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arXiv 2024年

作者： Fanelli, Federica Melo, Hygor P.M. Bruno, Matteo Loreto, Vittorio Sapienza Univ. of Rome Physics Dept Piazzale A. Moro 2 Rome00185 Italy Sony Computer Science Laboratories - Rome Joint Initiative CREF-Sony Centro Ricerche Enrico Fermi Via Panisperna 89/A Rome00184 Italy Via Panisperna 89/A Rome00184 Italy Postgraduate Program in Applied Informatics University of Fortaleza CE Fortaleza60811-905 Brazil Complexity Science Hub Josefstädter Strasse 39 A 1080 Vienna Austria

The distribution of urban services reveals critical patterns of human activity and accessibility. Proximity to amenities like restaurants, banks, and hospitals can reduce access barriers, but these services are often unevenly distributed, exacerbating spatial inequalities and socioeconomic disparities. In this study, we present a novel accessibility measure based on the spatial distribution of Points of Interest (POIs) within cities. Using the radial distribution function from statistical physics, we analyze the dispersion of services across different urban zones, combining local and remote access to services. This approach allows us to identify a city’s central core, intermediate areas or secondary cores, and its periphery. Comparing the areas that we find with the resident population distribution highlights clusters of urban services and helps uncover disparities in access to opportunities. Copyright © 2024, The Authors. All rights reserved.

关键词： Population statistics

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P1864RENAL HEALTH MOBILE APPLICATION: AN ANALYSIS OF ITS USE IN BRAZIL

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Nephrology Dialysis Transplantation 2020年第SUPPLEMENT_3期35卷

作者： Rodrigues de Araújo, Erica Maria Bezerra da Silva Junior, Geraldo Almeida de Freitas Filho, Ronaldo Gomes Ramalho de Oliveira, Juliana Albuquerque, Ana Carolina De Sousa Lucas, Ricardo Askari, Marjan De Francesco Daher, Elizabeth Vasconcelos Filho, Jose Eurico Formico Rodrigues, Maria Andreia Universidade de Fortaleza Public Health Medical Sciences and Applied Informatics Graduate Programs Fortaleza Brazil Erasmus University School of Health Policy & Management Rotterdam Netherlands Federal University of Ceará School of Medicine Fortaleza Brazil

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Dynamic Deployment and Control of an NDN Network for Military Multi-UAVs Based Surveillance Applications

Dynamic Deployment and Control of an NDN Network for Militar...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Otávio A. Rocha da Cruz Carlos Eduardo Pereira Antonio Santos da Silva João Paulo J. da Costa Paulo Mendes Edison Pignaton de Freitas Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul Rio Grande do Sul Brazil Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Hamm Germany Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Airbus Taufkirchen Germany

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The military usage of Unmanned Aerial Vehicles (UAVs) has garnered attention, especially after their employment in the Ukrainian war. Despite the most commented lethal usage, they have many other applications from which surveillance for imagery acquisition is one of primal importance. Using a standalone UAV for this purpose is well-known, but to cope with the scale of battlefield operations, using multi- UAV systems is an asset of great value. However, these systems rely on ad hoc networks that require solutions beyond conventional ones based on the Internet Protocol (IP). This paper addresses this concern by proposing a communication support mechanism for multi-UAV military surveillance systems based on the Information-Centric Networks (ICN) paradigm. The proposed approach consists of the dynamic deployment of an ICN network based on microservices architecture, where the communication services of each UAV are deployed according to their resources. The solution is validated in a simulated battlefield scenario where a surveillance UAV provides data demanded by other nodes. The results demonstrate that the proposed solution minimizes the data delivery delays by successfully deploying the customized set of microservices to support the transmission, even when a UAV with a low battery level is replaced at runtime.

