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Environmental legacy: the impact of the manufactured gas industry in the United States

Journal of Environmental Studies and Sciences 2015年第1期5卷 1-10页

作者： Tarr, Joel A. McMichael, Francis C. Departments of History Engineering & Public Policy and Heinz College of Public Policy and Management Carnegie Mellon University Pittsburgh PA United States Departments of Civil and Environmental Engineering and Engineering and Public Policy Carnegie Mellon University Pittsburgh PA United States

Levels of concern over the air and water impacts of U.S. industrial wastes have existed for over a century. A focus on land contamination is a more recent development but also has earlier antecedents. Air, water, and land pollution were a matter of societal concern, even though they were not necessarily identified as “environmental” effects, and information about them can be found in a variety of reports and court cases. This paper will examine the pollution effects of the manufactured gas industry—an industry that was widely distributed in cities and towns throughout the nation in the period from approximately 1825–1950. © 2014, AESS.

关键词： Environmental legacy Industrial wastes Manufactured gas industry

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Distributed transmission expansion planning in multi-area electric power systems

Distributed transmission expansion planning in multi-area el...

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Innovative Smart Grid Technologies (ISGT)

作者： Amin Kargarian Mohsen Rahmani Bamdad Falahati division of Electrical and Computer Engineering Louisiana State University Baton Rouge LA USA Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh PA USA Schweitzer Engineering Laboratories (SEL) Irvine CA

ISBN: (纸本)9781509051687

This paper presents a distributed transmission expansion planning (TEP) approach for multi-area power systems. A local TEP is formulated for each area with respect to local characteristics of that area as well as power flow in existing and candidate tie-lines that interconnect the area with its neighbors. Based on the auxiliary problem principle, a distributed decision-making algorithm is presented to coordinates the local TEP problems and find an overall solution for the entire power system. Limited information, only related to the tie-lines and not local lines, is exchanged between transmission areas, so that information privacy of the planners is respected. Numerical results on the Garver Test system show the effectiveness of the proposed distributed TEP algorithm.

关键词： Planning Optimization Power transmission lines Privacy Reliability Electronic mail

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Neglecting model structural uncertainty underestimates upper tails of flood hazard

arXiv

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arXiv 2017年

作者： Wong, Tony E. Klufas, Alexandra Srikrishnan, Vivek Keller, Klaus Earth and Environmental Systems Institute Pennsylvania State University University ParkPA16802 United States Department of Mathematics Wellesley College WellesleyMA02481 United States Department of Energy and Mineral Engineering Pennsylvania State University University ParkPA16802 United States Department of Geosciences Pennsylvania State University University ParkPA16802 United States Department of Engineering and Public Policy Carnegie Mellon University PittsburghPA15289 United States Department of Computer Science University of Colorado BoulderCO80309 United States

Coastal flooding drives considerable risks to many communities, but projections of future flood risks are deeply uncertain. The paucity of observations of extreme events often motivates the use of statistical approaches to model the distribution of extreme storm surge events. One key deep uncertainty that is often overlooked is model structural uncertainty. There is currently no strong consensus among experts regarding which class of statistical model to use as a "best practice". Robust management of coastal flooding risks requires coastal managers to consider the distinct possibility of non-stationarity in storm surges. This increases the complexity of the potential models to use, which tends to increase the data required to constrain the model. Here, we use a Bayesian model averaging approach to analyze the balance between (i) model complexity sufficient to capture decision-relevant risks and (ii) data availability to constrain complex model structures. We characterize deep model structural uncertainty through a set of calibration experiments. Specifically, we calibrate a set of models ranging in complexity using long-term tide gauge observations from the Netherlands and the United States. We find that in both considered cases, roughly half of the model weight is associated with the non-stationary models. Our approach provides a formal framework to integrate information across model structures, in light of the potentially sizable modeling uncertainties. By combining information from multiple models, our inference sharpens for the projected storm surge 100-year return levels, and estimated return levels increase by several centimeters. We assess the impacts of data availability through a set of experiments with temporal subsets and model comparison metrics. Our analysis suggests that about 70 years of data are required to stabilize estimates of the 100-year return level, for the locations and methods considered here. Copyright © 2017, The Authors. All rights reserv

