Resultados de investigación

README – SPAREMETAL DATASET 4 Title: Experimental and predictive data from sustainable machining of light alloys (SPAREMETAL project) Project: PID2021-125988OB-I00 Sistema experto para la mejora de la integridad superficial en el mecanizado sostenible de aleaciones ligeras Institution: University of Malaga (Universidad de Málaga) Principal Investigator: Lorenzo Sevilla Hurtado (ORCID: 0000-0002-2236-5807) Contributors: Francisco Javier Trujillo Vilches Carolina Bermudo Gamboa Sergio Martín Béjar Manuel Herrera Fernández Tobias Andersson Daniel Svensson Yezika Sánchez Hernández Description: This dataset contains experimental and processed data obtained during dry machining processes of light alloys (UNS A97075 aluminum alloy and Ti6Al4V titanium alloy). The data were collected within the SPAREMETAL research project and include both online monitoring data and offline characterization results. This is dataset 4 (there are 4 in total for this project), which analyzes the effect of cutting parameters on the cutting forces, temperature, vibration, macrogeometrical deviations, Roughness and Microhardness in the machining of the alloy UNS Ti6Al4V. Data include: - Online monitoring data: cutting forces, temperature, vibrations, energy consumption - Offline measurements: surface roughness (2D/3D), microhardness, geometrical deviations - Derived datasets: aggregated databases linking input machining parameters and output variables - Predictive models: artificial neural networks (ANN) developed in MATLAB/Simulink Folder structure: 01. RAW DATA: Folder containing the raw data for the output variables; 02. PROCESSED DATA: Folder with the processed databases of the output variables; 03. MODELS: Developed mathematical models for the output variables as a function of the input variables; 04. Documentation: Documentation associated with the Project; File formats: - CSV, XLSX, FEL, DWD, ATS, DXD (data) - MAT, M, SLX, MALAPP, MLX, EXE (models) - PDF, DOCX (documentation) - PNG, TIFF, JPG (images) Methodology: The dataset was generated through controlled machining experiments using CNC turning equipment. Input parameters include cutting speed, feed rate and depth of cut. Output variables were measured using dynamometers, thermographic cameras, accelerometers and optical metrology systems. Units: All variables are expressed in SI units unless otherwise specified in metadata files. Reuse: Data can be reused for: - Validation of machining models - Development of predictive algorithms - Comparative studies in manufacturing engineering License: Creative Commons Attribution 4.0 (CC BY 4.0) Funding: Spanish State Research Agency (AEI) – Projects of Knowledge Generation 2021 Contact: Lorenzo Sevilla Hurtado University of Malaga Email: (lsevilla@uma.es) Date of publication: 2026 Version: v2.0, Este dataset contiene datos experimentales y procesados obtenidos durante procesos de mecanizado en seco de aleaciones ligeras (aleación de aluminio UNS A97075 y aleación de titanio Ti6Al4V). Los datos se recopilaron en el marco del proyecto de investigación SPAREMETAL e incluyen tanto datos de monitorización en tiempo real como resultados de caracterización fuera de línea. Se trata del dataset 4 (existen 4 para este proyecto), en el que se analiza el efecto de los parámetros de corte sobre las fuerzas de corte, la temperatura, la vibración, las desviaciones macrogeométricas, la rugosidad y la microdureza en el mecanizado de la aleación UNS Ti6Al4V. Datos generados/recogidos: En el proyecto SPAREMETAL se han generado datos experimentales derivados de procesos de mecanizado en seco de aleaciones ligeras (UNS A97075 y Ti6Al4V), incluyendo: • Datos de monitorización online (fuerzas de corte, temperatura, vibraciones, consumo energético) • Datos de monitorización offline (rugosidad, desviaciones geométricas, microdureza, fatiga) • Bases de datos integradas de parámetros de corte y variables de salida • Modelos predictivos basados en redes neuronales artificiales • Archivos de simulación (MATLAB/Simulink) • Documentación metodológica y protocolos experimentales Formatos y estándares utilizados: • CSV, XLSX, FEL, DWD, ATS, DXD (data) • MAT, M, SLX, MALAPP, MLX, EXE (models) • Documentación: PDF, DOCX • Imágenes: TIFF, PNG Valor para otros investigadores: Los datos permiten: • Reproducir experimentos de mecanizado sostenible • Validar modelos predictivos • Desarrollar nuevos algoritmos de optimización, Ministerio de Ciencia e Innovación

PID2021-125091OB-I00, Smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes., Spanish Government with a Formación de Profesorado Universitario (FPU) Grant given by the Ministerio de Ciencia, Innovación y Universidades, European Regional Development Fund (ERDF) Program Funds

