Search Results (44)

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data. Full article

Coral reefs play an essential ecological role in maintaining marine water quality by naturally filtering contaminants. This study investigates the quantitative capability of coral reef ecosystems to reduce waterborne pollutants using biologically mediated processes. A systematic methodology, combining in situ observations, laboratory simulations, and analytical modeling, was adopted to determine the filtration efficiency of coral reefs. Remote sensing and photogrammetry characterized reef morphology, while microbial consortia transformations and coral polyp assimilation rates were quantified using biochemical assays. Results demonstrated significant nutrient uptake by coral polyps, particularly nitrogenous compounds, with higher removal efficiencies under stable salinity conditions. Temperature-induced stress was found to reduce polyp functionality. Enhanced sediment attenuation near reef structures improved coastal water transparency. The integration of vegetation buffers adjacent to reefs further augmented pollutant removal efficiency, with combined ecological strategies for effective pollution management. Full article

Contrast-induced nephropathy (CIN) represents a significant complication associated with the use of iodinated contrast media (ICM), especially in individuals with preexisting renal impairment. The pathophysiology of CIN encompasses oxidative stress, inflammation, endothelial dysfunction, and hemodynamic disturbances, resulting in acute kidney injury (AKI). Early detection is essential for effective management; however, conventional markers like serum creatinine (sCr) and estimated glomerular filtration rate (eGFR) exhibit limitations in sensitivity and timeliness. This review emphasizes the increasing significance of novel biomarkers in enhancing early detection and risk stratification of contrast-induced nephropathy (CIN). Recent advancements in artificial intelligence and computational analytics have improved the predictive capabilities of these biomarkers, enabling personalized risk assessment and precision medicine strategies. Additionally, we discuss mitigation strategies, including hydration protocols, pharmacological interventions, and procedural modifications, aimed at reducing CIN incidence. Incorporating biomarker-driven assessments into clinical decision-making can enhance patient management and outcomes. Future research must prioritize the standardization of biomarker assays, the validation of predictive models across diverse patient populations, and the exploration of novel therapeutic targets. Utilizing advancements in biomarkers and risk mitigation strategies allows clinicians to improve the safety of contrast-enhanced imaging and reduce the likelihood of renal injury. Full article

To obtain fundamental information on the Tequila 100% agave Cristalino commercial samples were characterized in their different classes. For this purpose, 12 samples were chosen, defined as: G1 (aged; n = 3, or extra-aged; n = 3) and G2 (aged-Cristalino; n = 3 or extra-aged-Cristalino; n = 3). Analytical characterization was performed on these beverages, consisting of isotope ratio mass spectrometry, gas and liquid chromatography, UV-Vis spectroscopy, and color using digital image processing. The results corroborate that the chromatographic characterization (mg/100 mL A.A.)—higher alcohols (299.53 ± 46.56), methanol (212.02 ± 32.28), esters (26.02 ± 4.60), aldehydes (8.93 ± 4.61), and furfural (1.02 ± 0.56)—and isotopic characterization—δ¹³C_VPDB = −13.02 ± 0.35 ‰ and δ¹⁸O_VSMOW = 21.31 ± 1.33 ‰—do not present statistically significant differences (p > 0.05) between groups. From these techniques, it was possible to reinforce that isotopic ratios can provide information about that the ethanol of these alcoholic beverages come from Agave tequilana Weber blue variety and it is not affected in the filtration process. Based on the UV-Vis analysis, I₂₈₀ and I₃₆₅ were obtained, which were related to the presence of polyphenols and flavonoids—expressed as mg quercetin equivalents/L—only found in group 1. Due to the presence of flavonoids in aged beverages, the oxidation process results in the formation of an amber color, which can be measured by an RGB color model; therefore, the analysis shows that there is a statistically significant difference (p < 0.05) between groups. It can be concluded that Tequila 100% agave Cristalino is a Tequila 100% agave aged or extra-aged without color in which its chromatographic and isotopic profile is not affected. Full article

