SOUTHERN JOURNAL OF SCIENCES

New tool for authors.

Background: A new management model for construction and demolition waste (CDW) was proposed for the city of Rio de Janeiro, hence waste the city planning area estimated generation; the results were compared with the data published by COMLURB and SNIS through the statistics tests was proved that CDW had been dispersed, which support the hypothesis that the current model cause dispersion of the rubble. Aim: This study proposes a new management model for CDW to the city, according to regions where occur the higher production of rubble. Methods: The estimation of rubble was obtained through the issued licenses from 2006 to 2020 published by SMPU. The quantification of rubble in the city was carried out by adopting the generation indicator from licensed construction activities. The amount of CDW published by COMLURB and SNIS was compared through statistics tests ANOVA and T-Test, the second period from 2006 to 2020 and 2011 to 2020. To measure the area to implant a CDW recycling plant, criteria from production capacity were used. Results: ANOVA test to data from 2006 to 2020 and 2011 to 2020, according to a confidence interval of 95%, found the P value 0.589 and 0.022, respectively, it was verified that the significant difference is between data from COMLURB and Estimated. The T-test was applied from the same period in data from SNIS and estimated the P values 0.399 and 0.014. Discussion: The data from Estimating between 2006 and 2020 showed the best representation. The Área de Planejamento – AP (Planning Area) 4 was where 50.70% of the total rubble, after the AP 3 with 17.66%, the AP 5 was estimated at 16.59%, last of all, AP 2 and AP 1 were calculated the generation of 8.28% and 6.77%, respectively. Therefore, sizing the CDW recycling producing 361,99.00 tons per year demands an area of 32,397.50 m². Conclusions: It was concluded that the receipt of CDW in transfer stations managed by COMLURB is not allowed; therefore, the need to create a construction waste recycling plant was presented, to this end, it is necessary to have an available area of approximately 32,397.50 m².

Background: Medical institutions collect a vast amount of sensitive patient data for personalized treatments and health trend analysis. However, this raises concerns regarding the privacy of patient data, as it contains sensitive and confidential information. Aims: Develop an anonymization tool using diverse techniques to protect data while preserving its utility. Methods: A Python-based data anonymization tool for medical datasets supporting both categorical and numerical data is developed. It employs various methods, including data perturbation, binning, scaling, transformation, and differential privacy. Results: The tool was able to anonymize sensitive data, both categorical and numerical, while preserving its utility for further analysis. Discussion: The Privacy-Preserving Data Anonymization Tool advances sensitive medical data management by anonymizing both categorical and numerical data using various techniques while retaining data utility. Conclusions: The Anonymization Tool addresses patient data privacy concerns by balancing data utility with privacy, enabling secure medical data use in research.