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arXiv 2025年

作者： Nabaei, Seyed Hamidreza Lenfant, Ryan Rajan, Viswajith Govinda Chen, Dong Timko, Michael P. Campbell, Bradford Heydarian, Arsalan Department of Systems and Information Engineering link lab University of Virginia CharlottesvilleVA22904 United States Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Department of Biology University of Virginia CharlottesvilleVA22904 United States Department of Civil and Environmental Engineering University of Virginia CharlottesvilleVA22904 United States

In the era of increasing interest in healthy buildings and the advent of smart homes, the significance of sustainable and health-conscious living spaces is paramount. Smart tools, including VOC sensors, play a pivotal role in monitoring indoor air quality. However, effective monitoring and interpreting signals from indoor VOC sources remains a challenge. To address this challenge, a new approach lies in understanding how indoor plants act in response to environmental conditions. Indoor plants produce terpenes, a type of VOC, in response to various abiotic and biotic stressors, such as detection of airborne pathogens, defense against predators, and intense light and temperature. Changing indoor plant VOCs thus offer the potential for monitoring indoor air quality. Existing research on signal interpretation relies heavily on specialized laboratory sensors to monitor plant responses. Our work aimed to leverage readily available commercial sensors to interpret and classify VOC emitted from plants as a potential means for monitoring changes in indoor air that contribute to the well-being and living experience of occupants in smart buildings. Specifically, we quantified the sensitivity of commercial air quality sensors by measuring 16 terpenes in a controlled setting. We identified the most promising terpenes and measured their concentrations in a realistic indoor environment. We investigated the use of physics-based models to map the response of the VOCs, but recognized that these models struggle to match the complexities of real-world environments. We then trained machine learning models to accurately classify terpenes using commercial VOC sensors and identified the optimal location for placing a sensor. To corroborate our findings, we analyze the terpene emissions of a living basil plant and successfully demonstrate the system’s ability to detect VOC emissions from the plant. Our findings established a foundation for overcoming the challenges associated with detecting

关键词： Smart homes

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Link Prediction and Navigability of Multiplex Energy Networks

arXiv

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arXiv 2025年

作者： Kazim, Muhammad Pirim, Harun Le, Chau Le, Trung Yadav, Om Prakash Industrial & Manufacturing Engineering North Dakota State University Dept. 2485 PO Box 6050 FargoND58102 United States Civil Construction and Environmental Engineering North Dakota State University Dept. 2470 PO Box 6050 FargoND58102 United States Industrial & Management Systems Engineering University of South Florida P.O Box 4202 FL33620-000 United States Industrial & Systems Engineering North Carolina Agriculture & Tech State University P.O Box 408 NC 27411 United States

In modern energy networks, where operational efficiency and resilience are critical, this study introduces an in-depth analysis from a multiplex network perspective—defined as a network where multiple types of connections exist between the same set of nodes. Utilizing Belgium's electricity and gas networks, we construct a five-layer multiplex network to simulate random node shutdown scenarios. We tailored the Jaccard and Adamic-Adar link prediction algorithms by integrating the concept of exclusive neighbors, thereby enhancing prediction accuracy with such multi-layered information. Emphasizing navigability, i.e., the network's ability to maintain resilience and efficiency under random failures, we analyze the impact of different random walk strategies and strategic link additions at various stages - individual layers, two-layer combinations, and three-layer combinations - on the network's navigability. Directed networks show modest improvements with new links, partly due to trapping effects—where a random walker can become circumscribed within certain network loops, limiting reachability across the network. In contrast, the undirected networks demonstrate notable increases in navigability with new link additions. Spectral gap analysis in directed networks indicates that new link additions can aid and impede navigability, depending on their configuration. This study deepens our understanding of multiplex energy network navigability and highlights the importance of strategic link additions, influenced by random walk strategies, in these networks. © 2025, CC BY.

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Artificial Intelligence:A new era for spatial modelling and interpreting climate-induced hazard assessment

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Geoscience Frontiers 2024年第4期15卷 1-13页

作者： Abhirup Dikshit Biswajeet Pradhan Sahar S.Matin Ghassan Beydoun M.Santosh Hyuck-Jin Park Khairul Nizam Abdul Maulud Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil&Environmental EngineeringFaculty of Engineering and ITUniversity of Technology SydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKM BangiSelangorMalaysia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209 Neungdong-roGwangjin-guSeoul 05006Republic of Korea School of Earth Sciences and Resources China University of Geosciences BeijingXueyuan RoadBeijing 100083China Department of Earth Sciences University of AdelaideAdelaideSouth AustraliaAustralia Faculty of Science Kochi UniversityKochi 780-8520Japan

The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent *** field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms,the availability of large quantities of data,and the increase in computational *** enhancement in algorithms can be largely attributed to the elevated complexity of the network architecture and the heightened level of abstraction found in the network's later *** a result,AI models lack transparency and accountability,often being dubbed as"black box"*** AI(XAI)is emerging as a solution to make AI models more transparent,especially in domains where transparency is *** discussion surrounds the use of XAI for diverse purposes,as researchers explore its applications across various *** the growing body of research papers on XAI case studies,it has become increasingly important to address existing gaps in the *** current literature lacks a comprehensive understanding of the capabilities,limitations,and practical implications of *** study provides a comprehensive overview of what constitutes XAI,how it is being used and potential applications in hydrometeorological natural *** aims to serve as a useful reference for researchers,practitioners,and stakeholders who are currently using or intending to adopt XAI,thereby contributing to the advancements for wider acceptance of XAI in the future.

