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arXiv 2020年

作者： Rivlin, Or Hazan, Tamir Karpas, Erez Technion Autonomous Systems Program Technion - Israel Institute of Technology Haifa3200003 Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa3200003 Israel

A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Social vulnerability: A driving force in amplifying the overall vulnerability of protected areas to natural hazards

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Heliyon 2025年第4期11卷 e42617页

作者： Pirasteh, Saied Mafi-Gholami, Davood Li, Huxiong Wang, Tao Zenner, Eric K. Nouri-Kamari, Akram Frazier, Tim G. Ghaffarian, Saman Institute of Artificial Intelligence Shaoxing University 508 West Huancheng Road Yuecheng District Zhejiang Province Shaoxing 312000 China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Forest Sciences Faculty of Natural Resources and Earth Sciences Shahrekord University Shahrekord *** Iran Metrology Research Group Quality Assessment and Management Systems Research Center Standard Research Institute PO Box 31585-163 Alborz Karaj Iran Department of Ecosystem Science and Management The Pennsylvania State University Forest Resources Building University Park 16802 PA United States Department of Geography Education Farhangian University Tehran Iran Emergency and Disaster Management Program School of Continuing Studies (SCS) Georgetown University Washington DC United States Institute for Risk and Disaster Reduction University College London United Kingdom

Incorporating social vulnerability (SoV) into vulnerability assessments for protected areas provides critical insights for enhancing the resilience and adaptability of these regions to environmental hazards. This study aimed to develop and apply an integrated social-ecological vulnerability assessment framework for a forest-protected area, focusing on scales relevant to management decisions. We collected data from 70 villages within the protected area through structured surveys, interviews, and reviews of secondary socio-economic and environmental datasets. A total of 24 socio-economic and environmental indicators were selected, based on expert consultation and literature review, to quantify the indices of exposure, sensitivity, adaptive capacity, and SoV. These indicators were weighted using the Fuzzy Analytical Hierarchy Process (FAHP) to ensure objective and expert-driven prioritization. Subsequently, the SoV index, derived from socio-economic metrics, was incorporated as a key variable in the exposure assessment. This integration involved 18 additional ecological indicators measured across a spatial grid, enabling a fine-scale analysis of the protected area's exposure, sensitivity, adaptive capacity, and overall vulnerability. Integrating social and ecological dimensions allowed for a spatially explicit evaluation of vulnerability at the grid-cell level, providing granular insights for localized management interventions. The results revealed that SoV significantly influenced the levels of exposure and overall vulnerability across the study area. High SoV values corresponded to areas with elevated overall vulnerability, emphasizing the interplay between social conditions and ecological risks. To enhance resilience in the medium and long term, the study recommends shifting investments toward active community engagement in conservation planning, increasing environmental awareness through education programs, and improving the socio-economic well-being of local commu

关键词： Environmental hazard resilience FAHP Integrated social-ecological framework and vulnerability assessment Protected area management Spatial pattern

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Consumer behavior in clothing industry and its relationship with open innovation dynamics during the covid-19 pandemic

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Journal of Open Innovation: Technology, Market, and Complexity 2021年第4期7卷 211页

作者： Ong, Ardvin Kester S. Cleofas, Maria Arielle Prasetyo, Yogi Tri Chuenyindee, Thanatorn Young, Michael Nayat Diaz, John Francis T. Nadlifatin, Reny Redi, Anak Agung Ngurah Perwira School of Industrial Engineering and Engineering Management Mapúa University 658 Muralla St. Manila 1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila 1002 Philippines Department of Industrial Engineering Faculty of Engineering University of Santo Tomas Manila 1015 Philippines Navaminda Kasatriyadhiraj Royal Thai Air Force Academy Bangkok 10220 Thailand Department of Finance and Accounting Asian Institute of Management Manila 1229 Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Industrial Engineering Department BINUS Graduate Program—Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

The fashion industry has been continuously growing over the years, yet it is an industry that was greatly affected by the COVID-19 pandemic. The purpose of the study was to determine the factors affecting buying behavior of Filipinos towards clothing apparel during the COVID-19 pandemic. There were 457 respondents who voluntarily participated and answered an online questionnaire. Structural equation modeling (SEM) indicated that marketing mix was found to have significant effects on attitude, subjective norm, and perceived behavioral control, which subsequently led to purchase intention. In addition, the COVID-19 latent variable was found to have significant effects on self-perceived severity and self-efficacy, which subsequently led to attitude and purchase intention. Interestingly, marketing mix was found to have the highest effect on actual purchase, which indicated that innovation dynamics are the keys for the buying behavior. The SEM construct can be applied to determine the clothing apparel buying behaviors of consumers in other countries, particularly during the COVID-19 pandemic. Finally, advertisements, promotions, sales, and health safety should be considered as innovation dynamics during the COVID-19 pandemic. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Consumer behavior COVID-19 pandemic Fashion industry Innovation dynamic Open innovation Protection motivation theory Theory of planned behavior

