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Community recovery from hazards occurs through various diffusion processes within social and spatial networks of communities. Existing knowledge regarding the diffusion of recovery in community socio-spatial networks, however, is rather limited. To bridge this gap, we created a network diffusion model to characterize the unfolding of population activity recovery in spatial networks of communities. In particular, this study aims to answer the research question "To what extent can the diffusion model capture the spatial patterns of recovery?" Using population activity recovery data derived from location-based information associated with 2017 Hurricane Harvey in the Houston area, we parameterized the threshold-based network diffusion model using the genetic algorithm and then simulated the recovery diffusion process. The results show that the spatial effects of recovery are rather heterogeneous across different areas; some spatial areas demonstrate a greater spatial effect in their recovery. Also, the results show that low-income and minority areas are community recovery multipliers; with faster recovery in these areas corresponding to accelerated recovery for the entire community. Hence, prioritizing these areas in resource allocation during recovery has the potential to accelerate could expedite the recovery of the entire community's recovery process while promoting recovery equality and equity. [ABSTRACT FROM AUTHOR]

In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topological network properties indicate that the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities. [ABSTRACT FROM AUTHOR]

In this study, the potassium adsorption for the purification of crude ammonium perrhenate solutions in the presence of various cheap and available ion exchange resins is reported for the first time. After the resin screening test, the optimum resin (Purolite® C100 resin as a strong acid cation ion exchange resin in the sodium form) was selected for morphological analysis using field emission scanning electron microscopy, which was further examined by energy-dispersive spectroscopy. Moreover, X-ray elemental mapping of the surface of C100 resin revealed homogeneous potassium adsorption. BET and TGA tests confirmed the adsorbed potassium on the C100 resin. Among the isotherm models, the Langmuir isotherm provided the best fit according to the correlation coefficients. The adsorption parameters have been scrutinized, including contact time, initial potassium concentration, temperature, and competitive binary potassium and sodium adsorption. The result of the binary system exhibited a decrease in the potassium adsorption capacity of the resin in the presence of secondary ions due to competition for adsorption sites. Kinetic characteristics of the potassium adsorption process were investigated by pseudo-first-order and pseudo-second-order, intraparticle diffusion, and Elovich models. The adsorption process has been found to match the pseudo-first-order reaction. The continuous/extended experiments and the elution test were also evaluated. [ABSTRACT FROM AUTHOR]

We present a latent characteristic in socio-spatial networks, hazard-exposure heterophily, to capture the extent to which populations with dissimilar hazard exposure could assist each other through social ties. Heterophily is the tendency of unlike individuals to form social ties. Conversely, populations in hazard-prone spatial areas with significant hazard-exposure similarity, homophily, would lack sufficient resourcefulness to aid each other to lessen the impact of hazards. In the context of the Houston metropolitan area, we use Meta's Social Connectedness data to construct a socio-spatial network in juxtaposition with flood exposure data from National Flood Hazard Layer to analyze flood hazard exposure of spatial areas. The results reveal the extent and spatial variation of hazard-exposure heterophily in the study area. Notably, the results show that lower-income areas have lower hazard-exposure heterophily possibly caused by income segregation and the tendency of affordable housing development to be located in flood zones. Less resourceful social ties in hazard-prone areas due to their high-hazard-exposure homophily may inhibit low-income areas from better coping with hazard impacts and could contribute to their slower recovery. Overall, the results underscore the significance of characterizing hazard-exposure heterophily in socio-spatial networks to reveal community vulnerability and resilience to hazards. [ABSTRACT FROM AUTHOR]

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public's hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents' preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points-of-interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents' hurricane preparedness. Four categories of points-of-interest, grocery stores, gas stations, pharmacies and home improvement stores, were identified as they have close relationship with hurricane preparedness, and the daily number of visits from each CBG to these four categories of POIs were calculated during preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, CBGs in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents' disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner. [ABSTRACT FROM AUTHOR]

