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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Compost-Soil Mixture Respiration and Organic Carbon Degradation

Compost-Soil Mixture Respiration and Organic Carbon Degradat...

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The 1nd Symposium of International Waste Working Group Asian Regional Branch (IWWG-ARB)(国际固体废物工作组亚洲区域第一届研讨会)

作者： Emenda Sembiring Dwina Lubna Environmental Engineering Study Program Faculty of Civil and Environmental EngineeringBandung Institute of TechnologyIndonesia

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Health-associated Cost of Urban Informal Industrial Sector: An Assessment Tool

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Procedia - Social and Behavioral Sciences 2012年 36卷 112-122页

作者： Anindrya Nastiti Indrawan Prabaharyaka Dwina Roosmini Tresna Dermawan Kunaefi Study Program of Environmental Engineering Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Jalan Ganeca 10 Bandung 40132 Indonesia

Marginalized urban migrants thrive in informal economy, where health and safety are often neglected. Findings on previous studies have listed several informal characteristics; occupational injuries and diseases in informal setting; and components of health expenditure. This paper attempts to assess health-associated cost in informal industry through questionnaire containing basic information of respondent and household members, economic and occupational assessment. Different types of jobs surveyed, target population, biases from occupational/non-occupational cost and household/individual expenditure, and validation method are challenges that must be addressed. This paper serves as policy-tool reference to improve the well-being of informal workers by improving nationwide workforce survey.

关键词： Urban informal sector occupational health and safety health expenditure

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Mitigating flood exposure: Reducing disaster risk and trauma signature

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Disaster health 2013年第1期1卷 30-44页

作者： James M Shultz Andrew McLean Holly B Herberman Mash Alexa Rosen Fiona Kelly Helena M Solo-Gabriele Georgia A Youngs Jessica Jensen Oscar Bernal Yuval Neria Center for Disaster & Extreme Event Preparedness (DEEP Center) University of Miami Miller School of Medicine Miami FL USA. School of Medicine and Health Sciences University of North Dakota Fargo ND USA. Department of Psychology Center for the Study of Traumatic Stress Uniformed Services University of the Health Sciences Bethesda MD USA. Clinical & Health Psychology University of Edinburgh Scotland UK. Department of Civil Architectural and Environmental Engineering College of Engineering University of Miami Department of Emergency Management North Dakota State University School of Medicine Universidad de Los Andes Bogota Colombia. Trauma and PTSD Program Department of Psychiatry & The New York State Psychiatric Institute Columbia University New York NY USA.

In 2011, following heavy winter snowfall, two cities bordering two rivers in North Dakota, USA faced major flood threats. Flooding was foreseeable and predictable although the extent of risk was uncertain. One community, Fargo, situated in a shallow river basin, successfully mitigated and prevented flooding. For the other community, Minot, located in a deep river valley, prevention was not possible and downtown businesses and one-quarter of the homes were inundated, in the city's worst flood on record. We aimed at contrasting the respective hazards, vulnerabilities, stressors, psychological risk factors, psychosocial consequences, and disaster risk reduction strategies under conditions where flood prevention was, and was not, possible. . We applied the "trauma signature analysis" (TSIG) approach to compare the hazard profiles, identify salient disaster stressors, document the key components of disaster risk reduction response, and examine indicators of community resilience. . Two demographically-comparable communities, Fargo and Minot, faced challenging river flood threats and exhibited effective coordination across community sectors. We examined the implementation of disaster risk reduction strategies in situations where coordinated citizen action was able to prevent disaster impact (hazard avoidance) compared to the more common scenario when unpreventable disaster strikes, causing destruction, harm, and distress. Across a range of indicators, it is clear that successful mitigation diminishes both physical and psychological impact, thereby reducing the trauma signature of the event. . In contrast to experience of historic flooding in Minot, the city of Fargo succeeded in reducing the trauma signature by way of reducing risk through mitigation.

