Artículos (Tecnología Electrónica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11417

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Cost-Effective Operation of Microgrids: A MILP-Based Energy Management System for Active and Reactive Power Control
(Elsevier, 2025-04) García Caro, Sebastián; Bracco, Stefano; Parejo Matos, Antonio; Fresia, Matteo; Guerrero Alonso, Juan Ignacio; León de Mora, Carlos; Universidad de Sevilla. Departamento de Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Microgrids (MGs) have emerged as a potential solution for the integration of Distributed Energy Resources (DERs) into the distribution network. In this sense, to effectively manage MGs, it is essential to implement Energy Management Systems (EMSs). This entails not only performing the unit commitment but also considering the voltage and reactive power technical constraints and managing ancillary services. This paper contributes with a comprehensive EMS for the optimal management of active and reactive power of a generic grid-tied MG composed of Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Diesel Generator (DGs) units and loads, with the goal of reducing the operating costs of the facility. The EMS includes models for the power electronics units to apply reactive power management and a generic formulation for the management of the startup and shutdown cycles of dispatchable units. Furthermore, a detailed modeling of BESS and DG units is presented, reflecting the actual behavior of the devices. The MG is modeled as a multi-busbar network, with the application of the power flow equations to establish the link between power flows and nodal voltages. All the constraints are linearized to formulate the EMS as a Mixed-Integer Linear Programming (MILP) optimization problem. The EMS is validated in a real facility: the CATEPS Microgrid Living-Lab. The results demonstrate the operational effectiveness of the EMS in different seasons, exhibiting a reduction in costs ranging from 21.84 % in summer to a 5.69 % in winter compared to a scenario with RES production but without energy management. In addition, a comprehensive examination of reactive power and voltage management is presented. Furthermore, an empirical assessment of the power flow equations linearization demonstrated minimal discrepancy in the results when compared with those obtained with the non-linear equations, exhibiting a mean absolute error of 8.8e-5 p.u. and 3.2e-5 rad in voltage magnitude and phase angle, respectively, in the most unfavorable scenario. A sensitivity analysis of the startup and shutdown cycles management of the BESS reveals a negligible effect on operational costs, yet it provides a mechanism for managing the battery stress by reducing the number of startups in a complete week from 28 to 16 in summer and from 37 to 24 in winter. The dependence between the maximum charging and discharging power on the state of charge of the BESS is also assessed in the use case.
A data-driven topology identification method for low-voltage distribution networks based on the wavelet transform
(Elsevier, 2025-06) García Caro, Sebastián; Fresia, Matteo; Mora-Merchán, Javier María; Carrasco Muñoz, Alejandro; Personal Vázquez, Enrique; León de Mora, Carlos; Universidad de Sevilla. Departamento de Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
A comprehensive knowledge of topology is of great importance for the effective operation and maintenance of distribution networks. This paper contributes with a novel data-driven topology identification method for low-voltage distribution networks based on the wavelet transform. The method uses only energy measurements from smart meters, being compatible with the current European smart meter capabilities. The method identifies the feeder and phase topology of single and three-phase customers, even in unbalanced situations. A computationally-efficient methodology to link customers' time-frequency features with their network connection is proposed. The performance of the method is assessed on eleven non-synthetic networks, with a robustness assessment of factors such as network observability, dataset size, measurement errors, and Renewable Energy Sources (RES) penetration. Accuracy rates exceeding 95 % are obtained in most cases, outperforming an energy-conservation approach. A 98 % accuracy can be achieved with a 30-day hourly dataset if at least 80 % of network observability is provided. For lower observability levels, 45 or 60 days of data are needed to reach similar rates. The sensitivity analysis of measurement error demonstrated that it had a negligible influence on the results. The method showed favorable results even in scenarios with high-RES penetration, with accuracy values exceeding 95 %.
