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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]

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]

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]

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]

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]

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]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UAS). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle - Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 days was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. [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]

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]

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]

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]

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.

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]

Recent classifications in Earth Observation (EO) commonly involve a combination of Hyperspectral Image (HSI) and Light Detection and Ranging (LiDAR) signals. However, many current methods fail to consider the HSI-LiDAR information concurrently, especially in terms of both its intra- and inter-modality aspects. Additionally, current methods are generally limited in their ability to fuse the features extracted from different modalities. Hence, this paper proposes a center-bridged framework, called Interaction Fusion (IF), that can leverage diverse information concerning the intra- and inter-modality relationships at the same time. More specifically, intra- and inter-modality information can be enriched by introducing the center patch of HSI (cp-HSI) as an extra input, This introduces additional contextual information within and across modalities that can be leverage for deeper insights. Further, we propose a fusion matrix as a structural feature map designed to integrate nine views generated by a view generator, enabling the adaptive combination of intra- and inter-modality information. Overall, our approach allows potential patterns to be captured, while mitigating any bias resulting from incomplete information. Extensive experiments conducted on three widely recognized datasets – Trento, MUUFL, and Houston – demonstrate that the IF framework achieves state-of-the-art results, surpassing existing methods. [ABSTRACT FROM AUTHOR]

• Integrated techno-economic assessment of the hydrogen value chain in the Permian Basin, spanning production, storage, and transportation. • Identified cost benefits in feedstock and tax credits for Permian-based hydrogen, balanced by transport expenses to Houston. • Scaling up to 412,000 metric tons/year can counterbalance transportation costs, positioning the Permian Basin competitively in the hydrogen market. This paper investigates the untapped potential of the Permian Basin, a multifaceted energy axis in Texas and adjoining states, in the emerging era of decarbonization. Aligned with current policy directives on regional hydrogen hubs, this study explores the viability of developing a hydrogen energy hub in the Permian Basin, thereby producing low-carbon intensity hydrogen from natural gas in the Basin and transporting it to the Greater Houston area. Diverging from existing literature, this study provides an integrated techno-economic evaluation of the entire hydrogen value chain in the Permian Basin, encompassing production, storage, and transportation. Furthermore, it comparatively analyzes the scenario of interest against an optimized base scenario, thereby underlining comparative advantages and disadvantages. The paper concludes that the delivered cost of Permian-based low-carbon intensity hydrogen to the Greater Houston area is $1.85/kg, benchmarked to the scenario, with hydrogen produced close to the Greater Houston area and delivered at $1.42/kg. Our findings reveal that Permian-based low-carbon intensity hydrogen production can achieve cost savings in feedstock ($0.25/kg) and potentially accrue a higher production tax credit due to a shorter gas supply chain to production ($0.33/kg). Nevertheless, a significant cost barrier is the expense of long-haul pipeline transport ($0.90/kg) from the Permian Basin to Houston as opposed to local production. Despite the obstacles, the study identifies a potential breakeven solution where increasing the production scale to at least 412,000 metric ton per year (about 3 steam-reforming plants) in the Permian Basin can effectively lower costs in the transport sector. Hence a scaled-up production can mitigate the cost difference and establish the Permian Basin as a competitive player in the hydrogen market. In conclusion, a SWOT analysis presents Strengths, Weaknesses, Opportunities, and Threats associated with Permian-based hydrogen production. [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]

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]

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]

While much research investigates how social capital relates to mental health after disasters, less work employs a multi‐scalar, multi‐dimensional social capital framework. This study applies such a construct to an analysis of novel survey data of approximately 1,000 rural and urban Texans after Hurricane Harvey struck the United States in August 2017. On the individual level, it finds that greater social support is linked to fewer mental health impacts, but that greater civic and organisational engagement is connected to greater mental health impacts. At the community level, it finds that neither a density of bridging social capital organisations nor of bonding social capital organisations is associated with poorer mental health, although a greater number of bonding organisations is related to negative mental health impacts on rural residents. The paper concludes by focusing on how individual and community social capital relationships with mental health are contingent on measurement, scale, and rural or urban location. [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]

Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. [ABSTRACT FROM AUTHOR]

Produce prescription programs are gaining traction in the U.S.; however, data on the impact of such approaches in pediatric populations are limited. The purpose of our clinic-based comparative effectiveness randomized controlled trial (CE RCT) is to evaluate the preliminary effectiveness of two produce prescription strategies (at-home delivery and grocery store vouchers) implemented by the Brighter Bites non-profit organization in improving obesity-related health outcomes and dietary behaviors among low-income 5–12-year-olds in Houston, Texas. This paper presents the study design, intervention components, and the study measures. Participants (n = 150) are being recruited from two pediatric clinics in Houston, Texas. Child eligibility criteria are aged 5–12 years, Medicaid recipients, body-mass index (BMI) percentile ≥85 and living within 10 miles of a Brighter Bites distribution site. Following consent and baseline measures, children are randomized into one of three arms: (1) Bi-weekly $25 vouchers redeemable for produce at stores (n = 50), (2) Bi-weekly produce delivery to participants' homes through DoorDash (n = 50), and (3) wait-list usual care controls (n = 50). Intervention participants also receive Brighter Bites nutrition education materials. Main child outcome measures are BMI z-scores, blood pressure, hemoglobin A1c, liver panels, and lipid panels. Other outcomes including household food insecurity, child diet quality, and home nutrition environment will be collected through parent surveys. Outcome measures are collected at baseline and post-intervention. Process evaluation will measure program dosage, reach, acceptability, and feasibility. Our paper presents the design and next steps to ensure the successful implementation of a produce prescription program in a pediatric clinic setting. [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]

As a result of more racially inclusive immigration policies in the U.S., the African and Caribbean population has increased. Thus far, scholarly inquiry on Black immigrants have focused on their incorporation into the racial hierarchy, their experiences with racism, and their relationships with African Americans. While beneficial, these studies overlook the impact of ethnic discrimination for Black immigrants. Although all individuals of African descent share similar racialized experiences in the U.S., I hypothesize that diverse Black immigrant groups endure unique discriminatory experiences because of their ethnic identities. Thus, through in-depth interviews with 27 Nigerian and 20 Jamaican respondents, this paper explores Black immigrants' experiences with racial and ethnic discrimination. I found that, regardless of ethnic background, 80% of my Nigerian and Jamaican respondents encountered racism. In contrast, ethnic discrimination varied between my Nigerian and Jamaican respondents. My Nigerian respondents were more likely to report their encounters with ethnic discrimination. These experiences ranged from being accused of internet scamming to being mocked for having an accent. On the other hand, not only were my Jamaican respondents less likely to report ethnic discrimination, but they were also more likely to see their ethnicity as an advantage. [ABSTRACT FROM AUTHOR]

Communal rainwater harvesting (RWH) systems allow a community to collect rainwater from multiple roofs, store and treat it in a central location, and then distribute it back to the community. This paper proposes a novel distributed rainwater harvesting approach to communal rainwater harvesting in which individual households connect the outflow of their RWH systems to a communal storage from which they can retrieve water when their system is not able to meet their water demands. We simulated the performance of the system in two cities, Houston and Jacksonville, for multiple private and communal storage combinations. We measure the performance of the system using the volumetric reliability (VR) metric, which is the ratio of rainwater that the communal system is able to provide to the total water demand. Results showed that the VR gains over a private system of 1%–6% and 1%–4%, can be achieved for up to 10 and 7 connected households, respectively, for Houston and Jacksonville if the emphasis is on VR gain >1.5%. The system achieved higher VR gains for lower total storage capacity in Houston, whereas the system achieved higher VR gains for higher total storage capacities in Jacksonville. This proposed decentralized rainwater harvesting system is attractive in the face of climate change, increases the resilience of water/stormwater infrastructure, and potentially could decrease the likely effects of flooding and property damage from stormwater. [ABSTRACT FROM AUTHOR]

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.

