Universidad Politécnica de Baja California

Stenocereus fruits are appreciated for their flavor and color, and their cultivation is highly sustainable, as they grow in arid zones without the need for fertilizers or agrochemicals. However, their nutritional and bioactive composition remains underexplored. This study evaluated the physicochemical and nutritional properties, bioactive compound content, and antioxidant capacity (AOX) of Stenocereus thurberi (red, white, purple, and orange), Stenocereus martinezzi (red), and Stenocereus gummosus (red). All fruits exhibited low total soluble solids (12.6 ± 0.2–14.7 ± 0.3 °Brix), acidity (0.81 ± 0.03–1.12 ± 0.03%), and moderate dietary fiber content (3.71 ± 0.05–4.86 ± 0.09%). S. martinezzi stood out for its high levels of betalains (33.7 ± 0.65 mg/100 g_fw), vitamin E (84.7 ± 0.2 µg/100 g_fw), and vitamin C (147.6 ± 11.4 mg/100 g_fw). At the same time, potassium, magnesium, and calcium were the predominant minerals in all samples. S. gummosus showed the highest total soluble phenols (120.6 ± 2.2 mg/100 g_fw) and was also notable for its flavonoid content. Flavonoids, hydroxycinnamic, and hydroxybenzoic acids were detected in all fruits. Red fruits had the highest AOX levels, followed by white, orange, and purple varieties. In conclusion, these fruits are nutritious, low in sugars, and rich in bioactive compounds, suggesting their potential as functional foods, particularly beneficial for individuals with chronic degenerative diseases.

Phase-change materials (PCMs) integrated into multi-pane windows can reduce building energy consumption and improve thermal comfort by increasing energy storage capacity. In this study, three window configurations with PCMs (TGP-1, TGP-2, and TGP-3) were thermally evaluated and compared with a conventional triple-pane (TG) window. The evaluation was performed for each season’s warmest and coldest days, which were selected based on the ambient temperature. A numerical code was developed in C + + based on the finite volume method to model the transient heat transfer in windows. For the modeling, we used meteorological variables (solar radiation, ambient temperature, and wind velocity) of Mérida, Yucatán, for the boundary conditions. We found that the TGP-3 reduced heat fluxes through it by up to 37.4 % regarding TG on the warmest day of spring. Furthermore, the annual electricity consumption cost and CO 2 emissions obtained by the TGP-3 (27.9 USD/m 2 and 281.2 kgCO 2 e/m 2 ) were 7.1 % lower than those of TG. Furthermore, the TGP-3 exhibited satisfactory dynamic thermal performance in the spring and summer; therefore, it is recommended to be used in these seasons. And for autumn and winter seasons, it is proposed to combine this configuration with technologies that reduce direct solar radiation. • Unsteady heat transfer in a triple glazing window (TG) with PCM, TGP-3, was modeled. • TGP-3 decreases the surface temperature up to 2.5 °C compared to TG on the spring day. • TGP-3 reduces the indoor heat flux on the spring day by 37.4 % compared to the TG. • TGP-3 reduces the CO 2 emission up to 7.1 % compared to one without PCM. • TGP-3 presents high values of time lag on both spring and summer seasons.

ARIMA, LSTM, and GRU are examined in this study, while COVID-19 trends and financial time series are also examined. A traditional statistical model, such as ARIMA, has long been used to model linear data but falls short when it comes to working with complex, non-linear data. Models such as Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) are employed to assess deep learning's capacity to handle long-range dependencies and dynamic patterns. Analysing their performance across various datasets, the study emphasises accuracy, computational efficiency, and suitability for diverse forecasting tasks. According to empirical results, ARIMA is effective for stationary time series, but LSTM and GRU perform better for non-linear time series. A real-time application can benefit from the GRU's competitive accuracy and fast execution, making it an optimal deep-learning model. Data characteristics and resource constraints must be considered when selecting forecasting models.

The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition.

