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arXiv 2022年

作者： Tang, Yihong Qu, Ao Chow, Andy H.F. Lam, William H.K. Wong, S.C. Ma, Wei Department of Urban Planning and Design University of Hong Kong Hong Kong Laboratory for Information and Decision Systems Massachusetts Institute of Technology Boston United States Department of Advanced Design and Systems Engineering City University of Hong Kong Hong Kong Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong Department of Civil Engineering University of Hong Kong Hong Kong Department of Civil and Environmental Engineering Research Institute for Sustainable Urban Development The Hong Kong Polytechnic University Hong Kong

Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the "forecasting-related knowledge" across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DastNet). DastNet is pre-trained on multiple source networks and fine-tuned with the target network's traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DastNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DastNet is applied to Hong Kong's new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data. Source codes of DastNet are available at https://***/YihongT/DASTNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

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NNI nanoinformatics conference 2023: Movement toward a common infrastructure for federal nanoEHS data computational toxicology: Short communication

NNI nanoinformatics conference 2023: Movement toward a commo...

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作者： Mortensen, Holly M. Amos, Jaleesia D. Exner, Thomas E. Flores, Kenneth Harper, Stacey Jarabek, Annie M. Klaessig, Fred Lobaskin, Vladimir Lynch, Iseult Marcum, Christopher S. Martens, Marvin Brough, Branden Spadola, Quinn Bjorkland, Rhema Public Health and Integrated Toxicology Division Center for Public Health and Environmental Assessment Office of Research and Development US Environmental Protection Agency Research Triangle Park NC United States Civil & Environmental Engineering Duke University DurhamNC27708 United States Seven Past Nine GmbH Rebacker 68 Schopfheim79650 Germany NSF MPS Ascend Fellow Arizona State University TempeAZ85287 United States Department of Environmental and Molecular Toxicology and the School of Chemical Biological and Environmental Engineering Oregon State University CorvallisOR97331 United States Health and Environmental Effects Assessment Division Center for Public Health and Environmental Assessment Office of Research and Development US Environmental Protection Agency Research Triangle Park NC27711 United States Pennsylvania Bio Nano Systems PA United States School of Physics University College Dublin Dublin 4 Ireland University of Birmingham Birmingham United Kingdom Office of the Chief Statistician of the United States Office of Management and Budget WashingtonDC20503 United States Department of Bioinformatics - BiGCaT NUTRIM Maastricht University Maastricht Netherlands National Nanotechnology Coordination Office AlexandriaVA United States Contractor to the National Nanotechnology Coordination Office AlexandriaVA United States

The National Nanotechnology Initiative organized a Nanoinformatics Conference in the 2023 Biden-Harris Administration's Year of Open Science, which included interested U.S. and EU stakeholders, and preceded the U.S.-EU COR meeting on November 15th, 2023 in Washington, D.C. Progress in the development of a common nanoinformatics infrastructure in the European Union and United States were discussed. Development of contributing, individual database projects, and their strengths and weaknesses, were highlighted. Recommendations and next steps for a U.S. nanoEHS common infrastructure were discussed in light of the pending update of the National Nanotechnology Initiative (NNI)’s environmental, Health and Safety Research Strategy, and U.S. efforts to curate and house nano environmental Health and Safety (nanoEHS) data from U.S. federal stakeholder groups. Improved data standards, for reporting and storage have been identified as areas where concerted efforts could most benefit initially. Areas that were not addressed at the conference, but that are critical to progress of the U.S. federal consortium effort are the evaluation of data formats according to use and sustainability measures;modeler and end user, including risk-assessor and regulator perspectives;a need for a community forum or shared data location that is not hosted by any individual U.S. federal agency, and is accessible to the public;as well as emerging needs for integration with new data types such as micro and nano plastics, and interoperability with other data and meta-data, such as adverse outcome pathway information. Future progress will depend on continued interaction of the U.S. and EU CORs, stakeholders and partners in the continued development goals for shared or interoperable infrastructure for nanoEHS. © 2024

关键词： Digital storage

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The Impact of Decarbonization on Particulate Soiling of Solar Panels

