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Stability-Aware Security–Performance Trade-Off Analysis in Resource-Constrained IoT Systems: A Time-Series and Bootstrap-Based Evaluation of TLS and Hybrid ECC–AES Mechanisms
(MDPI AG, 2026-05-02)
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Alvarez-Garcia, Maria Fernanda
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Visconti, Paolo
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The increasing deployment of resource-constrained Internet of Things (IoT) devices requires security mechanisms that preserve confidentiality without compromising energy efficiency or responsiveness. Although Transport Layer Security (TLS) provides standardized protection for MQTT-based communication, its computational overhead may significantly affect embedded architectures. This study presents a controlled experimental evaluation of three communication configurations implemented on ESP32-based nodes: unencrypted Message Queuing Telemetry Transport (MQTT), MQTT over TLS 1.2, and an application-layer hybrid scheme combining Elliptic Curve Diffie–Hellman key exchange with AES-128 encryption. Second-level measurements of instantaneous current, accumulated energy, end-to-end latency, and memory footprint were collected across repeated experimental runs. Time-series diagnostics were performed to assess autocorrelation and stationarity, and block bootstrap resampling was applied to ensure dependence-aware statistical inference. The results indicate that TLS introduces the highest cumulative energy growth and latency dispersion, while the hybrid ECC–AES configuration demonstrates intermediate behavior with reduced overhead relative to TLS. Pareto frontier analysis shows that TLS is dominated in the joint energy–latency space, whereas the hybrid scheme represents a non-dominated compromise between security and efficiency. These findings provide a stability-aware and statistically robust framework for evaluating security–performance trade-offs in embedded IoT systems.
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Generative AI and the scientific landscape: a bibliometric exploration of its global impact
(Editorial Académica Dragón Azteca, 2026-02-16)
Cossio Franco, Edgar Gonzalo
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Sossa Azuela, Juan Humberto
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Larios Rosillo, Víctor Manuel
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Maciel Arellano, Rocio
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Arreola Marín, María Esmeralda
The present comparative bibliometric study (2020-2025) of the Scopus and WoS databases on Generative Artificial Intelligence (GenAI) reveals accelerated growth, concentrating more than 95% of the production and reaching its peak impact in 2025. Thematically, the intersection of communication and technology/education dominates. Geographically, the United States leads production, but Asia-Pacific institutions (Hong Kong) are key. The field of GenAI is a massive trend driven by concentrated collaboration between North America and Asia.
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Aerodynamic drag reduction strategies for box-shaped payloads in delivery drones: a multimodal experimental study
(American Institute of Aeronautics and Astronautics, 2026-01-08)
This work presents a multimodal aerodynamic evaluation methodology integrating computational fluid dynamics (CFD), wind-tunnel testing, and full-scale flight experiments to characterize the aerodynamic behavior of standardized box-shaped payloads carried by multirotor unmanned aerial vehicles (UAVs). Three representative configurations—a baseline parcel, a front-fairing modification, and a combined fairing–boat-tail arrangement—were examined to demonstrate the methodology. Across all phases, environment-specific corrective procedures were implemented to address the limitations of each evaluation mode, including turbulence-model verification in CFD, moving-average force filtering in wind-tunnel testing at reduced Reynolds number, and atmospheric-density correction, stabilizer-tail implementation, and force-vector alignment correction with independent measurement of UAV and payload pitch angles during flight. These corrective steps minimized the influence of environmental variability, scale effects, and dynamic flight behavior, allowing the aerodynamic characteristics of each configuration to emerge consistently across the three platforms. Cross-validation across CFD, wind-tunnel, and flight testing showed close agreement in configuration-dependent aerodynamic trends, with all three phases reproducing similar variations in drag coefficient C_D and comparable drag-reduction performance C_(D,RED). The results demonstrate that the proposed multimodal methodology provides a robust and physically consistent framework for assessing UAV payload aerodynamics and establishes a foundation for future studies evaluating additional payload configurations and aerodynamic devices.
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From Aristotle via Aquinas: Understanding Formal Cause in Marshall McLuhan’s Philosophy
(Intellect, 2017)
This book brings together a number of prominent scholars to explore a relatively under-studied area of Marshall McLuhan’s thought: his idea of formal cause and the role that formal cause plays in the emergence of new technologies and in structuring societal relations. ©The authors ©Intellect.
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Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Science and Business Media Deutschland GmbH, 2026)
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book presents a multidisciplinary collection of machine learning approaches applied to the biomedical field, with a focus on computer-aided diagnostic systems, healthcare support tools, biological applications, and sustainable development in health. Computer-aided diagnostic systems leverage machine learning methods to support medical diagnosis, while healthcare support tools, biological applications, and sustainability-oriented studies aim to improve patients’ quality of life, propose innovative solutions to biological challenges, and incorporate sustainability into healthcare processes. The contributions in this book offer readers a holistic view of new methods used to address current biomedical challenges in medicine, biology, and health sciences. By applying artificial intelligence algorithms, mathematical theories, and emergent systems, these works demonstrate how such approaches can improve specific problems or propose innovative solutions. This book is valuable for readers interested in recent advances in machine learning for diagnostic systems, healthcare applications, biological research, and sustainability-related issues. ©The authors ©Springer.
