Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.

There is a great deficiency in the collection and disposal of solid waste, with a considerable amount disposed of in dumps instead of in landfills. In this sense, the objective of this research is to propose a solid waste mitigation plan through recovery in the District of Santa Rosa, Ayacucho. For this, a solid waste characterization plan was executed in eight days, and through ANOVA it was shown that there is a significant difference in means between business pairs except between a bakery and a hotel. Through clustering, zones A and B are highly correlated, reflecting that the amount of organic waste was greater than inorganic waste. In the organic waste valorization plan, the results through ANOVA indicate a significant difference for monthly and daily averages, and the clustering shows the different behavior of each month, drawing attention to August, concluding that the valorization pilot plan is viable due to the contribution of a large amount of organic solid waste to the valorization plant.

Rapid advancements in artificial intelligence (AI) technology have brought about a plethora of new challenges in terms of governance and regulation. AI systems are being integrated into various industries and sectors, creating a demand from decision-makers to possess a comprehensive and nuanced understanding of the capabilities and limitations of these systems. One critical aspect of this demand is the ability to explain the results of machine learning models, which is crucial to promoting transparency and trust in AI systems, as well as fundamental in helping machine learning models to be trained ethically. In this paper, we present novel quantitative metrics frameworks for interpreting the predictions of classifier and regressor models. The proposed metrics are model agnostic and are defined in order to be able to quantify: (i) the interpretability factors based on global and local feature importance distributions; (ii) the variability of feature impact on the model output; and (iii) the complexity of feature interactions within model decisions. We employ publicly available datasets to apply our proposed metrics to various machine learning models focused on predicting customers' credit risk (classification task) and real estate price valuation (regression task). The results expose how these metrics can provide a more comprehensive understanding of model predictions and facilitate better communication between decision-makers and stakeholders, thereby increasing the overall transparency and accountability of AI systems.

As cooperativas de catadores dependem de políticas públicas voltadas para o setor de saneamento básico. Nos governos que adotam/implementam políticas de fomento à coleta seletiva de resíduos sólidos, as cooperativas tendem a se desenvolver: o número de cooperados e o volume processado aumentam. A experiência da ASMARE e da COOPAMARE ilustra perfeitamente esse padrão. Nos governos progressistas, em que há o objetivo claro de estimular a atividade, ambas cooperativas floresceram. No entanto, elas perderam cooperados e reduziram sua atividade nos períodos em que as mesmas políticas foram esvaziadas. A análise reforça a necessidade de reativação das políticas de estímulo à coleta seletiva de resíduos sólidos, de forma inclusiva e solidária.