Enhancing the safety and shelf life of beef and plant-based burgers by combining High Hydrostatic Pressure (HHP) with nisin or a blueberry-derived product

The growing demand for sustainable and healthy dietary options has led to significant interest in plant-based meat alternatives though traditional meats, such as beef, remain dominant in the protein market. High Hydrostatic Pressure (HHP) stands out as a promising technology improving food safety and extending shelf life, while combining HHP with clean-label additives offers potential for process optimization. This study investigates the synergistic effect of HHP combined with nisin (500 IU/g) or blueberry-derived product (4 %) in beef and plant-based burgers to control L. monocytogenes and extend shelf life under slight temperature abuse. In plant-based burgers, HHP (600 MPa, 3 min) combined with additives, effectively delayed L. monocytogenes growth for 104 days during storage, outperforming HHP alone. At lower pressures (300–500 MPa), HHP combined with nisin or blueberry product significantly enhanced pathogen reduction in both matrices, achieving a synergistic effect of up to 1.4 log reduction. HHP (600 MPa), with or without the additives, also extended the storage period of non-inoculated plant-based burgers, maintaining the natural microflora below 3 log CFU/g for 83 days. The blueberry product notably influenced the physicochemical properties (e.g. pH, color) of both matrices, while HHP significantly affected the color of beef burgers. This study provides novel insights into the potential applications of HHP combined with natural antimicrobials, highlighting its effectiveness in plant-based meat alternatives and the significant role of the matrix in the synergistic effect. Future research should focus on sensory analysis and consumer acceptance to align these advancements with market demands.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 955431. It is also part of the project PID2021-125400OB-I00 supported by the Spanish Ministry of Science and Innovation

La triada oscura de la personalidad. Relación entre estilos de liderazgo y personalidad en el ámbito laboral y escolar. Un análisis comparativo

Tesis doctoral en período de exposición públicaDidacticas Especifica

Developing university competencies through learning and exchange of ideas. A multidisciplinary approach

This study evaluated an educational strategy based on Socrative and social networks to develop competencies in 103 university students of optics and economics. The objective was to analyze how this methodology affects the self-perception of skills such as communication, leadership and collaborative learning. A quantitative, cross-sectional approach was used with pretest and post-test questionnaires and Likert scale items on five key competencies. The results revealed an increase in motivation and leadership self-perception, highlighting positive interaction in social networks and teamwork. Factor analysis confirmed the coherence of the competency factors studied. However, limitations were identified in the stability of the groups due to frequent changes of spokesperson and lack of control in the dynamics. This strategy is innovative in stimulating collaborative learning, enhancing students’ motivation by using social networks

Multi-dimensional modeling of green self-compacting concrete with recycled aggregate through response surface methodology

Recycled Aggregate (RA) incorporation to Self-Compacting Concrete (SCC) typically reduces mechanical performance and durability. The aim of this research is to model these variations as a function of RA content using advanced statistical tools such as Central Composite Design (CCD, α = 1) and Response Surface Methodology (RSM) to facilitate the optimization of RA additions. This study examines the behavior of SCC produced with 0 %–100 % recycled aggregate (RA), both coarse and fine, while maintaining 300 kg/m 3 of Portland cement. Five performance dimensions were assessed: fresh properties, compression-related mechanical properties, bending- tensile mechanical properties, durability (effective porosity and water absorption), and eco-environmental indicators (global warming potential and cost). Most resulting models with R 2 values above 0.95 were dependent on the square of the RA contents, and showed minimal interaction between coarse and fine RA. Therefore, their direction of maximum variation was approximately the vector i + j in a Cartesian coordinate system. According to models’ slopes, the greatest property variations occurred above 50 % replacement, but a higher rate for fresh and durability properties. Simultaneous optimization of all the models recommended using 35 %–45 % coarse RA and 30 %–45 % fine RA. Additionally, range optimization yielded specific RA amounts for high-performance SCC, comprising 0 %–20 % coarse RA and 20 %–40 % fine RA, and for conventional-performance SCC, which would admit 60 %–100 % coarse RA and 0 %–70 % fine RA.This research work was supported by grant PID2023-146642OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU; grants UIC-231 and BU033P23 funded by the Junta de Castilla y León (Regional Government) and ERDF/EU; and grant SUCONS, Y135.GI funded by the University of Burgos

Labelled IoT Flow-Based Network Traffic Dataset for Cyberattack Detection [Dataset]

This study presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by hardware-based IoT devices during normal operation, including MQTT messaging, database synchronization, and web-based monitoring, as well as during the execution of predefined cyber-attack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAP format. The packet captures were subsequently processed into bidirectional network flows using a flow-based traffic extraction pipeline, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were identified based on predefined attack execution time intervals obtained from experimental metadata. Network flows observed outside these intervals were labelled as benign and correspond to regular device communication. The dataset is distributed through a structured repository that includes raw packet captures, processed flow-level datasets in tabular format, and metadata files describing the experimental setup, attack scenarios, and labelling criteria. The data support flow-based analysis of IoT network traffic and the evaluation of cyberattack detection methods.This publication is part of the AI4SECIoT project (“Artificial Intelligence for Securing IoT Devices”), funded by the National Cybersecurity Institute (INCIBE), derived from a collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of Burgos. This initiative was carried out within the framework of the Recovery, Transformation, and Resilience Plan funds, financed by the European Union (Next Generation)

Exploring Environmental Element Monitoring Data Using Chemometric Techniques: A Practical Case Study from the Tremiti Islands (Italy)

