Actividad física y la formación integral en estudiantes futbolistas de la Institución Educativa San Pedro-Distrito de la Molina 2018

La presente investigación tiene como determinar la relación que existe entre la actividad física y la formación integral en estudiantes futbolistas de la Institución Educativa San Pedro-Distrito de la Molina 2018. La metodología es de enfoque cuantitativo, de tipo básico, con diseño descriptivo correlacional y de método hipotético deductivo. La población está conformada por todos los estudiantes, que practican el futbol, de la Institución Educativa San Pedro, del distrito de La Molina, quienes prosiguieron estudios en el año lectivo 2018. La muestra es de carácter no probabilístico, intencionado y censal y la muestra estará conformada por 32 estudiantes futbolistas de la Institución Educativa San Pedro, del distrito de La Molina, quienes prosiguieron estudios en el año lectivo 2018. La técnica a utilizar en el presente estudio es la encuesta (cuestionario). Los resultados alcanzados por medio de rho Spearman = 0,8721 y una p= 0,000 menor al nivel de 0,05 estadísticamente significativa, la actividad física está relacionada con la formación integral, aceptándose la hipótesis alterna y rechazándose la hipótesis nula confirmando que: Existe relación significativa entre la actividad física y la formación integral en estudiantes futbolistas de la Institución Educativa San Pedro-Distrito de la Molina 2018.The present research aims to determine the relationship between physical activity and comprehensive training in soccer students of the San Pedro Educational Institution-La Molina District 2018. The methodology is of a quantitative approach, of a basic type, with a correlational descriptive design and hypothetical deductive method. The population is made up of all the students, who play soccer, from the San Pedro Educational Institution, in the La Molina district, who continued their studies in the 2018 school year. The sample is non-probabilistic, intentional and census and the sample It will be made up of 32 soccer students from the San Pedro Educational Institution, in the La Molina district, who continued their studies in the 2018 school year. The technique to be used in this study is the survey (questionnaire). The results achieved by means of rho Spearman = 0.8721 and a p = 0.000 less than the level of 0.05 statistically significant, physical activity is related to comprehensive training, accepting the alternative hypothesis and rejecting the null hypothesis confirming that: There is significant relationship between physical activity and comprehensive training in soccer students of the San Pedro-Distrito de la Molina Educational Institution 2018

Participatory rainfall monitoring: strengthening hydrometeorological risk management and community resilience in Peru

Heavy rainfall, floods and debris flow on the Rimac river watershed are recurring events that impact Peruvian people in vulnerable situations.There are few historical records, in terms of hydrometeorological variables, with sufficient temporal and spatial accuracy. As a result, Early Warning Systems (EWS) efficiency, dealing with these hazards, is critically limited. In order to tackle this challenge, among other objectives, the Participatory Monitoring Network (Red de Monitoreo Participativo or Red MoP, in spanish) was formed: an alternative monitoring system supported by voluntary community collaboration of local population under a citizen science approach. This network collects and communicates data captured with standardized manual rain gauges (< 3USD). So far, it covers districts in the east metropolitan area of the capital city of Lima, on dense peri-urban areas, districts on the upper Rimac watershed on rural towns, and expanding to other upper watersheds as well. Initially led by Practical Action as part of the Zurich Flood Resilience Alliance, it is now also supported by SENAMHI (National Meteorological and Hydrological Service) and INICTEL-UNI (National Telecommunications Research and Training Institute), as an activity of the National EWS Network (RNAT)

Cost Estimation for Underwater Tunnel Projects based on Uncertainty and Risk Analysis

Cost estimation is a complex and critical process, particularly during pre-investment phases of large undersea tunnel projects, where major decisions must be made under a high level of uncertainty. The high level of uncertainty regarding geological and construction performance aspects, as well as the occurrence of undesirable risk events may certainly affect the actual execution cost, making cost estimation a difficult task to be performed during the early phases.This work presents a cost estimation model based on uncertainty and risk analysis that may help project organisations to obtain more realistic cost estimates. The specific model was designed for Drill and Blast excavation method, and it is focused on the cost estimation of the tunnelling activities. Through standard project management tools, this model estimates the total tunnelling cost (CTT) as a random function of the normal (CNT) and extraordinary tunnelling cost (CET). The model assumes that normal cost is controlled by geological and construction aspects, while the extraordinary tunnelling cost may be derived for the occurrence of undesirable events. Both are modelled as random processes and integrated in @Risk, which allows performing Monte Carlo Simulations (MCS) and obtain the final cost distributions (PDF).The model was tested in a specific case study, and the results demonstrate the suitability of the model for determine the total tunnelling cost. Even though the model has demonstrated to be valid, the model robustness and accuracy may be improved by more advanced research in areas related to rock support and water inflow control. Finally, the results have confirmed that the integration of stochastic and driver-based and risk management tools may provide a powerful tool to improve the pre investment decision process of undersea tunnel project

