Resultados de investigación

This dataset is directly associated with the article “Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times” (Swarm and Evolutionary Computation, 2022) and contains the complete set of benchmark instances and experimental results reported in the publication., This dataset contains benchmark problem instances and detailed experimental results associated with the SWEVO 2022 article on green job shop scheduling with uncertain times. It includes the solved instances used in the experimental evaluation and the corresponding numerical results, provided both in the original spreadsheet format and in interoperable CSV files (one per instance). The dataset has been archived to support transparency, reproducibility and reuse of the experimental evidence reported in the associated publication., This research has been supported by the Spanish Government under research grants TIN2016-79190-R and PID2019-106263RB-I00, and by the Principality of Asturias Government under grant IDI/2018/000176.

Dataset used in the evaluation of the paper "Heuristic-Based Incremental Local Domain Model Generation", currently under review in "Information and Software Technology". Context: Current front-end frameworks and technologies enable rich clients to operate autonomously without frequent server requests. To achieve this autonomy, clients must maintain a Local Domain Model (LDM), often derived from the Global Domain Model (GDM) on the backend. Manually designing an LDM that is consistent with the GDM requires handling nuanced dependencies, an error-prone task where oversight is easy. Objective: To address these challenges we aim to: (a) formally map dependencies between GDM and LDM; (b) analyze effort and errors when modelling without assistance; and (c) provide a semi-automated method leveraging these dependencies to significantly reduce both effort and errors. Method: To achieve these objectives, we propose a heuristic-based, step-by-step guided method. This approach leverages pre-existing GDM information to incrementally uncover dependencies and automate LDM construction as designers identify local behavior of GDM elements. We assessed this method's impact through an empirical experiment where we aimed to identify common mistakes and quantify effort during LDM construction. Expert UML modelers completed an LDM creation task both manually and with our tool-supported method. We recorded errors and cognitive effort to establish a baseline and measure impact. User perceptions were gathered via a survey; an analytical usability study based on GOMS complemented findings. Results: The proportion of users committing errors decreased by 77.8% with the tool, and the average error count per user was reduced by 97.3%. Time to complete the task decreased by 35.0% and interactive effort by 44.6%, consistent with GOMS predictions. Surveys showed majority positive responses across all items. Conclusions: Our approach effectively streamlines Local Domain Model creation. By automatically detecting dependencies and guiding designers, the tool drastically reduces error rates, cuts completion time, and lowers interaction volume. Expert users rated the method positively, affirming that benefits of guided, incremental LDM construction outweigh adoption effort., This work has been funded by the Government of the Principality of Asturias, with support from the European Regional Development Fund (ERDF) under project IDE/2024/000751 (GRU-GIC-24-070). Additional funding was provided by the University of Oviedo through its support for official research groups (PAPI-24-GR-REFLECTION).

Datasets associated with the scientific paper "A fast magnetic vector characterization method for quasi two‑dimensional systems and heterostructures" published in Sci.Rep. 13, 9639 (2023)

Introduction. Hemophilic knee arthropathy leads to a reduction in muscle mass and strength. Blood flow restriction (BFR) involves the application of a compressive cuff that enables strength training with reduced loads. Objective. To evaluate safety as the primary outcome and the efficacy (muscle strength and activation, functional capacity, and quality of life) as secondary outcomes of a quadriceps strengthening programme using blood flow restriction in patients with haemophilia. Methods. A randomized clinical trial was conducted with 33 participants assigned to either an experimental group (strength training with BFR) or a control group. The four-week intervention included three weekly sessions and two strength exercises (leg extension and squat). Outcomes assessed were safety (clinical hemarthrosis), strength (dynamometry), muscle activation (surface electromyography), pain threshold (pressure algometry), range of motion (goniometry), functional capacity (Timed Up and Go), and quality of life (36-Item Short Form Survey). Follow-up assessments were performed four weeks post-intervention to evaluate effect persistence. Results. Thirty participants completed the study. No participants experienced clinical hemarthrosis or hematomas during the intervention, confirming safety as the primary outcome. Additionally, blood flow restriction training produced significant gains in quadriceps strength (p<0.001;η²p=0.19) and activation (p<0.001; η²p=0.28). Further improvements (p<0.05) were observed in pain threshold, knee range of motion, and the physical component of quality of life. Conclusions. Blood flow restriction strength training is safe for patients with hemophilia. It may enhance quadriceps strength and activation, raise pain thresholds, and improve knee mobility. Patients show better physical functioning, less bodily pain, and improved physical quality of life., This work has received financial support from Swedish Orphan Biovitrum S.L. (medical grant: HCC-14177). The funders played no role in the design, conduct, or reporting of this study

Datos relativos a la publicación: Calvo González, Soraya & Trujillo Barbadillo, Gracia. (2024). Discursos contrarios a la educación sexual en España: argumentario mediático y agentes de referencia en Twitter (X). ICONO 14. Revista Científica De Comunicación Y Tecnologías Emergentes, 22(1): e2086. https://doi.org/10.7195/ri14.v22i1.2086

Raw data gut microbiota analysis from mice.