关键词： Military communication Runtime Protocols Surveillance Information-centric networking Microservice architectures Autonomous aerial vehicles

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P1854SOCIAL NETWORK AS A SOURCE OF HEALTH EDUCATION IN NEPHROLOGY: THE CASE OF RENAL HEALTH INSTAGRAM

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Nephrology Dialysis Transplantation 2020年第SUPPLEMENT_3期35卷

作者： Bezerra da Silva Junior, Geraldo Askari, Marjan Dourado, Diovana Praça Brasil, Thiago Carneiro de Holanda, Barbara Gomes Ramalho de Oliveira, Juliana De Francesco Daher, Elizabeth Vasconcelos Filho, Jose Eurico Formico Rodrigues, Maria Andreia UNIFOR - Universidade de Fortaleza Public Health Medical Sciences and Applied Informatics Graduate Programs Fortaleza Brazil Erasmus University Rotterdam School of Health Policy & Management Rotterdam Netherlands Federal University of Ceará School of Medicine Fortaleza Brazil

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Science (New York, N.Y.) 2025年第6734期387卷 eadp4753页

作者： Liu, Hongbo Abedini, Amin Ha, Eunji Ma, Ziyuan Sheng, Xin Dumoulin, Bernhard Qiu, Chengxiang Aranyi, Tamas Li, Shen Dittrich, Nicole Chen, Hua-Chang Tao, Ran Tarng, Der-Cherng Hsieh, Feng-Jen Chen, Shih-Ann Yang, Shun-Fa Lee, Mei-Yueh Kwok, Pui-Yan Wu, Jer-Yuarn Chen, Chien-Hsiun Khan, Atlas Limdi, Nita A. Wei, Wei-Qi Walunas, Theresa L. Karlson, Elizabeth W. Kenny, Eimear E. Luo, Yuan Kottyan, Leah Connolly, John J. Jarvik, Gail P. Weng, Chunhua Shang, Ning Cole, Joanne B. Mercader, Josep M. Mandla, Ravi Majarian, Timothy D. Florez, Jose C. Haas, Mary E. Lotta, Luca A. Drivas, Theodore G. Vy, Ha My T Nadkarni, Girish N. Wiley, Laura K. Wilson, Melissa P. Gignoux, Christopher R. Rasheed, Humaira Thomas, Laurent F. Åsvold, Bjørn Olav Brumpton, Ben M. Hallan, Stein I. Hveem, Kristian Zheng, Jie Hellwege, Jacklyn N. Zawistowski, Matthew Zöllner, Sebastian Franceschini, Nora Hu, Hailong Zhou, Jianfu Kiryluk, Krzysztof Ritchie, Marylyn D. Palmer, Matthew Edwards, Todd L. Voight, Benjamin F. Hung, Adriana M. Susztak, Katalin Department of Medicine Renal Electrolyte and Hypertension Division University of Pennsylvania Philadelphia PA United States Institute of Diabetes Obesity and Metabolism University of Pennsylvania Philadelphia PA United States Department of Genetics University of Pennsylvania Philadelphia PA United States Penn-CHOP Kidney Innovation Center University of Pennsylvania Philadelphia PA United States Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou Zhejiang China Department of Nephrology Children' Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou Zhejiang China Institute of Molecular Life Sciences HUN-REN Research Center for Natural Sciences Budapest Hungary Department of Molecular Biology Semmelweis University Budapest Hungary Department of Medicine Federal University of São Paulo São Paulo Brazil Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN United States Institute of Clinical Medicine School of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Division of Nephrology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Institute of Biomedical Sciences Academia Sinica ROC Taipei Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung Taiwan ROC National Chung Hsing University Taichung Taiwan ROC Heart Rhythm Center Division of Cardiology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Department of Internal Medicine College of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan ROC Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan ROC Division of Endocrinology and Metabolism Department of Internal Medicine Kaohsiung Medical Universit

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

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Session introduction: Characterizing the importance of environmental exposures, interactions between the environment and genetic architecture, and genetic interactions: New methods for understanding the etiology of complex traits and disease 20

Session introduction: Characterizing the importance of envir...

