Magnesium alloys stand as promising candidates for orthopedic implants due to their biodegradability, which eliminates the need for secondary removal surgery and potentially reduces hospital care costs. However, the biodegradation of magnesium alloys AZ31 and ZK60A in vivo may occur before bone implants stabilize. Therefore, the objective of this study was to enhance the corrosion resistance and cytocompatibility of two magnesium alloys through coating treatments. Specifically, both alloys, AZ31 and ZK60A, were exposed to surface modification by plasma electrolytic oxidation (PEO) in a sodium metasilicate and potassium hydroxide-based electrolyte. Subsequently, polylactic acid (PLA) was applied using a dip-coating method. Comprehensive physical and chemical characterizations of the materials were conducted. The coatings exhibited a thickness of approximately 3.7 mu m with pore sizes ranging from 0.8 to 1.4 mu m. The PEO-PLA coatings significatively reduced the degradation rate. In particular, PEO-AZ31and ZK60-PEO exhibited a 93 % and 95 % reduction, respectively, in hydrogen evolution compared to bare metals. In addition, the AZ31 coating demonstrated superior corrosion resistance in a simulated biological fluid (SBF). Moreover, the coated ZK60 and AZ31 alloys displayed enhanced cell viability when compared to the uncoated alloys. The AZ31-PEO-PLA and ZK60-PEO-PLA samples exhibited mitochondrial activity greater than 80 % at dilutions greater than 1:2. As a result, PEO-PLA films were proposed as a physical barrier to corrosion.
IntroductionUniversity dropout remains a persistent challenge in higher education, reflecting structural tensions between inclusivity, student retention, and educational equity. Rather than approaching dropout as a simple aggregation of causes, this study adopts a comprehensive perspective that integrates academic, economic, and psychosocial dimensions, emphasizing their interaction with institutional contexts and responses.MethodsA systematic literature review was conducted following the PRISMA 2020 guidelines. Nineteen empirical studies were selected and analyzed to identify the main determinants of university dropout, as well as the institutional strategies and theoretical models used to understand and prevent student attrition. Data were synthesized through a comparative and thematic analysis, focusing on the frequency and articulation of determinants across studies.ResultsThe analysis indicates that motivation (73.7%), academic performance (57.9%), and financial hardship (31.6%) are the most recurrent determinants of dropout. Psychosocial factors, particularly emotional well-being and social integration, also show a substantial influence on student retention. In terms of institutional responses, predictive analytics, early warning systems, and comprehensive student support programs emerge as the most effective strategies for identifying at-risk students and reducing dropout rates.DiscussionCompared with prior reviews, the findings reveal limited theoretical integration and methodological consistency, as most studies focus on isolated variables without adequately linking them to broader institutional frameworks. The evidence highlights the need for multidimensional and theoretically grounded approaches that connect academic, economic, and psychosocial factors with coordinated institutional action. Such frameworks are essential for strengthening student retention strategies and advancing equity in higher education.
This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times.
One of the relevant underlying drivers of land use and cover (LUC) allocation decisions is governance. It, however, is often equated solely with the government and its formal policies, neglecting the role that other actors and regulations play in it. To address this gap, this study aims to examine the impact of the governance structure on farmers’ LUC decisions, particularly in the basin of the Grande and Chico rivers in Colombia. To that end, governance is conceptualized based on three functions that are part of adaptive co-management. For data collection, we administered a questionnaire to 73 farmers. In addition, we estimated a Seemingly Unrelated Regression (SUR) model, with variables representing the characteristics of farmers and the governance system. According to the results, variables associated with all three functions have an impact on decision-making, with the actors and institutions promoting production projects having the greatest influence and favoring pasture. Using the model proposed in this study, we recommend exploring the LUC trajectories that result from different configurations of the governance structure.
In this paper, the optimal operation of Battery Energy Storage Systems (BESS) in AC microgrids is addressed through coordinated active and reactive power management under both grid-connected and standalone modes, aiming to minimize energy losses and CO_2 emissions. The proposed framework integrates a parallel Crow Search Algorithm (PCSA) with the Successive Approximation power flow method, providing a robust and computationally efficient solution. Its performance is evaluated on a 33-bus AC microgrid representative of Medellín City, using real demand and generation profiles. A parallel JAYA algorithm is also implemented for benchmarking, with 100 independent runs in both operating modes. The main contributions include: (i) a unified optimization framework for simultaneous active and reactive power control of BESS; (ii) validation of PCSA as a fast and reliable alternative to conventional convex optimizers; and (iii) evidence of BESS benefits in reducing losses, mitigating emissions, and enhancing voltage stability. Results confirm that the proposed methodology consistently outperforms the benchmark with superior accuracy and significantly lower processing times across all scenarios.
