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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Saki Tsumoto Fusae Kawana Kazumasa Horie Minori Masaki Kei Nishida Kazuya Miyanishi Jaehoon Seol Morie Tominaga Takashi Amemiya Tetsuro Hiei Akihiro Tani Masaki Matsubara Atsuyuki Morishima Hiroyuki Kitagawa Masashi Yanagisawa Ph.D. Program in Humanics University of Tsukuba Tsukuba Japan Juntendo University Graduate School of Medicine Tokyo Japan Center for Computational Sciences (CCS) University of Tsukuba Tsukuba Japan Faculty of Health and Sports Sciences University of Tsukuba Tokyo Japan S ‘ UIMIN Inc Tokyo Japan Institute of Library Information and Media Science University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine (WPI-IIIS)) University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Sleep is a very important activity, but many people do not know their own sleep conditions. A sleep test personalizes sleep quality assessment and detects potential sleep disorders by measuring biological signals. The rise in sleep-related issues has necessitated the development of automated testing methods. Machine learning plays a pivotal role in interpreting sleep data and determining sleep stages. However, the generation of detailed reports and tailored recommendations still demands expert intervention. Automating the report generation to provide personalized sleep insights is a crucial and desired step for the future of sleep healthcare. Recently emerged Generative AI, such as ChatGPT, has attracted considerable attention in recent years. It can generate new sentences and images from input data. In this study, we investigate the practicality and applicability of using ChatGPT to generate narrative sleep reports for sleep test. In our proposed method, GPT-4 receives the information about the sleep habits of the participants and the sleep assessment automatically summarized by the rule-based algorithm. In the evaluation, we used in-home sleep EEG data obtained from 100 subjects by S'UIMIN inc. The generated reports were evaluated by experienced technicians and physicians on a 5- point scale for medical correctness and appropriateness as informative reports. The results of the evaluation showed that 60 % of the reports were the acceptable or above range in both aspects. While more than half of the results were judged to be above the acceptable range, differences between the generative AI and humans were also identified. Whereas humans comment on semantically weighted important findings such as medication and subjective insomnia, ChatGPT tends to make broad, shallow and flat comments on the input data. These facts suggest that although practical report generation only using generative AI is at present not easy, generative AI is a promising tool for improving the ef

关键词： Sleep Generative AI Medical services Machine learning Chatbots Electroencephalography Quality assessment

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SRAMM: Short read alignment mapping metrics

arXiv

引用

arXiv 2021年

作者： Chon, Alvin Huang, Xiaoqiu Bioinformatics and Computational Biology Graduate Program Iowa State University AmesIA50010 United States Department of Computer Science Iowa State University AmesIA50010 United States

Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile command line tool providing additional short read mapping metrics, filtering, and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods generally are neither standardized nor well described in literature or software manuals. Additionally, third party mapping quality programs are typically computationally intensive or designed for specific applications. SRAMM efficiently generates multiple different concept-based mapping scores to provide for an informative post alignment examination and filtering process of aligned short reads for various downstream applications. SRAMM is compatible with Python 2.6+ and Python 3.6+ on all operating systems. It works with any short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS' tags. It is freely available under the MIT license at http://***/achon/sramm. © 2021, CC BY.

关键词： Mapping

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Improving health awareness with real-time monitoring through a three-dimensional visualized digital health avatar

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Smart Health 2024年 34卷

作者： Chokphukhiao, Chaturapron Pattaranit, Pattrawan Tun, Wonn Shweyi Thet Masa, Sakaowrat Leemananil, Rattikorn Natteerapong, Nuttaphorn Phetcharaburanin, Jutarop Boonlue, Sophon Sunat, Khamron Patramanon, Rina Information Technology International Program College of Computing Khon Kaen University Khon Kaen 40002 Thailand Center of Excellence in Digital Innovation Faculty of Education Khon Kaen University Khon Kaen 40002 Thailand Integrative Biomedical Informatics and Computational Imaging Center I-BIC Faculty of Associated Medical Science Khon Kaen University Khon Kaen 40002 Thailand Department of System Biosciences and Computational Medicine Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Khon Kaen University National Phenome Institute Khon Kaen University Khon Kaen 40002 Thailand Department of Biochemistry Faculty of Science. Khon Kaen University Khon Kaen 40002 Thailand Department of Microbiology Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Computer Science College of Computing Khon Kaen University 40002 Thailand

