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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]

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]

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]

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]

• 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]

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management. • We compare the performance of the GA and PS methods for forecasting optimal flow releases in a multi-storage system for flood control. • The results of the GA and PS methods are very similar, however the run time required by PS is significantly smaller than that required by GA. • Dynamic water management according to the optimization results can significantly mitigate flooding compared to the case without management. • A key factor for flood control is to partially empty the storage systems before the rainfall event and during the initial period of rainfall. [ABSTRACT FROM AUTHOR]

This paper presents an integrated infrastructure-policy framework to analyze policy attention on addressing road infrastructure network vulnerability in terms of accessing critical facilities in the aftermath of a flood. Coping with network vulnerability, particularly physical access to various critical facilities and the services they provide, is an essential step in achieving a resilient community. However, the extent to which the network of local plans addresses such vulnerability remains unclear. To bridge this gap, this paper uses the Plan Integration for Resilience Scorecard method to examine the infrastructure-related policy attention in relation to community vulnerability vis-a-vis disrupted access to critical facilities. The proposed framework is tested in a set of super neighborhoods in Houston, Texas. Findings reveal a discrepancy between the policy effort and network vulnerability and identifies strengths and weaknesses of various plans in addressing disrupted access to critical facilities. The framework introduced in this paper provides a tool for stakeholders to evaluate an existing network of plans and identify gaps for future resilience improvement. • Network analysis reveals disparities in community post-disaster access to critical facilities. • Resilience scorecard method can help examine the infrastructure-related policy attention. • Analysis show that policy scores do not target vulnerable districts with low critical facility access. • This paper enables an infrastructure-plan integration analysis framework. [ABSTRACT FROM AUTHOR]

To predict atmospheric conditions in an urban environment, the land surface processes must be accurately described through the use of detailed land use (LU) and land cover (LC) data. Use of the U.S. Geological Survey (USGS) 25-category data, currently in the Fifth-generation Mesoscale Model (MM5), with the Noah land surface model (LSM) and MRF (medium-range forecast) planetary boundary layer (PBL) schemes resulted in the over-prediction of daytime temperatures in the Houston downtown area due to the inaccurate representation as a completely impervious surface. This bias could be corrected with the addition of canopy water in the urban areas from the evapotranspiration effects of urban vegetation. A more fundamental approach would be to utilize an LULC dataset that represents land surface features accurately. The Texas Forest Service (TFS) LULC dataset established with the LANDSAT satellite imagery correctly represents the Houston–Galveston–Brazoria (HGB) area as mixtures of urban, residential, grass, and forest LULC types. This paper describes how the Noah LSM and PBL schemes in the MM5 were modified to accommodate the TFS-LULC data. Comparisons with various meteorological measurements show that the MM5 simulation made with the high resolution LULC data improves the boundary layer mixing conditions and local wind patterns in the Houston Ship Channel, which is a critically important anthropogenic emission area affecting the HGB air pollution problems. In particular, when the synoptic flows are weak, the improved LULC data simulates the asymmetrically elongated Houston heat island convergence zone influencing the location of the afternoon Gulf of Mexico sea-breeze front and the Galveston Bay breeze flows. This paper is part I of a two-part study and focuses on the meteorological simulation. In part II, effects of using the different meteorological inputs on air quality simulations are discussed. [Copyright &y& Elsevier]

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas. [ABSTRACT FROM AUTHOR]

