مریم سیادت

The aim of this study was to investigate the model of predicting job burnout in nurses of Corona ward based on perceived stress mediated by personality type in nurses serving in the medical wards of patients with Covid 19. The research method was descriptive and structural equation modeling. The statistical population of this study was all nurses working in Imam Khomeini, Sina, Shohada Yaftabad and Atieh hospitals in Tehran in 1399, from which 200 nurses were selected and completed the questionnaires online. The research instruments included Maslash Burnout Scale (1981), Cohen et al. (1983) Perceived Stress Questionnaire and Denolt-D (2000) Personality Type Scale. The results of data analysis showed that the fit indices of the nasal model are in a favorable condition. Perceived stress has a significant direct and indirect effect on burnout (p> 0.05). This means that with increasing perceived stress, the level of burnout increases. Personality type D (negative emotions and social inhibition) on the one hand is affected by perceived stress levels and on the other hand affects the burnout of nurses serving in the medical wards of patients with Covid 19. Therefore, applying perceived stress reduction strategies can reduce nurses 'burnout by promoting nurses' mental health.

Tomato is the second most important crop in the world, which is often consumed freshly. One of the most importance tomato products is paste that considered being the stuffing ingredient in Iranian foods. Therefore, the safety of tomato paste is so important. The aim of this study was diagnosis adulteration in tomato paste. For this purpose, olfactory machine system based on five gas sensors (TGS2600, TGS2620, MQ3, TGS880, TGS2610) was constructed and its potential was evaluated in determining the different levels of pumpkin adulteration in tomato paste (0, 5, 10, 15 and 20%). Pure tomato paste detection from adulteration samples was based on the samples smell in the head space and the receipt of smell by sensors. The principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) and partial least squares (PLS) were used to classify and analyze the extracted features of the sensor response. Results of PCA and PLS indicated that 99 and 94 percent of data variance was covered by two main components. TGS2610 and MQ-3 sensors and TGS880 sensor had the highest and lowest application in detection of tomato paste adulteration. The accuracy of the classification by the LDA method was 79.7%. The polynomial function with accuracy of 77.78% of the training and 76.66% validation in the C-SVM method and the radial base function with a precision of 98.8% of the training and 88.14% of Validation in the Nu-SVM method had the highest classification accuracy. In total, the olfactory machine system had acceptable performance in separation of different levels of adulteration.