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arXiv 2021年

作者： Salehinejad, Hojjat Kitamura, Jumpei Ditkofsky, Noah Lin, Amy Bharatha, Aditya Suthiphosuwan, Suradech Lin, Hui-Ming Wilson, Jefferson R. Mamdani, Muhammad Colak, Errol Li Ka Shing Centre for Healthcare Analytics Research & Training St. Michael’s Hospital Toronto Canada Department of Electrical & Computer Engineering University of Toronto Toronto Canada Fujisawa-city Kanagawa Japan Department of Medical Imaging St. Michael’s Hospital Unity Health Toronto Toronto Canada Faculty of Medicine University of Toronto Toronto Canada Division of Neurosurgery Department of Surgery University of Toronto Toronto Canada Leslie Dan Faculty of Pharmacy University of Toronto Toronto Canada Dalla Lana Faculty of Public Health University of Toronto Toronto Canada

Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demonstrate that ML model generalizability is achievable in medical imaging with the detection of intracranial hemorrhage (ICH) on non-contrast computed tomography (CT) scans serving as the use case. An ML model was trained using 21,784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center. This real world external validation dataset consisted of every unenhanced head CT scan (n = 5,965) performed in our emergency department in 2019 without exclusion. The model demonstrated an AUC of 98.4%, sensitivity of 98.8%, and specificity of 98.0%, on the test dataset. On external validation, the model demonstrated an AUC of 95.4%, sensitivity of 91.3%, and specificity of 94.1%. Evaluating the ML model using a real world external validation dataset that is temporally and geographically distinct from the training dataset indicates that ML generalizability is achievable in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical imaging

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Combating Food Insecurity with Large Scale Aquaponics: A Case Study in Silicon Valley

Combating Food Insecurity with Large Scale Aquaponics: A Cas...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： Riley Albright-Borden Petra Nelken Sophia Sparagana Sydney Thompson James Wang Laura Doyle Hohyun Lee Michele T. Parker Sarah Kate Wilson Mechanical Engineering Santa Clara University Santa Clara CA Public Health Santa Clara University Santa Clara CA Electrical & Computer Engineering Santa Clara University Santa Clara CA Civil Environmental Sustainable Engineering Santa Clara University Santa Clara CA

ISBN: (数字)9781728117805

ISBN: (纸本)9781728117812

Food insecurity affects 20 percent of residents in wealthy Santa Clara County, California. To combat this inequity, an aquaponics system was designed in partnership with a local soup kitchen and urban farm to increase produce yield and meal nutrition. The system consists of ninety feet of growing space, a 600 gallon fish pond, and sensors to measure water quality. The fish waste will fertilize the plants while the plants remove chemicals and oxygenate the water for the fish, and nitrifying bacteria convert the ammonia into nitrate to nourish the plants. This quality, low-maintenance, closed-loop system should significantly reduce water usage compared to traditional farming and should improve the efficiency and sustainability of the current farm.

关键词： Fish Temperature measurement Media Sensor systems Solar panels Conferences

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Evaluating the effectiveness of electric vehicle subsidies in Canada

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Procedia computer Science 2019年 155卷 519-526页

作者： Zachary Thorne Larry Hughes MASc Candidate Electrical & Computer Engineering Dalhousie University 6299 South St Halifax NS B3H 4R2 Canada Founding Fellow at the MacEachen Institute of Public Policy Dalhousie University 6299 South St Halifax NS B3H 4R2 Canada

The electrification of transportation has been identified as a necessary aspect of climate change mitigation in order to limit global temperatures to 1.5°C above pre-industrial levels and prevent potentially catastrophic effects on the environment. Despite this, in Canada and most other developed countries, consumers have been slow to adopt electric vehicles. In March of 2019, to encourage increased adoption of electric vehicles, the government of Canada announced a new federal EV subsidy program that will offer a rebate of up to $5000 on the purchase of zero-emission vehicles. When evaluating the costs and benefits of such a program, it is important to consider that the magnitude of emissions reductions achieved through EV subsidies relates to the emissions intensity of the electricity used to power the EV. In this paper, the cost-effectiveness of achieving emissions reductions through EV subsidy programs is evaluated with respect to each provincial electricity system in Canada. These results were then used to compare the cost-efficiency of EV subsidies to other investments in climate change mitigation initiatives, and ultimately decide whether EV subsidies are feasible to implement in any of Canada’s provinces.

