University of the Coast

Generalized anxiety has significantly increased in the general population during and after the COVID-19 pandemic, highlighting the need for rapid screening tools. In this context, the present study analyzed the psychometric properties and internal consistency of the Generalized Anxiety Disorder Scale (GAD-7) in healthcare workers and the general population in Latin America. A cross-sectional e-health study was conducted, surveying 11,279 Latin Americans online using snowball sampling. The sample included healthcare professionals, hospital populations, community members from various occupations, and university students from six countries (Argentina, Bolivia, Ecuador, Chile, Colombia, and Peru). Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed separately for each country. Additionally, the instrument's internal consistency was evaluated by calculating McDonald's W index and item-total correlations based on the final items. The EFA revealed a unidimen-sional structure comprising the seven items of the instrument, which explained between 62.8% and 66.1% of the variance (KMO = between .900 and .910; p < .000). The CFA confirmed adequate fit indices for each country. The omega index ranged from 0.85 (Peru: CI = 0.800-0.884) to 0.95 (Argentina-Bolivia: CI = 0.901-0.985), and item-total correlations were high, ranging from .642 to .869, demonstrating the instrument's reliability. In conclusion, the findings of this study indicate that the GAD-7 is a valid and reliable instrument for assessing generalized anxiety symptoms in the Latin American population.

Accurate determination of volume percentages in three-phase fluids is paramount for the success of various industrial processes, ranging from oil and gas production to chemical engineering. This study presents a comprehensive approach to this challenge by leveraging advanced signal processing techniques and machine learning paradigms. Our methodology integrates the time, frequency, and wavelet transform features extracted from X-ray-based measurement systems whose structure consists of an X-ray tube source, two sodium iodide detectors, and a test pipe, all of which were simulated using the Monte Carlo N Particle code. The amalgamation of these features provides a rich representation of the fluid composition that captures both temporal and spectral characteristics. To enhance the discriminative power of the features, we employ a simulated annealing algorithm to strategically reduce their dimensionality and select pertinent features. The simulated annealing unit systematically evaluates the contribution of each feature to predictive accuracy. Further, through iterative elimination and re-evaluation, the algorithm refines the feature set, retaining only those with the highest relevance to the three-phase fluid composition. This feature selection process optimises the performance of subsequent machine learning models, streamlining the input space for enhanced interpretability and efficiency. Finally, to determine the volume percentages, we employ a support vector regression (SVR) neural network, which is trained on a refined dataset with capability to handle complex relationships and high-dimensional data. The proposed approach demonstrates superior accuracy in determining volume percentages of three-phase fluids compared to traditional methods, thereby making it an effective and integrated technique to analyse fluid composition in a variety of industrial settings and applications.

Background/Objectives: The global shift towards vegan and vegetarian diets has garnered attention for their ethical, environmental, and potential health benefits. These diets are often rich in phytonutrients and antioxidants, which have been associated with lower levels of inflammatory markers, such as C-reactive protein (CRP) and interleukin-6 (IL-6), suggesting a potential protective effect against systemic inflammation and oxidative stress. However, despite these benefits, concerns remain regarding their impact on neurological health due to the possible deficiencies of critical nutrients such as vitamin B12, DHA, EPA, and iron. This review critically evaluates the influence of these dietary patterns on neurological outcomes, emphasizing their nutritional composition, potential deficiencies, and their interplay with inflammation and oxidative stress. Methods: A systematic review of the literature published between 2010 and 2023 was conducted, focusing on studies that explore the relationship between vegan and vegetarian diets and neurological health. Key nutrients such as vitamin B12, omega-3 fatty acids, iron, and zinc were analyzed alongside antinutritional factors and their effects on the nervous system. Results: Evidence suggests that vegan and vegetarian diets, when well planned, can be rich in phytonutrients and antioxidants, which have been associated with lower levels of inflammatory markers, such as C-reactive protein (CRP) and interleukin-6 (IL-6). These findings indicate a potential role in reducing systemic inflammation and oxidative stress, both of which are linked to neurodegenerative diseases. However, deficiencies in critical nutrients such as vitamin B12, DHA, EPA, and iron have been consistently associated with an increased risk of cognitive decline, mood disturbances, and neurodegenerative disorders. Additionally, the presence of antinutritional factors like phytates and oxalates may further impair nutrient absorption, necessitating careful dietary planning and supplementation. Conclusions: While plant-based diets provide anti-inflammatory and antioxidant benefits, their neurological implications depend on nutrient adequacy. Proper planning, supplementation, and food preparation techniques are essential to mitigate risks and enhance cognitive health. Further research is needed to explore long-term neurological outcomes and optimize dietary strategies.

