Resultados de investigación

This work is part of the R+D+i projects “Development and monitoring of pre-service and novice science teachers’ teaching identity. Study of the influence of the inquiry processes, emotional and gender profiles (INQUIRY-IDEG)” (reference PID2022-140001OA-I00), funded by MICIU/AEI/1.13039/501100011033 and FEDER (UE) and “Visibility of UMA STEM researchers in pre-service teacher training. Perception and aping as strategies towards the inclusion of the gender perspective in STEM education” (reference JA-B2-02), funded by IIPPIT UMA. Dr García-Ruiz thanks the RYC2020 program (reference: RYC2020-029033-I), funded by MICIU/AEI/1.13039/501100011033 and ESF “Investing in your future”., Analysis of PSTs' gender perspective in STEM education. It includes the quantitative dataset, the R-syntax description used in the analysis and the readme file, IIPPIT UMA, R+D+i project PID2022-140001OA-I00, R+D+i project JA-B2-02, RYC2020 program RYC2020-029033-I

This package provides the DEM-AIA (Digital Elevation Map – Any-Angle Inclination-Aware) trajectory planner for unmanned ground vehicles (UGVs). The planner computes feasible and efficient trajectories over digital elevation models (DEMs), explicitly considering: - Terrain slopes (pitch and roll). - Vehicle dynamic and geometric constraints (speed, center of gravity, length, width, slope tolerance). - Any-angle variant of the A* algorithm for more realistic and shorter paths. - Optional heuristic search and node re-expansion. The distribution includes both synthetic and real DEMs, MATLAB source code, precompiled MEX binaries, and a test script., Metadata: - Application domain: Off-road robotics, terrain-aware trajectory planning. - Data type: Digital Elevation Models (DEMs). - Format: MATLAB .mat. - Spatial resolution: depends on the selected DEM. - Units: meters (altitude and coordinates). - Restrictions: Distributed MEX files only run on MATLAB R2021b (Windows x64). Recompilation is required for other versions or platforms., Planning safe and effective trajectories for off-road unmanned ground vehicles (UGV) is a critical Artificial Intelligence (AI) challenge that can benefit from recent advances in digital elevation models (DEM) for readily capturing accurate terrain geometry. Considering path slopes is crucial to preserve stability and assess terrain traversability at feasible speeds to optimize travel time, which is highly dependent on direction (i.e., pitch and roll). In this article, we propose a new DEM-based asymmetric inclination-aware (DEM-AIA) trajectory planner for ground vehicles. The planner is an any-angle variant of the A algorithm that computes pitch and roll estimations for each segment crossing cell triangles in the line-of-sight. Furthermore, we define a non-linear velocity constraints function that integrates information about tip-over safety limitations, maximum uphill and downhill slopes for the vehicle, and asymmetric modulation of nominal flat-ground velocity for all pitch and roll combinations. The planner produces a time sub-optimal trajectory with feasible speed references for each segment crossing a cell triangle. Moreover, we provide an extensive experimental analysis of inclination-aware performance on simulated and real-world DEMs as well as a comparison with state-of-the-art path planners adapted to travel-time optimization. An executable version of the planner with parameterizable variations is publicly available.

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

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

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., This work was supported by grants awarded by Ministerio de Sanidad del gobierno de España, Delegación del Gobierno para el Plan Nacional sobre Drogas, Fondos de Recuperación, Transformación y Resiliencia (PRTR) Unión Europea EXP2022/008766, by Ministerio de Ciencia e Innovación del gobierno de España PID2020–114392RB-I00/AEI/10.13039/501100011033, and by Universidad de Málaga PPRO-B4–2024–010.

Exploratory analysis (Principal Component Analysis) and hierarchical cluster analysis of a questionnaire about personal and contextual factors shaping science and technology pre-service teacher professional identity. It includes the dataset, the readme file and the R-code used in the analysis, R+D+i Project “Development and monitoring of pre-service and novice science teachers’ teaching identity. Study of the influence of the inquiry processes, emotional and gender profiles (INQUIRY-IDEG)” (reference PID2022-140001OA-I00), RYC2020 program (reference: RYC2020- 029033-I), funded by MICIU/AEI/1.13039/501100011033 and ESF “Investing in your future”