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A Humanist Reconquista: Hernando de Talavera’s Pedagogy of Good Manners and His Residential School for Morisco Boys
(Cambridge University Press (CUP), 2025)
Arenas Pacheco, Carlos Diego
From 1493 to 1507, Hernando de Talavera, the first archbishop of Granada after the Spanish Reconquista, ran a residential school for Morisco noble boys in his palace. This article argues that Talavera’s school set the foundation for the long history of residential schooling as a tool to transform or eradicate a conquered culture through the cultural assimilation of children. A champion of Christian humanism, Talavera thought that cultivating good manners (that is, adopting Spanish customs) was the main marker of a true Christian. Thus, his pedagogy aimed to educate everyone, particularly Morisco children, in what he considered the most reasonable and natural ways of living. By examining Talavera’s spiritual pedagogy, his humanist influences, and the educational experiences of Morisco boys at his palace, this paper lays the groundwork for a genealogical study of modern European colonial residential schooling for non-European children. ©The author ©Cambridge University Press (CUP) ©History of Education Quarterly.
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Ethical Challenges in Demand Prediction: A Case Study in the Wholesale Grocery Sector
(Instituto Politécnico Nacional. Centro de Investigación en Computación, 2025)
Duarte, Jorge
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Artificial Intelligence (AI) has emergedas a transformative tool in inventory management and demand prediction within the wholes ale grocerysector. By leveraging machine learning algorithms, businesses can analyze historical sales data, market trends, and seasonal variations to optimize inventory levels, reducing overstock and stockouts. AI-drivendemand prediction models provide accurate forecasts, enabling whole salers to anticipate customer needs and streamline supply chain operations. Thisarticle examines the ethical challenges associated with developing and implementing AI-driven demand prediction models in the wholesale grocery sector. As businesses seek to optimize their operations through artificial intelligence, significant ethical concerns arise that must be addressed to ensure responsible and fair implementation. This case study highlights the main ethical challenges identified in a grocery wholesaler, focusing on issues such as transparency, accountability, fairness, and human control. Through the analysis of aspecific demand prediction model, we discuss how these ethical concerns not only influence user acceptance of the model but also impact operational efficiency and customer satisfaction. The article aims to contribute to the ongoing dialogue on ethics in data science, providing insights and recommendations for companies looking to adopt predictive technologies ethically. ©The authors ©Computación y Sistemas © Instituto Politécnico Nacional. Centro de Investigación en Computación.
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How Financial Literacy Factors Influence Households’ Income and Expenses
(Social Sciences Research Society, 2024)
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Financial literacy equips individuals with the knowledge and skills necessary to manage money effectively, thereby fostering financial well-being and supporting societal development through the promotion of financial responsibility. This study investigates the key determinants of financial literacy that influence household income and expenditure. The research employs an empirical analysis, utilising data from a biannual National Income and Expenses Survey. Two models were compared using multivariate estimations to examine the cause-and-effect relationships between income, expenditure, and financial literacy variables. The first model applied the least-squares method, while the second utilised a robust least-squares method, which accommodates outliers and mitigates the impact of assumption violations. Findings reveal that certain factors, including savings, education, medical expense insurance, life insurance, and credit card usage, significantly and positively influence household income and expenses over time. Notably, the acquisition of medical expense insurance, life insurance, and credit card usage emerged as the most impactful factors. Although savings and education were statistically significant, their overall influence on household financial outcomes was comparatively limited. This study contributes by identifying and highlighting the most influential factors affecting household income and expenditure, with implications for policy and practice. It is recommended to enhance financial literacy by improving public understanding and practical engagement with medical expense insurance, life insurance, and credit card usage, thereby promoting more sustainable and prosperous financial outcomes for households. © The authors (2024), International Journal of Economics and Finance Studies ©Social Sciences Research Society.
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Social mobility through technological assets: a regional microsimulation model
(Social Sciences Research Society, 2025)
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Access to technology is a significant factor in facilitating upward social mobility by reducing disparities in access to information and fostering educational and employment opportunities. This study develops a microsimulation model for a region in Mexico to estimate the probabilities of households achieving social mobility through a two-year renewable microcredit scheme for acquiring a computer with internet access over a 14-year period. The data utilised include the National Income and Expenses Survey and a regional social mobility survey. The methodology incorporates principal component analysis to construct an Asset Index, ordered logistic regression for asset selection, a Markov Chain for transition probability estimation, and Monte Carlo simulation. The findings reveal that 13.75% of households could progress to a higher wealth quintile while sustaining their ability to repay the microcredit. The study advocates for targeting public programmes aimed at social mobility towards specific sectors rather than implementing universal initiatives. Additionally, it recommends enhancing credit conditions through public-private partnerships to mitigate inequality. ©The authors © (2024), (Social Sciences Research Society). All rights reserved.
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The effectiveness of curriculum standardization in data analysis and tools proficiency for undergraduate education: a case study
(Frontiers Media SA, 2025)
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Introduction: The rapid evolution of technology necessitates the development of advanced computing and data analysis skills in undergraduate education. Standardizing curricula is a strategy to ensure consistent learning outcomes and align educational objectives with industry requirements. This study investigates the impact of a standardized curriculum on students' academic performance and professional certification outcomes. Methods: A quasi-experimental design was used to analyze 1,597 students enrolled in a data analysis course before and after implementing a standardized curriculum at a private university in Mexico City. The study assessed course grades and certification exam scores to evaluate the effectiveness of standardization. Parametric and non-parametric tests were applied to ensure robust analysis. Results: Implementing the standardized curriculum resulted in a slight decrease in average course grades but significantly improved certification exam scores, exceeding the threshold for certification. The findings highlight enhanced proficiency in data analysis tools and consistency in achieving educational objectives across groups. Discussion: The results suggest that curriculum standardization effectively addresses teaching methodologies and assessment criteria discrepancies. While increased curriculum difficulty temporarily impacted grades, the improved certification outcomes demonstrate the value of standardization in preparing students for industry demands. These insights provide a foundation for future curriculum development to align academic instruction with the evolving requirements of a technology-driven workforce. ©The authors ©Frontiers Media.

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