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    Leveraging mesh modularization to lower the computational cost of localized updates to regional 2D hydrodynamic model outputs
    (Taylor & Francis, 2023) Garcia, M.; Juan, A.; Doss-Gollin, J.; Bedient, P.
    Hydrodynamic model outputs are used in urban flood risk modelling, flood alert systems, and Monte Carlo hazard assessment. This study tackles an under-explored challenge wherein regular updates to the spatial characteristics of the watershed – due to factors such as changing land use – alter the watershed’s response to rainfall forcing, thus rendering existing model outputs obsolete. Because state-of-the-art hydrodynamic models are computationally expensive, frequently re-running simulations can be costly. Modularization addresses this problem by requiring re-computation only for a limited domain affected by the land use changes. This article introduces a novel approach by modularizing the 2D domain into independent sub-domains before (‘discrete’) and after (‘abstract’) the numerical computations. Using the Hydrologic Engineering Center River Analysis System (HEC-RAS) 2D model of a large urban watershed in Houston as an illustrative and generalizable testbed, we show that both the discrete and abstract modularization closely approximates the results from re-running the entire model. The computational cost of modularization scales linearly with model size for memory requirements as storing the solution on the interior boundaries (discrete) or throughout the domain (abstract) are necessary. This trade-off of memory for computation may facilitate advances in surrogate modelling or Monte Carlo flood risk assessment.
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    A global analysis of coastal flood risk to the petrochemical distribution network in a changing climate
    (Elsevier, 2022) Capshaw, Kendall M.; Padgett, Jamie E.
    The global petroleum distribution network already faces a significant threat of disruption due to annual coastal flooding of major refining centers, which is expected to further increase with the effects of climate change. This study considers the impacts that sea level rise projections might have on the annual flood risk to coastal refineries, and how regional disruptions propagate across the network. Both the annual regional risk in terms of expected production disruption under a range of climate scenarios, as well as the expected production disruption due to a major flood event impacting refining hubs of high importance are assessed throughout the 21st century. These risks are propagated across the network to model the global impact of coastal flood-induced refining disruptions. This analysis provides insights on the relative risks that different climate scenarios and flood events pose globally, informing potential mitigation and adaptation needs of critical facilities. Due to the highly interconnected nature of the global petroleum product distribution network, these results highlight the need for mitigation considerations for even regions with low domestic production disruption risk due to coastal flood hazards, as disruptions in remote regions can have cascading consequences resulting in significant disruption to petroleum product supply around the world. Furthermore, such results can inform decisions regarding technology transitions or energy diversification in light of the new understanding of climate risks to coastal refineries and the global petroleum distribution network.
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    Global Increase of Antibiotic Resistance Genes in Conjugative Plasmids
    (American Society for Microbiology, 2023) Wang, Xiaolong; Zhang, Hanhui; Long, Xiang; Xu, Ximing; Ren, Hongqiang; Mao, Daqing; Alvarez, Pedro J. J.; Luo, Yi
    Antibiotic resistance is propagating worldwide, but the predominant dissemination mechanisms are not fully understood. Here, we report that antibiotic resistance gene (ARG) abundance in conjugative plasmids that are recorded in the National Center for Biotechnology Information (NCBI) RefSeq plasmid database is increasing globally, which is likely a key factor in the propagation of resistance. ARG abundance in plasmids increased by 10-fold on a global scale from the year 2000 to the year 2020 (from 0.25 to 2.93 ARG copies/plasmid), with a more pronounced increase being observed in low-to-middle income countries. This increasing trend of plasmid-borne ARGs was corroborated by bootstrap resampling from each year of the NCBI RefSeq plasmid database. The results of a correlation analysis imply that if antibiotic consumption keeps growing at the current rates, a 2.7-fold global increase in the ARG abundance of clinically relevant plasmids may be reached by 2030. High sequence similarities of clinically relevant, conjugative plasmids that are isolated both from clinics and from the environment raise concerns about the environmental resistome serving as a potential ARG maintenance reservoir that facilitates transmission across these ecological boundaries. IMPORTANCE Antibiotic resistance propagation is a significant concern due to its projected impacts on both global health and the economy. However, global propagation mechanisms are not fully understood, including regional and temporal trends in the abundance of resistance plasmids that facilitate antibiotic resistance gene (ARG) dissemination. This unprecedented study reports that ARG abundance in the conjugative plasmids that are recorded in the National Center for Biotechnology Information (NCBI) database and harbor ARGs is increasing globally with antibiotic consumption, especially in low-to-medium income countries. Through network and comparative genomic analyses, we also found high sequence similarities of clinically relevant conjugative resistance plasmids that were isolated from clinical and environmental sources, suggesting transmission between these ecological boundaries. Therefore, this study informs the One Health perspective to develop effective strategies by which to curtail the propagation of plasmid-borne antibiotic resistance.