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets.he PhD fellowship of M.F. was co-funded by PNRR resources from the European Union (Innovative PhD programs addressing the innovation needs of companies, CUP J11J22001830006) and by Ecotechsystems srl, Ancona (Marche, Italy)

The effects of sky diffuse light on indoor illuminance through radiosity models: a case study in Burgos

Diffuse radiation can play a critical role in the design of sustainable urban environments, in so far as it can transmit natural light to areas that direct sunlight cannot reach because of buildings and other structures. This characteristic of sky luminance is crucial for radiosity-based methods where luminance is used to determine energy transfer between surfaces. Consequently, the accuracy of a radiosity-based model will depend upon how well it can capture the subtle variations of sky luminance. In this study, both the accuracy and the performance of three luminance models are evaluated: the All-Weather model, the All-Sky model, and the CIE Standard General Sky model, focusing on their capability to replicate luminance at any point in the sky and at any given time. The results showed that while the CIE Standard Sky model offered the highest accuracy, it required more complex input data. The All-Weather and the All-Sky models rely on radiometric measurements. Both produced reliable results, with the All-Weather model standing out, because of its efficiency and minimal data requirements. Despite those strong points, all the models demonstrated higher error rates near the horizon, due to the challenges of accurately modeling luminance in this region. In this study, two radiosity methods were compared for calculating indoor illuminance: the Simplified Radiosity Algorithm (SRA), which considers spatial luminance variations across the openings, and the DeLight method, which assumes a uniform luminance distribution throughout the window view. The analysis of the results showed that the error rates produced in the luminance pattern estimations were reflected in the Radiosity model. Taking that effect into account, the combination of the All-Sky model with the SRA algorithm demonstrated a strong balance between accuracy and resource efficiency, offering a practical approach for sustainable urban lighting design

Impacto del síndrome PFAPA en la calidad de vida, alimentación y salud emocional en la infancia

Tesis doctoral en período de exposición públicaAvances en Ciencia y Biotecnología Alimentaria

Multicriteria and response surface-based selection of coarse recycled aggregate and blade waste contents in environmentally friendly low-strength concrete

The use of Coarse Recycled Aggregate (CRA) in Low-Strength Concrete (LSC), suitable for pavements, allows the sustainable revaluation of this waste material, but worsens its performance. Recycled fibers of Glass Fiber-Reinforced Polymer (GFRP) present in Raw-Crushed Wind-Turbine Blade (RCWTB) may counterbalance the detrimental effects caused by CRA addition. This research analyzes the mechanical properties, durability, cost and carbon footprint of LSC mixes with 50 % and 100 % CRA combined with 0 % and 10 % RCWTB. Stitching of the cementitious matrix by GFRP fibers improved flexural strength and energy absorption of LSC containing CRA. In addition, these fibers exerted a barrier effect against water passage and exhibited higher surface hardness, the addition of RCWTB therefore did not affect the water-absorption rate and effective porosity and increased the abrasion resistance of LSC. Finally, RCWTB also reduced the cost and carbon footprint of LSC. These enhancements caused by RCWTB were analyzed through Multi-Criteria Decision-Making (MCDM) and Response Surface Methodology (RSM), approaches novel in the literature. According to them, the joint use of CRA and RCWTB in LSC was recommended. PROMETHEE II algorithm indicated that the use of 10 % RCWTB combined with 100 % CRA was the best option due to economic and environmental advantages, as this algorithm only considered which LSC mixture was the best for each property. ELECTRE algorithm showed indifference between both CRA contents, due to the better mechanical and durability performance of 50 %-CRA LSC. TOPSIS algorithm and RSM models revealed a preference for 50 % CRA and 10 % RCWTB following quantitative calculations based on the magnitudes of the property differences between the mixes. This research demonstrates the broad suitability of LSC containing both CRA and RCWTB in construction applications, mainly regarding pavements.This research work was supported by grant TED2021-129715B-I00 funded by MICIU/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR; grant PID2023-146642OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU; grants UIC-231 and BU033P23 funded by the Junta de Castilla y León (Regional Government) and ERDF/EU; and grant SUCONS, Y135.GI funded by the University of Burgos

Modelling the yield stress of cement pastes and mortars containing heterogeneous and unconventional aggregates like raw crushed wind turbine blade

Determining the yield stress of cementitious materials is crucial for casting and concrete mix design. Fresh concrete possesses yield stress, behaving as a solid with viscoelastic properties below this threshold. When the yield stress is exceeded, concrete flows with a steady-state behavior commonly described by the Bingham or Herschel-Bulkley models. As the construction industry increasingly consumes more and more scarce raw materials, there is a growing need to develop and explore alternative construction materials to replace traditional ones while valorizing waste. Raw Crushed Wind Turbine Blade (RCWTB) has demonstrated interesting results when included in cementitious mixtures. However, a full characterization of rheology including the yield stress of mixtures containing RCWTB is still missing and would be of great practical interest. In this paper, the yield stress of cementitious pastes and mortars containing RCWTB with two different water/cement ratios is measured. Results demonstrate higher yield stress for higher RCWTB inclusion, this is mainly due to the bridge effect of the Glass Fiber Reinforced Polymer (GFRP) contained in the RCWTB. Finally, a physical model is applied for RCWTB to predict GFRP fibers maximum packing fraction based on their geometry, elastic properties, and the rheology of the surrounding cement-based material. This model is then validated with experimental yield stress of cement pastes and mortars.Open access funding provided by Università degli Studi di Padova within the CRUI-CARE Agreement