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In response to the COVID-19 pandemic, many community engagement efforts were moved exclusively online. Robust community engagement practices are vital to ensure equitable, inclusive stormwater management and infrastructure decision-making, and the impact of this online shift on community access, representation, and quality of interaction and feedback is not well understood. While in-person community engagement as currently practiced in stormwater management poses several challenges, including achieving representative participation by the target community, cost and time barriers, limited training of on-the-ground facilitators, and the lack of standardization and assessment methods, the challenges, advantages, and best practices of community engagement in online settings are unknown. The need to understand these aspects of online community engagement became more urgent as a result of the rapid and unanticipated shift to online approaches during the COVID-19 pandemic. This paper provides an exploration of the advantages, challenges, and opportunities of online community engagement through a thematic analysis of interviews conducted with 10 facilitators of stormwater projects in the greater Houston, Texas region during the COVID-19 pandemic. Using qualitative thematic analysis, responses were coded into categories and then themes and subthemes based on frequency and salience. Key themes that characterize challenges include perceived access limitations (digital divide—physical), digital literacy (digital divide—cognitive), quality of interaction, community trust, and resistance to online formats. Online community engagement is likely to continue well beyond the pandemic. Therefore, designing community engagement programs with these challenges in mind is essential for building upon the advantages afforded by online tools. As found in this study, these include increased attendance, removal of transportation barriers and time conflicts, access to non-local experts, resilient communication strategies, organizational efficiencies, improved data collection, and expanded access to information and participation opportunities through recorded events posted online. These findings contribute to improved online community engagement practices for infrastructure decision-making. [ABSTRACT FROM AUTHOR]

The joint utilization of multi-source data is of great significance in geospatial observation applications, such as urban planning, disaster assessment, and military applications. However, this approach is confronted with challenges including inconsistent data structures, irrelevant physical properties, scarce training data, insufficient utilization of information and an imperfect feature fusion method. Therefore, this paper proposes a novel binary-tree Transformer network (BTRF-Net), which is used to fuse heterogeneous information and utilize complementarity among multi-source remote sensing data to enhance the joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Firstly, a hyperspectral network (HSI-Net) is employed to extract spectral and spatial features of hyperspectral images, while the elevation information of LiDAR data is extracted using the LiDAR network (LiDAR-Net). Secondly, a multi-source transformer complementor (MSTC) is designed that utilizes the complementarity and cooperation among multi-modal feature information in remote sensing images to better capture their correlation. The multi-head complementarity attention mechanism (MHCA) within this complementor can effectively capture global features and local texture information of images, hence achieving full feature fusion. Then, to fully obtain feature information of multi-source remote sensing images, this paper designs a complete binary tree structure, binary feature search tree (BFST), which fuses multi-modal features at different network levels to obtain multiple image features with stronger representation abilities, effectively enhancing the stability and robustness of the network. Finally, several groups of experiments are designed to compare and analyze the proposed BTRF-Net with traditional methods and several advanced deep learning networks using two datasets: Houston and Trento. The results show that the proposed network outperforms other state-of-the-art methods even with small training samples. [ABSTRACT FROM AUTHOR]

The article presents information on the fourth International Conference on Environmental Science and Technology held in Houston, Texas, on July 28-31, 2008. The conference was chaired by Dr. George A. Sorial, professor at University of Cincinnati. The objective of the conference is to provide a major interdisciplinary forum for presenting new approaches from relevant areas of environmental science and engineering.

How does household exposure to a natural disaster affect higher education investments? Using variation in flooding from Hurricane Harvey (2017), we find that college-aged adults from flooded blocks in Houston are 7% less likely than counterparts to have student loans after Harvey, with larger effects in areas with more potential first-generation students. We find a similar relative decline in enrollment at more exposed Texas universities and colleges and a shift toward majors with higher expected earnings. Our results highlight a decrease in the quantity but an increase in the intensity of investments in human capital after the storm. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

In 2022, Houston, TX became a nexus for field campaigns aiming to further our understanding of the feedbacks between convective clouds, aerosols and atmospheric boundary layer (ABL) properties. Houston's proximity to the Gulf of Mexico and Galveston Bay motivated the collection of spatially distributed observations to disentangle coastal and urban processes. This paper presents a value-added ABL dataset derived from observations collected by eight research teams over 46 days between 2 June - 18 September 2022. The dataset spans 14 sites distributed within a ~80-km radius around Houston. Measurements from three types of instruments are analyzed to objectively provide estimates of nine ABL parameters, both thermodynamic (potential temperature, and relative humidity profiles and thermodynamic ABL depth) and dynamic (horizontal wind speed and direction, mean vertical velocity, updraft and downdraft speed profiles, and dynamical ABL depth). Contextual information about cloud occurrence is also provided. The dataset is prepared on a uniform time-height grid of 1 h and 30 m resolution to facilitate its use as a benchmark for forthcoming numerical simulations and the fundamental study of atmospheric processes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Living and working in comfort while a building's energy consumption is kept under control requires monitoring a system's consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings. [ABSTRACT FROM AUTHOR]

Presents abstracts of papers which will be presented at the 25th annual meeting of the Society for Psychophysiological Research in October 1985 in Houston, Texas. "Infants are not distractible during physiologically defined periods of sustained attention" by J.E. Richards; "Comparison of blink inhibition in infants, children, and young and old adults" by W.K. Berg et al.

