Diabetes is a disease that is increasing worldwide, the main causes are: a sedentary lifestyle, aging population and economic factors. On the other hand, in Peru there is also prevalence of diabetes, especially in older adults, due to this problem, in this research work a mobile application is designed to motivate and improve the state of patience with diabetes, this seeks to help these people to improve their quality of life. The Agile Scrum methodology was used for the design because of its constant meetings that better involve the team in the project, in addition to having tools that better organize the information. For the development of the design a survey was conducted to 25 university medical students, thanks to their answers we obtained the requirements of the application, which were taken as a guide to develop its functionality, finally the work includes the design of an interactive mobile application, intuitive and easy to use, in addition to having tools capable of motivating and informing the patient about diabetes.
— Quail farming ranks 3rd at the national level for poultry production and has growth projections of 3.9% in the coming years. In Peru, due to the decreases in temperature and cold in the southern part of the country, it presents a significant problem to the development of quail farms, of which its primary derivative is the egg that provides food contributions to the population and only with the 18.1% cholesterol compared to the eggs of other birds. This study aims to develop an automatic farm system to increase the production in the laying stage of quails in the homes of Arequipa. The control system was developed with the Arduino Nano platform, DHT11, LM35 sensors, actuators such as a 25W bulb, a fan, and a humidifier, which allow continuous temperature and humidity control. In addition, all the variables can be visualized through an LCD screen. A simple 3D model was developed with a capacity of between 10 and 25 quails. In charge of the control processes, the Arduino module reached an error rate of 0.9% in the temperature variable, and the humidity variable does not present an error rate. The total power of the prototype was measured and converted into a monetary value. Average power of 0.02667 kW / hour was obtained, which is reflected in a saving of 82.22% compared to using a 100W bulb. Keywords— Poultry production, farm quail, quail egg, automation, Arduino.
In the world and in Peru, Acute Respiratory Infections are the main cause of death, especially in the most vulnerable population, children under 5 years of age and older adults. Pneumonia is the leading cause of death of children in the world. 60.2% of pneumonia cases affect children under 5 years of age. Thus, prevention and timely treatment of lung diseases are crucial to reduce infant mortality in Peru. Among the main problems associated with this high is percentage the lack of medical professionals and resources, especially in remote areas, such as Puno, Huancavelica and Arequipa, which experience temperatures as low as -20°C during the cold season. This study develops an algorithm based on computational neural networks to differentiate between normal and abnormal lung sounds. The initial base of 917 sounds was used, through a process of data augmentation, this base was increased to 8253 sounds in total, and this process was carried out due to the need of a large number of data for the use of computational neural networks. From each signal, features were extracted using three methods: MFCC, Melspectogram and STFT. Three models were generated, the first one to classify normal and abnormal, which obtained a training Accuracy of 1 and a testing accuracy of 0.998. The second one classifies normal sound, pneumonia and other abnormalities and obtained training Accuracy values of 0.9959 and a testing accuracy of 0.9885. Finally, we classified by specific ailment where we obtained a training Accuracy of 0.9967 and a testing accuracy of 0.9909. This research provides interesting findings about the diagnosis and classification of lung sounds automatically using convolutional neural networks, which is the beginning for the development of a platform to assess the risk of pneumonia in the first moment, thus allowing rapid care and referral that seeks to reduce mortality associated mainly with pneumonia.
Abstract—MAPATÓN 2021 was organized by CONIDA (Peruvian Space Agency) as part of the celebrations for the bicentennial of Peru's independence. MAPATÓN was oriented to disaster management. This work aims to transmit the experience of participation in MAPATON and communicate the results achieved. The authors participated in developing flood simulation models and analyzing SENTINEL-1 satellite images to identify areas affected by floods that occurred in 2017, 2019 and 2020. The results obtained, shown in a story map, show the great utility of this type of analysis for risk management and disaster management. Keywords—MAPATON 2021; natural disasters; Peru; disaster management; Sentinel; satellite images
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