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    Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
    (Springer Nature, 2023) Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G.; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.
    As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).
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    Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events
    (MDPI, 2023) Li, Wenzhao; Jiang, Han; Li, Dongfeng; Bedient, Philip B.; Fang, Zheng N.
    Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement.
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    Editorial: Advanced technologies for industrial wastewater reclamation
    (Frontiers Media S.A., 2023) Deng, Shihai; Hu, Jiangyong; Ong, Say-Leong; Li, Qilin; Han, Jie
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    Impact of a natural disturbance on the performance and microbial communities in a full-scale constructed wetland for industrial wastewater treatment
    (Frontiers Media S.A., 2023) Hollstein, Marielle; Comerford, Mattheau; Uhl, Michael; Abel, Michael; Egan, Scott P.; Stadler, Lauren B.
    Constructed Wetlands (CWs) are a cost-effective, versatile and sustainable choice for wastewater treatment. In these environments, microbial communities play a significant role in pollutant removal. However, little is known about how microbial communities in full-scale CWs contribute to maintaining water quality or how their dynamics change in response to pulse disturbances such as fire or freezes. Furthermore, few studies have examined the relationship between CW microbial community structure and performance in full-scale industrial operations. We characterized the water-column and leaf-litter layer microbial communities in a 110-acre free water surface CW that provides tertiary wastewater treatment to a plastics manufacturing plant. The CW’s sampling campaign was conducted over a 12-month period that included Winter Storm Uri, a 100-year freeze event. Analysis of 16S rRNA gene amplicon sequences revealed that the bacterial communities experienced a temporal shift. There was also a shift in microbial community structure between the influent and the first segment of the CW. However, no differences in microbial community structure were observed in the second segment of the CW. There was a negative association between microbial community diversity and chlorophyll a, as well as microbial community diversity and total suspended solids (TSS); demonstrating an increase in microbial biodiversity as water quality improved throughout the CW. Six months after the freeze, CW performance in terms of removal of water quality constituents began to return to former removal trends. Yet, there was still a significant difference in microbial community structure within the CW relative to the previous year. This suggests CW functional resilience despite a shift in microbial community structure in the wetland.
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    Frequency independent damped outrigger systems for multi-mode seismic control of super tall buildings with frequency independent negative stiffness enhancement
    (Wiley, 2023) Wang, Meng; Sun, Fei-Fei; Koetaka, Yuji; Chen, Lin; Nagarajaiah, Satish; Du, Xiu-Li
    Damped outrigger system is effective for improving energy dissipation for tall buildings. However, conventional damped outrigger (CDO) system with viscous damping has two limitations: (i) its maximum damping ratio cannot be improved when outrigger/column stiffness is inadequate; (ii) different modes achieve their maximum damping ratios at different outrigger damping values, and thus the dampers cannot be optimized to simultaneously reduce vibrations of multiple modes of concern to their minimum. In this paper, a purely frequency-independent negative stiffness damped outrigger (FI-NSDO) system is proposed by combining frequency-independent damper (FID) and negative stiffness device (NSD). The damped outrigger with FID can achieve the maximum damping ratio for all modes as compared to frequency-dependent damper like viscous damper. As the NSD has the features of assisting and enhancing motion and frequency-independence, the utilization of NSD will considerably improve the maximum damping ratios when outrigger/column stiffness is inadequate and maintain the frequency-independent feature of the whole system. Therefore, the FI-NSDO has the capability of simultaneously increasing the damping ratios of all target modes to their maximum values. Analysis in frequency domain and time domain, demonstrate that the proposed FI-NSDO performs better in controlling the multi-mode vibration of seismic responses.