Reports on the call for papers for the Behavioral Safety Now Conference in Houston, Texas, issued by the Cambridge Center for Behavioral Studies and Quality Safety Edge. Schedule of the conference.

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data. [ABSTRACT FROM AUTHOR]

Natural disasters devastate communities by damaging built infrastructure systems that support residential habitation, forcing people to evacuate from their homes, find alternative housing, and eventually rehabitate. Disaster mitigation projects can reduce damage to built infrastructure and community recovery time. Most mitigation projects are analyzed using monetary measures, thereby not fully accounting for benefits due to other factors, such as residential habitation, which are difficult to quantify and model. A recent US Army Corps of Engineers policy requires the inclusion of nonmonetary impacts in mitigation project analyses. However, rigorous modeling and quantification methods of nonmonetary impacts are needed. This paper describes such a modeling approach and the Disaster Habitation Model (DHM) for rigorously modeling and quantifying the habitation impacts of disaster mitigation projects for project analysis and selection. The DHM combines the impacts of critical internal infrastructure systems in a community to simulate habitation over a community's disaster experience. The approach and model are illustrated using the Halls Bayou watershed in Houston, Texas. Results estimate the habitation benefits of a proposed mitigation program and thereby provide a basis for including other social effects of projects in mitigation project analyses. The approach and method are found to be capable of realistically portraying disaster habitation behavior and are useful in quantifying improvements due to mitigation efforts. Application across different disaster types, mitigation efforts, limitations, and future development opportunities are discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to address the following four research questions: first, whether auditors report critical audit matters (CAMs) to shield themselves against possible litigation; second, whether reporting quality affects auditors' propensity to report CAMs; third, whether auditors' tenure length – reflecting familiarity with clients' financial reporting – affects their likelihood to report CAMs; and fourth, whether auditors' conservatism increases the likelihood of CAMs reporting. Design/methodology/approach: Data are manually collected from audit reports including CAMs in 10-K, then financial data are collected from the Capital IQ database, and market data are collected from the CRSP database. Using propensity score matching, the initial sample of companies with CAMs is matched with companies without reported CAMs. Performance adjusted discretionary accruals, real earnings management proxy, Khan and Watts' (2009) C-score, propensity to issue a going concern opinion, Dechow et al.'s (2011) F-Score, Rogers and Stocken's (2005) model and Houston et al.'s (2010) model are used to measure reporting quality, auditor conservatism, misstatement risk and litigation risk, respectively. Findings: The results do not show that auditors report CAMs opportunistically to shield themselves from litigation risk. However, the results do suggest that auditors have a greater tendency to report CAMs when reporting quality is low and when they are more conservative. On the other hand, they have less tendency to report CAMs in their first year of engagement. Research limitations/implications: The findings of this study have important implications for the auditor behavior literature as it shows that, when it comes to reporting CAMs, auditors actually behave objectively and do not report in a trite way. This study also provides early archival evidence on a standard that relates to the first major change to the auditor's report in decades. To the best of the author's knowledge, it is the first to provide evidence on the association between auditor conservatism and auditors tendency to report CAMs and the first to triangulate prior research on auditor litigation risk by providing the first archival evidence on the auditors "litigation-shielding" concern. Practical implications: This study examines whether auditors attempt to meet the stated objective of reporting CAMs by signaling information about reporting quality. This study demonstrates that reporting CAMs is not a "boilerplate" communication. This study has implications for standards setters, as it shows that CAMs are reported in a way consistent with the objectives of the new standard, namely, via signaling information in the audit report on the quality of the financial statements. Originality/value: In terms of originality, this paper uses a manually collected sample and, to the best of the author's knowledge, is the first to focus on auditor's behavior rather than on investors or clients reactions to CAMs. Also, this paper addresses a recently issued standard using US data and archival approach, rather than experimental. This paper also provides relevant evidence related to concerns raised earlier but were not empirically examined, such as reporting CAMS as "boilerplate" expectations. This paper provides new evidence on the auditors' behavior with regard to litigation risk. [ABSTRACT FROM AUTHOR]

In the original publication of the 2020 ASCPT Annual Meeting abstract supplement, the following errors occurred. (1) The authors for several abstracts were listed in an incorrect order due to a transcription error with the online abstract submission system. The author bylines have now been corrected; and (2) The following three abstracts were mis-numbered. [Extracted from the article]

