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Permanent URI for this collectionhttps://hdl.handle.net/11285/345284
Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.
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- Comparative multi-objective optimization using neural networks for ejector refrigeration systems with LiBr and LiCl working agents(Science Direct, 2024-08) Khanmohammadi, Shoaib; Ahmadi, Pouria; Jahangiri, Ali; Izadi, Ali; Tariq, Rasikh; https://ror.org/05hkxne09; https://ror.org/05hkxne09; https://ror.org/05vf56z40; https://ror.org/0091vmj44; https://ror.org/03ayjn504Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar-powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi-objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two-objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchanger have also been studied in the ejector asset system. To find the best values of decision variables, multi-objective optimization for both arrangements is conducted, and results are presented. The results have indicated that the refrigeration system using lithium chloride working fluid without an ejector achieves a coefficient of performance of 0.766 and a cost of 0.922 $/h at the optimal point, while the system with an ejector yields a higher coefficient of performance (1.047) and a slightly lower cost rate (0.991 $/h). The outcomes of this work can play a critical role for higher education institutions in advancing innovative solutions to pressing energy challenges. Lifelong learning, at the heart of educational innovation, can benefit from the integration of sustainable energy systems as a core component of informal education through the optimization of ejector refrigeration systems.
- Predicting open education competency level: A machine learning approach(Science Direct, 2023-11) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; Buenestado Fernández, Mariana; Olague, Gustavo; https://ror.org/03ayjn504; https://ror.org/046ffzj20; https://ror.org/04znhwb73This article aims to study open education competency data through machine learning models to determine whether models can be built on decision rules using the features from the students' perceptions and classify them by the level of competency. Data was collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. Based on a quantitative research approach, we analyzed the eOpen data using two machine learning models considering these findings: 1) derivation of decision rules from students' perceptions of knowledge, skills, and attitudes or values related to open education to predict their competence level using Decision Trees and Random Forests models, 2) analysis of the prediction errors in the machine learning models to find bias, and 3) description of decision trees from the machine learning models to understand the choices that both models made to predict the competency levels. The results confirmed our hypothesis that the students' perceptions of their knowledge, skills, and attitudes or values related to open education and its sub-competencies produced satisfactory data for building machine learning models to predict the participants' competency levels.
- Data analysis in factors of social entrepreneurship to design planning tools in complex thinkin(Science Direct, 2023-08-22) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; Miranda Mendoza, Jhonattan; https://ror.org/03ayjn504This work presents the results of an exploratory pilot that analyzes the factors that influence self-reported social entrepreneurship competency and previous family backgrounds that might positively influence the development of the set of sub-competencies of complex thinking. It has been observed that individuals who put into practice the competencies that make up complex thinking perform better in overcoming the challenges of generating social value in contemporary society. Data were collected from a convenience sample of 47 students attending a private Higher Education Institution in Mexico using the “Profile of the Social Entrepreneur Instrumentóó. The data analysis comprised 1) validation of the instruments reliability, 2) tendencies and the frequency distribution of the data, 3) grouping by entrepreneurial family background, 4) the principal component analysis and 5) a clustering analysis. Our results support that forming Complex Thinking competencies for social entrepreneurship is not directly influenced by previous family experiences. Still, they recognize that these experiences are relevant in helping students become familiar with entrepreneurship-related issues. This paper empirically supports the hypotheses that social entrepreneurship experiences affect the correlations between social innovations and resolving complex global public problems.
- Design and construction of an omnidirectional sound source with inverse filtering approach for optimization(Science Direct, 2018-05-31) Ibarra Zarate, David Isaac; Ledesma, Rodrigo; López Caudana, Edgar Omar; https://ror.org/03ayjn504The aim of this study is to design an efficient omnidirectional point source whose analysis and design is also described here. The point source was designed with the help of MATLAB and SolidWorks software respectively for calculating the optimal dimensions. This point source is composed of a base in which the speaker is settle, and a cone that provides the omnidirectionality. Three different bases with three different cones were implemented and tested to determine which combination gives less reflection on rear part of cabinet. To enhance a flat response and true omnidirectionality, an Inverse Filtering Method will be introduced to the study. As a result, we observe that the point source is best suited with a cylindrical base and a 20 cm long cone. The radiation patterns showed omnidirectional results for frequencies lower than 15 kHz, and maximum deviation of 4 dB for 30 degrees.