An important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave heightH_s, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the totalH_sis inappropriate for EVA, leading to potential over- or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for projecting extreme values.

Ecuador’s energy system, heavily reliant on hydropower, is increasingly exposed to climate-related disruptions. The 2023–2024 crisis triggered by a historic drought revealed critical structural weaknesses. During this period, the government implemented scheduled electricity rationing of up to 14 h per day in major cities and industrial zones. The blackouts led to cascading economic and social impacts, with an estimated economic toll of USD 2 billion from the energy crisis, equivalent to 2% of Ecuadorian GDP. Hence, this study aims to apply the LEAP model to quantitatively simulate demand, supply, and policy outcomes under two long-term scenarios through 2050. The findings underscore the urgent need for energy diversification, efficiency improvements, and decarbonization of the transport sector to enhance system resilience. The results offer actionable insights for building a more resilient and low-carbon energy future in Ecuador and in similar hydropower-dependent economies. Additionally, the analysis highlights that institutional reforms, technological modernization, and energy integration are essential to mitigating long-term climate risks. By incorporating scenario-based projections, this study provides evidence to guide public policy and investment decisions. These findings contribute to the broader discourse on sustainable energy transitions in vulnerable economies under climate stress.

Introduction: Differential equations' complexity and abstractness make teaching challenging in university settings. The use of educational software emerges as a valuable pedagogical tool to enhance the learning process.Objective: This research aimed to design and validate a questionnaire to know university students' perception of learning differential equations through software.Method: This mixed-methods research employed descriptive-explanatory methods to investigate the topic. A comprehensive literature review was conducted, key aspects of the study topic were identified, the target population and sample were determined, experts were selected, an initial 27-question questionnaire was developed, content validity and reliability were assessed, and data were analyzed. Content validity was established using the Delphi method with eight mathematics experts and validated further using Aiken's V coefficient. Reliability was assessed using Cronbach's alpha and Omega McDonald's coefficients, employing Statistical Package for Social Sciences (SPSS) statistical software.Results: The experts assessed the adequacy, relevance, and clarity of the questions, ultimately validating 17 questions with an Aiken's V greater than 0.80 (with a 95% confidence interval). For reliability, an Alpha of 0.799 and an Omega of 0.798 were obtained, confirming that the 17 questions are well-correlated and consistently measure the same construct.Conclusions: A 17-item questionnaire was developed with validated and consistent questions to gather reliable and accurate information on the perception of learning differential equations using educational software.

The global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Although hydropower is generally considered environmentally friendly, it can cause environmental and social impacts. As the biggest and most representative hydropower project in Ecuador, the Coca Codo Sinclair hydropower project (CCSHP) provides a relevant case of water use competition between local communities and the country’s development. In this study, perspectives of four communities near the CCSHP were analyzed through a survey with 183 responses collected in 52 days through door-to-door household visits in two upstream and two downstream towns. The analysis highlights that limited community participation in project design and insufficient communication strategies have undermined public acceptance, despite government promotion of its national benefits. Survey results reveal that 79% of respondents expressed negative perceptions, primarily about environmental change, displacement, and lack of compensation, while only 15% expressed positive views. It is important to note that the communities had no role in selecting the project location, and their involvement was limited, particularly regarding transportation, environmental changes, and the loss of local species. These findings suggest that project managers should strengthen dialogue with local communities and design participatory mechanisms that can improve trust and long-term project acceptance.

In both turbulent and laminar pipe flows, we were able to accurately forecast the beginning range of the convective thermal transferring coefficients of Al2O3 magnetized nanofluids using machine learning approaches. The simulations utilized two machine learning techniques: Radial Basis Function-Backpropagation (RB) and Multiple Linear Regression Analysis (MLR). First, we used MLR to fit the polynomial equation. Afterwards, grid search cross-validation was employed to determine the optimal RB model with six hidden layer neurons. To evaluate the RB model, we compared numerical patterns of the parameters used to measure accuracy. The regression coefficient and mean square error (MSE) were the most commonly utilized parameters in Reynolds number (RN)-mass percentage simulations (R2). In the case of a laminar flow, these numbers were found to be 0.99994 and 0.34, respectively. Additionally, the results for laminar flow conditions using Reynolds number-magnetic field strength simplification were ideal, with an MSE of 3.85 and an R2 value of 0.99993. By comparing the predicted values with the experimental results visually using Three-Dimensional smoothed surface plots, we were able to further prove that the model was valid and accurate. These revolutionary findings could spark new developments and encourage substantial improvements in nanotechnology and machine intelligence. These findings are an important asset for driving future Research and Development, which in turn makes significant contributions to the ever-expanding frontiers of these innovative fields.

La investigación analiza el impacto del clima organizacional en la prestación de servicios de la administración pública, identificando los factores críticos que contribuyen a mejorar la eficiencia operativa y la satisfacción de los usuarios. La metodología se basa en modelos de ecuaciones estructurales para evaluar la relación entre el clima organizacional y el desempeño institucional, considerando diversas dimensiones. Se analizaron la fiabilidad y validez de los constructos mediante cargas y pesos factoriales, fiabilidad compuesta (FC) y varianza media extraída (AVE). Los resultados evidencian que todos los constructos presentan valores aceptables de fiabilidad y validez (AVE > 0.5, FC > 0.7), con una fuerte correlación entre el clima organizacional y el desempeño (0.721), lo que sugiere que un mejor ambiente organizacional está asociado con un mayor rendimiento.