The joint use of hyperspectral image (HSI) and Light Detection And Ranging (LiDAR) data has been widely applied for land cover classification because it can comprehensively represent the urban structures and land material properties. However, existing methods fail to combine the different image information effectively, which limits the semantic relevance of different data sources. To solve this problem, in this paper, an Attention-guided Fusion and Classification framework based on Convolutional Neural Network (AFC-CNN) is proposed to classify the land cover based on the joint use of HSI and LiDAR data. In the feature extraction module, AFC-CNN employs the three dimensional convolutional neural network (3D-CNN) combined with a multi-scale structure to extract the spatial-spectral features of HSI, and uses a 2D-CNN to extract the spatial features from LiDAR data. Simultaneously, the spectral attention mechanism is adopted to assign weights to the spectral channels, and the cross attention mechanism is introduced to impart significant spatial weights from LiDAR to HSI, which enhance the interaction between HSI and LiDAR data and leverage the fusion information. Then two feature branches are concatenated and transferred to the feature fusion module for higher-level feature extraction and fusion. In the fusion module, AFC-CNN adopts the depth separable convolution connected through the residual structures to obtain the advanced features, which can help reduce computational complexity and improve the fitting ability of the model. Finally, the fused features are sent into the linear classification module for final classification. Experimental results on three datasets, i.e., Houston, MUUFL and Trento datasets show that the proposed AFC-CNN framework achieves better classification accuracy compared with the state-of-the-art algorithms. The overall accuracy of AFC-CNN on Houston, MUUFL and Trento datasets are 94.2%, 95.3% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) classification. First, MBLBP operator is adopted to characterize the overall structural information of HSI, where the averaging strategy allocates same weights for the pixels in a local sub-region, so that the edges tend to be blurred due to being isotropic. To solve this question, the steering kernel is first introduced into MBLBP for learning the local structure prior of HSI. Then, a support vector machine classifier is used to calculate the soft classified probabilities of pixels. Furthermore, BEEPS is adopted to smooth the soft classified probabilities maps in the post-processing stage, and the purpose is to further improve classification accuracy of HSI by considering context-aware information for each class label. Experiments are performed on three real hyperspectral datasets, namely, Indian Pines, KSC, and Houston 2013, only 1%, 6, and 5 labeled samples are randomly selected for training, the overall accuracy(kappa) obtained by MSF is 99.47%(99.40), 99.52%(99.47), and 94.25%(93.78), respectively, which is far better than the contrast methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intervention methods to establish commitment to (collaborative) action are of potential interest to researchers and policymakers intent upon including stakeholder perspectives in natural risk governance (Scolobig, Nat Hazards 81:27–43, 2016). In this paper, a 6-step co-design method for engaging with local people in collaboratively envisioning nature-based solutions for flood defence is described. The problem structuring base of the participatory method is extended to accommodate the multi-actor situation and the local context of flood risk management. The intervention method is applied in a workshop in the Houston–Galveston Bay area in October 2014. At that time there was strong contestation surrounding the proposed Ike Dike with alternative combinations of nature-based and smaller conventional engineering solutions being proposed. The results indicate that the local participants were able to envision a wide range of future outcomes for the bay and were able to use the insights on nature-based solutions and the social contacts that they acquired at the transdisciplinary workshop to mobilize commitment to joint action. This action focused on collaboration rather than specifying ecological or technical infrastructural requirements and was instrumental in initiating more open discourse on flood defence options for the Houston–Galveston Bay area. The paper concludes that the generic applicability of the co-design method is limited by the requirement to understand and accommodate local circumstances and participants' insights within the workshop. [ABSTRACT FROM AUTHOR]

Reliable sensing of roadway conditions during flooding is a long-standing, challenging problem with societal importance for roadway safety. Tools that provide real-time data on road conditions during floods can facilitate safer mobility, reduce vehicle-related drownings, enhance flood response efficiency, and support emergency response decision-making. Following the tenets of user-centered design, such tools ideally should address the needs of diverse stakeholders involved in flood response. Currently, the existing literature lacks a thorough understanding of stakeholder needs to guide situational awareness tool development in the area of roadway mobility during flood events. This paper addresses this gap by studying the needs of stakeholders responsible for managing flood response in Houston. Semi structured one-on-one interviews were conducted with stakeholders from different Houston-based organizations responsible for managing and responding to flood hazard events in the downtown metropolitan area. Interview responses were systematically analyzed to identify (1) data needs for facilitating efficient and safe emergency response, (2) the most and least valuable information available during flooding, (3) communication and visualization strategies, (4) factors influencing stakeholder trust, and (5) factors influencing occupational stress during flood response. Finally, interview insights were used to develop a conceptual situational awareness framework and a prototype map-based tool that provides real-time road condition data during flood events. This study elucidates vital information for improving existing tools and providing preliminary guidance for future mobility-centric situational awareness tools that promote safer mobility and facilitate emergency response decision-making during flooding. Although the study focused on Houston, insights gained may be useful for comparable flood-prone regions. In developed countries, 40%–60% of flood fatalities are attributed to vehicle-related incidents. Flooded roads and lack of real-time road condition data pose safety risks to first responders and reduce emergency response efficiency. Understanding stakeholder needs and developing tools that address them are essential for improving the safety and efficiency of emergency response, especially considering a potential increase in flood risk to urban mobility due to climate change and other factors. Following the tenets of the user-centered design process, this study identified stakeholder needs, conceptualized a framework for sensing road conditions, and developed an open-source prototype tool in the context of flood response in Houston. Insights gained in this study can improve the efficacy of existing mobility-centric situational awareness tools and provide preliminary guidance for quick prototyping of new situational awareness tools. Furthermore, organizations can use the insights presented here to help reduce work-related stress among emergency response personnel, thereby improving emergency response efficiency and organizational resilience. [ABSTRACT FROM AUTHOR]

