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

Climate change is playing a significant role in agriculture. As an agriculturally efficient country like Bangladesh, smart farming is needed to effectively help farmers adapt to this climate change. The main objective of this IOT based project is to increase efficiency, especially in mushroom farming and shrimp fishing. In both cases, smart farming requires real-time weather information from farmers. This system enables farmers to monitor temperature, humidity, water level at any moment from anywhere in the world using their smartphones. This system focuses on local farmers in different districts of Bangladesh, where they are able to monitor the weather for their decision-making and optimize the concept of real-time scheduling for specific food crops. Local rice farmers who are using real-time weather monitoring systems can know the exact moment of their rains, which helps them determine the time to harvest their mature rice. This approach will open up a new avenue for our local farmers through which they can increase their capacity through mushroom cultivation and shrimp farming more than before, which will help boost the economy of a country like Bangladesh a lot.

This work describes the implementation of a video-oculography (VOG) and pupillometry system to measure pupillary reflexes in 20 volunteers (21 ± 3 years old) under blue and white light stimuli in a controlled environment. Although it's a very small sample, the results demonstrate that the system can be useful for obtaining pupillary data, with both the system and the infrastructure employed receiving a positive evaluation. Three 10 ms stimuli were used with 10 s intervals, recording pupillary areas between 3000 and 9000 pixels, with maximum contractions of 3000-5000 pixels. The results show that blue light induces slightly more pronounced contractions (average PCA: 444-449 pixels²) than white light (average PCA: 410-417 pixels²), with a decrease in post-illumination pupillary response (PIPR) in the third cycle due to fatigue of the intrinsically photosensitive retinal ganglion cells (ipRGCs). This system provides a preliminary database for future studies using artificial intelligence to identify potential biomarkers for parasympathetic ocular disorders.

In this article a systematic mapping is performed applying Petersen's methodology, with the objective of providing an overview of the relationship between emotion and user satisfaction and how this relationship is evaluated. For this purpose, the period of the decade from January 2014 to January 2024 is considered where the primary studies focus on these two variables, specifically in the field of Human Computer Interaction. The search is carried out in five scientific databases, the analysis is performed based on the frequency of publications by area of application, research contributions, in addition to the type of evaluation and the instruments used, which allows answering the four research questions posed. The results show that emotions and user satisfaction are topics of current interest to researchers, especially in Artificial Intelligence. The contributions that have been made are mainly tools, while most of the works evaluated emotions and satisfaction independently, the most used instrument for this purpose being the questionnaire.

This study compares Julia, Python, and R programming languages in the context of data science and software development. By evaluating performance, libraries, community support, and licensing, through conducting speed tests —including matrix operations, data manipulation, and visualization— it was observed that Julia is the fastest in numerical computations, making it ideal for scientific computing, while Python offers a balanced solution, it is efficient in data processing and graphics, and features a robust ecosystem and a large online community. R stands out in statistical analysis and visualization but is slower in computationally intensive tasks.  In the field of web development, Python leads with mature frameworks such as Django and Flask, while the alternatives in R (Shiny) and Julia (Genie) have more limited reach. Additionally, licensing differs: Julia and Python use MIT and PSF licenses, respectively, which are flexible and suitable for commercial projects, whereas R uses the GPL license, which is more restrictive.  In conclusion, Python emerges as the most versatile option for general-purpose projects, Julia excels in high-performance computing, and R maintains advantages in statistical analysis. Therefore, the choice of a programming language depends on the context and objectives of the project.

Nanoemulsions represent an emerging platform in controlled drug delivery due to their ability to transport and release bioactive compounds, improve their bioavailability, and enable sustained release at the site of action. Their nanometric size and high kinetic stability facilitate penetration into biological tissues and protect therapeutic molecules from enzymatic degradation. In the biomedical field, these nanostructures have shown great potential in the administration of hydrophobic agents and combination therapies. Growing scientific evidence supports their potential to revolutionise pharmaceutical delivery, although key barriers to their widespread clinical application remain. This paper is a brief summary presenting a description of nanoemulsions, the most commonly used synthesis methods and characterisation techniques. Finally, one of their many applications, controlled drug release, is demonstrated.  