Confectionery stoving is the process used to produce soft-jelled candies. The stoving process has many steps, but the most energy-intensive step is heating and drying the candies, which can consume up to 84% of the energy at a factory. Drying is achieved by heating and ventilating the candy stove with large volumes of outside air, so the impact of the local climate on confectionery facilities can be significant. However, there is a lack of literature investigating local climate impact on the stoving process. This work demonstrates how Multiobjective Optimization (MOO) can be used to find a balanced solution between first cost and energy usage. Furthermore, the work investigates local climate impact to study whether strategic facility siting in a specific climate zone can significantly reduce energy needs and installation costs. The paper proposes a generic energy model that uses actual meteorological data and a product-driven recipe for calculating the energy requirement of a confectionery stove. Four distinct climate zones were analyzed, and it was found that operating a stove in a dry, hot climate, such as Las Vegas, requires 15% less energy than a hot-humid climate, such as Houston. The energy model incorporates a Multiobjective Evolutionary Algorithm (MOEA) procedure to minimize energy usage and initial cost. The algorithm varies the airflow rate and percentage of outdoor air used in the stove. A Pareto Front search procedure is defined, which compares all solutions against a "base case" solution. The base case can be the minimum of any objective, but for this work, it is the lowest energy consuming solution. The search procedure found a preferred solution where a small increase in energy consumption, 0.2%, reduces first cost by 12%–45%. The methods outlined in this paper apply to any energy-consuming manufacturing process that is influenced by climate or any such process that has a broad set of functional operating parameters. • A new recipe driven energy model for confectionery stove was developed. • Multi-objective optimization was used to select system parameters. • Pareto front search procedure demonstrates trade-offs for determining preferred solution. • Investigation of local climate shows significant impact on confectionery stove energy consumption and system cost. • Highly efficient stoving system may cost little more than a traditional system. [ABSTRACT FROM AUTHOR]

Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. • The study developed a method that integrates computer vision and flow direction model to assess surface drainage condition. • This study applied the developed method to 50 street blocks in a neighborhood in Houston, Texas. • The developed model can identify key drainage and land features within a street block with an accuracy of 88.5%. • The results can show problematic areas susceptible to water ponding and inform drainage maintenance decisions. [ABSTRACT FROM AUTHOR]

This paper presents a MATLAB framework for forecasting optimal flow releases in a multi-storage system for flood control. This framework combines four widely-used models intended for (1) performing hydrologic analysis for a watershed and for level-pool routing in the storage systems, (2) simulating river inundation, (3) solving the optimization problem of determining hourly optimal flow releases in a multi-storage system, and (4) data management and plotting. The integration of all software is performed in MATLAB, which is a state-of-the-art and an easy-to-use environment for integrating computation, visualization, and programming. This paper focuses on (1) presenting the MATLAB scripts for interfacing the aforementioned four software, (2) describing the rationale for the objective function of the optimization, and (3) demonstrating the practical use of the MATLAB framework by applying it to the operation of a hypothetical eight-pond system in the Cypress Creek watershed in Houston, Texas. The results of the aforementioned application show that this framework could help in mitigating flooding. • This paper presents MATLAB scripts for interfacing four models of hydrology, hydraulics, optimization and data management. • The MATLAB scripts are exemplified through the flood control operation of a hypothetical eight-pond system. [ABSTRACT FROM AUTHOR]

• The scheduling model for seaport integrated energy system is proposed accounting of the operating flexibility of berth allocation problems, reefer area and cold-ironing. • The energy models of BAP, reefer area and cold-ironing management are presented in the first time, which is the basic models for the demand response research of seaport integrated system. • Dynamic pricing mode is incorporated with the proposed scheme for seaport. • The effectiveness of the proposed scheme in seaport energy management and cost reduction is verified with real-world data. As a bridge to connect the land and sea, the emissions within a seaport have significant effects on the urban environment. In order to reduce gas emissions, electrification of seaports has been a promising trend. Besides the integrated thermal network for cold-chain supply, the future seaport can be viewed as a transportation integrated energy system, and the coordination between the shipside and portside will highly influence the system behaviors of seaport. In this paper, the energy models of two basic ship-port coordination, i.e., on-shore power supply management (cold-ironing) and berth allocation are proposed, and an integrated energy system scheduling model is proposed to simultaneously meet the electric and thermal power demands. The port of Houston is set as the test case to show the validity of proposed method and the simulation results demonstrate berth allocation can provide adequate flexibility to facilitate the seaport operation. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the U-turn made by ships far away from their destination docks (called off-destination-dock U-turns) in the narrow waterway using automatic identification system (AIS) data. Such U-turns are time-consuming and would occupy a long section of channel, causing the travel delay for other vessels. The Houston Ship Channel (HSC) was used as the case study. The purpose of the study was two-folded. First, for each cargo or tanker having trips in the channel, we aimed to build a methodological framework and corresponding solution algorithms to capture significant directional changes of a ship's trajectory so that the calculation could be based on much fewer points for U-turn analysis. Second, the proposed algorithms were applied to the HSC to identify the sections frequently used by those off-destination-dock U-turns. In a narrow busy waterway, cargos and tankers, may be forced to wait or make the U-turn at predetermined sections of channel due to their larger length and/or width. Identifying such sections (i.e., "hotspots") can be useful for future channel developments and daily traffic management. The proposed methods and algorithms are generalized, so that they can be also applied to other narrow waterways in the same or similar situations. • A novel model was proposed to identify vessels' U-turn behavior based on AIS data. • The solution algorithm was proposed and tested efficiently using real AIS data of 6 months. • Hot spots for U-turn in the Houston Ship Channel (HSC) were identified and analyzed. [ABSTRACT FROM AUTHOR]

