Development of an air purifier with UVC radiation and HEPA filter in the context of COVID-19

The transmission of respiratory infections has an important role on human health, especially in the current context of the COVID-19 pandemic. In this work, we present the assessment of an air purifier that uses ultraviolet-C (UVC) radiation and a "High Efficiency Particulate Air” (HEPA) filter as mechanisms to decontaminate indoor environments with low air circulation. To assess the physicochemical and microbicidal characteristics of the equipment, the irradiance produced by the lamp, the flow rate at the entrance and exit of the device, possible changes in the ozone concentration and the equipment's decontamination potential for Staphylococcus aureus, Escherichia coli and Candida albicans. The total dose of UVC radiation that the air receives when passing through the equipment was 801.4 μJ cm-2, which would represent an inactivation of up to 80% of SARS-CoV-2 in the air. Furthermore, the filtration efficiency dropped with smaller particle diameter, and reduced to around 60% for particles with less than 1 μm and remained above 90% for PM2.5 and PM10 . In microbiological tests, there was a reduction of 99.4%, 99.9% and 99.5% for S aureus, E. coli and C. albicans, respectively, in 11 minutes. © 2022 Associação Brasileira de Engenharia Sanitária e Ambiental Este é um artigo de acesso aberto distribuído nos termos de licença Creative Commons.

Socio-Economic and Governance Conditions Corresponding to Change in Animal Agriculture: South Dakota Case Study

Understanding sustainable livestock production requires consideration of both qualitative and quantitative factors in a temporal and/or spatial frame. This study adapted Qualitative Comparative Analysis (QCA) to relate conditions of social, economic, and governance factors to changes in livestock inventory across several counties and over time. This paper presents an approach that (1) identified factors with the potential to relate to a change in livestock inventory and (2) analyzed commonalities within these factors related to changes spatially and temporally. This paper illustrates the approach and results when applied to five counties in eastern South Dakota. The specific response variables were periods of increasing, no change, or decreasing beef cattle, dairy cattle, and swine inventories in the specific counties for five-year census periods between 1992 and 2017. In the spatial analysis of counties, stable beef inventories and decreasing dairy inventories related to counties with increasing gross domestic products. The presence of specific social communities related to increases in county swine inventories. In the temporal analysis of census periods, local governance and economic factors, particularly market price influences, were more prevalent. Swine inventory showed a stronger link to cash crop markets than to livestock markets, whereas cattle market price increases associated with stable inventories for all animal types. Local governance tools had mixed effects for the different animal types across space and time. The factors and analysis results are context-specific. However, the process considers the various socio-economic processes in livestock production and community development applicable to agricultural sustainability questions in the Midwest and beyond.

COVID-19 classification in X-ray chest images using a new convolutional neural network: CNN-COVID

Purpose: COVID-19 causes lung inflammation and lesions, and chest X-ray and computed tomography images are remarkably suitable for differentiating the new disease from patients with other lung diseases. In this paper, we propose a computer model to classify X-ray images of patients diagnosed with COVID-19. Chest X-ray exams were chosen over computed tomography scans because they are low cost, results are quickly obtained, and X-ray equipment is readily available. Methods: A new CNN network, called CNN-COVID, has been developed to classify X-ray patient’s images. Images from two different datasets were used. The images of Dataset I is originated from the COVID-19 image data collection and the ChestXray14 repository, and the images of Dataset II belong to the BIMCV COVID-19+ repository. To assess the accuracy of the network, 10 training and testing sessions were performed in both datasets. A confusion matrix was generated to evaluate the model’s performance and calculate the following metrics: accuracy (ACC), sensitivity (SE), and specificity (SP). In addition, Receiver Operating Characteristic (ROC) curves and Areas Under the Curve (AUCs) were also considered. Results: After running 10 tests, the average accuracy for Dataset I and Dataset II was 0.9787 and 0.9839, respectively. Since the weights of the best test results were applied in the validation, it was obtained the accuracy of 0.9722 for Dataset I and 0.9884 for Dataset II. Conclusions: The results showed that the CNN-COVID is a promising tool to help physicians classify chest images with pneumonia, considering pneumonia caused by COVID-19 and pneumonia due to other causes. © 2021, Sociedade Brasileira de Engenharia Biomedica.