关键词： electric vehicles EV subsidies sustainable energy policy climate change mitigation

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Power system dispatch with electrochemical energy storage

arXiv

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arXiv 2019年

作者： He, Guannan Kar, Soummya Mohammadi, Javad Moutis, Panayiotis Whitacre, Jay F. Department of Engineering and Public Policy Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA02139 United States

Electrochemical energy storage (EES) is essential for the future smart grid. The inevitable cell degradation renders the EES lifetime volatile and highly dependent on EES dispatch, and thus incurs opportunity cost. This paper rigorously derives the marginal degradation cost of EES for power system dispatch. The derived optimal marginal degradation cost is time-variant to reflect the time value of money and the functionality fade of EES and takes the form of a constant value divided by a discount factor plus a term related to EES state of health. In case studies, we demonstrate the evolution of the optimal marginal costs of degradation that corresponds to the optimal long-term dispatch outcome. We also show that the optimal marginal cost of degradation depends on the marginal cost of generation in the grid. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric load dispatching

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The Performance of Correlation-Based Support Vector Machine in Illiteracy Dataset 2

The Performance of Correlation-Based Support Vector Machine ...

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2nd East Indonesia Conference on computer and Information Technology, EIConCIT 2018

作者： Gunawan, Indra Widyaningtyas, Triyanna Wibawa, Aji Prasetya Haviluddin Darusalam, Darusalam Pranolo, Andri Department of Electrical Engineering State University of Malang Malang Indonesia Department of Computer Science Mulawarman University Samarinda Indonesia Department of Technology Policy and Management Delft University of Technology Delft Netherlands College of Computer and Information Engineering Hohai University Nanjing China

ISBN: (纸本)9781538680483

SVM method performs a non-linear mapping of original data space into a high-dimensional feature space. The construction of linear discrimination function is useful for replacing the non-linear function in the original data space. This paper aims to efficiently explore the accuracy of SVM with the feature selection method. The selected feature selection method is Correlation-based Feature Selection (CFS), due to the approach's simplicity and speed. This research used an illiteracy rate dataset in Indonesia. The research result showed that the optimised method has overcome the original SVM, with 94 % of accuracy. © 2018 IEEE.

关键词： Support vector machines

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arXiv

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arXiv 2020年

作者： Li, Jiashuo Zhou, Sili Wei, Wendong Qi, Jianchuan Li, Yumeng Chen, Bin Zhang, Ning Guan, Dabo Qian, Haoqi Wu, Xiaohui Miao, Jiawen Chen, Long Liang, Sai Feng, Kuishuang Institute of Blue and Green Development Shandong University Weihai264209 State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China School of International and Public Affairs Shanghai Jiao Tong University Shanghai200030 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China Laboratory of Anthropogenic Systems Ecology College of Engineering Peking University Beijing100871 China Department of Earth System Science Tsinghua University Beijing100871 China Institute for Global Public Policy Fudan University Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+. Copyright © 2020, The Authors. All rights reserved.

关键词： Fossil fuel power plants

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Examining Interconnection and Net Metering policy for Distributed Generation in the United States

SSRN

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SSRN 2019年

作者： Schelly, Chelsea Louie, Edward P. Pearce, Joshua M. Dept. 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 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. © 2019, The Authors. All rights reserved.