The objective of this article is to examine how higher education contributes to students' employment rates and socioeconomic standing once they graduate. A literature review has been conducted, looking to identify the main particularities that exist in the study of higher education's impact in the employment and social status. For the empirical application this study uses a unique primary dataset that includes information about their employment status and socioeconomic standing at the start and at the end of their studies between 2016 and 2021 for 5400 students at Universidad de la Costa. This study employs a multinomial logit model, the result shows that the transition of student-graduates' increases (from 12.4% to 44.4%) the number of persons who are employed when they obtain their degrees. In contrast with the evidence showing significant changes in socioeconomic level, there are no significant differences between men and women regarding employment status. The findings of this paper offer relevant information to government and education policymaker to design strategies that promote the higher education in Colombia.

Background: Anxiety disorders have been rising globally, particularly among adolescents and women. However, the relationship between diet, psychological traits, and anxiety levels in athletes remains underexplored. Objectives: This study aimed to analyze the nutritional and psychological differences between athletes with varying anxiety levels, hypothesizing that higher anxiety correlates with unhealthier dietary habits, greater body distortion, and less adaptive psychological profiles. Methods: A total of 58 athletes (23 women, 35 men), aged 18 to 45 years (mean age = 30.2 years), participated in this cross-sectional study. Data were collected using validated online questionnaires, including the Big Five Inventory, Spielberger State–Trait Anxiety Inventory (STAI), Acceptance and Action Questionnaire-II (AAQ-II), UCLA Loneliness Scale, and Eating Disorder Inventory (EDI), as well as surveys assessing nutritional habits and physical activity levels. Statistical analyses were conducted using SPSS (v24.0), with independent t-tests to compare differences between higher and lower anxiety groups (p < 0.05). Results: It has beenindicated that higher anxiety was associated with greater neuroticism, lower psychological flexibility, and higher eating disorder symptomatology, while better sleep quality and psychological profiles correlated with lower anxiety levels. Additionally, athletes who cooked their own meals exhibited higher anxiety, whereas greater water intake and whole grain consumption were linked to lower anxiety. More frequent and intense training, particularly weight training, was also associated with reduced anxiety. Conclusion: This study concludes that anxiety in athletes is influenced by multiple lifestyle factors, including sleep quality, dietary habits, psychological traits, and exercise patterns. These findings emphasize the need for holistic approaches integrating nutrition, psychological interventions, and structured physical training to manage anxiety in athletes.

Abstract: Although not frequently lethal, dermatological diseases represent a common cause of consultation worldwide. Due to the natural and non-invasive approach of phy-totherapy, research for novel alternatives, such as polyphenols, to treat skin disorders is a subject of interest in modern medicine. Polyphenols, in particular, have been considered because of their anti-inflammatory, antitumoral, antimicrobial, and antioxidant properties, low molecular weight, and lipophilic nature that enables the passage of these compounds through the skin barrier. This review discusses the treatment of common dermatological diseases such as acne vulgaris, fungal infections, dermatitis, alopecia, and skin cancer, using polyphenols as therapeutic and prophylactic options. The specific molecules considered for each disorder, mechanisms of action, current clinical trials, and proposed applications are also reviewed.