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Explainable Machine Learning for Anaerobic Digestion: A Critical Review

SSRN

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SSRN 2022年

作者： Li, Wangliang Gupta, Rohit Zhang, Le Liu, Hongtao Zhang, Zhikai Hou, Jiayi You, Siming Ok, Yong Sik James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Nanoengineered Systems Laboratory UCL Mechanical Engineering University College London LondonWC1E 7JE United Kingdom Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London LondonW1W 7TS United Kingdom Department of Resources and Environment School of Agriculture and Biology Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing100101 China CAS Key Laboratory of Green Process and Engineering Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Water Resources and Environment Hebei GEO University Hebei Shijiazhuang050031 China Korea Biochar Research Center APRU Sustainable Waste Management Program Division of Environmental Science and Ecological Engineering Korea University Seoul02841 Korea Republic of

Anaerobic digestion (AD) is a traditional process to achieve energy recovery from a variety of organic waste streams. However, the AD performance in terms of biogas production and process stability relies on the cooperation of the functional microbial communities and the operating parameters. The latest progress in machine learning for AD processes including optimization, parametric prediction, perturbation detection, and long-term monitoring were critically reviewed. The explainable data-driven models including black-box machine learning (ML) models and nature-inspired optimization techniques were compared, while summarizing their applications. The review also highlighted the challenges, perspectives, and opportunities for future research of applying ML for AD processes. © 2022, The Authors. All rights reserved.

关键词： Optimization

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Radiomics in liver research: A paradigm shift in disease detection and staging

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology Artificial Intelligence 2025年 2卷 100016-100016页

作者： Le, Minh Huu Nhat Kha, Hien Quang Tran, Nghia Minh Nguyen, Phat Ky Huynh, Han H. Huynh, Phat Kim Lam, Han Le, Nguyen Quoc Khanh International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan AIBioMed Research Group Taipei Medical University Taipei 110 Taiwan Translational Science and Precision Medicine Talent Cultivation Program College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan International Master Program for Translational Science College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Department of Industrial and Systems Engineering North Carolina A&T State University USA Industrial & Management Systems Engineering Department University of South Florida Tampa FL 33620 USA In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei 110 Taiwan

AbstractThis narrative review explores radiomics in the broader context of liver disease research, an interdisciplinary field bridging medical imaging, oncology, and data science. It begins with an introduction to data analysis techniques, from foundational methods like descriptive statistics, comparative and regression analysis, and survival analysis, to advanced approaches like machine learning (ML), meta-analysis, and bioinformatics. Contextualizing these methodologies within liver disease research enhances our understanding of liver diseases. The focus is on radiomics, an emerging field, which is first placed in historical context before its applications are explained across fatty liver diseases (FLD), fibrosis, cirrhosis, and liver cancers. The role of radiomics in early detection, disease staging, and outcome prediction, particularly as a non-invasive alternative to liver biopsy, is analyzed in-depth. Current imaging modalities for liver cancer diagnosis are also examined, highlighting their advantages and limitations. Alongside radiomics, these imaging tools enable comprehensive liver assessment. Lastly, the integration of radiomics with other data types, especially genomic data, is explored, emphasizing its role in a holistic approach to liver disease management. The review concludes with a forward-looking perspective on future directions and advancements in radiomics and liver cancer research, addressing the challenges and opportunities in the field. It underscores the transformative impact of these developments on liver cancer research, patient care, personalized medicine, and clinical decision-making.

关键词： AFP alpha-fetoprotein AI artificial intelligence AST aspartate transaminase AUC area under the curve CCrCC clear cell renal cell carcinoma CEUS contrast-enhanced ultrasound CNNs convolutional neural networks DCNN deep convoluted neural network DOR diagnostic odds ratio FLD fatty liver disease GWAS genome-wide association studies HCC hepatocellular carcinoma MAFLD metabolic associated fatty liver disease ML machine learning MRE magnetic resonance elastography NAFLD nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NPV negative predictive value PPV positive predictive value VCTE vibration-controlled transient elastography Radiomics Liver disease Cancer Non-invasive diagnostic techniques Presicion medicine Deep learning Machine learning

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Renormalizing individual performance metrics for cultural heritage management of sports records

arXiv

引用

arXiv 2020年

作者： Petersen, Alexander M. Penner, Orion Management of Complex Systems Department Ernest and Julio Gallo Management Program School of Engineering University of California MercedCA95343 United States Chair of Innovation and Intellectual Property Policy College of Management of Technology Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

