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arXiv 2024年

作者： Evans, Matthew L. Bergsma, Johan Merkys, Andrius Andersen, Casper W. Andersson, Oskar B. Beltrán, Daniel Blokhin, Evgeny Boland, Tara M. Balderas, Rubén Castañeda Choudhary, Kamal Díaz, Alberto Díaz García, Rodrigo Domínguez Eckert, Hagen Eimre, Kristjan Montero, María Elena Fuentes Krajewski, Adam M. Mortensen, Jens Jørgen Duarte, José Manuel Nápoles Pietryga, Jacob Qi, Ji de Jesús Trejo Carrillo, Felipe Vaitkus, Antanas Yu, Jusong Zettel, Adam de Castro, Pedro Baptista Carlsson, Johan Cerqueira, Tiago F.T. Divilov, Simon Hajiyani, Hamidreza Hanke, Felix Jose, Kevin Oses, Corey Riebesell, Janosh Schmidt, Jonathan Winston, Donald Xie, Christen Yang, Xiaoyu Bonella, Sara Botti, Silvana Curtarolo, Stefano Draxl, Claudia Cobas, Luis Edmundo Fuentes Hospital, Adam Liu, Zi-Kui Marques, Miguel A.L. Marzari, Nicola Morris, Andrew J. Ong, Shyue Ping Orozco, Modesto Persson, Kristin A. Thygesen, Kristian S. Wolverton, Chris Scheidgen, Markus Toher, Cormac Conduit, Gareth J. Pizzi, Giovanni Gražulis, Saulius Rignanese, Gian-Marco Armiento, Rickard Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Matgenix SRL 185 Rue Armand Bury Gozée6534 Belgium École Polytechnique Fédérale de Lausanne Avenue de Forel 3 Lausanne1015 Switzerland Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio av. 7 VilniusLT-10257 Lithuania SINTEF P.O. Box 4760 Torgarden TrondheimNO-7465 Norway Materials Design and Informatics unit Department of Physics Chemistry and Biology Linköping University Sweden Baldiri i Reixac 10-12 Barcelona08028 Spain Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Computational Atomic-Scale Materials Design Technical University of Denmark Kgs. Lyngby Denmark Av. Miguel de Cervantes 120 Complejo Industrial Chihuahua Chihuahua Chih. 31136 Mexico Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Extreme Materials Duke University DurhamNC27708 United States École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Universidad Autónoma de Chihuahua Facultad de Ciencias Quimicas Chihuahua31125 Mexico Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of NanoEngineering University of California 9500 Gilman Drive La Jolla San DiegoCA92093-0448 United States National Institute for Materials Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan Dassault Systèmes Germany GmbH Am Kabellager 11-13 Cologne51063 Germany CFisUC Department of Physics University of Coimbra Rua Larga Coimbra3004-516 Portugal Dassault Systèmes 22 Science Park CB4 0FJ United Kingdom Theory of Condensed Matter Cavendis

The Open databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. © 2024, CC BY.

关键词： Electronic data interchange

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

arXiv

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arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

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Magnetic-resonance-based electrical property mapping using global maxwell tomography with an 8-channel head coil at 7 Tesla: A simulation study

arXiv

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arXiv 2020年

作者： Giannakopoulos, Ilias I. Serrallés, José E.C. Daniel, Luca Sodickson, Daniel K. Polimeridis, Athanasios G. White, Jacob K. Lattanzi, Riccardo Skoltech Center for Computational Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow143026 Russia Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Radiology New York University School of Medicine New YorkNY United States Q Bio CA94063 United States