关键词： community resilience disaster flood mental health mitigation psychological psychosocial resilience trauma trauma signature analysis

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Prediction of Leachate Generation in a Landfill Using Artificial Neural Networks

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Journal of Environmental Science and engineering(B) 2012年第11期1卷 1233-1238页

作者： Samin Tohru Furuichi Kiasei Ishhi Enri Damanhuri Suprihanto Notodarmodjo Kuntjoro Adji Sidarta Department of Civil Engineering University of Muhammadiyah Malang East Java 062-65144 Indonesia School of Engineering Hokkaido University Sapporo 060-8628 Japan Study Program of Environmental Engineering lnstitut Teknologi Bandung West Java 062-40116 Indonesia Study Program of Mathematic Institut Teknologi Bandung West Java 062-40116 Indonesia

One of the problems encountered in the operation of a leachate treatment in a landfill is the quantity of the fluctuating leachate. Therefore, information on the precise prediction about the quantity of leachate produced in a landfill is required. This information can be obtained by using an ANN （artificial neural networks） model. In this study, a prediction on a leachate generation for a period of 15 days was made. The input for the ANN model consists of data such as rainfall, temperature, humidity, duration of solar radiation, and the landfill characteristics, while the output is the leachate landfills production in Minamiashigara, Japan. The ANN algorithm uses a BP （back propagation） with LM （Levenberg-Marquadrt） training type. By using the input-output data pairs, the training of ANN model was conducted in order to obtain the values of the weights that describe the relationship between the input-output data. Furthermore, with the trained ANN model, the prediction of leachate generation for a period of 15 days was made. The study result shows that the prediction accuracy ofleachate generation of ANN-C model, with a correlation coefficient （r） of 0.924, is quite good. Thus, the prediction of leachate generation using artificial neural network model can be recommended for predicting leachate generation in the future. In this study, a prediction on a leachate generation for a period of 15 days was made. The quantity of leachate generation in a landfill can be obtained by using ANN for future periods. By entering data for future periods （t ＋1） in ANN models, the leachate generation for the period （t ＋1） can be predicted.

关键词： Artificial neural network back-propagation leachate neurons landfills.

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Mapping Pattlerns of Cracking and Moisture Migration in Early Brickwork and Replicating Shell Lime Mortar

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MRS Online Proceedings Library 2011年第1期352卷 333-340页

作者： Gregory Clancey Michael Stetson Robert Henry John Leeke Program in the History and Social Study of Science and Technology Massachusetts Institute of Technology Waltham USA Department of Civil Engineering University of New Hampshire Durham USA Sanford USA

This paper reports three aspects of an ongoing building archeology project at the MacPheadris-Warner House (ca. 1716–23) in Portsmouth, N.H., U.S.A. One is the use of a portable moisture meter to map rising damp levels and areas of water infiltration through brickwork. The second is the mapping of crack patterns and other defects on exterior elevations. We will also refer to a second site, the Juliette Gordon Low House in Savannah, Georgia, in discussing the problem of crack mapping. The third project is an ongoing attempt to replicate a shell lime mortar.

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Determination of intrinsic permeability for packed waste of Indonesian solid waste

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ITB Journal of engineering Science 2010年第2期42 B卷 137-150页

作者： Rahardyan, Benno Dwirestiani, Retno Padmi, Tri Environmental Engineering Study Program Faculty of Civil and Environmental Engineering Institute of Technology Bandung

Gas permeability and intrinsic permeability are the major parameters to promote aeration for packed waste. The objectives of this research are to identify physical parameters of gas transfer from a various type of packed wastes and examine ventilation design theory for landfill to enhance waste stabilization. Method to determine value of gas permeability and intrinsic permeability for packed waste is by flushing the packed column containing various type and physical characteristics of wastes with an air pump. Permeability was calculated by measuring pressure gradient on sampling points of the column using inclined manometer at distance 10 cm, 23 cm, 46 cm, 69 cm, 92 cm and 115 cm from origin. Gas permeability is specifically relied on physical parameters of wastes as follows, density, moisture content, particle size and gas velocity on the surface of compacted waste layer. Compost has finer pore structure and smaller pore size than leaves as well as mixed organic (65%) and inorganic wastes (35%). The experiment found the intrinsic permeability of leaves waste are in the order of 10-11 to 10-8 m2, 10-11 to 10-9 m2 for compost and 10-9 m2 for mixed organic (65%) and inorganic wastes (35%).

