Artículo
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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- Utilization of locally sourced waste fats for biodiesel production: Experimental characterization and environmental life cycle assessment(Elsevier, 2025-02-08) Muhammad Aqil, Khan; Nadeem Ahmed, Sheikh; Khan Zaib, Jadoon; Abubakr, Ayub; Tabbi, Wilberforce Awotwe; Rasikh, Tariq; Manuel Garcia PerezThis study presents the production, chemical characterization, and life cycle assessment (LCA) of biodiesel derived from various local waste feedstocks using experimental setup. Biodiesel was produced via transesterification in a 50-L batch reactor using oils sourced from a five-star restaurant (A), three-star café (B), rancid palm oil (C), and chicken feather oil (D). Fourier Transform Infrared Spectroscopy (FTIR) was used for identification of functional groups, while gas chromatography-mass spectrometry (GC-MS) analyzed fatty acid methyl ester (FAME) composition, revealing key variations such as Hexadecanoic acid (C16:0) and Octadecenoic acid (C18:1). Type A biodiesel exhibited the highest saturation, while Types B and C contained more unsaturated FAMEs, influencing their heating values. Conversion efficiencies were significantly influenced by the acid values of the feedstocks, with a maximum yield of 85 % achieved for a sample with an acid value of 3.5 mgKOH/g and a heating value of 35.8 MJ/kg. LCA performed using Simapro V9.5.0.2 demonstrated that biodiesels from Types A, B, C, and D reduced carbon footprints by 70 %, 64 %, 63 %, and 65 %, respectively, compared to fossil diesel. Feedstocks with lower free fatty acid (FFA) levels resulted in lower environmental impacts, while extensively reused cooking oils with higher FFA values contributed to increased carbon footprints. This study underscores the potential for scalable biodiesel production from waste resources, aligning with global and regional sustainability goals.
- Computational thinking in STEM education: current state-of-the-art and future research directions(Frontiers, 2025-01-08) Rasikh, Tariq; Aponte Babines, Bertha María; Ramírez, Jesús; Icaza Longoria, Inés Álvarez; Naseer, Fawad; Todor GanchevThe knowledge society exists mainly due to advancing technology and the exponential development of professionals’ capabilities. Digital transformation and new technologies generate complex environments demanding high-level skills. This work analyzes the current state of pedagogical approaches with a special focus on project-based learning that develops computational thinking in STEM students. A Systematic Literature Review examined the current state of pedagogical approaches along with project-based learning aimed at enhancing computational thinking within the context of higher education. Results allowed us to infer that (a) computational thinking promotes sustainable development through STEM education and novel teaching practices; (b) it is a fundamental skill for the problem-solving processes that evolve with technological progress; (c) its development is a global concern, not limited to a country’s development level; and (d) its introduction at an early stage provides opportunities for the advancement of vulnerable groups. Outlining, this study conducts a Systematic Literature Review (SLR) using PRISMA 2020 guidelines to analyze pedagogical approaches including project-based learning for enhancing computational thinking in STEM higher education, identifying global research trends, common strategies, and areas for improvement, while proposing a framework to align computational thinking skills with emerging technological challenges and promote sustainable educational practices. This study presents relevant results on the construction of state-of-the-art computational thinking and education; it is valuable for curricular design underpinning disciplinary and interdisciplinary approaches.
- Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society(PLOS ONE, 2024-12-19) Muhammad, Tayyab Zamir; Fida, Ullah; Rasikh, Tariq; Waqas, Haider Bangyal; Muhammad, Arif; Alexander, Gelbukh; Fredrick Romanus IshengomaInformal education via social media plays a crucial role in modern learning, offering selfdirected and community-driven opportunities to gain knowledge, skills, and attitudes beyond traditional educational settings. These platforms provide access to a broad range of learning materials, such as tutorials, blogs, forums, and interactive content, making education more accessible and tailored to individual interests and needs. However, challenges like information overload and the spread of misinformation highlight the importance of digital literacy in ensuring users can critically evaluate the credibility of information. Consequently, the significance of sentiment analysis has grown in contemporary times due to the widespread utilization of social media platforms as a means for individuals to articulate their viewpoints. Twitter (now X) is well recognized as a prominent social media platform that is predominantly utilized for microblogging. Individuals commonly engage in expressing their viewpoints regarding contemporary events, hence presenting a significant difficulty for scholars to categorize the sentiment associated with such expressions effectively. This research study introduces a highly effective technique for detecting misinformation related to the COVID-19 pandemic. The spread of fake news during the COVID-19 pandemic has created significant challenges for public health and safety because misinformation about the virus, its transmission, and treatments has led to confusion and distrust among the public. This research study introduce highly effective techniques for detecting misinformation related to the COVID-19 pandemic. The methodology of this work includes gathering a dataset comprising fabricated news articles sourced from a corpus and subjected to the natural language processing (NLP) cycle. After applying some filters, a total of five machine learning classifiers and three deep learning classifiers were employed to forecast the sentiment of news articles, distinguishing between those that are authentic and those that are fabricated. This research employs machine learning classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forest, to analyze and compare the obtained results. This research employs Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers, and afterwards compares the obtained results. The results indicate that the BiGRU deep learning classifier demonstrates high accuracy and efficiency, with the following indicators: accuracy of 0.91, precision of 0.90, recall of 0.93, and F1-score of 0.92. For the same algorithm, the true negatives, and true positives came out to be 555 and 580, respectively, whereas, the false negatives and false positives came out to be 81, and 68, respectively. In conclusion, this research highlights the effectiveness of the BiGRU deep learning classifier in detecting misinformation related to COVID-19, emphasizing its significance for fostering media literacy and resilience against fake news in contemporary society. The implications of this research are significant for higher education and lifelong learners as it highlights the potential for using advanced machine learning to help educators and institutions in the process of combating the spread of misinformation and promoting critical thinking skills among students. By applying these methods to analyze and classify news articles, educators can develop more effective tools and curricula for teaching media literacy and information validation, equipping students with the skills needed to discern between authentic and fabricated information in the context of the COVID-19 pandemic and beyond. The implications of this research extrapolate to the creation of a society that is resistant to the spread of fake news through social media platforms.
- Perceived competency in complex thinking skills among university community members in Pakistan: insights across disciplines(2024) José Carlos Vázquez Parra; Rasikh, Tariq; Castillo Martínez, Isolda Margarita; Naseer, FawadThis article aims to evaluate university community members’ (faculty members and students, in this case) perceptions of their complex thinking competency and its sub-competencies – including systemic, scientific, critical, and innovative thinking – across various disciplines at eight universities in Pakistan (Objective). Using a validated eComplexity instrument, descriptive statistical analysis of means and standard deviations, a Kruskal–Wallis test, a correlation matrix, and a correlation coefficient heatmap of complex thinking were applied to uncover key patterns and disparities (Methodology). The novelty of this study lies in its focus on how participants perceive their achievement of complex thinking competencies, offering unique insights into the specific challenges faced by different academic disciplines (Novelty). Notably, Humanities and Education profiles reported considerably low levels of competency (mean of 2.39), with statistically significant differences regarding knowledge of research report structures (scientific thinking) and interdisciplinary problem-solving and contextual analysis (innovative thinking) (Results). However, the study’s geographic context and reliance on self-perceived competencies pose limitations, potentially introducing social desirability bias (Limitations). These findings emphasise the need to adapt teaching methods to bridge competency gaps and promote equitable skill development (Conclusions). Future research should extend the study to broader educational contexts to explore regional and international variations, and assess interventions to enhance competencies in underperforming areas – particularly Humanities and Education – improving discipline performance and confidence in complex thinking (Implications).