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Exploring the Bioavailability of Red Grape Skin Extract Polyphenols: A Caco-2 Cell Model Study
Full text linkPublisher Copyright: © 2025 by the authors.Grapes are a rich source of polyphenols with a positive impact on human health. Polyphenols need to be bioavailable to exert any beneficial effect. However, there is limited knowledge on the bioavailability of polyphenols in grape extracts. The intestinal permeability of nine polyphenols of a red grape skin extract (GSE) was investigated using the Caco-2 cell model that simulates the human intestinal epithelium: three anthocyanins (delphinidin-3-O-glucoside, petunidin-3-O-glucoside and malvidin-3-O-glucoside), three flavonols (quercetin-3-glucoside, kaempferol-3-galactoside and kaempferol-3-glucoside), two hydroxybenzoic acids (gallic acid and syringic acid) and one hydroxycinnamic acid (caftaric acid). Two concentrations of GSE (15 mg/mL and 22 mg/mL) were used. The transport efficiency (TE) through the Caco-2 monolayer was studied. Among anthocyanins, only malvidin-3-O-glucoside was detected at the basolateral side, which represents the bloodstream, with a TE of 1.08 ± 0.01%. Flavonols resulted in a variety of results depending on the GSE concentration. Among flavonols, kaempferol-3-glucoside showed the highest TE of 130 ± 3%. Gallic acid showed the highest TE among the investigated polyphenols with 188 ± 3%. This study provides data on the intestinal transport of red grape skin extract polyphenols that can be used to explore the underlying mechanisms of the intestinal absorption and the bioactivity of natural grape extracts.Peer reviewe
Non-invasive identification of food fluids from the outside of opaque containers by low-power ultrasound
Full text linkPublisher Copyright: Copyright © 2025. Published by Elsevier Ltd.Cleaning in place (CIP) is widely utilized in the beverage industry to clean and lower bacteria levels in pipelines, tanks, and other processing equipment without the need for disassembly or manual cleaning. In this process, it is crucial to distinguish between fluids to determine its duration and to minimize product and water losses. The feasibility of low-power ultrasound (LPU) as a suitable technology to perform an in-line and non-invasive method to identify different food fluids (orange juice, banana puree, banana & apple puree and water) in a stainless steel (SS) opaque container was studied. Different ultrasonic key parameters (time of flight difference (DTOF), gain 80 %, full screen high (FSH) and amplitude difference) have been analysed by using both pulse-echo (PE) and through-transmission (TT) techniques and also, different frequencies (1, 2.25 and 5 MHz). Furthermore, the temperature fluctuation influence on the ultrasound response was studied, considering a typical overall temperature range in industrial juice production (14 °C–22 °C). DTOF together with the temperature and amplitude, was the combination of key parameters able to distinguish all the analysed products. Finally, a simple and easily scalable at an industrial level methodology for non-invasive identification of food fluids is proposed. This work highlights the potential of LPU technology to distinguish and control food fluids in-line, in a harmless way for the products and in a non-invasive way for the facility. This would make such monitoring feasible in many situations where it would otherwise be impractical, thereby improving the efficiency of the processes involved.Peer reviewe
A review on lossy mode resonance-based sensors: Fundamentals and applications
Full text linkPublisher Copyright: © 2025 The AuthorsLossy mode resonance (LMR) sensors have garnered significant attention over the past 20 years due to their high sensitivity, broad applicability, and multiparameter detection capability. This review systematically summarizes progress in theoretical models, experimental validations, and applications during this period. Originating from optical fiber structures and later extended to planar waveguides and integrated circuits, LMR sensors have evolved significantly with the introduction of modeling methods, including geometrical optics and modal analysis. These methods, especially modal analysis, offer a new perspective on LMRs as lossy directional couplers, facilitating deeper understanding of their behavior and guiding sensor design optimization. The performance of LMR-based sensors depends on parameters such as sensitivity and spectral width, which can be optimized to enhance their operation, with applications spanning two primary domains: aqueous media, including biosensors and chemical sensors, and air, with sensors for humidity, gases, and volatile organic compounds (VOCs). In addition, emerging designs, including multi-parameter sensing and integration with other phenomena such as surface plasmon resonances (SPRs) and surface acoustic waves (SAWs), highlight their versatility. Despite challenges like environmental cross-sensitivity and light coupling, advances in temperature compensation and machine learning provide promising pathways for overcoming these limitations, paving the way for next-generation sensing technologies.Peer reviewe
