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Anisotropic exchange spin model to investigate the Curie temperature dispersion of L10-FePt magnetic nanoparticles

Journal of Applied Physics 2025年第18期137卷

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic exchange spin model that accounts for magnetic anisotropy and evaluated the Curie temperature ( T c ) dispersion due to finite-size effects in L1 0 -FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L1 0 -FePt nanoparticles near their T c and rapidly cooling them. However, variations in T c caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of T c dispersion critical. In this study, we constructed atomistic Landau-Lifshitz-Gilbert models to explicitly incorporate the spin exchange anisotropy of L 1 0 -FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on T c dispersion. As a result, (1) the T c dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70 % of the experimentally observed T c dispersion can be attributed to particle-size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. © 2025 Author(s).

关键词： Magnetic nanoparticles

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Cybersecurity Enhancement in IoT-Enabled Public Health Information Systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health Information Systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare Information systems Testing

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Transparent Correlated Metallic Perovskites with Conducive Chemical Disorder

arXiv

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arXiv 2025年

作者： Almishal, Saeed S.I. Kezer, Pat Iwabuchi, Yasuyuki Sivak, Jacob T. Ayyagari, Sai Venkata Gayathri Sarker, Saugata Furst, Matthew Bejger, Gerald Yang, Billy Gelin, Simon Alem, Nasim Dabo, Ismaila Rost, Christina M. Sinnott, Susan B. Crespi, Vincent Gopalan, Venkatraman Engel-Herbert, Roman Heron, John T. Maria, Jon-Paul Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Electrical Engineering University of Michigan Ann ArborMI48109 United States Department of Chemistry The Pennsylvania State University University ParkPA16802 United States Department of Physics The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Paul-Drude Institute for Solid State Electronics Hausvogteiplatz 5-7 Berlin10117 Germany Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States

This manuscript presents a working model linking chemical disorder and transport properties in correlated-electron perovskites with high-entropy formulations and a framework to actively design them. We demonstrate this new learning in epitaxial Srx(Ti,Cr,Nb,Mo,W)O3 thin films that exhibit exceptional crystalline fidelity despite a diverse chemical formulation where most B-site species are highly misfit with respect to valence and radius. X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy confirm a unique combination of chemical disorder and structural perfection in thick epitaxial layers. This combination produces significant electron correlation, low electrical resistivity, and an optical transparency window that surpasses that of constituent end-members, with a flattened frequency- and temperature-dependent response. We address the computational challenges of modeling such systems and investigate short-range ordering using cluster expansion. These results showcase that unusual d-metal combinations access an expanded property design space that is predictable using end-member characteristics – though unavailable to them – thus offering performance advances in optical, spintronic, and quantum devices. © 2025, CC BY-NC-SA.

关键词： Perovskite

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Leveraging Ensemble Learning with Deep Learning for Accurate Customer Churn Prediction in Subscription-Based Mode

Leveraging Ensemble Learning with Deep Learning for Accurate...

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Intelligent Systems and computational Networks (ICISCN), International Conference on

作者： Janeshwar Raj Y Divya Nimma Vijay Kumar Veera Ankalu Vuyyuru Ravindra Changala B Kiran Bala Saveetha School of Law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Physics Graphic Era Hill University Dehardun Graphic Era Deemed to be University Dehradun India Department of CSE Koneru Lakshmaiah Education Foundation India Department of CSE Guru Nanak Institutions Technical Campus Hyderabad India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

An ensemble learning approach to address customer churn prediction for businesses which utilize subscription models. Ensemble methods, including bagging, boosting, and stacking, are combined with multiple base learners to improve predictive accuracy, leveraging deep learning architectures like convolutional and recurrent neural networks to capture temporal and spatial dependencies in customer behavior. The research utilizes a comprehensive dataset comprising historical customer interactions, demographic details, service usage, and churn outcomes. Preprocessing ensures the dataset’s readiness for machine learning. Ensemble models aggregate predictions from multiple classifiers, mitigating overfitting risks and achieving superior performance in precision, recall, and F1 score compared to individual models. Thus, the proposed methodology helps to enhance customer lifetime value, promote long-term business development and increase customer satisfaction with 98% accuracy. This approach offers more reliable churn predictions and actionable insights, enabling businesses to design targeted retention strategies and optimize services. By maximizing customer lifetime value, the proposed methodology contributes to sustained business growth and improved customer satisfaction.

