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125th ASEE Annual Conference and Exposition

作者： Riskin, Eve A. Milford, Jana Callahan, Janet Cosman, Pamela Schneider, John B. Pitts, Kevin Knaphus-Soran, Emily Llewellyn, Donna C. Delaney, Ann E. Myers, Beth A. Tetrick, Katherine Christine Cunningham, Sonya Ennis, Tanya D. O'Connor, Kevin Ferrez, Michelle Pan, Tiffany D. Baldis, Jessica EE Department University of Washington United States Engineering GoldShirt Program University of Colorado Boulder United States Micron School of Materials Science and Engineering Boise State University United States Department of Electrical and Computer Engineering University of California San Diego United States Voiland College of Engineering and Architecture United States Department of Physics University of Illinois Urbana-Champaign United States University of Washington United States Institute for STEM and Diversity Initiatives Boise State University United States Public Policy and Administration Boise State University United States Department of Analytics Assessment and Accreditation University of Colorado Boulder United States Program Washington State University United States University of Washington STARS Program Diversity and Access College of Engineering United States Engineering GoldShirt Program University of Colorado Boulder College of Engineering and Applied Science United States Department of Educational Psychology and Learning Sciences University of Colorado Boulder United States United States

The NSF-funded Redshirt in engineering Consortium was formed in 2016 with the goal of enhancing the ability of academically talented but underprepared students coming from low-income backgrounds to successfully graduate with engineering degrees. The Consortium takes its name from the practice of redshirting in college athletics, with the idea of providing an extra year and support to help promising engineering students complete a bachelor's degree. The Consortium builds on the success of three existing "academic redshirt" programs and expands the model to three new schools. The Existing Redshirt Institutions (ERIs) help mentor and train the new Student Success Partners (SSPs), and SSPs contribute their unique expertise to help ERIs improve existing Redshirt programs. The Redshirt model consists of seven main programmatic components aimed at improving the engagement, retention, and graduation of students underrepresented in engineering. These components include: "intrusive" academic advising and support services, an intensive first-year academic curriculum, community-building (including pre-matriculation summer programs), career awareness and vision, faculty mentorship, NSF S-STEM scholarships, and second-year support. Successful implementation of these activities is intended to produce two main long-term outcomes: a six-year graduation rate of 60%-75% for Redshirt students, and increased rates of enrollment and graduation of Pell-eligible, URM, and women students in engineering at participating universities. In the first year of the grant (AY 16-17), SSPs developed their own Redshirt programs, hired and trained staff, and got their programs off the ground. ERIs implemented faculty mentorship programs and expanded support to Redshirt students into their sophomore year. In the second year (AY 17-18), Redshirt programs were expanded at the ERIs while SSPs welcomed their first cohorts of Redshirt students. This Work in Progress paper describes the Redshirt programs at e

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An Open Natural Language Processing (NLP) Framework for EHR-based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3C)

arXiv

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arXiv 2021年

作者： Liu, Sijia Wen, Andrew Wang, Liwei He, Huan Fu, Sunyang Miller, Robert Williams, Andrew Harris, Daniel Kavuluru, Ramakanth Liu, Mei Abu-El-rub, Noor Schutte, Dalton Zhang, Rui Rouhizadeh, Masoud Osborne, John D. He, Yongqun Topaloglu, Umit Hong, Stephanie S. Saltz, Joel H. Schaffter, Thomas Pfaff, Emily Chute, Christopher G. Duong, Tim Haendel, Melissa A. Fuentes, Rafael Szolovits, Peter Xu, Hua Liu, Hongfang Department of Artificial Intelligence and Informatics Mayo Clinic United States Tufts Clinical and Translational Science Institute Tufts Medical Center United States Department of Internal Medicine University of Kentucky United Kingdom Department of Internal Medicine University of Kansas Medical Center United States Department of Pharmaceutical Care & Health Systems University of Minnesota Twin Cities United States Department of Pharmaceutical Outcomes & Policy University of Florida United States Department of Computer Science University of Alabama at Birmingham United States University of Michigan Medical School United States Wake Forest Baptist Medical Center United States Department of Medicine Johns Hopkins University United States Department of Biomedical Informatics Stony Brook University United States Sage Bionetwork United States Department of Medicine University of North Carolina Chapel Hill United States Albert Einstein College of Medicine United States Center for Health AI University of Colorado Anschutz Medical Campus United States Alex Informatics United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Biomedical Informatics University of Texas Health Science Center Houston United States