Hurricanes can have adverse effects on residential communities and pose a significant risk to their economic prosperity. The ingress of water into a building due to wind-driven rain (WDR) and inland flooding can cause significant damage leading to downtime or temporary loss of housing. Existing frameworks focus on estimating the amount of water ingress due to WDR and inland flooding separately. This paper provides a comprehensive framework that considers both WDR and inland flooding when estimating the amount of water ingress into residential buildings due to hurricane rainfall. The framework estimates the water ingress due to WDR by combining the WDR intensity with the perforated area of the building envelopes. The intensity of the WDR is quantified using an Eulerian Multi-phase Model. The buildings' envelope is considered susceptible to damage from the impact of windborne debris and excessive dynamic wind pressure. The framework to characterize and quantify inland flooding uses a coupled hydrologic-hydrodynamic model to estimate the inundation depth at each building. A case study consisting of a residential community in Houston, TX, which is subject to Hurricane Harvey illustrates the ability of the framework to capture the influence of WDR and inland flooding when quantifying water ingress. • A framework is presented for building water ingress estimation due to hurricane rainfall. • Both wind driven rain and inland flooding are considered simultaneously. • Wind driven rain is modeled through an Eulerian multiphase model. • Inland flooding is estimated using a coupled hydrologic-hydrodynamic model. • The importance of both WDR and inland flooding when estimating losses is shown. [ABSTRACT FROM AUTHOR]

Abstract: The remotely sensed land surface temperature (LST) is widely used in studying the surface urban heat island (SUHI). Due to the influence of clouds, composite products usually are preferred; however, exactly what the impact of temporal aggregation is on LST and SUHI is still unknown. In this paper, we quantify this impact focusing on Houston, Texas and its surroundings using MODIS LST products from 2000 to 2010. The results show that 1) the SUHI values are more notably enhanced in the daytime than nighttime with an increasing trend with larger composite periods. 2) The influences of aggregation in the spring and summer are larger than autumn and winter for the daytime. 3) The temporal aggregation impacts the spatial pattern of the SUHI implying that the higher SUHI regions are more likely to have a larger gap between two composite scales and this is related to the amount and distribution of clouds. [Copyright &y& Elsevier]

Abstract: Surface deformation in the Houston area has been quantified by using a variety of methods including LIDAR, InSAR, extensometers, drilling (to approximately 100m), and Ground Penetrating Radar. In this paper we report on GPS data acquired during the period between 1995 and 2005 that found evidence of ongoing subsidence (up to −56mm/year) in northwestern Houston and of possible horizontal surface movement towards the Gulf of Mexico (up to 6mm/year). We describe the methods of data-processing used in the study and speculate on the possibility that the active elevation of salt domes, mainly at the south and east of the city, may indirectly influence other surface movements including fault movements and subsidence over areas >1km2. Making use of our observations and analysis could help in natural hazard mitigation in the Houston area and possibly also indicate approaches to surface subsidence study that might be used in other urban areas. [Copyright &y& Elsevier]