Deepwater risers are susceptible to vortex-induced vibrations (VIV) when subjected to currents. When responding at high modes, fatigue damage in the inline (IL) direction may become equally important as the crossflow (CF) components. Accurate calculation of both IL and CF responses is therefore needed. Empirical VIV prediction programs, such as VIVANA "Passano et al. (2016, "VIVANA--Theory Manual Version 4.8," Trondheim, Norway)," SHEAR7 "(Vandiver, J. K., and Li, L., 2007, "Shear7 v4.5 Program Theoretical Manual," Department of Ocean Engineering, Massachusetts Institute of Technology, Cambridge, MA)," and VIVA "Triantafyllou et al. (1999, "Pragmatic Riser VIV Analysis," Offshore Technology Conference, Houston, TX, May 3-6, Paper No. OTC-10931-MS.)" are the most common tools used by the offshore industry. Progress has been seen in the prediction of CF responses. Efforts have also been made to include an IL load model in VIVANA. A set of excitation coefficient parameters were obtained from rigid cylinder test and adjusted using measured responses of one of the flexible cylinder VIV tests. This set of excitation coefficient parameters is still considered preliminary and further validation is required. Without an accurate IL response prediction, a conservative approach in VIV analysis has to be followed, i.e., all current profiles have to be assumed to be unidirectional or acting in the same direction. The purpose of this paper is to provide a reliable combined IL and CF load model for the empirical VIV prediction programs. VIV prediction using the existing combined IL and CF load model in VIVANA is validated against selected flexible cylinder test data. A case study of a deepwater top tension riser (TTR) has been carried out. The results indicate that VIV fatigue damage using two-dimensional directional current profiles is less conservative compared to the traditional way of using unidirectional current profiles. [ABSTRACT FROM AUTHOR]

The car fulfills not only instrumental transportation functions, but also holds important symbolic and affective meaning for its owners and users. In particular, owning and using a car can be a symbol of an individual's social status or personal image ('car pride'). This paper introduces and validates a standard measure of car pride estimated from 12 survey statements using a cross-sectional sample of 1236 commuters in New York City and Houston metropolitan statistical areas. We find that car pride is higher in Houston than in New York City. We then empirically examine the bidirectional relation between car pride (attitude) and household car ownership (behavior) using structural equation modeling. To identify the bidirectional relationship we use an individual's general pride as the instrumental variable (IV) for that same individual's car pride; in the opposite direction, we use the average household vehicle ownership in the respondent's census block group as the IV for the respondent's household car ownership. We find that positive and statistically significant relations exist from car pride to car ownership, while the relation in the reverse direction is not statistically significant. On average and in both city subsamples, the relation from car pride to household car ownership (attitude-to-behavior) is much stronger than the reverse (behavior-to-attitude). In fact, in our models car pride is more predictive of car ownership than most individual and household socio-demographics included in traditional ownership forecasting models, including income. Empowered with a well-validated, standard measure for car pride and a robust approach for exploring reciprocal attitude-behavior relations in cross-sectional data, future research can extend the current understanding presented in this paper to explore car pride's relation with other travel behaviors, the dynamics of these attitude-behavior relations over time, and their implications for policies to promote sustainable travel behavior. [ABSTRACT FROM AUTHOR]

In epidemiology, the effective reproduction number R e is used to characterize the growth rate of an epidemic outbreak. If R e > 1 , the epidemic worsens, and if R e < 1 , then it subsides and eventually dies out. In this paper, we investigate properties of R e for a modified SEIR model of COVID-19 in the city of Houston, TX USA, in which the population is divided into low-risk and high-risk subpopulations. The response of R e to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized. A nonlinear cost model is used for control measures, to include the effects of diminishing returns. Lowest-cost control combinations for reducing instantaneous R e to a given value are computed. We propose three types of heuristic strategies for mitigating COVID-19 that are targeted at reducing R e , and we exhibit the tradeoffs between strategy implementation costs and number of deaths. We also consider two variants of each type of strategy: basic strategies, which consider only the effects of controls on R e , without regard to subpopulation; and high-risk prioritizing strategies, which maximize control of the high-risk subpopulation. Results showed that of the three heuristic strategy types, the most cost-effective involved setting a target value for R e and applying sufficient controls to attain that target value. This heuristic led to strategies that begin with strict distancing of the entire population, later followed by increased testing. Strategies that maximize control on high-risk individuals were less cost-effective than basic strategies that emphasize reduction of the rate of spreading of the disease. The model shows that delaying the start of control measures past a certain point greatly worsens strategy outcomes. We conclude that the effective reproduction can be a valuable real-time indicator in determining cost-effective control strategies. [ABSTRACT FROM AUTHOR]

