Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

Governance and Corporate Management Systems have become key tools for the comprehensive management of organizations, which, when implemented, guarantee corporate success, which can materialize in obtaining corporate goals, strategic objectives, KPIs, and KRP. This paper is the product of research exercises where a Governance and Corporate Management System is exposed, supported by three main pillars: Technology Management, Innovation Management, and Digital Transformation, which we will call GCMS+ from now on. The work developed is based on a quasi-experimental longitudinal research methodology, in which three key phases are addressed; the first phase corresponds to the systematic review of the literature, which allows the identification of the key components of the proposed GCMS+ system. The second phase of the methodology focused on the modelling of the GCMS+ system, which visually and descriptively describes the structural components, flows, and control mechanisms. The third phase of the methodology focuses on the validation of the GCMS+ system proposed from the discussed literature and contrasting the position of the authors, which is identified through a conceptual discussion. In this case, it seeks to validate the structural elements of GCMS+, the constraints, adoption of good practices, integration of standards, adoption of principles, integration of drivers, strategic taxonomy, organizational structures, governance and management approach, as well as methodologies, roles, initiatives, metrics, and indicators.

Digital simulation methods are transforming engineering education, offering accessible alternatives to practices traditionally dependent on physical resources. In the field of strength of materials, tensile testing—defined as a standardized method used to assess the response of materials under uniaxial tensile load—is essential for characterizing the mechanical behavior of metals, but its implementation in academic settings faces challenges such as excessive costs, equipment limitations, and irreversible sample destruction. Recent studies suggest that agent-based simulation models can mitigate these obstacles, although questions remain regarding their pedagogical effectiveness, technological evolution, and methodological rigor. Through a literature review, it is demonstrated that simulation models significantly improve the conceptual understanding of tensile testing by 35% compared to traditional methods, while reducing dependence on physical laboratories without compromising accuracy. These findings redefine the potential of simulations in engineering education, not only as logistical solutions but also as pedagogical tools that democratize access to experiential learning. The review proposes a framework for future research, highlighting the need to standardize quantitative assessments and evaluate socioaffective dimensions to integrate these types of simulations into academic curricula where infrastructure limit practical experiences.