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Education 4.0 arises to provide citizens with the technical/digital competencies and cognitive/interpersonal skills demanded by Industry 4.0. New technologies drive this change, though time-independent learning remains a challenge, because students might face a lack of support, advice and surveillance when teachers are unavailable. This study proposes complementing presential lessons with online learning driven by ChatGPT, applied as an educational tool able to mentor K-12 students learning science at home. First, ChatGPT’s performance in the field of K-12 science is evaluated, scoring A (9.3/10 in 2023, and 9.7/10 in 2024) and providing detailed, analytic, meaningful, and human-like answers. Then, an empirical interventional study is performed to assess the impact of using ChatGPT as a virtual mentor on real K-12 students. After the intervention, the grades of students in the experimental group improved by 30%, and 70% of students stated a positive perception of the AI, suggesting a positive impact of the proposed educational approach. After discussion, the study concludes ChatGPT might be a useful educational tool able to provide K-12 students learning science with the functional and social/emotional support they might require, democratizing a higher level of knowledge acquisition and promoting students’ autonomy, security and self-efficacy. The results probe ChatGPT’s remarkable capacity (and immense potential) to assist teachers in their mentoring tasks, laying the foundations of virtual mentoring and paving the way for future research aimed at extending the study to other areas and levels, obtaining a more realistic view of AI’s impact on education.

Hepatitis C virus (HCV) disproportionately affects certain sub-populations, including people with experience of incarceration (PWEI). Little is known about how perceptions of HCV and treatment have changed despite simplifications in testing and treatment in carceral settings. Nineteen semi-structured interviews were conducted with people living with or having a history of HCV infection released from Quebec provincial prison. Interviews were guided by the Common Sense Self-Regulation Model (CS-SRM) and aimed to explore cognitive and emotional representations of HCV and coping strategies. Among the 19 participants, seven (37%) were diagnosed with HCV in prison and 14 (74%) had previously received HCV treatment. Participants’ HCV illness perceptions were influenced by fear (of HCV transmission, death, and the well-being of family) and stigma (related to HCV, injection drug use, and incarceration). While some sought education and social and professional support, others self-isolated or engaged in high-risk behaviors to cope. Despite advances in HCV treatment, PWEI continue to experience various forms of stigma and fear surrounding their HCV diagnosis, resulting in delayed HCV care. These findings provide insights into how prison-based healthcare providers can better utilize HCV illness perceptions to evaluate willingness to engage in HCV care among PWEI.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed.

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission-founded PHOTONGATE project. This concept will allow for the quantification of multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a biochemical technology (molecular gates), which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which, combined with porous substrates, will increase the sensitivity, and a photonic technology based on localized surface plasmonic resonance (LSPR) structures that serve as transducers for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm2). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought of as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, which is fully European Union Made, avoiding optical fibers and expensive optical components.

Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images is a notable example. Although SeaDronesSee is the most extensive and complete dataset for this task, it suffers from significant class imbalance. To address this issue, we present POSEIDON, a data augmentation tool specifically designed for object detection datasets. Our approach generates new training samples by combining objects and samples from the original training set while utilizing the image metadata to make informed decisions. We evaluate our method using YOLOv5 and YOLOv8 and demonstrate its superiority over other balancing techniques, such as error weighting, by an overall improvement of 2.33% and 4.6%, respectively.

Porous materials are currently the basis of many optical sensors because of their ability to provide a higher interaction between the light and the analyte, directly within the optical structure. In this study, mesoporous TiO2 layers were fabricated using a bottom-up synthesis approach in order to develop optical sensing structures. In comparison with more typical top-down fabrication strategies where the bulk constitutive material is etched in order to obtain the required porous medium, the use of a bottom-up fabrication approach potentially allows increasing the interconnectivity of the pore network, hence improving the surface and depth homogeneity of the fabricated layer and reducing production costs by synthesizing the layers on a larger scale. The sensing performance of the fabricated mesoporous TiO2 layers was assessed by means of the measurement of several glucose dilutions in water, estimating a limit of detection even below 0.15 mg/mL (15 mg/dL). All of these advantages make this platform a very promising candidate for the development of low-cost and high-performance optical sensors.

Many optical sensors exploit the interesting properties of porous materials, as they ensure a stronger interaction between the light and the analyte directly within the optical structure. Most porous optical sensors are mainly based on porous silicon and anodized aluminum oxide, showing high sensitivities. However, the top-down strategies usually employed to produce those materials might offer a limited control over the properties of the porous layer, which could affect the homogeneity, reducing the sensor reproducibility. In this work, we present the bottom-up synthesis of mesoporous TiO2 Fabry-Pérot optical sensors displaying high sensitivity, high homogeneity, and low production cost, making this platform a very promising candidate for the development of high-performance optical sensors.

