Residual solids retained in the sand bed after backwashing are commonly used to assess the cleaning efficiency of pressurized sand filters employed in irrigation systems. However, procedures originally proposed for slow sand filters are not fully suitable for pressurized filters because these systems differ in operating conditions and bed configuration. In this study, an adapted procedure was developed to determine residual total suspended solids (TSSf) retained in pressurized sand filter media after backwashing. The method involves collecting the upper 0-100 mm layer of the sand bed, followed by drying, homogenization, controlled subsampling, sequential washing cycles under rotational agitation, and determination of suspended solids by vacuum filtration with 0.45 μm glass microfiber filters. Method validation included analysis of grain-size representativeness and reproducibility. The grain-size distribution curves obtained from different subsample masses showed Pearson correlation coefficients higher than 0.99 (p < 0.01), while coefficients of variation for TSSf determination remained below 20% for all evaluated samples. The procedure allows direct evaluation of residual solids retained in pressurized sand filters after backwashing and may be useful for comparing backwashing conditions and assessing filter cleaning performance in irrigation systems. The main features and applications of this method are as follows:•Quantification of residual solids retained in pressurized sand filter media after backwashing.•Representative sand subsampling and extraction of retained solids using sequential washing cycles.•Evaluation of backwashing performance in irrigation sand filters.
Vegetation restoration is widely regarded as a key measure for mitigating soil erosion in the middle reaches of the Yellow River. However, the regulation of flood sediment transport by forest-grass vegetation coverage (Ve) shows strong nonlinear characteristics and threshold effects. This makes it difficult for traditional physical models to accurately describe sediment production responses across different vegetation stages. To address this issue, this study proposes a stage-specific water-sediment simulation framework that integrates Ve threshold identification with machine learning, namely a threshold-aware modeling framework. Four typical basins in the middle reaches of the Yellow River, namely the Kuye, Wuding, Fen, and Wei River basins, were selected as the study areas. Based on multi-source data from 1979 to 2025, including flood events, rainfall, Ve, and land use, random forest was first used to identify the dominant factors controlling sediment production. The exponential function, piecewise linear regression, and Copula model were then combined to identify Ve threshold bands from three perspectives: functional form, structural breakpoint, and probability dependence. Based on the identified thresholds, threshold information was embedded into LSTM, XGBoost, and SVR models to construct overall and stage-specific simulation scenarios.The results show that: (1) Ve and rainfall are the key factors controlling sediment production; (2) clear Ve threshold bands exist in all basins, with recommended ranges of 31%∼35% for the Kuye River Basin, 27%∼32% for the Wuding River Basin, 23%∼28% for the Fen River Basin, and 20%∼23% for the Wei River Basin; (3) model performance improved significantly after introducing threshold information. Taking the Kuye River Basin as an example, the NSE values of the LSTM, XGBoost, and SVR models increased to 0.858, 0.861, and 0.830, respectively; and (4) vegetation exerted a strong regulatory effect on suspended sediment concentration in the pre-threshold stage, whereas the system gradually shifted to a sediment-supply-limited state in the post-threshold stage, with a markedly weakened marginal vegetation effect. This study reveals the stage-specific regulatory mechanism of vegetation restoration on water-sediment processes. It also provides new theoretical and methodological support for intelligent modeling of complex water-sediment systems and ecological management of river basins.
Effective early warning systems for aquatic contamination require monitoring strategies capable of detecting subtle, long-term shifts in mixture-driven biological activity. Suspended particulate matter (SPM) serves as a carrier and reservoir for complex contaminant mixtures, facilitating their transport and persistence in aquatic systems, yet systematic toxicological time series for archived SPM remain scarce. Regulatory monitoring predominantly targets Priority Substances and River Basin Specific Pollutants, leaving the temporal trends of particle-associated mixture toxicity largely unresolved. Leveraging 18 years (2005-2022) of cryogenically archived annual SPM composites from the Rhine River, we conducted a spatiotemporal effect-based assessment integrating receptor-mediated effects, oxidative stress analysis and untargeted Cell Painting phenomics. This integrated toolbox enabled evaluation of pathway-specific responses and multi-compartment cellular perturbations associated with particle-bound contaminant mixtures. Polar SPM-associated chemicals elicited oxidative stress response and caused endocrine disruption through estrogen receptor α (ERα) activation and androgen receptor inhibition (anti-AR). Trend analysis showed spatiotemporal variation along the river, with statistically increasing trends of oxidative stress and anti-AR activity over time at Koblenz, driven by polar chemicals. Both polar and non-polar SPM extracts activated the aryl hydrocarbon receptor (AhR), indicating presence of compounds capable of triggering xenobiotic response pathways. Several subcellular compartments were affected, with mitochondrial features being among the most affected. These findings demonstrate that SPM-associated chemicals elicit diverse toxicological effects by acting on several receptors and impacting diverse cellular structures. Combining targeted and phenomics-based effect approaches provided comprehensive mechanistic insights and valuable information to support the early warning systems for chemical contamination in aquatic environments.