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines; several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines.

The determination of void fraction in various two-phase flows holds great significance across a range of industries, including gas, oil, chemical, and petrochemical sectors. Scientists have proposed a wide array of methods for measuring void fractions. In comparison to other methods, capacitive-based sensors stand out as a good choice due to their affordability, nondestructively, robustness, and reliability. However, one of the factors that can affect the accuracy of these sensors is changes in the fluid composition. For instance, even a minor alteration in the fluid within the pipe can result in a significant void fraction measurement error. To address this issue, regular calibration is necessary, which can be a laborious task. In this paper, an Artificial Neural Network (ANN) is employed in order to make sensor measurements independent of fluid changes, which allows for more reliable and precise measurements without the need for frequent calibration. Our focus is on studying stratified two-phase flow. In this research, four different combinations of electrodes of a four-concave sensor are utilized as the input of an ANN. As a result, the ANN’s output accurately quantifies the void fraction. COMSOL Multiphysics software is utilized to simulate the behavior and measure the capacitance value of different combinations of this sensor. Additionally, a Multilayer Perceptron (MLP) neural network in MATLAB is designed and implemented, which can forecast the gas percentage within a two-phase fluid containing different liquids, achieving a remarkable mean absolute error of only 0.0031.

The method developed for the determination of lapachone isomers ( α ‐ and β ‐lapachones) involves the use of square wave voltammetry (SWV) with an electrode based on epoxy–graphite composite. The electrolytic aqueous solution contained a cationic surfactant (CTAB), phosphate buffer (pH 6.0), and KNO 3 . The addition of CTAB enhanced analyte diffusion into the electrode–solution interface, improving detection through SWV. Under chosen conditions, β ‐lapachone and α ‐lapachone respectively present reversible and quasireversible processes. The analytical signals were detected at the specific potentials of −370 mV ( α ‐lapachone) and −190 mV ( β ‐lapachone) using 140 s preconcentration at +400 mV. The SWV parameters used include 30 Hz frequency, 40 mV pulse amplitude, and 20 mV potential step. Instrumental detection limits were 2.4 × 10 ⁻⁷ mol L ⁻¹ and 1.4 × 10 ⁻⁷ mol L ⁻¹ respectively for α ‐lapachone and β ‐lapachone. Lapachol and, in a lesser extent, sulfonated β ‐lapachone interfere with both analyte signals, requiring liquid–liquid extraction prior to the determination of α ‐lapachone and β ‐lapachone in ethanolic plant (heartwood of the Tabebuia impetiginosa ) extract. The results obtained using SWV agreed with those achieved by high‐performance liquid chromatography.

The fast growth worldwide of linkable scientific datasets supposes significant challenges in their management and reuse. Large experiments, such as the Latin American Giant Observatory, generate volumes of data that can benefit other kinds of studies. In this sense, there is a modular ecosystem of external radiation tools that should harvest and supply datasets without being part of the main pipeline. Workflows for personal dose estimation, muongraphy in volcanology or mining, or aircraft dose calculations are built with different privacy policies and exploitation licenses. Every numerical method has its own requirements and only parts could make use of the Collaboration’s resources, which implies the convergence with other computing infrastructures. Our work focuses on developing an agnostic methodology to address these challenges while promoting open science. Leveraging the encapsulation of software in nested containers, where the inner layers accomplish specific standardization slices and calculations, the wrapper compiles metadata and data generated and publishes them. All this allows researchers to build a data-driven computer continuum that complies with the findable, accessible, interoperable, and reusable principles. The approach has been successfully tested in the computer-demanding field of radiation-matter interaction with humans, showing the orchestration with the regular pipeline for diverse applications. Moreover, it has been integrated into public or federated cloud environments as well as into local clusters and personal computers to ensure the portability and scalability of the simulations. We postulate that this successful use case can be customized to any other field.

