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A machine learning framework to predict PPCP removal through various wastewater and water reuse treatment trains
No full textThe persistence of pharmaceuticals and personal care products (PPCPs) through wastewater treatment and resulting contamination of aquatic environments and drinking water is a pervasive concern, necessitating means of identifying effective treatment strategies for PPCP removal. In this study, we employed machine learning (ML) models to classify 149 PPCPs based on their chemical properties and predict their removal via wastewater and water reuse treatment trains. We evaluated two distinct clustering approaches: C1 (clustering based on the most efficient individual treatment process) and C2 (clustering based on the removal pattern of PPCPs across treatments). For this, we grouped PPCPs based on their relative abundances by comparing peak areas measured via non-target profiling using ultra-performance liquid chromatography-tandem mass spectrometry through two field-scale treatment trains. The resulting clusters were then classified using Abraham descriptors and log Kow as input to the three ML models: support vector machines (SVM), logistic regression, and random forest (RF). SVM achieved the highest accuracy, 79.1%, in predicting PPCP removal. Notably, a 58–75% overlap was observed between the ML clusters of PPCPs and the Abraham descriptor and log Kow clusters of PPCPs, indicating the potential of using Abraham descriptors and log Kow to predict the fate of PPCPs through various treatment trains. Given the myriad of PPCPs of concern, this approach can supplement information gathered from experimental testing to help optimize the design of wastewater and water reuse treatment trains for PPCP removal
Search for the Anomalous Events Detected by ANITA Using the Pierre Auger Observatory
Full text linkA dedicated search for upward-going air showers at zenith angles exceeding 110° and energies E\u3e0.1 EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two anomalous radio pulses observed by the ANITA flights I and III that appear inconsistent with the standard model of particle physics. Using simulations of both regular cosmic-ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between January 1, 2004, and December 31, 2018, consistent with an expected background of 0.27±0.12 events from misreconstructed cosmic-ray showers. This translates to an upper bound on the integral flux of (7.2±0.2)×10-21 cm-2 sr-1 y-1 and (3.6±0.2)×10-20 cm-2 sr-1 y-1 for an E-1 and E-2 spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an E-3 spectrum and over 8.1 events for a conservative E-5 spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers
Prediction of compressive and flexural strength of coal gangue-based geopolymer using machine learning method
No full textThe mechanical properties of coal gangue-based geopolymers are influenced by various factors, including source location, activator type, and liquid-to-solid ratio. Among these, the precursor Si/Al ratio exhibits vary significantly based on the source location. This study focuses on investigating the effectiveness of machine learning models in predicting the mechanical properties of coal gangue-based geopolymers and offers guidance for mix design tailored to coal gangue from different sources. By utilizing machine learning models, this research aims to optimize the mix proportions of geopolymer materials, enhancing the sustainable recycling of coal gangue. To achieve this, orthogonal experiments were conducted by adjusting the Si/Al ratio of coal gangue-based geopolymers by incorporating fly ash and slag. The experimental results, combined with data from the literature, formed a dataset that was analyzed using various machine learning techniques. Generalization tests were also conducted to assess the predictive accuracy of the best-performing models. The results indicate that XGBoost and Random Forest exhibited strong predictive accuracy, with R² values of 0.865 and 0.882 for 3-day and 28-day compressive strength, and 0.788 for 28-day flexural strength. Prediction errors remained minimal, with MAE values of 2.78 MPa, 3.38 MPa, and 0.594 MPa for 3-day compressive, 28-day compressive, and 28-day flexural strength, respectively, confirming model reliability. Furthermore, the generalization test results indicate that Random Forest is less sensitive to variations in precursor material composition under identical Si/Al conditions, exhibiting more excellent generalization capabilities. This study provides theoretical support for the intelligent optimization of geopolymer mix design, which not only reduces experimental costs but also contributes to the sustainable utilization of coal gangue as a cementitious material alternative, thereby mitigating environmental impacts associated with conventional cement production
Diversity and Distribution of Hydrocarbon-Degrading Genes in the Cold Seeps from the Mediterranean and Caspian Seas