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20th Pacific Symposium on Biocomputing, PSB 2015

作者： Hall, Molly A. Verma, Shefali Setia Wall, Dennis P. Moore, Jason H. Keating, Brendan Campbell, Daniel B. Gibson, Gregory Asselbergs, Folkert W. Pendergrass, Sarah Center for Systems Genomics Department of Biochemistry and Molecular Biology Pennsylvania State University University ParkPA16802 United States Department of Pediatrics Division of Systems Medicine Program in Biomedical Informatics Stanford University StanfordCA94305 United States Institute for Quantitative Biomedical Sciences Department of Genetics Geisel School of Medicine Dartmouth College HanoverNH03755 United States Center for Applied Genomics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Psychiatry and the Behavioral Sciences Zilkha Neurogenetic Institute Center for Genomic Psychiatry Neuroscience Graduate Program Keck School of Medicine University of Southern California Los AngelesCA90089 United States Center for Integrative Genomics Department of Biology Georgia Institute of Technology AtlantaGA30332 United States Department of Cardiology Division of Heart and Lungs University Medical Center Ultrecht Netherlands

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Time-dependent modelling of short-term variability in the TeV-blazar VER J0521+211 during the major flare in 2020

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Astronomy and Astrophysics 2025年 694卷

作者： MAGIC Collaboration Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Barres De Almeida, U. Barrio, J.A. Batković, I. Bautista, A. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. De Menezes, R. Delfino, M. Delgado, J. Delgado Mendez, C. Di Pierro, F. Di Tria, R. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Eisenberger, L. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera Llorente, J. Hrupec, D. Imazawa, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado De Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. ETH Zürich ZürichCH-8093 Switzerland Università di Siena and INFN Pisa SienaI-53100 Italy BarcelonaE-08193 Spain Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain RomeI-00136 Italy Università di Udine and INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik GarchingD-85748 Germany Università di Padova and INFN PadovaI-35131 Italy Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia Rio de Janeiro22290-180 URCA Brazil IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La LagunaE-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Università di Pisa and INFN Pisa PisaI-56126 Italy INFN MAGIC Group: INFN Sezione di Bari Dipartimento Interateneo di Fisica dell'Università e del Politecnico di Bari BariI-70125 Italy Department for Physics and Technology University of Bergen Bergen Norway INFN MAGIC Group: INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group: University of Rijeka Faculty of Physics Rijeka51000 Croatia Universität Würzburg WürzburgD-97074 Germany Japanese MAGIC Group: Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan ZeuthenD-15738 Germany Armenian MAGIC Group: ICRANet-Armenia Yerevan0019 Armenia Croatian MAGIC Group: Josip Juraj Strossmayer University of Osijek Department of Physics Osijek31000 Croatia Finnish MAGIC Group: Finnish Centre for Astronomy with ESO Department of Physics and Astronomy U

Self-repairing graphite protective layer has been discovered as a suitable protective layer in blast furnace (BF) hearth in recent years. In the current study, actual samples of self-repairing graphite protective layer taken from a commercial BF were analyzed in detail. The results revealed that the hot face of graphite protective layer exhibits a distinct white graphite luster, with large areas of graphite adhering to the surface. Along the direction of its formation, the sample displays a striped pattern with alternating layers. The graphite is strip-shaped, it is relatively coarse and unevenly distributed. The coarse graphite runs in the same direction, unlike graphite in molten iron which has no fixed direction in a chaotic state. The formation process of selfrepairing graphite protective layer can be concluded, graphite precipitates at the interface through heterogeneous nucleation. Crystal nuclei often preferentially adhere to the surface of these impurities to form, owing to the fact that the nucleation energy of heterogeneous nucleation is lower than that of homogeneous nucleation. Titanium is discovered during the observation of microscopic morphology of graphite protective layer, graphite protective layer is more robust due to the strengthening effect of titanium. Titanium strengthening mechanism of self-repairing graphite protective layer is summarized, the strengthening mechanism can be divided into four steps. TiC particles are dispersed around graphite, which reduces the difficulty of the orientation of flake graphite growth. The presence of TiC increases the growth rate of crystals. The four steps are cyclically performed, so the self-repairing graphite protective layer can precipitate layer by layer through titanium strengthening mechanism, which serves to protect the carbon brick in BF hearth. © EDP Sciences, 2025.

关键词： Blast furnaces

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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