Artículos (Tecnología Electrónica)

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

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Design of wearable textile electrodes for the monitorization of patients with heart failure
(MDPI, 2024-06-04) Sánchez, María Jesús; Scagliusi, Santiago Joaquín F. ; Giménez-Miranda, L; Pérez García, Pablo; Medrano Ortega, Francisco Javier; Olmo Fernández, Alberto; Medicina; Tecnología Electrónica; TIC178: Diseño y Test de Circuitos Integrados de Señal Mixta
Heart failure is a severe medical condition with an important worldwide incidence that occurs when the heart is unable to efficiently pump the patient's blood throughout the body. The monitoring of edema in the lower limbs is one of the most efficient ways to control the evolution of the condition. Impedance spectroscopy has been proposed as an efficient technique to monitor body volume in patients with heart failure. It is necessary to research new wearable devices for remote patient monitoring, which can be easily worn by patients in a continuous way. In this work, we design and implement new wearable textile electrodes for the monitoring of edema evolution in patients with heart failure. Impedance spectroscopy measurements were carried out in 5 healthy controls and 2 patients with heart failure using our wearable electrodes for 3 days. The results show the appropriateness of impedance spectroscopy and our wearable electrodes to monitor body volume evolution. Impedance spectroscopy is shown to be an efficient marker of the presence of edema in heart failure patients. Initial patient positive feedback was obtained for the use of the wearable device.
Design and Evaluation of Combined Hardware FIA and SCA Countermeasures for AES Cipher
(IEEE, 2024-08) Potestad Ordóñez, Francisco Eugenio; Tena Sánchez, Erica; Zuniga-Gonzalez, V.; Acosta Jiménez, Antonio José; Electrónica y Electromagnetismo; Tecnología Electrónica; Agencia Estatal de Investigación. España; Ministerio de Ciencia e Innovación (MICIN). España; Ministerio de Asuntos Económicos y Transformación Digital
The development of hardware countermeasures against hardware attacks has attracted the attention of the scientific community over the past two decades. Side-Channel Analysis (SCA) and Fault Injection Attacks (FIA) represent a significant risk to the security of cryptographic circuits. Combined countermeasures against these types of attack must be carefully designed if both protections are required in the same implementation without interfering with the other. In this paper, a combined proposal is presented to counteract the SCA and FIA attacks. SCA countermeasures are based on a low-entropy masking scheme, while the FIA countermeasure is based on a signature generator detection scheme. Both have been experimentally implemented in an AES cipher in FPGA. Experimental FIA attacks, first-order Correlation Power Analysis (CPA) attacks, and Test Vector Leakage Assessment (TVLA) tests have been carried out to evaluate the improvement in the security levels. The experimental results show that for an increase in area 28.77% and a frequency degradation of 4.05% with respect to the unprotected implementation, the protected Advanced Encryption Standard (AES) scheme shows great security metrics against FIA (99.87% fault coverage) and SCA attacks.
Low Entropy Masking Protection Scheme for Ascon Cipher to Counteract Side-Channel Attacks
(IEEE, 2025-11) Tena Sánchez, Erica; Potestad Ordóñez, Francisco Eugenio; Martín González, Miguel; Casado Galán, Alejandro; Acosta Jiménez, Antonio José; Electrónica y Electromagnetismo; Tecnología Electrónica; Ministerio para la Transformación Digital y de la Función Pública. España; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España
Since NIST selected the ASCON cipher as a finalist in the lightweight cipher competition for constrained environments in February 2023, the cipher has been a focus of researchers, industry and government. On the other hand, hardware-implemented cryptographic algorithms have had to deal with so-called Side-Channel Attacks (SCA) since their emergence in the late 1990s. Although the ASCON algorithm is relatively recent, SCA attacks that breach its security have been proposed in the literature. In this paper, we present a design methodology for a low entropy masking protection scheme in order to raise the ASCON algorithm’s security levels against SCA. To evaluate the proposed methodology, the ASCON’s permutation has been implemented in an Artix-7 Xilinx FPGA. The implemented design area overhead is 5.45% with respect to the unprotected implementation. A complete ASCON algorithm has been manufactured in a 65nm TSMC ASIC technology. To perform experimental SCA attacks on the ASCON ASIC, a PCB has been designed and manufactured to specifically perform power measurements on the ASIC core.