关键词： Floods

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Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories

NPP - digital psychiatry and neuroscience

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NPP - digital psychiatry and neuroscience 2025年第1期3卷 8页

作者： Lisa Vizer Jennifer Pierce Yinyao Ji Meredith A Bucher Mochuan Liu Lyle Ungar Salvatore Giorgi Zhaopeng Xing Stacey L House Francesca L Beaudoin Jennifer S Stevens Thomas C Neylan Gari D Clifford Tanja Jovanovic Sarah D Linnstaedt Donglin Zeng Laura T Germine Kenneth A Bollen Scott L Rauch John P Haran Alan B Storrow Christopher Lewandowski Paul I Musey Phyllis L Hendry Sophia Sheikh Christopher W Jones Brittany E Punches Lauren A Hudak Jose L Pascual Mark J Seamon Erica Harris Claire Pearson David A Peak Roland C Merchant Robert M Domeier Brian J O'Neil Paulina Sergot Leon D Sanchez Steven E Bruce Steven E Harte Ronald C Kessler Karestan C Koenen Samuel A McLean Xinming An Department of Medicine Division of General Medicine and Clinical Epidemiology University of North Carolina at Chapel Hill Chapel Hill NC 27559 USA. Department of Anesthesiology University of Michigan Ann Arbor MI 48109 USA. Institute for Trauma Recovery Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill NC 27559 USA. Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill NC 27559 USA. Computer and Information Science University of Pennsylvania Philadelphia PA 19104 USA. CVS Health New York NY 10029 USA. Department of Emergency Medicine Washington University School of Medicine St. Louis MO 63110 USA. Department of Epidemiology Brown University Providence RI 02930 USA. Department of Emergency Medicine Brown University Providence RI 02930 USA. Department of Psychiatry and Behavioral Sciences Emory University School of Medicine Atlanta GA 30329 USA. Departments of Psychiatry and Neurology University of California San Francisco San Francisco CA 94143 USA. Department of Biomedical Informatics Emory University School of Medicine Atlanta GA 30332 USA. Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA 30332 USA. Department of Psychiatry and Behavioral Neurosciences Wayne State University Detroit MI 48202 USA. Institute for Trauma Recovery Department of Anesthesiology University of North Carolina at Chapel Hill Chapel Hill NC 27559 USA. Institute for Technology in Psychiatry McLean Hospital Belmont MA 02478 USA. The Many Brains Project Belmont MA 02478 USA. Department of Psychiatry Harvard Medical School Boston MA 02115 USA. Department of Psychology and Neuroscience & Department of Sociology University of North Carolina at Chapel Hill Chapel Hill NC 27559 USA. Department of Psychiatry McLean Hospital Belmont MA 02478 USA. Department of Emergency Medicine University of Massachusetts Chan Medical School Worcester MA 01655 USA. Department of Emergency Med

Language features may reflect underlying cognitive and emotional processes following a traumatic event that portend clinical outcomes. The authors sought to determine whether language features from usual smartphone use were markers associated with concurrent posttraumatic symptoms and worsening or improving posttraumatic symptoms over time following a traumatic exposure. This investigation was a secondary analysis of the Advancing Understanding of RecOvery afteR traumA study, a longitudinal study of traumatic outcomes among survivors recruited from 33 emergency departments across the United States. Adverse posttraumatic sequelae were assessed over the six months following the initial traumatic exposure. Language features were extracted from usual smartphone use in a specialized app. Bivariate linear mixed models were used to identify and validate language features that are markers associated with posttraumatic symptoms. Participants were 1744 trauma survivors, with a mean age of 39 [SD = 13] years old, and 56% were female. Fourteen language features were associated with severity level of posttraumatic symptoms at specific timepoints (cross-sectional markers) and five features were associated with change in severity level of posttraumatic symptoms (longitudinal markers). References to the body and health or illness were predictive of worsening pain, somatic, and thinking/concentration/fatigue symptom severity over time. An increase in references to others was associated with improvement in somatic symptom severity over time and increases in expressions of causation or cognitive processes were associated with improvement in pain symptom severity over time. Language features derived from usual smartphone use can convey important information about health, functioning, and recovery following a traumatic event. Clinicians might utilize such information to determine who may experience a high symptom burden or risk of worsening posttraumatic symptoms.

