Resultados de investigación

Antioxidants are used as markers of the functional potential of foods, making their accurate quantification essential for assessing bioactivity and possible health benefits. A smart polymer incorporating 2,2-diphenyl-1-picrylhydrazyl (DPPH) motifs (FDPPH) was synthesized via bulk polymerization of N-vinylpyrrolidone, methyl methacrylate, and a diphenylamine-functionalized monomer, followed by solid-phase reactions to introduce the DPPH functionality. FTIR and thermal analysis confirmed successful synthesis. The FDPPH sensor showed a colorimetric response consistent with DPPH in solution, demonstrating its reliability. Twenty-three beet samples were analyzed using both the conventional DPPH assay and the FDPPH method, where polymer discs were immersed and colour changes recorded via smartphone. The hue (H) parameter in the HSV colour-space showed the highest correlation. Calibration using beet samples was more accurate than with Trolox. The optimal response time was 3 hours, with a LOD and LOQ of 0.063 and 0.191 mmol Trolox/L, respectively., We gratefully acknowledge the financial support provided by all funders. Author S. Vallejos received Grant PID2023-147301OB-I00 and Grant 3101166576-166576-29-325 funded by MICIU/AEI /10.13039/501100011033 and FEDER, EU. The financial support provided by Fondo Europeo de Desarrollo Regional-European Regional Development Fund (FEDER, ERDF) and Regional Government of Castilla y León -Consejería de Educación, Junta de Castilla y León- (BU025P23) is gratefully acknowledged. This work was supported by the Regional Government of Castilla y León (Junta de Castilla y León) and by the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU PRTR. Author Saul Vallejos received grant BG22/00086 funded by Spanish Ministerio de Universidades. J. L. Vallejo-García received the grant PRE2021-09812 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. Author María Gaona-Ruiz received a research assistant contract (UBU-08-B) funded by the Regional Government of Castilla y León.

The data set consists of a spreadsheet file containing the data used in the research project PID2020-120288GB-I00, The authors are grateful for the grants: Agencia Estatal de Investigación, Proyecto PID2020-120288GB-I00

This dataset contains anonymized data from a randomized controlled trial investigating the use of multi-field functional electrical stimulation (FES) with the Fesia Grasp device for hand rehabilitation in patients with Multiple Sclerosis (MS). The dataset includes baseline and post-intervention measures of hand strength, fine motor control, and activities of daily living. Data were collected during the rehabilitation program and analyzed to assess the effectiveness of the intervention., The APC was funded by Colegio Profesional de Terapeutas Ocupacionales de Castilla y León (COPTOCYL).

The dataset contains all raw data of the work "Red-Edge (725nm) Monochrome Imaging of Vine Leaves Treated with Antifungal Products", This dataset is part of the project TED2021-131551B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR

This dataset is a proportionally downsampled version of the NF-ToN-IoT V1 dataset [Sarhan et al., 2021; Sarhan et al., 2023]., 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 is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.

This work presents a new polymeric material in the form of a film (FLAC) containing immobilized laccase enzyme through diazo bonds, designed for the degradation of indigo carmine dye, which is highly toxic and commonly found in wastewater from the textile industry. The immobilization of the enzyme in the film was characterized by SEM, FT-IR, DSC, TGA and EDXS. The degradation of the dye by FLAC initiates in the presence of a mediator due to the high redox potential of the dye. Six natural mediators (ferulic acid, syringaldazine, guaiacol, eugenol, thymol, and p-coumaric acid) were tested, and complete degradation of the dye was achieved in 180 min, with a mediator concentration of 1 ppm (syringaldazine) and a dye concentration of 10 ppm. A novelty in this study is the short exposure time of the dye-mediator solution to FLAC (15 min) which allowed the degradation process to continue autonomously after the film was removed. Additionally, it was observed that the material was more effective in the presence of textile washing products, achieving over 99 % degradation in 40 min, surpassing its efficacy in distilled water. Regarding reusability, the material retained >90 % of its activity after five cycles of use and washing. Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) identified key levers for techno-economic and environmental viability: shorten cycle time (surfactant media), maximise re-use and percycle volume/concentration, implement solvent recovery, and energy decarbonization thereby outlining a roadmap to sustainable scale-up., We gratefully acknowledge the financial support provided by all funders. Author S. Vallejos received Grant PID2023-147301OB-I00 and Grant 3101166576-166576-29-325 funded by MICIU/AEI /10.13039/ 501100011033 and FEDER, EU. The financial support provided by Fondo Europeo de Desarrollo Regional-European Regional Development Fund (FEDER, ERDF) and Regional Government of Castilla y Le´on -Consejería de Educaci´on, Junta de Castilla y Le´on- (BU025P23) is gratefully acknowledged. This work was supported by the Regional Government of Castilla y Le´on (Junta de Castilla y Le´on) and by the Ministry of Science and Innovation MICIN and the European Union NextGenerationEU PRTR. J. L. Vallejo-García received the grant PRE2021-09812 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. Author Saul Vallejos received grant BG22/00086 funded by Spanish Ministerio de Universidades.