Emerging Digital Interventions for ADHD: An Overview of Ongoing Clinical Trials
(IOS Press, 2026) Gabarron, Elia; López-Resa, Patricia; Rivera-Romero, Octavio; Lopez-Campos, Guillermo; Denecke, Kerstin; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Introduction: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by inattention, hyperactivity and impulsivity. Analyzing registered clinical trials on digital interventions for ADHD can provide early insight into planned research directions. The study aims to review registered clinical trials to identify and characterize emerging research on digital interventions for ADHD. Methods: We systematically searched ClinicalTrials.gov, the EU Clinical Trials Registry and the WHO ICTRP for ongoing trials of digital interventions for ADHD. Results: Of 149 identified trials, 35 ongoing studies met inclusion criteria. The most common interventions were mobile apps (14), computerized training (6), virtual reality (5), and web-based programs (5). Intervention durations were typically 6–15 weeks (23), most commonly 12 weeks. Primary outcomes assessed included ADHD symptoms and neurocognitive functions (23), followed by emotional/psychological outcomes (18) and family-related outcomes (12). Discussion and conclusion: Registered trial protocols indicate a growing interest in digital approaches for ADHD, particularly app-based, medium-term interventions targeting both children and adults. However, as these findings are based on ongoing trials registrations rather than completed studies, conclusions regarding effectiveness cannot yet be drawn. The limited conclusion of safety-reported outcomes represents a critical gap that may constrain the future real-world applicability of these interventions.
Comparative Post-Market Evaluation of Two Generations of Digital Devices for Growth Hormone Therapy: Adherence and Performance Support
(IOS Press, 2026) Dimitri, Paul; Arnaud, Lilian; Simonin, Melissande; Castel, Marie Nathalie; Rivera-Romero, Octavio; Koledova, Ekaterina; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Adherence to recombinant human growth hormone (r-hGH) therapy is essential for achieving optimal growth outcomes. This retrospective post-market clinical follow-up analysis compared adherence outcomes from real-world device logs of Easypod® 3 (EP3) and Easypod® 2 (EP2). At 12 months, mean adherence was 92.5% (EP3) and 92.4% (EP2), with more than 85% patients maintaining acceptable threshold adherence. These findings support the reliability of device-recorded adherence data and highlight the value of connected injection devices for monitoring adherence with r-hGH therapy and facilitating patient engagement.
Evaluating Caregiver Satisfaction with the Use of a Digital Self-Management Device and Its Support Programme for Paediatric Growth Hormone Therapy in Italy: A Research Protocol
(IOS Press, 2026) Longo, Ilaria; Rizzi, Caterina; Paolillo, Andrea; Koledova, Ekaterina; Rivera-Romero, Octavio; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
This protocol describes a web-based survey questionnaire developed to gather information on user satisfaction with digital devices and ClickService®, a patient support programme (PSP) in Italy for caregivers and paediatric patients receiving recombinant human growth hormone (r-hGH) therapy (Saizen®, Merck KGaA, Darmstadt, Germany) for growth disorders. Anonymous data is being collected from patients registered in the PSP in Italy and the results will be analyzed to evaluate the caregiver and patient satisfaction with the digital device and the PSP.
Frequency of Use of the MyFood4Senior App, a Personalised Digital Intervention to Empower Seniors to Follow a Healthy Lifestyle
(IOS Press, 2026) Sivianes Castillo, Francisco; Alonso-Aperte, Elena; Álvarez-Martín, Cristina; Hernández Velázquez, María Dolores; Ropero Rodríguez, Jorge; Iglesia González, Rocío de la; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Empowering older people to improve their eating habits and physical activity is essential for them to enjoy a better quality of life. There is no doubt that older people are a group of great interest, one that is growing in number and yet is sometimes overlooked. Most health interventions are carried out in childhood and young adulthood. Objectives: To analyze the frequency of use of MyFood4Senior, an app for personalized nutrition education and physical activity in people over 65 years of age. Methods: A group of 40 people over the age of 65 were invited to participate in this study. After signing an informed consent form to participate, in an initial face-to-face session, data was collected from the participants to establish a baseline, assistance was offered to install the application, and brief training was provided on its use. After 4 weeks of use we assessed frequency of use based on logs. Results: 23 of the 40 individuals invited used the app at least once per week for three weeks. 13 of them used the app all the 4 weeks. Three of them used it just one day per week. Only 3 out of the 13 participants showed a decreasing frequency of use over the weeks. Conclusions: This paper presents the results of a study on frequency of use of the MyFood4Senior app among older adults. The results show how participants used the app. Most of the participants followed the usage recommendations.
