Conference Agenda

Pneumonia is a leading cause of illness and death worldwide, and early diagnosis is essential to improve patient outcomes. However, in many hospitals, the lack of advanced technology and the use of manual methods to detect the disease make accurate identification difficult. In this context, Deep Learning applied to the analysis of medical images, such as chest X-rays, is presented as a promising solution to improve both diagnostic speed and accuracy. This article reviews the literature on the use of Deep Learning models in early detection of pneumonia, showing that these models outperform or match the performance of expert radiologists. The PICOC methodology and the PRISMA diagram were used to select 30 relevant studies from the last five years. Although progress has been made, challenges remain, such as variable image quality and adaptation to different clinical contexts. The findings suggest that Deep Learning contributes to the reduction of diagnostic times and improves accuracy, but there are still areas to be improved for optimal integration into clinical practice. These tools are expected to become more adaptable and continue to improve their accuracy over time

The research utilizes Lean Manufacturing tools to enhance productivity in a small company that specializes in the production of industrial glue. For this purpose, we conducted a methodological process to diagnose the industrial glue production activities through direct observation and interviews. We quantified this data in a time study and constructed a Process Operations Diagram (POD). Over-processing waste was identified in the cleaning and drying stages, resulting in waiting times in the shelling and milling sub-processes. Kanban cards and a visual control board (Andon) were implemented, in addition to standardizing activities in the cleaning and drying subprocesses at the simulation level using the FlexSim tool. As a result, the times of the cleaning and drying subprocesses were improved, in addition to a reduction in reprocessing during the production of industrial glue. Likewise, an increase in raw material, labor, and energy productivity of 29.8%, 17.1%, and 33.4%, respectively, is projected. This results in a 16.67% increase in total productivity, due to improved resource management and the minimization of waste in the production process. From the economic perspective, the use of Lean Manufacturing tools results in a positive NPV of S/94,829.56 and a Cost Benefit Index of 3.43, demonstrating the significant economic benefits of the proposal

Currently, the protection of digital works is a great challenge, since it is easy to copy or use them without permission, affecting the rights of creators. Blockchain technology has emerged as a possible solution, since it allows recording information securely, transparently and without the possibility of alteration. In this study, the PRISMA and PICO methodology is used, which facilitated the search for scientific studies of great relevance with the help of its standardized steps for this research; in addition, the LIA Method was applied for the synthesis of the articles included, simplifying the writing of the results, providing greater transparency and reliability in the information written. Therefore, the results were divided into two parts: thematic and bibliometric results, in the bibliometric section figures are presented with respect to the main characteristics of the articles that have been included in the review (year, affiliation, country of publication, database), on the other hand, in the thematic aspect the application tools used as support for the blockchain and the effects found in each of the most relevant scientific productions in the current research carried out were highlighted.

This study focuses on the practical implementation of classical digital signal processing techniques, specifically the Fast Fourier Transform (FFT) and the Z-transform, on the ESP32 microcontroller, an inexpensive and versatile hardware. Through the Arduino IDE environment, recurring examples of spectral analysis, frequency filtering, and manipulation of synthetic audio signals are demonstrated. The main objective is to illustrate how these tools enable the identification of spectral components, the removal of unwanted frequencies, and the reconstruction of signals in real time. The methodology combines a detailed theoretical review of these mathematical tools with their application in concrete examples employing synthetic signals. The research highlights how the FFT enables fast and accurate spectral analysis, facilitating tasks such as filtering and the removal of unwanted components in the frequency domain. Furthermore, the Z-transform offers a comprehensive approach for the analysis and design of digital systems, enabling the manipulation and stability of signals through the manipulation of poles and zeros. The results, presented through visual simulations, demonstrate the usefulness of these techniques in reconstructing and filtering audio signals in real time in low-cost embedded systems. The integration of these transformations into platforms such as the ESP32 constitutes an educational and practical resource for students and developers, promoting learning and innovation in digital audio processing in educational applications and prototypes.

The pandemic generated by the COVID 19, led to a series of demands at the educational level, especially those related to the digital competencies of teachers and students necessary for the teaching-learning process. The general objective of this research was to analyze the studies conducted regarding digital competencies in teachers and students during the pandemic through a systematic review. It is a basic, qualitative, phenomenological research, having as method the systematic review under the Prisma approach, using the documentary analysis technique, as well as the individual analysis matrix instrument. The population consisted of 3472 articles from the Scopus database, written in English, Spanish and Portuguese on digital competencies, digital skills or technological competencies during the years 2020 to 2023. Likewise, the total sample consisted of 82 articles and the Rayyan program was used to arrive at it. Among the results, it was found that high school teachers have higher digital competencies, and among them, female teachers have slightly higher digital competencies than male teachers. On the other hand, undergraduate students have better digital competencies. Likewise, as digital competencies favor the learning of students and teachers, that information literacy is associated with digital awareness, and that, although there are trainings for teachers and students, they are insufficient and without a previous diagnosis, since it is necessary to consider levels, previous knowledge and accompaniment. Also, in the use of digital tools, several digital resources are employed, and the use of the Flipped Classroom methodology is scarce.

This systematic literature review (SLR) aims to analyze real-life use cases, adoption barriers, and critical success factors, providing a comprehensive view of the role of virtual reality (VR) as a driver of technological innovation and digital transformation in the corporate world. The methodology used integrates the PICOC method, which allowed for the formulation of research questions and the development of a general search equation in Scopus. Likewise, the PRISMA protocol was applied to select, using inclusion and exclusion criteria, 71 original articles that answered the PICOC questions. In this sense, the results show VR applications in sectors such as education, healthcare, industry, and culture, highlighting benefits such as improved learning, risk reduction, process optimization, and personalization of the user experience. Barriers such as high initial costs, lack of standards, and accessibility and inclusion challenges are also identified. It is concluded that VR has transformative potential when integrated with emerging technologies, underscoring the need for inclusive, collaborative, and adaptive strategies to drive effective and sustainable digital transformation in diverse organizational environments.

This study examines the impact of Industry 4.0 and Industry 5.0 technologies on nanostore supply chains through a meta-analysis of 52 recent studies. While prior research often centers on operational efficiency and inventory control, this study emphasizes the combined value of digitalization and human-centric, sustainable approaches. Industry 4.0 technologies—such as IoT, AI, and blockchain—demonstrate clear benefits in improving performance metrics, while Industry 5.0 enhances adaptability, resilience, and competitiveness in small-scale retail. A key contribution is the identification of how Industry 5.0 supports the transition toward customer-centric, sustainable models without compromising efficiency. The analysis also uncovers critical gaps in the literature, including limited research on implementation challenges at the micro-business level. Despite methodological variability, findings suggest the need for longitudinal studies, broader regional representation, and standardized research designs to ensure comparability. Practically, nanostores stand to gain from these technologies and policy support—through targeted funding, training, and partnerships—can accelerate adoption. This study contributes to the theoretical discourse on digital transformation in informal retail and offers actionable insights for enhancing nanostore performance while aligning with inclusive and sustainable development goals.