Rodrigo Simon Bavaresco

Mental health issues significantly affect workers and industries by reducing productivity, generating economic losses, and contributing to psycho-physical conditions. Technological advancements are transforming workplaces, emphasizing mental health care through digitalization and automation. This study introduces Kaire, an intelligent model designed to identify and predict work-related stressors in industrial settings. Kaire utilizes an ontology-based framework combined with semantic rules and machine learning algorithms, including Support Vector Machine (SVM) and Random Forest (RF), to classify stressors and forecast events. Synthetic data simulating industrial routines evaluated Kaire’s performance, with SVM achieving 99.36% accuracy, 99.55% precision, 98.84% recall, and 99.19% F1 score. The Group Stress Index (GSI) analysis showed 88.39% similarity between calculated results and simulator outputs. While event prediction scored lower across all evaluated configurations, stressor identification and GSI estimation showed consistent results in the simulated scenario. Kaire contributes to stress identification and monitoring, supporting industries in promoting workers’ mental health and well-being.

Stress is a mental disorder responsible for impacting the industry through psychosomatic illnesses, loss of productivity, and accidents caused by stressful workplaces. Conversely, the literature indicates that fostering mental well-being among workers can boost motivation and performance while alleviating symptoms of stress. The fourth industrial revolution incorporated technologies into work that allowed the automation of processes and control of environments. The fifth revolution introduced the application of research and innovation aimed at a human-centered consciousness, enabling the advancement of mental health through sensors and wearables. Despite advancements in stress classification technologies, there remain opportunities for further research into identifying stress motivators within industrial work environments. In this sense, this paper proposes an ontology to identify stressors considering personal and environmental data, allowing knowledge generation related to work stressors for mitigating the problem. The methodology utilized in this ontology development consisted of seven stages and two evaluation phases. The findings addressed four key questions related to competency as outlined in the model. The results revealed potential stressful scenarios, including the timing of occurrence, shared locations, environmental factors, and identifying groups experiencing moments of stress. This study presents as a scientific contribution the first ontology to address the identification of work-related stressors in the industrial environment.

The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.

The interest in human phenotypes has leveraged interdisciplinary efforts encouraging a better understanding of the broad spectrum of psychological and behavioral disorders. Moreover, the usage of mobile and wearable devices along with unobtrusive computational capabilities provides an extensive amount of information that allows the characterization of phenotypes. This article describes the human phenotype through the lens of computational range and reviews state-of-the-art computational phenotyping. Furthermore, the article discusses computational phenotyping’s extension concerning the combination of intelligent environments and personal mobile devices, addressing technical, managerial, and ethical challenges. This combination reinforces ubiquitous computational capabilities for phenotyping as a facilitator of interdisciplinary information convergence in favor of clinical and biomedical research.

The growth in data generated by private and public organizations leads to several opportunities to obtain valuable knowledge. In this scenario, data science becomes pertinent to define a structured methodology to extract valuable knowledge from raw data. It encompasses a heterogeneous group of techniques that challenge the implementation of a single platform capable of incorporating all the available resources. Thus, it is necessary to formulate a data science workflow based on different tools to extract knowledge from massive datasets. In this context, high-performance computing (HPC) provides the infrastructure required to optimize the processing time of data science workflows, which become a collection of tasks that must be efficiently scheduled to provide results in acceptable time intervals. While few studies explore the use of HPC for data science tasks, in the best of our knowledge, none conducts an in-depth analysis of scheduling and load balancing on such workflows. In this context, this chapter proposes an analysis of scheduling and load balancing from the perspective of data science scenarios. It presents concepts, environments, and tools to summarize the theoretical background required to define, assign, and execute data science workflows. Furthermore, we are also presenting new trends concerning the intersection of data science, scheduling, and load balance.

O estresse mental é um dos principais causadores de problemas na saúde fı́sica e emocional. Desta forma, torna-se estratégico mensurar o estado de estresse diário por meio fisiológico a fim de utilizá-lo em aplicações que visem o bem-estar do indivı́duo. Entretanto, as movimentações diárias influenciam na coleta de dados fisiológicos, causando ruı́do. Este trabalho coleta e analisa os históricos de contextos de 5 indivı́duos com o intuito da classificação do estresse mental por meio da variabilidade da frequência cardı́aca. Como contribuição cientı́fica, o método visa identificar e remover ruı́do bem como mostrar uma relação entre os nı́veis de estresse e a localização em que foram mensurados. A melhor performance obtida, dentre três algoritmos de aprendizado de máquina utilizados foi do classificador Support Vector Machine, resultando em 82% de acurácia.