Participatory Assessment of Ethics in AI: A Review
(IOS Press, 2026) Zamora-Lorence, María; Ropero Rodríguez, Jorge; Rivera-Romero, Octavio; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Introduction: Artificial Intelligence (AI) is reshaping digital healthcare by advancing disease detection, treatment options, and individualized patient care. In this context, responsible AI emphasizes building systems that follow ethical standards, reduce bias, and safeguard personal data, preserving patient safety and dignity. Moreover, its algorithms should be clear and open to evaluation. Although it brings major opportunities, responsible AI still encounters notable obstacles and limitations. Objectives: To understand the nature of participatory AI ethics assessment, from the nature of the evaluators to the way in which ethics are assessed. Methods: A scoping review of literature on evaluation of responsible AI was performed using several scientific databases. Results: 7 papers were found that explicitly mentioned “ethics” in their titles or abstracts and reported on studies evaluating responsible AI systems using a participatory approach. The studies show considerable heterogeneity in terms of the experts who participated in the assessment. Only one study involved patients. The factors evaluated also show a high degree of heterogeneity. The most used participatory methods were interviews and surveys. Conclusions: participatory studies assessing ethics in AI should report information in detail about participants. Participant background, gender, and level of expertise should be reported, together with the multidisciplinary character or not of the study, and the methods used to collect information. Furthermore, ethics assessment of AI should consider ethical principles contained in common frameworks and should use an unambiguous nomenclature.
Hardware implementations, SCA/FIA attacks, and countermeasures for the ASCON AEAD cipher: A review
(IEEE, 2024-11) Martín González, Miguel; Tena Sánchez, Erica; Potestad Ordóñez, Francisco Eugenio; Acosta Jiménez, Antonio José; Electrónica y Electromagnetismo; Tecnología Electrónica; Agencia Estatal de Investigación. España; Ministerio de Ciencia e Innovación (MICIN). España
The design and implementation of lightweightoriented ciphers on hardware is an ever more important topic in Internet of Things (IoT) given the increasing abundance of devices needing secure communication in our modern society. ASCON was selected in 2023 as the new standard algorithm with authenticated encryption with associated data (AEAD) for lightweight applications by the National Institute of Standards and Technology (NIST). This paper offers a full bibliographic recollection of hardware implementations, attacks and countermeasures published about ASCON. From which ASCON has proven its simplicity allows many implementation design approaches to achieve great performance while staying lightweight. However, its unprotected implementations are not safe from hardware attacks, some of the published attacks even being able to dodge countermeasures. ASCON is expected to thrive as the new standard in its field, although further work is required in the development of secure implementations before it does so. Index Terms—Hardware security, ASCON AEAD, side-channel attacks, fault injection attacks, hardware countermeasures.
Electromagnetic Fault Injection Attack Methodology against AES Hardware Implementation
(IEEE, 2024-11) Casado Galán, Alejandro; Potestad Ordóñez, Francisco Eugenio; Acosta Jiménez, Antonio José; Tena Sánchez, Erica; Electrónica y Electromagnetismo; Tecnología Electrónica; Agencia Estatal de Investigación. España; Ministerio de Ciencia e Innovación (MICIN). España
Implementation attacks are a serious threat in the field of cryptography and the IoT. One particular type of implementation attack is Fault Injection Attacks (FIA). These attacks aim to modify the functionality of a certain cryptographic scheme by altering the value of certain bits or bytes inside the electronic circuit. Differential Fault Analysis (DFA) is a technique that exploits these introduced faults to retrieve sensible information about the cryptographic algorithm, like the secret key. There are several ways to perform an FIA, one of them is the electromagnetic FIA, which aims to insert faults in a circuit by generating an electromagnetic pulse (EMP) to induce Foucault currents inside the chip that affect the components inside. In this paper, a methodology to find the optimal locations to perform an electromagnetic FIA and test it in two circuits is presented: The first one is an implementation of a single S-Box of the Advanced Encryption Standard (AES) algorithm and the second one is a full implementation of the AES, both on an Artix-7 FPGA. Both examples show that inserting several types of faults that the DFA literature uses to break the cipher is possible.