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    Practical negative stiffness device with viscoelastic damper in parallel or series configuration for cable damping improvement
    (Elsevier, 2023) Chen, Lin; Liu, Zhanhang; Zou, Yiqing; Wang, Meng; Nagarajaiah, Satish; Sun, Feifei; Sun, Limin
    Negative stiffness mechanism has been found able to improve damping performance of dampers on a stay cable which otherwise is limited by the damper installation distance from a cable end. This study provides a practical negative stiffness device (NSD) with adjustable negative stiffness and experiments are performed to validate the negative stiffness effect. The NSD is then combined with a viscoelastic damper in parallel or series for cable damping improvement. Explicit design formulas are derived for optimal design with a target enhancement effect in damping considering the damper described respectively using the Kelvin model and the linear hysteretic damping model. The formulas are verified by analytical and numerical solutions. Parametric analyses show damping enhancement effects of the NSD and it is found more efficient when combined with a damper in series because both deformation amplitudes of the damper and the NSD are further increased in this configuration. Subsequently, case studies are carried out based on two cables of the Sutong Bridge respectively with a shear-type viscous damper and a high damping rubber damper. The results show that the designed NSD can fulfill practical requirements. Particularly, a 100% increase in damping can be achieved by the presented NSD when combined with the damper installed on a cable of 546.9 m long.
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    Mesoscale Modeling of Distributed Water Systems Enables Policy Search
    (Wiley, 2023) Zhou, Xiangnan; Duenas-Osorio, Leonardo; Doss-Gollin, James; Liu, Lu; Stadler, Lauren; Li, Qilin; Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment
    It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, it is unclear which are beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a mesoscale representation model that approximates DWSs with reduced backbone networks to enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multiobjective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston's water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The mesoscale modeling approach and the multiobjective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.
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    Impregnation of KOAc on PdAu/SiO2 causes Pd-acetate formation and metal restructuring
    (Royal Society of Chemistry, 2023) Jacobs, Hunter P.; Elias, Welman C.; Heck, Kimberly N.; Dean, David P.; Dodson, Justin J.; Zhang, Wenqing; Arredondo, Jacob H.; Breckner, Christian J.; Hong, Kiheon; Botello, Christopher R.; Chen, Laiyuan; Mueller, Sean G.; Alexander, Steven R.; Miller, Jeffrey T.; Wong, Michael S.
    Potassium-promoted, oxide-supported PdAu is catalytically active for the gas-phase acetoxylation of ethylene to form vinyl acetate monomer (VAM), in which the potassium improves long-term activity and VAM selectivity. The alkali metal is incorporated into the catalyst via wet impregnation of its salt solution, and it is generally assumed that this common catalyst preparation step has no effect on the catalyst structure. However, in this work, we report evidence to the contrary. We synthesized a silica-supported PdAu (PdAu/SiO2, 8 wt% Pd, 4 wt% Au) model catalyst containing Pd-rich PdAu alloy and pure Au phases. Impregnation with potassium acetate (KOAc) aqueous solution and subsequent drying did not cause XRD-detectible changes to the bimetal structure. However, DRIFTS indicated the presence of Pd3(OAc)6 species, which is correlated to up to 2% Pd loss after washing of the dried KOAc-promoted PdAu/SiO2. Carrying out the impregnation step with an AcOH-only solution and subsequent drying caused significant enlargement of the pure Au grain size and generated a smaller amount of Pd3(OAc)6. During co-impregnation of AcOH and KOAc, grain sizes were enlarged slightly, and substantial amounts of K2Pd2(OAc)6 and Pd3(OAc)6 were detected by DRIFTS and correlated to up to 32% Pd loss after washing. Synchrotron XAS analysis showed that approximately half the Pd atoms were oxidized, corroborating the presence of the Pd-acetate species. These results indicate wet-impregnation-induced metal leaching can occur and be substantial during catalyst preparation.
