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• A quantitative approach to examine policy preference conflicts among diverse stakeholders across multiple plans. • Hazard mitigation plan incorporated the most overall stakeholder policy preferences among the four examined plans. • The regional transportation plan incorporated the fewest overall stakeholder policy preferences. • The hazard mitigation plan and the regional conservation plan had the highest level of policy consistency. • The hazard mitigation plan and regional transportation plan had the lowest level of policy consistency. The objective of this paper was to create and test a methodological framework for examining the extent to which various plans captured diverse stakeholder policy preferences related to resilience planning and management of interdependent urban systems. Policy preferences represent what were important for stakeholders and determine the priorities of stakeholders in resilience planning of urban systems. Stakeholders in different urban sectors may have conflicts of policy preferences in the resilience planning process. A comprehensive understanding of the extent to which plans incorporated and reflected policy preferences of different stakeholders would greatly improve the quality of resilience planning. Hence, we proposed a plan evaluation framework to examine the extent to which various plans captured diverse stakeholder policy preferences in resilience planning of interdependent infrastructure systems. We showed the application of the proposed framework in the evaluation of four plans affecting flood resilience planning in the Houston area. The proposed tool could help identify conflicted stakeholder policy preferences in planning and enable evaluation of policy consistency in networks of plans. [ABSTRACT FROM AUTHOR]

Abstract In the present study, for the first time, we develop a sensitive voltammetric technique for the determination of pemetrexed using Pd/carbon nanofibers/methyl (trioctyl)ammonium bis(trifluoromethylsulfonyl)imide ([M3OA]+[NTF2]−) ionic liquid modified carbon paste electrode in which Nafion was utilized as binder (CPE/Pd/CNF/[M3OA]+[NTF2]−/Nafion). Composite nanofibers were synthesized by electrospinning method and characterized by SEM, TEM, XRD and FT-IR and the synthesized ionic liquid was also investigated by FT-IR and UV–Vis. Square wave voltammetry (SWV) measurements were performed to investigate the electrochemical oxidation of pemetrexed using the proposed electrode in 0.1 M phosphate buffer solution at pH 6.0. Several parameters such as pH amount, scan rate and the type of supporting electrolyte were studied for pemetrexed oxidation. In addition, selectivity, repeatability, reproducibility and stability were also evaluated. Under optimized conditions, a linear concentration was detected in the range of 1.00–35.0 nM with a detection limit of 0.33 nM by SWV. The proposed sensor was successfully applied for the quantification of pemetrexed in cancerous human plasma, pharmaceutical and health urine samples. Graphical abstract Unlabelled Image Highlights • Fabrication of Pd/carbon nanofibers by electrospinning method • Modification of carbon paste electrode by Pd/CNF/[M3OA]+[NTF2]−. • Electrochemical pemetrexed determination using SWV technique • The developed sensor being successfully applied for the determination of pemetrexed in real samples [ABSTRACT FROM AUTHOR]

The objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings. [ABSTRACT FROM AUTHOR]

Sustainability of infrastructure systems involves multiple interrelated objectives which are dependent on the dynamic behaviors of stakeholders, interdependencies between infrastructure assets, and evolving conditions in the social and environmental systems. This research contributes to the body of knowledge by creating a framework that integrates: (1) different dimensions of sustainability in infrastructure networks, and (2) dynamic changes in the behaviors of stakeholders, asset performance conditions, and the social and environmental conditions. The proposed framework includes an agent based simulation model to capture the dynamic behaviors of decision makers and the evolving performance of infrastructure assets under different socio-environmental conditions. The outcomes of the simulation model include the short- and long-term costs and environmental impacts of infrastructure networks under different scenarios related to the changes in the socio-environmental conditions. The application of the framework is shown in a numerical case study of a road network. The results include: (i) identification of infrastructure management strategies (e.g., maintenance planning and funding allocation) that optimize the tradeoff between annual cost and environmental impacts of the network; and (ii) discovering robust management strategies that lead to highly-likely desired sustainability outcomes across different uncertain scenarios related to the future socio-environmental conditions. The results show the capability of the proposed framework for sustainability assessment of infrastructure systems based on integrated analysis of the dynamic behaviors of decision-makers, infrastructure characteristics, and evolving socio-environmental conditions. Hence, it provides a predictive quantitative tool to inform decision-making and policy formulation toward improving the sustainability of infrastructure networks. [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]