The present research demonstrates the application of light-induced enhancement of cellular uptake of genetic materials (DNA and siRNA) into the living suspension cells. Based on the position of the photothermal conversion substrate, two distinct experimental setups were evaluated: (1) the bottom surface of a cell culture plate coated with a gold nanofilm functioning as the substrate, and (2) a setup where a cover glass coated with a gold nanofilm functioning as the substrate was placed over the cell culture medium. In both setups, the near-infrared laser was focused onto the gold nanofilm, inducing convection in the extracellular medium through light-induced condensation of cells and genetic material. Despite not using any transfection reagents whatsoever, this experimental technique significantly promoted the cellular uptake, cytosolic release, and expression of the target genetic material. In particular, the setup (2) exhibited lower cytotoxicity after light irradiation compared to setup (1) due to the photothermally controlled Marangoni convection. Additionally, the simulation results also confirmed the superiority of this system. These results demonstrate that the inverted optical condensation with the photothermal source at the top of the reaction container has the potential to enhance the transfection of genetic material into suspension cells avoiding the damage, and such a mechanism would be used for highly efficient transfection of various biochemical substances leading to the unconventional drug delivery.
The polarized scattering properties of particles constitute one of the fundamental issues in marine scientific research and serve as a cornerstone for active and passive optical remote sensing. Complex particle types influence the optical properties of water, thereby affecting the interpretation of remote sensing data. To gain a deeper understanding of the polarized scattering characteristics of different particles, we developed a Scheimpflug lidar and a backward small-angle Mueller matrix measurement system, and conducted measurement experiments on various standard particles and marine algal species. The Mueller matrix measurement system, designed with an off-axis parabolic mirror and a bilateral telecentric optical path, enables high-resolution measurement of the polarized volume scattering function at near-180° backward scattering angles. Based on rigorous calibration, the independent measurement results of the volume scattering function for the same sample by the two systems show good consistency, with a correlation coefficient of approximately 0.77 and a mean relative error of about 0.24. Within the effective detection range, Mie scattering simulations can effectively reproduce the measured results of the standard particle volume scattering functions. Three parameters were used for particle classification exploration: color ratio, depolarization ratio, and volume scattering function. Different types of particles exhibit different characteristics in this three-parameter classification, verifying the potential of multi-wavelength polarized oceanic lidar for the retrieval of particle groups, concentrations, and other parameters.