These files consist of raw network packet captures (PCAP format) obtained from controlled laboratory experiments that emulate benign and malicious Internet of Things (IoT) traffic in fog computing environments. The experiments involved resource-constrained edge devices, specifically a Raspberry Pi 4 and a Jetson Nano. Malicious traffic corresponds to Denial-of-Service (DoS) attacks executed from the Jetson Nano against the Raspberry Pi 4 within an isolated local network, while benign traffic reflects typical IoT device behavior, including alert generation, DNS requests, and telemetry or device-management communications (e.g., MQTT updates). All captures were performed under reproducible laboratory conditions and are intended to support research on intrusion detection, IoT network behavior analysis, and the development and training of cybersecurity attack detection models., This work was carried out under the project *AI4SECIoT – Artificial Intelligence for Securing IoT Devices*, funded by the Instituto Nacional de Ciberseguridad (INCIBE) within the framework of the *Plan de Recuperación, Transformación y Resiliencia*, financed by the European Union (Next Generation EU).

This repository provides flow-based IoT traffic datasets generated through NFStream and Tranalyzer for cyberattack detection and generalization studies. The datasets originate from two data sources: (1) **UBU-LAB**, representing traffic captured in controlled IoT laboratory conditions, and (2) **ToN-IoT**, a public dataset containing benign and Denial-of-Service (DoS) traffic traces (available at https://research.unsw.edu.au/projects/toniot-datasets). All datasets have been processed and structured to support experiments on flow extraction tool comparison and cross-domain generalization using Multilayer Perceptron (MLP) models. The repository contains: - Raw extracted flows for both NFStream and Tranalyzer, - Balanced datasets used in comparative studies (Paper 2), and - Datasets generated for generalization strategies (Paper 3). The data files are ready to be reused or integrated into new modelling pipelines for IoT network security research., This research was supported by the project *AI4SECIoT – Artificial Intelligence for Securing IoT Devices*, funded by the Instituto Nacional de Ciberseguridad (INCIBE) within the framework of the *Plan de Recuperación, Transformación y Resiliencia*, financed by the European Union (Next Generation EU).

The data set is a set of txt files with the fictitious instaces used in the paper "Efficient model for shift planning in the health and social care sector"., The authors are grateful for the following entities, projects, and grants: The Spanish Research Agency and European Union (Projects PDC2021-121021-C22 and PID2022-139543OB-C44) Spanish Ministry of Universities (Grant FPU/00606-2021) Regional Government of Castilla y León and FEDER founds (Grants BDNS 540787 and BU021G24).

This dataset contains simulation data generated from two individual-based stochastic models for malware propagation in an Internet-of-Things (IoT) network: a continuous-time Gillespie SIR model and a discrete-time Monte Carlo SIR model. For each modeling framework, two variants are included: the standard version (Gil / LMC) and the version incorporating the Topological Overlap Measure (TOM) (GilT / LMCTOM). All simulations are executed on a 128-node IoT communication network generated as a power-law cluster graph. Each simulation is stored as an independent CSV file in pivoted format, where rows represent network nodes and columns represent temporal steps produced by the algorithm (event steps in the Gillespie method and iteration steps in the Monte Carlo method). The dataset is suitable for research on malware propagation, stochastic processes on networks, graph-based machine learning models, and cybersecurity analytics., 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 is carried out within the framework of the Recovery, Transformation and Resilience Plan funds, financed by the European Union (Next Generation), the project of the Government of Spain that outlines the roadmap for the modernization of the Spanish economy, the recovery of economic growth and job creation, for solid, inclusive and resilient economic reconstruction after the COVID19 crisis, and to respond to the challenges of the next decade.