Evaluation the effects of temperature and packaging conditions on the quality of button mushroom during storage using e-nose system.

In this study, the effects of different packaging conditions on the quality of button mushrooms and some its chemical properties (pH and TSS) using an e-nose system equipped with ten sensors have been investigated. The button mushrooms were packaged using two types of films in two atmospheric modes. They were stored at 25 and 4 °C for ten days. During the storage, they were tested every other day. The results showed a mild increase in pH levels in all treatments during the ten days. Changes in TSS in ordinary polyethylene film-packed samples and ambient atmosphere at room temperature showed the highest value. Moreover, investigating the sensor response during the storage period showed that the most significant changes in the response of all sensors occurred in samples packed with polyethylene film and ambient atmosphere at 25 °C. Also, the scoring diagram of principal component analysis (PCA) showed that the completely distinct groups were detectable at two temperatures, two packaging films, and two different packaging atmosphere. At the same time, there was an overlap between the groups in six storage times. The support vector machine (SVM-C) and artificial neural network (ANN)classified the samples with 81 and 66% accuracy in six different storage times. The values of R 2 for predicting TSS and pH using PLS (partial least squares regression), MLR (multiple linear regression) and PCR (principal component regression) ranged between 51 and 68 and 54-59%, respectively, however prediction of TSS had a higher accuracy.

Development of an android app for estimating the water quality parameters in fish pond.

In this research, a new android app for smartphones for estimating some water quality parameters in carp fish ponds such as pH, electrical conductivity (EC), total dissolved solids (TDS), and turbidity is presented. Contact imaging was used to acquire images from the samples. To estimate pH, EC, TDS, and turbidity values, 12 features were extracted from each image. Features were used as input to the artificial neural network models. The performance of the models was evaluated by the R2 and RMSE parameters. Based on the results, the network with a structure of 12-15-4 was selected as the best model. The values of R2 for estimating pH, TDS, EC, and turbidity were 0.913, 0.993, 0.994, and 0.958, respectively, while the corresponding values for the RMSE were 0.054, 1.835, 3.766, and 0.262, respectively. Finally, this model was successfully implemented on an app named WaterApp on the android smartphone. For testing the app on the smartphone, the performance of the model was evaluated again using new images. According to the results, the R2 values for validation data by the developed WaterApp for pH, EC, TDS, and turbidity were 0.88, 0.913, 0.884, and 0.944, respectively.

Analysis of noise pollution emitted by stationary MF285 tractor using different mixtures of biodiesel, bioethanol, and diesel through artificial intelligence.

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in a four-cylinder and four-stroke engine of MF285 tractor was studied. Further, the noise pollution was measured from two positions, the driver and bystander, at 1000, 1600, and 2000 revolutions, and ten different fuel levels resulting from different compositions of biodiesel, bioethanol, and diesel fuels. For data analysis, adaptive network-based fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) were applied. Comparing the means of noise pollution at different levels demonstrated that the B25E6D69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 rpm, and with the increase of engine speed, the noise pollution intensified. The models laid by the RSM were better than other.

Different blends of biodiesel, bioethanol, diesel and noise pollution emitted by stationary and moving MF285 tractor.

Given the significance of the relationships between human beings, environment, machines and ergonomics as well as the necessity of using renewable fuels, the present study aimed to investigate the effects of different blends of biodiesel, bioethanol and diesel on noise pollution emitted by a MF285 tractor in stationary and moving modes by the aid of statistical methods. In this respect, the emitted noise was measured using the noise dosimeters and sound level meters in the driver and the bystander's positions, at 1000, 1600 and 2000 RPM in both stationary and moving modes. Then, nine fuel blends of biodiesel, bioethanol and diesel with different volumetric percentages as well as pure diesel were studied. To study the effects of key factors on noise pollution, the factorial experiment was conducted in the form of a completely randomized design, followed by the application of the SPSS Statistics Software Version 19.0. The fuel type nearly affected the noise pollution at the level of 5%, and other factors such as engine rotational speed and the fuel type-engine rotational speed interaction influenced it at the level of 1%. In both the driver and bystander's positions, the minimum and maximum noise pollution occurred at 1000 and 2000 RPM, respectively. The effects of gears along with their twofold and threefold interaction with other factors were not significant. Finally, the results of the present study demonstrated that the B25E4D71 fuel, composed of 25% biodiesel and 4% bioethanol, had the lowest noise pollution.