In this study, a method is proposed to develop a model for the estimation of chemical oxygen demand (effluent chemical oxygen demand [e-COD]), suspended solids (effluent suspended solids [e-SS]), and pH (effluent pH [e-pH]) parameters from the discharge water parameters using various parameters of the wastewater treatment plant of the Kayseri Organized Industrial Zone (KOIZ-WWTP). A gradient descent algorithm (GDA) based on machine learning, which is widely used to detect linear regression parameters, is proposed. In the first stage of the two-stage study, the relationships between each parameter were determined, and correlational selection was applied to the input parameters accordingly. Thus, the relationships of 11 input parameters with the target parameters were determined and the parameters with high correlation between them were determined and selected. A median filter was applied to all datasets used, thus smoothing out potential outliers and reducing noise. In the second stage the batch gradient descent variant of the GDA machine learning algorithm was used to determine model parameter values. The model was applied to the training dataset and its accuracies were obtained on the test dataset. Root mean square error (e-COD: 0.2771, e-SS: 0.2792, e-pH: 0.3240), variance accounting factor (e-COD: 91.9362, e-SS: 92.2587, e-pH: 88.6390), and R2 adj (e-COD: 0.910, e-SS: 0.913, e-pH: 0.873) were obtained as performance metrics. Thanks to this study, the model parameters were determined fairly accurately iteratively without overfitting. With this study, it is planned to reduce the labor force of this and similar facilities in terms of consumables, equipment, and most importantly time, by accurately estimating the effluent parameters of KOIZ-WWTP. Furthermore, this framework directly contributes to environmental sustainability and operational efficiency by offering potential energy savings in aeration processes and reducing the dependency on extensive laboratory analyses. A novel gradient descent‐based linear regression framework was developed to estimate effluent parameters (effluent chemical oxygen demand [e‐COD], effluent suspended solids [e‐SS], and effluent pH [e‐pH]) in the wastewater treatment plant of the Kayseri Organized Industrial Zone. Correlation‐based feature selection and median filtering were applied to enhance model robustness and reduce noise from operational anomalies. The batch gradient descent algorithm was used to iteratively optimize regression coefficients, yielding high predictive accuracy without overfitting. Strong correlations were identified between influent and biological treatment parameters, especially biological treatment–mixed liquor suspended solids, which showed dominant influence across all output variables. The model achieved high performance metrics on test data: Root mean square error (e‐COD: 0.2771, e‐SS: 0.2792, e‐pH: 0.3240), variance accounting factor (VAF) (>88%), and R2adj (>0.87). The proposed approach offers a cost‐effective alternative to sensor‐based monitoring, reducing labor, maintenance, and analysis time in wastewater operations.
This paper presents the design, fabrication, and characterization of a sub-milliwatt graphene-based micro thermal conductivity detector (µTCD) that utilizes a suspended multilayer graphene (MLG) bridge to sense volatile organic compounds (VOCs) in the gas phase based on their thermal transport properties. The graphene bridge is transferred onto a silicon chip with integrated microchannels using a photolithography-free process. By incorporating microchannel designs, this approach enables precise transfer of suspended MLG dimensions without conventional patterning steps. A key innovation of this work lies in the use of an ultra-low thermal mass suspended graphene architecture, which significantly increases temperature rise per unit input power, thereby enhancing sensitivity per unit power compared to conventional metal-based TCDs. The fabricated µTCD successfully produces chromatograms of multiple VOC species, closely matching those obtained using a standard flame ionization detector (FID). The device demonstrates an estimated limit of detection (LOD) of 190 ppm while consuming an average power of 151 µW under DC operation.
Heavy metals (HMs) pollution in mining areas poses significant threats for environmental integrity and public health, yet the transport pathways of HMs from mining wastes remain inadequately characterized. This study assessed contamination levels and human health risks of nine HMs across multiple environmental media within a small catchment in an iron-ore region and identified HM migration pathways from tailings using Pb isotopic tracing. Cd and Sb exhibited the highest contamination, while Zn, Pb, Mn, Cu, and Fe showed moderate enrichment, and Cr and Ni were relatively low. Pollution was most severe in tailings, ore mountain soils, and suspended particles, moderate in sediments and dust, and no-to-moderate in farmland soils. Although anthropogenic activities primarily contributed to HM sources, iron-ore mining had a limited impact on surrounding metal pollution. Over 80% and 50% of Pb accumulation in ore mountain soils and suspended particles were derived from the tailings, respectively, suggesting sediment transport as the main migration pathway of Pb in the region. Noncarcinogenic risk of HMs was acceptable for adults but exceeded safe levels for children in tailings, ore mountain soils, and suspended particles. Additionally, carcinogenic risk from Cr surpassed acceptable thresholds for both adults and children across all samples. These findings highlight the necessity for long-term monitoring of HMs in the orefield and urgent management of HM migration via riverine transport to mitigate potential pollution risks.