Key Components of Participatory Design Workshops for Digital Health Solutions: Nominal Group Technique and Feasibility Study
(Springer, 2025-05-14) Denecke, Kerstin; Rivera-Romero, Octavio; Giunti, Guido; Holten, Karin van; Gabarrón, Elia; Universidad de Sevilla. Departamento de Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); European Union (UE). H2020; Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Participatory design (PD) is an essential method in the development of digital health solutions since it promises to increase acceptance, usability, and trust in the developed solution. Although careful planning and preparation is crucial for the success of PD workshops, a framework of key components to consider is still missing. The objective of this work is to develop such framework enriched with examples for aspects to be considered when planning and conducting PD workshops for designing and developing digital interventions in healthcare. We applied the nominal group technique with four participants with backgrounds in computer science, health informatics, psychology, and social anthropology to identify key components of PD workshops. The resulting framework was applied by an expert in PD to a case of a digital health solution for fatigue self-management for multiple sclerosis. The feasibility and applicability of the framework and its shortcomings were assessed. As a result, a framework consisting of five main categories and a total of 36 factors were assigned and defined in relation to the categories. The categories are participatory process, involved persons and their roles, workshop definition, setting, privacy and ethics, including regulations. The application of the framework to the test case demonstrated the feasibility and applicability of the suggested framework as well as the shortcomings of the analyzed PD process. This framework provides practical guidance while highlighting the complexity of PD workshops, encouraging their broad adoption, critical reflection, and continuous refinement. It has potential to improve the conduct of PD workshops and, in this way, potential to improve usability, acceptance, and usefulness of digital health solutions. In future work, the user perspective could be used to extend the framework.
The Unexpected Harms of Artificial Intelligence in Healthcare: Reflections on Four Real-World Cases
(IOS Press, 2025-05) Denecke, Kerstin; Lopez-Campos, Guillermo; Rivera-Romero, Octavio; Gabarrón, Elia; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Rapid advances in Artificial Intelligence (AI), especially with large language models, present both opportunities and challenges in healthcare. This article analyzes real-world AI-related harms in healthcare. Methods: We selected four recent AI-related incidents from the AIAAIC Repository. Results: The incidents discussed include: Whisper’s harmful hallucinations; UNOS’s algorithm delaying transplants for black patients; the WHO’s S.A.R.A.H. chatbot providing inaccurate health information; and Character AI’s chatbot promoting disordered eating among teens. Discussion and conclusion: These incidents highlight diverse risks, from misinformation to safety concerns, involving both industry and institutional providers. The article emphasizes the need for systematic reporting of AI-related harms, concerns about security, privacy, and ethics, and calls for a centralized health-specific database to enhance patient safety and understanding.
Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review
(JMIR Publications, 2024-01-01) Gabarrón, Elia; Larbi, Dillys; Rivera-Romero, Octavio; Denecke, Kerstin; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Background: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. Objective: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. Methods: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases—PubMed, Embase, and IEEE Xplore—and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Results: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. Conclusions: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI’s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.
Healthcare professionals’ perspectives towards the digitalisation of paediatric growth hormone therapies: expert panels in Italy and Korea
(Frontiers Media, 2024-07-10) Rivera-Romero, Octavio; Chae, Hyun Wook; Faienza, Maria Felicia; Vergani, Edoardo; Cheon, Chong Kun; Di Mase, Raffaella; Frasca, Francesco; Lee, Hae Sang; Giavoli, Claudia; Kim, Jihyun; Klain, Antonella; Moon, Jung Eun; Iezzi, Maria Laura; Yeh, James; Aversa, Antonio; Rhie, Young Jun; Koledova, Ekaterina; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Introduction: To analyse the perspectives of healthcare professionals (HCPs) regarding the acceptance of digital health solutions for growth hormone (GH) deficiency care. This study identified factors impacting HCPs’ intent to use and recommend digital solutions supporting recombinant-human growth hormone (r-hGH) therapy in Italy and Korea with a use case of connected drug delivery system (Aluetta® with Smartdot™) integrated in a platform for GH treatment support (the Growzen™ digital health ecosystem). Methods: Participatory workshops were conducted in Rome, Italy, and Seoul, Korea, to collect the perspectives of 22 HCPs on various predefined topics. HCPs were divided into two teams, each moderated by a facilitator. The workshops progressed in five phases: introduction of the project and experts, capturing views on the current context of digitalisation, perceived usefulness and ease of use of Aluetta® with Smartdot™, exploration of the perception of health technology evolution, and combined team recommendations. Data shared by HCPs on technology acceptance were independently analysed using thematic analysis, and relevant findings were shared and validated with experts. Results: HCPs from both Italy and Korea perceived Aluetta® with Smartdot™ and the Growzen™ based digital health ecosystem as user-friendly, intuitive, and easy-to-use solutions. These solutions can result in increased adherence, a cost-effective healthcare system, and medication self-management. Although technology adoption and readiness may vary across countries, it was agreed that using digital solutions tailored to the needs of users may help in data-driven clinical decisions and strengthen HCP–patient relationships. Conclusion: HCPs’ perspectives on the digitalisation in paediatric GH therapies suggested that digital solutions enable automatic, real-time injection data transmission to support adherence monitoring and evidence-based therapy, strengthen HCP–patient relationships, and empower patients throughout the GH treatment process.
