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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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Validating the predictions of mathematical models describing tumor growth and treatment response

arXiv

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arXiv 2025年

作者： Lorenzo, Guillermo Hormuth, David A. Wu, Chengyue Pash, Graham Chaudhuri, Anirban Lima, Ernesto A.B.F. Okereke, Lois C. Patel, Reshmi Willcox, Karen Yankeelov, Thomas E. Group of Numerical Methods in Engineering Department of Mathematics University of A Coruña Spain Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX United States Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Breast Imaging The University of Texas MD Anderson Cancer Center HoustonTX United States Department of Biostatistics The University of Texas MD Anderson Cancer Center HoustonTX United States Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Texas Advanced Computing Center The University of Texas at Austin AustinTX United States Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX United States Department of Oncology The University of Texas at Austin AustinTX United States

Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticipation of tumor outcomes to hasten the discovery of cancer mechanisms and personalize disease management. To address these limitations, individualized cancer forecasts have been shown to predict tumor growth and therapeutic response, inform treatment optimization, and guide experimental efforts. These predictions are obtained via computer simulations of mathematical models that are constrained with data from a patient’s cancer and experiments. This book chapter addresses the validation of these mathematical models to forecast tumor growth and treatment response. We start with an overview of mathematical modeling frameworks, model selection techniques, and fundamental metrics. We then describe the usual strategies employed to validate cancer forecasts in preclinical and clinical scenarios. Finally, we discuss existing barriers in validating these predictions along with potential strategies to address them. © 2025, CC BY-NC-ND.

关键词： Prediction models

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Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, Ronan Lucor, Didier Iooss, Bertrand Brajard, Julien Xiao, Dunhui Janjic, Tijana Ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, Rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： Dynamical systems

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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White spot syndrome virus (WSSV) modulates lipid metabolism in white shrimp

Communications Biology

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Communications Biology 2023年第1期6卷 546页

作者： Ng, Yen Siong Cheng, Cheng-Shun Ando, Masahiro Tseng, Yi-Ting He, Shu-Ting Li, Chun-Yuan Cheng, Shu-Wen Chen, Yi-Min Kumar, Ramya Liu, Chun-Hung Takeyama, Haruko Hirono, Ikuo Wang, Han-Ching Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan Research Organization for Nano and Life Innovations Waseda University Tokyo Japan International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan Department of Aquaculture National Pingtung University of Science and Technology Pingtung Taiwan Department of Life Science and Medical Bioscience Waseda University Tokyo Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology Tokyo Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and Engineering Tokyo Japan Department of Marine Biosciences Tokyo University of Marine Science and Technology Tokyo Japan

In addition to the Warburg effect, which increases the availability of energy and biosynthetic building blocks in WSSV-infected shrimp, WSSV also induces both lipolysis at the viral genome replication stage (12 hpi) to provide material and energy for the virus replication, and lipogenesis at the viral late stage (24 hpi) to complete virus morphogenesis by supplying particular species of long-chain fatty acids (LCFAs). Here, we further show that WSSV causes a reduction in lipid droplets (LDs) in hemocytes at the viral genome replication stage, and an increase in LDs in the nuclei of WSSV-infected hemocytes at the viral late stage. In the hepatopancreas, lipolysis is triggered by WSSV infection, and this leads to fatty acids being released into the hemolymph. β-oxidation inhibition experiment reveals that the fatty acids generated by WSSV-induced lipolysis can be diverted into β-oxidation for energy production. At the viral late stage, WSSV infection leads to lipogenesis in both the stomach and hepatopancreas, suggesting that fatty acids are in high demand at this stage for virion morphogenesis. Our results demonstrate that WSSV modulates lipid metabolism specifically at different stages to facilitate its replication. © 2023, The Author(s).

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Closed-looped sensing and stimulation system for Parkinson's disease early diagnosis and rehabilitation

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Smart Health 2022年 26卷

作者： Cai, Yi Qian, Xiaoye Li, Qin Lin, Feng Huang, Ming-Chun Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China ZJU-Hangzhou Global Scientific and Technological Innovation Center School of Cyber Science and Technology Zhejiang University Zhejiang 310058 China Suzhou Huanmu Intelligence Technology Co. Ltd. Jiangsu China

Parkinson's disease (PD) patients are involved in motor dysfunctions and gait issues. The absence of long-term reliable gait rehabilitations could result in poor gait function, gait deficits, and locomotion problems. Meanwhile, PD patients usually face significant challenges to complete the rehabilitation programs effectively without a specialized gait laboratory or after discharge from the hospital. This paper presents a closed-loop sensing and computing system to facilitate long-term medical care for PD patients. The proposed system consists of wearable sensing, data streaming, online data processing, real-time auditory stimulation for PD gait rehabilitation, and data services for medical providers. To address the potential issues of user-friendly and comforts, we implemented a wrapped shoe based plantar pressure sensing device for under-feet pressure data collection. Afterward, quantitative measurements of gait are fulfilled through online data streaming and processing architecture. Physical therapy is generally preferred for PD rehabilitation because it provides a more significant benefit superior to the drugs. Hence, applications of gait activity recognition and closed-loop stimulation are proposed to build a reliable closed-loop system and support self-contained training. To evaluate the proposed system, we conduct experiments with three gait and PD related datasets. It broads Parkinson's gait health care prospects that make long-term PD rehabilitation possible in community-living environment. © 2022 Elsevier Inc.

