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Pig iron, the source for a variety of iron-based products, is traded in commodity markets. Therefore, enhanced productivity has significant economic implications for the producers. Pig iron is mainly produced inside of tall, vertical, thermodynamic reactors called blast furnaces that run 24 hours a day. The blast furnaces are too complex to model explicitly and are generally regarded as black boxes. In this study, we design, develop, and deploy novel machine learning models on a rich data sample covering more than 20 production variables spanning nine years of actual operational period, collected at one of the largest pig iron production plants in Brazil. We show that, given the blast furnace parameters, machine learning models are capable of unveiling novel insights by illuminating the black box and successfully predicting production levels at different configurations. These prediction models can be used as decision aids to improve production efficiencies. We also perform a sensitivity analysis of the trained models to identify and rank the input variables according to their relative importance. We present our findings, which are largely in line with the existing literature, and confirm their validity, practicality, and usefulness through consultations with subject matter experts. [ABSTRACT FROM AUTHOR]

• We developed machine learning models for the classification of suicide risk in Brazil. • The Random Forests model achieved the best AUC ROC (0.814). • The Naive Bayes classifier presented the best sensitivity (0.922). • Features representing depression were the most relevant for the classification task. • Objective suicide risk stratification can provide the means to early preventive interventions. Even though suicide is a relatively preventable poor outcome, its prediction remains an elusive task. The main goal of this study was to develop machine learning classifiers to identify increased suicide risk in Brazilians with common mental disorders. With the use of clinical and sociodemographic baseline data (n = 4039 adult participants) from a large Brazilian community sample, we developed several models (Elastic Net, Random Forests, Naïve Bayes, and ensemble) for the classification of increased suicide risk among individuals with common mental disorders. 1120 participants (27.7%) presented increased suicide risk. The Random Forests model achieved the best AUC ROC (0.814), followed by Naive Bayes (0.798) and Elastic Net (0.773). Sensitivity varied from 0.922 (Naive Bayes) to 0.630 (Random Forests), while specificity varied from 0.792 (Random Forests) to 0.473 (Naive Bayes). The ensemble model presented an AUC ROC of 0.811, sensitivity of 0.899, and specificity of 0.510. Features representing depression symptoms were the most relevant for the classification of increased suicide risk. Some of our models presented good performance metrics in the classification of increased suicide risk in the investigated sample, which can provide the means to early preventive interventions. [ABSTRACT FROM AUTHOR]

The global increase of urban areas highlights the need to improve their adaptation to extreme weather events, in particular heavy rainfall. This study analyzes the impacts of in-situ rain gauges' distribution (by applying the fractal dimension concept) associated with a spatial diagnosis of flood occurrences in the municipality of Itaperuna, Rio de Janeiro–Brazil, performing an investigation of flood susceptibility maps based on transitory (considering precipitation) and on permanent factors (natural flood susceptibility). The fractal analysis results pointed out that the rain gauges' distribution presented a scaling break behavior with a low fractal dimension (0.416) at the small-scale range, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation method was used to validate both predictive maps with recorded data of the major January 2020 event, which indicated that the transitory factors' flood map presented an unsatisfactory Probability of Detection of floods (P O D = 0.552) when compared to the permanent factors' map (P O D = 0.944) . These issues allowed to consider the hydrological uncertainties associated with the sparse gauge network distribution and its impacts on the use of flood susceptibility maps. Such methodology enables the evaluation of other municipalities and regions, constituting essential information in aid of territorial management. [ABSTRACT FROM AUTHOR]