Abstract: A previous hypothesis advanced from observational studies such as METROMEX suggests that the intensity, frequency, and organization of cumulus convection may be impacted by the forcing of enhanced merger activity downstream of urban zones. A resulting corollary is that cities may exert an indirect anthropogenic “forcing” of parameters related to convection and associated phenomena such as lightning and precipitation. This paper investigates the urban-merger hypothesis by examining the role of convective cell mergers on the existence and persistence of the Houston lightning “anomaly”, a local maximum in cloud-to-ground (CG) lightning activity documented to exist over and east of Houston. Using eight summer seasons of peak columnar radar reflectivity, CG lightning data and a cell-tracking algorithm, a two-dimensional cell merger climatology is created for portions of Eastern Texas and Louisiana. Results from the tracking and analysis of over 3.8 million cells indicate that merger-driven enhancements in convection induce a positive response (O 46%) in ground flash densities throughout the domain, with areas of enhanced lightning typically being co-located with areas of enhanced merger activity. However, while mergers over the Houston area (relative to elsewhere in the domain) do result in more vigorous convective cells that produce larger CG flash densities, we find that CG lightning contributions due to mergers are distributed similarly throughout the domain. Hence while we demonstrate that cell mergers do greatly impact the production of lightning, the urban cell merger hypothesis does not uniquely explain the presence of a local lightning maximum near and downstream of Houston. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to investigate the effects of the growth of the Houston metropolitan area on the characteristics and intensity of convection and precipitation. For this purpose, we implemented the Town Energy Budget (TEB) urban model into Regional Atmospheric Modeling System (RAMS). The Landsat Thematic Mapper National Land Cover Data (NLCD) for the Houston area corresponding to the years 1992, 2001 and 2006 were used for an objective experimental design of land-use sensitivity experiments. We analyzed the impact on two distinct groups of convective cells triggered by the sea breeze circulation on August 24 2000 and compared the model simulated precipitation to RADAR data. The first group of storms occurred southwest of the city and was not influenced by the urban cloud condensation nuclei (CCN), while the second was and occurred north of the city (downwind) a few hours later. The effect of land-use on convection and precipitation was dramatic for the storms SW of the city and it was linked to a monotonic increase in the intensity of the sea breeze. The intensification of the sea-breeze circulation when using the 1992, 2001, and 2006 NLCD land use datasets generated a monotonic increase of the total precipitated volume of 9, 11, and 25%, respectively (over a run with no city). Due to increased exposure to aerosols, the upper levels of the convective cells downwind of the city were invigorated by a greater latent heat release linked to higher amounts of liquid water transported to supercooled levels. However, precipitation did not show a monotonic behavior when we varied the intensity of the urban aerosol sources. With the highest aerosol concentrations, riming growth of ice particles became so inefficient that larger amounts of condensate was transported upwards into the storm anvil, contributing to a reduction in precipitation efficiency. [Copyright &y& Elsevier]

Abstract: Houston is the only major city in North America without zoning. The growth of Houston illustrates a traditional free market philosophy in which land use zoning is seen as a violation of private property and personal liberty. This paper explores how the lack of zoning has an impact on land use controls and urban development in Houston. Using a theoretical framework derived from institutional economics and public choice theories for institutional analyses of land development controls, it explores how local land use policies made by both the local government and non-governmental sectors shape urban development in Houston. The study results show that despite the city’s lack of zoning, local land use regulatory policies made by the municipality have significant influence on urban development. Additionally, civic and private organizations such as super neighborhoods and homeowner associations fill the gaps left by the lack of land use zoning. These two aspects contribute to land use controls and urban development of the city. The study finds that land use controls by private contract and by government legislative intervention are not mutually exclusive or immutable; that equity goals are not met in market approaches, and public planning intervention is necessary; and that deed restrictions might be better at facilitating property sales and maintenance than at improving neighborhood welfare and governance. [Copyright &y& Elsevier]

Abstract: This paper is aimed to introduce an approach for evaluating compressional and shear wave velocities via the neutron, density, gamma ray logs and poissons ratio (σ ) in a very simple way for different types of lithologies provided that this approach assumed matrix to be known from any one of the sources employed for such objectives. The approach taken in this present study based on the well known Dresser Atlas [Dresser Atlas, 1979: Log Interpretation Charts. Dresser Industries Inc., Houston, Texas, 107p.] equations for computing porosity. This can be achieved by merging such equations with the well known effective porosity equation in order to end up with a formula for both compressional and shear wave velocities as a function of neutron, density, shale volume poissons ratio; taking into account the effect of both matrix and fluid nature. A number of measurements, including compressional, shear and all elastic moduli, are used to test the proposed approach and compare its results with those obtained. The success of such comparison give us the opportunity to apply the suggested formula to real field data in the northern part of the Gulf Of Suez basin. The results of both test and application came in a good agreement and the small existing error can be negligible. [Copyright &y& Elsevier]