Rivers are major pathways for microplastic transport, yet controls on microplastic storage across different riverine environments remain poorly constrained, particularly in managed systems. Here, we quantify microplastic abundance and characteristics in water and sediments from the Kaskaskia River (Illinois), a regulated tributary of the Mississippi River, with a specific focus on differences between main channel and backwater environments. Water and sediment samples were collected from three locations downstream of Carlyle Lake and analyzed for microplastic concentration, morphology, size, and color. Microplastic (MP) concentrations averaged 2.9 MP/L in the water column and 34.4 MP/kg in sediments, with clear fibers dominating both matrices. Contrary to expectations that low-energy backwaters would act as preferential sinks, sediment microplastic concentrations were significantly (p < 0.05) higher in main channel environments in comparison to adjacent backwaters. In contrast, microplastic concentrations in water did not vary by site, riverine environment, or depth, and were not correlated with total suspended solids (p > 0.05), indicating a decoupling between microplastic and suspended sediment transport. Microplastics in backwater sediments were larger than those in the main channel (p < 0.05), suggesting enhanced fragmentation in high-energy main channel environments. These results demonstrate that in regulated, low-gradient rivers, main channels can function as microplastic storage zones, rather than solely as transport pathways, and backwaters may consistently serve as ecological refuges. Our findings emphasize the importance of cross-sectional sampling and environment-specific assessments to accurately characterize fluvial microplastic dynamics and inform freshwater monitoring and management strategies across all riverine environments. Rivers are main transport pathways for plastic pollution but can also act as microplastic storage zones within bed sediments. This study examined how microplastics are distributed in water and sediments across different environments in the Kaskaskia River (United States) downstream of a regulated reservoir. Samples were collected from the main channel (i.e., high energy) and adjacent backwaters (i.e., low energy). Sediment microplastic concentrations were higher in the main channel, while concentrations in the water column were similar across sites. Microplastics in main channel sediments were smaller, indicating relatively greater fragmentation in higher energy main channels. We show that in low‐gradient, regulated rivers, main channels can function as microplastic storage zones, while backwaters may serve as ecological refuges. Accounting for differences among river environments is important for accurately assessing microplastic dynamics and informing monitoring and management efforts.
ASP flooding wastewater contains crude oil, suspended solids, anionic polymers and surfactants, with high viscosity, high zeta potential, difficult demulsification, flocculation and slow separation and sedimentation. In order to solve the problem of wastewater treatment of ASP flooding in oil fields, a magnetic branched core was prepared from ethyl silicate (TEOS), nano Fe3O4 and aminopropyl triethoxysilane (APTES), and then reacted with polyamine and methyl acrylate to synthesize the magnetic hyperbranched molecule FSNMN with demulsification ability. Using acrylamide (AM), acryloxyethyl trimethylammonium chloride (DAC) and maleic anhydride (MA) as raw materials, cationic polymer long chain (CAMHA) with flocculating properties was synthesized and grafted with hyperbranched molecules. The demulsification flocculation ability of the product regarding ASP flooding wastewater was evaluated, and the demulsification flocculation mechanism was summarized. The results showed that the average molecular weight of 3-FSNMN4-C was 4.7 million, the cationic degree was 20.5%, and the saturation magnetization was 20 EMU/g. The removal rate of oil and suspended solids was 93.82% and 91.95% respectively when the simulated sewage was treated by magnetic field for 30 min. Magnetic hyperbranched star chain polymer provides a solution to the serious ecological environment problems caused by ASP flooding.
Mechanical failure of bone - not bone density - is the primary clinical concern for skeletal disorders and diseases. Bone mass and density are major contributors to whole bone strength, which is why therapeutics that regulate bone volume by altering resorption and formation are so effective. However, there are limits to the increases in bone mass and density achieved with existing therapeutics and challenges in maintaining gains after treatment is suspended. This perspective focuses on a major contributor to whole bone strength that is not directly addressed by existing therapeutics: the quality of bone extracellular matrix as measured by matrix mechanical properties. We review studies showing: a) whole bone strength is much more sensitive to variation in bone matrix quality than to bone mass/density, b) bone matrix quality varies in humans in ways sufficient to influence whole bone strength, and c) interventions may alter bone matrix quality with minimal effects on bone mass/density. A major limitation to discovering methods for improving bone matrix quality is that most preclinical studies focus on bone formation and bone density/mass and do not measure or report bone matrix mechanical properties. Identifying mechanisms that enhance bone matrix quality will require faster and more precise biomechanical assessments of bone matrix and studies specifying the molecular mechanisms that regulate the composition of bone extracellular matrix. While there are many observational reports of differences in bone matrix among individuals, here we argue it is time to go beyond observational studies and consider bone matrix as an attractive therapeutic target. Medical treatments for bone disease focus on increasing bone size or density. However, there are limits to what existing treatments can achieve. Here we argue that instead of just focusing on the amount of bone we should also address the quality of the mineralized material the bone is made from (the bone matrix). We review the importance of bone matrix quality and discuss recent studies showing that bone matrix may be modifiable in adults. We review new experimental methods that could help to identify the next generation of treatments for bone disease.