Making the MIRACLE Possible: Concrete as a Radiative Cooling Material
No full textPublisher Copyright: © 2025, META Conference. All rights reserved.Over the past four years, the MIRACLE project has been dedicated to developing innovative cement-based materials with radiative cooling properties. This work highlights some of the most promising results achieved, emphasizing the significant impact of these new concretes in reducing building energy consumption and mitigating the urban heat island effect.Peer reviewe
AiGAS-dEVL-RC: An Adaptive Growing Neural Gas Model for Recurrently Drifting Unsupervised Data Streams
No full textAbstract—Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments. This work introduces a novel method based on the Growing Neural Gas (GNG) algorithm, designed to effectively handle abrupt recurrent drifts while adapting to incrementally evolving data distributions (incremental drifts). Leveraging the self-organizing and topological adaptability of GNG, the proposed approach maintains a compact yet informative memory structure, allowing it to efficiently store and retrieve knowledge of past or recurring concepts, even under conditions of delayed or sparse stream supervision. Our experiments highlight the superiority of our approach over existing data stream learning methods designed to cope with incremental non-stationarities and verification latency, demonstrating its ability to quickly adapt to new drifts, robustly manage recurring patterns, and maintain high predictive accuracy with a minimal memory footprint. Unlike other techniques that fail to leverage recurring knowledge, our proposed approach is proven to be a robust and efficient online learning solution for unsupervised drifting data flows. Index Terms—Data stream learning, extreme verification latency, concept drift, Growing Neural Gas.Peer reviewe
A systemic model for lossy mode resonances (LMRs)
No full textPublisher Copyright: © 2024Lossy mode resonances (LMRs) have been widely employed for the development of sensors in the last years. However, the theoretical frameworks for LMRs are scarce and difficult to systematize, hampering the development of this technology. In this work, we propose a new systemic model for assessing LMRs in arbitrary waveguide configurations, based solely on modal analysis of the unperturbed waveguide and the waveguide with a thin film optimized for LMR generation. The model is first developed for a generic waveguide, and leveraged to design, for the first time, LMRs in a silicon nitride photonic wire waveguide. It is furthermore demonstrated that the model only requires a few modes to reliably describe LMRs in D-shaped fibers, reducing the computational cost of simulating them. Therefore, the suggested model is valid for both high and low contrast waveguides, and it is considered it provides new insights about LMRs, which will help in the design of new LMR-based devices and its extension to novel platforms.Peer reviewe
An In Situ Study of the Topochemical Transformation of Hybrid Layered Hydroxides Into Metallic Nanocomposites
No full textPublisher Copyright: © 2025 The Author(s). Advanced Functional Materials published by Wiley-VCH GmbH.The urgent transition toward sustainable energy systems requires the development of advanced nanomaterials. Among them, nanocomposites composed of inorganic nanoparticles embedded in a graphitic matrix offer exceptional redox properties, electrical conductivity, and mechanical stability, making them highly attractive for electrochemical applications. While typically synthesized via high-temperature calcination of Metal-Organic Frameworks, Layered Hydroxides (LHs) represent a promising alternative due to their anisotropic nature, chemical versatility, and scalable, well-established synthesis routes. However, the mechanism behind their transformation into nanocomposites remains poorly unexplored. Herein, in situ synchrotron-based techniques are employed to investigate the topochemical transformation of 2D cobalt-based LHs into nanocomposites thanks to the templating and reducing effect exerted by intercalated carboxylic molecules. Experiments reveal that the length of dicarboxylic anions governs the transformation mechanism, balancing the inherent anisotropy and reactivity: short chains hinder nanocomposite formation, whereas longer chains promote it. Furthermore, in situ experiments comparing samples with and without nanocomposite formation provided crucial insights into the decomposition dynamics. In situ tracking allows to decipher the initial topochemical transformation of the layered precursor into a metal oxide phase, with the carbon content determining the extent of reduction. These findings provide fundamental understanding for the rational design of advanced energy materials of special industrial interest.Peer reviewe