Abstract: Scientists and educators at Baylor College of Medicine are using space life sciences research areas as themes for middle school science and health instructional materials. This paper discusses study findings of the most recent unit, Food and Fitness, which teaches concepts related to energy and nutrition through guided inquiry. Results of a field test involving more than 750 students are reported. Use of the teaching materials resulted in significant knowledge gains by students as measured on a pre/post assessment administered by teachers. In addition, an analysis of the time spent by each teacher on each activity suggested that it is preferable to conduct all of the activities in the unit with students rather than allocating the same total amount of time on just a subset of the activities. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of the hybrid control of autonomous vehicles driving on automated highways. Vehicles are autonomous, so they do not communicate with each other nor with the infrastructure. Two problems have to be dealt with: a vehicle driving in a single-lane highway must never collide with its leading vehicle; and a vehicle entering the highway at a designated entry junction must be able to merge from the merging lane to the main lane, again without any collision. To solve these problems, we equip each vehicle with a hybrid controller, consisting of several continuous control laws embedded inside a finite state automaton. The automaton specifies when a given vehicle must enter the highway, merge into the main lane, yield to other vehicles, exit from the highway, and so on. The continuous control laws specify what acceleration the vehicle must have in order to avoid collisions with nearby vehicles. By carefully designing these control laws and the conditions guarding the automaton transitions, we are able to demonstrate three important results. First, we state the initial conditions guaranteeing that a following vehicle never collides with its leading vehicle. Second, we extend this first result to a lane of autonomous vehicles. Third, we prove that if all the vehicles are equipped with our hybrid controller, then no collision can ever occur, and all vehicles either merge successfully or are forced to drop out when they reach the end of their merging lane. Finally, we show the outcome of a highway microsimulation modelled after the Katy Corridor near Houston, Texas: our single-lane highway can accommodate 4000 vehicles per hour with neither drop-outs nor traffic congestion. It is entirely programmed in Shift, a hybrid systems simulation language developed at UC Berkeley by the PATH group. This shows that Shift is a well suited language for designing safe control laws for autonomous highway systems, among others. [Copyright &y& Elsevier]

The mission operations directorate (MOD) at the Johnson Space Center (JSC) is responsible for the planning and operation of human space flight missions. MOD is being challenged with sustaining and developing new operations capabilities to support increasingly demanding requirements and to improve its processes to accomplish these missions at higher levels of safety, mission success, and effectiveness. Automation is being considered as an enabling technology to meet the aforementioned challenges. The synergistic combination of flight controllers and intelligent software providing the function of ‘operations assistants’ (OA) is being pursued as the key implementation of this technology in the Mission Control Center (MCC).The flight control team (FCT) assesses the condition and operability of the major systems such as electrical power, thermal control, life support, communications, altitude control, and data handling at MCC. OA assist the flight controllers with their tasks of monitoring the status and health of the flight systems. They also help maintain the flight controller''s awareness of the operations being performed during the mission and help assure that operational objectives are being met. The station duty officer (SDO) performs the lead operations role for the International Space Station (ISS) during quiescent times when FCT and the flight director are off-duty. SDOs assess the condition and operability of the major ISS systems. This assessment involves monitoring and controlling several periodic processes on systems such as the electrical power, thermal control, life support, communications, altitude control, and data handling systems. The SDO is also responsible for coordinating operations with the Russian FCT. The OA will help the SDO maintain an awareness of all the processes performed on board and will assist with the responses to anomalous conditions. The OA for this position will support the concept of reduced control center staffing during quiescent times. The purpose of this paper is twofold: (1) to present a unique two-stage specification methodology that combines data flow diagrams and petri nets and (2) to apply the proposed methodology in a complex space station system. [Copyright &y& Elsevier]

