The Development and Testing of Pour-Flush Toilet Sensors for Understanding User Interaction in Peri-Urban Households

Rapid worldwide urbanization has created peri-urban environments that often lack services and infrastructure for water and sanitation. Globally, around 4.5 billion people do not have access to safely managed sanitation, as is often the case in such environments. Efforts to develop appropriate sanitation alternatives in these contexts recognize the value of understanding users’ preferences and interaction with their sanitation systems, however, the traditional tools for assessing technology usage and adoption are based on physical observation, which presents limitations. In this work, we developed a toilet sensor to identify usage patterns of pour-flush toilets by quantifying flushing and defecation events. The device has a methane gas sensor, IR distance sensor and a motion sensor connected to a microcontroller. Its small footprint allows for unobtrusive installation inside a toilet bowl and operates battery-powered for about 5 days depending on usage patterns. To evaluate the sensor performance, units were installed for a field trial in nine participants’ households in a Mexican peri-urban community and an algorithm for automated data analysis was developed. Surveys were also conducted to benchmark the sensor performance and determine the potential value of the approach. Results showed that on average people underreported their daily toilet usage by two events compared to the measurements and they flushed only 75% of the time after defecation. By monitoring the usage of the current pour-flush toilets lacking piped water and sewerage and complementing the data with users’ feedback, we can gain an understanding of the existing limitations so more suitable sanitation alternatives can be proposed.

Artificial Intelligence in Healthcare: Validating an AI Algorithm in Health Institutions in the COVID-19 Pandemic (a Use Case)

The paper considers new challenges related to public health. Action is needed to improve access to healthcare while maintaining its quality. The introduction of AI-based automated data analysis systems can be a solution to that. The present study seeks to assess the use of AI in outpatient care to detect pathological changes in the lungs typical of a coronavirus amidst the pandemic. The sample size was 600 patients. The results were statistically and analytically processed. The sensitivity attained 94%; the specificity, accuracy and the area under the ROC curve were 77%, 83%, and 87%, respectively. The negative predictive value was 97%; the positive predictive value was 66%. The data obtained show that the algorithm separates the CT scan results having no abnormalities in the lungs. The authors conclude that the usage of AI technologies helped to improve diagnostic accuracy during the COVID-19 pandemic. Artificial intelligence algorithms can also work with patients in non-pandemic times, thus improving healthcare access.

Intelligent System for Solving Problems of Veterinary Medicine on the Example of Dairy Farms

This article describes an automated expert system developed to diagnose cow diseases and assist veterinarians in treatment. We set before a diagnostic method based on the analysis of observed symptoms and experience of veterinarians. The system represents a web interface for maintaining a database of diseases, their symptoms and treatment methods, as well as a smartphone application for the diagnostics in offline mode. The developed intelligent system will allow agricultural producers to make specific decisions based on automated data analysis. Also presented in the article the information on the developed expert system, and the results of tests and testing during its use. The economic efficiency and importance of the work is determined by the possibility of automated recording of data on the livestock of animals, zoo technical and veterinary operations.