Digital Health Program to Support Family Caregivers of Children Undergoing Growth Hormone Therapy: Qualitative Feasibility Study
(JMIR Publications, 2025-02-05) Jiménez-Díaz, Alba; Pierantonelli, Maitena; Coscolín, Patricia Morte; Salinas-Uhalte, Amaia; Quer-Palomas, Silvia; Rivera-Romero, Octavio; Herrero, Rocío; Fernández Luque, Luis; Baños, Rosa; Berrios, Ricardo C.; Arriba, Antonio de; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission. Fondo Social Europeo (FSO); Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
Background: Caregivers of children with growth hormone deficiency often face emotional challenges (eg, stress) associated with their children’s health conditions. This psychological burden might affect children’s adherence to treatment and hinder their health-related quality of life (HrQoL). This assumption is leading to seriously considering multidimensional clinical approaches to pediatric health conditions where the emotional well-being of caregivers should be accounted for to optimize children’s health outcomes. Novel mobile health (mHealth) solutions based on emotional and behavioral change techniques can play a promising role because they are increasingly used within different health areas to support adaptive psychological functioning. However, whether and how mHealth solutions of this type of emotional well-being support caregivers of children with growth-related problems is an issue that needs to be clarified. Objective: This study aimed to gather qualitative information to better understand individualized experiences of caregiving of children undergoing growth hormone therapy (GHt) and perceived barriers or facilitators for the adoption of an mHealth solution called Adhera Caring Digital Program (ACDP).Methods: A total of 10 family caregivers were recruited at Miguel Servet Children’s Hospital, and they engaged with the ACDP for 1 month. The ACDP is a mobile-based digital intervention focused on promoting the overall well-being of family caregivers which provides access to personalized education, motivational mobile-based messages, and mental well-being exercises such as mindfulness or respiratory exercises. Subsequently, an individual semistructured interview was performed to gather qualitative user experience information. Results: The digital intervention was well-received. The ACDP was found to be useful, easy to use, and understandable, addressing all the difficulties expressed by caregivers. It was also noted to be particularly helpful at the beginning of the treatment and, for some families, became a natural tool that strengthened the parent-child relationship. Conclusions: The ACDP is a promising and well-accepted tool that enhances the experience of patients and caregivers. It improves the management of growth hormone deficiency and promotes the overall well-being of family caregivers.
Gestión óptima en microrredes con soporte fotovoltaico e hidrógeno verde
(Comité Español de Automática, 2025) Moliner Heredia, Rubén; Vivas Venegas, Carlos; Rodríguez Rubio, Francisco; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España
La gestión de la energía es esencial para un correcto control de una microrred. Es importante que dicha gestión tenga en cuenta la optimización de la vida útil de los componentes de la microrred. Uno de los posibles comportamientos que se desean evitar es el apagado y encendido continuo de determinados componentes, tales como las celdas de combustible y los electrolizadores. En este artículo se ha propuesto un algoritmo usando control predictivo basado en modelo (MPC) utilizando diversas restricciones de tiempos mínimos de activación de dichos componentes para evitar efectos perjudiciales sobre los equipos de hidrógeno verde. Además, se ha propuesto un método de transmisión entre iteraciones del algoritmo para que la aplicación de dichas restricciones sea compatible con errores en las predicciones del algoritmo y de perturbaciones en el sistema. Para validar el algoritmo propuesto, se ha desarrollado un modelo simplificado de la microrred, y se han realizado simulaciones y comparaciones con varios algoritmos utilizando Matlab.
Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
(MDPI, 2025-03-12) Tena Sánchez, Erica; Potestad Ordóñez, Francisco Eugenio; Zúñiga González, Virginia; Acosta Jiménez, Antonio José; Universidad de Sevilla. Departamento de Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Junta de Andalucía; European Union (UE). H2020; Ministerio para la Transformacion Digital y Función Pública; European Commission. Fondo Social Europeo (FSO); Universidad de Sevilla. TIC180: Diseño de Circuitos Integrados Digitales y Mixtos
Considerable attention has been given to addressing side-channel attacks to improve the security of cryptographic hardware implementations. These attacks encourage the exploration of various countermeasures across different levels of abstraction, through masking and hiding techniques, mainly. In this paper, we introduce a novel hiding countermeasure designed to mitigate Correlation Power Analysis (CPA) attacks without significant overhead. The new countermeasure interferes with the processed data, minimizing the power correlation with the secret key. The proposed method involves using a Correlated-Power-Noise Generator (CPNG). This study is supported by experimental results using CPA attacks on a SAKURA-G board with a SPARTAN-6 Xilinx FPGA. An Advanced Encryption Standard (AES) cipher with 128/256-bit key size is employed for this purpose. The proposed secure design of AES has an area overhead of 29.04% compared to unprotected AES. After conducting a CPA attack, the acquisition of information about the private key has been reduced drastically by 44.5%.