Introduction: The use of modern technologies has become crucial for enhancing people's awareness of health problems. By early health risk detection, there will be better treatment outcomes, the severity of illness will decrease, and costs for treatment will also reduce. Performing routine checkups could help patients feel less anxious about their health in the future. In this study, the innovation of “Digital Health Avatar” enables users to monitor physical changes, raise awareness of medical conditions, and encourage healthy behaviors by visualizing health status as a 3D figure. Methods: Health data were collected using medical devices like blood test strips, body composition meters, and automatic blood pressure monitors. API technology and cloud computing systems were used to process these collected data and produced a three-dimensional avatar that indicated the user's health status. User satisfaction survey on using the health avatar system was assessed through a survey of 61 participants. Results: Health avatar system successfully generate the data and display the measurement results on the health report page of the system. Users can easily check and record their physiological conditions through the avatar. Moreover, users showed satisfaction on the use of the health avatar system and its effectiveness in health check-ups in the survey. Conclusions: The ‘Digital Health Avatar’ has the potential to significantly promote individual well-being and health awareness. Its development will be improved by user feedback and continued study, ensuring it becomes an efficient tool for promoting healthier lifestyles. © 2024 The Authors

关键词： Digital health avatar Health sensors Healthcare Three-dimensional model

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On the elastoplastic behavior in collisional compression of spherical dust aggregates

arXiv

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arXiv 2024年

作者： Arakawa, Sota Tanaka, Hidekazu Kokubo, Eiichiro Okuzumi, Satoshi Tatsuuma, Misako Nishiura, Daisuke Furuichi, Mikito Center for Mathematical Science and Advanced Technology Japan Agency for Marine-Earth Science and Technology 3173-25 Showa-machi Kanazawa-ku Yokohama236-0001 Japan Astronomical Institute Graduate School of Science Tohoku University 6-3 Aramaki Aoba-ku Sendai980-8578 Japan Center for Computational Astrophysics National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Earth and Planetary Sciences Tokyo Institute of Technology 2-12-1 Ookayama Meguro Tokyo152-8550 Japan Interdisciplinary Theoretical and Mathematical Sciences Program RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

Aggregates consisting of submicron-sized cohesive dust grains are ubiquitous, and understanding the collisional behavior of dust aggregates is essential. It is known that low-speed collisions of dust aggregates result in either sticking or bouncing, and local and permanent compaction occurs near the contact area upon collision. In this study, we perform numerical simulations of collisions between two aggregates and investigate their compressive behavior. We find that the maximum compression length is proportional to the radius of aggregates and increases with the collision velocity. We also reveal that a theoretical model of contact between two elastoplastic spheres successfully reproduces the size- and velocity-dependence of the maximum compression length observed in our numerical simulations. Our findings on the plastic deformation of aggregates during collisional compression provide a clue to understanding the collisional growth process of aggregates. Copyright © 2024, The Authors. All rights reserved.

关键词： Dust

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Wire Before You Walk

Wire Before You Walk

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Asilomar Conference on Signals, Systems & Computers

作者： Tesfa Asmara Dhananjay Bhaskar Ian Adelstein Smita Krishnaswamy Michael Perlmutter Department of Computer Science Pomona College Claremont CA USA Mathematics and Statistics Pomona College Claremont CA USA Department of Genetics Yale School of Medicine New Haven CT USA Department of Mathematics Yale University New Haven CT USA Department of Computer Science Yale University New Haven CT USA Program for Applied Mathematics Yale University New Haven CT USA Computational Biology and Bioinformatics Program Yale University New Haven CT USA Wu Tsai Institute Yale University New Haven CT USA Department of Mathematics Boise State University Boise ID USA

Node embeddings aim to associate a vector to every vertex of a graph which can then be used for downstream tasks such as clustering, classification, or link prediction. Many popular node embeddings such as no $d$ e2vec and DeepWalk are based upon counting which nodes frequently co-occur in random walks of the graph. In this paper, we show that the performance of such algorithms can be improved by rewiring the edges of the graph through a variety of network indices before running DeepWalk. These rewirings effectively give the random walker an inductive bias and increase the accuracy of a logistic regression classifier applied to the node embedding on several benchmark data sets.