Nanomaterials with very specific features (purity, colloidal stability, composition, size, shape, location…) are commonly requested by cutting-edge technologic applications, and hence a sustainable process for the mass-production of tunable/engineered nanomaterials would be desirable. Despite this, tuning nano-scale features when scaling-up the production of nanoparticles/nanomaterials has been considered the main technological barrier for the development of nanotechnology. Aimed at overcoming these challenging frontier, a new gas-phase reactor design providing a shorter residence time, and thus a faster quenching of nanoclusters growth, is proposed for the green, sustainable, versatile, cost-effective, and scalable manufacture of ultrapure engineered nanomaterials (ranging from nanoclusters and nanoalloys to engineered nanostructures) with a tunable degree of agglomeration, composition, size, shape, and location. This method enables: (1) more homogeneous, non-agglomerated ultrapure Au-Ag nanoalloys under 10 nm; (2) 3-nm non-agglomerated ultrapure Au nanoclusters with lower gas flow rates; (3) shape-controlled Ag NPs; and (4) stable Au and Ag engineered nanostructures: nanodisks, nanocrosses, and 3D nanopillars. In conclusion, this new approach paves the way for the green and sustainable mass-production of ultrapure engineered nanomaterials.

This paper presents preliminary polarization measurements and systematic-error characterization of the Thirty Gigahertz Instrument receiver developed for the QUIJOTE experiment. The instrument has been designed to measure the polarization of Cosmic Microwave Background radiation from the sky, obtaining the Q, U, and I Stokes parameters of the incoming signal simultaneously. Two kinds of linearly polarized input signals have been used as excitations in the polarimeter measurement tests in the laboratory; these show consistent results in terms of the Stokes parameters obtained. A measurement-based systematic-error characterization technique has been used in order to determine the possible sources of instrumental errors and to assist in the polarimeter calibration process.

This paper presents a 10 to 20 GHz bandwidth microwave polarimeter demonstrator, based on the implementation of a near-infra-red frequency up-conversion stage that allows both the optical correlation, when operating as a synthesized-image interferometer, and signal detection, when operating as a direct-image instrument. The proposed idea is oriented towards the implementation of ultra-sensitive instruments presenting several dozens or even thousands of microwave receivers operating in the lowest bands of the cosmic microwave background. In this work, an electro-optical back-end module replaces the usual microwave detection stage with Mach–Zehnder modulators for the frequency up-conversion, and an optical stage for the signals correlation and detection at near-infra-red wavelengths (1550 nm). As interferometer, the instrument is able to correlate the signals of large-format instruments, while operating as a direct imaging instrument also presents advantages in terms of the possibility of implementing the optical back end by means of photonic integrated circuits to achieve reductions in cost, weight, size, and power consumption. A linearly polarized input wave, with a variable polar angle, is used as a signal source for laboratory tests. The receiver demonstrator has proved its capabilities of being used as a new microwave-photonic polarimeter for the study of the lowest bands of cosmic microwave background.

Salmonella enterica is a pathogen capable of colonizing various environments, including the intestinal tract of different animals such as mammals, birds, and reptiles, which can act as carriers. S. enterica infection induces different clinical diseases, gastroenteritis being the most common, which in some cases, can evolve to septicemia and meningitis. Reptiles and amphibians have been reported as a reservoir of Salmonella, and transmission of the pathogen to humans has been documented. This study aimed to determine the presence of virulence genes and characterize the genotypic antibiotic resistance profile in Salmonella strains isolated from Caiman crocodilus fuscus obtained in situ (natural habitat) in Prado, Tolima, Colombia in a previous study and stored in a strain bank in our laboratory. Fifteen Salmonella strains were evaluated through endpoint PCR to determine the presence of resistance genes and virulence genes. The genes blaTEM, strB, and sul1 were detected in all the strains that confer resistance to ampicillin, streptomycin, and sulfamethoxazole, as well as the virulence genes invA, pefA, prgH, spaN, tolC, sipB, sitC, pagC, msgA, spiA, sopB, sifA, lpfA, csgA, hilA, orgA, iroN, avrA, and sivH, indicating the possible role of babilla (Caiman crocodilus fuscus) as a carrier of multidrug-resistant bacteria.

This paper presents the design and implementation of an on-grid microinverter control technique for managing active and reactive power based on a dq transformation. The system was implemented in a solar microinverter development kit (Texas Instruments—TMDSSOLARUINVKIT). This microinverter has two stages: DC-DC and DC-AC. The DC-DC stage contains an active clamp flyback converter, where the maximum power point tracking (MPPT) of the solar panel is obtained with a current-based incremental conductance algorithm. The DC-AC stage comprises a dual-buck inverter in which voltage-, current-, and phase-tracking control loops are implemented to control the active and reactive power. These techniques were simulated in MATLAB using the proposed mathematical model and experimentally validated in the solar development kit. The results show that the simulated model behaved similarly to the real system, and the control techniques presented good performance. The maximum power point (MPP) of the solar panel was monitored in the DC-DC stage using a current reference provided by the incremental conductance MPPT algorithm and was regulated by a 2P2Z control. The algorithm is robust against continuous changes in irradiance, as it quickly follows the ideal power and continually operates at a point close to the MPP. In addition, the active and reactive power control in the DC-AC stage enables the microinverter to supply the maximum active power. Moreover, the microinverter supplies reactive power according to a defined reference and within the established limits. The proposed mathematical model of the microinverter can be used to design new control techniques and other microinverter topologies. In addition, this active and reactive power-control technique can be implemented in low-power and low-cost microinverters to successfully maintain power quality in small microgrids.