In a new approach, we considered the special affinity between Ni and poly-histidine tags of recombinant urate oxidase to utilize Ni-MOF for immobilizing the enzyme. In this study, a carbon paste electrode (CPE) was modified by histidine-tailed urate oxidase (H-UOX) and nickel-metal–organic framework (Ni-MOF) to construct H-UOX/Ni-MOF/CPE, which is a rapid, sensitive, and simple electrochemical biosensor for UA detection. The use of carboxy-terminal histidine-tailed urate oxidase in the construction of the electrode allows the urate oxidase enzyme to be positioned correctly in the electrode. This, in turn, enhances the efficiency of the biosensor. Characterization was carried out by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET), and field emission scanning electron microscopy (FE-SEM). At optimum conditions, the biosensor provided a short response time, linear response within 0.3–10 µM and 10–140 µM for UA with a detection limit of 0.084 µM, repeatability of 3.06%, and reproducibility of 4.9%. Furthermore, the biosensor revealed acceptable stability and selectivity of UA detection in the presence of the commonly coexisted ascorbic acid, dopamine, L-cysteine, urea, and glucose. The detection potential was at 0.4 V vs. Ag/AgCl. [ABSTRACT FROM AUTHOR]

One of the new electrochemical sensors has been devised and fabricated for the highly sensitive determination of venlafaxine on the basis of ZnO nanoflowers (ZnO-NF) as modified materials. Therefore, hydrothermal methods have been used to synthesize zinc oxide nanoflowers. The formation of ZnO nanoflower has been confirmed via Fourier transform infrared (FTIR), XRD, and FESEM. Owing to the ZnO nanoflowers with the greater surface areas to oxidation of venlafaxine, venlafaxine has been sensitively determined with the linear ranges between 0.04 and 475.0 µM and LOD equal to 0.01 µM (S/N = 3). This new technique has been substantially utilized to determine venlafaxine in real samples. [ABSTRACT FROM AUTHOR]

The major problems of the semiconductor nanomaterials in the photocatalytic applications for degradation of organic contaminants are fast recombination of photogenerated electron-hole pairs and low visible light harvesting. Therefore, we tried to resolve these drawbacks by coupling LaFeO 3 nanoparticles with Bi 2 WO 6 nanoflakes and constructing of binary Z-scheme heterojunction through the facile solvothermal synthesis approach. The photocatalytic activity of the LaFeO 3 /Bi 2 WO 6 heterojunction was studied through degradation of tetracycline (TC) upon visible light irradiation. The effects of experimental parameters, including LaFeO 3 /Bi 2 WO 6 dosage, initial concentration of TC, visible light irradiation time, and solution pH on the photocatalyst performance of binary nanocomposite were analyzed by response surface methodology. The results of analysis of variance confirmed that these parameters have a significant effect on the TC degradation efficiency. The value of the first-order kinetic rate constant for removal of TC using LaFeO 3 /Bi 2 WO 6 was calculated 0.1291 min−1, which was 20 and 10 times higher than pure LaFeO 3 and Bi 2 WO 6 , respectively. Besides, LaFeO 3 /Bi 2 WO 6 was shown the excellent photocatalytic stability after 4 cycles. Furthermore, the photocatalytic pathway for photodegradation of TC by binary LaFeO 3 /Bi 2 WO 6 nanocomposite was proposed according to the trapping experiments of active species and the results were signify that the h+ and . OH are the predominant reactive species for the photodegradation of TC. Overall, we believe that current novel research opens a promising route for the preparation of highly efficient LaFeO 3 /Bi 2 WO 6 photocatalyst and its usage for wastewater treatment. [ABSTRACT FROM AUTHOR]

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such as motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks. [ABSTRACT FROM AUTHOR]

This paper presents damage assessment using a hierarchical transformer architecture (DAHiTrA), a novel deep‐learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real‐time and high‐coverage information and offers opportunities to inform large‐scale postdisaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer‐based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the feature domain after applying a transformer encoder to the spatial features. The proposed network achieves state‐of‐the‐art performance when tested on a large‐scale disaster damage data set (xBD) for building localization and damage classification, as well as on LEVIR‐CD data set for change detection tasks. In addition, this work introduces a new high‐resolution satellite imagery data set, Ida‐BD (related to 2021 Hurricane Ida in Louisiana) for domain adaptation. Further, it demonstrates an approach of using this data set by adapting the model with limited fine‐tuning and hence applying the model to newly damaged areas with scarce data. [ABSTRACT FROM AUTHOR]