Objectives: The aim of this study was to compare the acute effects of static and dynamic bouncing stretching during a warm-up on flexibility performance, assessed by the Sit-and-Reach (SR) test, in primary schoolchildren. Methods: The sample consisted of 98 schoolchildren (45 girls and 53 boys), aged 8–12 years, from 3rd to 6th grades of Primary Education. A randomized controlled crossover design was used, in which each partici-pant completed three warm-up conditions: static stretching (SS), bouncing stretching (BS), and no stretching (NS). All participants performed a standardized warm-up consisting of light jogging, joint mobility exercises, and general dynamic mobility. Subsequently, par-ticipants in the SS condition performed static stretching, those in the BS condition per-formed bouncing stretching, and those in the NS condition received theoretical instruction related to warm-up. Following each condition, flexibility was assessed using the classic Sit-and-Reach test. Results: Flexibility performance differed across warm-up conditions. Mean SR scores were 15.51 ± 5.53 cm for SS, 16.77 ± 5.59 cm for BS, and 13.82 ± 5.89 cm for NS. One-way ANOVA revealed significant differences among conditions. Post-hoc analy-sis using Tukey’s HSD test showed that the bouncing stretching condition resulted in sig-nificantly higher flexibility scores compared to no stretching (mean difference = 2.96 cm, p =0.0006). No significant differences were observed between static stretching and no stretching (mean difference = 1.69 cm, p = 0.08), nor between static and bouncing stretch-ing (mean difference = −1.27 cm, p= 0.24). Conclusions: These findings suggest that incor-porating bouncing stretching at the end of a warm-up may enhance flexibility performance in primary schoolchildren and represent a suitable option prior to lower-body physical activities.

The database (UMATBrush Dataset [15]) has a main folder and two subfolders named: Traces (with the sensed data traces measured) and Scripts. The first one contains the collected accelerometer data while the second one includes two scripts that automatically download, unzip and process the dataset. The Traces subfolder in turn consists of four subfolders, each corresponding to one of the experimental subjects. These are named with the word Subject, followed by an underscore (_) and the subject’s identifier (a number from 1 to 4). The names of the monitored data files with the acceleration measurements are presented according to the layout Subject_X_Watch_WWW_Date_YYYYMMDD_Time_HHMMSS.csv, where: • X is the subject identification number (1 to 4). • WWW is the smartwatch model name used to capture the acceleration signals. It can take one of the following values: LEO-DLXX, TicWatch-Pro or TicWatch-Pro-3-GPS, as indicated in the next section. • YYYYMMDD is the year (YYYY), month (MM) and day (DD) when the trace was collected. • HHMMSS is the hour (HH), minute (MM) and second (SS) of the instant when the gathering of the samples was initiated. Data files are plain text files in CSV (Comma Separated Values) format without headers. Every line in each data file contains a single measurement of the triaxial accelerometer of the IMU embedded in the smartwatch. The values of each line are arranged as it follows: • Timestamp, Ax, Ay, Az, Class Label where: Timestamp is the time (in μs) elapsed from the starting time of the recording to the instant in which the sample was captured. Accordingly, the first sample has a zero value and the rest of samples have a timestamp relative to this first sample. Ax, Ay, Az are the measurements of the three axes of the triaxial accelerometer (expressed in g units). Class Label is a binary value that indicates whether the sample corresponds to a toothbrush activity (1) or not (0)., The UMATBrush dataset provides accelerometer measurements captured by three different commercial smartwatches and from four participants during the execution of toothbrushing activities and other daily life activities in real-life conditions. The dataset can be used to characterize the dynamics of the toothbrushing movements as well as a dataset to train, validate and test AI-based HAR (Human Activity Recognition) systems., Spanish Ministry of Science, Innovation, and Universities, NextGenerationEU/PRTR Funds, Campus de Excelencia Internacional Andalucia Tech, ED2021-130456B-I00, B4-2023-12

Results from a sample of 223 university professors on knowledge, attitudes, and teaching uses regarding Generative Artificial Intelligence.

The dataset was collected from a cohort of 69 experimental subjects during the execution of several repetitions of the TUG (Timed-Up-and-Go) test. The dataset includes the measurements gathered with four wearables SHIMMER devices embedding four sensors (accelerometer, gyroscope magnetometer and barometer) located on four body locations (waist, dominant wrist, ankle and chest). Younger participants repeat the tests wearing a geriatric simulator., Silva Villafuerte, Carlos Alfredo; CASILARI, EDUARDO (2024), “UMATUG”, Mendeley Data, V1, doi: 10.17632/8z96ds3jsp.1, UMATUG dataset contains the inertial data collected from a cohort of 69 experimental subjects during the execution of TUG (Timed-Up-and-Go) mobility tests., NextGenerationEU/PRTR Funds, Ministerio de Ciencia, Innovación y Universidades, Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech, DIANA Research Group, ED2021-130456B-I00, B4-2023-12, PAIDI group TIC171

La base de datos contiene los ejemplares de camaleón común (Chamaeleo chamaeleon) hallados durante el periodo de estudio. Para cada individuo se indica el día del muestreo, la especie vegetal sobre la que el ejemplar fue hallado, el tipo de sustrato (herbáceo o leñoso), la altura y el diámetro de la percha, el peso y la clase de edad diferenciando entre juvenil, subadulto y adulto.

Datos de investigación correspondiente a un cuestionario para evaluar podcasts educativos