This paper contributes to research on neighborhood effects and knowledge about the conditions associated with community-level patterns of police behavior by examining whether levels of violence, racial and ethnic composition, and socioeconomic disadvantage are associated with officer-involved shootings in Houston, TX. In addition, drawing from the ecological theory of policing, this study explores whether spatial heterogeneity exists in the observed relationships. Poisson regression models reveal that community violence and neighborhood racial and ethnic context are meaningful indicators of on-duty officer-involved shootings. Socioeconomic disadvantage did not emerge as a significant variable in the models estimated. Results from Geographically Weighted Poisson Regression (GWPR) models indicate statistically significant spatial variation in the effects for all the key community factors. The findings highlight the importance of local context for developing a greater understanding of police violence across the urban landscape. [ABSTRACT FROM AUTHOR]

Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural network (RNN) applied to sequential models has motivated groups to pursue RR modeling using RNN. Existing RNN based methods generally use either sequence input single output or unsynced sequence input and output architectures. In this paper, we propose a synced sequence input and output long short-term memory (LSTM) network architecture for hydrologic analysis and compare it to existing methods (sequence input single output LSTM). We expect the model will improve RR prediction in terms of accuracy, calibration training time, and computational cost. The key idea is to efficiently learn the long term dependency of runoff on past rainfall history. To be more specific, we use the indigenous ability of the LSTM network to preserve long term memory instead of artificially setting a time window for input data. In this way, we can avoid losing long term memory of the input, the calibration of the time window length, and excessive computation. The whole procedure mimics the traditional process-driven methods and is closer to the physics interpretation of the RR process. We conducted experiments on real-world hydrologic data from the Brays Bayou in Houston, Texas. Extensive experimental results clearly validate the effectiveness of our proposed method in terms of various statistical and hydrological related evaluation metrics. Notably, our experiment shows that some rainfall events could affect the runoff process in the test watershed for at least a week. For fine temporal resolution prediction, this long term effect needs to be carefully handled, and our proposed method is superior in this case. [ABSTRACT FROM AUTHOR]

This paper draws on data from 12 focus group discussions of a faith-based prison ministry program to examine how ordinary Americans talk about the separation of church and state. The focus groups – conducted in Houston, Minneapolis-St. Paul, and Boston – revealed that program supporters and opponents draw on different moral frameworks, with supporters focusing on a framework of care and opponents focusing on a framework of justice. The findings affirm the centrality of moral concerns in shaping citizen stances on church-state separation and suggest that meaningful political compromise is possible on the issue of government support of faith-based social services. [ABSTRACT FROM AUTHOR]