Cost Estimation for Underwater Tunnel Projects based on Uncertainty and Risk Analysis

Cost estimation is a complex and critical process, particularly during pre-investment phases of large undersea tunnel projects, where major decisions must be made under a high level of uncertainty. The high level of uncertainty regarding geological and construction performance aspects, as well as the occurrence of undesirable risk events may certainly affect the actual execution cost, making cost estimation a difficult task to be performed during the early phases.This work presents a cost estimation model based on uncertainty and risk analysis that may help project organisations to obtain more realistic cost estimates. The specific model was designed for Drill and Blast excavation method, and it is focused on the cost estimation of the tunnelling activities. Through standard project management tools, this model estimates the total tunnelling cost (CTT) as a random function of the normal (CNT) and extraordinary tunnelling cost (CET). The model assumes that normal cost is controlled by geological and construction aspects, while the extraordinary tunnelling cost may be derived for the occurrence of undesirable events. Both are modelled as random processes and integrated in @Risk, which allows performing Monte Carlo Simulations (MCS) and obtain the final cost distributions (PDF).The model was tested in a specific case study, and the results demonstrate the suitability of the model for determine the total tunnelling cost. Even though the model has demonstrated to be valid, the model robustness and accuracy may be improved by more advanced research in areas related to rock support and water inflow control. Finally, the results have confirmed that the integration of stochastic and driver-based and risk management tools may provide a powerful tool to improve the pre investment decision process of undersea tunnel project

Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin

Flood modeling and forecasting are crucial for managing and preparing for extreme flood events, such as those in the Tropical Andes. In this context, assimilating streamflow data is essential. Data Assimilation (DA) seeks to combine errors between forecasting models and discharge measurements through the updating of model states. This study aims to assess the applicability and performance of streamflow DA in a sub-daily forecasting system of the Peruvian Tropical Andes using the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) algorithms. The study was conducted in a data-sparse Andean basin during the period February–March 2022. For this purpose, the lumped GR4H rainfall–runoff model was run forward with 100 ensemble members in four different DA experiments based on IMERG-E and GSMaP-NRT precipitation sources and assimilated real-time hourly discharges at the basin outlet. Ensemble modeling with EnKF and PF displayed that perturbation introduced by GSMaP-NRT’-driven experiments reduced the model uncertainties more than IMERG-E’ ones, and the reduction in high-flow subestimation was more notable for the GSMaP-NRT’+EnKF configuration. The ensemble forecasting framework from 1 to 24 h proposed here showed that the updating of model states using DA techniques improved the accuracy of streamflow prediction at least during the first 6–8 h on average, especially for the GSMaP-NRT’+EnKF scheme. Finally, this study benchmarks the application of streamflow DA in data-sparse basins in the Tropical Andes and will support the development of more accurate climate services in Peru

gekk0-r

An Adaptable Universal Rainfall Logging and Data Communication System for Raspberry Pi

Universal rainfall loggers gekk0-r

Rainfall monitoring stations are a critical component of operational flood early warning systems (EWS) and climate resilience initiatives, especially in mountainous landscapes where rainfall patterns are complex. Furthermore, in data-scarce regions they are necessary to properly define warning thresholds that might otherwise only be based on hazard assessment models. However, use of traditional proprietary-technology rainfall monitoring stations can be challenging due to operational and resource-related limitations. In this paper, we describe gekk0-r, a data logging and communication system designed to support the use of (potentially any) commercially available rain gauges to operate as a quasi-automatic rainfall station as part of a monitoring network. gekk0-r uses open and free technologies to record without any degradation of data quality. It is controlled by a Raspberry Pi microcomputer with a custom-made printed circuit board (PCB) as a hat for easy connection with the selected rain gauge. It can send data to web platforms through the cellular network or any other available internet connection. To demonstrate its performance, we conducted a calibration experiment in a laboratory setting which showed that the combination of the gekk0-r logger with a commercial rain gauge achieved World Meteorological Organization (WMO) standards for rainfall intensity measurements