Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In the realm of digital ecosystems, the integration of Lean UX principles into virtual environments for older adults presents a promising avenue for enhancing cognitive health and addressing neurocognitive disorders. This research builds upon the foundation that virtual reality (VR) and augmented reality (AR) technologies can significantly improve the quality of life for the elderly, particularly in fostering social interaction and cognitive stimulation. Previous studies have demonstrated the efficacy of VR and AR in engaging older adults, yet there remains a gap in the application of Lean UX methodologies to optimize these digital experiences. This study investigates the incorporation of Lean UX into digital ecosystems for older adults, aiming to tailor virtual environments to their unique needs and preferences, thereby aiding in the treatment and management of neurocognitive disorders. Employing a user-centered design approach, iterative prototyping, and multidisciplinary collaboration, the research analyzes user engagement, adaptability, and satisfaction. The findings reveal that Lean UX can lead to more intuitive, accessible, and personalized VR interfaces, resulting in increased user satisfaction and potentially mitigating the effects of neurocognitive decline. The implications of this research underscore the importance of empathetic design in creating inclusive digital solutions that support the well-being of older adults.

Currently, robotics and technology are increasingly present in different areas of human development. New technologies are entering the field of education, where the student acquires and develops skills to solve solutions. This article presents the development of robotic prototype projects as an apprenticeship for higher education, in order for the student to develop didactic and playful activities, using low-cost robotics, thus encouraging creativity, logical thinking, robot programming, teamwork and problem solving. The methodology used was experimental through learning sessions in which 100 students participated, and a rubric and checklist were used for evaluation. Significant differences were obtained in the performance of the five groups when completing an activity. The calculated means showed on average, 10 students achieve the activity without help, 7.4 students achieve with minimal help and 2.6 students achieve with considerable help. It was concluded that the proposed learning was accepted by the students, and its application allowed detecting difficulties in the students when interacting with robotics and arduino.

Aligned with the principles of justice, peace, and sustainable development, Artificial Intelligence (AI) has the potential to make learning more equitable, fair, accessible, and inclusive. To achieve this, teachers need specialized training and low-cost, easily accessible educational resources that facilitate their incorporation into pedagogical practice. This article aims to contribute to this goal, specifically in the context of natural science education. To this end, it presents the design, implementation, and evaluation of a proposal for educational activities intended to enhance the teaching strategies of natural science teachers by incorporating Supervised Machine Learning. We evaluated the activities in two workshops involving a total of 56 science teachers; the first workshop was conducted online, and the second was held in person. During the evaluation, we examined changes in teachers' self-perception through surveys, with assessments conducted at the beginning and end of both workshops. The results highlight significant improvements in the science teachers' perceptions in key areas, such as knowledge about Machine Learning, the selection of resources to support their teaching, and more positive attitudes towards integrating Machine Learning in the science classroom. Challenges related to the conceptualization and application of Machine Learning in the educational environment were also identified. This study underscores the need for additional support and specific preparation to overcome digital gaps in the adoption of AI in multidisciplinary education. The findings are discussed in light of recent professional development trends based on AI teacher training strategies.

This study presents the complexity and sensitivity of chaotic system dynamics in the case of the double pendulum. It applied detailed numerical analyses of the double pendulum in multiple computing platforms in order to demonstrate the complexity in behavior of the system of double pendulums. The equations of motion were derived from the Euler-Lagrange formalism, in order to capture the system's dynamics, which is coupled nonlinearly. These were solved numerically using the efficient Runge-Kutta-Fehlberg method, implemented in Python, R, GNU Octave, and Julia, while runtimes and memory usage were extensively benchmarked across these environments. Time series analyses, including the calculation of Shannon entropy and the Kolmogorov - Smirnov test, quantified the system's unpredictability and sensitivity to infinitesimal perturbations of the initial conditions. Phase space diagrams illustrated the intricate trajectories and strange attractors, as further confirmation of the chaotic nature of the double pendulum. All the findings have a clear indication of the importance of accurate measurements of the initial condition in a chaotic system, contributing to an increased understanding of nonlinear dynamics. Future research directions are faster simulations using Numba and GPU computing, stochastic effects, chaotic synchronization, and applications in climate modeling. This work will be useful for understanding chaos theory and efficient computational approaches in complex systems of dynamical nature.

This paper provides a systematic review focused on diagnosing learning difficulties and implementing teaching strategies in the context of linear algebra. The research aims to deepen the understanding of this topic over the last decade. The study, guided by four questions, analyzed 84 articles and ultimately included 41 in the review. The search strategy was based on the PRISM protocol, and specific indicators were used. The findings indicate that most authors in the review primarily use the APOE theory and genetic decomposition for formal diagnosis of learning problems. This approach helps build knowledge frameworks, especially in vector spaces and linear transformations. A key finding is the prevalent use of digital technology in both the models and strategies proposed in these studies. This review highlights opportunities for future research in diagnosing learning problems and developing innovative, technology-integrated strategies in education.

The implementation of qualitative and quantitative methods in Ordinary Differential Equations (ODEs) requires the use of mathematical software to achieve a modern approach with effectiveness in geometric and numerical analysis. This work was carried out with the objective of analyzing the solution of mathematical problems related to first-order ODEs. The Maple software was used, due to its great symbolic capacity, with an interface that makes it easy to analyze, explore, visualize and solve mathematical problems. First-order ODEs are analyzed and solved with Maple, considering existence, uniqueness, and stability. Also, a qualitative approach is discussed, obtaining qualitative information about the solutions directly from the equation, without the use of a formula for the solution. Worksheets have been built and developed in Maple containing the solution to some problems posed in the literature. Graphic and numerical representations are obtained that help carry out a convenient analysis and interpretation of the problems posed.