Computational and Memory Efficiency in Heartbeat Rate Detection: A Review of ECG and PPG Techniques
(MDPI, 2026-04-14) Merino Monge, Manuel; Lebrato Vázquez, Clara; Castro García, Juan Antonio; Sánchez Antón, Gemma; Molina Cantero, Alberto Jesús; Tecnología Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TIC022: Tecnologías para la Asistencia, la Integración y la Salud
(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational and memory constraints inherent to resource-limited systems. (2) Methods: A scoping review of 52 studies published between 2017 and 2024 was conducted, covering time-domain, frequency-domain, matrix-based, and machine learning approaches. The methods were evaluated according to estimation accuracy, computational complexity, memory footprint, and suitability for on-device implementation. (3) Results: Time-domain peak detection methods consistently provide high accuracy (minimum of 79.25%, maximum of 99.96%, and median ≥99.69%) for ECG and reliable heart rate estimation for PPG with linear computational complexity, low memory requirements and low energy consumption. Frequency-domain approaches are suitable for average heart rate estimation from PPG but do not preserve inter-beat intervals (error range of [1.07, 6.4] beats per minute (BPM)). Matrix-based and machine learning methods often entail higher computational cost without proportional performance gains in wearable contexts (error range of [1.07, 6.4] BPM for PPG signals; accuracy in range of [95.4, 99.96]% for ECG). (4) Conclusions: Lightweight signal-processing techniques offer the most favorable trade-off between accuracy and efficiency for wearable implementations, whereas computationally intensive approaches are better suited for edge- or cloud-based processing.
An Organic Electrochemical Transistor-Based Sensor for IgG Levels Detection of Relevance in SARS-CoV-2 Infections
(MDPI AG, 2024) Algarín Pérez, Antonio; Acedo, Pablo; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; TIC178: Diseño y Test de Circuitos Integrados de Señal Mixta
Organic electrochemical transistors appear as an alternative for relatively low-cost, easy-to operate biosensors due to their intrinsic amplification. Herein, we present the fabrication, characterization, and validation of an immuno-detection system based on commercial sensors using gold electrodes where no additional surface treatment is performed on the gate electrode. The steady-state response of these sensors has been studied by analyzing different semiconductor organic channels in order to optimize the biomolecular detection process and its the application to monitoring human IgG levels due to SARS-CoV-2 infections. Detection levels of up to tens of µg mL−1 with sensitivities up to 13.75% [µg/mL]−1, concentration ranges of medical relevance in seroprevalence studies, have been achieved.
Persuasive chatbot-based interventions for depression: a list of recommendations for improving reporting standards
(Frontiers Media, 2025-06-19) Denecke, Kerstin; Rivera-Romero, Octavio; Wynn, Rolf; Gabarrón, Elia; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Background: Depression is the leading cause of disability worldwide. Digital interventions based on chatbots could be an alternative or complementary approach to the treatment of depression. However, the absence of technical information in papers on depression-related chatbots often obstructs study reproducibility and hampers evaluating intervention efficacy.Objective: This study aims to identify specific characteristics of chatbots for depression and formulate recommendations for improving reporting standards.Methods: In an initial step, a list of items that must be reported was defined based on a previous review on digital interventions for depression, the Behavior Change Wheel framework, and a taxonomy for defining archetypes of chatbots. To capture the existing knowledge on the development of chatbots for depression, a literature review was conducted in a second step. From the identified studies, we tried to extract information related to the items from our initial list and described in this way the chatbots and their evaluation. As a third step, the findings of the literature review were analyzed, leading to an agreement on a list of recommendations for reporting chatbot-based interventions for depression. Results: The items of the recommendation list for reporting fall into four dimensions: General information; Chatbot-based depression intervention functions; Technical data; and Study. Through a literature review, a total of 23 studies on chatbots for depression were identified. We found that a lot of information as requested by our initial reporting list was missing, specifically regarding the involvement of natural language processing, data privacy handling, data exchange with third-party providers, and hosting. Additionally, technical evaluation details were often unreported in many papers.Conclusion: Studies on chatbots for depression can improve reporting by specifically adding more technical details and chatbot evaluation. Such reporting of technical details is important even in papers on clinical trials that utilize chatbots in order to allow reproducibility and advance this field. Future work could obtain expert consensus on the recommended reporting items for chatbot-based interventions for depression.