This study focuses on the topographic structure of optical anisotropy maps (theziograms) of dehydrated blood plasma films (facies) to identify and utilize markers for diagnosing self-similarity (multifractality) in the birefringence parameters of supramolecular protein networks. The research is based on the Jones-matrix analytical framework, which describes the formation of polarization-structural speckle fields in polycrystalline blood plasma facies. In the proposed model, algorithms were developed to relate the real and imaginary parts of the complex elements of the Jones matrix to the theziograms of linear and circular birefringence. To experimentally implement these algorithms, a novel optical technology was introduced for polarization-interference registration and phase scanning of the laser speckle field of blood plasma facies. The laser-based Jones-matrix layer-by-layer theziography relies on polarization filtration and the digital recording of interference patterns from microscopic images of blood plasma facies. This process includes digital 2D Fourier reconstruction and phase-by-phase scanning of the object field of complex amplitudes, enabling the acquisition of phase sections of laser polarization-structural speckle field components scattered with varying multiplicities. Jones-matrix images of supramolecular networks, along with their corresponding theziograms of linear and circular birefringence, were obtained for each phase plane. The experimental data derived from laser layer-by-layer Jones-matrix theziography were quantitatively analyzed using two complementary approaches: statistical analysis (central moments of the 1st to 4th orders) and multifractal analysis (spectra of fractal dimension distributions). As a result, the most sensitive markers—namely asymmetry and kurtosis—were identified, highlighting changes in the statistical and scale self-similar structures of the theziograms of linear and circular birefringence in blood plasma facies. The practical aspect of this work is to evaluate the diagnostic potential of the Jones-matrix theziography method for identifying and differentiating changes in the birefringence of supramolecular networks in blood plasma facies caused by the long-term effects of COVID-19. For this purpose, a control group (healthy donors) and three experimental groups of patients, confirmed to have had COVID-19 one-to-three years prior, were formed. Within the framework of evidence-based medicine, the operational characteristics of the method—sensitivity, specificity, and accuracy—were assessed. The method demonstrated excellent accuracy in the differential diagnosis of the long-term effects of COVID-19. This was achieved by statistically analyzing the spectra of fractal dimensions of Jones-matrix theziograms reconstructed in the phase plane of single scattering within the volume of blood plasma facies. Full article

In this study, polysulfone/polyvinylpyrrolidone (PSf/PVP, 20 wt%/5 wt%)-based ultrafiltration (UF) membranes reinforced with different ratios (0.5 and 1 wt%) of cellulose nanocrystals (CNCs) and cellulose nanofibres (CNFs) were prepared by the phase inversion method. The effect of CNC, CNF, and CNC-CNF reinforcement on the morphology, roughness, crystallinity, porosity, average pore size, mechanical properties, and filtration performance of PSf/PVP-based membrane was investigated. Distilled water and surface water (lake water) fluxes of the membranes were determined at 3 bar using a dead-end filtration system. The distilled water flux of the fouled–hydraulic cleaned membranes was determined, and scanning electron microscopy (SEM) images of the fouled–cleaned membranes were examined. The flux recovery ratio (FRR) and fouling parameters were calculated to examine the fouling behaviour of the membranes. The mechanical properties of the membranes were modelled by the Mori–Tanaka, finite element, Voigt–Reuss, self-consistent scheme, and Halpin–Tsai methods using Digimat and/or analytically. In addition, the von Mises equivalent stress distributions of the nanocomposites were presented. Among the investigated membranes, PSf/PVP/CNC-0.5 had the highest distilled water flux (475.5 ± 17.77 L/m².h), PSf/PVP/CNF-1 exhibited the stiffest behaviour with an elasticity modulus of 70.63 ± 3.15 MPa, and PSf/PVP/CNC-1 had the best organic matter removal efficiency. The finite element was the most successful modelling method for estimating the mechanical properties of nanocellulose-reinforced flat sheet membranes. Full article