Individual performance metrics are commonly used to compare players from different eras. However, such cross-era comparison is often biased due to significant changes in success factors underlying player achievement rates (e.g. performance enhancing drugs and modern training regimens). Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multibillion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics – despite the fact that many player careers span different eras. Remarkably, we find that the method applied to comprehensive Major League Baseball and National Basketball Association player data preserves the overall functional form of the distribution of career achievement, both at the season and career level. As such, subsequent re-ranking of the top-50 all-time records in MLB and the NBA using renormalized metrics indicates reordering at the local rank level, as opposed to bulk reordering by era. This local order refinement signals time-independent mechanisms underlying annual and career achievement in professional sports, meaning that appropriately renormalized achievement metrics can be used to compare players from eras with different season lengths, team strategies, rules – and possibly even different sports. Copyright © 2020, The Authors. All rights reserved.

关键词： Sports

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Why solve NASA open innovation challenges? An analysis of solver motivations from NASA's Astrobee challenge series 70

Why solve NASA open innovation challenges? An analysis of so...

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70th International Astronautical Congress, IAC 2019

作者： Zhang, Lihui Lydia Szajnfarber, Zoe Technology and Policy Program Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Engineering Management and Systems Engineering George Washington University 800 22nd St NW WashingtonDC20052 United States

In the past decade, NASA and organizations around the world have exploited open challenges as both a source of innovation and a way to increase awareness of their products and business. Some famous examples include the NASA Centennial Challenges, Google Lunar X-Prize, and ESA Grand Challenge. While extensive research has been focused on the novelty and appropriateness of the solutions, and challenge winners have been profiled extensively, little is known about the broader group of participants, and particularly what drives them to participate. Knowing why solvers enter the solving pool can enable organizations, like NASA, to more effectively tap into their motivations and thereby improve challenge efficiency. This paper takes a step towards that goal, analyzing a unique dataset of 9000+ self-reported motivations, produced during the NASA-Freelancer Astrobee Challenge Series. With inductive coding and supervised Natural Language Processing algorithm, we learned the different categories and distributions of motivation from this data set. We then compare such empirical classification to those discussed in literature and by practitioners of open challenges. This enabled a test of whether NASA challenges activate different motivations than do typical challenges. We also coded motivations for specific references to "NASA" and its mission. We found evidence to support "NASA bump", a phenomenon widely discussed in the field but rarely studied, where people are motivated to participate open challenges due to some level of emotional affiliation to NASA. Implications of these findings are discussed. Copyright © 2019 by the International Astronautical Federation (IAF). All rights reserved.

关键词： NASA

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Impacts of Groundwater Pumping for Hydraulic Fracturing on Other Sector Wells in Aquifers Overlying the Eagle Ford Shale, Texas

SSRN

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SSRN 2022年

作者： Brien, John A. Obkirchner, Gabrielle Knappett, Peter Shawn Kuehl Miller, Gretchen R. Burnett, David Bhatia, Mukul Water Management and Hydrologic Sciences Program Texas A&M University College StationTX77845 United States Brien Well Drilling HearneTX United States Luhdorff and Scalmanini Consulting Engineers WoodlandCA United States Geology & Geophysics Texas A&M University College StationTX77843 United States Civil & Environmental Engineering Texas A&M University College StationTX77843 United States Petroleum Engineering Texas A&M University College StationTX77843 United States Berg-Hughes Center for Petroleum and Sedimentary Systems Texas A&M University College StationTX77843 United States

In regions with limited surface water supplies, hydraulic fracturing (HF) events consume high volumes of groundwater over the span of a few weeks. The sporadic pumping schedules from Rig/Frack Supply Wells (RFSWs) contrast with the seasonal pumping from irrigation wells and the steady year-round pumping from municipal wells. The objective of this study is to assess the impacts of groundwater pumping for HF on hydraulic heads in other sector water wells and layer-to-layer volumetric flux between the four layers that comprise the Carrizo-Wilcox Aquifer in south Texas. The study period spanned from 2011 to 2020. The pumping schedules of 2,500 RFSWs were estimated from reported water volumes for each of the 22,500 HF events. To assess the importance of spatial resolution in groundwater models the impacts of RFSW pumping were quantified using both an existing numerical groundwater model with 1 mile 2 grid cells and a simpler analytical model. The median annual drawdowns predicted by the numerical and analytical models ranged from 0.2 to 0.8 m and 0.2 to 6.6 m, respectively. The analytical model predicted more severe, but less frequent drawdowns than the numerical model. High temporal and spatial resolution models are required to plan for the impacts of pumping for HF supply on other sector wells. © 2022, The Authors. All rights reserved.