While it is well known where the best entrepreneurial ecosystems can be found in general, such information is missing in the case of sustainability entrepreneurial ecosystems. These are entrepreneurial ecosystems with high shares of startups that are active in industries which address the Sustainable Development Goals through innovation. Not knowing what the best sustainability entrepreneurial ecosystems are, inhibits sustainability startups to locate to regions with the most favourable supportive conditions. This paper addresses the above-mentioned gap by establishing a ranking of entrepreneurial ecosystems according to their share of sustainability enterprises. For this purpose, we have analysed the websites of 19,997 startups in the 28 largest entrepreneurial ecosystems according to the Startup Genome project using Latent Dirichlet Allocation. We find that the highest proportion of sustainability startups among these entrepreneurial ecosystems is located in Boston, followed by Houston, Seattle and Lagos, respectively. A qualitative comparative analysis of the causal patterns underlying our results reveals that high GDP in combination with either (1) high shares of female founders of startups or (2) high shares of non-religious people in the population induce entrepreneurial ecosystems with relatively high levels of sustainability enterprises. Our work aims to stimulate further scholarly interest in sustainability-oriented entrepreneurial ecosystems. Policy makers can use the causal drivers to increase the share of sustainability startups in their region. [ABSTRACT FROM AUTHOR]

• COVID-19 is a viral disease caused by SARS-CoV-2. • Twenty-three autopsy cases demonstrate that COVID-19 is a systemic disease with major pulmonary and cardiac manifestations. • COVID-19 produces an acute interstitial pneumonia, usually with a prominent diffuse alveolar damage (DAD) component, often coupled with a thrombotic microangiopathy. • The heart frequently shows acute cardiomyocyte injury and, in some cases, pericarditis and/or myocarditis. • Patients with fatal COVID-19 frequently are obese and have pre-existing cardiac disease, hypertension and/or diabetes mellitus. This paper collates the pathological findings from initial published autopsy reports on 23 patients with coronavirus disease 2019 (COVID-19) from 5 centers in the United States of America, including 3 cases from Houston, Texas. Findings confirm that COVID-19 is a systemic disease with major involvement of the lungs and heart. Acute COVID-19 pneumonia has features of a distinctive acute interstitial pneumonia with a diffuse alveolar damage component, coupled with microvascular involvement with intra- and extravascular fibrin deposition and intravascular trapping of neutrophils, and, frequently, with formation of microthombi in arterioles. Major pulmonary thromboemboli with pulmonary infarcts and/or hemorrhage occurred in 5 of the 23 patients. Two of the Houston cases had interstitial pneumonia with diffuse alveolar damage pattern. One of the Houston cases had multiple bilateral segmental pulmonary thromboemboli with infarcts and hemorrhages coupled with, in nonhemorrhagic areas, a distinctive interstitial lymphocytic pneumonitis with intra-alveolar fibrin deposits and no hyaline membranes, possibly representing a transition form to acute fibrinous and organizing pneumonia. Multifocal acute injury of cardiac myocytes was frequently observed. Lymphocytic myocarditis was reported in 1 case. In addition to major pulmonary pathology, the 3 Houston cases had evidence of lymphocytic pericarditis, multifocal acute injury of cardiomyocytes without inflammatory cellular infiltrates, depletion of splenic white pulp, focal hepatocellular degeneration and rare glomerular capillary thrombosis. Each had evidence of chronic cardiac disease: hypertensive left ventricular hypertrophy (420 g heart), dilated cardiomyopathy (1070 g heart), and hypertrophic cardiomyopathy (670 g heart). All 3 subjects were obese (BMIs of 33.8, 51.65, and 35.2 Kg/m2). Overall, the autopsy findings support the concept that the pathogenesis of severe COVID-19 disease involves direct viral-induced injury of multiple organs, including heart and lungs, coupled with the consequences of a procoagulant state with coagulopathy. [ABSTRACT FROM AUTHOR]