Participatory Health and Artificial Intelligence: A Literature Review
(IOS Press, 2025) Denecke, Kerstin; López-Campos, Guillermo; Ropero Rodríguez, Jorge; Torres Santos-Olmo, Rosario; Rivera-Romero, Octavio; Martín-Sánchez, Fernando; Gabarrón, Elia; Tecnología Electrónica; TIC277: Informática de la Salud Participativa y Personalización
Introduction: Participatory Health (PH) emerges as a consequence of the rise of the internet, which has led to a patient-centered approach. Participatory Health Informatics (PHI) uses information technologies and evaluates the use of tools. The emergence of new Artificial Intelligence (AI) techniques represents a great advance for PH. The objective of this article is to study the facilitators and opportunities that AI offers to PH, but also the challenges and barriers it faces. Methods: A literature review on barriers and facilitators of AI in PH was conducted, including articles published in the last 10 years. Results: 38 articles were eventually selected for review. Several aspects and applications of AI in PH were identified, including health domains and types of participation; types of AI used; reported barriers and challenges; facilitators and opportunities; impact on participatory health; and ethical, legal and patient safety considerations. Discussion and conclusion: 6 main thematic areas of interaction between AI and PHI were identified. There is a wide variety of applications, with special impact on predictive analysis, the management of healthcare data and conversational agents. Legal and privacy issues are seen as the main barriers for the use of AI in PHI, whereas improving diagnostic accuracy, optimizing patient flow, and patient empowerment are considered the main opportunities.
Optimized energy and task management in sustainable warehouses with Automated Forklifts and V2G-enabled Electric Vehicles
(Elsevier, 2025-12) Francis, Alphonse; Fresia, Matteo; Ghavidel, Bahareh; García Caro, Sebastián; Siri, Silvia; Bracco, Stefano; Tecnología Electrónica; European Commission (EC); TIC150: Tecnología Electrónica e Informática Industrial
The improvement of energy efficiency in the logistics sector is central to the European Union’s sustainability goals. To this purpose, the electrification of delivery fleets and the adoption of smart warehouses equipped with Renewable Energy Sources (RESs) and Battery Energy Storage Systems (BESSs) represent the main solutions. Current research often treats the logistics and warehouse task scheduling aspects and the energy management of warehouses and charging infrastructres for Electric Vehicles (EVs) as separate challenges, leaving a gap in solutions that capture the interdependence of logistics and energy flows. To fill this gap, the present paper proposes a comprehensive Energy Management System (EMS) that couples logistic task planning with energy optimization through a Mixed Integer Linear Programming (MILP) model. The proposed EMS coordinates a warehouse equipped with a Photovoltaic (PV) power plant, a BESS, Vehicle-to-Grid (V2G)-enabled EVs and a fleet of Automated Forklifts (AFs) minimizing, on the energy side, the electricity costs of the warehouse and, on the logistics side, the penalties related to unexecuted tasks. Dedicated task scheduling constraints are included in the EMS. Three operational scenarios are analyzed: (I) both the BESS and V2G-enabled EVs are in operation, (II) BESS is out of service but EVs still provide V2G support, and (III) BESS is unavailable and EVs cannot operate in V2G mode. The results demonstrate a 75% reduction in operating costs in Scenario I compared to Scenario III, while a 42% reduction in operating costs is observed when compared to Scenario II. Also, self-consumption increases by 15% in Scenario I with respect to Scenario III, while it increases by 6% in Scenario I with respect to Scenario II. The impact of EV arrival time and transportation demand is assessed too, showing how costs are negatively affected when considering longer traveled distances or shifted arrivals.