关键词： data streaming processing Gait rehabilitation Rhythmic auditory stimulation Wearable sensing

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A comparative analysis of thermal and pressure distribution in rotating flows of von Karman and Bodewadt using multivariate regression models: A case of machine learning techniques

引用

International Communications in Heat and Mass Transfer 2025年 166卷

作者： Upreti, Himanshu Pandey, Alok Kumar Pandey, Ankita Bartwal, Priya School of Engineering and Technology BML Munjal University Haryana Gurugram India Center for Advanced Data and Computational Science BML Munjal University Haryana Gurugram India Uttarakhand Dehradun India Department of Mathematics GLA University Uttar Pradesh Mathura India

This work is related to fluid flow over a rotating disk, which has attracted significant interest due to its applications in engineering, aerodynamics, and industrial processes. This research presents a detailed numerical solution of rotating flows of von Karman and Bodewadt. The system of boundary layer approximation consists of the external forces i.e. Hall current, magnetic field, thermal radiation, Darcy-Forchheimer porous model and slip mechanism. The governing equations are boundary value problem which is solved using bvp4c solver, that efficiently handles non-linear differential equations with boundary conditions at two different points. To further analyze the flow characteristics, we employ multivariate linear and polynomial regression models, providing data driven perspective on the dependency of SFC (skin friction coefficient) and LNN (local Nusselt number) on governing parameters. The study reports that, multivariate linear regression and polynomial regression are the machine learning approaches used to estimate the SFC and LNN values for the von Karman and Bodewadt flows, respectively. The MAE (mean absolute error), MSE (mean square error), andR2are used to evaluate the prediction's performance. The highest R2values for the implemented method is obtained as 0.98905073. The results obtained demonstrate the efficacy of machine learning techniques in this field. © 2025 Elsevier Ltd

关键词： Maximum principle

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Comparative analysis of time irreversibility and amplitude irreversibility based on joint permutation

arXiv

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arXiv 2022年

作者： Yao, Wenpo Yao, Wenli Xu, Rongshuang Wang, Jun School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China Department of Hydraulic Engineering School of Civil Engineering Tsinghua University Beijing100084 China School of Applied Meteorology Nanjing University of Information Science & Technology Nanjing210044 China

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of TIR and AIR, the amplitude permutation and global information of the associated vector are combined to produce a joint probability estimation. Chaotic logistic, Henon, and Lorenz series are generated to evaluate TIR and AIR according to surrogate theory, and the distributions of joint permutations for these model series are measured to compare the degree of TIR and AIR. The joint permutation TIR and AIR are then used to investigate nonequilibrium pathological features in epileptic electroencephalography data. Test results suggest that epileptic brain electrical activities, particular those during the onset of seizure, manifest higher nonequilibrium characteristics. According to the statistical definitions and targeted pairs of joint permutations in the chaotic model data, TIR and AIR are fundamentally different nonequilibrium descriptors from time- and amplitude-reversibility, respectively, and thus require different forms of numerical analysis. At the same time, TIR and AIR both provide measures for fluctuation theorems associated with nonequilibrium processes, and have similar probabilistic differences in the pairs of joint permutations and consistent outcomes when used to analyze both the model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures. Additionally, the construction of joint permutations contributes to the development of symbolic time series analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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14 Examples of How LLMs Can Transform Materials science and Chemistry: A Reflection on a Large Language Model Hackathon

arXiv

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arXiv 2023年

作者： Jablonka, Kevin Maik Ai, Qianxiang Al-Feghali, Alexander Badhwar, Shruti Bocarsly, Joshua D. Bran, Andres M. Bringuier, Stefan Brinson, L. Catherine Choudhary, Kamal Circi, Defne Cox, Sam de Jong, Wibe A. Evans, Matthew L. Gastellu, Nicolas Genzling, Jerome Gil, María Victoria Gupta, Ankur K. Hong, Zhi Imran, Alishba Kruschwitz, Sabine Labarre, Anne Lála, Jakub Liu, Tao Ma, Steven Majumdar, Sauradeep Merz, Garrett W. Moitessier, Nicolas Moubarak, Elias Mouriño, Beatriz Pelkie, Brenden Pieler, Michael Ramos, Mayk Caldas Ranković, Bojana Rodriques, Samuel G. Sanders, Jacob N. Schwaller, Philippe Schwarting, Marcus Shi, Jiale Smit, Berend Smith, Ben E. Van Herck, Joren Völker, Christoph Ward, Logan Warren, Sean Weiser, Benjamin Zhang, Sylvester Zhang, Xiaoqi Zia, Ghezal Ahmad Scourtas, Aristana Schmidt, K.J. Foster, Ian White, Andrew D. Blaiszik, Ben Sion Valais Switzerland Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Chemistry McGill University MontrealQC Canada Reincarnate Inc. United States Yusuf Hamied Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom Lausanne Switzerland Lausanne Switzerland San Diego CA United States Mechanical Engineering and Materials Science Duke University United States Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Chemical Engineering University of Rochester United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States UCLouvain Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée 6534 Belgium CSIC Francisco Pintado Fe 26 Oviedo33011 Spain Department of Computer Science University of Chicago ChicagoIL60637 United States Computer Science University of California Berkeley BerkeleyCA94704 United States Bundesanstalt für Materialforschung und -Prüfung Unter den Eichen 87 Berlin12205 Germany Francis Crick Institute 1 Midland Rd LondonNW1 1AT United Kingdom American Family Insurance Data Science Institute University of Wisconsin-Madison MadisonWI53706 United States Department of Chemical Engineering University of Washington SeattleWA98105 United States *** Stability.AI United Kingdom Department of Chemistry and Biochemistry University of California Los AngelesCA90095 United States Department of Computer Science University of Chicago ChicagoIL60490 United States Data Science and Learning Division Argonne National Lab United States Globus University of Chicago Data Science and Learning Division Argonne National Lab United States Department of Computer Science University of Chicago Data Science and Learning Division Argonne National Lab United

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. © 2023, CC BY.

关键词： computational linguistics

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