Assessing the impact of vaccines on COVID-19 efficacy in survival rates: a survival analysis approach for clinical decision support
(Frontiers Media, 2024-11) González Rodríguez, Juan Luis; Oprescu, Andreea M.; Muñoz Lezcano, Sergio; Cordero Ramos, Jaime; Romero Cabrera, Juan Luis; Armengol de la Hoz, Miguel Ángel; Estella, Ángel; Universidad de Sevilla. Departamento de Tecnología Electrónica
Background: The global COVID-19 pandemic, caused by the SARS-CoV-2 virus, has presented significant challenges to healthcare systems worldwide. Objective: This study, based on an analysis of a cohort from the Public Health System of Andalusia (Spain), aims to evaluate how vaccination affects case-fatality rate in patients hospitalized due to COVID-19 infection in Andalusia. Methods: The cohort consists of 37,274 individuals after applying the inclusion criteria. We conducted survival analyses employing the Cox proportional hazards models and generated adjusted survival curves to examine the outcomes. The analyses were performed from three perspectives: vaccinated vs. unvaccinated patients, vaccinated and unvaccinated patients grouped by age, and stratified by vaccination status. Results: Results indicate a substantial correlation between vaccination and a 20% reduction in the risk of case-fatality. Age-specific effects reveal varying degrees of protection across different age groups. Conclusion: These findings emphasize the pivotal role of vaccination status in COVID-19 risk assessment, supporting the development of a clinical decision support system for accurate predictions and optimizing healthcare management at admission.
TinyJAMBU Hardware Implementation For Low Power
(2024-08-05) Fernández García, Carlos; Mora Gutiérrez, José Miguel; Jiménez Fernández, Carlos Jesús; Universidad de Sevilla. Departamento de Tecnología Electrónica
In this paper, we present hardware implementations of the lightweight TinyJAMBU cipher with reduced power consumption using a mechanism based on shift register parallelization. The power consumption in digital circuits depends linearly on the switching activity of the logic gates. The parallelization technique reduces the number of switches per clock cycle of the shift registers, which can significantly reduce power consumption. This technique has been applied to the TinyJAMBU cipher, a f inalist in the NIST lightweight cryptography standardization process with the lowest resource and power consumption. The implementations we present use the logical parallelization technique in the cipher’s NLFSR, which is the basic block of TinyJAMBU, and in the key register. Simulation results are presented to demonstrate the effectiveness of the proposed technique in reducing power consumption, achieving a reduction of more than 30% in dynamic power consumption compared to the standard implementation, with almost no increase in resource consumption. Therefore, the ciphers proposed in this paper are highly suitable for use in applications with severe constraints on available resources and power.
Application specific integrated circuit solution for multi-input multi-output piecewise-affine functions
(Wiley, 2016-01) Brox Jiménez, Piedad; Martínez Rodríguez, Macarena Cristina; Tena Sánchez, Erica; Baturone Castillo, María Iluminada; Acosta Jiménez, Antonio José; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; European Commission (EC); Gobierno de España; Consejo Superior de Investigaciones Científicas (CSIC); Universidad de Sevilla. TIC180: Diseño de Circuitos Integrados Digitales y Mixtos
This paper presents a fully-digital architecture and its ASIC implementation for computing Multi- Input Multi-Output (MIMO) Piecewise-Affine (PWA) functions. The work considers both PWA functions defined over regular hyper-rectangular and simplicial partitions of the input domains and also lattice piecewise-affine representations. The proposed architecture is able to implement PWA functions following different realization strategies, using a common structure with a minimized number of blocks, thus reducing power consumption and hardware resources. Experimental results obtained with an ASIC integrated in a 90-nm CMOS standard technology are provided. The proposed architecture is compared with other digital architectures in the state-of-the-art habitually used to implement model predictive control applications. The proposal is superior in power consumption (saving up to 86%) and economy of hardware resources (saving up to 40% in comparison with a mere replication of the three representations) to other proposals described in literature, being ready to be used in applications where high-performance and minimum unitary cost are required.