ACS ES&T Air

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ACS ES&T Air 2024年第12期1卷 1531-1540页

作者： Drew Shindell Ivan Petropoulos Greg Faluvegi Luke Parsons Michael Bergin Nicholas School of the Environment Duke University Durham North Carolina 27708 United States NASA Goddard Institute for Space Studies & Center for Climate Systems Research Columbia University New York New York 11215 United States Civil and Environmental Engineering Duke University Durham North Carolina 27708 United States

Climate researchers have examined many impacts of climate change on energy supply and demand under various scenarios. However, the effect of changing particulate deposition onto solar panel surfaces on solar power production efficiency (i.e., soiling) has not been studied. We therefore characterize probabilistic outcomes across multiple climate models and scenarios. We find large current regional losses (up to 40% without manual cleaning, up to 20% with monthly cleaning and rain removal) in generation that grow slightly under a high-emission scenario, largely due to regional increases in windblown dust. In contrast, under a low-emissions scenario, potential production increases significantly (2–8% interquartile range with only rain removal) due to reduced soiling, especially in regions of Asia and Africa where anthropogenic aerosols are major contributors to soiling. Projected changes vary widely across models in many dusty areas outside of the Sahara and Arabia. Differences can also be large in regions dominated by anthropogenic aerosols, such as Nigeria, eastern China, and northern India, where the full range across modeled potential power production changes extends from −1 to +11% for the end of the century (without manual cleaning), underscoring the need to consider multiple climate models. With large increases in projected solar power deployment, the relatively small potential production increases reported here could nevertheless represent a large dividend in additional energy production. Hence, reductions in air pollution attributable to decarbonization could provide positive feedback under which a greater deployment of solar power (or other renewables) increases the production of solar power, facilitating the transition to renewable energy.

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Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach

arXiv

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arXiv 2025年

作者： Jahan, Israt Schreck, John S. Gagne, David John Becker, Charlie Astitha, Marina School of Civil and Environmental Engineering University of Connecticut StorrsCT United States Eversource Energy Center University of Connecticut StorrsCT United States Research Applications Laboratory BoulderCO United States Computational & Information Systems Lab BoulderCO United States

Machine learning has shown promise in reducing bias in numerical weather model predictions of wind gusts. Yet, they underperform to predict high gusts even with additional observations due to the right-skewed distribution of gusts. Uncertainty quantification (UQ) addresses this by identifying when predictions are reliable or needs cautious interpretation. Using data from 61 extratropical storms in the Northeastern USA, we introduce evidential neural network (ENN) as a novel approach for UQ in gust predictions, leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model as features and gust observations as targets. Explainable artificial intelligence (XAI) techniques demonstrated that key predictive features also contributed to higher uncertainty. Estimated uncertainty correlated with storm intensity and spatial gust gradients. ENN allowed constructing gust prediction intervals without requiring an ensemble. From an operational perspective, providing gust forecasts with quantified uncertainty enhances stakeholders’ confidence in risk assessment and response planning for extreme gust events. © 2025, CC BY-NC-ND.

关键词： Wind forecasting

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A Review of Prospects and Opportunities in Disassembly with Human-Robot Collaboration

arXiv

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arXiv 2023年

作者： Lee, Meng-Lun Liang, Xiao Hu, Boyi Onel, Gulcan Behdad, Sara Zheng, Minghui The Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY14260 United States The Department of Civil Structural and Environmental Engineering University at Buffalo BuffaloNY14260 United States The Department of Industrial & Systems Engineering University of Florida GainesvilleFL32611 United States The Department of Food and Resource Economics University of Florida GainesvilleFL32611 United States The Department of Environmental Engineering Sciences University of Florida GainesvilleFL32611 United States

