The use of cell phones has increased worldwide in the past few decades, particularly in children and adolescents. Using these electronic devices provides personal benefits. Communicating through cell phones was a very important factor in the socioeconomic progress of developed countries. However, it is beyond doubt that its indiscriminate use can bring up certain psychiatric disorders or cause some disorder in a person, within the phobic group of anxiety disorders called nomophobia; basically associated with anxiety, nervousness, discomfort, and distress when contact with the smartphone is lost, mainly in the youngest users. This research proposal aims to identify symptoms that have not yet been detected by unceasing cell phone use, considering that in Peru there are few studies of human health engineering and the physical mental health. For that reason, we sought to identify the symptomatic factors of nomophobia presented by students at the National University of Engineering and its interference with their academic life. To accomplish this study, we designed a questionnaire according to our reality with the use of focus groups techniques when the test was taken in class. Three symptomatic factors of nomophobia were identified: feelings of anxiety, compulsive smartphone use, and feelings of anxiety and panic. The study included a representative sample of 461 students in different years of study engineering (21% women, 79% men, over 17 years of age). Finally, given the widespread adoption of smartphones and their integration into educational environments, the results of this study can help educators understand students’ inclination to use their smartphones at all times.

The use of cell phones has increased worldwide in the past few decades, particularly in children and adolescents. Using these devices provides personal benefits. Communicating through cell phones was a very important factor in the socioeconomic progress of developed countries. However, it is beyond doubt that its indiscriminate use can accompany certain psychiatric disorders or cause some disorder in a person, within the phobic group of anxiety disorders called nomophobia—associated with anxiety, nervousness, discomfort, and distress when contact with the smartphone is lost, mainly in young users. This research proposal aims to identify symptoms that have not yet been detected by intensive cell phone use, considering that in Peru there are few studies of human health engineering and the physical mental health. Is for this reason that in our study, we sought to identify the symptomatic factors of nomophobia presented by students at the National University of Engineering and its interference with their academic life. To accomplish this study, we designed a questionnaire according to our reality with the use of focus groups techniques when the test was taken in class. Three symptomatic factors of nomophobia were identified: feelings of anxiety, compulsive smartphone use, and feelings of anxiety and panic. The study included a representative sample of 461 students of different years of study engineering (21\% women, 79\% men, over 17 years of age). Finally, given the widespread adoption of smartphones and their integration into educational environments, the results of this study can help educators understand students’ inclination to use their smartphones at all times.

This study presents a novel approach that combines agricultural census data with remotely sensed time series to develop accurate predictive models for paddy rice yield across the different regions of Peru. By leveraging sparse regression and Elastic-Net regularization techniques, the study uncovers causal relationships between key remotely sensed variables such as Normalized Difference Vegetation Index (NDVI), precipitation (PREC), temperature (TEMP), and agricultural yield. To further enhance prediction accuracy, first- and second-order dynamic transformations (velocity and acceleration) of these variables were applied to capture non-linear patterns and lagged effects on yield. The findings demonstrate improved predictive performance when integrating regularization techniques with climatic and geospatial variables, allowing for more accurate forecasts of yield variability. The results confirm the presence of causal relationships in the Granger sense, underscoring the value of this methodology to strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.

This study introduces a novel approach that integrates agricultural census data with remotely sensed time series to develop precise predictive models for paddy rice yield across various regions of Peru. By utilizing sparse regression and Elastic-Net regularization techniques, the study identifies causal relationships between key remotely sensed variables-such as NDVI, precipitation, and temperature-and agricultural yield. To further enhance prediction accuracy, the first- and second-order dynamic transformations (velocity and acceleration) of these variables are applied, capturing non-linear patterns and delayed effects on yield. The findings highlight the improved predictive performance when combining regularization techniques with climatic and geospatial variables, enabling more precise forecasts of yield variability. The results confirm the existence of causal relationships in the Granger sense, emphasizing the value of this methodology for strategic agricultural management. This contributes to more efficient and sustainable production in paddy rice cultivation.