Infrastructure-Based Smart Positioning System for Automated Shuttles Using 3D Object Detection
No full textPublisher Copyright: © 2025 IEEE.Automated vehicles need high positioning accuracy to execute driving maneuver effectively. This accuracy is crucial for the viability of dependent systems such as planning, decision-making, and perception. However, achieving precise localization typically necessitates expensive onboard sensors, that increase vehicle costs, complicate maintenance, and pose significant scalability challenges for large fleets of trucks or buses. To address these issues without compromising vehicle interoperability, this work proposes an infrastructure-based positioning system for critical areas. The system utilizes off- board sensors to collect data from a shuttle moving on a test track. The data collection is automated through a custom- designed labeling tool, eliminating the need for manual tagging. A deep learning model based on 3D object detection has been trained to localize the vehicle accurately during normal operation. Rigorous assessments have been conducted to evalu-ate localization performance, achieving an Average Trajectory Error of 0.17 m for position, and 9.4 deg for rotation. To demonstrate real-world applicability, a complete architecture based on ROS2 was developed and tested with actual data, confirming its functionality in practical scenarios.Peer reviewe
Statistical Approach for Monitoring the Lack of Quality in Aerospace Manufacturing Operations
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Mitigating the number of defective parts in the aerospace sector presents an excellent challenge, particularly during the final stages of manufacturing operations. The final processes are critical, and stringent cutting conditions are enforced due to the added value of the manufactured components. Furthermore, the manufacturing batches are either unique or composed of a limited number of units. Therefore, reducing waste and manufacturing time rates is imperative to maintaining competitiveness in the aerospace market. The study proposes developing a robust statistical strategy capable of predicting the lack of quality in industrial components. This strategy must consider that it is a final operation in the manufacturing cell and that industrial production batches are small-scale and tailored to the aerospace sector.Peer reviewe
Synthesizing Conductive Metal-Organic Framework Nanosheets for High-Performing Chemiresistive Sensors
No full textPublisher Copyright: © 2025 The Authors. Published by American Chemical Society.Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) are emerging as unique electrode materials with great potential for electronic applications. However, traditional devices based on c-MOFs often utilize them directly in the powder or nanoparticle form, leading to weak adhesion to the device substrate and resulting in low stability and high noise levels in the final device. In this study, we present a novel approach utilizing thin c-MOFs synthesized via a general MOF nanosheet sacrifice approach, enhancing their aspect ratio and flexibility for high-performance electronic applications. The resultant benzene-based Cu-BHT nanosheets feature a thin thickness (around 5 nm) and a high aspect ratio (>100), affording Cu-BHT exceptional flexibility with a 10-fold decrease in Young’s modulus (0.98 GPa) and hardness (0.09 GPa) compared to bulk Cu-BHT nanoparticles (10.79 and 0.75 GPa, respectively). This heightened flexibility enables the Cu-BHT nanosheets to conform to the channels of the electrodes, ensuring robust adhesion to the electrode substrate and improving device stability. As a proof-of-concept, the chemiresistive nanosensor based on Cu-BHT nanosheets demonstrates an 8.0-fold decrease in the coefficient of variation of the response intensity and a 47.1-fold increase in the signal-to-noise ratio compared to sensors based on bulk Cu-BHT nanoparticles. Combined with the machine learning algorithms, the Cu-BHT nanosensor demonstrates outstanding performance in identifying and discriminating multiple volatile organic compounds at room temperature with an average accuracy of 97.9%, surpassing the thus-far-reported chemiresistive sensors.Peer reviewe