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    Compound Climate Risk: Diagnosing Clustered Regional Flooding at Inter-Annual and Longer Time Scales
    (MDPI, 2023) Amonkar, Yash; Doss-Gollin, James; Lall, Upmanu
    The potential for extreme climate events to cluster in space and time has driven increased interest in understanding and predicting compound climate risks. Through a case study on floods in the Ohio River Basin, we demonstrated that low-frequency climate variability could drive spatial and temporal clustering of the risk of regional climate extremes. Long records of annual maximum streamflow from 24 USGS gauges were used to explore the regional spatiotemporal patterns of flooding and their associated large-scale climate modes. We found that the dominant time scales of flood risk in this basin were in the interannual (6–7 years), decadal (11–13 years), and secular bands and that different sub-regions within the Ohio River Basin responded differently to large-scale forcing. We showed that the leading modes of streamflow variability were associated with ENSO and secular trends. The low-frequency climate modes translated into epochs of increased and decreased flood risk with multiple extreme floods or the absence of extreme floods, thus informing the nature of compound climate-induced flood risk. A notable finding is that the secular trend was associated with an east-to-west shift in the flood incidence and the associated storm track. This is consistent with some expectations of climate change projections.
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    Case studies of multihazard damage: Investigation of the interaction of Hurricane Maria and the January 2020 earthquake sequence in Puerto Rico
    (Frontiers, 2023) Hain, Alexandra; Zaghi, Arash E.; Padgett, Jamie E.; Tafur, Anibal
    This paper is motivated by the unique findings and observations from reconnaissance visits after the earthquake series in Puerto Rico in January 2020. It aims to discuss the potential interactions of Hurricane Maria and 2020 earthquake series and the considerations they underscore for future field reconnaissance missions. Traditionally, post-disaster damage assessment activities focus on one hazard and overlook the potentially cascading effects of multiple hazards on structures and infrastructure. This paper provides case studies showing the possible interaction of multiple hazards and their cascading effects observed in Puerto Rico. Infrastructure surveyed includes port facilities, buildings (particularly historical structures), and bridge structures. The data collected during the reconnaissance missions reveal how the impacts of Hurricane Maria, along with infrastructure aging and delayed repair and recovery activities, may have influenced the damage level and failure modes observed during the earthquake sequence a few years after. These case studies illustrate the nature of multihazard interactions and how these effects should be documented during post-disaster assessments. Beyond the insights gained from the case studies illustrated in this paper, the field survey instrument is provided as a basis for future reconnaissance studies, and the full set of reconnaissance data collected are published on the NSF funded NHERI DesignSafe cyberinfrastructure. As a result, this work not only provides data from Puerto Rico that can inform future damage and recovery modeling efforts, but also offers survey instruments and a field data collection process that is particularly tailored to cases where multihazard effects are at play.
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    Snapshot ARG Removal Rates across Wastewater Treatment Plants Are Not Representative Due to Diurnal Variations
    (American Chemical Society, 2023) Lou, Esther G.; Ali, Priyanka; Lu, Karen; Kalvapalle, Prashant; Stadler, Lauren B.
    To evaluate the threat of the environmental dissemination of antibiotic resistance associated with wastewater treatment plants (WWTPs), the removal efficiency of antibiotic resistance genes (ARGs) during wastewater treatment needs to be assessed. The sample collection strategy is one factor that is often overlooked in study design and most studies on ARGs in wastewater perform grab sampling. Here, we hypothesized that wastewater sampling (i.e., grab and composite sampling) influences the observed ARG concentrations and calculated removal rates across WWTPs. We compared the removal rates calculated based on the two different sampling methods for several genes, including some clinically relevant ARGs (blaNDM-1, blaOXA-1, MCR-1, MCR-5, MCR-10, and qnrA). We conducted summer and winter 24 h sampling campaigns where grab samples were collected every 2 h from the influent, secondary effluent, and final effluent. The snapshot removal rate of each target gene calculated based on the 12 grab samples fluctuated by 0.5–1.6 log in the winter and 0.9–2.7 log in the summer, indicating diurnal variation. Overall, for each target gene, the removal rates calculated based on 24 h composite samples were approximately equal to the median of the 12 snapshot removal rates. Our study confirms the importance of using composite samples to monitor ARGs in wastewater.