Wastewater-based epidemiology (WBE) is a promising tool for monitoring respiratory pathogens, yet the rapid in-sewer decay of viruses limits its application in low-incidence settings. This study investigates the decay rates of respiratory syncytial virus (RSV) in wastewater under various conditions to enhance the utility of WBE for forecasting RSV outbreaks. Laboratory-scale recirculating sewer systems were employed to simulate in-sewer decay of RSV at different temperatures, organic concentrations, and total suspended solids (TSS) levels. A first-order decay model revealed a significant temperature dependence, with the decay rate increasing approximately 30-fold from 4 to 35 °C. Conversely, higher TSS concentrations provided substantial protection to RSV particles, extending the T90 by up to sevenfold, whereas SCOD exhibited minimal impact. Based on these findings, a multiple linear regression model was established to identify key predictors. This study underscores the importance of understanding RSV decay kinetics for accurate WBE and highlights the value of kinetic modelling in correcting in-sewer viral loss for optimized wastewater surveillance.
Tissue-engineered tumour models utilizing organ-specific three-dimensional (3D) tumour spheroids are crucial for developing and validating novel imaging systems targeted to their detection. This study explores the use of 3D bioprinting to develop and standardise the precise placement of tumour spheroids on prefabricated tissue models. These models can be used as validation platform for novel biomedical diagnostic devices. Such integrated systems enable rapid, minimally invasive detection and characterisation of cancer cells, reducing reliance on traditional biopsy-based methods. The standardised deposition of spheroids onto prefabricated tissue models required, 1. the development of a spring-loaded bracket to keep tissue samples in a defined position during 3D printing; 2. a custom-made, Python-based graphical user interface (GUI) to facilitate the determination of spheroid printing positions within a standard 12-well cell culture plate. The program digitally reproduces the plate layout within a Cartesian coordinate system centred at the plate's geometric midpoint, ensuring compatibility with standard 3D bioprinter coordinate systems; 3. a standardized workflow to define the time-point of extrusion of spheroids and placement onto tissue, which is predetermined accurately by suspending the spheroids in density-matched methyl cellulose solution. The accurate placement of individual tumour spheroids (~500µm) onto prefabricated tissue models was validated using a custom-made positioning analysis plate, yielding results of translational offset at x = 27.4 µm, and y = 43 µm. This work utilizes simple-to-follow and readily available lab instruments and customized 3D bioprinters, rather than acquiring specialised equipment. Future studies will focus on shortening the workflow presented in-here, and assessing its adaptability to other particle-based tissue models, e.g., microgels.
The anaerobic/aerobic/anoxic (AOA) process involves the inevitable consumption of intracellular carbon sources during the aerobic phase, which limits the nitrogen removal efficiency of subsequent endogenous denitrification. Therefore, elucidating the effects of different aeration patterns on endogenous metabolic mechanisms and microbial interactions is crucial. In this study, the characteristics of intracellular carbon source utilization under continuous and intermittent aeration patterns were comparatively analyzed in continuous-flow suspended sludge reactors treating low C/N municipal wastewater. During 210 days of operation, both the continuous aeration system (AOAC) and intermittent aeration system (AOAI) achieved a total inorganic nitrogen (TIN) removal efficiency of approximately 90%. The results demonstrated that the AOAI system effectively reduced the oxidation of intracellular carbon sources within the aerobic zone. However, intermittent aeration was prone to introducing inter-zonal dissolved oxygen (DO) interference, which caused a metabolic lag in the anoxic phase and consequently constrained the rapid utilization of electron donors. Conversely, the prolonged continuous anoxic conditions in the AOAC system enriched the hydrolytic-fermentative bacterium Caldithrix. This enrichment of Caldithrix enhanced the utilization of extracellular polymers and cellular decay products, providing additional electron donors for endogenous denitrification. Furthermore, glycogen was identified as the core carbon source driving endogenous denitrification in both systems, with the dominant genus Rubrivivax playing a pivotal role. In conclusion, this study elucidated the impact of aeration patterns on the endogenous metabolic networks of the AOA process from the perspective of carbon utilization mechanisms, providing valuable insights for advancing low-energy, high-efficiency nitrogen removal.
