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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.
- Complex competencies for leader education: artificial intelligence analysis in student achievement profiling(Taylor @ Francis Online, 2024-07-21) Ramírez Montoya, María Soledad; Morales Menendez, Ruben; Tworek, Michael; Escobar Díaz, Carlos Alberto; Tariq, Rasikh; Tenorio Sepúlveda, Gloria Concepción; https://ror.org/03ayjn504; https://ror.org/03vek6s52Future education requires fostering high-level competencies to enhance student talent, and artificial intelligence (AI) can help in profile analysis. The aim was to determine the variables that predict the GPA of students in the ‘Leaders of Tomorrow’ program through an integrated methodology of data analytics, machine learning modeling, and feature engineering in order to generate knowledge about the application of AI in social impact programs. This research focused on 466 graduates of a ‘Leaders of Tomorrow’. A regression analysis was performed to model the relationship between the dependent variable and multiple independent variables. The findings revealed: (a) Analysis of variance (ANOVA) demonstrated exceptional model fit for predicting ‘student.term_Grade Academic Performance (GPA)_program’ with an R-squared of 0.999; (b) Visual analysis showed that significant variables like age and origin-school Grade-Point Average (GPA) affect term GPA; (c) Kendall tau correlation revealed a positive correlation of origin-school GPA with term GPA and a slightly negative one with age; (d) Support Vector Machine (SVM) regression aligned actual and predicted GPAs closely, indicating high accuracy; and (e) Recursive Feature Elimination (RFE) identified ‘student_originSchool.gpa’ as the most predictive feature. This study is intended to be of value to academic communities interested in enhancing the academic profiles of students with complex competencies, as well as communities interested in applying AI in education for predictions that contribute to trajectories for training.
- Complex artificial intelligence models for energy sustainability in educational buildings(Springer Nature, 2024-07-01) Tariq, Rasikh; Mohammed, Awsan; Alshibani, Adel; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/03yez3163Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence‑based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K‑nearest neighbors, gradient boosting, and long‑term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While ’Type of School’ is less direct or weaker correlation with ’Annual Consumption’. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K‑Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real‑world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI‑driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.
- Synergy of Internet of things and education: cyber-physical systems contributing towards remote laboratories, improved learning, and school management(2024-06) Tariq, Rasikh; Casillas Muñoz, Fidel Antonio Guadalupe; Hassan, Syed Tauseef; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/041sj0284The modern Industrial Revolution has ushered in a wave of technological advancements, including the proliferation of over 20 billion digital identities associated with the Internet of Things (IoT) devices worldwide. Amid this complexity, IoT has emerged as a beacon of hope, offering multitudinous solutions from the perspectives of school management, instructors, and learners. The prime objective of this article is to review the current state-of-the-art in IoT and education specifically in areas like remote laboratories, improved learning, and school management. The implemented method is systematic literature review of IoT technology's practical applications and case studies to meet these key educational stakeholders' unique needs. The studies focus on remote labs, learning experiences, and campus administration. This comprehensive analysis gathered data from sources like Scopus and WoS and summarized insights from 122 articles. These cases encompass the foundational principles of IoT, its diverse applications in higher education, its challenges, and future avenues for research. Our findings indicate that (a) the implementation of IoT-based remote laboratories has transformed engineering education by enhancing the safety and operational efficiency of labs, improving students' comprehension of complex concepts, and facilitating a more interactive and engaging educational experience, (b) the integration of IoT systems within educational settings has profoundly enhanced both teaching and learning experiences by creating interactive, immersive environments and significantly improving student engagement and understanding through personalized and hands-on learning approaches, and (c) the integration of IoT technology within educational administration has significantly advanced the digitalization of traditional school management systems, enhancing administrative efficiency in areas such as schedule management, student records, and financial operations through automation, thereby streamlining processes and enhancing responsiveness in educational institutions. However, it must be noted that the current infrastructure, particularly in public universities, often falls short of fully harnessing IoT technologies to optimize the learning experience. Investments in infrastructure, teacher training, and curriculum design are imperative to fully leverage IoT's benefits for education.
- Biosorption of Pb(II) using natural and treated ardisia compressa K. Leaves: simulation framework extended through the application of artificial neural network and genetic algorithmVázquez Sánchez, Alma Yolanda; Lima, Eder Claudio; Abatal, Mohamed; Tariq, Rasikh; Arizbe Santiago, Arlette; Alfonso, Ismeli; Aguilar Ucán, Claudia Alejandra; Vazquez Olmos, América Rosalba; https://ror.org/05hpfkn88; https://ror.org/01v5y3463; https://ror.org/03ayjn504; https://ror.org/01tmp8f25This study explored the effects of solution pH, biosorbent dose, contact time, and temperature on the Pb(II) biosorption process of natural and chemically treated leaves of A. compressa K. (Raw-AC and AC-OH, respectively). The results show that the surface characteristics of Raw-AC changed following alkali treatment. FT-IR analysis showed the presence of various functional groups on the surface of the biosorbent, which were binding sites for the Pb(II) biosorption. The nonlinear pseudo-second-order kinetic model was found to be the best fitted to the experimental kinetic data. Adsorption equilibrium data at pH = 2–6, biosorbents dose from 5 to 20 mg/L, and temperature from 300.15 to 333.15 K were adjusted to the Langmuir, Freundlich, and Dubinin–Radushkevich (D-R) isotherm models. The results show that the adsorption capacity was enhanced with the increase in the solution pH and diminished with the increase in the temperature and biosorbent dose. It was also found that AC-OH is more effective than Raw-AC in removing Pb(II) from aqueous solutions. This was also confirmed using artificial neural networks and genetic algorithms, where it was demonstrated that the improvement was around 57.7%. The nonlinear Langmuir isotherm model was the best fitted, and the maximum adsorption capacities of Raw-AC and AC-OH were 96 mg/g and 170 mg/g, respectively. The removal efficiency of Pb(II) was maintained approximately after three adsorption and desorption cycles using 0.5 M HCl as an eluent. This research delved into the impact of solution pH, biosorbent characteristics, and operational parameters on Pb(II) biosorption, offering valuable insights for engineering education by illustrating the practical application of fundamental chemical and kinetic principles to enhance the design and optimization of sustainable water treatment systems.