关键词： Deep learning Accuracy Recurrent neural networks Stacking Predictive models Reliability engineering Ensemble learning Churn Business Overfitting

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Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

arXiv

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arXiv 2025年

作者： Sang, Shengtian Jahanandish, Hassan Li, Cynthia Xinran Bhattachary, Indrani Lee, Jeong Hoon Zhang, Lichun Vesal, Sulaiman Ghanouni, Pejman Fan, Richard Sonn, Geoffrey A. Rusu, Mirabela Stanford University Department of Radiology 300 Pasteur Drive StanfordCA94305 United States University of Miami Department of Computer Science Coral GablesFL33124 United States Stanford University Department of Urology 300 Pasteur Drive StanfordCA94305 United States Stanford University Institute of Computational and Mathematical Engineering 475 Via Ortega StanfordCA94305 United States Dartmouth College Department of Biomedical Data Science 1 Medical Center Drive LebanonNH03756 United States

Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI’s detailed visualization with TRUS’s real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p Copyright © 2025, The Authors. All rights reserved.

关键词： Urology

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Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study

arXiv

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arXiv 2025年

作者： Jahanandish, Hassan Sang, Shengtian Li, Cynthia Xinran Vesal, Sulaiman Bhattacharya, Indrani Lee, Jeong Hoon Fan, Richard Sonn, Geoffrey A. Rusu, Mirabela Stanford University Department of Urology 300 Pasteur Drive StanfordCA94305 United States Stanford University Department of Radiology 300 Pasteur Drive StanfordCA94305 United States University of Miami Department of Computer Science Coral GablesFL33124 United States Stanford University Institute of Computational and Mathematical Engineering 475 Via Ortega StanfordCA94305 United States Dartmouth College Department of Biomedical Data Science 1 Medical Center Drive LebanonNH03756 United States

Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target suspicious prostate lesions. This has led to artificial intelligence (AI) applications improving MRI-based detection of clinically significant prostate cancer (CsPCa). However, MRI-detected lesions must still be mapped to transrectal ultrasound (TRUS) images during biopsy, which results in missing CsPCa. This study systematically evaluates a multimodal AI framework integrating MRI and TRUS image sequences to enhance CsPCa identification. The study included 3110 patients from three cohorts across two institutions who underwent prostate biopsy. The proposed framework, based on the 3D UNet architecture, was evaluated on 1700 test cases, comparing performance to unimodal AI models that use either MRI or TRUS alone. Additionally, the proposed model was compared to radiologists in a cohort of 110 patients. The multimodal AI approach achieved superior sensitivity (80%) and Lesion Dice (42%) compared to unimodal MRI (73%, 30%) and TRUS models (49%, 27%). Compared to radiologists, the multimodal model showed higher specificity (88% vs. 78%) and Lesion Dice (38% vs. 33%), with equivalent sensitivity (79%). Our findings demonstrate multimodal AI’s potential to improve CsPCa lesion targeting during biopsy and treatment planning, surpassing current unimodal models and radiologists;ultimately improving outcomes for prostate cancer patients. Copyright © 2025, The Authors. All rights reserved.

关键词： Biopsy

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Anisotropic Exchange Spin Model to Investigate the Curie Temperature Dispersion of Finite-Size L10-FePt Magnetic Nanoparticles

arXiv

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arXiv 2025年

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic spin model that accounts for magnetic anisotropy and evaluated the Curie temperature (Tc) dispersion due to finite size effects in L10-FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L10-FePt nanoparticles near their Tc and rapidly cooling them. However, variations in Tc caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of Tc dispersion critical. In this study, we constructed atomistic LLG models to explicitly incorporate the spin exchange anisotropy of L10-FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on Tc dispersion. As a result, (1) the Tc dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70% of the experimentally observed Tc dispersion can be attributed to particle size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. Copyright © 2025, The Authors. All rights reserved.

关键词： Curie temperature

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Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

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Big data Mining and Analytics 2023年第1期6卷 72-84页

作者： Samin Poudel Marwan Bikdash Department of Computational Data Science and Engineering North Carolina A&T State UniversityGreensboroNC 27401USA

We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary *** SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)*** dataset characteristics considered include various densities of the rating matrix and the numbers of users and *** simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data *** SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)*** dropping ***,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero *** importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens *** 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF *** validation was conducted using the 1M MovieLens and the Yahoo!Music Rating *** models were constant and significant across all 3 datasets.

关键词： collaborative filtering subsampling accuracy loss models performance loss recommendation system simulation rating matrix root mean square error

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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