As public awareness and concern about air quality grows, companies and researchers have begun to develop small, low-cost sensors to measure local air quality. These sensors have been used in citizen science projects, in distributed networks within cities, and in combination with public health studies on asthma and other air-quality-associated diseases. However, sensor long-term performance under different environmental conditions and pollutant levels is not fully understood. In addition, further evaluation is needed for other long-term performance trends such as performance among sensors of the same model, comparison between sensors from different companies and comparison of sensor data to federal equivalence or reference method (FEM/FRM) measurements. A 10-month evaluation of two popular particulate matter (PM) sensors, Dylos DC1100 and AirBeam, and a popular ozone (O3) sensor, Aeroqual 500, was performed as part of this study. Data from these sensors were compared to each other and to FEM/FRM data and local meteorology. The study took place at the Houston Regional Monitoring (HRM) site 3, located between the Houston Ship Channel and Houston's urban center. PM sensor performance was found to vary in time, with multivariate analysis, binning of data by meteorological parameter, and machine learning techniques able to account for some but not all performance variations. PM type (i.e., size distribution, fiber-flake-spheroid shape and black-brown-white color) likely played a role in the changing sensor performance. Triplicate individual Aeroqual O3 sensors tracked reasonably well with the FEM data for most of the measurement period but had irregular periods of O3 measurement offset. While the FEM data indicated 4 days where ozone levels were above the NAAQS, the Aeroqual ozone sensors indicated a substantially higher number of days, ranging from 9 to 16 for the three sensors. Implications: This paper evaluated the long-term performance of several commercial low-cost sensors (PM2.5 and ozone) as compared to federal equivalence method (FEM) monitors under a range of meteorological and air quality conditions. PM2.5 sensors performed well on low humidity days with winds indicative of sea salt or dust PM sources but had poor correlation with FEM data under other conditions. Two types of PM sensors were studied (Dylos 1100 and AirBeam) and data only correlated well between sensors of the same type. Sensor networks with multiple PM sensor types would not be as useful for comparative purposes as sensor networks of the same type. Relative humidity corrections alone did not increase sensor agreement with FEM to acceptable levels, specific information about PM sources and sensor response in the area measured is needed. Low-cost ozone sensors tested (Aeroqual) performed well but were biased high and overestimated days above ozone NAAQS. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper was to design, manufacture, and evaluate a tissue equivalent, dual magnetic resonance/computed tomography (MR/CT) visible anthropomorphic head and neck (H&N) phantom. This phantom was specially designed as an end‐to‐end quality assurance (QA) tool for MR imaging guided radiotherapy (MRIgRT) systems participating in NCI‐sponsored clinical trials. Method: The MRIgRT H&N phantom was constructed using a water‐fillable acrylic shell and a custom insert that mimics an organ at risk (OAR) and target structures. The insert consists of a primary and secondary planning target volume (PTV) manufactured of a synthetic Clear Ballistic gel, an acrylic OAR and surrounding tissue fabricated using melted Superflab. Radiochromic EBT3 film and thermoluminescent detectors (TLDs) were used to measure the dose distribution and absolute dose, respectively. The phantom was evaluated by conducting an end‐to‐end test that included: imaging on a GE Lightspeed CT simulator, planning on Monaco treatment planning software (TPS), verifying treatment setup with MR, and irradiating on Elekta's 1.5 T Unity MR linac system. The phantom was irradiated three times using the same plan to determine reproducibility. Three institutions, equipped with either ViewRay MRIdian 60Co or ViewRay MRIdian Linac, were used to conduct a feasibility study by performing independent end‐to‐end studies. Thermoluminescent detectors were evaluated in both reproducibility and feasibility studies by comparing ratios of measured TLD to reported TPS calculated values. Radiochromic film was used to compare measured planar dose distributions to expected TPS distributions. Film was evaluated by using an in‐house gamma analysis software to measure the discrepancies between film and TPS. Results: The MRIgRT H&N phantom on the Unity system resulted in reproducible TLD doses (SD < 1.5%). The measured TLD to calculated dose ratios for the Unity system ranged from 0.94 to 0.98. The Viewray dose result comparisons had a larger range (0.95–1.03) but these depended on the TPS dose calculations from each site. Using a 7%/4 mm gamma analysis, Viewray institutions had average axial and sagittal passing rates of 97.3% and 96.2% and the Unity system had average passing rates of 97.8% and 89.7%, respectively. All of the results were within the Imaging and Radiation Oncology Core in Houston (IROC‐Houston) standard credentialing criteria of 7% on TLDs, and >85% of pixels passing gamma analysis using 7%/4 mm on films. Conclusions: An MRIgRT H&N phantom that is tissue equivalent and visible on both CT and MR was developed. The results from initial reproducibility and feasibility testing of the MRIgRT H&N phantom using the tested MGIgRT systems suggests the phantom's potential utility as a credentialing tool for NCI‐clinical trials. [ABSTRACT FROM AUTHOR]