Day-labor worker centers are labor market intermediaries that target their interventions to underregulated segments of residential construction and allied industries. As sites of rulemaking in the informal economy, worker centers raise standards and enforce worker protections in sectors that lie beyond the reach of government enforcement. In addition to strengthening wage floors, worker centers are now acting as "disaster recovery hubs" that can help local communities following natural disasters. As the economy was shuttered by the COVID-19 pandemic, worker centers pivoted to provide emergency assistance to unemployed workers. This paper assesses these two emerging areas of worker center activity through a survey of disaster-recovery workers in Houston in the aftermath of Hurricane Harvey and a national survey of worker centers that administered emergency assistance to immigrant workers during the COVID-19 pandemic. These case studies reveal promising new interventions that could lead to more inclusive forms of workforce development. [ABSTRACT FROM AUTHOR]

In view that cities will continue to house the majority of the world's population at an increasing rate in the face of climate change, this paper studies urban resilience by examining the response history of the mean-square displacement of the citizens of large cities prior to and upon historic natural hazards strike. The recorded mean-square displacements of large numbers of cellphone users from the cities of Houston, Miami, and Jacksonville when struck by hurricanes Harvey 2017, Irma 2017, and Dorian 2019 together with the recorded mean-square displacements of the citizens of Dallas and Houston when experiencing the 2021 North American winter storm suggest that large cities of average population density when struck by natural hazards are inherently resilient. The recorded mean-square displacements presented in this study also validate a mechanical model for cities, previously developed by the authors, that is rooted in Langevin dynamics and predicts that following a natural hazard, large cities revert immediately to their initial steady-state behavior and resume their normal, preevent activities. The inherent ability of large American cities to revert to their normal, preevent, steady-state response as evidenced in this study by the recorded mean-square displacement of their citizens needs to be further explored for other cities around the world with different resources, and socioeconomic structures. [ABSTRACT FROM AUTHOR]

Addicks and Barker reservoirs were built in the 1940s to protect downtown Houston from flooding and have generally worked very well until 2017 when Hurricane Harvey devastated much of Houston and surroundings with up to 40 inches (102 cm) of rainfall causing flooding of 154,000 homes in over 22 watersheds in Houston/Harris County alone. However, the story of how Addicks and Barker flooded upstream residential areas from a hydrologic standpoint is a harsh lesson in flood infrastructure policy and funding. This failure to protect both downstream properties in Buffalo Bayou and upstream areas behind the dams ended up with tens of thousands of flooded homes and properties, with many having flood waters for over 10 days. This paper explores the main causes for the flooding and addresses the hydrologic issues upstream in both reservoirs. The main causes of flooding were not just related to a massive rainfall event, but also explosive urban expansion of land use upstream of reservoirs, altered and updated reservoir design issues, and lack of governmental action in the years leading up to the disaster. Potential long‐term solutions to the flooding and design problems are addressed in this article as well. [ABSTRACT FROM AUTHOR]

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the community recovery efforts undertaken by Houston, Texas, sport organizations following Hurricane Harvey in 2017. Design/methodology/approach: Forty-eight media articles, 138 social media posts from Houston athletes and five semi-structured interviews with Houston sport organization executives underwent a content analysis to categorize responses of disaster relief activities. All eleven categories were identified. Three themes emerged from additional analysis: organizations serving as communication hubs, earned trust and internal organizational support. Benchmark examples in key categories are also discussed. Findings: This paper provided focused analysis of the reactions of several Houston area sport organizations during the immediate disaster recovery period. Organizations participated in both tangible and emotional recovery efforts. The long-term impacts of these efforts will require additional investigation. The findings of this case study are specific to the relief efforts in Houston, Texas, following Hurricane Harvey in 2017 and may not be generalizable beyond this scope. Practical implications: Sport organizations and community leaders can better prepare for future disaster responses by gaining insight into the roles and procedures enacted by the Houston teams following the Hurricane in 2017. Originality/value: This study provides a detailed examination of the responses of several Houston sport organizations following Hurricane Harvey, including perspectives from executives inside of the organizations. Utilizing social anchor theory, this paper expands our understanding of the impacts sport organizations may produce in their roles as social anchors during disaster relief and recovery. [ABSTRACT FROM AUTHOR]

Drawing on ethnographic fieldwork with energy financiers in Houston, Texas, this paper explores how experts use a lexicon of models and metrics to conceptualize and construct allegories about future hydrocarbon projects and companies. I show that allegorical narratives built with this lexicon advance a kind of energy ethics – distinguishing what is good and advocating for particular hydrocarbon futures. As the energy industry pivots toward renewables, I conclude that these metrics, models and allegories are coming to bear on new forms of extraction. This paper contributes to a better understanding of the financial and managerial processes on which extractive energy practices are imagined, valued and decided. [ABSTRACT FROM AUTHOR]

