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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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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application
(MDPI AG, 2025)
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Marmolejo-Saucedo, José Antonio
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Köse, Utku
Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. ©The authors ©MDPI.
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A Conceptual Framework for Digital Transformation of Business Models: Advancing Towards Industry 5.0
(Springer Nature Switzerland, 2026)
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Hernández-Lara, Ana Beatriz
Digital transformation is progressing unevenly across industries, with varying levels of success influenced by organizational and sector-specific factors. Understanding where to focus investments and what type of transformation to adopt has become a crucial challenge for companies seeking competitiveness and market relevance in the digital era. This paper aims to analyze companies’ strategic decision making to foster digital transformation, conducting a literature review, and proposing a conceptual framework for digital transformation of business models. The study identifies key drivers of successful digital transformation, including digital strategy, human capital, scalability, customer focus, security and risk management. Integrating these factors, the proposed model emphasizes the strategic alignment of digital initiatives with organizational goals, fostering a culture of continuous innovation and adaptability. The findings contribute to a deeper understanding of the mechanisms and prerequisites for effective digital transformation, offering insights for organizations navigating the shift toward Industry 5.0. ©The authors ©Springer.
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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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