Full text linkMarine cold seeps are unique ecological niches characterized by the emergence of hydrocarbons, including methane, which fosters diverse microbial communities. This study investigates the diversity and distribution of hydrocarbon-degrading genes and organisms in sediments from the Caspian and Mediterranean Seas, utilizing 16S rRNA and metagenomic sequencing to elucidate microbial community structure and functional potential. Our findings reveal distinct differences in hydrocarbon degrading gene profiles between the two seas, with pathways for aerobic and anaerobic hydrocarbon degradation co-existing in sediments from both basins. Aerobic pathways predominate in the surface sediments of the Mediterranean Sea, while anaerobic pathways are favored in the surface sediments of the anoxic Caspian Sea. Additionally, sediment depths significantly influence microbial diversity, with variations in gene abundance and community composition observed at different depths. Aerobic hydrocarbon-degrading genes decrease in diversity with depth in the Mediterranean Sea, whereas the diversity of aerobic hydrocarbon-degrading genes increases with depth in the Caspian Sea. These results enhance our understanding of microbial ecology in cold seep environments and have implications for bioremediation practices targeting hydrocarbon pollutants in marine ecosystems
Improving geospatial coastal vulnerability indices for the Great Lakes
No full textIn response to record-high water levels in the Great Lakes, there has been a notable surge in engineering interventions and the construction of armoring structures to mitigate shoreline erosion. However, the efficacy of these defensive measures against erosion and their broader implications for the physical vulnerability of coastal communities remain critical concerns. Our pilot study applied the Coastal Vulnerability Index (CVI) method to the Muskegon shoreline, enhancing it by calculating CVI values for individual parcels and integrating the shoreline rate of change and shoreline armaments. This approach localized variations and provided a precise understanding of factors influencing vulnerability. We found that using the shoreline rate of change allowed us to identify vulnerable areas prone to erosion due to dynamic shoreline processes and seasonal variations. In the study, seasonality significantly influenced vulnerability, particularly through ice cover, which aligns with findings on seasonal shoreline erosion risks from previous studies. It also underscores the importance of considering temporal dynamics in assessing coastal vulnerability in the Great Lakes region. We observed higher vulnerability in the northern and southern parts of the county\u27s shoreline compared to the central areas. Sites near heavily armored properties exhibited increased vulnerability, highlighting the complex impacts of shoreline armors on adjacent areas. The developed CVI holds the promise of providing coastal managers with invaluable insights. Specifically, it guides the reclassification of high-vulnerability areas and informs the formulation of policies that address the multifaceted challenges associated with shoreline armoring
Understanding the Growth Mechanism of Thiol-Conjugated Au25 Cluster
Full text linkThe synthesis of ligand-conjugated gold nanoclusters has attracted significant attention due to its ability to achieve precise control over cluster size selectivity. Among these, Au25(SR)18-, where R represents an alkyl group, is one of the earliest being synthesized with a very high yield, although its growth mechanism is yet to be fully understood. Using density functional theory, we present the results of a theoretical investigation on the growth process of Au25(SR)18-, beginning from Au13(SR)12-. Our findings indicate that the sulfur atoms in the core structure of Au13(SR)12- preferentially bond with Au-thiol monomers. Monomers attached to two adjacent triangular faces form a staple motif of the gold-sulfur chain, releasing a single linear thiol radical. These reactions occur along the six mutually perpendicular ridges of the Au13 core. The remaining eight triangular faces, linked with linear alkyl parts, cannot bind additional Au-thiol monomers, stopping cluster growth. Furthermore, the capping gold-sulfur chains play a protective role for the core, facilitating the stable formation of the Au25(SR)18-cluster, as confirmed experimentally
Hardware-Assisted Runtime In-vehicle ECU Firmware Self-attestation and Self-repair
No full textModern vehicles are largely controlled by many embedded computers, known as Electronic Control Units (ECUs). The increased use of ECUs has brought many in-vehicle security concerns. Specifically, injection of malware into ECUs poses a significant risk to vehicle operation. Indeed, many ECU malware injection attacks have been performed, and much work has been introduced towards mitigating these vulnerabilities. A main defense is for ECUs to perform a self-attestation over their firmware state. However, most current self-attestation solutions do not enable runtime checking due to their high computational cost. Additionally, existing solutions mostly do not incorporate any ECU self-repairing in coordination with the attestation mechanisms. In this work, we have designed FSAVER, a highly efficient self-attestation and self-repair framework for in-vehicle ECUs. For the self-attestation, we adapt highly efficient spot-checking techniques, so that the firmware can be checked periodically at runtime. To perform these attestations, we rely on the TEE already equipped within each ECU. For self-repair, we take advantage of the isolated flash memory controller (FMC) in the storage device. Specifically, we coordinate it with the update mechanism and self-attestations to guarantee that the latest benign firmware version can always be restored. To realize this while malware is running, a special mechanism has been carefully developed to notify the FMC of the malicious presence