The manufacturing industry has motivated the development of the region in which it is located. In the last few years manufacturing has adopted innovations and advanced digital process technology, among which can be mentioned Machine Learning (ML), a subset of artificial intelligence (AI) to make industrial processes smarter, more efficient, and highly optimized operations. In this review, research studies related with the application of ML algorithms to problems and situations of manufacturing value chain are explored. After presenting the applications, benefits, and challenges, this chapter recalls the future trends of machine learning in this context, like in predictive maintenance, quality control, process optimization, and supply chain management, accomplished in Latin America. In that way it explores industrial process optimization.

A Geographic Information System (GIS) is a technological tool that allows for the capture, storage, analysis, and visualization of geographically referenced data. These systems integrate various forms of spatial and non-spatial data, facilitating the analysis of geographic phenomena and patterns.The integration of Machine Learning (ML) into Geographic Information Systems (GIS) has revolutionized the way geospatial data is analyzed and used. Machine Learning, with its ability to learn from large volumes of data and make accurate predictions, complements the analytical capabilities of GIS, allowing for the extraction of complex patterns and the performance of advanced predictions that were not previously possible. The purpose of this chapter is to explore the applications of Geographic Information Systems (GIS) empowered by the use of machine learning, highlighting their impact on spatial analysis and environmental management.

The current era of globalization has exacerbated the problem of climate change, affecting all ecosystems, The anthropogenic footprint has been the leading cause of the current environmental impact on the planet, and socioeconomically developed countries have implemented various strategies to mitigate the environmental impact of anthropogenic damage on marine and terrestrial ecosystems. The two main challenges are environmental protection and education. It is necessary to develop a linkage between education, government, society, and industry to address climate change as the principal measure for creating programs with international reach. Governmental and educational institutions must develop quality educational policies. The implementation of a triad between public institutions, private institutions, and education, which collaborate with society and the government through programs that integrate institutional internal plans and programs with economic support for the incorporation of public educational policies, the promotion of R&D projects.

Iron inclusion in the composition of the zeolite crystal structure endows it with new and useful properties. However, direct synthesis involving Fe, frequently creates unfavorable conditions that hinder the crystallization process and generate impurity phases. For this reason, novel methods, which include iron within the zeolitic matrix, are being sought out. This article presents a one-pot synthesis of iron-modified zeolite X. The resulting materials were characterized, and their textural, electrical, and magnetic properties were studied.

This article describes the application of the Higher Education Multiple Contributions Fund for the Technological and Polytechnic Universities in Mexico from 2000 to 2018. This information was obtained from the Ministry of Public Education and from the General Office of Technological and Polytechnic Universities. The variables for this analysis were: the federal resource investments on physical infrastructure conducted for each fiscal year for the aforementioned period, and the evolution of enrolled students. A quantitative method was used, non-experimental, transactional, descriptive. The analysis universe was 179 universities from the technological and polytechnic universities. There was a comparison conducted of the resources invested in the Multiple Contributions Fund on infrastructure and the yearly enrolled students in the subsystem. Based on this information, this paper describes if there is a relationship between the increase of students enrolled and the authorization of federal resources for the Higher Education Multiple Contributions Fund. The results obtained in the years from 2000 to 2018 were taken into consideration where the number of students enrolled in universities increased by 851%, and the cost per student invested in the fund decreased -88.04%. Results indicate that there is no relationship between the application of investment in education infrastructure and the evolution of enrolled students.

We shall present a descriptive, quantitative study with the purpose of analyzing the technological and connectivity resources that teachers had during the preventive period of COVID-19 in the year 2021. We analyzed the technological and connectivity resources at home; the resources, tools or strategies used by teachers, the platforms used to communicate, as well as the difficulties that students faced to be able to carry out distance education. A survey was applied to 225 teachers in the Public Teacher Training Schools in the state of Baja California Sur. We found that most of the teachers had connectivity (Internet) and computer equipment at home. The main technological resource used was the cellular phone and their main support was the Google Classroom platform. They communicated with their students through WhatsApp; most of the students had lack of connectivity and access to equipment.