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International Conference on computer Supported Cooperative Work in Design

作者： Daniel Schneider António Correia Jano Moreira de Souza Tercio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Tràs-os-Montes e Alto Douro Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165981

Citizen engagement in building user-curated narratives of complex or long-lasting news stories has been the key foundation of the design and implementation of the Acropolis virtual environment. Previous user studies have shown, by positive evidence, that this goal can be pragmatically achieved, but the challenge now lies in assessing: a) the extent to which an environment like Acropolis can be used to empower citizens; and b) whether and how the tool could be used to support the work of professional curators. Findings from a focus group study highlighted the tool's potential to engage citizens with news, the usefulness of the environment to build virtual memories, and the convenience of using Acropolis to support professional journalistic work.

关键词： Conferences Buildings Virtual environments Tools Collaborative work

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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COVID-19 as Opportunity to Test Digital Nomad Lifestyle

COVID-19 as Opportunity to Test Digital Nomad Lifestyle

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Daniel Schneider Jano Moreira de Souza Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

We report the first findings of an empirical study aimed at investigating how COVID-19 pandemic has impacted the work practices and lifestyles of digital nomads (DN). To do this, we analyzed messages, questions and comments posted by digital nomads in a specific online discussion community of the Reddit social network. Preliminary findings indicate COVID-19 as an opportunity to test DN lifestyle by aspiring digital nomads who want to plan their careers and also present evidence of an overload of online channels for actual DNs. On the other hand, we found that much of the literature on digital nomadism is fragmented and scattered through different disciplines and perspectives, with a strong focus on digital nomads' lifestyles. In order to obtain a holistic and unified understanding of digital nomads, we conducted a comprehensive literature review to further conceptualize the phenomenon under study.

关键词： COVID-19 Social networking (online) Pandemics engineering profession Conferences Bibliographies Collaborative work

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Crowdsourcing Urban Narratives for a Post-Pandemic World

Crowdsourcing Urban Narratives for a Post-Pandemic World

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International Conference on computer Supported Cooperative Work in Design

作者： Ramon Chaves Daniel Schneider Claudia Motta António Correia Hugo Paredes Bárbara Caetano Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

关键词： Crowdsourcing Social networking (online) Smart cities Instruments Urban planning Collective intelligence Tools

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Improving debris flow evacuation alerts in Taiwan using machine learning

arXiv

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arXiv 2022年

作者： Tsai, Yi-Lin Irvin, Jeremy Chundi, Suhas Ng, Andrew Y. Field, Christopher B. Kitanidis, Peter K. Department of Civil and Environmental Engineering Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Interdisciplinary Environmental Studies Program Stanford University StanfordCA United States Department of Earth System Science Stanford University StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts. Copyright © 2022, The Authors. All rights reserved.

关键词： Rain

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Blockchain Technology Based Credibility Verification in E-Journal Entities

Blockchain Technology Based Credibility Verification in E-Jo...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Sri Watini Zulkarnain Kedah Chandra Lukita Petrus Sokibi Gadis Mustika Reymund Rahardja Arsi Yulianjani Husni Teja Sukmana Early Childhood Teacher Education University of Panca Sakti Bekasi Bekasi Indonesia Vice Chancellor University of Malaysian Computer Science Selangor Malaysia Information System University of Catur Insan Cendekia Cirebon Indonesia Information System University of Raharja Tangerang Indonesia Digital Business University of Raharja Tangerang Indonesia Information Management Study Program University of Raharja Tangerang Indonesia Lecture of Information Engineering Syarif Hidayatullah Islamic University Jakarta Indonesia

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era.

关键词： COVID-19 Economics Epidemics Soft sensors Focusing Forgery Blockchains

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Implantable Biosensor for Continuous Serotonin Detection in Freely Moving Crayfish

Implantable Biosensor for Continuous Serotonin Detection in ...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Jinjing Han Tawen Ho Justin M. Stine Michael A. Straker Jens Herberholz Reza Ghodssi Department of Electrical and Computer Engineering University of Maryland College Park MD USA Institute for System Research University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA Department of Psychology University of Maryland College Park MD USA Program in Neuroscience and Cognitive Science University of Maryland College Park MD USA Fischell Department of Bioengineering University of Maryland College Park MD USA

This work presents, for the first time, an implantable biosensor for continuous in vivo serotonin (5-HT) monitoring in freely moving crayfish. 5-HT is an important neurotransmitter that regulates animal behaviors. Conventional methods for detecting 5-HT do not provide real-time information. Our approach facilitates continuous electrochemical monitoring of 5-HT levels without impeding animal natural behavior. The sensing system shows a linear range of 0.4 - 1.2 μΜ with a sensitivity of 0.07 μΑ/μΜ for 5-HT detection. With a 3D-printed package, the sensor can be easily attached to crayfish dorsal carapace allowing electrodes implantation into crayfish heart to monitor neurohormonal dynamics of 5-HT. The implantable biosensor system displayed a significant increase in oxidation currents after 5-HT injections. The device successfully demonstrates the application for in vivo detection of 5-HT in the hemolymph (i.e., blood) of freely behaving crayfish underwater.

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Quantitative Analysis of Colonic Epithelial Cell Aging in a Cell-cycle like Model: Changes in Nucleus and Cytoplasm Along the Crypt Axis

Quantitative Analysis of Colonic Epithelial Cell Aging in a ...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Bao, Shunxing Wang, Yaohong Rudravaram, Gaurav Zhang, Rendong Deng, Ruining Yu, Xin Gao, Chenyu Li, Jia Li, Yike Roland, Joseph T. Zuo, Lianrui Liu, Qi Lau, Ken S. Coburn, Lori A. Wilson, Keith T. Landman, Bennett A. Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Anatomical Pathology UT MD Anderson Cancer Center HoustonTX United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center Nashville United States Department of Otolaryngology-Head and Neck Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

ISBN: (纸本)9781510686045

Understanding epithelial cell aging in bowel tissue can provide critical insights into gastrointestinal (GI) health, potentially leading to improved diagnosis, treatment, and prevention strategies for various GI conditions. Despite the significance of cellular aging in the GI tract, previous studies have not quantified epithelial cell maturity development along the base-to-surface axis of colon crypts due to the labor-intensive nature of manual quantification and the lack of a clear definition of cellular age. Moreover, the process faces tremendous challenges when tissue is cut in 2D and examines results solely from one or a few biopsy cuts without reconstructing the full 3D space, as the original cell structure is 3D, leading to variability based on the cut angle. This research addresses these challenges by systematically analyzing the relationship between epithelial cell morphology and their position from the base to the surface in colon crypt tissue. Our approach involves using multiplex immunofluorescence (MxIF) imaging for cell nuclei segmentation, identifying the potential full longitudinal cut, and manually annotating the surface and base of the colon crypts. By analyzing the epithelial cells, we create density maps to investigate the relationship between nucleus size and the surface-to-base distance ratio, defining this ratio as cell age. We also examine the nucleus-to-cytoplasm (N/C) ratio in relation to cell age. For the statistical analysis, we use a robust linear regression model to remove outliers and a random effects model for the final regression. Our results show a decreasing trend in both the N/C ratio and nucleus size as epithelial cells move from the base to the surface. These findings provide a quantitative pipeline for single-cell profiling in a spatial manner, facilitating broader analysis of epithelial cell aging and its implications for GI health. © 2025 SPIE.

关键词： Linear regression

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Biomedical data and AI

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

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

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

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