关键词： Artificial Intelligence Explainable AI(XAI) Climate change Spatial modelling Natural hazards

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Personalized Context-Aware Multi-Modal Transportation Recommendation

Personalized Context-Aware Multi-Modal Transportation Recomm...

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IEEE Symposium on Intelligent Vehicle

作者： Xianda Chen Meixin Zhu PakHin Tiu Yinhai Wang Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Guangdong Provincial Key Lab of Integrated Communication Sensing and Computation for Ubiquitous Internet of Things Department of Civil and Environmental Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This study proposes to find the most appropriate transport modes with an awareness of user preferences (e.g., costs, times) and trip characteristics (e.g., purpose, distance). The work was based on real-life trips obtained from a map application. Several methods including gradient boosting tree, learning to rank, multinomial logit model, automated machine learning, random forest, and shallow neural network have been tried. For some methods, feature selection and over-sampling techniques were also tried. The results show that the best-performing method is a gradient-boosting tree model with the synthetic minority oversampling technique (SMOTE). Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that contains metro, i.e., compared to other modes, people would be more willing to tolerate long-distance metro trips. This indicates that metro lines might be a good candidate for large cities.

关键词： Logistic regression Costs Navigation Intelligent vehicles Urban areas Neural networks Transportation

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Crack Face Contact Modeling is Essential to Predict Crack-Parallel Stresses

arXiv

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arXiv 2025年

作者： Hakimzadeh, Maryam Walkington, Noel Mora-Corral, Carlos Gazonas, George Dayal, Kaushik Department of Civil and Systems Engineering Johns Hopkins University United States Center for Nonlinear Analysis Department of Mathematical Sciences Carnegie Mellon University United States Departamento de Matemáticas Universidad Autónoma de Madrid Spain Instituto de Ciencias Matemáticas CSIC-UAM-UC3M-UCM Spain DEVCOM Army Research Laboratory United States Department of Civil and Environmental Engineering Carnegie Mellon University United States Department of Mechanical Engineering Carnegie Mellon University United States

Phase-field fracture models provide a powerful approach to modeling fracture, potentially enabling the unguided prediction of crack growth in complex patterns. To ensure that only tensile stresses and not compressive stresses drive crack growth, several models have been proposed that aim to distinguish between compressive and tensile loads. However, these models have a critical shortcoming: they do not account for the crack direction, and hence they cannot distinguish between crack-normal tensile stresses that drive crack growth and crack-parallel stresses that do not. In this study, we apply a phase-field fracture model, developed in our earlier work, that uses the crack direction to distinguish crack-parallel stresses from crack-normal stresses. This provides a transparent energetic formulation that drives cracks to grow in when crack faces open or slide past each other, while the cracks respond like the intact solid when the crack faces contact under normal compressive loads. We compare our approach against two widely used approaches, Spectral splitting and the Volumetric-Deviatoric splitting, and find that these predict unphysical crack growth and unphysical stress concentrations under loading conditions in which these should not occur. Specifically, we show that the splitting models predict spurious crack growth and stress concentration under pure crack-parallel normal ***, our formulation, which resolves the crack-parallel stresses from the crack-normal stresses, predicts these correctly. © 2025, CC BY.

关键词： Stress concentration

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Exploring walkability patterns in diverse urban landscapes: A comprehensive spatial geographic analysis of a megacity in Thailand

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Multimodal Transportation 2025年第3期4卷

作者： Iamtrakul, Pawinee Chayphong, Sararad Vichiensan, Varameth Luathep, Paramet Chollacoop, Nuwong Fukuda, Atsushi Fukuda, Tuenjai Center of Excellence in Urban Mobility Research and Innovation Faculty of Architecture and Planning Thammasat University Pathum Thani 12120 Thailand Department of Civil Engineering Faculty of Engineering Kasetsart University Bangkok 10900 Thailand Department of Civil and Environmental Engineering Faculty of Engineering Prince of Songkla University Songkhla 90110 Thailand Thailand National Energy Technology Center (ENTEC) Pathum Thani 12120 Thailand Department of Transportation Systems Engineering College of Science and Technology Nihon University Chiba 274-8501 Japan Asian Transportation Research Society (ATRANS) Bangkok Chatuchak 10900 Thailand

