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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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Sphonic: Development of a Mobile Application Using AI and AR for Learning Biomedical Concepts
(Springer Nature Switzerland, 2025)
Escobar-Castillejos, Daisy
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Báez Gómez Tagle, Enrique Ulises
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Chavarría-Reyes, Fernando Mauricio
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Sigüenza Noriega, Iñaki
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Cruz-Ledesma, Iván
This chapter covers the development of Sphonic, an iOS mobile application that combines artificial intelligence (AI) and augmented reality (AR) to improve biomedical education. Sphonic was designed to employ SwiftUI for its front end, while its back end is based on Django and hosted on Amazon Web Services. Sphonic intends to assist in explaining complex topics in chemistry and biology. Using Apple’s ARKit framework, Sphonic allows students to explore dynamic representations of DNA architecture, chemical bonding interactions, and biological processes to promote engagement and retention. On the other hand, the application features an OCR-based chemical equation solver, using Amazon Bedrock’s generative AI capabilities, that balances equations and offers comprehensive, step-by-step guidance, addressing conceptual deficiencies for learners. Additionally, Sphonic implements secure authentication protocols to protect user data and features a user-friendly interface that simplifies navigation. This chapter concludes by emphasizing the importance of AI and AR in modern education, demonstrating how these technologies could democratize access and understanding of scientific information and foster creativity in academic environments. ©The authors ©Srpinger.
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Development of a Mobile Application for Dermatological Diagnosis Using Image Recognition: The DermAware Case Study
(Springer Nature Switzerland, 2025)
Cedillo-Maldonado, Luis
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Sigüenza-Noriega, Iñaki
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Miranda-Mateos, Sara Rocio
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Reinoso-Fuentes, Lorenzo
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Pérez-Aguirre, Mauricio
This chapter discusses the development of DermAware, an iOS mobile application intended for assisting dermatological diagnosis through image recognition and machine learning. DermAware aims to serve as a supplementary resource for specialized medical professionals and patients, aiding the early identification and supervision of dermatological illnesses. The application was developed with SwiftUI for the front end and Django for the back end, providing scalability and secure data management. The application incorporates multiple components designed to enhance its usability. A real-time messaging module was designed to facilitate direct communication among users for prompt consultation scheduling. Health tracking functionalities, supported by Apple’s HealthKit, enable the collection and monitoring of patient data. The application integrates patient history management, allowing doctors to review previous assessments and track disease development conveniently. Ultimately, an authentication system guarantees data confidentiality and adherence to regulations. DermAware uses Apple’s CoreML framework alongside the ResNet50 convolutional neural network model to classify skin diseases. The system was trained using publicly accessible dermatological datasets, achieving an accuracy of 85% for detecting various skin conditions, including melanoma. The chapter finishes by addressing the technical challenges faced during development, evaluating potential enhancements, and discussing future developments in the field. These factors highlight the significance of AI-driven applications for improving medical diagnostics and healthcare accessibility. ©The authors ©Springer.
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Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Nature Switzerland, 2026)
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions. Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions. This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development. ©The authors ©Springer.
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Orthosis Control Based on Electromyographic Signals and Machine Learning
(Springer Nature Switzerland, 2025)
Escobedo-Gordillo, Andrés
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Díaz, Fernando
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Villa, Jesús
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Sepúlveda, Miguel
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García-Casas, Sebastián
The human hand is indispensable for daily activities, and those who suffer from dysfunction due to strokes or accidents often require therapy to improve their condition. This study has developed a hand orthosis that uses surface Electromyographic (sEMG) signals and machine learning to address therapeutic needs and improve the quality of life for individuals with reduced motor skills in their hands and/or wrists. While current orthoses meet therapy requirements, they do not incorporate machine learning (ML) or sEMG sensors to optimize performance and accessibility. This chapter describes a remote-controlled, electro-mechanical orthosis that can replicate six basic movements of the human hand using three sEMG channels and ML. Our dataset of 14,400 samples, each labeled with a hand gesture, was generated by eight participants. The orthosis is comfortable and customizable for different users, as shown in prototype testing. The convolutional neural network (CNN) used achieves an accuracy of 90.38% with an inference time of 1.515 ms. Therefore, this orthosis system has significant potential for further development and practical application in patients who require such intervention. ©The authors ©Springer.
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Digital financial inclusion as a catalyst for innovation, economic growth, and sustainability: A bibliometric analysis (2014-2024)
(Pro-Metrics, 2025)
Salazar-Uribe, Mayra Yvette
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Salgado-García, Jorge Arturo
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Hernández-Lara, Ana Beatriz
Objective. This study aimed to conduct a bibliometric analysis of keywords to identify strategic topics in digital financial inclusion (DFI) and their relationship with sustainability and economic growth between 2014 and 2024. Design/Methodology/Approach. A bibliometric analysis was conducted on a sample of 1,234 academic articles indexed in Scopus using the Bibliometrix tool in R. Keyword co-occurrence was examined using multiple correspondence analysis and K-means clustering to reveal thematic structures. Results/Discussion. A total of six thematic clusters were identified: (1) threshold effect, (2) digital transformation, (3) central bank digital currencies (CBDCs), (4) sustainable development, (5) financial and digital literacy, and (6) fintech. These clusters demonstrated the evolution of DFI from its initial role as a technological enabler, such as fintech and blockchain, to its current impact on economic development, growth, and sustainability. This analysis proposed a conceptual model of DFI. In this model, digital literacy and fintech functioned as enablers. Meanwhile, CBDCs and blockchain technology served as structural tools. Digital financial inclusion was defined as a mechanism for inclusive economic development. Conclusion. The findings contributed to an understanding of how financial digitization is linked to sustainability strategies and long-term economic growth. ©The authors ©Iberoamerican Journal of Science Measurement and Communication ©Pro-Metrics
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WEFE nexus unveiled: a comprehensive review of monitoring and modelling methods in the water-energy-food-ecosystems nexus
(Purpose Led Publishing, 2025-10)
Sustainable resource management in the face of climate change is a pressing challenge for our society. This paper delves into the water-energy-food-ecosystems (WEFE) nexus, a scientific framework that supports the integrated assessment and management of the interconnected resources. Shifting from sectoral to cross-sectoral and transdisciplinary perspectives, the WEFE nexus addresses interdependencies and interactions among water, energy, food, ecosystems, and climate. This paper focuses on the extended nexus, incorporating ecosystems as a fourth pillar, underscoring the importance of considering ecosystems on an equal footing with water, energy, and food sectors. In addition, the paper emphasizes the significance of monitoring and modelling techniques, laying the foundations for understanding the nexus complexities and assessing uncertainty. The paper offers an overview of integrated nexus modelling, system analysis and socio-economic modelling, bridging the gap between nexus science and practice. It highlights the role of multifaceted stakeholder engagement methods, policy assessment, and institutional analysis in nexus models. Quantifying the nexus through indicators, and its alignment with the Sustainable Development Goals, EU Green Deal, and EU Blue Deal are also key focal points. Finally, the last part of the paper addresses challenges in existing nexus modelling attempts, advocates for the integration of transdisciplinary information, and presents lessons learned. The paper concludes with recommendations for the future of the WEFE nexus, emphasizing its potential in fostering transformative change toward sustainable resource management and inclusive policymaking.
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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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