关键词： Gas permeability

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A model for building and sustaining communities of engineering education research scholars

A model for building and sustaining communities of engineeri...

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113th Annual ASEE Conference and Exposition, 2006

作者： Adams, Robin Allendoerfer, Cheryl Bell, Philip Chen, Helen Fleming, Lorraine Leifer, Larry Maring, Bayta Williams, Dawn Purdue University United States Department of Engineering Education Purdue University United States University of Washington United States Center for the Advancement of Engineering Education University of Washington United States Cognitive Studies in Education University of Washington United States Stanford University United States United States Center for the Advancement Engineering Education Academic Pathways Study Research Team Howard University United States Department of Civil Engineering Howard University United States National Science Foundation HBCU Undergraduate Program United States Department of Mechanical Engineering Design Stanford Center for Design Research United States United States United States Center for the Advancement of Engineering Education Department of Educational Administration and Policy Howard University United States Institute for Scholarship on Engineering Education

Building and sustaining communities of new engineering education researchers is crucial for bringing people into an area of scholarship, supporting them working in this area, providing opportunities for long term professional development within a community of practice, and building capacity in the engineering education research infrastructure. The Institute for Scholarship on engineering Education (ISEE), an element of the NSF-funded Center for the Advancement of engineering Education (CAEE), is designed to build and sustain communities of engineering education scholars who can investigate student learning issues and transform findings into actionable improvements. The Institute has a revolving host structure across three schools - University of Washington, Stanford University, and Howard University. Over three consecutive year-long cycles, ISEE builds and extends a cohort of scholars at host and partner schools. In this paper we explore the question "how do we build capacity in engineering education research?" We pursue this question by examining the ISEE model, the successes and challenges experienced as we near completion of the second of three cycles, and implications for future community building efforts. © American Society for engineering Education, 2006.

关键词： engineering research

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Probabilistic risk analysis of diesel power generators onboard ships

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NAVAL ENGINEERS JOURNAL 1999年第3期111卷 35-58页

作者： Ayyub, BM Karaszewski, ZJ Wade, M Bilal M. Ayyub.:is a Professor of Civil Enginnering and the Director of the Center for Technology and Systems Management at the University of Maryland (College Park). He is also a researcher and consultant in the areas of structural engineering inspection methods and practices reliability and risk analysis. He completed his B.S. degree in civil engineering in 1980 and completed both the M.S. (1981) and Ph.D. (1983) in civil engineering at the Georgia Institute of Technology. Dr. Ayyub has an extensive background in uncertainty modeling and marine structural reliability marine structures uncertainty modeling and anaylsis and mathematical medeling using the theories of probability statistics and fuzzy sets. He has completed several research projects that were funded by the NSF USCG USN the USACE ASME and several engineering companies. Dr. Ayyub served the engineering community in various capacities through societies that include ASNE ASCE ASME SNAME IEEECS and NAFIPS. He is the authour and co-author of about 250 publications in journals and conference proceedings and reports. His publications include edited books textbooks and book chapters. Dr. Ayyub is the only double recipient of the ASNE “Jimmie” Hamilton Award for the best papers in the Naval Engineers Journal in 1985 and 1992. Also he received the ASCE “Outstanding Research Oriented Paper” in the Journal of Walter L. Huber Research Prize in 1997 and the K.S. Fu Award of NAFIPS in 1995. He is a registered Professional Engineer (PE) with the State of Maryland. Zbigniew J. Karaszewski:is the Associate Director of the University of Maryland's Center for Technology and Systems Management and technical director of the Center for Maritime Leadership. He is a Systems Engineering and Management Consultant specializing in life cycle system support business systems auditing and risk management and loss prevention. Mr. Karasezewski has 30 years of experience in the field of systems performance analysis and management as a practitioner and teacher

In this paper, a methodology and guidelines for applying risk methods in design and operation of maritime systems were developed and demonstrated using a case study of marine diesel generators. The methodology consists of several modules that include system definition based on functional and performance requirements, definition of dependencies among subsystems, data collection and reduction, reliability and risk analyses, and results reduction for use in decision analysis. The methodology was used to assess and analyze typical service diesel generators found onboard ships. The analysis included the generation of cut sets for the overall reliability of the system. The most likely failure scenarios were determined by demonstration as the failure of fuel supply components. The results are consistent with previous findings of the U.S. Coast Guard.

