Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials by Jan Navrátil, Rafał Topolnicki, Michal Otyepka, Piotr Błoński

“…However, identifying optimal TM-substrate configurations is challenging when relying solely on density functional theory (DFT) calculations with spin-orbit coupling. …”
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Implementation of Arabic Language Learning Media in Islamic Universities: Benefits and Problems by Isna Mufidah, Siti Khoiriyah, Nurul Ainiy

“…As instructional aids, media support educators in delivering material and enhancing student comprehension. In Arabic language learning, the four linguistic skills—Istima’ (listening), Kalam (speaking), Qira’ah (reading), and Kitabah (writing)—require diverse media tailored to their specific functions and objectives. …”
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Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4 by Aizhong Xu, Jianjun Liu, Li Tong, Tingting Shen, Songlin Xing, Yujie Xia, Bosen Zhang, Zihao Wu, Wenkang Yuan, Anhai Yu, Zijie Kan, Wenqi Yang, Chao Zhang, Chong Zhang

“…Mfuzz expression pattern clustering and functional enrichment were applied to genes with BEX4 as the core to explore their regulatory mechanisms. …”
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Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification by Shao-Hsuan Chang, Lung-Kun Yeh, Kuo-Hsuan Hung, Yen-Jung Chiu, Chia-Hsun Hsieh, Chung-Pei Ma

“…<b>Objective:</b> This study employed multiple machine learning algorithms to analyze the transcriptomes of keratoconus patients, identifying feature gene combinations and their functional associations, with the aim of enhancing the understanding of keratoconus pathogenesis. …”
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Evaluative methodological proposal in the performance of the teaching learning process in the university policlinic by Maricel Castellanos González, Jorge Cañellas Granda, Mikhail Benet Rodríguez, Luis Senén Rodríguez Fernández

“…The instruments to be used for the evaluation of the implementation in the teaching learning process of this new pedagogical model were proposed. …”
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Federated Learning for a Dynamic Edge: A Modular and Resilient Approach by Leonardo Almeida, Rafael Teixeira, Gabriele Baldoni, Mário Antunes, Rui L. Aguiar

“…The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. …”
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Detection and location of EEG events using deep learning visual inspection. by Mohammad Amin Fraiwan

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Machine learning‐guided plasticity model in refractory high‐entropy alloys by Shang Zhao, Jinshan Li, Weijie Liao, Ruihao Yuan

“…Additionally, an interpretable machine learning algorithm is employed to derive explicit functional expressions describing the relationship between key features and fracture strain, achieving 88% accuracy. …”
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Deep learning molecular interaction motifs from receptor structures alone by Seeun Kim, Simaek Oh, Hyeonuk Woo, Jiho Sim, Chaok Seok, Hahnbeom Park

“…MotifGen generates motif profiles at the receptor surface for 14 types of functional groups or 6 chemical interaction classes. …”
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Interpretability in deep learning for finance: A case study for the Heston model by Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

“…Deep learning is a powerful tool whose applications in quantitative finance are growing every day. …”
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Machine learning-assisted Ru-N bond regulation for ammonia synthesis by Zichuang Li, Mingxin Zhang, Xiaozhi Su, Yangfan Lu, Jiang Li, Qing Zhang, Wenqian Li, Kailong Qian, Xiaojun Lu, Bo Dai, Hideo Hosono, Yanpeng Qi, Miao Xu, Renzhong Tai, Jie-Sheng Chen, Tian-Nan Ye

“…Herein, we demonstrate that a combination of machine learning (ML) and model mining techniques can effectively address these challenges. …”
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Industrial-scale prediction of cement clinker phases using machine learning by Sheikh Junaid Fayaz, Néstor Montiel-Bohórquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan

“…Here, using a comprehensive two-year industrial dataset, we develop machine learning models that outperform conventional Bogue equations with mean absolute percentage errors of 1.24%, 6.77%, and 2.53% for alite, belite, and ferrite prediction respectively, compared to 7.79%, 22.68%, and 24.54% for Bogue calculations. …”
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Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy by Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim, Kwan Yun

“…Abstract We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential equations alongside Monte-Carlo integration, we develop a method tailored for continuous training functions to extract the general isotropic bulk metric from entanglement entropy data. …”
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Psycho-Theological Learning Model to Improve Mental Resilience in Muslim Adolescents by Muhammad Zamzam, Winsidi Winsidi

“…Teachers functioned as both spiritual facilitators and emotional companions, while a spiritually nourishing learning environment proved pivotal. …”
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Education Paradigm Shifts in the Age of AI: A Spatiotemporal Analysis of Learning by Ruojun Zhong (仲若君), Yong Zhao (赵勇)

“…Design/Approach/Methods A theoretical analysis of functions of the five elements (curriculum, pedagogy, activities, learning environments, and assessments) of schooling in controlling time and space in learning. …”
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Recent advances in deep learning for protein-protein interaction: a review by Jiafu Cui, Siqi Yang, Litai Yi, Qilemuge Xi, Dezhi Yang, Yongchun Zuo

“…Protein-protein interactions (PPIs) are fundamental regulators of biological functions. With the inclusion of deep learning in PPI research, the field is undergoing transformative changes. …”
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Machine learning tools for deciphering the regulatory logic of enhancers in health and disease by Spyros Foutadakis, Vasiliki Bourika, Ioanna Styliara, Panagiotis Koufargyris, Asimina Safarika, Eleni Karakike

“…Recently, machine learning approaches and particularly deep learning models (Enformer, BPNet, DeepSTARR, etc.) allow the prediction of enhancers, the impact of variants on their activity and the inference of transcription factor binding sites, leading, among others, to the construction of the first completely synthetic enhancers. …”
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Design and Evaluation of an Augmented Reality Application for Learning Workshop Tool Recognition by Resa Pramudita, Dedi Rohendi, Roer Eka Pawinanto, Muhammad Adli Rizqulloh, Mochamad Rizal Fauzan, Dedy Suryadi, Iwa Kuntadi

“…Key usability aspects were rated positively, including comfort (4.2), ease of use (4.0), functional integration (3.9), stability (4.1), and ease of learning (4.3). …”
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A variational deep-learning approach to modeling memory T cell dynamics. by Christiaan H van Dorp, Joshua I Gray, Daniel H Paik, Donna L Farber, Andrew J Yates

“…This method uses deep learning and stochastic variational inference and is trained on the single-cell flow-cytometry data directly, rather than on the kinetics of pre-identified clusters. …”
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The Influence of School Principals' Learning Leadership and School Climate on Teacher Performance by A. Aulia Reski Novianti Alnisyar, Fatmiati Nur

“…Based on this, the researcher took the role of learning leadership as the object of research because the researcher considers that the leadership role of the school principal is considered capable of improving the quality of education and creating a conducive school atmosphere, not only carrying out his duties and functions as a school leader. …”
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