Knowledge, Volume 5, Issue 3 (September 2025) – 6 articles

Artificial neural networks have been used in a multitude of applications in various research areas in recent decades, providing excellent results in both data classification and data fitting. Their success is based on the effective identification (training) of their parameters using optimization techniques, and hence a series of programming methods have been developed for training these models. However, many times these techniques either can identity only some local minima of the error function with poor overall results or present overfitting problems in which the performance of the artificial neural network is significantly reduced when it is applied to different data from the training set. This manuscript introduces a method for the efficient training of artificial neural networks, where a series of genetic algorithms is applied to the network parameters in several stages. In the first stage, an initial identification of the network value interval is performed; in the second stage, the initial estimate of the value interval is improved; and in the third stage, the final adjustment of the network parameters within the previously identified value interval takes place. The new method was tested on some classification and regression problems found in the relevant literature, and the experimental results were compared against the results obtained by the application of other well-known methods used for neural network training. Full article

Rotating electrical machine maintenance is a core component of engineering education curricula worldwide. Within this context, vibration monitoring represents a widespread methodology for electrical rotating machinery monitoring. However, the multi-physical nature of vibration monitoring presents a complex learning scenario, including concepts from both mechanical and electrical engineering domains. This article proposes a novel knowledge-based educational experience design leveraging an integrated FEA-assisted test bench aimed at comprehensively addressing the electromechanical link between stator current and frame vibration. To this aim, a Finite Element Analysis (FEA) model is utilized to link excitation electrical signals with airgap radial forces acting in the stator. The subsequent correlation of these FEA predictions with measured frame vibrations on a physical test bench provides students with the theoretical concepts and practical tools to adequately comprehend this complex multi-physical phenomenon of wide application in real industrial scenarios. The pedagogical potential of the method also includes the development of critical thinking and problem-solving soft skills, and foundational understanding for digital twin concepts. A Delphi-style expert survey conducted with 25 specialists yielded strong support for the pedagogical robustness and relevance of the method, with mean ratings between 4.32 and 4.64 out of 5 across key dimensions. These results confirm the potential to enhance deep understanding and practical skills in vibration-based electrical machine diagnosis. Full article

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

This paper explores the concept of ‘competency’ in modern organisations. It emphasises the strategic importance of aligning organisational values, strategic goals, and employee competencies. It introduces competency mapping as a framework for ensuring such an alignment, as well as for developing a culture of continuous learning and development, where the emotions and feelings of the interactants are also taken into account based on intrapersonal and interpersonal aspects of human behaviour. The article also elucidates the interconnection among diverse human ‘intelligences’ that are of paramount importance in shaping human knowledge and guiding us in navigating through life more smoothly and efficiently. Thus, through an interdisciplinary scope, we have attempted to analyse the intrinsic value of competency mapping as a knowledge driver in modern organisational and educational settings. Full article

This article presents a comparative analysis of digital manufacturing laboratories (DMLs) in Mexico and Spain. It is argued that DMLs, also known as makerspaces or FabLabs, play a key role in innovation and experimentation, but that their success depends on the relationships they establish with social actors, such as local governments, universities, and firms. Key concepts of the transformative innovation approach such as “protective space” and “embeddedness” are introduced, which allow us to understand how DMLs operate within a complex system. The comparative analysis of a DML in Mexico City (Mexico) and a DML in Valencia (Spain) allows us to identify similarities and differences in their operational contexts. While the Mexican DML faces a lack of government support and dependence on the private sector, the Spanish one benefits from strong institutional support and public policies that facilitate its development. This results in greater stability and capacity for action for the Valencian FabLab VLC compared to the Mexican FabLab Finally, we reflect on how the embeddedness received from different social actors affects the autonomy and transformative capacity of DMLs, suggesting that while both labs have the potential to innovate, their contexts and relationships determine their effectiveness and sustainability in the digital sociotechnical system. Full article