Drawing on qualitative data from an exploratory study of the experiences of audit pair researchers—pairs of black, white, and Latinx field testers—in an urban nightlife study in Austin, Dallas, and Houston, Texas, this paper examines nightclub owners' use of what I identify as "exclusionary tools"—that is, "dress codes," "steering," and "fake guest lists." Using Critical Race Theory's critique of neutrality, I argue that these exclusionary tactics create a welcoming atmosphere for white patrons while maintaining limits on the participation of African Americans and other peoples of color. This paper offers empirical examples of how these exclusionary practices limit legal challenges to discrimination in the first place and concludes with a discussion of potential methodological approaches for expanding this exploratory study and further identifying systematic exclusionary practices. [ABSTRACT FROM AUTHOR]

Increasing resilience to flooding is a complex process that requires horizontal and vertical coordination between institutions in policy making and implementation. This paper explores the effect of institutional coordination on managing flood risk in two cities plagued by flooding. Our results show that efforts on building urban flood resilience can be undermined by lack of proper coordination between urban development, water management and land use planning. We find that this complexity is magnified by the emergence of the concept of resilience as an urban development goal that is increasingly pursued by various authorities, but that is inherently contested in practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose two multimodal circular distributions which are suitable for modeling circular data sets with two or more modes. Both distributions belong to the regular exponential family of distributions and are considered as extensions of the von Mises distribution. Hence, they possess the highly desirable properties, such as the existence of non-trivial sufficient statistics and optimal inferences for their parameters. Fine particulates (PM2.5) are generally emitted from activities such as industrial and residential combustion and from vehicle exhaust. We illustrate the utility of our proposed models using a real data set consisting of fine particulates (PM2.5) pollutant levels in Houston region during Fall season in 2019. Our results provide a strong evidence that its diurnal pattern exhibits four modes; two peaks during morning and evening rush hours and two peaks in between. [ABSTRACT FROM AUTHOR]

Traffic accidents directly influence public safety and economic development; thus, the prevention of traffic accidents is of great importance in urban transportation. The accurate prediction of traffic accidents can assist traffic departments to better control and prevent accidents. Thus, this paper proposes a deep learning method named attention-based residual dilated network (ARDN), to extract essential information from multi-source datasets and enhance accident prediction accuracy. The method utilizes bidirectional long short-term memory to model sequential information and incorporates an attention mechanism to recalibrate weights. Furthermore, a dilated residual layer is adopted to capture long term information effectively. Feature encoding is also employed to incorporate natural language descriptions and point-of-interest data. Experimental evaluations of datasets collected from Austin and Houston demonstrate that ARDN outperforms a range of machine learning methods, such as logistic regression, gradient boosting, Xgboost, and deep learning methods. The ablation experiments further confirm the indispensability of each component in the proposed method. [ABSTRACT FROM AUTHOR]

Objective: Given the decreased clinical training opportunities during the COVID-19 pandemic, this study aimed to provide insights into how training directors and supervising neuropsychologists from internships offering neuropsychology training adjusted expectations of competitive applicants. Method: Respondents (n = 50) from internships offering at least an "exposure" in neuropsychology completed questions about how training expectations of competitive applicants have changed because of the COVID-19 pandemic. Results: Most respondents reported decreased expectations for clinical hours and research productivity and increased expectations for telehealth experience and involvement in working with culturally diverse populations. Additionally, more than half of respondents from programs at university-affiliated and Veteran Affairs medical centers indicated reduced expectations for average number of integrated reports. Furthermore, compared to respondents at Veteran Affairs medical centers, respondents at university-affiliated medical centers stated decreased expectations for average number of paper presentations. Conclusions: The COVID-19 pandemic has motivated subtle changes in expectations of competitive neuropsychology-oriented internship applicants, specific to clinical experience, research productivity, and prioritization of certain application materials. Qualitative responses suggest that many respondents endeavored to improve applicant screening rather than lower expectations for applicants. As a result, consistent with previous recommendations, the importance of fit between trainee and training program should continue to be emphasized by prospective applicants. These findings have important implications for trainees for the next several years, as graduate students at all stages of training ultimately progress to the internship application stage. [ABSTRACT FROM AUTHOR]

Disasters are increasing in frequency and intensity. Much of the current disaster literature adopts a social vulnerability perspective, which considers how political, social, and economic factors influence pre-disaster preparation and post-disaster recovery. Even with this focus, however, there remains a dearth of literature on immigrant populations and their long-term recovery trajectories. This paper applies a racial formation framework to a disaster context. I seek to show how service providers from community-based organizations (CBOs) navigate racialized anti-immigrant disaster recovery policies to help the Mexican immigrant community in Houston, Texas with their long-term housing recovery after Hurricane Harvey. I conducted semi-structured interviews and ethnographic observations with service providers from CBOs located in Houston that serve this population with post-disaster housing. I argue that the disaster recovery system is comprised of racial structures and racialized anti-immigrant policies, passively and actively limiting the access to resources for the Mexican immigrant community. I found that to challenge the racial structures and racialized anti-immigrant policies of the disaster recovery system, service providers assist the community through direct assistance to Mexican immigrants excluded from other programs; collaboration with other organizations to combine limited resources; helping the community navigate racialized anti-immigrant bureaucracy; and building trust by embedding themselves in the victimized community. However, findings also show that these organizations face significant challenges in conducting their work. This research brings a much-needed theoretical expansion of race and racialization theories to disaster research. [ABSTRACT FROM AUTHOR]