The sheaf cohomology techniques are newly used to include Morse simplicial complexes in a rectangular-matrix chain, whose singular values are compatible with those of a square matrix, which can be used for multiple sequencing. The equivalence with the simplices of the corresponding graph is proven, as well as that the filtration of the corresponding probability space. The new protocol eliminates the problem of stochastic stability of deep Markov models. The paradigm can be implemented to develop deep-machine-learning multiple sequencing. The construction of the deep Markov models for sequencing, starting from a profile Markov model, is analytically written. Applications can be found as an amino-acid sequencing model. As a result, the nucleotide-dependence of the positions on the alignments are fully modelized. The metrics of the manifolds are discussed. The instance of the application of the new paradigm to the Jukes–Cantor model is successfully controlled on nucleotide-substitution models. Full article

Microfluidic-chip based hydrodynamic filtration is one of the passive sorting techniques that can separate cell or particle suspensions into subpopulations of different sizes. As the branch channels and side channels play an important role in maintaining particle focusing, their rational design is necessary for highly efficient sorting. A model framework involving multiple side and multiple branch channels has been developed by extending the analytical analysis of three-dimensional laminar flow in channel networks, which was previously validated by comparison with numerical simulations. Objective parameters were identified as the number of branch channels and each length of individual branches. The presence of multiple side channels causes an increase in the average fluid velocity in main and branch channels as the branch point shifts toward the end of the main channel, which differs from the behavior observed in a single side channel. The number of branches and their individual lengths decrease distinctly in the case of branch channels consisting of narrow and wide sections, which enables the compact design of a microfluidic-chip, being operated by a lower pressure drop under the same throughput. Sorting of bidisperse particles was accomplished with an optimally designed chip to verify this framework by achieving very high recovery and purity. Full article

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities. Full article

In the wake of the COVID-19 pandemic, the scientific community has been galvanized to unravel the enigmatic role of bioaerosols in the transmission of infectious agents. This literature review, anchored in the extensive Web of Science Core Collection database covering the period from 1990 to 2023, utilizes a bibliometric approach to chart the dynamic landscape of bioaerosol research. It meticulously documents the paradigm shifts and burgeoning areas of inquiry that have emerged in the aftermath of the pandemic. This review meticulously maps out the sources and detection strategies of pathogens in a variety of ecosystems. It clearly shows that impaction and filtration sampling methods, followed by colony counting and PCR-based detection techniques, were predominantly used in the scientific works within the previous three decades. It synthesizes the progress and limitations inherent in a range of models for predicting aerosol-mediated pathogen spread and provides a comparative analysis of eDNA technology and traditional analytical techniques for bioaerosols. The accuracy of these detection methods and forecasting models is paramount for the early recognition of transmission risks, which, in turn, paves the way for prompt and effective disease mitigation strategies. By providing a thorough analysis of the historical progression and current state of bioaerosol research, this review illuminates the path ahead, identifying the critical research needs that will drive the field’s advancement in the years to come. Full article

Pavement runoff contains complex pollutants that can lead to environmental pollution and health risks. A pavement-runoff-integrated treatment system has been recognized as an effective way to deal with pavement runoff pollution. However, there is little support for selecting appropriate materials for physical pools due to a lack of understanding of the selective filtration and physical adsorption characteristics. In this study, gravel and activated carbon were chosen as the substrate materials for physical filtration and adsorption pools, and their corresponding purification characteristics were investigated using an indoor scaled down model. The results showed that the removal rate of all pollutants was related to the size of the gravel used. This was mainly due to the increased gravel particle size and voids, which resulted in a higher water velocity, shorter hydraulic retention time, and inadequate filtration. Compared with coconut shell granular activated carbon (GAC) and coal column activated carbon (EAC), analytically pure granular activated carbon (ARAC) showed a better removal rate for petroleum and heavy metals. This is mainly because ARAC has a larger specific surface area, higher pore volume, and wider pore size distribution, resulting in a remarkable adsorption capacity for pollutants. Overall, the combination of 0.3 mm gravel and ARAC was found to be the most suitable for use as filtration and adsorption materials for physical pools. These findings offer a gravel- and ARAC-based pavement-runoff-integrated treatment system, which has excellent potential to enhance the removal of pollutants from pavement runoff. Full article