Deviation from the traction path affects the operation of composting machines that work in an offset manner and reduces their performance. In this article, a compost mixing machine behind a tractor, which deviates from the movement path during operation, was investigated and evaluated. The equations governing the performance of the extraction machine and the effective parameters in the optimal performance of the device were determined and their effect on the deviation of the device was investigated. The results of this research show that by changing the parameters of the machine without increasing its weight in order to reduce the deviation from the movement path, it is possible to significantly increase the balance of the machine and work efficiency and reduce the deviation from the movement path. In this investigation, it was found that by changing several parameters such as the longitudinal and transverse distance of the composting machine, the friction coefficient, etc., the deviation angle of the machine decreases from 17 degrees to less than 4 degrees. Based on the theory presented in this article, the device was designed, built and used in Gulab Zahra Kerman Company, and its performance is satisfactory, and the practical review of each of the parameters presented in this article will be presented in other articles. [ABSTRACT FROM AUTHOR]

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood. [ABSTRACT FROM AUTHOR]

Adsorption process is an effective and promising procedure for the removal of contaminations from wastewater. Nanocomposites have attracted extensive attention in the adsorption field owning to large surface/volumes ratio and large number of active sites. We aim at fabricating a novel binary g‐C3N4/ZnV2O4 nanocomposite, which can be used for removal of crystal violet dye from aqueous solution via the adsorption process. The as‐synthesized nanocomposite was well characterized by X‐ray diffraction (XRD), Fourier‐transform infrared spectroscopy (FTIR), field emission scanning microscope (FESEM), energy‐dispersive X‐ray (EDX), and mapping analyses. The effects of four key experimental parameters on the adsorption process, including adsorbent dosage, initial concentration of crystal violet dye, adsorption time, and solution pH, were investigated by using central composite design‐based response surface methodology. The high adsorption efficiency of 99.67% was obtained at the adsorbent dosage of 19.776 mg, initial crystal violet concentration of 20.048 ppm, contact time of 59.20 min, and pH of 8. The correlation coefficients values (R2 of 0.9969, predicted‐R2 of 0.9910, and adjusted‐R2 of 0.9948), model p value = 0.0001, and model F value = 489.78 confirm the adequacy and predictability of the proposed model. The results of adsorption kinetics demonstrate that the adsorption of crystal violet by g‐C3N4/ZnV2O4 was performed by chemical adsorption. Also, the Langmuir model was fitted effectively with the adsorption data. Furthermore, the mechanism of adsorption was explained via π–π stacking interaction, n–π stacking interaction, electrostatic attraction, coordination bonding, and hydrogen bonding. [ABSTRACT FROM AUTHOR]

Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner. [ABSTRACT FROM AUTHOR]

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner. [ABSTRACT FROM AUTHOR]

The development of new technologies for the efficient degradation of toxic organic pollutants from water resources has received great attention. In this study, we described an effective method for the preparation of Pd nanoparticles (NPs) decorated on nitrogen rich ionic liquid (IL) modified magnetic nanoparticles and its application as the green and recoverable heterogeneous catalyst in the reductive degradation of organic dyes and reduction of nitro compounds. The Fe3O4/SiO2−IL−Pd nanocatalyst was characterized by several techniques namely, FT‐IR, FE‐SEM, EDX‐ elemental mapping, XRD, TEM, XPS, TGA and VSM. The Fe3O4/SiO2‐IL−Pd nanocatalyst was applied for the efficient reduction of structurally different nitroarenes, methyl orange (MO), methylene blue (MB) and methyl red (MR) in aqueous media using NaBH4 as reducing agent at room temperature. Using this catalyst nitroarenes were reduced to corresponding amines very efficiently and organic dyes reduced quantitatively under short reaction times. The catalyst can be recovered and recycled for at least ten runs without a notable decrease in the activity. [ABSTRACT FROM AUTHOR]

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction. • Interpretable machine learning models are utilized to provide reliable prediction of flash flood hotspots. • Built environment variables are extracted for constructing model features. • The models can achieve very good accuracy in identifying flooded/non-flooded locations. • Hydrological and topological features have greater impacts on flash flood risk than other features • Localized specifications of models are needed for accurate flash flood prediction for particular cities [ABSTRACT FROM AUTHOR]