Engineered biochar-attapulgite clay composite: A novel slow-release phosphorus fertilizer
Full text linkSustainable agricultural practices are in high demand, and the development of innovative materials for slow nutrient release holds utmost importance. Phosphorus (P) stratification limits plant P availability in no-till farming systems. To address this, a nano composite-based P fertilizer to release nutrients in the root zone following surface soil application is constructed. The nanocomposite fertilizers (BAP-SRFs) were formed by a combination of different amounts of biochar (BC), attapulgite clay (ATP) and KH2PO4, followed by a high-temperature treatment (500 °C). XANES analysis revealed the enrichment of the surface with carbon (C), oxygen (O) and P-containing functional groups, and the bond formation of P with calcium (Ca) and magnesium (Mg) and organic C. Batch desorption and dynamic release experiments provided conclusive evidence that an interplay between ATP and P contents is crucial for the optimum delivery of P in both aqueous and soil media. The slowest release of P in water was achieved in BAP-SRF, which had a BC to ATP ratio of 2 and was loaded with 200 mg of KH2PO4. Pseudo-second-order kinetics presented the best fit for P release, demonstrating that the release mechanism of P is primarily based on diffusion. The visual diffusion of P in soil provides critical evidence of the slow-release nature of the BAP-SRFs as compared to commercial single super phosphate. The experimental findings were further corroborated by the first principles density functional theory calculations, which highlighted that BC and ATP make an excellent combination to interact with the KH2PO4 and slow down its release. The engineered nanocomposite P fertilizers developed here may provide a solution for the intractable issue of P stratification in no-till farming systems
How does the sewer microbiome impact wastewater surveillance for antibiotic resistance?
Full text linkAntibiotic resistance is a global health threat that is difficult to directly monitor prior to clinical presentation. Wastewater surveillance has emerged as a public health tool that could aid in identifying the community wide circulation of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB), however this is complicated by the potential growth and decay of ARB within the sewer network. Sewer systems have been suggested as a primary place where sub-lethal doses of antibiotics are present with human and environmental adapted bacterial strains, however little is understood about what physiochemical properties might enhance or inhibit ARG propagation. Hence, key concerns exist about the sewer system being a potential reservoir for ARGs and a source of their persistence in wastewater with sewer microbiome serving as potential evolution points for bacterial gene transfer. The objective of this review is to summarize what is currently known about sewer microbiomes and the incidence of ARGs. Studies on occurrence, conveyance and fate of ARGs in sewer systems are presented and the role and impact of sewer residence time of wastewater in influencing the transmission and type of transfer mechanisms is assessed
Paving Equity: Unveiling Socioeconomic Patterns in Pavement Conditions Using Data Mining
No full textAccessibility is a key metric in transportation equity, yet a critical aspect of accessibility is the quality of access. This study presents a data-driven social equity assessment of pavement conditions across the United States using the International Roughness Index (IRI) and data mining techniques applied to over 8 million records from the Highway Performance Monitoring System (HPMS). Data mining, as an exploratory tool, revealed hidden patterns that conventional methods might overlook. The analysis uncovered disparities in pavement conditions across socioeconomic and demographic groups. On average, road segments in the National Highway System with lower traffic volumes, higher minority populations, greater racial diversity, thinner pavement designs, and more non-English speakers tend to have poorer pavement conditions, as observed in the HPMS data. While the study identifies correlations rather than causal relationships, the findings underscore the inequities in access quality and highlight the need for transportation agencies to integrate social equity considerations into pavement maintenance and budgetary strategies. The observed gap is larger within clusters representing rural and small urban areas compared to urban sections. By factoring socioeconomic variables into decision-making processes, policymakers can better allocate resources to ensure equitable access to well-maintained infrastructure, regardless of community demographics, traffic levels, or other influencing factors