Despite recent methodology advancements in clinical natural language processing (NLP), adoption of clinical NLP models within the clinical and translational research community remains hindered by issues with ETL process heterogeneity and human factor variations. In this study, we proposed an open NLP development framework with the aim of addressing these issues. The viability of such a platform was evaluated on a COVID-19 use case through sites participating in the National COVID Cohort Collaborative (N3C). As part of our assessment of the impact of single vs. multi-site NLP algorithm development, we evaluated the performance of both an NLP ruleset developed solely using a single site’s clinical narratives as well as one further refined using a synthetic derived dataset sourced from three sites (Mayo, UKen, and UMN). The single-site ruleset resulted in performances of 0.876, 0.706, and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets, respectively, while the multi-site NLP ruleset improved performances to 0.884, 0.769 and 0.806. The results of our use case test run inform us of the importance of a multisite federated development, evaluation, and implementation framework. As such, we aim to meet this need with our framework by providing the tools necessary to conduct NLP development in a collaborative manner through consensus building, process coordination, and best practice sharing. © 2021, CC BY.

关键词： Natural language processing systems

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Examining interconnection and net metering policy for distributed generation in the United States

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Renewable Energy Focus 2017年 22-23卷 10-19页

作者： Schelly, Chelsea Louie, Edward P. Pearce, Joshua M. Department of Social Sciences Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States School of Public Policy Oregon State University CorvallisOR97331-3703 United States Materials Science and Engineering and Department of Electrical & Computer Engineering Michigan Technological University 1400 Townsend Drive HoughtonMI49931 United States

Following requirements of the Energy policy Act of 2005, most U.S. states require utility companies to adopt interconnection and net metering policies, allowing customers to become prosumers who both consume and produce electricity, generating electricity using distributed renewable energy technologies, connecting to the existing electric utility grid and receiving compensation for excess electricity generation. This paper reviews existing interconnection and net metering policies instituted by investor owned utilities (IOUs) across the U.S., specifically focused on policies regulating installations of small scale, residential or Tier 1 (a term used to indicate policies applicable to smaller scale rather than larger scale, although the size at which DG systems are classified as either Tier 1 or higher tiers varies by utility). publicly available data from each IOU reveal inconsistencies in interconnection and net metering policies, within states and even within individual companies. In addition, accurate information is often unavailable to consumers. Perhaps most importantly, results suggest that compensation for excess distributed generation often lacks transparent articulation in utility policy. The results of this study provide important insight into interconnection and net metering policies for distributed renewable energy generation, as states and utilities continue to modify interconnection and net metering policies in response to increased adoption of distributed renewable energy systems. © 2017 Elsevier Ltd

关键词： Distributed power generation

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Characterizing weibo social media posts from wuhan, china during the early stages of the COVID-19 pandemic: Qualitative content analysis

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JMIR public Health and Surveillance 2020年第4期6卷 e24125页

作者： Xu, Qing Shen, Ziyi Shah, Neal Cuomo, Raphael Cai, Mingxiang Brown, Matthew Li, Jiawei Mackey, Tim Department of Healthcare Research and Policy University of California San Diego - Extension San Diego CA United States Global Health Policy and Data Institute San Diego CA United States S-3 Research LLC San Diego CA United States Masters Program in Computer Science Jacobs School of Engineering University of California San Diego San Diego CA United States Department of Anesthesiology School of Medicine University of California San Diego San Diego CA United States US Embassy National Cancer Institute National Institutes of Health Beijing China Department of Anesthesiology Division of Infectious Diseases and Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: The COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. Objective: This study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. Methods: We used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. Results: We identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. public reaction included both protective and elevated health risk behavior. Conclusions: Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxie

关键词： Attitude Behavior China Content analysis COVID-19 Data mining Infodemic Infodemiology Infoveillance Knowledge Social media Weibo

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A structural haplotype in the 17q21.31 MAPT region is associated with increased risk for chronic traumatic encephalopathy endophenotypes

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Cell Reports Medicine 2025年第5期6卷 102084-102084页