Inequality in cities is a phenomenon arising from the complex interactions among urban systems and population activities. Conventional statistics and mathematical models like multiple regression models require assumptions of feature interactions with specified mathematical forms that may fail to fully capture complex interactions of heterogeneous urban components, creating challenges in systematically assessing socio‐economic inequality in cities. To overcome the limitations of these conventional mathematical models, in this work, we propose an interpretable machine learning model to capture the complex interactions of urban variables and the main interaction effects on socio‐economic statuses. We extract urban features from high‐resolution anonymized mobile phone data with billions of activity records related to people and facilities in 47 US metropolitan areas and predict the attributes of urban areas from six income and race groups. We show that socio‐economic inequality in cities can be effectively measured by the predictability of trained machine learning models in controlled experiments. We also examine the tradeoff between spatial resolution, sample size, and model accuracy; test the presence of influential features; and measure the transferability of the trained models to identify the optimal values for controlled factors. The results show that metropolitan areas share similar patterns of inequality, which could be moderated by improved polycentric facility distribution and road density. The generality of associated factors and transferability of machine learning models can help bridge data gaps between cities and inform about inequality alleviation strategies. Despite similarities, 50% to 90% of variations among cities are still present, which shows the need for localized policies for inequality alleviation and mitigation. Our study shows that machine learning models could be an effective approach to examine inequality, which opens avenues for more data‐centric and complexity‐informed planning, design, policymaking, and engineering toward equitable cities. [ABSTRACT FROM AUTHOR]

Traffic congestion occurs daily but the transportation network still functions to meet people's travel needs. We propose that a road meets operation requirements as long as the quality of its links (i.e., speed or travel time) satisfies an acceptable threshold. This paper incorporates the link reliability concept into the road travel performance using the link quality threshold to offer new perspectives on network resilience measurement. We introduce two aggregated macroscopic metrics: network reliability scale index, and network stability, based on the link reliability to quantify the road travel performance change in facing external disturbance. We use the temporal traffic-embedded Harris County, TX transportation network during the 2017 Hurricane Harvey as a case study. We show that the proposed metrics can well capture the transportation network performance variation during flooding. Also, we develop an integrated resilience metric that encapsulates the network resistance, recoverability, and rapidity in facing flooding. The results move us closer to better understanding transportation network resilience behavior in different link quality conditions (q). The findings of this research also provide important insights for city planners and traffic operators to examine transportation network resilience through a reliability and stability lens. • Empirical traffic during disaster reveals unique reliability and stability pattern. • Link reliability captures transportation network resilience reduction and recovery. • Distribution of all link's dysfunction time ratio follows a power-law distribution. • Fluctuation of link reliability exceeds the normal range during Hurricane Harvey. • Each city's transportation resilience is differently sensitive to varying link quality thresholds. [ABSTRACT FROM AUTHOR]

Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformers (BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events. • Manually labeled a dataset that trains NLP models for classifying rescue requesting tweets. • Evaluated the performance of 10 different models, including three based on milestone NLP algorithms and seven BERT-based models. • Designed a customized CNN classifier placed on the top of BERT model that achieved best performance. • Laid the foundation of developing future victim finding applications. [ABSTRACT FROM AUTHOR]

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin–destination (O–D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics. [ABSTRACT FROM AUTHOR]

Background: The outcome of coronavirus disease 2019 (COVID-19) is complicated by various comorbidities; asthma, a common chronic disease, may be considered one of these conditions. This study aimed to investigate the effect of asthma as a potential comorbid condition on the COVID-19 prognosis. Materials and Methods: This retrospective study included all RT-PCR confirmed COVID-19 cases recorded on the Shiraz health department's electronic database from January to May 2020. A questionnaire was designed to collect information about patients' demographics, their history of asthma and other comorbidities, and the severity of COVID-19 by contacting them by phone. Results: Of 3163 COVID-19 patients, 109 (3.4%) had self-reported asthma with a mean age of 42.7 ± 19.1 years. Most patients (98%) had mild-to-moderate asthma, while 2% had severe disease. Among asthmatic patients, fourteen (12.8%) were admitted to the hospital, and five (4.6%) died. Univariate logistic regression results showed that asthma had no significant effect on hospitalization (OR 0.95, 95% CI: 0.54-1.63) and mortality (OR 1.18, 95% CI: 0.48-2.94) in patients with COVID-19. Compared living and deceased patients with COVID-19, the pooled OR was 18.2 (95% CI: 7.3–40.1) for cancer, 13.5 (95% CI: 8.2–22.5) for age 40-70 years, 3.1 (95% CI: 2–4.8) for hypertension, 3.1 (95% CI: 1.8–5.3) for cardiac disease and 2.1 (95% CI: 1.3–3.5) for diabetes mellitus. Conclusion: This study showed that asthma is not associated with an increased risk of hospitalization and mortality in patients with COVID-19. Further studies are needed to investigate the risk of different asthma phenotypes on the severity of COVID-19 disease. [ABSTRACT FROM AUTHOR]

