Objectives: Cardiovascular diseases (CVD) remain a leading cause of premature mortality globally, despite the proven efficacy of physical activity in reducing risks. This research aims to identify risk characteristics and characterise pathologies related to the onset of CVD in relation to physical activity levels. The study tests the hypothesis that adequate physical activity is associated with CVD-related events, while sedentary behaviour is a factor related to increased risk factors. Methods: A cross-sectional, observational, descriptive, and analytical study was conducted with 116 participants of both sexes (aged 16 to 77 years) in El Espinal, Tolima. Clinical, anthropometric, and biochemical assessments were performed, including blood pressure, Body Mass Index (BMI), visceral fat, and lipid profiles. Physical activity was self-reported and categorised as weekly, monthly, and occasional exercise. Descriptive and bivariate statistical analyses were performed. Quantitative variables were expressed as means and standard deviations. Qualitative variables were presented as absolute frequencies. Statistical interaction graphs were used to analyse the effects of age and exercise frequency on pulse pressure. Results: Weekly exercise was identified as a key modulator of hemodynamic stability; while BMI and visceral fat increased with age, pulse pressure remained stable (44.17-46.55 mmHg). In contrast, occasional exercise was linked to high cardiovascular vulnerability, with pulse pressure spiking to a critical 75.00 mmHg in elderly participants (77 years) and BMI reaching obesity levels (38.15 kg/m2). Monthly exercise showed high variability and progressive lipid profile deterioration, with total cholesterol reaching 282.00 mg/dL in late maturity. Conclusions: Regular weekly physical activity acts as a physiological buffer that dissociates chronological ageing from vascular damage. While weekly exercise maintains optimal hemodynamic and metabolic ranges, occasional or inconsistent activity fails to prevent critical increases in pulse pressure and arterial stiffness during senescence. These findings underscore the necessity of regular, rather than sporadic, exercise as a vital "medicine" for maintaining arterial integrity across the lifespan.
Detecting credit card fraud is a complex tabular learning problem due to the underrepresentation of fraudulent events, extreme class imbalance, and scattered minority areas. To address this limitation, a conservative minority synthesis strategy is proposed, combining the removal of minority outliers, synthetic data generation using SMOTE, geometric filtering, and supervised plausibility filtering. Unlike conventional oversampling, the method does not seek to artificially balance classes but rather to retain only locally consistent and discriminatively useful synthetic samples. The approach was evaluated on the ULB Credit Card Fraud Detection and IEEE-CIS Fraud Detection datasets using a 60/20/20 stratified partitioning, preventing information leakage between training, calibration, and testing. On ULB, the proposal retained 638 final synthetic samples, raising the fraud rate of the augmented dataset to 0.005396. In IEEE-CIS, 947 final synthetic samples were retained, with a final fraud rate of 0.037563. Compared to techniques such as SMOTE, Borderline-SMOTE, ADASYN, and SimpleVAE, the proposed method showed competitive performance, achieving the highest AUPRC in four of six classifiers in ULB and in three of six classifiers in IEEE-CIS. Ablation analyses and statistical validation show that supervised plausibility filtering helped control the quality of the generated samples, although its benefit is not uniform and depends on the classifier and dataset used.
Precision agriculture requires robotic platforms capable of covering large cultivation areas while providing accurate positioning for localized crop monitoring and management operations. Cable-driven parallel robots (CDPRs) offer large workspaces and lightweight moving structures; however, their usable workspace is constrained by cable tension requirements. This study presents the experimental validation of AgroCableBot, a reconfigurable CDPR for precision agriculture previously proposed by the authors, in which the proximal cable attachment points are vertically reconfigured to increase the wrench-feasible workspace (WFW). Previous simulation results showed that this reconfiguration increases the WFW from approximately 58% to 100% of the geometrically admissible workspace for a minimum cable tension of 5 N. In this work, a physical prototype with adjustable anchor height was built, and a vision-based sensing system was integrated for end-effector localization, automatic calibration, and closed-loop trajectory tracking. Experimental validation included square and circular trajectories and an agricultural inspection trajectory over a seedling tray, executed at three anchor heights (400, 600 and 900 mm). The system achieved a three-dimensional RMSE between 1.9 and 2.6 mm, a 95th-percentile error below 4.5 mm, and a maximum tracking error of 6.0 mm, measured with the vision system. These results demonstrate the potential of AgroCableBot as a reconfigurable robotic platform for accurate crop monitoring and localized agricultural operations in protected cultivation environments.
Magnetic shape memory alloys based on the Ni–Mn–Ga system are of strategic interest for aerospace and robotics applications due to their ability to respond to both thermal and magnetic stimuli. However, the NASA Shape Memory Materials Database a key resource for the community exhibits significant gaps in functional parameters, with up to 93.7% of records missing critical properties such as the Curie temperature, and over 88% lacking complete magnetic data. To address this limitation, this study proposes a data imputation strategy based on a stacking ensemble comprising twelve machine learning models (LGBM, XGBoost, CatBoost, GradientBoosting, RandomForest, MLP, BayesianRidge, KNN, SVR, GPR, MICE, and AutoEncoder), optimized via Optuna and evaluated using ten random seeds with 10 repetitions each. The approach was applied to reconstruct missing entries in NASA’s database. For heat treatment 1, the method achieved coefficients of determination (R2) of 0.95 for duration (h) and 0.88 for temperature (°C), respectively. For the phase transformation temperatures (Mf, Ms, As, and Af), the method yielded R2 values of 0.83, 0.82, 0.79, and 0.80, respectively. Magnetic properties saturation magnetization and maximum magnetic field were imputed with an R2 of 0.92. In contrast, the Curie temperature exhibited limited predictive performance (R2 = 0.15–0.35), primarily due to insufficient data availability. Overall, the proposed methodology integrates machine learning based imputation with physically supported constraints, providing a viable alternative to enhance the completeness and utility of materials databases.
Introduction: burnout syndrome is a global occupational crisis that seriously affects the mental health of healthcare workers and the quality of medical care. The COVID-19 pandemic has intensified this phenomenon, highlighting the urgent need for effective diagnosis and prevention strategies.Objective: to analyse the prevalence, risk factors, diagnostic tools, and intervention strategies for burnout syndrome in healthcare personnel, based on recent scientific evidence.Method: a systematic review of the scientific literature (2019–2024) was conducted following PRISMA guidelines. International databases (PubMed, Scopus, Cochrane, SciELO) were consulted, selecting 28 studies of high methodological quality that evaluated prevalence, risks, instrument validity, and the effectiveness of preventive interventions.Results: the overall prevalence of burnout among healthcare workers was 39%, reaching up to 59.5 % among nurses during the pandemic. The main risk factors were workplace bullying (OR: 4.05–15.01), low job satisfaction (OR: 5.05) and high perceived stress (OR: 4.21). Among the diagnostic instruments, the Maslach Burnout Inventory (MBI) and the Oldenburg Burnout Inventory (OLBI) showed the best psychometric properties. Mindfulness-based and coaching interventions moderately reduced burnout (SMD: -0.44).Conclusions: burnout is a multifactorial problem where organisational causes predominate. It is recommended to implement preventive institutional policies, strengthen workplace wellbeing and standardise diagnostic tools to improve the sustainability of the healthcare system.