作者： Han, Xudong Zhang, Yichi Petrosky, Jillian N. Bald, Sarah Sherva, Richard M. Labadorf, Adam Cherry, Jonathan D. Chung, Jaeyoon Farrell, Kurt Abdolmohammadi, Bobak Durape, Shruti Martin, Brett M. Palmisano, Joseph N. Farrell, John J. Alvarez, Victor E. Huber, Bertrand R. Dwyer, Brigid Daneshvar, Daniel H. Jun, Gyungah R. Lunetta, Kathryn L. Goldstein, Lee E. Katz, Douglas I. Cantu, Robert C. Shenton, Martha E. Cummings, Jeffrey L. Reiman, Eric M. Stern, Robert A. Alosco, Michael L. Tripodis, Yorghos Farrer, Lindsay A. Stein, Thor D. Crary, John F. McKee, Ann C. Mez, Jesse Bioinformatics Graduate Program Boston University Boston MA United States Section of Biomedical Genetics Department of Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston University Alzheimer's Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA United States Boston VA Healthcare System Boston MA United States Boston University Chronic Traumatic Encephalopathy Center Boston University Boston MA United States Department of Pathology and Laboratory Medicine Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Pathology Neuroscience and Artificial Intelligence & Human Health Neuropathology Brain Bank & Research CoRE Friedman Brain Institute Ronald M. Loeb Center for Alzheimer's Disease Icahn School of Medicine at Mount Sinai New York NY United States Biostatistics and Epidemiology Data Analytics Center Boston University School of Public Health Boston MA United States Bedford VA Healthcare System Bedford MA United States Braintree Rehabilitation Hospital Braintree MA United States Department of Physical Medicine and Rehabilitation Harvard Medical School Boston MA United States Department of Rehabilitation and Human Performance Brain Injury Research Center Icahn School of Medicine at Mount Sinai New York NY United States Department of Neurology Icahn School of Medicine at Mount Sinai New York NY United States Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Ophthalmology Boston University Chobanian & Avedisian School of Medicine Boston MA United States Departments of Biomedical Electrical & Computer Engineering Boston University College of Engineering Boston MA United States Departments of Radiology and Psychiatry Boston University Chobanian & Avedisia

Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impact (RHI) exposure. Genetic variation in the 17q21.31 region, containing microtubule-associated protein tau (MAPT), has been implicated in tauopathies but has not been investigated in CTE. The region includes a megabase-long inversion (H1/H2) and copy-number variations, including α, β, and γ segments, which can be characterized as nine segregating structural haplotypes. We leveraged array SNP data and a reference panel across the 17q21.31 region to impute structural haplotypes and test their association with CTE endophenotypes in 447 European ancestry brain donors with RHI exposure. The H1β1γ1 haplotype was significantly associated with dementia and semi-quantitative tau burden in multiple cortical and medial temporal regions commonly affected in CTE. H1β1γ1 differential expression analyses in dorsolateral frontal cortex implicated cis-acting genes and inflammatory pathways. Taken together, the H1β1γ1 haplotype may help explain CTE heterogeneity among those with similar RHI exposure. © 2025 The Authors

关键词： 17q21.31 chronic traumatic encephalopathy dementia differential gene expression immune system MAPT repetitive head impacts structural haplotypes tauopathy traumatic brain injury

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Towards A Task Taxonomy of Visual Analysis of Electronic Health or Medical Record Data

Towards A Task Taxonomy of Visual Analysis of Electronic Hea...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Xiaohui Bian Hadi Kharrazi Jesus J. Caban Gaoqi He Zhiquan Feng Jian Chen Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Department of Health Policy and Management The Johns Hopkins School of Public Health Baltimore USA The National Intrepid Center of Excellence Walter Reed National Military Medical Center Bethesda USA Department of Computer Science and Software Engineering East China Normal University Shanghai China School of Information Science and Engineering University of Jinan Jinan China Department of Computer Science and Engineering The Ohio State University Columbus USA

ISBN: (纸本)9781538676738;9781538676721

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

关键词： Task analysis Data visualization Medical diagnostic imaging Taxonomy History Data mining Visualization

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Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic Family for Energy Harvesting

Ultra-Low Power Subthreshold Quasi Floating Gate CMOS Logic ...

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International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS)

作者： M. Pilar Garde Antonio Lopez-Martin Daniel Orradre Jaime Ramírez-Angulo Institute of Smart Cities Public University of Navarra Pamplona Spain Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA

ISBN: (纸本)9781538663660

Subthreshold digital circuits are suitable for application niches where high performance is not required, but extremely low power consumption is a must. One of the most relevant applications of this kind nowadays is energy harvesting, where self-powered devices experience severe energy constraints. In this paper, a subthreshold CMOS logic family is proposed based on the Quasi-Floating Gate transistor, aimed to the design of different subcircuits required in energy harvesting. Measurement results of a test chip prototype confirm the feasibility of this approach.

关键词： Logic gates Inverters MOSFET Energy harvesting Threshold voltage Capacitance

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1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic

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Global Epidemiology 2020年 2卷 100023-100023页

作者： Koczkodaj, W.W. Mansournia, M.A. Pedrycz, W. Wolny-Dominiak, A. Zabrodskii, P.F. Strzałka, D. Armstrong, T. Zolfaghari, A.H. Dębski, M. Mazurek, J. Computer Science Laurentian University Sudbury P3E 2C6 ON Canada Department of Epidemiology and Biostatistics School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Electrical & Computer Engineering Alberta University Edmonton AB Canada University of Economics in Katowice 1 Maja 47 Katowice 40-287 Poland Saratov Medical University Reaviz Saratov Russian Federation Rzeszów University of Technology Powstańców Warszawy 12 Rzeszów 35-959 Poland Department of Sociology University of Gdańsk Bażyńskiego 8 Gdańsk 80-309 Poland Silesian University in Opava Opava Czech Republic

