A estratificação e o manejo adequado da dor: Stratification and proper pain management

A dor é um potencial de risco para a saúde pública, esta se caracteriza pela experiência multidimensional associada a aspectos físicos e emocionais. A dor alerta o perigo e evita danos orgânicos, mas quando está impossibilita atividades diárias e impacta a qualidade de vida do paciente é classificada como patológica e urge por interferência médica. O seguinte artigo objetivou descrever através da revisão narrativa de literatura os aspectos referentes da dor e o seu manejo adequado. A dor é um amplo complexo que possui a classificação conforme a localização, tipo, intensidade, periodicidade. As categorias referentes a dor são nociceptivas, neuropática, psicogênica. Ademais, as síndromes dolorosas são diversas e podem acometer qualquer sistema do corpo. Ressaltando ser essencial a categorização do máximo possível de informações para conduzir adequadamente ao tratamento destas enfermidades.&nbsp

Does the Soil Tillage Affect the Quality of the Peanut Picker?

Machine harvesting is an essential step of crop production, considering a dynamic operation, and is subject to losses due to several factors that affect its quality. The objective of this study was to evaluate the quality of mechanized peanut pickers in the three soil tillage operations using Statistical Quality Control (SQC) tools. We conducted the experiments in a peanut field located at 21°20′23″ S and 47°54′06″ W of Brazilian peanut farmers. We used Statistic Control Quality (SQC) experimental design to monitor peanut losses during machine harvesting. The treatments evaluated were three soil tillage operations: conventional (CT), rotary tillers (RT), and hoe (RH). The quality indicators were collected inside the picker’s bulk tank. Statistical analyses used were descriptive statistics and SQC tools (run charts, control charts, and the Ishikawa diagram). The process was considered stable for indicators: whole pods (CT, RT, and RH), broken pods (CT, RT, and RH), and hatched pods (CT, RT, and RH), while the other indicators showed points that were out of control. With the application of SQC tools, it was possible to identify the factors that caused the increase of variability in peanut harvesting, listing the points to be improved to support decision-making, always aiming to increase this operation’s quality

Application of Lactobacillus paracasei LPC02 and lactulose as a potential symbiotic system in the manufacture of dry-fermented sausage

The purpose of this study was to evaluate how the incorporation of the probiotic culture Lactobacillus paracasei and the prebiotic lactulose in dry-fermented sausage affects the technological, microbiological and sensorial characteristics of the products. Four formulations were analyzed: CONT (without prebiotic or probiotic); PREB (with 2% lactulose); PROB (with 108 CFU/g of L. paracasei); and SYMB (with 2% lactulose and 108 CFU/g of L. paracasei). The functional ingredients did not affect (P > 0.05) the water activity, pH, moisture, acidity and lactic acid bacteria counts during ripening process. Lower (P  0.05) by the treatments. A mean concentration of 2.16 ± 0.39% of lactulose was observed in PREB and SYMB sausages and a count of 7.59 ± 0.37 log CFU/g of L. paracasei was observe in PROB and SYMB sausages. The incorporation of lactulose and L. paracasei into this traditional meat product resulted in a potential probiotic and symbiotic salami, adding additional nutritional benefits for the consumer

Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=