The Covid-19 has presented an unprecedented challenge to public health worldwide. However, residents in different countries showed diverse levels of Covid-19 awareness during the outbreak and suffered from uneven health impacts. This study analyzed the global Twitter data from January 1st to June 30th, 2020, to answer two research questions. What are the linguistic and geographical disparities of public awareness in the Covid-19 outbreak period reflected on social media? Does significant association exist between the changing Covid-19 awareness and the pandemic outbreak? We established a Twitter data mining framework calculating the Ratio index to quantify and track awareness. The lag correlations between awareness and health impacts were examined at global and country levels. Results show that users presenting the highest Covid-19 awareness were mainly those tweeting in the official languages of India and Bangladesh. Asian countries showed more disparities in awareness than European countries, and awareness in Eastern Europe was higher than in central Europe. Finally, the Ratio index had high correlations with global mortality rate, global case fatality ratio, and country-level mortality rate, with 21-31, 35-42, and 13–18 leading days, respectively. This study yields timely insights into social media use in understanding human behaviors for public health research. [ABSTRACT FROM AUTHOR]

The burgeoning availability of sensing technology and location-based data is driving the expansion of analysis of human mobility networks in science and engineering research, as well as in epidemic forecasting and mitigation, urban planning, traffic engineering, emergency response, and business development. However, studies employ datasets provided by different location-based data providers, and the extent to which the human mobility measures and results obtained from different datasets are comparable is not known. To address this gap, in this study, we examined three prominent location-based data sources—Spectus, X-Mode, and Veraset—to analyze human mobility networks across metropolitan areas at different scales: global, sub-structure, and microscopic. Dissimilar results were obtained from the three datasets, suggesting the sensitivity of network models and measures to datasets. This finding has important implications for building generalized theories of human mobility and urban dynamics based on different datasets. The findings also highlighted the need for ground-truthed human movement datasets to serve as the benchmark for testing the representativeness of human mobility datasets. Researchers and decision-makers across different fields of science and technology should recognize the sensitivity of human mobility results to dataset choice and develop procedures for ground-truthing the selected datasets in terms of representativeness of data points and transferability of results. • Analyzed human mobility networks across metropolitan areas at different scales: global, sub-structure, and microscopic with three prominent location-based data sources—Spectus, X-Mode, and Veraset. • Recognize the sensitivity of human mobility results to dataset choice and transferability of results and representativeness of human mobility datasets. [ABSTRACT FROM AUTHOR]

The number of catastrophic events such as extreme rainfalls and hurricanes has been growing. These events pose a major threat to the life safety and economic prosperity of urban regions. Flood control networks play a pivotal role in mitigating the risk associated with the stormwater generated by extreme rainfalls and hurricanes. The objective of this study is to propose a framework to examine the vulnerability in flood control infrastructure networks. This framework applies graph theory concepts and tools to define a vulnerability index for flood control network components (e.g., channels and rivers). The topological attributes of flood control networks are used to determine the vulnerability index based on structural attributes of flood control networks. First, a flood control network is modeled as a directed graph and storage facilities are incorporated into the network. Second, co-location exposure, upstream channel susceptibility, and discharge redundancy are characterized as important vulnerability attributes of a channel in flood control network. Then, these three characteristics are formulized based on the topological attributes of the network and characteristics of channels. The vulnerability index is then determined based on the three vulnerability characteristics. The proposed vulnerability index can be used to evaluate the impact of different risk reduction policies on flood control network vulnerability and determine the optimal mitigation strategies aiming at flood risk reduction, such as widening vulnerable channels, placement of storage facilities in the network or increasing the redundancy of the network. The framework is implemented on two watersheds in Harris County (Texas, USA) and the results' implications for decision-making in infrastructure management and hazard mitigation planning are discussed. The results highlight the capability of the proposed graph-based framework to inform flood risk reduction through evaluation of the vulnerability of infrastructure networks. • A novel graph-based approach is proposed to assess the vulnerability of flood control networks in urban regions. • The graph-based approach enables identification of vulnerable channels considering exposure, susceptibility, and redundancy. • The application of the proposed framework is demonstrated in two watersheds in Harris County region. • The proposed framework supports decision-making for network enhancement aiming at flood risk and vulnerability reduction. • The proposed method provides implications for prioritizing channels for enhancement and selecting proper storage facilities. [ABSTRACT FROM AUTHOR]

Deriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle. [ABSTRACT FROM AUTHOR]

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Synthesize and characterize of nanocomposite of polyaniline and Au (PANI/Au). • Use of the DNA probe to detect the complementary sequence associated with the hepatitis B virus. • Employ esculetin as a novel electroactive indicator to produce an electrochemical DNA hybridization biosensor for the first time. • In-silico study to elucidate and validate the results obtained from experimental measurements of the esculetin and the target DNAs. In the present study, esculetin, as a novel electroactive indicator, was used to produce an electrochemical DNA hybridization biosensor for Hepatitis B Virus (HBV) detection. At first, alkanothiol-modified DNA probe (from Hepatitis B) was self-assembled on the surface of a glassy carbon electrode modified by the nanocomposite of polyaniline and Au nanoparticles (PANI-Au). Hybridization conditions were enhanced, including the immobilization of the probe strands, the hybridization process and the indicator accumulation. For evaluating the DNA hybridization, the differential pulse voltammetric response of the accumulated esculetin was used as an electroactive indicator on different electrode surfaces. According to the obtained results, the voltammetric responses of esculetin accumulated on the probes interacted with non-complementary, mismatch and complementary targets were significantly different. Therefore, in optimum conditions, electrochemical signals of esculetin were linear in the concentration range of 0.01–3.00 × 10−6 M of the complementary strands, and a detection limit of 3.80 × 10−9 M was obtained for the target. Subsequently, the modified electrode was utilized for the detection of the HBV in spiked serum and plasma samples. The results obtained from electrochemical technique were confirmed by computational molecular simulation using MOE software. Moreover, the simulation outcomes provided valuable information about quantum chemical properties including the HOMO–LUMO maps and chemical reactivity for esculetin-the DNA target components. [ABSTRACT FROM AUTHOR]

In the present work, 2D monolayers of WS 2 and g-C 3 N 4 were synthesized for the preparation of WS 2 /g-C 3 N 4 nanostructure through a two-step process. 1D graphene oxide nanoribbons (GONRs) were synthesized via unzipping of multi-walled carbon nanotubes (MWCNTs) and applied for the preparation of three-component g-C 3 N 4 /WS 2 @rGONR (x) (x = 1, 2, and 5 wt%) nanocomposites. The synthesized nanocomposites were evaluated via Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), high-resolution transmission electron microscopy (HRTEM), and diffuse reflectance spectroscopy (DRS). The photocatalytic performance of the nanostructures was investigated in the hydrogen evolution reaction (HER) using the Fluorine-doped tin oxide (FTO) as the electrode. Subsequently, a possible photocatalytic mechanism was estimated. According to the results, the g-C 3 N 4 /WS 2 @rGONR (x) modified FTO has the lowest Tafel slope (60 mV dec−1), and overpotential (150 mV), the highest current density (47 mA cm−2) and also the maximum stability (19 mA for 3000s). Therefore, g-C 3 N 4 /WS 2 @rGONR (2) photocatalyst can be a novel and efficient candidate for hydrogen production as a clean fuel. • g-C 3 N 4 /WS 2 naosheets were prepared and placed on the graphene oxide nanoribbons. • Heterojunction g-C 3 N 4 /WS 2 @rGONR (x) nanocomposites were used in water splitting process. • rGONR act as an effective bridge for improving the photocatalytic properties of g-C 3 N 4 /WS 2. • g-C 3 N 4 /WS 2 @rGONR (2) photocatalyst showed promising performance for H 2 production. [ABSTRACT FROM AUTHOR]

Using quantitative measures to assess road network resilience, this study proposes a system-level framework that offers insights into existing road network resilience that could inform the planning and development processes of transportation agencies. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on dimensions of road network resilience. Integrating the four metrics of classification using two mathematical approaches, this study arrived at overall resilience performance metrics for assessing road segment criticality. A case study was conducted on Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. The data used in this study is available to other states and countries; thus, the framework presented in this study can be adopted by transportation agencies for regional transportation resilience assessments. [ABSTRACT FROM AUTHOR]

