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In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (Rw) from AC for the Operational Land Imager lite (ACOLITE)’s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining Rw from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy (ε) by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and ε by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (|β|) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0–300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options.

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided.

Satellite-derived bathymetry (SDB) is a cost-effective method for estimating water depth in inland and coastal waters, but is only applicable to optically shallow water (OSW). Determining the appropriate extent of SDB maps and the depth threshold for accurate SDB model predictions has therefore been a challenge for practical applications of SDB. Previous studies have used either a numeric cut-off value or manually delineated OSW to determine where to apply, and where not to apply, SDB models. We compared the use of a threshold applied to the predicted depth, automated delineation of OSW using a published model, and manual delineation of OSW, to determine which method of masking unsuitable pixels for SDB performs best. We used a water-leaving reflectance Sentinel-2 image of the St. Lawrence River, and a Random Forest model using neighbouring pixel information to predict SDB. We then compared the different approaches to masking unsuitable pixels in terms of the mean absolute error (MAE) of the retained predictions and the total mapped area. The application of a model-predicted depth threshold is easy to implement and achieved an MAE of 0.54 m, outperforming automated and manual OSW delineation methods, which had MAEs of 1.39 m and 1.64 m respectively over an approximately 100 km2 study area. Future studies should further investigate these and other methods for masking pixels unsuitable for SDB under a wider range of environmental conditions.

Background Phthalic acid esters, including diethyl phthalate (DEP), which are considered as top-priority and hazardous pollutants, have received significant attention over the last decades. It is vital for industries to select the best treatment technology, especially when the DEP concentration in wastewater is high. Meanwhile, anaerobic biofilm-based reactors are considered as a promising option. Therefore, in the present study, for the biological removal of DEP from synthetic wastewater, two different anaerobic biofilm-based reactors, including anaerobic fixed film baffled reactor (AnFFBR) and up-flow anaerobic fixed film fixed bed reactor (UAnFFFBR), were compared from kinetic and performance standpoints. As in the previous studies, only the kinetic coefficients have been calculated and the relationship between kinetic coefficients and their interpretation has not been evaluated, the other aim of the present study was to fill this research gap. Results In optimum conditions, 90.31 and 86.91% of COD as well as 91.11 and 88.72% of DEP removal were achieved for the AnFFBR and UAnFFFBR, respectively. According to kinetic coefficients (except biomass yield), the AnFFBR had better performance as it provided a more favorable condition for microbial growth. The Grau model was selected as the best mathematical model for designing and predicting the bioreactors’ performance due to its high coefficients of determination (0.97 < R 2). With regard to the insignificant variations of the calculated Grau kinetic coefficients (K G) when the organic loading rate (with constant HRT) increased, it can be concluded that both of the bioreactors can tolerate high organic loading rate and their performance is not affected by the applied DEP concentrations. Conclusions Both the bioreactors were capable of treating low-to-high strength DEP wastewater; however, according to the experimental results and obtained kinetic coefficients, the AnFFBR indicated higher performance. Although the AnFFBR can be considered as a safer treatment option than the UAnFFFBR due to its lower DEP concentrations in sludge, the UAnFFFBR had lower VSS/TSS ratio and sludge yield, which could make it more practical for digestion. Finally, both the bioreactors showed considerable methane yield; however, compared to the UAnFFFBR, the AnFFBR had more potential for bioenergy production. Although both the selected bioreactors achieved nearly 90% of DEP removal, they can only be considered as pre-treatment methods according to the standard regulations and should be coupled with further technology.

Background and Aims it is important to identify patients at higher risk for severity and poor outcomes of COVID‐19 infection, to have better disease management and pandemic control. In this study, we aimed to assess the distribution of ABO and Rh blood groups in hospitalized COVID‐19 infected patients and demonstrate its association with severity and outcomes of the disease. Methods This is a cross‐sectional study at Ziaeian Specialist Hospital, in Tehran, Iran. Of all confirmed COVID‐19 infected patients who were admitted to this hospital, 273 patients were enrolled in this study and categorized based on their disease severity or clinical outcomes including intensive care unit (ICU) admission, need for mechanical ventilation and mortality. The distribution of ABO and Rh blood groups was assessed and compared between different groups, to investigate the association of blood group types with disease severity or outcomes. Also, the study population was categorized based on their blood group types to demonstrate the association of laboratory parameters, radiologic findings, and length of hospitalization with blood groups. Sex, age and underlying disease were adjusted in the final model by multivariate regression analysis. Results This study showed that Blood group A (35.9%) was the most prevalent among hospitalized COVID‐19 patients followed by O (34.8%), B (21.6%), and AB (7.7%) (A > O > B > AB). ABO and Rh blood group was not associated with disease severity, need for mechanical ventilation, or ICU admission, while blood group B was associated with an increased risk of death in comparison with type O, in hospitalized COVID‐19 patients (p = 0.02). The number of patients with severe levels of C‐reactive protein (CRP) test results was lower in O blood group patients in comparison with non‐O blood groups (p = 0.01). Conclusion No significant association was found between blood groups and other lab tests, radiologic findings, and length of hospitalization.