A Methodology for Optimized Design of Secure Differential Logic Gates for DPA Resistant Circuits
(Institute of Electrical and Electronics Engineers, 2014-06) Tena Sánchez, Erica; Castro, Javier; Acosta Jiménez, Antonio José; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; Consejo Superior de Investigaciones Científicas (CSIC); Gobierno de España; Universidad de Sevilla. TIC180: Diseño de Circuitos Integrados Digitales y Mixtos
Cryptocircuits can be attacked by third parties using differential power analysis (DPA), which uses power consumption dependence on data being processed to reveal critical information. To protect security devices against this issue, differential logic styles with (almost) constant power dissipation are widely used. However, to use such circuits effectively for secure applications it is necessary to eliminate any energy-secure flaw in security in the shape of memory effects that could leak information. This paper proposes a design methodology to improve pull-down logic configuration for secure differential gates by redistributing the charge stored in internal nodes and thus, removing memory effects that represent a significant threat to security. To evaluate the methodology, it was applied to the design of AND/NAND and XOR/XNOR gates in a 90 nm technology, adopting the sense amplifier based logic (SABL) style for the pull-up network. The proposed solutions leak less information than typical SABL gates, increasing security by at least two orders of magnitude and with negligible performance degradation. A simulation-based DPA attack on the Sbox9 cryptographic module used in the Kasumi algorithm, implemented with complementary metal-oxide-semiconductor, SABL and proposed gates, was performed. The results obtained illustrate that the number of measurements needed to disclose the key increased by much more than one order of magnitude when using our proposal. This paper also discusses how the effectivenness of DPA attacks is influenced by operating temperature and details how to insure energy-secure operations in the new proposals.
Logic Minimization and Wide Fan-In Issues in DPL-Based Cryptocircuits Against Power Analysis Attacks
(Wiley, 2019-02) Tena Sánchez, Erica; Acosta Jiménez, Antonio José; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; Consejo Superior de Investigaciones Científicas (CSIC); Gobierno de España; Universidad de Sevilla. TIC180: Diseño de Circuitos Integrados Digitales y Mixtos
This paper discusses the use of logic minimization techniques and wide fan-in primitives and how the design and evaluation of combinational blocks for full-custom dual-precharge-logic-based cryptocircuits affect security, power consumption, and hardware resources. Generalized procedures for obtaining optimized solutions were developed and applied to the gate-level design of substitution boxes, widely used in block ciphers, using sense-amplifier–based logic in a 90-nm technology. The security of several proposals was evaluated with simulation-based correlation power analysis attacks, using the secret key measurements to disclosure metric. The simulation results showed increased security-power-delay figures for our proposals and, surprisingly, indicated that those solutions which minimized area occupation were both the most secure and the most power-efficient.
Potential of Large Language Models in Health Care: Delphi Study
(JMIR Publications, INC, 2024-05-13) Denecke, Kerstin; May, Richard; Rivera-Romero, Octavio; de Arriba-Muñoz, Antonio; Chapman, Wendy; Chow, James C.L.; Lacalle Remigio, Juan Ramón; Ropero Rodríguez, Jorge; Sevillano Ramos, José Luis; Verspoor, Karin; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Medicina Preventiva y Salud Pública; Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Background: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. Objective: The aim of this adapted Delphi study was to collect researchers’ opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. Methods: We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. Results: The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. Conclusions: Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.
Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks
(Springer Nature, 2024) Denecke, Kerstin; May, Richard; Rivera-Romero, Octavio; Universidad de Sevilla. Departamento de Tecnología Electrónica
Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation.
Engine and oil condition analysis using a hybrid supervised model with multi-layer neural network and expert rules
(Institute of Electrical and Electronics Engineers, 2024-10) Ochando Terreros, Francisco José; Guerrero Alonso, Juan Ignacio; Luque Rodríguez, Joaquín; León de Mora, Carlos; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. TIC150: Tecnología Electrónica e Informática Industrial
This article relies on using machine learning algorithms for improving maintenance in military vehicles by means of a condition-based approach, allowing more precise maintenance task scheduling. The engine was chosen among several vehicle systems for its faults, effects, and criticality. We adopt a novel approach, using real-time captured operational engine data for condition-based and prognostic methods without additional sensors. Unlike other work that transmits data externally or performs oil chemical analysis, this research leverages data and expert rules for simultaneous analysis of oil use and engine health. The methodology involves data processing techniques, model training, and model testing with operational data. Operational data are used to predict friction power based on several engine parameters. The model combines expert lubrication rules with machine learning algorithms. These rules serve as the foundation for unknown friction power prediction using a multilayer perceptron, among other algorithms. Consequently, the model provides a crucial indicator of engine wear based on friction power. Unlike other models, the proposed model eliminates the need for incorporating viscosity sensors, thus avoiding the complexity of installing such sensors in vehicles. A performance analysis was conducted to identify the most efficient algorithm for developing a lightweight model suitable for installation on an edge computer. Empirical validation revealed a correlation between the cumulative friction power and metal concentration in the oil samples.