Objective: Global Maxwell Tomography (GMT) is a recently introduced volumetric technique for noninvasive estimation of electrical properties (EP) from magnetic resonance measurements. Previous work evaluated GMT using ideal radiofrequency (RF) excitations. The aim of this simulation study was to assess GMT performance with a realistic RF coil. Methods: We designed a transmit-receive RF coil with 8 decoupled channels for 7T head imaging. We calculated the RF transmit field (B1+) inside heterogeneous head models for different RF shimming approaches, and used them as input for GMT to reconstruct EP for all voxels. Results: Coil tuning/decoupling remained relatively stable when the coil was loaded with different head models. Mean error in EP estimation changed from 7.5% to 9.5% and from 4.8% to 7.2% for relative permittivity and conductivity, respectively, when changing head model without re-tuning the coil. Results slightly improved when an SVD-based RF shimming algorithm was applied, in place of excitation with one coil at a time. Despite errors in EP, RF transmit field (B1+) and absorbed power could be predicted with less than 0.5% error over the entire head. GMT could accurately detect a numerically inserted tumor. Conclusion: This work demonstrates that GMT can reliably reconstruct EP in realistic simulated scenarios using a tailored 8-channel RF coil design at 7T. Future work will focus on construction of the coil and optimization of GMT's robustness to noise, to enable in-vivo GMT experiments. Significance: GMT could provide accurate estimations of tissue EP, which could be used as biomarkers and could enable patient-specific estimation of RF power deposition, which is an unsolved problem for ultrahigh-field magnetic resonance imaging. Copyright © 2020, The Authors. All rights reserved.

关键词： Integral equations

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Skeletal trait measurements for thousands of bird species

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Scientific data 2025年第1期12卷 1-7页

作者： Weeks, Brian C. Zhou, Zhizhuo Probst, Charlotte M. Berv, Jacob S. O’Brien, Bruce Benz, Brett W. Skeen, Heather R. Ziebell, Mark Bodt, Louise Fouhey, David F. School for Environment and Sustainability University of Michigan Ann Arbor MI United States School of Computer Science Carnegie Mellon University Pittsburgh PA United States Michigan Institute for Data Science University of Michigan Ann Arbor MI United States Department of Ecology and Evolutionary Biology and Museum of Zoology University of Michigan Ann Arbor MI United States Department of Ecology and Evolutionary Biology Princeton University Princeton NJ United States Negaunee Integrative Research Center Field Museum of Natural History Chicago IL United States Committee on Evolutionary Biology University of Chicago Chicago IL United States Computer Science Courant Institute of Mathematical Sciences New York University New York NY United States Electrical & Computer Engineering Tandon School of Engineering New York University New York NY United States

Large comparative datasets of avian functional traits have been used to address a wide range of questions in ecology and evolution. To date, this work has been constrained by the limited availability of skeletal trait datasets that include extensive inter- and intra-specific sampling. We use computer vision to identify and measure bones from photographs of museum skeletal specimens to assemble an extensive dataset of functionally important skeletal elements in birds. The dataset spans 2,057 species of birds (Aves: Passeriformes) and includes measurements of 12 skeletal elements from 14,419 individuals. In addition to the trait values directly measured from photographs, we leverage the multi-dimensional nature of our dataset and known phylogenetic relationships of the species to impute missing data under an evolutionary model. To facilitate use of the dataset, the taxonomy has been reconciled with an existing comprehensive avian phylogeny and an additional dataset of external functional traits for all birds. © The Author(s) 2025.

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Hypertrophic cardiomyopathy diagnosis based on cardiovascular magnetic resonance using deep learning techniques