This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications. Full article

A new mathematical model of an aerosol fibrous filter, composed of a variety of nano- and microfibers, is developed. The combination of nano- and microfibers in a mixed-type filter provides a higher overall quality factor compared with filters with monodisperse fibers. In this paper, we propose a mathematical model of the flow of an incompressible fluid in a porous region consisting of a set of cylinders of various diameters in the range of nano- and micrometers to describe a mixed-type aerosol filter. The flow domain is a rectangular periodic cell with one microfiber and many nanofibers. The motion of the carrier medium is described by the boundary value problem in Stokes flow approximation with the no-slip boundary condition for microfibers and the slip condition for nanofibers. The boundary element method taking into account the slip and non-slip conditions is developed. The calculated velocity field, streamlines, vorticity distribution, and drag of separate fibers and the entire periodic cell are presented. Numerical results for the drag force of the porous medium of a mixed-type filter for the various ratios of mass proportion of nano- and microfibers, porosity, and filtration velocity are presented. The obtained results are compared with the analytical formulas based on the approximate theory of filtration of bimodal filters and with known experimental data. It is shown that with an increase in the mass fraction of nanofibers, the total drag force of the cell increases, while the relative contribution of nanofibers to the total drag force tends toward the value that is less than unity. An approximate analytical formula for the drag coefficient of a mixed aerosol filter is derived. The developed flow model and analytical formulas allow for estimating the aerodynamic drag of a mixed filter composed by nano- and microfibers. Full article

A high-throughput cell-based monitoring platform was fabricated to rapidly measure the specific toxicity of unknown waters, based on AuNPs@aptamer fluorescence bioassays. The aptamer is employed in the platform for capturing the toxicity indicator, wherein hybrid chain-reaction (HCR)-induced DNA functional gold nanoparticle (AuNPs) self-assembly was carried out for signal amplification, which is essential for sensitively measuring the sub-lethal effects caused by target compounds. Moreover, the excellent stability given by the synthesized DNA nanostructure provides mild conditions for the aptamer thus used to bind the analyte. Herein, ATP was treated as a toxicity indicator and verified using lead-caused cell damage as a model. Under optimized conditions, excellent performance for water sample measurement was observed, yielding satisfactory accuracy (recovery rate: 82.69–114.20%; CV, 2.57%–4.65%) and sensitivity (LOD, 0.26 µM) without sample pretreatment other than filtration, indicating the method’s simplicity, high efficiency, and reliability. Most importantly, this bioassay could be used as a universal platform to encourage its application in the rapid quantification of specific toxicity in varied sources of samples, ranging from drinking water to highly contaminated wastewater. Full article

Computer simulation models are a useful tool in planning, enabling reliable yet affordable what-if scenario analysis. Many simulation models have been proposed and used for urban planning and management. Still, there are a few modeling options available for the purpose of evaluating the effects of various stormwater control measures (SCM), including LID (low-impact development) controls (green roof, rain garden, porous pavement, rainwater harvesting), upland off-line controls (sedimentation, filtration, retention–irrigation) and online controls (detention, wet pond). We explored the utility and potential of the Soil and Water Assessment Tool (SWAT) as a modeling tool for urban stormwater planning and management. This study demonstrates how the hydrologic modeling strategies of SWAT and recent enhancements could help to develop efficient measures for solving urban stormwater issues. The case studies presented in this paper focus on urban watersheds in the City of Austin (COA), TX, where rapid urbanization and population growth have put pressure on the urban stormwater system. Using the enhanced SWAT, COA developed a framework to assess the impacts on erosion, flooding, and aquatic life due to changes in runoff characteristics associated with land use changes. Five catchments in Austin were modeled to test the validity of the SWAT enhancements and the analytical framework. These case studies demonstrate the efficacy of using SWAT and the COA framework to evaluate the impacts of changes in hydrology and the effects of different regulatory schemes. Full article