关键词： Electric utilities

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The cysteine-altering p.R544C variant in the NOTCH3 gene is a probable candidate for blood pressure and relevant traits in the Taiwan Biobank

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Journal of neurology 2023年第11期270卷 5536-5544页

作者： Eugene Lin Po-Hsiu Kuo Yu-Li Liu Tso-Jen Wang Albert C Yang Shih-Jen Tsai Department of Biostatistics University of Washington 3980 15th Avenue NE Box 351617 Seattle WA 98195 USA. lines@uw.edu. Department of Electrical and Computer Engineering University of Washington Seattle WA 98195 USA. lines@uw.edu. Graduate Institute of Biomedical Sciences China Medical University Taichung 40402 Taiwan. lines@uw.edu. Department of Public Health Institute of Epidemiology and Preventive Medicine National Taiwan University Taipei 10617 Taiwan. Department of Psychiatry National Taiwan University Hospital Taipei 100 Taiwan. Center for Neuropsychiatric Research National Health Research Institutes Miaoli County 35053 Taiwan. Taoyuan Psychiatric Center Ministry of Health and Welfare Taoyuan 33058 Taiwan. Brain Research Center National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Department of Psychiatry Taipei Veterans General Hospital No. 201 Shih-Pai Road Sec. 2 Taipei 11217 Taiwan. tsai610913@***. Division of Psychiatry National Yang Ming Chiao Tung University Taipei 112304 Taiwan. tsai610913@***.

BACKGROUND:The cysteine-altering variants in NOTCH3 have been suggested to be associated with stroke, dementia, and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), where aberrant blood pressure levels represent the characteristics of these diseases. We aimed to assess whether the cysteine-altering ***544Cys (p.R544C; rs201118034) variant and common single nucleotide variants (SNVs) in NOTCH3 could contribute to systolic and diastolic blood pressure and related phenotypes in the Taiwan Biobank. METHODS:We employed a discovery sample of 68,925 individuals, an independent replication sample of 45,676 individuals, and a combined/total sample of 114,601 individuals; all from the Taiwan Biobank. Blood pressure, such as systolic and diastolic blood pressure, was measured for all participants. Association was evaluated using a general linear model, where results were considered statistically significant if the P value < 0.05 divided by the number of independent tests per model. RESULTS:From our analysis, we identified and replicated three novel candidates for blood pressure that have not previously been reported: the cysteine-altering p.R544C variant for systolic blood pressure, the common SNV rs11669950 for diastolic blood pressure, and the common SNV rs4808235 for diastolic blood pressure. We also generalized two previously identified SNVs (i.e., rs10418305 and rs7408868) in NOTCH3 for blood pressure in European and non-Taiwanese East Asian populations to the Taiwanese population. Moreover, the participants with NOTCH3 p.R544C had an increased stroke frequency (P < 1.0 × 10) and a higher dementia frequency (P = 2.0 × 10) compared with the whole Taiwan Biobank population in the combined/total sample. CONCLUSION:NOTCH3 is a strong candidate for a role in stroke, dementia, and CADASIL, which has previously been linked to blood pressure changes. While our preliminary study suggests that NOTCH3 p.R544C may influence blood

关键词： Biobank Blood pressure CADASIL Dementia NOTCH3 Neurology Stroke

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Event detection in micro-PMU data: A generative adversarial network scoring method

arXiv

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arXiv 2019年

作者： Aligholian, Armin Shahsavari, Alireza Cortez, Ed Stewart, Emma Mohsenian-Rad, Hamed Department of Electrical and Computer Engineering University of California RiversideCA United States Energy Delivery Engineering Group Riverside Public Utilities RiversideCA United States Defense Infrastructure Group Lawrence Livermore National Laboratory LivermoreCA United States

A new data-driven method is proposed to detect events in the data streams from distribution-level phasor measurement units, a.k.a., micro-PMUs. The proposed method is developed by constructing unsupervised deep learning anomaly detection models;thus, providing event detection algorithms that require no or minimal human knowledge. First, we develop the core components of our approach based on a Generative Adversarial Network (GAN) model. We refer to this method as the basic method. It uses the same features that are often used in the literature to detect events in micro-PMU data. Next, we propose a second method, which we refer to as the enhanced method, which is enforced with additional feature analysis. Both methods can detect point signatures on single features and also group signatures on multiple features. This capability can address the unbalanced nature of power distribution circuits. The proposed methods are evaluated using real-world micro-PMU data. We show that both methods highly outperform a state-of-the-art statistical method in terms of the event detection accuracy. The enhanced method also outperforms the basic method. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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