We forecast 1,000,000 COVID-19 cases outside of China by March 31st, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic;we model only the number of cases. The proposed heuristic is based on a simple observation that the plot of the given data is well approximated by an exponential curve. The exponential curve is used for forecasting the growth of new cases. It has been tested for the last situation report of the last day. Its accuracy has been 1.29% for the last day added and predicted by the 57 previous WHO situation reports (the date 18 March 2020). Prediction, forecast, pandemic, COVID-19, coronavirus, exponential growth curve parameter, heuristic, epidemiology, extrapolation, abductive reasoning, WHO situation report. © 2020

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Author Correction: Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries

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Nature communications 2021年第1期12卷 6827页

作者： Yong Suk Oh Jae-Hwan Kim Zhaoqian Xie Seokjoo Cho Hyeonseok Han Sung Woo Jeon Minsu Park Myeong Namkoong Raudel Avila Zhen Song Sung-Uk Lee Kabseok Ko Jungyup Lee Je-Sang Lee Weon Gi Min Byeong-Ju Lee Myungwoo Choi Ha Uk Chung Jongwon Kim Mengdi Han Jahyun Koo Yeon Sik Choi Sung Soo Kwak Sung Bong Kim Jeonghyun Kim Jungil Choi Chang-Mo Kang Jong Uk Kim Kyeongha Kwon Sang Min Won Janice Mihyun Baek Yujin Lee So Young Kim Wei Lu Abraham Vazquez-Guardado Hyoyoung Jeong Hanjun Ryu Geumbee Lee Kyuyoung Kim Seunghwan Kim Min Seong Kim Jungrak Choi Dong Yun Choi Quansan Yang Hangbo Zhao Wubin Bai Hokyung Jang Yongjoon Yu Jaeman Lim Xu Guo Bong Hoon Kim Seokwoo Jeon Charles Davies Anthony Banks Hyung Jin Sung Yonggang Huang Inkyu Park John A Rogers Center for Bio-Integrated Electronics Northwestern University Evanston IL USA. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA. Department of Materials Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA. Querrey Simpson Institute for Bioelectronics Northwestern University Evanston IL USA. State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian People's Republic of China. Ningbo Institute of Dalian University of Technology Ningbo People's Republic of China. Department of Biomedical Engineering Texas A&M University College Station TX USA. Department of Mechanical Engineering Northwestern University Evanston IL USA. Advanced 3D Printing Technology Development Division Korea Atomic Energy Research Institute Daejeon Republic of Korea. Qualcomm Institute La Jolla CA USA. Sibel Health Inc Niles IL USA. Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Planning and Development Gimhae Hansol Rehabilitation & Convalescent Hospital Gimhae Republic of Korea. Department of Rehabilitation Medicine Pusan national university hospital Busan Republic of Korea. Department of Materials Science and Engineering KAIST Institute for The Nanocentury (KINC) Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Materials Science and Engineering Northwestern University Evanston IL USA. Department of Mechanical Engineering Kyung Hee University Yongin Republic of Korea. Department of Biomedical Engineering College of Future Technology Peking University Beijing People's Republic of China. School of Biomedical Engineering Korea University Seoul Republic of Korea. Interdisci

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OC12.08: Observer-reality agreement for tumour spread and prediction non-resectability in ovarian cancer (ISAAC study, imaging study on advanced ovarian cancer)

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Ultrasound in Obstetrics & Gynecology 2023年第S1期62卷

作者： D. Fischerová M. Pesta M. Blazko M. Moruzzi K. Hundarova U. Scovazzi N. de Sousa M. Ambrosio A. C. Testa D. Franchi V. Chiappa J. Alcazar P. Pinto D. Cibula Obstetrics and Gynecology First Faculty of Medicine Charles University and General University Hospital Prague Czechia Faculty of Mathematics and Physics Charles University Prague Czechia Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czechia Women Children and Public Health Sciences Policlinico Universitario Agostino Gemelli IRCSS Rome Italy Gynecology Coimbra Hospital and University Center Coimbra Portugal Ospedale Policlinico San Martino and University of Genoa Genoa Italy Gynecology and Obstetrics Braga Hospital Braga Portugal Obstetrics and Gynecology Sant'Orsola Malpighi Hospital University of Bologna Bologna Italy Obstetrics and Gynecology Catholic University of the Sacred Heart Rome Italy European Institute of Oncology Milan Italy Gynecologic Oncology National Cancer Institute of Milan Milan Italy Obstetrics and Gynecology University of Navarra Pamplona Spain Gynecology Portuguese Institute of Oncology Francisco Gentil Lisbon Portugal

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