Machine learning estimation of battery state of health in residential photovoltaic systems
(Elsevier, 2025-02) Luque Rodríguez, Joaquín; Schroeder, Benedikt; Carrasco Muñoz, Alejandro; Heidarabadi, Houman; León de Mora, Carlos; Hesse, Holger; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; TIC150: Tecnología Electrónica e Informática Industrial
As the global adoption of residential battery storage systems paired with local photovoltaic (PV) generation increases, prosumers are increasingly motivated to reduce both their electricity costs and dependence on the grid. This shift highlights the importance of accurately evaluating and predicting the battery's State of Health (SOH) and Remaining Useful Life (RUL). These factors are crucial for determining the operational costs and longevity of battery systems. Traditionally, SOH predictions have relied heavily on detailed measurement data and time-intensive simulations. In response, we introduce a new AI-based approach that simplifies SOH estimation. Our method, named "ML Battery Life Predictor (MLBatLife)," leverages forecasted or historical PV generation data and load consumption patterns to quickly forecast the SOH for various battery configurations. Tested on simulated data, this tool demonstrated a high accuracy, with a coefficient of determination of 0.986 for predictions one day ahead, and an impressively low average error of 0.1 % for projections five years into the future. This innovative AI-driven technique offers substantial benefits for evaluating the economic viability and warranty parameters of battery installations in different regions. It provides a valuable resource for both industry stakeholders and energy system planners aiming to assess and anticipate battery health outcomes efficiently.
Framework for Asset Digitalization: IoT Platforms and Asset Health Index in Maintenance Applications
(MDPI, 2025-02-02) Candón Fernández, Eduardo; Crespo Márquez, Adolfo; Guillén López, Antonio Jesús; Hidalgo Fort, Eduardo; Organización Industrial y Gestión de Empresas I; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; TEP134: Organización Industrial; TIC192: Ingeniería Electrónica
This study proposes a comprehensive framework for digitalizing and managing assets with low initial digital maturity, focusing on their operation and maintenance (O&M) lifecycle. The framework integrates Internet of Things (IoT) networks with Asset Health Index (AHI) models through four interconnected components. The Asset Definition Model ensures standardized data representation based on IEC 81346-1:2022 and ISO 14224:2016, while the Asset Criticality Model prioritizes maintenance actions using risk-informed analysis. The Asset Monitoring Model enables real-time data acquisition through IoT sensors, facilitating condition-based monitoring and dynamic decision-making. Finally, the Intelligent Asset Management Models support long-term planning by simplifying data complexity and aligning with advanced maintenance strategies. A case study on bridge maintenance demonstrates the practical value of the framework, showcasing its ability to integrate real-time monitoring with predictive decision-making tools. By bridging asset monitoring and lifecycle planning, the framework enhances operational efficiency, reduces maintenance costs, and addresses the challenges posed by limited digital maturity in critical infrastructure. This approach represents a significant advancement in the digital transformation of maintenance management.
Multiobjective Environmental Cleanup with AutonomousSurface Vehicle Fleets Using Multitask Multiagent DeepReinforcement Learning
(Wiley, 2026-02) Seck Diop, Dame; Yanes Luis, Samuel; Perales Esteve, Manuel Ángel; Gutiérrez Reina, Daniel; Toral, S. L.; Ingeniería Electrónica; Tecnología Electrónica; European Commission. Fondo Social Europeo (FSO); Junta de Andalucía; TIC201: ACE-TI; TEP203: Física Interdisciplinar Fundamentos y Aplicaciones
Plastic pollution in water bodies threatens and disrupts aquatic life, requiring effective cleanup solutions. This paper proposes a strategy for plastic cleanup using a fleet of autonomous surface vehicles in a multitask scenario, with a focus on both exploration and cleaning tasks. The mission is decoupled into two phases: an exploration phase for locating trash and a cleaning phase for collection. A Multitask Deep Q-Network with two heads estimates Q-values for each task, and all ASVs share the same policy through an egocentric state formulation to enhance scalability. A multiobjective learning approach is applied, resulting in distinct policies that balance the duration of the exploration and cleaning phases, leading to the construction of a Pareto front, which provides a visual representation of trade-offs between task priorities. The framework adapts to various environmental conditions, demonstrated in both the larger Malaga Port and the smaller Alamillo Lake. The study also highlights the importance of a dedicated exploration phase for larger areas, while minimal exploration is sufficient for smaller spaces. Compared to the decomposition weighting sum strategy, the approach consistently produces superior Pareto-optimal policies, ensuring broader and more effective exploration of the objective space.