Introduction The prevalence of Generalized Anxiety Disorder (GAD) has increased rapidly, highlighting the importance of its detection using quick tools applicable to men and women from different countries. Objective To analyze the psychometric properties of the Generalized Anxiety Disorder Test (GAD-7) by gender and country in Latin America and the Caribbean (LAC). Method A cross-sectional e-health study with 12,124 participants from 15 LAC countries (54.32% women, 45.68% men) was conducted, including participants from Argentina (7.3%), Bolivia (6.7%), Colombia (10.3%), Chile (6.9%), Costa Rica (4.9%), El Salvador (5.7%), Ecuador (7.2%), Guatemala (4.7%), Panama (5.1%), Paraguay (5.7%), Peru (8.6%), Puerto Rico (5.8%), the Dominican Republic (6.6%), Uruguay (6.3%), and Venezuela (8.2%). All participants completed the GAD-7 scale digitally. Results A unidimensional structure of the GAD-7 was confirmed, explaining 70% of the variance. The model fit indices were adequate (RMSEA = 0.062; CFI = 0.997; TLI = 0.995; SRMR = 0.017; p < 0.001), and the factor loadings for each item were satisfactory (> 0.70). Additionally, the factor structure showed measurement invariance between genders and countries, with adequate fit indices at all levels (configural, metric, scalar, and strict), suggesting that the measurements are equivalent in both contexts. Finally, the internal consistency of the GAD-7 was high, with a McDonald’s Omega coefficient of 0.91. Conclusions The GAD-7 exhibits a factor structure that is equivalent across genders and countries, demonstrating its validity and reliability for the rapid detection of GAD symptoms in different countries within the region.

Background: Body composition is a determinant of physical fitness and sports performance. Aim: To describe the anthropometric characteristics, body composition, somatotype, and asymmetries of the 2023 world champion in the C1-1000 canoeing event. Methods: Dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and anthropometry were used to describe the athlete's body composition. Results: The results showed a fat tissue distribution of 16.3% by DXA, 15.9% by BIA, and 19.0% by anthropometry. Muscle tissue was reported at 32.0 kg (47.5%) by BIA and 34.1 kg (50.6%) by anthropometry. Conclusions: The athlete exhibits low levels of fat mass with high lean mass, factors that enable optimal development in world-class sports.

Background/Objectives: This systematic review focused on collecting the most significant findings on the impact of the administration of Bifidobacterium infantis (or Bifidobacterium longum subps. infantis) and Bifidobacterium breve, alone, in conjunction, or in combination with other strains, in the treatment of neurodegenerative diseases including Alzheimer’s disease (AD) and Parkinson’s disease (PD). These diseases are characterized by the progressive degeneration of neurons, resulting in a broad spectrum of clinical manifestations. AD is typified by a progressive decline in cognitive abilities, while PD is marked by motor symptoms associated with the loss of dopamine (DA). Methods: Five different databases, ScienceDirect, Scopus, Wiley, PubMed, and Web of Science (WoS), were reviewed and the studies were screened for inclusion by the following criteria: (i) studies that specifically evaluated the use of Bifidobacterium infantis, Bifidobacterium longum subsp. infantis, or Bifidobacterium breve as a therapeutic intervention, either in human or animal models, in the context of neurodegenerative diseases; (ii) the studies were required to address one or more of the pathologies examined in this article, and the pathologies included, but were not limited to, neurodegeneration, Alzheimer’s disease, Parkinson’s disease, and oxidative stress; (iii) the full text was accessible online; and (iv) the article was written in English. Results: The data suggest that these probiotics have neuroprotective effects that may delay disease progression. Conclusions: This study provides updated insights into the use of these Bifidobacterium strains in neurodegenerative diseases like AD and PD, with the main limitation being the limited number of clinical trials available.