Product disassembly plays a crucial role in the recycling, remanufacturing, and reuse of end-of-use (EoU) products. However, the current manual disassembly process is inefficient due to the complexity and variation of EoU products. While fully automating disassembly is not economically viable given the intricate nature of the task, there is potential in using human-robot collaboration (HRC) to enhance disassembly operations. HRC combines the flexibility and problem-solving abilities of humans with the precise repetition and handling of unsafe tasks by robots. Nevertheless, numerous challenges persist in technology, human workers, and remanufacturing work, that require comprehensive multidisciplinary research to bridge critical gaps. These challenges have motivated the authors to provide a detailed discussion on the opportunities and obstacles associated with introducing HRC to disassembly. In this regard, the authors have conducted a thorough review of the recent progress in HRC disassembly and present the insights gained from this analysis from three distinct perspectives: technology, workers, and work. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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A Deep Generative Learning Approach to Two-stage Adaptive Robust Optimization

arXiv

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arXiv 2024年

作者： Brenner, Aron Khorramfar, Rahman Sun, Jennifer Amin, Saurabh Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA United States MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA United States Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA United States Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

Two-stage adaptive robust optimization (ARO) is a powerful approach for planning under uncertainty, balancing first-stage decisions with recourse decisions made after uncertainty is realized. To account for uncertainty, modelers typically define a simple uncertainty set over which potential outcomes are considered. However, classical methods for defining these sets unintentionally capture a wide range of unrealistic outcomes, resulting in overly-conservative and costly planning in anticipation of unlikely contingencies. In this work, we introduce AGRO, a solution algorithm that performs adversarial generation for two-stage adaptive robust optimization using a variational autoencoder. AGRO generates high-dimensional contingencies that are simultaneously adversarial and realistic, improving the robustness of first-stage decisions at a lower planning cost than standard methods. To ensure generated contingencies lie in high-density regions of the uncertainty distribution, AGRO defines a tight uncertainty set as the image of "latent" uncertainty sets under the VAE decoding transformation. Projected gradient ascent is then used to maximize recourse costs over the latent uncertainty sets by leveraging differentiable optimization methods. We demonstrate the cost-efficiency of AGRO by applying it to both a synthetic production-distribution problem and a real-world power system expansion setting. We show that AGRO outperforms the standard column-and-constraint algorithm by up to 1.8% in production-distribution planning and up to 11.6% in power system expansion. © 2024, CC BY.

关键词： Generative adversarial networks

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Direct Evaluation of the Modification Coefficient Kmod for the Static-Fatigue Design of Annealed Glass Panes Under Wind Load

SSRN

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SSRN 2024年

作者： Vozzella, Stefano Boni, Claudio Massabò, Roberta Royer-Carfagni, Gianni Department of Civil Chemical and Environmental Engineering University of Genova Via Montallegro 1 GenovaI-16145 Italy Department of Systems Engineering and Industrial Technologies University of Parma Parco Area delle Scienze 181/A ParmaI-43124 Italy Via Lombardia 49 San Giuliano Milanese MilanoI-20098 Italy University of Parma Italy

To account for static fatigue due to subcritical surface-crack propagation in annealed glass, most standards introduce the modification coefficient kmod to reduce the design material strength. For wind actions, kmod is defined from the nominal durations of applied pressures on the glass plate, corresponding to maximum wind gusts or cumulative winds, so as to produce a damage level equivalent to real exposure. However, the nominal durations are based on tradition and practice, and are not calibrated from real ***, we consider wind velocities recorded for years in nine anemometric stations located in a region of Italy close to the sea. They provide probabilistic distributions, which are incorporated in the equations of subcritical crack propagation to calculate kmod as a function of the return period of the action. The nominal duration of a wind with fixed probabilistic reference velocity, which is equivalent in terms of produced damage to the actual action, is then determined. For the available data, kmod is also calculated by direct integration of the time-histories of mean and peak wind *** find that, in general, the probabilistic approach provides the most conservative results. Prescriptions by some standards are not on the safe side;other standards give values on average consistent, but not always conservative. We conclude that the evaluation of kmod should be better based on a probabilistic approach, explicitly considering the return period of the wind action in the design. © 2024, The Authors. All rights reserved.

