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The internal audits of a company are a fundamental process for quality assurance and proper compliance with different processes within a company. For this reason, it is also important that companies are able to adequately plan their implementation; But it often happens that companies do not have the necessary tools to make these adequate plans. Within the topic of planning, in this context, the factors of effort of internal auditors in work hours and capacity (number of audits) are taken into account. These aspects are vital for the organization of times and audits over a period of time, since, if an adequate calculation is not made, it can significantly affect planning and trigger different consequences for the company and its processes. Given the above, it was proposed to develop and implement predictive and prescriptive analytics models to facilitate the planning and optimization of internal audits in terms of hours and number of audits (2 models); improving an (existing) digital planning tool. Historical audit information was obtained, worked and processed within an IT company. Different Machine Learning Python libraries were applied to carry out the modeling process, which include: PyCaret, H20, FLAML, TPOT, LazyRegressor, among others. Various evaluation metrics such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) were used to evaluate the models. The best results were obtained by Random Forest models, where R2 metrics of 94.40% and a MAPE of 6.26% were obtained for the hours of effort model; An R2 of 90.49% and MAPE of 6.77% were obtained for the capacity model. [ABSTRACT FROM AUTHOR]

Lithium, a lightweight metal, is used in various applications such as batteries, cell phones, ceramics, glass, and bipolar disorder medications. It’s been brought to worldwide attention due to its high charge storage capacity in small places. However, concerns arise regarding the environmental and social impact of lithium extraction near mining communities. Issues include groundwater depletion, land destruction and community displacement. This study aims to develop a dynamic model using machine learning algorithms to measure the socio-economic and environmental impact of lithium mining projects. The proposed dynamic model reveals that, if keeping demand and production as of today, lithium mining will become an irresponsible practice by 2040. Hence, it is crucial to implement policies like battery recycling, reforestation initiatives by mining entities, and potential production limitations to ensure a sustainable lithium industry. [ABSTRACT FROM AUTHOR]

Internal auditing is a fundamental process for quality assurance and compliance of processes within a company. For this reason, it is very important that companies can adequately plan its execution. One of the most important aspects to consider for effective planning is the calculation of the estimated time it takes to execute the audits. This project proposes the use of machine learning to develop a model that can predict the effort time (referred to as Actual Effort) that is required to carry out a certain number of audits in a period of two weeks (referred to as Sprint) in an information technology services company. In the early stages of the project, it was intended to develop an algorithm that predicts the Actual Effort for a single audit, but due to inefficient results, the approach of predicting the Actual Effort per Sprint was preferred. Many types of models were implemented along the project and various evaluation metrics, such as coefficient of determination (R²), symmetric mean absolute percentage error (SMAPE), and others, were evaluated. The best results were obtained by an ETR model where an R² of 96% and a SMAPE of 8.47% were obtained. [ABSTRACT FROM AUTHOR]