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Hemocompatible Coatings of Catheters Equipped with Electrochemical Sensors for Real-Time Monitoring of Critical Parameters in the Blood
No full textPublisher Copyright: © 2025 American Chemical Society.Continuous monitoring of blood parameters at the bedside is crucial for critically ill patients. Miniaturized biosensors integrated into medical devices in direct contact with blood can improve the quality of monitoring by reducing response time in emergencies as well as the invasiveness and blood consumption during the measurements. In such settings, coating biosensors with hemocompatible materials is essential for achieving biocompatibility and maximum acceptance of the devices for direct measurements of blood parameters in the human body. Here, we present an instrumented catheter equipped with a hemocompatible electrochemical sensor to continuously monitor one of the key parameters- glucose, lactate, and pH, directly in blood. Selective amperometric and potentiometric sensors were obtained by modifying the electrodes with enzymes and pH-responsive toluidine blue O. The glucose sensors exhibited a linear response from 0.06 to 10 mM with a sensitivity of −116 ± 16 nA/mMGlucose, while the lactate sensor had a linear response between 5 and 20 mM with a sensitivity of −38 ± 6 nA/mMLactate. A pH sensitivity of −20.17 ± 2.37 mV/pH for sensors with the hydrogel-covered TBO film has been reached. Hemocompatible properties, crucial for in vivo applications, were achieved by coating the functional electrode surfaces with an additional hydrogel layer based on a four-armed poly(ethylene glycol), cross-linked with the anticoagulant polysaccharide heparin. Initially, the functionalization strategy was thoroughly evaluated in terms of sensor response and hemocompatibility in a planar format; followed by a proof-of-principle demonstration of glucose sensing with a catheter imprinted with the respective electrochemical sensor.Peer reviewe
An Infrastructure-Based Localization Method for Articulated Vehicles
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Automating articulated vehicles in valet parking maneuvers is becoming more significant, due to the growing need for efficient freight transportation and logistics. Thus, this paper introduces a novel Infrastructure to Vehicle (I2V) localization approach for articulated vehicles. The proposed method focuses on accurately classifying trucks and trailers and hitch angle estimation between them. Validations with real-world LiDAR data on different articulated vehicles show that our solution improves safety and efficiency in automated docking scenarios without vehicle modifications.Peer reviewe
Generation of humanized bone for disease modeling using porcine adipose tissue-derived extracellular matrix scaffolds and human dental pulp stem cells
No full textPublisher Copyright: © 2025Human Dental Pulp Stem Cells (hDPSCs) represent a remarkable cell source for tissue engineering and regenerative medicine, offering significant potential for use in personalized medicine and autologous therapies. Decellularized extracellular matrix (ECM)-derived biological scaffolds show excellent properties for supporting cell delivery and growth in both in vitro and in vivo applications. These scaffolds provide essential biochemical cues that regulate cellular functions and offer a more accurate representation of the in vivo environment. Porcine decellularized adipose tissue (pDAT) is a very abundant source of ECM, constituting an ideal material for biologic scaffold preparation. The integration of hDPSCs with pDAT-derived ECM enables the patient-specific generation of diverse humanized tissues and their application in personalized drug screening platforms. This chapter details a three dimensional (3D) culture methodology utilizing hDPSCs and pDAT-derived scaffolds to engineer humanized bone tissue.Peer reviewe
Refractive Index Sensor Using a SMS Fiber in an Erbium-Doped Fiber Ring Laser
No full textPublisher Copyright: © 2017 IEEE.In this letter, a refractive index (RI) sensor was developed and experimentally validated using an interferometric design that integrates single-mode fiber (SMF), no-core multimode fiber, and another segment of SMF. The single-mode-multimode-single-mode structure was employed as a filter and inserted into an erbium-doped fiber ring laser to enhance the sensor's detection accuracy. Experimental findings show that the sensor provides an outstanding linear response, with an RI sensitivity of 96.639 nm/RIU over a measurement range of 1.3468-1.4061, along with strong stability.Peer reviewe
Analysis of Near-Zero Prices in the Iberian Electricity Market and Their Impact on the Profitability of Renewable Photovoltaic Plants
No full textPublisher Copyright: © 2025 IEEE.The increasing occurrence of near-zero market prices in the wholesale electricity markets has raised concerns about the market capture rate and impact on the viability of Photovoltaic (PV) assets. This article aims to conduct a statistical analysis of the occurrence of near-zero day-ahead prices in the Iberian electricity market along 2024 and evaluate the correlation between prices and various technologies in the energy mix. There are 838 hours of the year below 1 €/MWh. During the spring months, day-ahead prices are significantly lower, especially at daylight. Looking at spring months, the findings suggest that wind power tends to decrease them. As can be concluded, there is a strong linear correlation between the thermal gap and market prices. Finally, this article addresses the impact of such prices on the profitability of PV plants. PV is not able to influence prices (negative Pearson coefficient), resulting in a capture rate of 65 %.Peer reviewe
A comparative analysis of high speed and conventional laser-based directed energy deposition techniques focused on coating applications for high value-added components