The coalescence time of bubbles in a liquid depends on the nature of the liquid, which determines both its surface thermodynamics and the molecular interactions between the gas/liquid interfaces, and on the geometry, prescribed by the curvature of the bubbles. Coalescence is well described in pure liquids that have the same composition in bulk and at interfaces and in which the interactions are attractive. In contrast, the mechanisms are poorly understood in more complex liquids in which coalescence times are orders of magnitudes larger than in pure liquids and are unpredictable. To provide insight on these mechanisms, we use model systems: binary mixtures of miscible oils. In these liquids, interfaces have purely attractive molecular interactions and the surface thermodynamics can simply be described using a well-determined Gibbs elastic modulus, which is controlled by the composition of the mixture. We measure the coalescence rate by forming periodic trains of bubbles in millifluidic tubes whose radius varies over 1.5 decade. We report coalescence times spanning more than three decades and, for a given composition, varying according to a power law with curvature, with an exponent larger than that reported in pure liquids and independent of Gibbs elasticity. The experimental behavior is in excellent agreement with a numerical solution of the coupled thermodynamical and hydrodynamical equations, performed in the simple geometry of a suspended liquid film. Our results clearly reveal how geometry and surface thermodynamics modify the coalescence process of bubbles in the limit of small Gibbs elasticity.
Nitrogen dioxide (NO2) is mainly discharged from the burning of fossil fuels and remains suspended in the air with other particulate pollutants, which has a significant impact on the Earth's ecological environment and is harmful to human health. Schizophrenia is a nervous system disease involving emotion, thinking, and behavior. There is no consistent conclusion about the etiology of schizophrenia, though numerous studies are ongoing. Previous studies suggest that exposure to NO2 air pollution may increase the risk of schizophrenia, though this remains in the early exploratory stages. We conducted a 2-sample Mendelian Randomization study to investigate the potential causal effect of NO2 exposure on schizophrenia risk. Genetic instruments were selected from large-scale genome-wide association studies of European ancestry, including NO2 exposure (n = 456,380) and schizophrenia (n = 640,808). To ensure the reliability of our findings, we also conducted sensitivity analyses. Across all Mendelian Randomization models, NO2 exposure showed a significant positive causal effect on schizophrenia risk. Beta values ranged from 0.246 (weighted median estimator model, 102 single nucleotide polymorphisms) to 0.478 (inverse variance weighting model, 128 single nucleotide polymorphisms), with all P < .05. odds ratios ranged from 1.30 (95% confidence interval: 1.03-1.65, weighted median estimator, exposure: ukb-b-9942; outcome: ieu-b-5099) to 1.60 (95% confidence interval: 1.39-1.87, inverse variance weighting fixed effects, exposure: ukb-b-5620; outcome: ieu-b-5099). Sensitivity analyses and heterogeneity tests confirmed the robustness of these findings. These findings help further our understanding of the etiology of schizophrenia and provide a new perspective for air pollution mitigation.
This study evaluates ultraviolet-visible (UV-Vis) spectroscopy as a quantitative approach for turbidity detection in clear freshwater systems, where conventional methods often show limited sensitivity and reduced accuracy at low particle concentrations, particularly in reservoirs used for drinking water supply, where maintaining low turbidity is essential. Water samples were collected in 26 surface and bottom sampling sites, totaling 52 samples, along a clear water drinking water supply reservoir. The samples were analyzed using UV-Vis spectrophotometry (200-900 nm). Principal Component Analysis (PCA) was applied to identify spectral patterns, and Partial Least Squares (PLS) regression was used to develop predictive models for turbidity based on absorbance spectra. The first two PCA components explained 91% of the total variance, and clearly distinguished surface and bottom samples, indicating consistent spectral differences associated with vertical gradients. The PLS model showed a strong correlation (0.71 R2) between predicted and measured turbidity, demonstrating reliable performance under low turbidity conditions. These results indicate that UV-Vis spectroscopy enhances sensitivity in clear waters and enables the detection of subtle variations in suspended particles, overcoming limitations of conventional methods. Given the contrasting results reported in the literature regarding the application of spectroscopy to water samples, this study contributes to improving the use of this technique. Also, by integrating spectral analysis with multivariate calibration, this study refines turbidity assessment in freshwater systems and advances the validation of in situ measurements, providing a non-invasive approach for water quality monitoring and sediment dynamics evaluation.
