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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]

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]

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]

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]

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]

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]