Get PDFPublisher Copyright: © The Author(s) 2025.This study presents a comparative analysis of two laser powder-based directed energy deposition (DED) techniques—conventional Laser Metal Deposition (LMD) and High Speed Laser Cladding (HSLC)—for coating high value-added components using the same hybrid machine. Experimental tests were performed to characterize the HSLC and compared with the results obtained in previous works associated with conventional LMD. The same base material (42CrMoS4 steel alloy) and two filler materials (a Ni-based alloy and an Fe-based alloy) were used in both cases, with tests performed on both preheated and non-preheated substrates. Powder efficiency, material deposition rate (MDR), coated surface per minute or coating rate (CR), argon consumption and microhardness were analyzed. The results indicate that for the Ni-based alloy, preheating the substrate is necessary in both techniques to avoid crack formation. In the case of the Fe-based alloy, cracks were only observed in the HSLC variant when the substrate was preheated. Significant differences in productivity were observed between the two technology variants for both materials, particularly in terms of CR and MDR, with HSLC showing a significant increase in both metrics. HSLC achieved coating rates between 4500 and 6860 mm2/min, representing a minimum productivity improvement of 130% compared to LMD. MDR values reached up to 1.72 kg/h for Ni-based and 1.39 kg/h for Fe-based alloys, significantly higher than the < 0.8 kg/h observed in LMD. Argon consumption per deposited kilogram was reduced by up to 80%. Maximum hardness values of 62–64 HRC (Fe-based) and 54–57 HRC (Ni-based) were achieved.Peer reviewe
ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines
Get PDFPublisher Copyright: © The Author(s) 2025.Machine learning is already integrated in diverse domains enhancing their performance and decision support. For laboratories, this approach is normally sufficient. However, in real environments, these models can not be generally deployed isolated since they require additional steps to satisfy an objective. These steps can range from different data transformations to the inclusion of extra machine learning models which compose an analytic pipeline. Moreover, the majority of software solutions wrap a model into an API and, rarely, focus on the whole pipeline. These are unresolved topics in the well-known MLOps methodology, specifically in packaging and service phases. In addition, these concerns can also be extrapolated to other paradigms like DevOps or DataOps. In the context of the Pliades European project, this paper approaches the conceptualization of diverse types of pipelines from different perspectives and for different contexts, instead of simplifying the deployment and serving to an API. Thus, ArtifactOps methodology is proposed aimed at unifying XXOps paradigms which share the majority of stages. Finally, ArtifactDL pipeline definition language is proposed to describe the key aspects identified when designing different pipelines types and to support the proposed ArtifactOps methodology. Moreover, the research presents two real scenarios to better illustrate both ArtifactOps methodology and ArtifactDL pipeline definition language and it is defined an expert evaluation conducted to validate the approach.Peer reviewe
Fear of COVID-19, Physical Activity, and Psychopathology A Cross-Sectional Study
Get PDFPublisher Copyright: © 2025 The Author(s).Background: The COVID-19 pandemic was associated with high fear of infection and consequences of the pandemic, decline in physical activity and increase in depressive symptoms. Aims: This study assessed whether fear of COVID-19 is cross-sectionally associated with symptoms of depression in a clinical outpatient sample and if physical activity moderates this effect. Methods: Data was collected between March 2021 and May 2022 at 10 study sites in a crosssectional assessment of 401 participants, aged 18 65 (M = 42.08, SD = 13.26, 71.0% female). All participants fulfilled diagnostic criteria for major depressive disorders, insomnia, panic disorder, agoraphobia, or posttraumatic stress disorder. Data was analyzed using linear regression models including fear of COVID-19 (disease anxiety; consequence anxiety), selfreported physical activity, physical activity measured by accelerometers (min/week), as well as the interaction of these variables as predictors, depressive symptoms as the outcome. Results: The primary model s fit was significant, F(15, 377.13) = 1.89, p = .022. Consequence anxiety was significantly and positively associated with depressive symptoms (? = 0.12, t = 2.33, p = .020). We further observed a negative association between self-reported physical activity and depressive symptoms (? = _0.15, t = _2.73, p = .007). There was no significant interaction effect. Limitations: These results should be interpreted as an observational association. Conclusion: Results show that fear of the consequences of COVID-19 was positively associated with depressive symptoms, but physical activity did not moderate this association. We report an independent, negative association between self-reported physical activity and depressive symptoms.Peer reviewe
Game On: Exploring the Potential for Soft Skill Development Through Video Games
Get PDFPublisher Copyright: © 2025 by the authors.Soft skills remain fundamental for employability and sustainable human development in an increasingly technology-driven society. These interpersonal and cognitive competencies—such as communication, adaptability, and critical thinking—represent uniquely human capabilities that current Artificial Intelligence (AI) systems cannot replicate. However, assessing and developing these skills consistently remains a challenge due to the lack of standardized evaluation frameworks. This study explores the potential of commercial video games as engaging environments for soft skills enhancement and introduces an AI-based assessment methodology to quantify such improvement. Using player data collected from the Steam platform, we designed and validated an AI model based on Gradient Boosting Regressor (GBR) to estimate participants’ soft skill progression. The model achieved high predictive performance (R2 ≈ 0.9; MAE/RMSE ≈ 1), demonstrating strong alignment between gameplay behavior and soft skill improvement. The results highlight that video game-based data analysis can provide a reliable, non-intrusive alternative to traditional testing methods, reducing test-related anxiety while maintaining assessment validity. This approach supports the integration of video games into educational and professional training frameworks as a scalable and data-driven tool for soft skills development.This research was funded by the European Union, MEGASKILLS—HORIZON-CL2-2022-TRANSFORMATIONS-01, grant number 101094275.Peer reviewe
Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms
No full textPublisher Copyright: © 2025 IEEE.This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.Peer reviewe