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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The study of structural properties and photocatalytic activity of ZnO prepared by ultrasonic assisted precipitation method 2

The study of structural properties and photocatalytic activi...

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sanusi, S. Setiadji, S. Hakim, N.D. Aziz, S.H.R.A. Sawitri, A. Aliah, H. Hadisantoso, E.P. Ivansyah, A.L. Syarif, D.G. Department of Chemistry Faculty of Science and Technology UIN Sunan Gunung Djati Bandung Jl. A.H. Nasution No.105 Bandung Indonesia Department of Physics Faculty of Science and Technology UIN Sunan Gunung Djati Bandung Jl. A.H. Nasution No.105 Bandung Indonesia Master Program in Computational Science Faculty of Mathematic and Natural Science Institut Teknologi Bandung Jl. Ganesha No. 10 Bandung Indonesia Lab. of Material Physics Badan Tenaga Atom Nasional Jl. Taman Sari Bandung No.71 Bandung Indonesia

ZnO is a semiconductor that has excellent activity as photocatalyst under UV light region. ZnO is precipitated by adding oxalate acid onto zinc acetate dehydrate solution with ultrasonic assisted at various times and flattened at 500 °C. The structural properties of the ZnO samples are characterized by X-Ray Diffraction (XRD) and Brunauer-Emmett-Teller (BET). XRD result has confirmed the ZnO structure as hexagonal phase. Rietveld refinement of XRD pattern by La Beil method is employed to analyze the lattice parameters of ZnO. It provides that ZnO as wurtzite with accurate values. Debye-scherrer equation examines the crystallite size. It is confirmed that the crystallite size of ZnO is decreased by ultrasonic assisted at precipitation. BET result shows that ultrasonic assisted could increase the surface area of ZnO, which are 45.64 m2/g of ZnO without ultrasonic assisted and 143,487 m2/g of ZnO with ultrasonic assisted along 2 hours. The photocatalyst experiment is set up to analyze the photocatalytic activity of ZnO for degradation of methylene blue under UV light irradiation. By significantly that decolorization of methylene blue occurred at pH 10 for 30 minutes irradiation. The photocatalytic activity for ZnO samples with an hour and 2 hours ultrasonic assisted are 79.54 % and 86.50 % respectively, whereas ZnO sample without ultrasonic assisted is 70.90 %, and almost all of methylene blue contaminant have done degraded successfully after 120 minutes irradiation. According to the results, the structural properties and photocatalytic activity of ZnO are increased as consequence to the presence of ultrasonic assisted on precipitation. © Published under licence by IOP Publishing Ltd.

关键词： Zinc oxide

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Subdiffusive wave transport and weak localization transition in three-dimensional stealthy hyperuniform disordered systems

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Physical Review B 2022年第6期105卷 064204-064204页

作者： F. Sgrignuoli S. Torquato L. Dal Negro Department of Electrical and Computer Engineering & Photonics Center Boston University 8 Saint Mary's Street Boston Massachusetts 02215 USA Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08540 USA Division of Material Science and Engineering Boston University 15 Saint Mary's Street Brookline Massachusetts 02446 USA Department of Physics Boston University 590 Commonwealth Avenue Boston Massachusetts 02215 USA

The purpose of this work is to understand the fundamental connection between structural correlations and light localization in three-dimensional (3D) open scattering systems of finite size. We numerically investigate the transport of vector electromagnetic waves scattered by resonant electric dipoles spatially arranged in 3D space by stealthy hyperuniform disordered point patterns. Three-dimensional stealthy hyperuniform disordered systems are engineered with different structural correlation properties determined by their degree of stealthiness χ. Such fine control of exotic states of amorphous matter enables the systematic design of optical media that interpolate in a tunable fashion between uncorrelated random structures and crystalline materials. By solving the electromagnetic multiple scattering problem using Green's matrix spectral method, we establish a transport phase diagram that demonstrates a distinctive transition from a diffusive to a weak localization regime beyond a critical scattering density that depends on χ. The transition is characterized by studying the Thouless number and the spectral statistics of the scattering resonances. In particular, by tuning the χ parameter, we demonstrate large spectral gaps and suppressed subradiant proximity resonances, facilitating light localization. Moreover, consistently with previous studies, our results show a region of the transport phase diagram where the investigated scattering systems become transparent. Our work provides a systematic description of the transport and weak localization properties of light in stealthy hyperuniform structures and motivates the engineering of photonic systems with enhanced light-matter interactions for applications to both classical and quantum devices.