Sea level rise, as a result of climate change, is expected to drive coastal hazards that could bring significant damages to coastal regions in the future. However, high uncertainties remain in the projections of sea level rise from different climate scenarios and sea level rise prediction models. Quantification and integration of these uncertainties are essential to better inform coastal planning and decision making for climate adaptation, critical for infrastructure sustainability and resilience. This paper advances knowledge cross-cutting structural engineering and climate change in the face of multihazards via a novel framework termed the Probabilistic Sea Level Rise Hazard Analysis (PSLRHA). This study uses the current generation of models and protocols from the climate science research community to better portray the future climate and project sea level rise. The aggregation process produces the probability of exceeding a specific sea level rise threshold at a certain location and facilitates the creation of the global sea level rise hazard map. The relative importance of each climate scenario and sea level rise contributing models are demonstrated via the deaggregation process. We identify the models that have most contribution to extreme sea level rise thresholds, with large fluctuations in the high thresholds among ice sheet models. Finally, we show the practical implementation of PSLRHA results via compound flooding analyses using Houston as an illustrative example. [ABSTRACT FROM AUTHOR]

Purpose: Be Well Communities™ is MD Anderson's signature place-based approach for cancer prevention and control, working with communities to promote wellness and address modifiable risk factors for cancer. The purpose of this paper is to describe implementation of the planning phase of the Be Well Communities model in Acres Homes which began in 2019. Methods: A community advisory group (Steering Committee) including residents, non-profit organizations, health care partners, city and county agencies, plus other stakeholders, was convened and aligned through a structured process to develop shared goals, foster multisector collaboration, as measured by a stakeholder survey administered twice, and enhance community capacity to improve health outcomes through development of a Community Action Plan. Results: Clear, achievable goals were developed, multisector collaboration was enhanced, and more than 400 h of capacity building support led to a Community Action Plan initially focused on healthy eating and active living, including 15 evidence-based interventions led by 18 organizations. The majority (93%) of the Steering Committee reports that this plan reflects community priorities and will reach the residents most in need. Conclusion: By listening and developing trust, the Be Well Communities team successfully worked with Acres Homes residents and organizations to enhance community capacity to address health inequities in one of Houston's most diverse and historic communities. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) is a brain function injury caused by external mechanical injury. Primary and secondary injuries cause neurological deficits that mature brain tissue cannot repair itself. Stem cells can self-renewal and differentiate, the research of stem cells in the pathogenesis and treatment of TBI has made significant progress in recent years. However, numerous articles must be summarized to analyze hot spots and predict trends. This study aims to provide a panorama of knowledge and research hotspots through bibliometrics. Method: We searched in the Web of Science Core Collection (WoSCC) database to identify articles pertaining to TBI and stem cells published between 2000 and 2022. Visualization knowledge maps, including co-authorship, co-citation, and co-occurrence analysis were generated by VOSviewer, CiteSpace, and the R package "bibliometrix.". Results: We retrieved a total of 459 articles from 45 countries. The United States and China contributed the majority of publications. The number of publications related to TBI and stem cells is increasing yearly. Tianjin Medical University was the most prolific institution, and Professor Charles S. Cox, Jr. from the University of Texas Health Science Center at Houston was the most influential author. The Journal of Neurotrauma has published the most research articles on TBI and stem cells. Based on the burst references, "immunomodulation," "TBI," and "cellular therapy" have been regarded as research hotspots in the field. The keywords co-occurrence analysis revealed that "exosomes," "neuroinflammation," and "microglia" were essential research directions in the future. Conclusion: Research on TBI and stem cells has shown a rapid growth trend in recent years. Existing studies mainly focus on the activation mechanism of endogenous neural stem cells and how to make exogenous stem cell therapy more effective. The combination with bioengineering technology is the trend in this field. Topics related to exosomes and immune regulation may be the future focus of TBI and stem cell research. [ABSTRACT FROM AUTHOR]

Deep learning has achieved promising results for hyperspectral image (HSI) classification in recent years due to its hierarchical structure and automatic feature extraction ability from raw data. The HSI has continuous spectral information, allowing for the precise identification of materials by capturing minute spectral differences. Convolutional neural networks (CNNs) have proven to be effective feature extractors for HSI classification. However, inherent network limitations prevent them from adequately mining and representing the sequence attributes of spectral signatures and learning critical and valuable features from both spectral and spatial dimensions simultaneously. This paper proposes a deep learning-based framework called a novel dual attention-based multiscale-multilevel ConvLSTM3D (DAMCL) to address these challenges. In this work, our contribution is threefold; firstly, a dual attention mechanism is proposed, effectively learning critical and valuable features from spectral and spatial dimensions. Secondly, multiscale ConvLSTM3D blocks can learn the discriminative features alongside handling long-range dependencies of spectral data. Thirdly, these features are combined by a multilevel feature fusion approach to maximize the impact of features learned at different levels. To assess the performance of the proposed method, extensive experiments are carried out on five different benchmark datasets containing complex and challenging land cover classes. The results confirm that the proposed method outperforms state-of-the-art techniques with a small number of training samples in terms of overall accuracy (OA), average accuracy (AA), and Kappa (k). The overall accuracy of 98.88%, 99.42%, 99.20%, 95.37%, and 92.57% is achieved over the Indian Pines, Salinas Valley, University of Pavia, Houston 2013, and Houston 2018 datasets, respectively. [ABSTRACT FROM AUTHOR]

