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Showing 7 results for Nahvi
Farshad Darvishi, Iraj Nahvi, Hamid Zarkesh,
Volume 2, Issue 1 (9-2011)
Volume 2, Issue 1 (9-2011)
Abstract
Lipase is used in the production of detergents, cosmetics, pharmaceuticals, flavour enhancers and foods. The lipase of yeast Yarrowia lipolytica can be used for production of important class of chemical intermediates in the pharmaceutical industry. Lipase production depends on media composition and environmental conditions. Y. lipolytica DSM 3286 strain was cultured on media containing different organic and inorganic nitrogen sources. Lipase production was investigated by measuring biomass and lipase activity was detected by ρ-nitrophenyl laurate (PNPL) spectrophotometric assay method at various times within a period of 7 days. In this study, the effect of different nitrogen sources was investigated on Y. lipolytica DSM 3286 lipase production. The maximal lipase production (34.7 U/ml after 48 h) was detected in medium containing yeast extract as nitrogen source. The optimum temperature and pH of the enzyme activity were 37 °C and 7, respectively. The final goal of this study is to develop and optimize lipase production by Y. lipolytica for use in pharmaceutical industry.
Volume 12, Issue 47 (7-2015)
Abstract
Xylitol is a naturally five – carbon polyol with a high sweetening power. Owing to its physicochemical and technological properties, make it of high value to pharmaceutical, food and chemical industries. The biotechnological method of producing xylitol by microorganisms has been studied as an alternative to the chemical method. This method is of interest because it requires little energy and is very specific. Among the microorganisms, yeasts are considered as the best xylitol producer. In this study xylitol was produced by Rhodotorola mucilaginosa that isolated from leaf of Benjamina. The produced xylitol by R. mucilaginosa was determined and measured by thin layer chromatography, kit and colorimetric methods. This strain produced 6.42 g l-1 xylitol after 48 hours in medium congaing of 40 g l-1 xylose. Consequences of increasing the initial xylose concentration from 60 to 140 g l-1, the final xylitol concentration and yield were also increased. Maximum concentration of produced xylitol by R. mucilaginosa was 49.28 g l-1 (yield of 0.59 g g-1) at 140 g l-1 initial xylose concentration. However further increasing of xylose concentration to 160 g l-1, led to a drastic decrease in xylitol production and yield.
Volume 14, Issue 9 (12-2014)
Abstract
Drowsy driving is a main cause of severe accidents. Drowsiness is responsible for 30% to 37% of fatal road accident in Iran. In this paper, driver drowsiness is detected based on features related to the steering wheel angle and the lateral position of the vehicle. Data from the vehicle and the virtual road are used to extract drowsiness features. Experimental results using a driving simulator are presented. Participants were 21 to 28 year-old males with a high tendency to sleep (Epworth Sleepiness Scale≥10). The subjects had to drive a lane keeping scenario on a long and monotonous virtual road in both drowsy and alert states. The drowsiness criteria are validated with Karolinska Sleepiness Scale (KSS) and video rating based on KSS measurements. The results illustrate that the phase diagram of the steering wheel angle (Ellipse criterion), the standard deviation of the steering wheel angle, and the mean and the standard deviation of the lateral position of the vehicle are highly correlated with drowsiness. The accuracy of the diagnosis was 77% for the Ellipse criterion, 76% for the standard deviation of the steering wheel angle, 67% for the standard deviation of the lateral position, and 65% for the mean value of the lateral position.
