Resultados de investigación

README – SPAREMETAL DATASET 2 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 2 (there are 4 in total for this project), which analyzes the effect of cutting parameters on the fatigue behaviour, geometrical devitions, cutting forces, cutting temperatura, energy consumption, Tensile strenght, and microhardness in the turning operation of UNS A97075. 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 1 (existen 4 para este proyecto), en el que se analiza el efecto de los parámetros de corte sobre el comportamiento en fatiga, desviaciones geométricas, fuerzas de corte, temperatura de corte, consumo de energía, resistencia a la tracción y microdureza en el torneado de la aleación UNS A97075. 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

Proyecto PID2021-126283NB-I00

This dataset supports the study of startle-related physiological responses during walking using a wireless smart assistive cane equipped with a Galvanic Skin Response (GSR) sensor. Data were collected from 22 healthy participants walking through university areas while exposed to sudden auditory stimuli designed to elicit startle responses. GSR, tonic, phasic and acceleration signals were recorded simultaneously using the smart cane and an Empatica EmbracePlus reference device. The dataset includes raw/preprocessed signals, extracted GSR features and statistical results from Linear Mixed-Effects Models (LMMs) assessing the effects of startle condition and motion. The analysis identified 12 of 20 extracted features as motion-robust on the smart cane, with moderate-to-large effect sizes (Cohen’s dz = 0.54–1.27). These results indicate that startle-related physiological information can be detected from a cane-embedded GSR sensor during walking despite motion artifacts, supporting its potential use in future assistive and wearable systems for ambulatory startle and stress monitoring.

Data compiled and processed by the authors from publicly available sources (MICIU and IUNE).

La base de datos está compuesta por 56 registros, correspondientes a distintos sujetos participantes en el estudio, y 16 variables que recogen información demográfica, antropométrica y de rendimiento físico. Variables incluidas Sujeto: Identificador anónimo de cada participante. Protocol: Tipo de protocolo experimental aplicado (por ejemplo, CMR). Body Mass (kg): Masa corporal del sujeto. Age (años): Edad del participante. Stature (cm): Estatura del sujeto. BMI (kg/m²): Índice de masa corporal. Pre-CMJ (cm): Altura del salto con contramovimiento antes de la intervención. Post-CMJ (cm): Altura del CMJ inmediatamente después de la intervención. 15 m Post CMJ (cm): Altura del CMJ 15 minutos después de la intervención. Reps.: Número total de repeticiones realizadas durante el protocolo. RPE Post: Percepción subjetiva del esfuerzo inmediatamente tras la intervención (escala RPE). RPE 15 min POST: Percepción subjetiva del esfuerzo 15 minutos después. FC Max (lpm): Frecuencia cardíaca máxima registrada. FC Media (lpm): Frecuencia cardíaca media durante el protocolo. delta CMJ (cm): Diferencia entre el CMJ pre y post intervención, utilizada como indicador del cambio en el rendimiento., La presente base de datos recoge información antropométrica, fisiológica y de rendimiento físico obtenida en un grupo de sujetos adultos sometidos a un protocolo experimental relacionado con el consumo de cafeína. El conjunto de datos incluye mediciones previas y posteriores a una intervención, evaluando principalmente el rendimiento en el salto con contramovimiento (Countermovement Jump, CMJ), la percepción subjetiva del esfuerzo y variables de frecuencia cardíaca. Esta base de datos está destinada a facilitar el análisis del efecto agudo de la cafeína sobre el rendimiento neuromuscular y la respuesta fisiológica en un contexto de ejercicio físico controlado.

Alcohol Use Disorder (AUD) is a highly prevalent psychiatric and represents a significant public health challenge. Naltrexone (NTX), a mu-opioid receptor antagonist widely used for AUD treatment, has limited efficacy due to side effects and variability in patient response. Interactions between the full-length GAL molecule and the opioid system have been demonstrated. In our recent studies, we showed that the Galanin (1− 15) fragment [GAL (1− 15)] decreased alcohol seeking along with alcohol consumption. This study aims to examine the effects of GAL(1− 15)+NTX on alcohol-seeking behavior and alcohol consumption, as well as the involvement of the mesolimbic system. In rats, we assessed GAL(1− 15)+NTX in reward-seeking and the role of GALR2 using the antagonist M871 in the self-administration test. In addition, GAL(1− 15)+NTX effects were studied on voluntary alcohol using the two-bottle choice paradigm. Locomotor activity and stereotyped behaviors, along with dopa- mine release in the dorsal striatum following alcohol injections, were assessed. Moreover, we have analyzed the transcriptional changes of C-Fos, MOR, POMPC, and dopamine receptors in the ventral tegmental area, nucleus accumbens and the hypothalamus. GAL(1− 15)+NTX combination reduced alcohol seeking in self-administration and two-bottle choice consumption, with GALR2 involved in the effect. In addition, GAL(1− 15)+NTX attenuated alcohol-induced locomotor activity and stereotyped behaviors linked to reduced dopamine release in the dorsal striatum. Notably, these effects were associated with C-Fos, MOR, and dopamine receptor changes, suggesting that the mesolimbic pathway, including the opioid system, is involved in GAL(1− 15)+NTX effects. These results open up the possibility of using GAL(1− 15) with NTX as a novel strategy in AUD.

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

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.

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.