Deep learning has achieved impressive success in computer vision, especially remote sensing. It is well known that different deep models are able to extract different kinds of features from remote sensing images. For example, the convolutional neural networks (CNN) can extract neighbourhood spatial features in the short-range region, the graph convolutional networks (GCN) can extract structural features in the middle- and long-range region, and the encoder-decoder (ED) can obtain the reconstruction features from an image. Thus, it is challenging to design a model that can combine the different models to extract fused features in a hyperspectral image classification task. To this end, this paper proposes a three-branch attention deep model (TADM) for the classification of hyperspectral images. The model can be divided into three branches: graph convolutional neural network, convolutional neural network, and deep encoder-decoder. These three branches first extract structural features, spatial-spectral joint features and reconstructed encoded features from hyperspectral images, respectively. Then, a cross-fusion strategy and an attention mechanism are employed to automatically learn the fusion parameters and complete the feature fusion. Finally, the hybrid features are fed into a standard classifier for pixel-level classification. Extensive experiments on two real-world hyperspectral datasets (Houston and Trento) demonstrate the effectiveness and superiority of the proposed method. Compared with other baseline classification methods, such as FuNet-C and Two-Branch CNN(H), proposed method achieves the highest classification results. Specifically, overall classification accuracies of 93.25% and 95.84% were obtained on the Houston and Trento data, respectively. [ABSTRACT FROM AUTHOR]

Coastal areas are subject to the joint risk associated with rainfall‐driven flooding and storm surge hazards. To capture this dependency and the compound nature of these hazards, bivariate modelling represents a straightforward and easy‐to‐implement approach that relies on observational records. Most existing applications focus on a single tide gauge–rain gauge/streamgauge combination, limiting the applicability of bivariate modelling to develop high‐resolution space–time design events that can be used to quantify the dynamic, that is, varying in space and time, compound flood hazard in coastal basins. Moreover, there is a need to recognize that not all extreme events always come from a single population, but can reflect a mixture of different generating mechanisms. Therefore, this paper describes an empirical approach to develop design storms with high‐resolution in space and time (i.e., ~5 km and hourly) for different joint annual exceedance probabilities. We also stratify extreme rainfall and storm surge events depending on whether they were caused by tropical cyclones (TCs) or not. We find that there are significant differences between the TC and non‐TC populations, with very different dependence structures that are missed if we treat all the events as coming from a single population. While we apply this methodology to one basin near Houston, Texas, our approach is general enough to make it applicable for any coastal basin exposed to compounding flood hazards from storm surge and rainfall‐induced flooding. [ABSTRACT FROM AUTHOR]

Hyperspectral images contain rich features in both spectral and spatial domains, which bring opportunities for accurate recognition of similar materials and promote various fine-grained remote sensing applications. Although deep learning models have been extensively investigated in the field of hyperspectral image classification (HSIC) tasks, classification performance is still limited under small sample conditions, and this has been a longstanding problem. The features extracted by complex network structures with large model size are redundant to some extent and prone to overfitting. This paper proposes a low-rank constrained attention-enhanced multiple feature fusion network (LAMFN). Firstly, factor analysis is used to extract very few components that can describe the original data using covariance information to perform spectral feature preprocessing. Then, a lightweight attention-enhanced 3D convolution module is used for deep feature extraction, and the position-sensitive information is supplemented using a 2D coordinate attention. The above widely varying spatial–spectral feature groups are fused through a simple composite residual structure. Finally, low-rank second-order pooling is adopted to enhance the convolutional feature selectivity and achieve classification. Extensive experiments were conducted on four representative hyperspectral datasets with different spatial–spectral characteristics, namely Indian Pines (IP), Pavia Center (PC), Houston (HU), and WHU-HongHu (WHU). The contrast methods include several advanced models proposed recently, including residual CNNs, attention-based CNNs, and transformer-based models. Using only five samples per class for training, LAMFN achieved overall accuracies of 78.15%, 97.18%, 81.35%, and 87.93% on the above datasets, which has an improvement of 0.82%, 1.12%, 1.67%, and 0.89% compared to the second-best model. The running time of LAMFN is moderate. For example, the training time of LAMFN on the WHU dataset was 29.1 s, and the contrast models ranged from 3.0 s to 341.4 s. In addition, ablation experiments and comparisons with some advanced semi-supervised learning methods further validated the effectiveness of the proposed model designs. [ABSTRACT FROM AUTHOR]