The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans • Using high-resolution traffic data to predict near-future road inundation status in urban floods • Adjusting spatio-temporal graph convolutional networks for road network inundation status predictions • Contributing near-future infrastructure failure prediction capability to the smart flood resilience [ABSTRACT FROM AUTHOR]

While several non-pharmacological measures have been implemented for a few months in an effort to slow the coronavirus disease (COVID-19) pandemic in the United States, the disease remains a danger in a number of counties as restrictions are lifted to revive the economy. Making a trade-off between economic recovery and infection control is a major challenge confronting many hard-hit counties. Understanding the transmission process and quantifying the costs of local policies are essential to the task of tackling this challenge. Here, we investigate the dynamic contact patterns of the populations from anonymized, geo-localized mobility data and census and demographic data to create data-driven, agent-based contact networks. We then simulate the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States and evaluate a combination of mobility reduction, mask use, and reopening policies. We find that our model captures the spatial-temporal and heterogeneous case trajectory within various counties based on dynamic population behaviors. Our results show that a decision-making tool that considers both economic cost and infection outcomes of policies can be informative in making decisions of local containment strategies for optimal balancing of economic slowdown and virus spread. • Data-driven contact network models are developed to learn social contact network patterns in COVID-19. • The model simulates the epidemic spread with a time-varying contagion model in ten large metropolitan counties in the United States. • The model is capable of evaluating the effectiveness of combinations of mobility reduction, mask use, and reopening policies. • A decision-making tool is provided for making decisions for optimal balancing of economic slowdown and virus spread. [ABSTRACT FROM AUTHOR]

• Successful synthesis of g-C 3 N 4 /Cu/TiO 2 composite as a photocatalyst. • High photocatalytic performance of the composite under visible light irradiation. • 60% degradation of endosulfan in aqueous medium after 80 min irradiation. • Enhanced photocatalytic activity compared to pure g-C 3 N 4 and Cu/TiO 2. The g-C 3 N 4 /Cu/TiO 2 (graphitic-carbon nitride/copper/titanium dioxide) nanocomposite as a photocatalyst was developed to degrade endosulfan, a very poisonous pesticide, in an aqueous medium under visible light irradiation. The structure, morphology, and optical properties of the synthesized nanocomposite were determined using SEM, XRD, FT-IR, DRS, EDX, and PL. The obtained results showed that incorporating the Cu and TiO 2 molecules onto g-C 3 N 4 led to enhance absorption ability in the visible region, as well as improved the separation of photo-excited electron–holes. Some operational factors such as initial pesticide concentration, pH, and the presence of anions were studied in the photocatalytic degradation of endosulfan. The 60% photocatalytic performance was achieved by 80 min irradiation of the endosulfan solution, which was three times higher than pure g-C 3 N 4. The improved photodegradation efficiency probably arose from the synergistic effect between three components of the composite photocatalyst. [ABSTRACT FROM AUTHOR]

• Observability considerations is neglected in the design of flood sensor networks. • A framework for the assessment of flood control network observability is proposed. • The framework enables monitoring flood status across critical regions. • The framework is tested on flood control network in Harris County, TX. • Results reveal insights from observability consideration in sensor network design. Monitoring the state of infrastructure systems proactively is crucial to ensure their proper functionality during extreme events. Flood control networks are designed to keep communities safe from inundation. Accurately monitoring the inundation status of flood control components could enhance flood situational awareness and risk management during extreme events. However, the design and placement of sensor networks that collect data to monitor the status of flooding do not often consider the principles of observability. We bridge the gap by creating a framework for the assessment of flood control network observability and determining the minimum number and locations of the flood gauges to achieve maximum monitoring across critical regions. We first delineate critical regions that are needed to be observed, then identify feasible solutions on sensor sets, and finally determine the candidate sensor set based on the importance of the nodes that exist in each sensor set. We tested the framework in the context of Harris County, Texas, as a case study. The results show that the current flood gauge placement is not sufficient for comprehensively monitoring the flooding status of critical areas, nor informed by the network observability principles. Results also offer insights to decision-makers to extend the current flood gauge network or design new flood gauge networks more effectively. [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]