This paper contextualizes the most recent discovery of 95 forgotten graves of incarcerated laborers at a public school construction site within ongoing tensions around public history, race, and development in Sugar Land, Texas, a Houston-area bedroom community. Unearthed along with the graves is the state's long history of Black labor exploitation, from enslavement to convict leasing to employment with Imperial Sugar. In this article, I engage the haunting of Black laboring bodies in the landscape from the perspective of both that of a researcher and former resident of Fort Bend County confronted with the purposeful forgetting of Black geographies, bodies, and lives. I expose through critical analysis of government documents, online digital exhibits, maps, photos, and autoethnographic recollection of the area, the haunting of Black laboring bodies in not only the site of burial discovery but also two sites within the Sugar Land cultural landscape: Mayfield Park and the Imperial Sugar Refinery. I argue developers and government agencies perpetuate a mythic local history that, until the discovery of the 95, allowed them to disassociate itself from Sugar Land's history of Black labor exploitation before its incorporation as a city. The 95's haunting allows for an inventive awareness of the Black laboring bodies and thus redefines the cultural landscape rooted in plantation logics as a Black geography. I conclude with a discussion of the ways Black laboring bodies' haunting creates a space for a critical cultural landscape solution. [ABSTRACT FROM AUTHOR]

Almost 40% of Veterans Health Administration (VHA) users have obesity. VHA's national weight management program is associated with weight loss and improved health. However, while 94% of eligible VHA users are offered weight management programs, <8% use them. We developed EMBER – a novel, Motivational Interviewing-based, self-help tool – with the goal of E nhancing M otivation for B etter E ngagement and R each for weight management. EMBER is not a weight management program; instead it engages people in existing programs by informing and guiding choices about weight management. The EMBER Trial is a randomized hybrid type 1 effectiveness implementation trial. Participants are Palo Alto or Houston VA Health Care System users with obesity who have not used a VHA weight management program in the past two years (target N = 470). Participants are randomly assigned to EMBER or an information-only control condition, after which they receive materials on paper or digitally, per their preference. The trial's primary goal is to determine whether participants randomized to EMBER are more likely to have any weight management engagement at two-month follow-up compared to those in the control condition. Secondary outcomes include 6-month retention in weight management, weight management behaviors, weight loss, quality of life, and implementation outcomes (e.g., reach, appropriateness). EMBER is the first self-directed, Motivational Interviewing-based intervention designed to increase weight management program engagement. The study takes a low-touch, population health approach that could be modified for other programs if effective. The Hybrid Type 1 design will ensure results can be scaled and sustained. • EMBER is a novel self-help intervention to increase weight management engagement. • EMBER is based on an integration of health services and health psychology theories. • The EMBER Trial's hybrid design will ensure results can be scaled and sustained. [ABSTRACT FROM AUTHOR]

