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Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans.In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don't always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don't like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm.Combining both optimization-and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks.We've developed a context-aware emotionbased model to design intelligent agents for group decision-making processes. To evaluate this model, we've incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion-and argumentbased factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

Chagas disease is a public health problem, affecting about 7 million people worldwide. Benznidazole (BZN) is the main treatment option, but it has limited effectiveness and can cause severe adverse effects. Drug delivery through nanoparticles has attracted the interest of the scientific community aiming to improve therapeutic options. The aim of this study was to evaluate the cytotoxicity of benznidazole-loaded calcium carbonate nanoparticles (BZN@CaCO3) on Trypanosoma cruzi strain Y. It was observed that BZN@CaCO3 was able to reduce the viability of epimastigote, trypomastigote and amastigote forms of T. cruzi with greater potency when compared with BZN. The amount of BZN necessary to obtain the same effect was up to 25 times smaller when loaded with CaCO3 nanoparticles. Also, it was observed that BZN@CaCO3 enhanced the selectivity index. Furthermore, the cell-death mechanism induced by both BZN and BZN@CaCO3 was evaluated, indicating that both substances caused necrosis and changed mitochondrial membrane potential.

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In austenitic stainless steels, plastic deformation can induce martensite formation. The induced martensite is related to the austenite (γ) instability at temperatures close or below room temperature. The metastability of austenite stainless steels increases with the decreasing of stacking fault energy (SFE). In this work, the deformation induced martensite was analyzed by X ray diffraction, electron back scatter diffraction (EBSD), magnetic methods and atomic force microscope (AFM) in samples of a low SFE austenitic stainless steel, AISI 301LN and compared with a medium SFE stainless steel, AISI 316L. Both techniques, X ray diffraction and EBSD, presented similar quantities for the α'-martensite. Texture results indicate that the crystallographic orientation of the formed α'-martensite is {001}<110> and {103}<110>. The morphology of α'-martensite was analyzed by AFM. Corrosion tests showed that deformation reduces pitting corrosion and generalized corrosion resistance in both steels.