关键词： Analytical models

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Correction to: Environmental consequences of interacting effects of changes in stratospheric ozone, ultraviolet radiation, and climate: UNEP Environmental Effects Assessment Panel, Update 2024 (Photochemical & Photobiological Sciences, (2025), 24, 3, (357-392), 10.1007/s43630-025-00687-x)

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Photochemical and Photobiological Sciences 2025年第5期24卷 863-865页

作者： Neale, Patrick J. Hylander, Samuel Banaszak, Anastazia T. Häder, Donat-P. Rose, Kevin C. Vione, Davide Wängberg, Sten-Åke Jansen, Marcel A. K. Busquets, Rosa Andersen, Mads P. Sulbæk Madronich, Sasha Hanson, Mark L. Schikowski, Tamara Solomon, Keith R. Sulzberger, Barbara Wallington, Timothy J. Heikkilä, Anu M. Pandey, Krishna K. Andrady, Anthony L. Bruckman, Laura S. White, Christopher C. Zhu, Liping Bernhard, Germar H. Bais, Alkiviadis Aucamp, Pieter J. Chiodo, Gabriel Cordero, Raúl R. Petropavlovskikh, Irina Neale, Rachel E. Olsen, Catherine M. Hales, Simon Lal, Aparna Lingham, Gareth Rhodes, Lesley E. Young, Antony R. Robson, T. Matthew Robinson, Sharon A. Barnes, Paul W. Bornman, Janet F. Harper, Anna B. Lee, Hanna Calderón, Roy Mackenzie Ossola, Rachele Paul, Nigel D. Revell, Laura E. Wang, Qing-Wei Zepp, Richard G. Environmental Research Center Smithsonian Institution EdgewaterMD United States Centre for Ecology and Evolution in Microbial Model Systems Linnaeus University Kalmar Sweden Unidad Académica de Sistemas Arrecifales Universidad Nacional Autónoma de México Puerto Morelos Mexico Erlangen Germany Department of Biological Sciences and Department of Civil and Environmental Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Chemistry University of Turin Turin Italy Department of Marine Sciences University of Gothenburg Gotheburg Sweden School of Biological Earth and Environmental Sciences Environmental Research Institute University College Cork Cork Ireland Chemical and Pharmaceutical Sciences Kingston University London Kingston Upon Thames United Kingdom Civil Environmental & Geomatic Engineering University College London London United Kingdom Department of Chemistry and Biochemistry California State University NorthridgeCA United States Atmospheric Chemistry Observations and Modeling National Center for Atmospheric Research BoulderCO United States USDA UV-B Monitoring and Research Program Colorado State University Fort. CollinsCO United States Department of Environment and Geography University of Manitoba WinnipegMB Canada Working Group Environmental Epidemiology IUF-Leibniz Research Institute for Environmental Medicine Düsseldorf Germany Department of Environment and Health School of Public Health University of Bielefeld Bielefeld Germany School of Environmental Sciences University of Guelph GuelphON Canada Retired From Eawag Swiss Federal Institute of Aquatic Science and Technology Dübendorf Switzerland Center for Sustainable Systems School for Environment and Sustainability University of Michigan Ann ArborMI United States Climate Research Finnish Meteorological Institute Helsinki Finland Indian Academy of Wood Science Bengaluru India Chemical and Biomolecular Engineering North Carolina State University RaleighN

This work is not a product of the U.S. Government or the U.S. Environmental Protection Agency, and the author is not doing this work in any governmental capacity. This research was not performed or funded by EPA and was not subject to EPA’s quality system requirements. The views expressed in this journal article are those of the author(s) and do not necessarily represent the views or the policies of the U.S. Environmental Protection Agency. The original article has been corrected. © The Author(s), under exclusive licence to the European Photochemistry Association, European Society for Photobiology 2025.

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Response to Comment on "Cannabis and the Environment: What Science Tells Us and What We Still Need to Know"

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ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2021年第6期8卷 486-486页

作者： Wartenberg, Ariani C. Holden, Patricia A. Bodwitch, Hekia Parker-Shames, Phoebe Novotny, Thomas Harmon, Thomas C. Hart, Stephen C. Beutel, Marc Gilmore, Michelle Hoh, Eunha Butsic, Van Department of Environmental Science Policy and Management University of California Berkeley California 94720 United States Bren School of Environmental Science & Management University of California Santa Barbara California 93117 United States School of Public Health San Diego State University San Diego California 92182 United States Environmental Systems Graduate Program School of Engineering University of California Merced California 95343 United States Department of Life & Environmental Sciences and Sierra Nevada Research Institute University of California Merced California 95343 United States

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