Late Dr. Sam Mannan promoted in his last year the concept of the Safety Triad as a basis for effective process safety. As he mentioned himself, nobody will be surprised that this triad consists of Prevention, Mitigation, and Response. If everything were perfect an accident would not occur, but in hindsight after an accident experience teaches that at least one of these pillars of the triad is missing or defective. Also, given the recent hurricane Harvey experience in the Houston region, socio-technical associated hazard threats emerge not only from inside a plant but can be external: NaTech or technological accidental occurrences evoked by natural forceful events. Hence to be acceptably safe, the robustness and reliability of the triad components must be well designed, and their performance over time must be periodically verified. This objective supposes a risk-based approach both for design and for reliable operation. However, risk assessment itself is fallible. Over the years it has been shown that there are many limitations, but fortunately during the last few years we have seen an advent of new approaches, methods, and techniques to perform a more realistic scenario definition of what can go wrong, enabling to cope with uncertainties in models and parameter values, to analyze the risk dynamics of situations, such as reliability and availability degradation, but also process risk evolving in continuous processes. In all this, not only the technical aspects but also the associated human and organizational factors must be included. The latter dominate the scene in many situations. Last but not least is the concept of resilience in case risks are overlooked due to lack of knowledge or even to the notorious "black swan" incidents. Preparing for unexpected events should provide not only error tolerant design but early warning agility, capability to improvise, and means to recover operations. It is business continuity that will be at stake! The paper will elaborate both the triad and Sam Mannan's view on risk assessment. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing has been widely used for flood mapping and monitoring. Nevertheless, flood detection in urban areas still proves to be particularly challenging by using SAR. In this paper, we assess the roles of SAR intensity and interferometric coherence in urban flood detection using multi-temporal TerraSAR-X data. We further introduce an active self-learning convolution neural network (A-SL CNN) framework to alleviate the effect of a limited annotated training dataset. The proposed framework selects informative unlabeled samples based on a temporal-ensembling CNN model. These samples are subsequently pseudo-labeled by a multi-scale spatial filter. Consistency regularization is introduced to penalize incorrect labels caused by pseudo-labeling. We show results for a case study that is centered on flooded areas in Houston, USA, during hurricane Harvey in August 2017. Our experiments show that multi-temporal intensity (pre- and co-event) plays the most important role in urban flood detection. Adding multi-temporal coherence can increase the reliability of the inundation map considerably. Meanwhile, encouraging results are achieved by the proposed A-SL CNN framework: the к statistic is improved from 0.614 to 0.686 in comparison to its supervised counterpart. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification has attracted wide attention in many fields. Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers, which has improved the HSI classification accuracy greatly. However, GNN-based methods have not been widely applied due to their time-consuming, inefficient information description as well as poor anti-noise robustness. To overcome the deficiencies, a novel multi-scale receptive fields graph attention neural network (MRGAT) is proposed for HSI classification in this paper. In this network, a superpixel segment method is adopted to abstract the original HSI local spatial features. A two-layer one-dimensional convolution neural network (1D CNN) spectral transformer mechanism, is designed to extract the spectral features of superpixels, with which the spectral features can be acquired automatically. Furthermore, graph edges are introduced into Graph Attention Network (GAT) to acquire the local semantic feature of the graph. Moreover, inspired by the transformer network, we design a novel multi-scale receptive field GAT to extract the local-global adjacent node-features and edges-features. Finally, a graph attention network and a softMax function are utilized for multi-receptive feature fusion and pixel-label predicting. On Pavia University, Salinas, and Houston 2013 datasets, the overall accuracies (OAs) of our MRGAT are 71.76%, 82.61%, and 63.82%, respectively. Moreover, the performances with limited labeled samples indicates that the MRGAT contains superior adaptability. Compared with the competitive classifiers, MRGAT achieves high classification efficiency verified by training time comparison experiment. [ABSTRACT FROM AUTHOR]

Abstract: From 2002 to 2011, West Nile virus mosquitoes (WNV) has been ever-present in traps across Harris County, TX which contains the city of Houston. Disease-positive trap locations have peaked twice, from 2002 to 2006 and then again from 2009 onwards. This paper will examine fine scale spatial and temporal patterns in disease-positive mosquito traps for the Houston area across this time frame, using three different analytical approaches: kernel density, spatial filtering and SaTScan. The purpose of this paper is twofold. Firstly, to identify spatial and space-time clusters of WNV in order to spatially prioritize subsequent research for causative associations. Secondly, to compare the effectiveness of three methods that vary in complexity and ease of use in order to suggest a transferable methodology for mosquito control and environmental health departments across the United States with only lower level GIS skillsets. This paper also illustrates a successful ongoing academic and mosquito control collaboration with the Harris County Public Health Services Mosquito Control Division's (MCD) program. [Copyright &y& Elsevier]

Although anti-immigrant sentiment is often attributed to economic competition with foreign-born workers, research has provided contradictory results. This paper combines survey and occupation data to re-examine the relationship between immigration attitudes and labour market competition in Houston, an established immigration gateway. I analyse data from multiple waves of the Houston Area Survey and the American Community Survey, the Bureau of Labor Statistics, and O*NET. Results indicate that labour market competition is associated with preferences for reducing immigration in Houston. Those working in occupations with recent increases in the number of foreign-born workers, that require less education, and that have higher rates of unemployment are more likely to prefer to reduce future legal immigration, even after controlling for perceived economic and cultural threats. These results support other research at the occupation and industry levels and suggest that labour market competition is a factor in shaping immigration attitudes. [ABSTRACT FROM AUTHOR]