关键词： Anderson localization Light-matter interaction Mesoscopics Photonics Scattering theory Weak localization Metamaterials

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Thermal Conductivity of Water at Extreme Conditions

arXiv

引用

arXiv 2023年

作者： Zhang, Cunzhi Puligheddu, Marcello Zhang, Linfeng Car, Roberto Galli, Giulia Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Center for Molecular Engineering Argonne National Laboratory LemontIL60439 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Chemistry University of Chicago ChicagoIL60637 United States

Measuring the thermal conductivity (κ) of water at extreme conditions is a challenging task and few experimental data are available. We predict κ for temperatures and pressures relevant to the conditions of the Earth mantle, between 1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium molecular dynamics simulations and a deep neural network potential fitted to density functional theory data. We then interpret our results by computing the equation of state of water on a fine grid of points and using a simple model for κ. We find that the thermal conductivity is weakly dependent on temperature and monotonically increases with pressure with an approximate square-root behavior. In addition we show how the increase of κ at high pressure, relative to ambient conditions, is related to the corresponding increase in the sound velocity. Although the relationships between the thermal conductivity, pressure and sound velocity established here are not rigorous, they are sufficiently accurate to allow for a robust estimate of the thermal conductivity of water in a broad range of temperatures and pressures, where experiments are still difficult to perform. Copyright © 2023, The Authors. All rights reserved.

关键词： Thermal conductivity

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Identifying Patterns in Dental Visit Attendance Among Pregnant Women: A Retrospective Cohort Study

AJPM Focus

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AJPM Focus 2025年第2期4卷 100322-100322页

作者： Al Jallad, Nisreen Manning, Samantha Mao, Xinyue Mehta, Parshad Wu, TongTong Cacciato, Rita Luo, Jiebo Li, Yihong Xiao, Jin Eastman Institute for Oral Health University of Rochester Medical Center Rochester New York United States Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester New York United States Master of Public Health Program Department of Public & Ecosystem Health Cornell University Ithaca New York United States Department of Computer Science University of Rochester Medical Center Rochester New York United States

Introduction: Understanding the factors influencing dental care utilization is crucial for enhancing treatment adherence and outcomes. This study evaluates dental care–seeking patterns among pregnant women in low-income community. Methods: The authors analyzed data from 311 pregnant patients and 1,111 visits (2019–2022) synchronized from dental and medical records. The primary outcome was showing up for scheduled dental visits. To identify visit-attending patterns, the authors used a model-based clustering method to cluster longitudinal data with categorical outcomes. A penalized generalized linear mixed-effects model was applied to identify relevant variables for the visit attendance trajectories within each cluster. Results: The study participants comprised 49.6% Black, 32.2% White, and 12.5% Hispanic women. The majority (89.07%) were holding Medicaid insurance. Among the 1,111 scheduled visits, 432 resulted in no-shows (38.8%), including failed and canceled appointments. The authors identified 3 distinct clusters of visit-attending patterns on the basis of their show-up rates: low demand/low appointment risk (85% attendance), high demand/high appointment risk (57% attendance despite multiple scheduled visits), and moderate demand/high appointment risk (55% attendance with fewer scheduled visits). Various determinants, such as race;age;inner-city residence;appointment timing;the COVID-19 era;type of scheduled dental treatment;and prior medical visits for conditions such as anxiety, depression, hypertension, and allergies, influenced the visit-attending behaviors within each patient group. Conclusions: The innovative clustering approach of this study successfully identified dental care–seeking patterns among pregnant women, suggesting its applicability to a broader demographic. Identifying potential modifiable factors that could enhance attendance at dental visits is essential for improving oral healthcare outcomes among underserved pregnant patients. © 2025 The Au

关键词： care-seeking patterns Pregnancy prenatal oral healthcare social determinants of health

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