关键词： Diagnostic markers Neurological manifestations

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DC Power Flow Estimation Utilizing Bayesian-based LMMSE Estimator

DC Power Flow Estimation Utilizing Bayesian-based LMMSE Esti...

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IEEE Power and Energy Society General Meeting

作者： M. H. Amini Marija D. Ilic O. Karabasoglu Joint Institute of Engineering Department of Electrical and Computer Engineering Sun Yat-sen University-Carnegie Mellon University Departments of Electrical and Computer Engineering and Engineering and Public Policy Carnegie Mellon University

ISBN: (纸本)9781467380416

In recent years, Smart Grid was introduced to achieve an environmentally-friendly, adequate, secure and fossil fuel-independent power system. The large scale smart grid studies require accurate state estimation to obtain an acceptable adequacy level. There exist some challenges regarding anomalous power flow studies which motivate grid operators to utilize robust and accurate estimation methods. Therefore, power system state estimators play a pivotal role in real-time grid management. In this paper, a sequential linear minimum mean square error (LMMSE) estimator is utilized to solve the DC power flow problem. First, we introduce the classic linear estimator model which assumes that to-be-estimated parameter values are unknown but deterministic. The LMMSE estimator will be discussed which treats the to-be-estimated parameter as a random variable with a known prior probability density function (pdf). We evaluate the accuracy of the LMMSE estimator by comparing it with maximum likelihood estimator (MLE). Finally, the effect of covariance matrix topology will be studied by defining three scenarios with different noise covariance matrices.

关键词： DC Power Flow Estimation Linear Minimum Mean Square Error (LMMSE) Maximum Likelihood Estimator (MLE)

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Provision of differentiated reliable services in distribution systems based on customer preferences

Provision of differentiated reliable services in distributio...

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Annual North-American Power Symposium

作者： Siripha Junlakarn Marija Ilíc Department of Engineering and Public Policy Carnegie Mellon University Department of Electrical and Computer Engineering Carnegie Mellon University

This paper proposes a market mechanism that provides differentiated higher reliability services beyond a standard level in distribution networks. To offer the higher reliability services, switching devices, as one of technologies for enhancing reliability, will be installed in the systems. The decisions on numbers and locations of these switches are made based on information from a service provider and customers.

关键词： Reliability engineering Investment computer network reliability Switches Circuit breakers Standards

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Model predictive excitation control for constrained frequency and voltage stabilization

Model predictive excitation control for constrained frequenc...

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IEEE General Meeting Power& Energy Society

作者： Xia Miao Marija D. Ilić Department of Electrical and Computer Engineering Department of Engineering and Public Policy Carnegie Mellon University PA USA

In this paper, a novel controller based on the combination of differential flatness property and model predictive technology for the generator excitation system is proposed. Such controller will maintain the terminal voltage of the generator within physical limits by better utilizing the existing excitation control. This eliminates the need for adding new fast storage. One preliminary real-world system is used to show the effectiveness of the proposed controller, and the results are compared with the performance of earlier proposed high-gain feedback linearizing excitation controller.

关键词： Voltage control Power system dynamics Aerospace electronics Trajectory Predictive control Nonlinear dynamical systems Transient analysis

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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ARIMA-based Demand Forecasting Method Considering Probabilistic Model of Electric Vehicles' Parking Lots

ARIMA-based Demand Forecasting Method Considering Probabilis...