Coastal water supply infrastructure systems are exposed to saltwater intrusion exacerbated by sea-level rise stressors. To enable assessing the long-term resilience of these systems to the impact of sea-level rise, this study developed a novel hazards-humans-infrastructure nexus framework that enables the integrated modeling of stochastic processes of hazard scenarios, decision-theoretic elements of adaptation planning processes of utility agencies, and dynamic processes of water supply infrastructure performance. Using the proposed framework and data collected from South Miami-Dade service area, a multi-agent simulation model was created to conduct exploratory assessments of the long-term resilience of water supply infrastructure under various sea-level rise scenarios and adaptation approaches. The results showed the capability of the proposed model for scenario landscape generation to discover robust adaptation pathways for enhanced infrastructure resilience under uncertainty. The analysis results could provide actionable scientific information to water infrastructure managers to improve their adaptation planning and investment decision-making processes. • Climatic hazard stressors are the most significant determinant of resilience in coastal water infrastructure systems. • Adaptive planning approach would increase the likelihood of achieving greater resilience. • Reactive planning approaches are unable to fully close the resilience gap induced by uncertainty. • Robust adaptation decision-making would enhance the long-term resilience of infrastructure systems. [ABSTRACT FROM AUTHOR]

Despite its useful properties, imiquimod (IMQ), known as an anti-cancer drug, can be harmful to the skin at high concentrations. Therefore, we have developed a novel electrochemical sensor to determine IMQ, for the first time. A glassy carbon electrode (GCE) was modified by a new composite comprising of ionic liquid-based molecularly imprinted polymer (MIP) and gold nanoparticles/graphene oxide (Au/GO). The MIP/Au/GO nanocomposite was synthesized through non-covalent imprinting process in the presence of IMQ, as template molecule and characterized by SEM and FT-IR. The square wave voltammetry technique (SWV) was applied for IMQ determination in 0.1 M phosphate buffer solution (PBS) at pH 7.0. Several parameters affecting the IMQ quantification were evaluated and optimized. Under the optimized conditions, the sensor presented a linear range of 0.02–20.0 μM, a limit of quantification and detection of 0.02 μM and 0.006 μM, respectively. Low RSD values indicate the good repeatability and reproducibility of the modified electrodes in preparation and determination procedures. The satisfactory results indicated that the proposed sensor could be successfully applied for IMQ determination in real samples. • The MIP was prepared using [VAIM]+Brˉ ionic liquid as monomer, imiquimod as template and EGDMA as a cross-linker. • The MIP/Au/GO nanocomposite was prepared through non-covalent imprinting process on the surface of Au/GO. • The MIP/Au/GO modified glassy carbon electrode was fabricated for selective determination of imiquimod. • Electrochemical determination of imiquimod was performed by square wave voltammetry and cyclic voltammetry techniques. • The novel sensitive and selective sensor was successfully applied for imiquimod determination in real samples with good merit figures. [ABSTRACT FROM AUTHOR]

Removal of cadmium ions from an aquatic solution was investigated by low-cost adsorbents and compared when modifying them by Fe-S nanoparticles. Nanocomposites were synthesized by a new experimental method under simple and inexpensive procedure by Fe-S nanoparticles, which possessed good characteristics of the surface area and magnetic property. These nanocomposite adsorbents removed cadmium at larger percentages than pure adsorbents. Iron filings had only a minor effect on the removal of cadmium, but when they were modified with Fe-S nanoparticles, the adsorption rate reached over 98.5%. Under operating conditions of pH 7, a concentration of 3 ppm, and a flow rate of 5 ml/min, the highest cadmium adsorption in water was caused by the iron nanocomposite. Display Omitted [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]

Pesticides are generally utilized one after another for crop conservation, and this pesticide utilization oftentimes resulting in combined pollution of their residues in the environment. An experimental study was performed on the degradation of dinoseb and imidacloprid. In this research, GO/CuFe 2 O 4 –CdS nanocomposite was synthesized as a high-efficiency photocatalyst for the process of pesticides pollutants from water. Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray (FESEM-EDX), X-Ray Diffraction (XRD), Vibrating-sample Magnetometer (VSM), and Fourier Transform Infrared spectroscopy (FTIR) analyses are applied to study the properties of this nanocomposite. Dinoseb and imidacloprid as pesticide materials have been utilized as water contaminants and their degradation performance using the synthesized photocatalyst is considered. The effect of initial pH, photocatalyst amount, initial pesticide concentration and degradation time are investigated in this research. The optimum values for both pesticides were as follows: pollutant concentration was 10 ppm, the nanocatalyst amount was 100 mg, irradiation time was 100 min and the pH of pesticide solution equaled to 8. The results showed that the GO/CuFe 2 O 4 –CdS nanocomposite is the efficient nanocatalyst for degradation of these pesticides. [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]

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization. • The relationships among sociodemographic factors, damage claims, and social media content attention on areas in disasters. • Identified a power-law relationship between social media attention and population size. • Social media users pay more attention to rescue and donation related information. • Informed emergency managers and public officials to effective use social media data for equitable disaster response. [ABSTRACT FROM AUTHOR]

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