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    Polydopamine-assisted one-step immobilization of lipase on α-alumina membrane for fouling control in the treatment of oily wastewater
    (Elsevier, 2023) Mulinari, Jéssica; Ambrosi, Alan; Feng, Yuren; He, Ze; Huang, Xiaochuan; Li, Qilin; Di Luccio, Marco; Hotza, Dachamir; Oliveira, J. Vladimir; Nanotechnology-Enabled Water Treatment Center (NEWT)
    Covalent enzyme immobilization is generally a time-consuming and multistep procedure that uses toxic solvents and requires more than one chemical, making industrial upscaling unattractive. Using an aqueous polydopamine (PDA) solution for enzyme immobilization is a greener alternative. Usually, enzyme immobilization using PDA is performed in two steps: dopamine polymerization on the material surface followed by enzyme immobilization. A few recent studies applied a one-step strategy by mixing dopamine and enzyme in the coating solution, reducing the immobilization time, chemical consumption, and wastewater generation. This study compares the two-step and one-step approaches to immobilizing the lipase Eversa Transform 2.0 (ET2) on an α-alumina membrane. The one-step immobilization method achieved similar enzyme loading, membrane hydrolytic activity, and enzyme-specific activity to those of the two-step method. The ET2 immobilized using both strategies showed excellent fouling resistance and self-cleaning performance in oily wastewater filtration. The membrane modified by the one-step approach exhibited a lower reduction in pure water permeance after oil fouling (35%) and a higher permeance recovery (90%) than the one modified by the two-step method (40% and 74%, respectively). This better performance can be due to the higher hydrophilicity of the modified membrane and higher stability over reaction time shown by the enzyme immobilized by the one-step strategy. The higher stability can be attributed to more attachment points between the enzyme and PDA, increasing the enzyme rigidity and preventing conformational changes.
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    A Polysulfone/Cobalt Metal–Organic Framework Nanocomposite Membrane with Enhanced Water Permeability and Fouling Resistance
    (American Chemical Society, 2022) Gil, Eva; Huang, Xiaochuan; Zuo, Kuichang; Kim, Jun; Rincón, Susana; Rivera, José María; Ranjbari, Kiarash; Perreault, François; Alvarez, Pedro; Zepeda, Alejandro; Li, Qilin; Nanosystems Engineering Research Center for Nanotechnology Enabled Water Treatment
    Ultrafiltration membranes are widely used in water and wastewater applications. The two most important membrane characteristics that determine the cost-effectiveness of an ultrafiltration membrane process are membrane permeability and fouling resistance. Metal–organic frameworks (MOFs) have been intensively investigated as highly selective sorbents and superior (photo) catalysts. Their potential as membrane modifiers has also received attention recently. In this study, a non-functionalized, water-stable, nanocrystalline mixed ligand octahedral MOF containing carboxylate and amine groups with a cobalt metal center (MOF-Co) was incorporated into polysulfone (PSF) ultrafiltration (UF) membranes at a very low nominal concentration (2 and 4 wt %) using the conventional phase inversion method. The resultant PSF/MOF-Co_4% membrane exhibited water permeability up to 360% higher than of the control PSF membrane without sacrificing the selectivity of the membrane, which had not been previously achieved by an unmodified MOF. In addition, the PSF/MOF-Co_4% membrane showed strong resistance to fouling by natural organic matter (NOM), with 87 and 83% reduction in reversible and irreversible NOM fouling, respectively, compared to the control PSF membrane. This improvement was attributed to the increases in membrane porosity and surface hydrophilicity resulting from the high hydrophilicity of the MOF-Co. The capability of increasing membrane water permeability and fouling resistance without compromising membrane selectivity makes the MOF-Co and potentially other hydrophilic MOFs excellent candidates as membrane additives.
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    Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections
    (Elsevier, 2023) Wolken, Madeline; Sun, Thomas; McCall, Camille; Schneider, Rebecca; Caton, Kelsey; Hundley, Courtney; Hopkins, Loren; Ensor, Katherine; Domakonda, Kaavya; Prashant, Kalvapalle; Persse, David; Williams, Stephen; Stadler, Lauren B.
    Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.
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    Application of a fully polynomial randomized approximation scheme (FPRAS) to infrastructure system reliability assessments
    (2017-08-06) Fu, Bowen; Dueñas-Osorio, Leonardo
    Networked systems make the reliability assessment of critical infrastructure computationally challenging given the combinatorial nature of system-level states. Several methods from numerical schemes to analytical approaches, such as Monte Carlo Simulation (MCS) and recursive decomposition algorithms (RDA), respectively, have been applied to this stochastic network problem. Despite progress over several decades, the problem remains open because of its intrinsic computational complexity. As the structural facilities of infrastructure systems continue to in terconnect in network forms, their study steers analysts to develop system reliability assessment methods based on graph theory and network science. A fully polynomial randomized approximation scheme (FPRAS) based on Karger’s graph contraction algorithm is an approximating method for reliability evaluation, which has a unique property rarely exploited in engineering reliability: that by performing a number of experiments in polynomial time (as a function of system size), it provides an a priori theoretical guarantee that the reliability estimate falls into the ϵ-neighborhood of its true value with (1−δ) confidence. We build upon the FPRAS ideas to develop an s-t reliability version that has practical appeal. Focusing on the relevant-cut enumeration stage of the FPRAS, we find correlations between the recurrence frequencies of links in minimum cuts within the randomization phase of the contraction algorithm, and typical network topological properties. We employ LASSO regression analysis to approximate the relationship between link recurrence frequencies and such topological metrics. With the topology-informed link recurrence frequencies, obtained at a much lower computational cost, we use a new biased contraction probability yielding 16.9% more distinct minimum cuts (MinCuts) than the original random contraction scheme. The biased contraction scheme proposed here can significantly improve the efficiency of reliability evaluation of networked infrastructure systems, while supporting infrastructure systems design, maintenance and restoration given its ability to offer error guarantees, which are ideal for future prescriptive guidelines in practice.
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    Resilience-informed infrastructure network dismantling
    (2022-09-13) Fu, Bowen; Dueñas-Osorio, Leonardo
    Large-scale networked infrastructure systems contribute significantly to modern society. Highly intra- and interconnnected systems enable communities to be more productive, at the expense of becoming more vulnerable to extreme events, cascading failures, and operational demands, including random failures and even targeted attacks. The resilience of infrastructure systems against common but random failure and rare but intentional attacks is critical for safe communities, as it covers multiple other types of contingencies in between. Network dismantling is a process to make the network dysfunctional by removi ng a fraction of components, which provides insights for robustness and resilience under many events, from common to rare. In particular, to protect networks from uncertain dismantling, we need to understand how to optimally fragment networks into small clusters by removing a fraction of their assets with minimal cost. Approximation methods are desirable because finding the optimal dismantling strategy is NP-hard, thus impractical on infrastructure networks. First attempts rely on iterative removal of the nodes with the highest adaptive importance, either from basic centralities, such as degree and betweeness, or from some more advanced metrics like collective influence. However, the additive nature of such methods fails to capture the synergistic nature of the dismantling problem. An algorithm connecting network dismantling problems with network decycling problems, identifies better the collective dismantling set. Other recent strategies add realism by adopting nonuniform node remo val costs, and applying a bisecting algorithm based on weighted spectral approximations iteratively. Despite these efforts, the combinatorial optimization nature of the network dismantling problem still requires global solutions, even if approximated. Additionally, the cost to remove components is the only factor considered in most previous methods. Network resilience, which can inform what to protect from dismantling to facilitate recovery, is seldom included as part of the cost. In this work, we propose a method employing Karger`s contraction algorithm and node-transferring heuristic optimization to approximate the optimal dismantling set, considering both component removal cost and network resilience after dismantling. The proposed method, resilDism, obtains good performance compared to state-of-the-art network dismantling methods, and provides valuable insights to guide network design and resilience enhancement in practice.
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    Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing
    (MDPI, 2023) Jana, Debasish; Nagarajaiah, Satish
    In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.