Bisphenol A (BPA) is an industrial chemical used primarily in the manufacture of polycarbonate plastics and epoxy resins. BPA is considered an endocrine-disrupting chemical (EDC) because it interferes with hormonal systems. Over the decades, several techniques have been proposed for BPA removal in wastewaters. This study discusses recent advancements and progress of effective techniques for BPA removal, including membrane, adsorption, advanced oxidation process (AOPs), and biodegradation. The mechanisms of BPA adsorption on modified adsorbents include pore-filling, hydrophobic interactions, hydrogen bonding, and electrostatic interactions. Among the various agricultural waste adsorbents, Argan nut shell-microporous carbon (ANS@H20–120) exhibited the highest efficiency in removing BPA. Furthermore, the performance of magnetic treatment for activated carbon (AC) regeneration is introduced. According to the present study, researchers should prioritize agricultural waste-based adsorbents such as ACs, highly microporous carbons, nanoparticles, and polymers for the removal of BPA. In particular, the combination of adsorption and AOPs (advanced oxidations) is regarded as an efficient method for BPA removal. A series of relevant studies should be conducted at laboratory, pilot, and industrial scales for optimizing the application of agricultural waste-based AC to reduce BPA or other refractory pollutants from an aqueous environment.

SARS-CoV-2, primarily affecting the respiratory system, is also found in fecal samples from COVID-19 patients, demonstrating wastewater as a significant route for viral RNA transmission. During high prevalence periods, healthcare facility wastewater became a potential contamination source. Understanding the role of wastewater in epidemiology is crucial for public health risk assessment. In hospitals, with a specific number of COVID-19 cases, wastewater analysis offers a unique opportunity to link virus presence in wastewater with COVID-19 hospitalizations, a connection that is not possible in urban wastewater treatment plants (WWTPs). Shorter wastewater transit times enable more accurate virus tracking. With documented infection rates and rigorous testing, hospitals are ideal for wastewater monitoring, revealing practicalities and limitations. This review updates global efforts in quantifying SARS-CoV-2 in hospital wastewater, considering hospitalization rates' influence on viral RNA levels and comparing disinfection methods. Insights gleaned from this study contribute to Wastewater-based Epidemiology (WBE) and can be applied to other virus strains, enhancing our understanding of disease transmission dynamics and aiding in public health response strategies.

Emerging and hazardous environmental pollutants like phthalic acid esters (PAEs) are one of the recent concerns worldwide. PAEs are considered to have diverse endocrine disrupting effects on human health. Industrial wastewater has been reported as an important environment with high concentrations of PAEs. In the present study, four short-chain PAEs including diallyl phthalate (DAP), diethyl phthalate (DEP), dimethyl phthalate (DMP), and phthalic acid (PA) were selected as a substrate for anaerobic fixed film fixed bed reactor (AnFFFBR). The process performances of AnFFFBR, and also its kinetic behavior, were evaluated to find the best eco-friendly phthalate from the biodegradability point of view. According to the results and kinetic coefficients, removing and mineralizing of DMP occurred at a higher rate than other phthalates. In optimum conditions 92.5, 84.41, and 80.39% of DMP, COD, and TOC were removed. DAP was found as the most bio-refractory phthalate. The second-order (Grau) model was selected as the best model for describing phthalates removal.

Deploying large language models (LLMs) on edge devices is challenging due to their limited memory and power resources. Cloud-only inference reduces device burden but introduces high latency and cost. Static edge-cloud partitions optimize a single metric and struggle when bandwidth fluctuates. We propose Splitwise, a novel Lyapunov-assisted deep reinforcement learning (DRL) framework for fine-grained, adaptive partitioning of LLMs across edge and cloud environments. Splitwise decomposes transformer layers into attention heads and feed-forward sub-blocks, exposing more partition choices than layer-wise schemes. A hierarchical DRL policy, guided by Lyapunov optimization, jointly minimizes latency, energy consumption, and accuracy degradation while guaranteeing queue stability under stochastic workloads and variable network bandwidth. Splitwise also guarantees robustness via partition checkpoints with exponential backoff recovery in case of communication failures. Experiments on Jetson Orin NX, Galaxy S23, and Raspberry Pi 5 with GPT-2 (1.5B), LLaMA-7B, and LLaMA-13B show that Splitwise reduces end-to-end latency by 1.4x-2.8x and cuts energy consumption by up to 41% compared with existing partitioners. It lowers the 95th-percentile latency by 53-61% relative to cloud-only execution, while maintaining accuracy and modest memory requirements.

Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and employs a heuristic-aided evolutionary game theory approach to solve it efficiently. To evaluate MOFCO, we simulate mobile users using SUMO, providing realistic mobility patterns. Experimental results show that MOFCO reduces system cost, defined as a combination of latency and energy consumption, by an average of 19% and up to 43% in certain scenarios compared to state-of-the-art methods.