When evolvability or maintainability are not considered during software development, the outcome may yield high costs and long modification hours, with an impact on the final quality and customer satisfaction. These two quality attributes contribute in considering future changes not only in requirements but also in environment and technology. A systematic literature review and a thematic synthesis were performed in order to determine the current state of evolvability and maintainability research. The search and selection processes were performed with IEEEXplore, ACM, Springer and ScienceDirect databases, obtaining a total of 37 primary studies. The thematic synthesis, in turn, allowed identifying leading  themes or topics related to evolvability and maintainability. The results helped to determine that research is scarce. Although several authors provide definitions for both attributes along with subattributes, features and even metrics, the only artefact that is taken into account is code. The most frequently mentioned metrics were Maintainability Index, Lines of Code, Cyclomatic Complexity y Coupling Between Objects.

Sudden death caused by cardiorespiratory arrest, is one of the main causes of death in Western countries. Timely care has been shown to have a very important positive effect on life expectancy, as well as on the reduction of the consequences derived from a cardiac arrest event. To contribute to the solution of this problem, in this article we address the design of a mobile application to support the management of a collaborative map of automatic external defibrillators, which allows "trackers" to quickly locate these devices in public and private spaces, to take them to the site of an emergency in the event of cardiorespiratory arrest.

El presente estudio tiene como fin fomentar el desarrollo de la lectoescritura en niños con Trastorno del Espectro Autista (TEA). El análisis de requisitos se hizo de manera documental para revisar los aspectos importantes del TEA; se identificaron las características de la enseñanza alfabética y silábica para niños de edades comprendidas de 3 a 8 años con apoyo de especialistas del Instituto de Investigaciones Cerebrales de la Universidad Veracruzana. Finalmente, se diseñó un prototipo lúdico denominado SilabaTEAndo, con elementos que captan la atención del infante y que pueda aprender de manera divertida a través de juegos. Dicha app complementa la enseñanza alfabética y silábica, a través de la emisión de los sonidos de las letras y sílabas, así como con la asociación de las palabras con su respectiva imagen. El alcance del proyecto se limita al Aula Bambú del Instituto Tecnológico Superior de Xalapa y a los niños con TEA que asisten a esta institución en busca de mejorar sus habilidades de lectoescritura.

X-ray Fluorescence Computed Tomography (XFCT) has emerged as a promising modality owing to the availability of high-energy polychromatic X-ray sources in the laboratory. As compared to other modalities, tabletop XFCT offers advantages such as easy accessibility, low instrumentation costs, and efficient operation. This approach allows for simultaneous transmission computed tomography (CT) along with XFCT, providing multimodal/multiplexed in vivo images and expanding applications with metallic probes such as gold nanoparticles (AuNPs). Despite its benefits, CT+XFCT imaging poses challenges, particularly in minimizing X-ray dose. Research into accurate methods for estimating the location of X-ray fluorescence emissions has led to various techniques and developments, applied both experimentally and through simulations.This work introduces an innovative methodology based on X-ray energy dispersion spectrometry, using DICOM images to create probability maps of fluorescence emission locations. We apply this methodology to localize nanoparticles in computer-generated phantoms, demonstrating its feasibility and versatility through Monte Carlo simulations and correlation with micro-tomography. This methodology emerges as a promising tool for obtaining functional information in complex biomedical environments. Keywords: Monte Carlo, XFCT, Methodology, Nanoparticles

About 95 % of wildfires are anthropogenic causes. The burning of grasslands as a method of sweeping for livestock and agricultural exploitation has been implemented for over two centuries on the American continent. Every winter fire is used to promote the regrowth of forage species in the Paraná River Delta. Atypical weather conditions and climate change have exacerbated the impact of fires worldwide. In 2020, the accumulation and spread of unprecedented wildfire emissions affected the inhabitants of the Rosario Metropolitan Area (AMR), Argentina. This work analyzed meteorological conditions, the number of active fires detected by the VIIRS satellite instrument, and the concentration of fine particulate matter (PM2.5) measured in the AMR. Anomalies of precipitation, relative humidity, and temperature recorded in situ between June and August in the period 2010-2020 were calculated. The results show that for the year 2020, ambient humidity and temperature had an average decrease of 10 % and 3 %, respectively, while accumulated precipitation had a reduction of about 70 % with respect to previous years. The clustering model showed that the highest median NI coincides with the lowest accumulated precipitation and low relative humidity, as well as prevailing winds from the northwest. Wind analysis reveals that currents from the northwest direction promoted larger surface fire impacts in the Paraná River Delta, and northeast winds increased PM2.5 levels in the AMR. These findings underline the importance of considering meteorological conditions in assessing environmental risks and in formulating mitigation strategies against air pollution and forest fires.