Background/Objectives: Several parameters have been proposed for the objective measurement of the quality of care (QoC) and breaches of care in patients with heart failure (HF). Therefore, the objective of this study was to evaluate the measures of QoC in inpatients with decompensated HF in the cardiology department of a tertiary Venezuelan hospital. Methods: An observational, descriptive, ambispective study was conducted with adults of all genders diagnosed with decompensated HF between 2022 and 2024. Sociodemographic, clinical, and therapeutic variables were assessed, as well as psychobiologic habits, measures of QoC, readmissions, and in-hospital mortality within the first 6 months of care. Results: Among the 131 subjects evaluated, the average age was 63.6 ± 14.1 years, with 58% (n = 76) being male. Among the in-hospital measures of QoC, the most common was the programming for follow-up consultations (98.5%; n = 129), followed by the prescription of beta-blockers (90.1%; n = 118). An upwards trend was also observed in the later years regarding the frequency of left ventricle ejection fraction (LVEF) assessment (p < 0.001), and the use of iSGLT2 (p = 0.03). During follow-up, 36.6% of the patients died within 6 months, with those in NYHA class IV showing a higher probability of death (OR: 3.84; CI95%: 0.89–16.55; 0.04). Conclusions: The in-hospital measures for QoC in this population were similar to those in previous reports, with LVEF assessment and iSGLT2 prescription showing a particularly significant increase in recent years.

Anticipating the timing and location of future emergency calls is crucial for making informed decisions about vehicle location and relocation, ultimately reducing response times and enhancing service quality. A predictive model for EMS (Emergency Medical Services) events is proposed to address this need. The proposed spatiotemporal approach integrates machine learning, signal analysis, and statistical features, capturing geographical, temporal, and event-specific factors. The model identifies patterns associated with the occurrence or absence of emergency calls, using clustering techniques for demand spatial splitting and then training an XGBoost model on the multivariate time series. The model uses signal analysis to extract valuable insights from time-series data, enhancing understanding of temporal patterns, while statistical features enhance predictive capabilities. Principal Component Analysis (PCA) enhances convergence and integrates diverse time series features. As a result, this novel integrated approach improves the estimation of spatiotemporal probabilities of emergency events, effectively addressing data sparsity challenges. This framework adapts effectively, predicting EMS zones and guiding system configuration. The model outperforms a Random Forest trained solely on time-series data, boosting accuracy by up to 26.9 % in Barranquilla's case study zones, with a mean improvement of 16.4 %. Accuracy improvement makes the model helpful in assisting city authorities in ambulance location/relocation and dispatching decisions.

Background Burnout is a prevalent condition in the healthcare sector, and although it has been extensively studied among healthcare professionals, less is known about its impact on non-professional workers, particularly in low-resource settings. This study aimed to test a preliminary predictive model based on basic socioeconomic and sociodemographic determinants to predict symptoms of burnout among support personnel and health services managers in a resource-limited health center. Methods A prospective cross-sectional study was conducted. Using simple random sampling, symptoms of burnout were surveyed among health service managers and support personnel using the Maslach Burnout Inventory (MBI). Statistical analyses included correlation tests and predictive models using random forest models to identify significant associations and cast predictions. Results A total of 76 participants were included. Of these, 34.21% exhibited high levels of emotional exhaustion (EE), 42.11% showed elevated depersonalization (DP), and 7.89% reported low personal accomplishment (PA). Significant negative correlations were observed between household income and the EE and DP dimensions. The predictive models demonstrated acceptable performance in identifying socioeconomic factors associated with burnout, with prediction errors ranging from 7.68% to 20.31%. Conclusions Burnout is common among support personnel and health services managers in resource-limited settings, particularly among those with lower incomes. The findings underscore the importance of implementing policies that address both working conditions and economic well-being to mitigate the risk of burnout. More robust predictive models could serve as a valuable tool for early identification and prevention of burnout in this type of setting.

This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon’s information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector.