Pablo Rodríguez-Gonzálvez

This study applies InSAR time series analysis derived from Sentinel-1 satellite data (ascending and descending orbits) processed with ISCE2 and StaMPS (v.4.1) software to evaluate deformation dynamics in three landfill types near Gijón, Spain. Initially, the data were validated against the European Ground Motion Service (EGMS) dataset using a set of Persistent Scatterers (PS) in an urban control area through two analytical approaches (EGMS protocol and PSDefoPAT(2023)). The results showed high consistency, with 82–85% of points classified as highly reliable. Subsequently, this control group was compared with PS from each landfill type (active sanitary, operational inert, and closed inert). Significant deformation differences were found in each landfill type: the active sanitary landfill exhibited distinct anomalies depending on orbit, with strong residual variance instability detected (p < 0.003) in both. Operational inert landfills showed significant anomalies (p < 0.001) in both orbits with variable stability, while closed inert landfills displayed strong stability (p > 0.7) and variable anomalies. These results confirm the efficacy of InSAR approaches for detecting active landfill zones to aid in locating illegal or unauthorized dumping sites and to direct in situ inspection planning.

This study presents a methodology to automatically assess visibility distance on secondary roads using mobile LiDAR systems. The method evaluates both braking and overtaking visibility distances based on the 3D geometry of the road, applying a dynamic analysis through a series of parametrised quadrangular pyramids that simulate the driver’s field of view. Road segments are classified into three risk levels, low, medium, and high, according to the feasibility of stopping or overtaking safely. The methodology was validated on three secondary roads in Spain, achieving an average accuracy of 92.7% when compared to existing road signage. These results demonstrate the method’s potential to improve road safety through continuous, data-driven visibility monitoring. Its application supports advanced driver assistance systems and offers road authorities a reliable tool for proactive risk assessment and road infrastructure planning.

This work presents a system that integrates infrared thermography with two specially designed devices to enhance learning and promote sustainability awareness among 14-year-old secondary school students in Spain. An experimental and a control group were included in an experimental research design. While the control group attended conventional problem-solving classes, the experimental group participated in practical exercises utilizing thermographic cameras and two custom-built devices. Pretests and post-tests were administered to evaluate students’ theoretical and practical understanding of infrared radiation, physics, sustainability, and energy efficiency. A gender-based stratified analysis was conducted to investigate the possible impact of gender on learning outcomes and to obtain information for encouraging female participation in STEM professions to guarantee objective results. The results revealed statistically significant improvements in post-test scores compared to pretest results, demonstrating enhanced learning outcomes. The experimental group outperformed the control group, confirming the effectiveness of the innovative proposed methodology for learning complex scientific concepts. Additionally, students in the experimental group displayed high levels of curiosity, intrinsic motivation, and satisfaction, as observed through participant observation and a perception survey. While the survey indicated favorable responses regarding satisfaction, self-confidence, and learning, scalability received mixed opinions, potentially due to limited student familiarity with thermography’s broader applications. Overall, these findings underscore the potential of thermography as a powerful educational tool to improve scientific literacy and sustainability awareness. Future research should expand on this approach, exploring applications emphasizing critical thinking and problem-solving skills while leveraging thermographic technology to promote interdisciplinary learning.

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection.

This article discusses the use of Near Infrared spectroscopy (NIR) combined with multivariate classification methods for detecting bacterial contamination in milk in the dairy industry. In a first experiment, the study found that Nir was accurate and reliable in detecting the presence of biofilms in milk. Our results showed that the technology was effective in distinguishing between contaminated and uncontaminated samples with an area under the ROC curve (AUC) greater than 99%. It was also effective in classifying the samples belonging to different strains. In a second experiment, we used the same methodology to assess their effectiveness in detecting bacterial contamination proportions in milk. Our results showed that the technology was effective in classifying milk samples contaminated with four different bacteria and uncontaminated controls with an AUC greater than 97%. Moreover, results were still good when data from all bacteria were analyzed together, even at low bacterial concentrations, obtaining an average precision of 70%. These results demonstrate the potential of this technology to be used as a rapid and accurate method for identifying bacterial contamination in the dairy sector.