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IEEE Power and Energy Society General Meeting

作者： M. H. Amini O. Karabasoglu Marija D. Ilic Kianoosh G. Boroojeni S. S. Iyengar Joint Institute of Engineering Department of Electrical and Computer Engineering Sun Yat-sen University-Carnegie Mellon University Departments of Electrical and Computer Engineering and Engineering and Public Policy Carnegie Mellon University School of Computing and Information Sciences Florida International University Florida International University's School of Computing and Information Sciences

ISBN: (纸本)9781467380416

Electric transportation is one of the key elements of the future power systems since conventional power networks are rapidly evolving towards smart grids. This transition creates the need for systematic utilization of electric vehicles (EV) in order to avoid unpredictable effects on the power systems. An accurate and efficient method for demand forecasting of EVs is needed to perform a feasible scheduling of resources in order to supply the predicted load sufficiently. This paper presents a method for electricity demand fore-casting considering EV parking lots' charging demand using historical load data. The method is based on auto-regressive integrated moving average (ARIMA) model for medium-term demand forecasting. The proposed approach improves the fore-casting accuracy. Probabilistic hierarchical EVs' parking lot demand modeling is used to estimate the expected load for each parking lots' daily charging demand. In order to evaluate the effectiveness of the proposed approach, it is implemented on PJM historical load data. The simulation results show the high accuracy of the proposed method for electricity demand forecasting.

关键词： Load modeling Predictive models Mathematical model Smart grids Probabilistic logic Demand forecasting

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Network priorities for social sustainability research and education: Memorandum of the Integrated Network on Social Sustainability Research Group

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Sustainability: Science, Practice, and policy 2016年第1期12卷 16-21页

作者： Hollander, Rachelle Amekudzi-Kennedy, Adjo Bell, Sarah Benya, Frazier Davidson, Cliff Farkos, Craig Fasenfest, David Guyer, Regina Hjarding, Angelique Lizotte, Michael Quigley, Dianne Watts, Diana Whitefoot, Kate Center for Engineering Ethics and Society National Academy of Engineering 500 Fifth Street NW Washington 20001 DC United States Department of Civil and Environmental Engineering Georgia Institute of Technology 709 Atlantic Drive Atlanta 30332 GA United States Department of Civil and Environmental Engineering University College London Chadwick 117 London WC1E 6BT United Kingdom Department of Civil and Environmental Engineering Syracuse University Syracuse 13244 NY United States American Society of Civil Engineering Task Committee on Sustainability 9105 Aspenshire Court Raleigh 27613 NC United States Sociology Department Wayne State University Detroit 48202 MI United States Energy and Environmental Assistance Office University of North Carolina Charlotte EPIC 1150 Charlotte 28223 NC United States Sustainability Office University of North Carolina Charlotte 9201 University City Boulevard Charlotte 28223 NC United States Northeast Ethics Education Partnership Brown University PO Box 1943 135 Angell Street Providence 20017 RI United States Department of Business Trinity Washington University 125 Michigan Avenue Washington 20017 DC United States Departments of Mechanical Engineering and Engineering and Public Policy Carnegie Mellon University Pittsburgh 15213 PA United States

The Integrated Network for Social Sustainability (INSS) is a research-coordination network supported by the National Science Foundation that is currently in its third year of activities. Individual and institutional members, representing a wide range of fields and interests, are devoted to addressing social sustainability as an important, understudied issue under the broader rubric of sustainability and sustainable development. The INSS has developed a number of affinity groups and a set of activities to facilitate its development. An annual conference draws members together to review and report on their efforts. At the first conference, a group interested in developing a research agenda formed. This Community Essay shares its members’ perspectives about priorities for future research and education on social sustainability, highlighting efforts for greater inclusion of marginalized populations in research. © 2016, ProQuest. All rights reserved.

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