Roadside channel systems are critical for the management of stormwater runoff and the protection of the structural integrity of roads; and thus, require systematic evaluation and maintenance. However, the evaluation of these systems remains ad hoc due to the lack of efficient inspection methods. This paper contributes to filling this gap by providing an automated process for the inspection and evaluation of roadside channel systems using data obtained from mobile lidar (Light Detection and Ranging) scanners. The Cloth Simulation Filtering algorithm was employed to split lidar point clouds into bare earth and object datasets, and then compute six key attributes of roadside channels based on the bare earth dataset. The six attributes are: channel depth, bottom width, side slope, longitudinal slope, and length and density of subsurface pipes and culverts. To test and demonstrate the new automated process, it was applied to six street sections in the City of Houston, Texas. The computed attributes were compared to the design and maintenance guidelines set by the City of Houston and Harris County. The evaluation results indicate that every channel examined in this study has its own condition issues and improvement needs. While no channel section in this study was in full compliance with the guidelines, no channel was utterly incompatible either. The results show that the developed automated process can effectively and efficiently evaluate roadside channels, providing an alternative to conventional manual inspection methods. Future work could use the output of the developed method to assess the risk of localized flooding and inform both the municipal government and property owners about effective mitigation measures. • A method for automating the evaluation of roadside drainage channels in urban settings has been developed and demonstrated. • The automated evaluation method consists of computing six key attributes of roadside channels from mobile lidar raw data. • The potential for evaluating roadside channels at shorter distances to inform targeted improvements is presented. [ABSTRACT FROM AUTHOR]

This paper proposes an AIS-data-based model to accurately estimate vessels' travel time and its distribution between any two points in a narrow channel. It is crucial to know the travel time (plus its distribution) between some critical points for traffic control. The model has two components: (1) to identify a vessel's destination dock, and its arrival and departure times (the key is how to separate a vessel's trips); and (2) to estimate a vessel's travel time from its destination dock (or a specific point) to another specific point, or vice versa. Travel time between two points is called traversal time. The proposed algorithms were then applied to the Houston Ship Channel (HSC) with the AIS data of 11 months in 2017, focusing on the outbound trips in the section above the Beltway-8 Bridge. Through the travel times of cargos and tankers from their destination docks to a point are likely to be normally distributed, their traversal times between two points are more likely to be lognormally distributed. The traversal time distribution is significantly impacted by vessel width, draft and type, but independent from time-of-day and month. The adjacent link traversal times are also highly positively correlated. • A novel model was proposed to estimate vessels' travel times and their distributions. • The model can determine a vessel's destination dock, and the number of trips. • A vessel's trips are separated, with each trip's arrival and departure times identified. • Traversal time between two points is more likely to be lognormally distributed. • The factors impacting vessels' traversal time were studied in the Houston Ship Channel. [ABSTRACT FROM AUTHOR]

This paper presents an integrated framework that combines a community's physical vulnerability to access disruption to critical facilities and their tolerance for such access disruption to services in order to inform the targeted communities protection and build an equitable resilience enhancement plan. The first component of the proposed framework includes a percolation simulation model capable of integrating the road network disruption probability into the flood propagation and encapsulates the road network's access to critical facilities (i.e., healthcare facilities). The discovered spatial reach of an areas' physical vulnerability dependence is 9 miles. Besides, physical disruptions in road networks and loss of access to emergency services (such as healthcare) have varying impacts on different sub-populations. To consider this aspect, the second component of the proposed framework involves a disruption tolerance index (DTI) to examine communities' tolerance towards access disruption to healthcare facilities in the face of the flooding. The proposed framework recognizes the importance of both infrastructure and human perspective of the vulnerability assessment and is tested using empirical data from Harris County, Texas, in the case of road network disruptions due to fluvial flooding. Houston, the fourth-largest city in the United States, is within Harris County. Integrated spatial analysis result reveals different spatial clusters of vulnerability across the study region and provides important insights regarding the critical infrastructure protection prioritization and hazard mitigation planning. The spatial clusters also unveil the existence of a homogeneous spatial pattern where similar vulnerable areas stay together. The proposed framework could be adopted by other cities and different critical facilities to enable decision-makers, infrastructure managers, and city planners to better evaluate their community vulnerability. [ABSTRACT FROM AUTHOR]