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The SARS-CoV-2 virus can cause thrombotic microangiopathy (TMA) by alternate pathway activation. We present a case of a young female patient who presented with fever and dialysis-dependent acute kidney injury. On evaluation, she was diagnosed with COVID-19-induced complement-mediated thrombotic microangiopathy (CM-TMA). She was initially managed with plasma exchange (PLEX). However, because of poor response to PLEX, she was administered Inj Eculizumab. She became dialysis independent and had normal renal function on follow-up visits. [ABSTRACT FROM AUTHOR]

This study contributes to the field of sustainable supply chain management by shedding light on the relationship between overdependence on supply chain partners, exploitation, and the willingness of business-to-business partner firms to focus on sustainability performance. This study further investigates how ethical culture moderates this relationship. Drawing on the resource dependency theory, the study involves developing a model and validating it using multivariate analysis among 120 dyads. The findings reveal that a business-to-business firm's overdependence on its supply chain partners can lead to it being exploited, which in turn negatively impacts its willingness to prioritize sustainability within the supply chain. Moreover, the study demonstrates that the stronger the firm's ethical culture, the weaker the effect of overdependence on exploitation, while at a lower level of the firm's ethical culture, the effect of overdependence on exploitation is stronger. The utilization of resource dependency theory unveils the potential downsides of overreliance on business-to-business supply chain partners and its consequences within the supply chain. [ABSTRACT FROM AUTHOR]

This study investigates the spatial and temporal variations of PM2.5 concentrations in Delhi from 2014 to 2023, utilizing ground-based measurements from the Central Pollution Control Board (CPCB) and MERRA-2 reanalysis data. The analysis reveals strong positive correlations (r > 0.90) across all districts, highlighting city-wide factors influencing PM2.5 levels, such as vehicular emissions, industrial activities, and regional weather patterns. Seasonal patterns show PM2.5 concentrations peaking during winter, attributed to lower temperatures, reduced wind speeds, and increased emissions from heating sources. To enhance the accuracy of PM2.5 predictions, various machine learning (ML) models were employed, including Extra Trees Regressor, Random Forest Regressor, Light Gradient Boosting Machine (LGBM) Regressor, and a Stacking Regressor. These models utilized MERRA-2 sub-parameters like Dust, Organic Carbon, Black Carbon, Sea Salt, and Sulfate. The Stacking Regressor demonstrated the best performance, achieving an R² value of 0.67 and a significant improvement in correlation with CPCB measurements (r = 0.86). The ML models significantly improved the prediction accuracy of PM2.5 concentrations compared to the original MERRA-2 data, reducing the Mean Bias from -39.4 µg/m³ to around 10.4µg/m³ and the Root Mean Squared Error (RMSE) from 71.1 µg/m³ to below 40 µg/m³. Additionally, the Fraction of predictions within a factor of 2 increased from 0.61 for MERRA-2 to over 0.89 for all ML models. These findings underscore the effectiveness of integrating machine learning models with MERRA-2 sub-parameters to accurately estimate PM2.5 concentrations. This approach provides more reliable predictions of air quality, essential for developing targeted and effective air quality management strategies in Delhi. [ABSTRACT FROM AUTHOR]

Cybersecurity threats have emerged as a critical concern affecting educational institutions, with schools becoming prime targets for cyberattacks. In response to this growing threat landscape, this paper critically analyses and assesses various cybersecurity awareness programs designed for the school education system. The introduction sets the stage by highlighting the increasing importance of cybersecurity in schools and outlining the objectives of the analysis. Firstly, it examines the array of cybersecurity threats that schools face, underscoring the potentially severe consequences of these threats. The analysis then elaborates on the imperative need for cybersecurity awareness in schools, emphasizing the vulnerabilities inherent in educational institutions and the vital role of education in mitigating these risks. A thorough examination of existing cybersecurity awareness programs in schools follows, offering insights into their objectives, methodologies, and target audiences. Despite their merits, existing programs confront various challenges and limitations, including financial constraints, scalability issues, and adaptability concerns, which are scrutinized in detail. Moreover, the paper addresses the pivotal roles played by teachers and school administrators in promoting cybersecurity awareness, emphasizing their training requirements and responsibilities. The paper also considers the legal and ethical dimensions of cybersecurity education in schools, with a focus on safeguarding student privacy and obtaining consent. Anticipating the evolving landscape of cybersecurity, the paper concludes with a forward-looking perspective, offering predictions about future trends in cybersecurity and providing actionable recommendations for enhancing cybersecurity awareness programs in schools. In summary, this critical analysis offers valuable insights into the complex world of cybersecurity education in schools, emphasizing the need for proactive measures to safeguard educational institutions and their stakeholders in an increasingly digital world. [ABSTRACT FROM AUTHOR]

In this study, a new adsorbent has been developed by modifying a low-cost and readily available waste, namely the dried orange (Citrus aurantium Linn) peel (CiR). The removal of heavy metals pollutants by binding of magnesium oxides (MgO) on the peel represents a simple, economical, and faster method. Various analysis, including X-ray diffraction, Fourier transforms infrared spectroscopy and application of mathematical models, have been performed on the modified adsorbent (dried Citrus reticulata peel + MgO; CiR-MgO matrix). The abundant free functional groups present in the biomass and metal oxides collectively exert interactive forces leading to the sorption of Zn2+ heavy metals. The Langmuir isotherm and pseudo-first order kinetic models are confirmed as the best fitted model of the adsorption process through chi-analysis. The rate-limiting step of intra-particle diffusion governs the adsorption process. This method is best suited for fixed-bed column application due to its fast kinetics, hydrophilic nature, and regeneration process with 0.005 M HCl. [ABSTRACT FROM AUTHOR]