WSNs experience due to densely dispersed nodes and high flow rates near sinks. However, few researches focus on node and channel traffic, increasing energy consumption and complexity, to alleviate energy efficient traffic using mobile nodes. The proposed method identifies and characterizes energy efficient traffic areas using a unique Water wave game theory algorithm.  Determining the fitness function allows us to estimate the player's stability over these variables. Mobile sinks and neighboring nodes are alerted if the fitness is low, which also predicts energy efficient traffic and creates an implicit alarm threshold. To address multi-energy efficient traffic situations, a novel LAFLC algorithm is used, which uses Learning Automata with Water wave game theory to learn the nature of the energy efficient traffic. In order to find the ideal choice for the input, the algorithm classifies system decisions, mobile data collectors, routing, and mobility. This eliminates the need to reroute data when moving and replacing several traffic nodes for mobile data collectors. The result reveals that the suggested approach attained high PDR, Throughput and Energy efficiency when contrasted with existing techniques.

The Electronic Medical Documentations have gained vitality and are stated as the pillar of digital healthcare. It is the main platform to store and retrieve the information of patients. It possesses numerous benefits like cost minimization, few medical errors with better healthcare access and tracking. Though the advantages and adoption of EMD have been extensively stated, the challenges entangled with their usage persist, particularly with the safety and integrity of the patient’s data that are stored in the cloud. Conventional data sharing methods have been centralized and encountered problems of sole point-of-failure. Thus, a decentralized system is required where the blockchain concept comes into play. Moreover, protecting personal details from unauthorized entrée by guaranteeing data integrity is vital where cryptography gains its significance. Though traditional methods have used algorithms based on these concepts for secured EMD sharing, they lacked with regard for privacy and security. To rectify this problem, the objective of the present study proposes Modified-Advanced Encryption Standard based on the chaos random key generation. In a traditional AES system, a static key finds applicability which must be swapped in advance and assured to be maintained safely. Nevertheless, in the proposed model, a chaotic system’s synchronization technology is introduced wherein the static key turns random and dynamic which avoids the need to be maintained or transferred in any open channels. Besides, a Modified Digest hashing algorithm is also used with M-AES for feeding the encrypted text and hash into data blocks. Performance of the proposed merging technique encompassing a hybrid pattern from both patient ID and medical records is assessed about significant metrics for determining its efficiency. The overall model performance are estimated using various estimation parameters such as the execution time, decryption time, encryption time, memory consumption and usage rates and the time required for key generation. Findings of the proposed work deliberates that higher level of security are ensured, whereby a long-term protection are used, which intends to prevents adversaries from gaining a static target for prolonged attacks. Additionally, the security level of the proposed system performs better in terms of integrity, privacy, authentication, access control, and cryptographic function.

Sales prediction is a significant task of every industries. A potential prediction may majorly impact the revenue loss, out of stock and excessive stock. Many existing research have been implemented to predict the dairy sales prediction, however there are some limitations. To address this problem, the proposed research uses DL (Deep Learning) based technique to forecast the dairy sales. The proposed research uses dairy supply chain dataset to assess the proposed model CNN (Convolutional Neural Network). The present research uses Universal Scale CNN, specifically 1D-CNN, that is able to acquiring the features in ideal and in effective rates. Followed by, the extracted features are fed as an input to Multi-Perspective based Bi-LSTM (Bidirectional Long Short Term Memory) that is able to acquiring the features in an effective manner in characteristics of reducing the error rates upon the prediction sales rate of dairy based products. The performance of proposed Multi-Perspective Fusion Bi-LSTM with Universal Scale CNN is evaluated by different performance metrics which includes RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Square Error) and R2 (R Square).   When compare to other models, the proposed Multi-Perspective Fusion Bi-LSTM with Universal Scale CNN outperforms with optimal performance value with high R2 value of 0.9824. The performance metrics provides broad analysis in terms of accurate prediction of the proposed model.    

The modern digital era depends on the transactional systems to maintain and process larger volumes of real-time transactions diagonally with the sectors such as e-commerce, banking, and online reservations which makes sure the protected payment processing, accurate account preservation, and real-time accessibility, are evolved from conventional  monolithic and client-server architectures to more advanced micro services and server less computing models to meet increasing demands for accessibility, agility, and flexibility. As transaction volumes increase, monolithic system which combines the databases, business logic, and user interfaces into one unit face challenges with scalability and complexity. By splitting the applications into tiny, separate services, each of which oversees a distinct business process, micro services designs, on the other hand, enable fault isolation, autonomous scaling, and greater flexibility.  Server less computing further converts the transactional systems by abstracting structural management, enabling businesses to focus on feature enhancement and cost-effectiveness through automatic scaling based on demand. This review inspects the evolution of transactional system architectures, highlighting the transference from monolithic structures to micro services and server less models. It investigates the role of these architectures in the sectors such as e-commerce, banking, and online transactions, addressing research questions about the most effective architectures for transactional applications and the influence of intelligent algorithms on real-time analytics and decision-making. By evaluating the benefits and challenges of each architectural approach, the review recommends a micro services architecture for one of the domains, highlighting its advantages in managing higher transactions, ensuring real-time accessibility, and providing scalable solutions, while also acknowledging difficulties in process management, data reliability, and system association.