Both random walk and respondent-driven sampling (RDS) exploit social networks and may reduce biases introduced by earlier methods for sampling from hidden populations. Although RDS has become much more widely used by social researchers than random walk (RW), there has been little discussion of the tradeoffs in choosing RDS over RW. This paper compares experiences of implementing RW and RDS to recruit drug users to a network-based study in Houston, Texas. Both recruitment methods were implemented over comparable periods of time, with the same population, by the same research staff. RDS methods recruited more participants with less strain on staff. However, participants recruited through RW were more forthcoming than RDS participants in helping to recruit members of their social networks. Findings indicate that, dependent upon study goals, researchers' choice of design may influence participant recruitment, participant commitment, and impact on staff, factors that may in turn affect overall study success. [ABSTRACT FROM AUTHOR]

• Pedestrian crash counts are modeled. • Disparities between majority Black and non-majority Black neighborhoods are examined. • Pedestrian crashes are strongly affected by the density of transit stops. • Black pedestrians are more likely to be involved in crashes in non-majority Black neighborhoods. • Policy and equity implications are identified. In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind. [ABSTRACT FROM AUTHOR]

Purpose Social scientific research is having a substantial impact on eyewitness identification procedural reforms. Police agencies in the USA have changed their eyewitness practices based on the results of social scientific research. The purpose of this paper is to contribute new knowledge by using a unique set of data to describe detailed aspects of eyewitness identification procedures conducted as part of robbery investigations in Houston, TX.Design/methodology/approach Robbery investigators completed surveys following identification procedures conducted during a six-month period of time. The sample includes 975 identification procedures. The analysis describes important features of identification procedures and places results in the context of existing research.Findings Results show that photo spreads were the most frequently used lineup procedure and selection outcomes were similar to recent field studies conducted in the USA. Results also show that the type of procedure, presence of a weapon, cross-race identifications, and viewing opportunity were significantly correlated with selection outcomes.Originality/value Police are reforming their eyewitness identification procedures based on findings from social science research. The study measures and describe the characteristics of a large sample eyewitness procedures conducted by investigators in the field. [ABSTRACT FROM AUTHOR]

Houston is the only major city in North America without zoning. The growth of Houston illustrates a traditional free market philosophy in which zoning is seen as a violation to private property rights. This paper examines how the lack of zoning has an impact on land use and urban form in Houston. It uses cluster analysis integrating socioeconomic factors to select three case study neighbourhoods, and then applies geographical information systems to analyse their urban form spatial characteristics. The study investigates the change of urban form in three neighbourhoods over two decades. The analysis is accompanied by a qualitative investigation of the neighbourhoods, which attempts to address why and how those quantified characteristics of urban form developed over the decades. The paper concludes by discussing the similarity and diversity of land-use patterns and the reasons, by outlining policy implications from the findings on urban form, and by contributing to the debate over urban form and government intervention in better land-use patterns. [ABSTRACT FROM AUTHOR]

This paper focuses on changing patterns of substance use among low income, African American drug users evacuated from New Orleans, Louisiana, during Hurricane Katrina of August 2005. It examines the relationship between increases and decreases in alcohol and tobacco (AT) use and illicit drug (ID) use after Katrina and pre-disaster and within-disaster factors. Data from structured interviews with 200 Katrina evacuees currently living in Houston were collected 8–14 months after the disaster. Multivariate analysis revealed that rises in AT use were positively associated with education. Females and younger evacuees were more likely to have increased AT use. ID use increase was positively associated with resource loss and leaving the city before Katrina. Decreases in AT and ID use were found to be associated with disaster-related exposure. The paper discusses the specific consequences of disasters on disadvantaged minority substance users and the importance of developing public health disaster policies that target this population. [ABSTRACT FROM AUTHOR]