Multiple exposure pathways and health risk assessment of potentially harmful elements for children and adults living in a coal region in Brazil.

Energy generated by coal can contaminate the environment by releasing toxic elements, including metals. The human health risk assessment (HHRA) associated with geographic information system (GIS) tools can assist the management of contaminated areas, such as coal mining areas. The objective of the study was to carry out the assessment and spatialization of the risk to human health of potentially hazards elements (PHEs) in the soil for children and adults, from multiple exposure routes (oral, inhalation and dermal) in the Candiota mines, largest coal mining region of Brazil. The non-carcinogenic risks (HQ) of PHEs (Cu, Pb, Zn, Ni, Cr, Fe, Mn, Cd, As and Se) and carcinogenic risks of As were estimated and spatialized. The results revealed a risk for children exposure to Mn, with greatest contribution through dermal route. Mn (HQderm 72.41-96.09% and HQinh 40.84-82.52%) and Fe (HQo 43.90-81.44%) were the metals with greatest contribution to human health risk among studied population. As did not present carinogenic risk to adults. The spatial distribution of non-carcinogenic risk showed that Cr, As, Fe, Pb, Ni, Zn and Cu have higher HInc close to the coal mining areas, while Mn, Se and Cd have the highest HInc values in surrounding municipalities (Pinheiro Machado; Pedras Altas and Hulha Negra). The use of HHRA associated with GIS tools provides important elements for decision-making in the management of contaminated sites, indicating chemical elements, locations, routes of exposure and priority target populations.

Business Simulation Games Analysis Supported by Human-Computer Interfaces: A Systematic Review.

This article performs a Systematic Review of studies to answer the question: What are the researches related to the learning process with (Serious) Business Games using data collection techniques with Electroencephalogram or Eye tracking signals? The PRISMA declaration method was used to guide the search and inclusion of works related to the elaboration of this study. The 19 references resulting from the critical evaluation initially point to a gap in investigations into using these devices to monitor serious games for learning in organizational environments. An approximation with equivalent sensing studies in serious games for the contribution of skills and competencies indicates that continuous monitoring measures, such as mental state and eye fixation, proved to identify the players' attention levels effectively. Also, these studies showed effectiveness in the flow at different moments of the task, motivating and justifying the replication of these studies as a source of insights for the optimized design of business learning tools. This study is the first systematic review and consolidates the existing literature on user experience analysis of business simulation games supported by human-computer interfaces.

Comunicação entre agentes de software especialistas em doenças cardíacas / Comunicación entre agentes de software especializados en enfermedades del corazón / Communication between software agents specializing in heart diseases

Objetivos: Este artigo relata a construção de um Sistema Multiagente para a tomada de decisão sobre o estado cardíaco de pacientes, usando uma base de dados real. Métodos: O sistema foi implementado com dois cardiologistas principais (Médico e Doutor) e um especialista (Especialista), onde, se houver discordância entre os dois primeiros, o Especialista é chamado para ser feito o desempate. Foi utilizado o framework Jason (que une AgentSpeak-L e Java) para o sistema e o software Weka para a criação da árvore de decisão do agente Especialista. Resultados: Como resultado, o agente Médico (com 72% de acerto) obteve melhores resultados que o agente Doutor, onde o Especialista foi utilizado como o validador. Conclusão: O uso de Sistemas Multiagentes pode auxiliar no processo de tomada de decisão em sistemas de área da saúde.

Objectives: This paper presents a Multiagent System to assist decision making on patients' heart state, using a real dataset. Methods: The system has two cardiologists (Physician and Doctor) and one expert (Specialist). If the Physician and Doctor Agents do not agree with each other, the Specialist agent is called to give a third opinion. We have used the Jason platform (which merge AgentSpeak-L and Java) for the system and the software Weka to generate the decision tree used by the Specialist. Results: As a result, the Physician (with 72% of accuracy) obtained better results than the Doctor. The Specialist was used as the validator. Conclusion: The use of Multiagent Systems can help in the decision-making process in healthcare systems.

Objetivos: Este artículo informa sobre la construcción de un Sistema Multiagente para la toma de decisiones sobre el estado cardíaco de los pacientes, utilizando una base de datos real. Métodos: El sistema se implementa de tal manera que hay dos cardiólogos principales (Médico y Doctor) y un especialista (Especialista), donde, si hay desacuerdo entre los dos primeros, el Especialista es llamado para que sea el desempate. Utilizamos la plataforma Jason (que une AgentSpeak-L y Java) para el sistema y el software Weka para crear el árbol de decisión del agente experto. Resultados: Como resultado, el Agente Médico (con 72% correcto) obtuvo los mejores resultados entre los dos y el Experto fue validado como experto para la base. Conclusión: El uso de sistemas multiagente puede ayudar en el proceso de toma de decisiones en los sistemas de salud.

Circadian Rhythm and Pain: Mathematical Model based on Multiagent Simulation.

The circadian rhythm is responsible for the daily variations in metabolism, and circadian rhythm disorders have direct implications for many diseases, such as obesity and mental disorders. The regulation of sleep time is the most common example of the importance of the circadian rhythm for the functioning of the human body. In this sense, this work aims to study a mathematical and computational model based on multiagent simulation that simulates the synchronization and desynchronization of the circadian rhythm in relation to the pain variables. The results from the multiagent simulation of circadian rhythms show that in relation to pain, sleep, especially its biological rhythms, is directly affected by pain. In this way, our mathematical model was able to show that pain causes changes in the circadian rhythm and it can contribute to the medical field analysis.

Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude.

An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.

Discovering Patterns in Brain Signals Using Decision Trees.

Even with emerging technologies, such as Brain-Computer Interfaces (BCI) systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain's behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT) to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain's behaviour.