KMFC-GWO: A Hybrid Fuzzy-Metaheuristic Algorithm for Privacy-Preservation in Graph-Based Social Networks
(Tech Science Press, 2026-04-27) Memarian, Saeideh; Oprescu, Andreea M.; Moreno-Naranjo, Natalia; Miró Amarante, Gloria; Romero Ternero, María del Carmen; Tecnología Electrónica; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TIC150: Tecnología Electrónica e Informática Industrial
In recent years, the proliferation of social networks has been remarkable, providing a rich source for data mining endeavors. However, a significant challenge lies in safeguarding the privacy of individuals while sharing these databases publicly. Current approaches, such as K-anonymity, L-diversity, and T-closeness, are commonly employed for data anonymization in social networks. However, these techniques entail considerable information loss due to random alterations in the graph-based datasets. To address these limitations, this paper introduces a new anonymization technique called KMFC-GWO, which combines K-Member Fuzzy Clustering with Grey Wolf Optimizer. This integrated method is designed to strengthen the anonymized graph against a range of threats, including identity, attribute, link disclosure, and similarity attacks, while significantly reducing information loss. Within the KMFC-GWO framework, K-member fuzzy c-means clustering is utilized to create well-balanced clusters, each meeting the K-anonymity requirement. Subsequently, the Grey Wolf Optimizer is applied to optimize cluster formation and effectively anonymize the social network graph. The objective function is carefully crafted to minimize both clustering error and information loss, while ensuring adherence to predefined anonymity criteria. Experimentation on three major graph-based social networks extracted from Facebook, Twitter, and YouTube validates the effectiveness of the KMFC-GWO approach. Results demonstrate its ability to significantly reduce information loss in published graph data, while concurrently satisfying requirements for K-anonymity, L-diversity, and T-closeness.
Optimizing Alternating Current Electrical Stimulation Parameters to Enhance Osteoblasts Differentiation
(Wiley, 2026-04-12) Bocio-Núñez, Jesús; Montoya García, María José; Vázquez Gámez, María de los Ángeles; Martín Fernández, Daniel; Chacón, Pedro; Rico Corral, Miguel Ángel; Colmenero, Miguel Ángel; Yúfera García, Alberto; Giner García, Mercedes; Citología e Histología Normal y Patológica; Medicina; Tecnología Electrónica; Junta de Andalucía; CTS211: Metabolismo Cálcico, Hipertensión y Arteriosclerosis; BIO132: CitoQuímica Ultraestructural; TIC178: Diseño y Test de Circuitos Integrados de Señal Mixta
Electrical stimulation (ES) has emerged as a promising technique in the field of bioengineering and biomedicine, particularly in bone regeneration and cell differentiation. ES using alternating current (AC) is based on the periodic reversal of current direction, which generates oscillating electric fields. The application of an electric field has effects on cell growth and differentiation, as well as on morphology and migration. This study aimed to explore the effect of applying AC electrostimulation within the proliferation, differentiation, and morphology process of osteoblastic cells. The electrical stimulation signals were daily applied for 3 h during 14 days. Different frequencies were tested (1 Hz, 10 Hz, 100 Hz, and 1 kHz), with amplitudes of 125, 250, 500, 750, 1000, and 1500 mV/mm. Cell viability was estimated using the AlamarBlue, and MC3T3-E1 differentiation levels were evaluated through alkaline phosphatase (ALP) activity. RUNX2, OSX, ALP, OPG, and RANKL gene expression was assessed by RT-PCR. Morphological analysis was performed through cell transfection followed by immunofluorescence. Statistical analysis was conducted by SPSS.23 and graphs generated through Graph-pad. Viability and ALP activity were optimal at 10 Hz. Once the frequency was defined, RUNX2, OSX, ALP, OPG, and RANKL gene expression revealed an increase in the differentiation and osteogenic activity levels at 10 Hz and 500–750 mV/mm. As well as, morphological studies showed an increase in the area, pseudopodia length, and numbers at 500 mV 10 Hz conditions. The optimal ES condition to differentiate MC3T3-E1 cells is 10 Hz 500–750 mV/mm. Electrostimulation has emerged as a promising technique in the field of bioengineering and biomedicine, particularly in bone regeneration and cell early maturation.

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