The use of high-intensity focused ultrasound for the rewarming of cryopreserved biological material
(IEEE, 2021) Olmo Fernández, Alberto; Barroso, Pablo; Barroso, Fátima; Risco, Ramón; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Física Aplicada III
High-intensity focused ultrasound (HIFU) has been used in differentmedical applications in the last years. In this work, we present for the first time the use of HIFU in the field of cryopreservation, the preservation of biological material at low temperatures. An HIFU system has been designed with the objective of achieving a fast and uniform rewarming in organs, key to overcome the critical problem of devitrification. The finite-element simulations have been carried out using COMSOL Multiphysics software. An array of 26 ultrasonic transducers was simulated, achieving an HIFU focal area in the order of magnitude of a model organ (ovary). A parametric study of the warming rate and temperature gradients, as a function of the frequency and power of ultrasonicwaves,was performed.An optimal value for these parameters was found. The results validate the appropriateness of the technique,which is of utmost importance for the future creation of cryopreserved organ banks.
Alternative General Fitting Methods for Real-Time Cell-Count Experimental Data Processing
(Institute of Electrical and Electronics Engineers, 2020-07-20) Serrano Viseas, Juan Alfonso; Pérez García, Pablo; Huertas Sánchez, Gloria; Yúfera García, Alberto; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; Ministerio de Ciencia, Innovación y Universidades (MICINN). España
This paper reports two general methods for extraction of cell-electrode electrical model parameters in cell culture (CC) assays. The presented approaches can be applied to CC assays based on electrical cell-substrate impedance spectroscopy (ECIS) technique for real-time supervision, providing the cell number per square centimeter, i.e., the cell density, as main result. Both of the proposed methods - minimization of system equations and data predictive model - search, during the experiment, the optimum values of the electrical model parameters employed for the electrode-cell model. The results of this search enable a fast and efficient calculation of the involved cell-electrode model parameters and supply real-time information on the cell number. For the sake of experimental validation, we applied the proposed methods to practical CCs in cell growth assays with a cell line of AA8 Chinese hamster ovarian fibroblasts and the Oscillation Based Test technique for bioimpedance measurements. These methods can be easily extrapolated to any general cell lines and/or other bioimpedance test methodologies.
Bioimpedance Spectroscopy-Based Edema Supervision Wearable System for Noninvasive Monitoring of Heart Failure
(Institute of Electrical and Electronics Engineers, 2023-05-17) Fernández Scagliusi, Santiago Joaquín; Giménez Miranda, Luis; Pérez García, Pablo; Martín Fernández, Daniel; Medrano Ortega, Francisco Javier; Huertas Sánchez, Gloria; Yúfera García, Alberto; Universidad de Sevilla. Departamento de Tecnología Electrónica; Universidad de Sevilla. Departamento de Medicina; Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo; Universidad de Sevilla. Departamento de Biología Celular; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Instituto de Salud Carlos III
Heart failure (HF) is a complex syndrome in which the heart is unable to pump enough blood containing oxygen and nutrients to meet the body's demands. HF is the leading cause of hospitalization for patients over 65 years of age. After a patient is diagnosed with HF, the mortality rate is 50% within the first five years. Presently, there are no unanimous diagnostic criteria for HF. Bioimpedance (BI) analysis has been proposed in recent years as a technique to detect one of the main symptoms: changes in body volume due to edema. This research presents a portable device (Volum), capable of performing real-time BI measurements in a low-cost and noninvasive way. The goal is to improve patient monitoring at home to ensure rapid intervention in cases of worsening conditions, either with timely hospitalizations or adjustments to a patient's usual treatment. Volum is a small, wearable, wireless, lightweight, low-power clinical pilot prototype that takes measurements through four electrodes and sends the data via Bluetooth to an Android device.

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