Optimization of microbial production of essential fatty acids by using of Mucor rouxii in oil wastes
Volume 14, Issue 62 (4-2017)
Abstract
Essential fatty acids (omega3/6) which are precursors of prostaglandins and leukotrienes and play an important rules in treatment of diseases. These fatty acids cannot be synthesized by human and should be obtained by nutritional dietary. Gamma linolenic acid (GLA) is one of the omega 6 fatty acids that is useful in cardiovascular and cancer diseases. Fungi especially zygomycetes are known as the best lipid producers containing essential fatty acids. The purpose of this research was using of several oil wastes as the renewable and cheap substrates to production of essential fatty acids by zygomycete fungi Mucor rouxii DSM1194. Five oil wastes were studied and the production of lipids, biomass, essential fatty acids like GLA, linoleate (omega6) and alpha linolenate (omega3) were determined. Since production of GLA was considerable, it was optimized by hierarchical experimental design, including a half fraction factorial and then design following by the response surface method (RSM). Yeast extract, ammonium sulphate and carbon source (oil wastes) were the significant factors on optimization of GLA production. Results showed after 72h growth of fungi in 28° C on R1 oil waste (obtained from restaurant), 56.4 mg/l GLA were produced which increased to 82.23 mg/l after optimization. The interaction of carbon and nitrogen sources was significant while yeast extract and ammonium sulphate didn`t have any interaction effects.
Volume 15, Issue 7 (9-2015)
Abstract
Nanotechnology has great potential applications in many fields such as chemistry, physics, material science, etc. In the recent years, due to the extraordinary properties of nanostructures, they are used in a wide range of nanodevices such as nanosensors, nanoactuators and nanocomposites. The effect of size on mechanical behavior of nanostructures whose size is comparable with molecule distances is important. Considering that classical continuum models are free scale and cannot capture the size effects, nonlocal continuum models are used for the analysis of mechanical properties of nanostructures. The nonlocal elasticity theory assumes that the stress at a reference point in the body depends not only on strain at that specific point, but also it depends on the strain at all other points. So, this theory contains long range interaction between atoms and internal scale length. This theory is capable to predict behavior of nanostructures without solving complicated equations. In the present work, the effect of considering small scale on the buckling of nanorings is studied. Governing equations are derived based on the nonlocal elasticity theory using the virtual displacement method and Hamilton's principle. Shear effect is achieved by Timoshenko beam theory. The governing equations are solved analytically. The effects of nonlocal parameter, radius, radius to thickness ratio and buckling mode number on the buckling loads of the nanorings are investigated.
Volume 19, Issue 1 (January 2019)
Abstract
Regarding the growing development of traffic perception systems, advanced driver assistance systems play a significant role in improving automotive safety. They should be able to guide intelligent vehicles through complicated driving scenarios. The complex nature of the driving process results in complicated control engineering methods. Modeling driver behavior based on psychological concepts would simplify the driving logic and human-machine interaction. In this research, psychological concepts and tire force limitations are formulated based on vehicle kinematics and kinetics as a function of speed and curvature. A multi-objective cost function is defined based on psychological concepts and tire force limits. The speed and the curvature, at which the cost function is minimal, are selected as the decided values. Saturated proportional controllers set the vehicle speed and path curvature on the decided values by adjusting the steering angle of the front wheels, accelerator pedal position, and brake force. The model performance is evaluated by a complicated driving scenario, which includes travelling in the same and opposite directions, presence of obstacles with different sizes and speeds, and high curvature paths. The model avoids face-to-face collisions with a time-to-collision close to 0.72 s. Also, it can avoid obstacles in tight spaces as narrow as 30 cm. Simulation results indicate that the proposed driver model performs safely at the presence of moving obstacles and tight spaces.
Volume 25, Issue 4 (Winter 2021)
Abstract
Introduction
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.
Methodology
The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.
Results and discussion
In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of this study is a homogeneous area of agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.
Conclusion
This study can be concluded as follows:
- All downscaling algorithms used in this research had an acceptable performance in simulating Landsat bands.
- Among the reflectance band-related downscaling methods, STARFM had the best performance, followed by ESTARFM and Regression, respectively.
- Among the thermal band-related downscaling methods, the SADFAT algorithm performed best, followed by Cokriging and Regression.
- The use of STARFM algorithm for reflectance bands and SADFAT algorithm for thermal bands in homogeneous areas is recommended.
- The difference between the different combinations of methods for estimating actual evapotranspiration is small.
Keywords: Downscaling; Landsat-8; MODIS; Evapotranspiration; Cokriging; STARFM
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