This paper explores how local governments have addressed abandonment of a high-dollar investment: publicly funded sport facilities. The issue of abandoned professional sport facilities is becoming more ubiquitous as teams seek new, more modern, state-of-the-art venues to maximize operational revenues. This creates a scenario where the average lifespan of a professional sport facility is only 27 years. Using a comparative case study analysis, this research examines how municipalities have approached the redevelopment of abandoned stadium infrastructure in Detroit, Houston, and St. Louis. Successful outcomes related to the redevelopment of abandoned stadiums in Detroit have seen public and private stakeholders take advantage of their unique assets in public-private partnerships. However, abandoned stadium infrastructure in Houston and St. Louis have remained idle for years as local governments failed to secure private investment to aid with redevelopment efforts and/or created additional bureaucratic red tape that limits the prospects for site redevelopment. [ABSTRACT FROM AUTHOR]

This paper examines the vulnerability of road networks to two types of disruptions by modeling the percolation dynamics in road networks under different disruption scenarios. The objective of this paper is threefold: (1) to examine if the theoretical network robustness measure proposed in the literature is applicable for measuring the integrity of road networks during disruptions; (2) to unveil the impacts of network size on the overall vulnerability of road networks; and (3) to compare the performance profile of road networks to random and nonrandom types of disruptions. To that end, this study first modeled the road system in a community as a planar graph. Then, the percolation dynamic in the road network during the flood is captured by assigning different removal probabilities to nodes in the road network according to Bayes' rule that take floodplain types, node-elevation, and street-grade as inputs. In the end, an overall road network robustness measure and its temporal changes were obtained and for random and nonrandom scenarios, using road networks with different sizes. The results were compared in order to characterize the vulnerability of road networks under different scenarios. The proposed method was applied to the road network in central Houston during Hurricane Harvey. The results show that: (1) the theoretical network robustness measure is applicable to assess the road network robustness; (2) compared to the random percolation model, the probability (Bayes' rule) based percolation could lead to a greater decrease in the network robustness; and (3) the percolation profiles of the road networks with different sizes are not significantly different. The findings of this study could not only inform resilience enhancing decisions by the stakeholders but also could serve as a foundation for future vulnerability related research. [ABSTRACT FROM AUTHOR]

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage. [ABSTRACT FROM AUTHOR]

Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry's interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians. [ABSTRACT FROM AUTHOR]

Summary: Real‐time health monitoring of stay cables in cable‐stayed bridges is necessary for timely maintenance and to avoid unforeseen fatigue damage due to vortex‐induced vibration—mainly due to combination rain and wind‐related dynamic loads. Conventional contact‐based sensors may often malfunction in harsh weather conditions and are expensive to install and maintain. Therefore, recently, the usage of non‐contact camera‐based measurement is burgeoning in the domain of structural sensing. Non‐contact video‐based sensing provides a higher spatial resolution compared to conventional sensors along with a lower cost. Therefore, in this paper, we present a framework that uses video‐based measurement as multiple sensors to reduce the estimation error in determining the real‐time cable tension. First, we calculate the vibration response using the phase‐based motion estimation algorithm for various locations of interest. We then intuitively fuse the data from all the locations to estimate the real‐time frequency variation using a blind source separation (BSS) technique named complexity pursuit (CP). Finally, the real‐time stay‐cable tension is calculated from the real‐time frequency history using the taut‐string theory. The proposed algorithm is applied to Fred‐Hartman cable‐stayed bridge in Houston, Texas. The algorithm is validated using actual tension in the cable. We also show that the estimation error in the proposed sliding window‐based CP framework is considerably lesser than the conventional real‐time tension estimation technique using Short‐time Fourier Transform (STFT). The accurate estimation of stay‐cable tension from the video‐based measurement shows the significant potential of the proposed framework in the domain of structural health monitoring. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the state of price transparency efforts for health care services within the industry including the need for price transparency and the benefits and challenges currently facing price transparency measures. The paper also examines the current landscape of price transparency measures to understand the different solutions that exist to facilitate transparency, and compares the solutions in use among various hospitals within the Houston area for their effectiveness. Finally, a price transparency model will be proposed as a solution that can be standardized for use within the industry, with a discussion for applying this price transparency concept in a hospital setting and possible issues that could arise with the proposed implementation of the price transparency solution. [ABSTRACT FROM AUTHOR]

Over the past years, the frequency and scope of disasters affecting the United States have significantly increased. Government agencies have made efforts in improving the nation's disaster response framework to minimize fatalities and economic loss due to disasters. Disaster response has evolved with the emergency management agencies incorporating systematic changes in their organization and emergency response functions to accommodate lessons learned from past disaster events. Technological advancements in disaster response have also improved the agencies' ability to prepare for and respond to natural hazards. The transportation and logistics sector has a primary role in emergency response during and after disasters. In this light, this paper seeks to identify how effective policy changes and new technology have aided the transportation and logistics sector in emergency response and identify gaps in current practices for further improvement. Specifically, this study compares and contrasts the transportation and logistical support to emergency relief efforts during and after two major Hurricane events in the U.S., namely Hurricane Katrina (which affected New Orleans in 2005) and Hurricane Harvey (which affected Houston in 2017). This comparison intends to outline the major steps taken by the government and the private entities in the transportation and logistics sector to facilitate emergency response and the issues faced during the process. Finally, the paper summarizes the lessons learned from both the Hurricane events and provides recommendations for further improvements in transportation and logistical support to disaster response. [ABSTRACT FROM AUTHOR]