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Based on a systematic review of convolutional neural networks (CNN), this study explores the efficacy of small imaging sensors in monitoring the real-time presence of cyanotoxins and hazardous contaminants in urban ecosystems. To develop a machine learning-based CNN, this study first investigated the relationships between the prevalence of hazardous algal blooms (HABs) and faecal indicator bacteria (FIB) in waterways and aquifers of certain semi-arid zones of Sri Lanka, Sweden and New York (United States). By incorporating a popularly known AbspectroscoPY framework to effectively process the spectrophotometric data of the obtained samples, the formulation subsequently reveals strong positive correlations between FIB coliforms and nutrient loads (particularly nitrate and phosphate). A corroborative association with the incidence of chronic kidney disease of uncertain aetiology (CKDu) among the residents of the studied regions further affirms the reliability of the methodology. These findings underline the need for policymakers to consider the geographical and land-use traits of urban habitats in strategies aimed at reducing water-borne health hazards. [ABSTRACT FROM AUTHOR]

The spread of infectious diseases such as COVID-19 depends on complex fluid dynamics interactions between pathogens and fluid phases, including individual droplets and multiphase clouds. Understanding these interactions is crucial for predicting and controlling disease spread. This applies to human and animal exhalations, such as coughs and sneezes, as well as bursting bubbles that create micron-sized droplets in various indoor and outdoor environments. By exploring case studies in this regard, this study examines the emerging field of fluid dynamics in disease transmission, focusing on multiphase flows, interfacial flows, turbulence, pathogens, human traffic, aerosol transmission, ventilation, and breathing microenvironments. These results indicate that increased ventilation rates and local ventilation methods can effectively reduce the concentration of SARS-CoV-2-laden aerosols in the immediate breathing spaces between individuals. In a displacement-ventilated room, both neutral and unstable conditions were more effective in removing breathed SARS-CoV-2-laden aerosols from the air, regardless of the presence of test subjects. However, stable conditions may increase the risk of infection in individuals living in confined spaces. Thus, the findings of this study are useful for providing practical guidance for managing the spread of airborne infections. [ABSTRACT FROM AUTHOR]