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A METHOD FOR ASSESSING RESIDUAL NAPL BASED ON ORGANIC-CHEMICAL CONCENTRATIONS IN SOIL SAMPLES

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GROUND WATER MONITORING AND REMEDIATION 1991年第2期11卷 128-136页

作者： FEENSTRA, S MACKAY, DM CHERRY, JA Stan Feenstra is a hydrogeochemist and president of Applied Groundwater Research Ltd. in Mississauga Ontario He received a B.Sc. in earth sciences and an M.Sc. in hydrogeology from the University of Waterloo in 1978 and 1980 respectively and was designated a Certified Ground Water Professional by AGWSE in 1989. Since 1980 he has been a ground water consultant with Golder Associates in Mississauga Ontario and Zenon Environmental in Burlington Ontario and founded Applied Groundwater Research Ltd. in 1987. Feenstra specializes in the hydrogeochemical evaluation of ground water contamination at waste disposal facilities and chemical spill sites. He is currently a Ph.D. candidate in hydrogeology and research associate in the Waterloo Centre for Ground Water Research at the University of Waterloo (Waterloo Ontario N2L 3G1) and is involved in research related to the behavior of dense organic solvents in ground water. Douglas M. Mackay is an adjunct professor at the Waterloo Centre for Groundwater Research at the University of Waterloo Waterloo Ontario (N2L3G1). Dr. Mackay received a B.S. in engineering and M.S. and Ph.D. in civil engineering from Stanford University in 1970 1973 and 1981 respectively. From 1986 to 1990 he was a faculty member of the Environmental Science and Engineering Program of the UCLA School of Public Health. His research focuses on field studies of transport and fate of organic chemicals in ground water various scale studies of decontamination of soil and ground water and ground water monitoring technologies. John A. Cherry is a professor at the Waterloo Centre for Groundwater Research at the University of Waterloo Waterloo Ontario (N2L 3G1). He received his B.S. in geological engineering from the University of Saskatchewan in 1962. He received his M.S. from the University of California at Berkeley in 1964 and his Ph.D. from the Department of Geology at the University of Illinois in 1966. His research interests include the field study and modeling of contaminants in ground wat

Ground water contamination by non-aqueous phase liquid (NAPL) chemicals is a serious concern at many industrial facilities and waste disposal sites. NAPL in the form of immobile residual contamination, or pools of mobile or potentially mobile NAPL, can represent continuing sources of ground water contamination. In order to develop rational and cost-effective plans for remediation of soil and ground water contamination at such sites, it is essential to determine if non-aqueous phase liquid (NAPL) chemicals are present in the subsurface and delineate the zones of NAPL contamination. The presence of NAPL pools may be evident as a floating or sinking phase in monitoring wells. The residual NAPL contamination may be identified in soil samples if residual contents are high and contaminated zones in the soil cores are thick. However, visual identification may not be effective if residual contents ar elow or if the NAPL residual is distributed heterogeneously in the samples. The chemical analysis of soil samples provides a measure of the total chemical concentration in the soil but cannot determine directly whether NAPL is present in the samples. Qualitatively, soil analyses that exhibit chemical concentrations in the percent range or > 10,000 mg/kg would generally be considered to indicate the presence of NAPL. However, the results of soil analyses are seldom used in a quantitative manner to assess the possible presence of residual NAPL contamination when chemical concentrations are lower and the presence of NAPL is not obvious. The assessment of the presence of NAPL in soil samples is possible using the results of chemical and physical analyses of the soil, and the fundamental principles of chemical partitioning in unsaturated or saturated soil. The method requires information on the soil of the type typically considered in ground water contamination studies and provides a simple tool for the investigators of chemical spill and waste disposal sites to assess whether soil

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