To determine whether personal responsibility incentives in Medicaid differentially affect enrollment and the comprehensiveness of plan benefits among members who are non-Hispanic Black and non-Hispanic White. We conducted an interrupted time series analysis to estimate trends in racial disparity ratios of enrollment across more comprehensive Healthy Indiana Plans (HIP) before and during the COVID-19 Public Health Emergency (PHE) when the state suspended personal responsibility incentives, including monthly premium contributions. We analyzed restricted-access administrative data from the Indiana Family and Social Services Administration from 2018 through 2023. The analytic cohort comprised 939,667 non-Hispanic Black and non-Hispanic White adults (19-64 years) enrolled in one of four HIP tiers, including HIP Plus or HIP Basic, and HIP State Plan Plus or HIP State Plan Basic in which presence of a qualifying health condition is required for eligibility. Before the PHE, members who are non-Hispanic Black were approximately 23 percentage points less likely to be in the more comprehensive HIP Plus plan relative to members who are non-Hispanic White. An increase in the disparity ratio of 0.076 points toward parity (95% CI, 0.054-0.097 points) for HIP Plus recipients was observed following suspension of personal responsibility incentives during the PHE. After an administrative upgrade of all HIP Basic recipients to HIP Plus plans during July 2021, this disparity ratio increased an additional 0.146 points from the start of the PHE (95% CI, 0.141-0.151 points) to 0.994 (95% CI, 0.993-0.994). Personal responsibility incentives in Medicaid are associated with substantial and persistent racial disparities in enrollment and plan comprehensiveness. The study indicates that while the temporary removal of these incentives can reduce disparities, proactive policy interventions may be necessary to achieve and maintain equitable access to care.
Nanoscale heat transport plays an important role in energy conversion and thermal management. Therefore, understanding how nanoscale heat transport can be tuned is critical for developing novel technologies, including cooling strategies for microelectronics and nanostructured materials and devices for high-efficiency energy conversion. To probe nanoscale thermal transport phenomena, many calorimetric tools and approaches have been developed. Specifically, suspended microcalorimeters featuring picowatt resolution have been extensively employed for measuring thermal transport in low-dimensional materials, radiative heat transfer in nanoscale gaps, and between subwavelength structures. Further, scanning calorimetric probes, combined with atomic force microscopy and scanning tunneling microscopy, have been utilized for probing atomic-scale thermal transport and near-field thermal radiation. Here, we discuss these advances in calorimetric tools and their use for studying nanoscale thermal transport. We conclude by discussing open experimental challenges and highlighting the importance of future developments in subpicowatt resolution calorimetric tools for accessing unexplored nanoscale thermal transport phenomena.
Immobilization cell systems provides an efficient and environment friendly alternative for the treatment of recalcitrant pollutants present in industrial wastewater. Immobilization of microbial cells can be achieved via various techniques including adsorption on solid surface, encapsulation in semi-permeable polymers and cross-linking with polymeric compounds. It provides high biomass concentration, reusability, mechanical/chemical stability, easy solid-liquid separation, and resilience to harsh effluent conditions. Moreover, some support matrices like nanocomposites and biopolymers may serve the dual role, as an adsorbent as well as an aid in biodegradation/biosorption of target pollutant. Immobilized cell systems have sequestered various contaminants present in industrial wastewater such as; dyes, hydrocarbons, heavy metals, phenol and surfactants. Recent advances in new carriers; biopolymers, nanocomposites, and carbon materials improve bacterial cell stability and improve the pollutant removal efficacy in complex waste streams. This review examines the different strategies available, targeting merits of the immobilized cell system over suspended ones, selection criteria of the support matrix, and critical analysis of the studies conducted using immobilized cell matrices for the treatment of different industrial effluents. Along with pollutants removal, the options for resource recovery/isolation of value-added products from industrial wastewater are also highlighted. Considering limited reports on exploitation of immobilized bacterial cell system for treatment of real industrial wastewaters, and looking into the current and future load of industrial pollution, the comprehensive appraisal of the recent advancement of sustainable bioremediation strategies will promote industrial scalability, cost-efficiency and will contribute to circular economy.