The University of Santander (UDES) is a private research university, approved by the Colombian government through the Ministry of Education, according to legal status 810 1996; organized under the provisions of Act 30 of 1992. This university has different locations in Colombia and Latin America being the main campus located in Bucaramanga, other campus are Panama City (Panama), Bogotá, Cúcuta and Valledupar. Provides technical, undergraduate, graduate, postgraduate, and continual education programs.
It is essential to describe performance profiles and implement training loads for MMA athletes participating in competitions, as well as to explore and develop new training models as a priority for MMA coaches. The objective of this study was to evaluate the anthropometric characteristics and physical performance of contemporary elite male mixed martial arts (MMA) athletes. Professional Colombian Mixed Martial Artists were assessed before participating in the sport combat championship (Mean age = 30.80 +/- 4.56 years, height = 174.00 +/- 3.43 cm, weight = 77.97 +/- 8.84 Kg). Athletes underwent evaluations on body composition and sports performance parameters including bioelectric impedance analysis (BIA), VO2max, maximal aerobic speed (MAS) and handgrip strength and other variables. The evaluations revealed a mean muscle mass of 64.67% +/- 5.20% (95% CI, 67.89%-61.44%), body fat percentage of 10.93% +/- 4.31% (95% CI, 13.60%-8.25%), Ponderal index of 14.79 +/- 1.50 (95% CI, 15.72-13.86) and bone mass 3.45 +/- 0.27 (95% CI, 3.61-3.28). Mean VO2max was 63.23 +/- 5.50 ml/min-1/Kg-1 (95% CI, 66.64-59.81). This information can assist in assessing and evaluating sports performance as well as in developing and optimizing specific training regimes and identifying talents.
Malaria remains a major public health challenge in Colombia, with a significant increase in cases in recent years. Climate variables-particularly temperature and rainfall-are key drivers of malaria transmission, yet their lagged, non-linear effects across space and time are poorly characterized in the Colombian context. We conducted an ecological, spatiotemporal analysis using weekly malaria case data from 970 municipalities in Colombia (2013-2023) combined with satellite-derived climate data. We applied distributed lag non-linear models (DLNMs) embedded within a Bayesian hierarchical framework using integrated nested Laplace approximation (INLA) to estimate the delayed, non-linear associations between weekly temperature and rainfall and malaria incidence, while accounting for spatial and temporal autocorrelation, forest cover, multidimensional poverty, altitude, population size, and prior case counts. Our results show that malaria risk increases non-linearly with temperature, peaking around 28 °C, with a global exposure of minimum risk (EMR) at 16.43 °C, and significant effects observed at lags of 0-6 weeks. In contrast, lower weekly rainfall was associated with higher malaria risk, with an EMR at 0.85 mm. Sensitivity analyses confirmed the robustness of these findings. These results challenge previous studies about climate-driven malaria risk and highlight accelerated transmission dynamics in Colombia's endemic zones. The identification of specific climate thresholds linked to elevated malaria incidence provides actionable evidence for climate-informed early warning systems and targeted interventions to support malaria elimination efforts in Colombia.
The notion of Erzeugungsgrad was introduced by Joos Heintz in (Theoret Comput Sci 24:239–277, 1983) to bound the number of non-empty cells occurring after a process of quantifier elimination. We extend this notion and the combinatorial bounds of Theorem 2 in Heintz (1983) using the degree for constructible sets defined in Pardo and Sebastián (J Complex 68:101588, 2022). We show that the Erzeugungsgrad is the key ingredient to connect affine Intersection Theory over algebraically closed fields and the VC-Theory of Computational Learning Theory for families of classifiers given by parameterized families of constructible sets. In particular, we prove that the VC-dimension and the Krull dimension are linearly related up to logarithmic factors based on Intersection Theory. Using this relation, we study the density of correct test sequences in evasive varieties. We apply these ideas to analyze parameterized families of neural networks with rational activation function.
Neurocardiogenic syncope (NCS), particularly cardioinhibitory and mixed subtypes, remains a clinical challenge when refractory to conventional therapy. Cardioneuroablation (CNA) targeting the parasympathetic ganglionated plexi has emerged as a novel interventional option. This study evaluates the efficacy of biatrial CNA with extracardiac vagal stimulation (ECVS) validation in preventing recurrence and modulating cardiac autonomic regulation. A single-center combined retrospective–prospective cohort study was conducted at a fourth-level institution in Colombia, including patients with cardioinhibitory or mixed-type NCS refractory to conventional treatment. CNA was performed targeting both atria with three-dimensional mapping, followed by ECVS via the internal jugular vein for pre/post validation. Primary outcome was the recurrence of syncope. Secondary outcomes included changes in heart rate variability (HRV) and quality of life (QoL) as assessed by 24-hour Holter monitoring and the SF-36 questionnaire, respectively. Mean follow-up was 24 months. Statistical analysis was performed using SPSS Statistics 28.0.0.0. Fifty-three patients (mean age 42.8 ± 10.1 years, 70 Cardioneuroablation significantly reduced syncope recurrence in patients with refractory cardioinhibitory and mixed-type neurocardiogenic syncope. Extracardiac vagal stimulation enabled reliable intraoperative confirmation of successful vagal denervation. Heart rate variability indices (SDNN, RMSSD, pNN50) showed significant parasympathetic suppression after cardioneuroablation. Patients experienced substantial improvement in health-related quality of life following the procedure.
Accurate prediction of performance degradation in proton exchange membrane fuel cells (PEMFCs) is essential for predictive maintenance and commercial viability. This task is challenged by data scarcity, complex operational noise, and highly dynamic conditions. Current data-driven models often require large datasets and lack adaptive optimization, compromising accuracy or efficiency. To overcome this, we propose a novel selfintelligent gradient descent search mechanism-driven grey neural network (SiGDSM-GNN). The model integrates grey system theory, adept at handling uncertain, limited data, with the nonlinear learning power of neural networks. Its key innovation is a self-intelligent mechanism that dynamically selects and adapts gradient descent strategies during training, significantly enhancing convergence and robustness. Extensive testing on three realistic PEMFC datasets-steady-state, dynamic loading with 5 kHz current ripples, and real-world vehicle operation-demonstrated superior performance. SiGDSM-GNN achieved a mean absolute percentage error of 0.11%, a noise-to-signal ratio of 0.005, and a prediction time of 0.005 s, outperforming benchmark models including optimization algorithms and deep learning hybrids. This work provides a highly accurate, dataefficient, and real-time capable framework for PEMFC degradation forecasting, advancing predictive maintenance strategies for cleaner energy systems.
