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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]

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]

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]

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]

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]

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]