Tropical cyclones are one of the most important causes of disasters in Central America. Using historical (1970–2010) tracks of cyclones in the Caribbean and Pacific basin, we identify critical path locations where these low-pressure systems cause the highest number of floods in a set of 88 precipitation stations in the region. Results show that tropical cyclones from the Caribbean and Pacific basin produce a large number of indirect impacts on the Pacific slope of the Central American isthmus. Although the direct impact of a tropical cyclone usually results in devastation in the affected region, the indirect effects are more common and sometimes equally severe. In fact, the storm does not need to be an intense hurricane to cause considerable impacts and damage. The location of even a lower intensity storm in critical positions of the oceanic basin can result in destructive indirect impacts in Central America. The identification of critical positions can be used for emergency agencies in the region to issue alerts of possible flooding and catastrophic events.

El objetivo fue medir el efecto de un concentrador de flujo tipo wind lens en el caso de flujo turbulento. El estudio se realizó en el túnel de viento de capa límite del Instituto de Investigación en Mecánica Aplicada de la Universidad de Kyushu, Japón, entre marzo y mayo de 2019. Se utilizó un anemómetro de hilo caliente con un mecanismo móvil para realizar tres pruebas. Primero se mide la velocidad en el eje del túnel, sin la rejilla de turbulencia instalada. En segundo lugar, se coloca la rejilla y se mide la intensidad de turbulencia en el eje del túnel de viento. En tercer lugar, se determina el efecto del WL en la velocidad del viento incidente, a diferentes niveles de intensidad de turbulencia. La velocidad del viento en el túnel sin la rejilla de turbulencia es casi constante, alrededor de 9.6 m/s. Al colocar la rejilla se registró en el eje del túnel de viento una intensidad de turbulencia decreciente, desde un valor de 28.6 % a 500 mm de la rejilla, hasta un 5 % de intensidad de turbulencia a 3 100 mm de la rejilla. Cuando se midió el efecto del wind lens en flujo turbulento, se obtuvo un aumento del 20 % o más en la velocidad del viento. El wind lens demostró ser adecuado para turbinas eólicas que operan en flujo turbulento, acrecentó la velocidad del viento en todas las pruebas, generando un mayor aumento a mayor intensidad de turbulencia.

Software development can be a time-consuming and costly process that requires a significant amount of effort. Developers are often tasked with completing programming tasks or making modifications to existing code without increasing overall complexity. It is essential for them to understand the dependencies between the program components before implementing any changes. However, as code evolves, it becomes increasingly challenging for project managers to detect indirect coupling links between components. These hidden links can complicate the system, cause inaccurate effort estimates, and compromise the quality of the code. To address these challenges, this study aims to provide a set of measures that leverage measurement theory and hidden links between software components to expand the scope, effectiveness, and utility of accepted software metrics. The research focuses on two primary topics: (1) how indirect coupling measurements can aid developers with maintenance tasks and (2) how indirect coupling metrics can quantify software complexity and size, leveraging weighted differences across techniques. The study presents a comprehensive set of measures designed to assist developers and project managers with project management and maintenance activities. Using the power of indirect coupling measurements, these measures can enhance the quality and efficiency of software development and maintenance processes.

There are few reports in the literature regarding wind speed near the ground. This work presents a model for wind speed from 4 m above the ground, based on year-round measurements in two meteorological towers. Each tower is equipped with anemometers at five heights, as well as thermometers and pressure and relative humidity sensors. The data is processed using Eureqa artificial intelligence software, which determines the functional relationship between variables using an evolutionary search technique called symbolic regression. Using this technique, models are found for each month under study, in which height and temperature are the variables that most affect wind speed. The model that best predicts the measured wind speeds is then selected. A polynomial function directly proportional to height and temperature is identified as the one that provides the best predictions of wind speed on average, within the rough sub-layer. Finally, future work is identified on testing the model at other locations.