Research Square

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research Square 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Izadi, Navid Hoseini Roshanzamir, Mohamad Shoeibi, Afshin Khozeimeh, Fahime Sani, Fariba Alizadeh Sani, Zahra Alizadeh Hussain, Sadiq Harlapur, Chandrashekhar Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Sarrafzadegan, Nizal Islam, Sheikh Mohammed Shariful Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Mashhad University of Medical science Mashhad Iran Department of Cardiac MRI Omid Hospital Tehran Iran Dibrugarh University Assam786004 India Emergency Department Epworth Hospital Geelong Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom Faculty of Medicine SPPH University of British Columbia VancouverBC Canada Isfahan Cardiovascular Research Center Cardiovascular Research Institute Isfahan University of Medical Sciences Khorram Ave Isfahan Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Hypertrophic cardiomyopathy (HCM) can lead to serious cardiac problems. HCM is often diagnosed by an expert using cardiovascular magnetic resonance (CMR) images obtained from patients. In this research, we aimed to develop a deep learning technique to automate HCM diagnosis. CMR images of 37421 healthy and 21846 HCM patients were obtained during two years. Images obtained from female patients form 53% of the collected dataset. The mean and standard deviation of the dataset patients’ age are 48.2 and 19.5 years, respectively. Three experts inspected images and determined whether a case has HCM or not. New data augmentation was used to generate new images by employing color filtering on the existing ones. To classify the augmented images, we used a deep convolutional neural network (CNN). To the best of our knowledge, this is the first time CNN is used for HCM diagnosis. We designed our CNN from scratch to reach acceptable diagnosis accuracy. Comparing the designed algorithm output with the experts’ opinions, the method could achieve accuracy of 95.23%, recall of 97.90%, and specificity of 93.06% on the original dataset. The same performance metrics for the designed algorithm on the augmented dataset were 98.53%, 98.70%, and 95.21%, respectively. We have also experimented with different optimizers (e.g. Adadelta and Adagrad) and other data augmentation methods (e.g. height shift and rotation) to further evaluate the proposed method. Using our data augmentation method, accuracy of 98.53% were achieved which is higher than the best accuracy (95.83%) obtained by the other data augmentation methods which have been evaluated. The upper bound on difference between true error rate and empirical error rate of the proposed method has also been provided in order to present better performance analysis. The advantages of employing the proposed method are elimination of contrast agent and its complications, decreased CMR examination time, lower costs for patients and cardiac imagi

关键词： Diagnosis

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (ai). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in ai and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in ai methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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A fundamental theorem for eco-environmental surface modelling and its applications

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science China Earth sciences 2020年第8期63卷 1092-1112页

作者： Tianxiang YUE Na ZHAO Yu LIU Yifu WANG Bin ZHANG Zhengping DU Zemeng FAN Wenjiao SHI Chuanfa CHEN Mingwei ZHAO Dunjiang SONG Shihai WANG Yinjun SONG Changqing YAN Qiquan LI Xiaofang SUN Lili ZHANG Yongzhong TIAN Wei WANG Ying’an WANG Shengnan MA Hongsheng HUANG Yimin LU Qing WANG Chenliang WANG Yuzhu WANG Ming LU Wei ZHOU Yi LIU Xiaozhe YIN Zong WANG Zhengyi BAO Miaomiao ZHAO Yapeng ZHAO Yimeng JIAO Ufra NASEER Bin FAN Saibo LI Yang YANG John PWILSON State Key L aboratory of Resources and Environment Information System Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China College of Land Resources and Environment Jiangxi Agricultural UniversityNanchang 330045China College of Geomatics Shandong University of Science and TechnologyQingdao 266590China College of Forestry Beijing Forestry UniversityBeijing 100083China School of Land and Resources China West Normal UniversityNanchong 637002China College of Geographical Information and Tourism Chuzhou UniversityChuzhou 239000China Institutes of Science and Development Chinese Academy of SciencesBeijing 100190China Department of Information Engineering Shandong University of Science and TechnologyTai'an 271019China College of Resources Sichuan Agricultural UniversityChengdu 611130China College of Geography and Tourism Qufu Normal UniversityRizhao 276828China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100101China School of Geographical Science Southwest UniversityChongqing 400715China National Disaster Reduction Center of China Ministry of Emergency ManagementBeijing 100721China Department for Population Data China Population and Development Research CenterBeijing 100081China Department for Industrial Standard China Standardization AdministrationBeijing 100088China The Academy of Digital China Fuzhou UniversityFuzhou 350002China National Institute of Environmental Health Chinese Center for Disease Control and PreventionBeijing 100021China School of Information Engineering China University of GeoscienceBeijing 100083China Qian Xuesen Laboratory of Space Technology Beijing 100094China College of Architecture and Urban Planning Chongqing Jiaotong UniversityChongqing 400074China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

关键词： HASM FTEEM Spatial upscaling Spatial downscaling Spatial interpolation data fusion Model-data assimilation Model coupling

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How do diseases spread at the critical state?

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Chaos, Solitons & Fractals 2025年 199卷

作者： Li-Feng Hou Shifu Wang Li Li Xin Lu Gui-Quan Sun Sino-Europe Complex Science Center School of Mathematics North University of China Shanxi Taiyuan 030051 China Complex Systems Research Center Shanxi University Taiyuan 030006 Shanxi China Department of Microbiology Laboratory Children’s Hospital Affiliated to Shandong University Jinan 250022 China School of Computer and Information Technology Shanxi University Taiyuan 030006 Shanxi China College of Systems Engineering National University of Defense Technology Changsha Hunan 410073 China Key Laboratory of Complex Systems and Data Science of Ministry of Education Taiyuan 030006 China

The transmission characteristics of infectious diseases near critical thresholds are essential for public health strategy formulation. This study employs reaction–diffusion SI with nonlinear and SIR models with saturated incidence rates, integrating optimal control theory to investigate epidemic propagation trends under critical conditions. The structural complexity of three epidemiological target states (extinction, quasi-uniform epidemic, and patterned epidemic) is quantitatively characterized using spatial entropy methods. A multi-indicator comparative analysis systematically reveals the evolutionary trends of epidemics in critical states from three dimensions: target attainability, average control intensity, and control complexity. The findings indicate that achieving a patterned epidemic state requires the lowest control intensity and spatial intervention complexity compared to extinction and quasi-uniform states, suggesting that epidemic systems in critical states are more inclined toward structured transmission patterns. The proposed framework for quantifying spatial structure and control complexity provides a theoretical basis and practical guidance for formulating spatial prevention and control strategies for infectious diseases in critical states.

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data driven assessment of built environment impacts on urban health across United States cities

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Scientific Reports 2025年第1期15卷 1-20页

作者： Ghorbany, Siavash Hu, Ming Yao, Siyuan Sisk, Matthew Wang, Chaoli Zhang, Kai Nguyen, Quynh Camthi Department of Civil and Environmental Engineering and Earth Sciences College of Engineering University of Notre Dame Notre Dame 46556 IN United States School of Architecture Walsh Family Hall of Architecture University of Notre Dame Notre Dame 46556 IN United States Faculty of Architecture Civil Engineering and Applied Arts Academy of Silesia Rolan 43 Katowice 40-555 Poland Department of Computer Science and Engineering College of Engineering University of Notre Dame Notre Dame 46556 IN United States Lucy Family Institute for Data & Society University of Notre Dame Notre Dame 46556 IN United States Department of Computer Science and Engineering University of Notre Dame Notre Dame 46556 IN United States Department of Population & Community Health College of Public Health | University of North Texas 1155 Union Cir Denton 76205 TX United States National Institute of Nursing Research 31 Center Drive Room 5B03 Bethesda 20892-2178 MD United States

The built environment plays a crucial role in urban health through its constant interaction with residents. Yet, a comprehensive analysis across multiple U.S. cities covering different geographical conditions has been missing. This study examines the impact of the built environment on mental, physical, and overall health in 19 major U.S. cities across diverse climate zones, aiming to discover the influential factors and create a predictive health model for the entire nation. Utilizing a convolutional neural network, this research extracted building characteristics from google street view and employed a multiple regression model, XGBoost, support vector regression (SVR), decision tree, and random forest, achieving R-squared values of 0.75, 0.76, and 0.82 for mental, physical, and general health, respectively. The findings revealed certain building features profoundly influence urban health, including lead paint as a persistent health hazard, while air conditioning boosts health outcomes. Traditional materials like wood and masonry, common in older buildings, are linked to better health compared to modern materials. The study also reveals significant geographic variations, underscoring the intricate relationship between architecture and public health. By highlighting the vital role of building features in shaping urban health, this research provides a foundation for policymakers to inform building regulations, prioritize vulnerable areas, and support health-oriented urban design, ultimately contributing to healthier, more livable urban environments. © The Author(s) 2025.

关键词： Built environment Convolutional neural network General health Google street view Mental health prediction Physical health

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