关键词： Glass

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Scalable analysis of stop-and-go waves

arXiv

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arXiv 2024年

作者： Ji, Junyi Gloudemans, Derek Wang, Yanbing Zachár, Gergely Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States The Transportation and Power Systems Division Argonne National Laboratory United States School of Sustainable Engineering and the Built Environment Arizona State University United States Department of Mathematical Sciences Rutgers University Camden United States

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in this data. This paper makes a first step towards addressing this shortcoming by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before;(b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane;(c) wave fronts and tails propagate at different speeds;(d) wave boundaries capture rich and non-trivial wave topologies (with

关键词： Traffic congestion

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Article direct measurement of mercury deposition at rural and suburban sites in Washington state, USA

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Atmosphere 2021年第1期12卷

作者： Beutel, Marc W. Desilva, Lanka Amegbletor, Louis Department of Civil and Environmental Engineering and Environmental Systems Graduate Group University of California MercedCA95343 United States Department of Civil and Environmental Engineering Washington State University PullmanWA99164 United States Environmental Systems Graduate Group University of California MercedCA95343 United States

Because of mercury’s (Hg) capacity for long-range transport in the atmosphere, and its tendency to bioaccumulate in aquatic biota, there is a critical need to measure spatial and temporal patterns of Hg atmospheric deposition. Dry deposition of Hg is commonly calculated as the product of a measured atmospheric concentration and an assumed deposition velocity. An alternative is to directly assess Hg deposition via accumulation on surrogate surfaces. Using a direct measurement approach, this study quantified Hg deposition at a rural site (Pullman) and suburban site (Puyallup) in Washington State using simple, low-cost equipment. Dry deposition was measured using an aerodynamic "wet sampler" consisting of a Teflon plate, 35 cm in diameter, holding a thin layer (2.5 mm) of recirculating acidic aqueous receiving solution. In addition, wet Hg deposition was measured using a borosilicate glass funnel with a 20-cm-diameter opening and a 1 L Teflon sampling bottle. Hg deposition was estimated based on changes in total Hg in the aqueous phase of the samplers. Dry Hg deposition was 2.4 ± 1.4 ng/m2·h (average plus/minus standard deviation;n = 4) in Pullman and 1.3 ± 0.3 ng/m2·h (n = 6) in Puyallup. Wet Hg deposition was 7.0 ± 4.8 ng/m2·h (n = 4) in Pullman and 1.1 ± 0.2 ng/m2·h (n = 3) in Puyallup. Relatively high rates of Hg deposition in Pullman were attributed to regional agricultural activities that enhance mercury re-emission and deposition including agricultural harvesting and field burning. Hg concentration in precipitation negatively correlated with precipitation depth, indicating that Hg was scavenged from the atmosphere during the beginning of storm events. Because of their relative simplicity and robustness, direct measurement approaches such as those described in this study are useful in assessing Hg deposition, and for comparing results to less direct estimates and model estimates of Hg deposition. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Deposition

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Railway-station-area vitality in response to COVID-19: A case study of diverse Japanese cities

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Cities 2025年 162卷

作者： Dai, Jiannan Sun, Wenzhe Lu, Qing-Long Schmöcker, Jan-Dirk Antoniou, Constantinos Department of Urban Management Kyoto University Kyoto 615-8540 Japan Business School University of Shanghai for Science and Technology Shanghai 200093 China Chair of Transportation Systems Engineering Technical University of Munich Munich 80333 Germany Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore

This study examines the impact of COVID-19 on the number of visitors weighted by their time spent at facilities within and nearby railway stations, analyzing short-term demand losses and long-term recovery trends at 69 major stations in diverse Japanese cities using aggregated mobile phone data. We refer to this as “station area vitality”. We extend previous research by integrating external variables, such as land use and Points of Interest (POIs), to explain vitality drops and forecast recovery over two years. The findings reveal that multifunctional station areas—those combining leisure shopping, daily-needs shopping, and transport purposes—showed greater resilience during the pandemic. This underscores the value of mixed-use development and flexible zoning for enhancing station resilience. Furthermore, our forecasting models, particularly ARIMAX and LSTM, can to some degree predict long-term recovery trends during or after the pandemic when external variables and extended learning periods are included. We hence suggest that this can offer critical insights for urban planners and policymakers to build more resilient station areas and to forecast their performance during a new pandemic. © 2025 Elsevier Ltd

关键词： COVID-19 pandemic Demand loss and recovery Demand pattern resilience Long-term forecast Railway station area Urban vitality

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