Urbanization, urban sprawl, and the increasing population contribute to challenges in density and diversity within each metropolitan area, creating the need to find sustainable solutions for urban transport. This study investigates the correlation between the urban context and walkability, specifically in Bangkok, Thailand. A spatial geographic approach was employed for urban typology analysis, utilizing multivariate clustering, including walkability assessment through a geographic information system and urban and mobility indices. The findings of the urban context analysis revealed five distinct forms: rural, rural town, suburban, urban, and urban core, each characterized by varying levels of walking abilities. The findings contribute to understanding the nature of activities characteristic of both urban environments and mobility in terms of spatial environmental conditions present in each urban context. Urban and transport planners should explore strategies that align with the specific environmental context of each area to promote walking effectively. © 2025 The Authors

关键词： Nonmotorization Physical activity Sustainable transport Walking environment

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Editorial: Intermittent water supply: Challenges, opportunities and solutions

Aqua Water Infrastructure, Ecosystems and Society

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Aqua Water Infrastructure, Ecosystems and Society 2023年第5期72卷

作者： Farmani, Raziyeh Charalambous, Bambos Nelson, Kara L. Dalton, Joe Centre for Water Systems Department of Engineering University of Exeter Exeter United Kingdom Hydrocontrol Ltd P.O. Box 71044 Limassol Cyprus Department of Civil & Environmental Engineering University of California BerkeleyCA United States HydrOptimise MAZ Business Development The Lagoon Amwaj Bahrain

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Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

arXiv

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arXiv 2023年

作者： Zhang, Haotian Gao, Shijian Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou511400 China Department of Electronic and Computer Engineering the Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios with adverse environment conditions, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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A FRAMEWORK TO EXTRAPOLATE AND EVALUATE HUMAN RELIABILITY CAUSAL MODELS FROM EVENT REPORT NARRATIVES

A FRAMEWORK TO EXTRAPOLATE AND EVALUATE HUMAN RELIABILITY CA...

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2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

作者： Ruiz-Tagle, Andres Paglioni, Vincent P. Lopez-Droguett, Enrique Groth, Katrina M. Systems Risk and Reliability Analysis Lab University of Maryland College ParkMD United States Department of Civil and Environmental Engineering Garrick Institute for the Risk Sciences University of California Los Angeles United States

Although the data required to build adequate human reliability analysis (HRA) models remains scarce, there is an abundance of operating experience that could inform HRA modeling. Especially through Bayesian Networks (BNs), HRA causal modeling could benefit from information extrapolated from narrative accounts of relatively common occurrences such as those documented in NRC Licensee Event Reports. Extracting useful information from narratives is a complicated and often subjective task. The information transmitted in a narrative account differs depending on the author’s writing style, situational understanding, and brevity. Narrative structures have often been employed to give an easily-accessible account of industrial incidents. Still, technical fields have just as often eschewed narrative in favor of quantitative evidence when available. This paper aims to show that narrative can be a valuable supplement to quantitative evidence for HRA causal modeling, not least because of the abundance of narrative information. We will show that a causal model of an event can be developed by reframing narrative information using a standardized 3-element structure composed of a: description, explanation, and recommendation. Using the widely known SPAR-H model, we will demonstrate that this narrative structure can be easily translated into a BN to model an incident event. Furthermore, the proposed narrative structure allows for applying interventional and counterfactual reasoning BN-based tools, which we will use to evaluate recommendations for work practice improvements to mitigate similar future scenarios. This paper shows that narrative information can be utilized beyond qualitative analyses/explanations of incidents to develop quantitative causal models to support HRA applications. © 2021 PSA. All Rights Reserved.

关键词： Bayesian networks

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The impact of integrating augmented reality into the production strategy process 37

The impact of integrating augmented reality into the product...

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37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020

作者： Nassereddine, Hala Veeramani, Dharmaraj Hanna, Awad Department of Civil Engineering University of Kentucky United States Department of Industrial and Systems Engineering University of Wisconsin-Madison United States Department of Civil and Environmental Engineering University of Wisconsin-Madison United States

ISBN: (纸本)9789529436347

Although execution is generally the phase during which challenges faced by the construction industry become apparent, it only represents the tip of the iceberg. Execution depends on the effectiveness of construction planning and control-A n area identified by researchers as in need of improvement. Production Strategy is a fundamental step and a critical component of production planning and control where decisions are collectively made to properly allocate and deploy resources to achieve project objectives. The Production Strategy Process (PSP) involves a massive information transfer and requires a high level of communication between the project team members. The increased complexity and sophistication of construction projects along with the rapid advances in emerging technologies has fueled construction companies' interest in technology as a source of innovation. One technology that has gained great interest in recent years is Augmented Reality (AR). AR, a pillar of the fourth industrial revolution, offers powerful capabilities to enable the next generation of PSP and provides companies significant opportunities to maintain their vitality and competitive edge. This research investigated the integration of AR with PSP and the impact that AR can have on the process. An ARenabled PSP prototype was developed for the Microsoft HoloLens headset and was validated on an ongoing construction project. The results showed that AR has the highest impact on PSP in three areas, namely analytical, tracking, and informational. © 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot. All rights reserved.

关键词： Augmented reality

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