng Li, Guohe Li

“…Neural Radiance Fields (NeRF) have been employed to generate 3D scenes by training models on images captured from different viewpoints. …”
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Improving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement by Zhiliang Yan, Haosong Huang, Rongmei Geng, Jingang Zhang, Yu Chen, Yunfeng Nie

“…Specifically, we employ K-means unsupervised clustering combined with human-guided selection to refine coarse annotations into cell-level masks based on spectral features. Our method is validated using a hyperspectral lung squamous cell carcinoma dataset containing 65 image samples. …”
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