Showing 4 results for Ahrari
Volume 2, Issue 2 (6-2016)
Abstract
The fauna of Onychiurinae were investigated in different parts of Kermanshah province during 2013–2014. Specimens were collected from both leaf litter and surface layer of the soil. Totally four species from three genera were found. All of them are new for Kermanshah province and Protaphorura levantina (Christiansen, 1956), Heteraphorura cf. japonica (Yosii, 1967) and Vibronychiurus archivari (Christiansen, 1956) were not previously recorded in Iran; it is also the first time that the genus of Vibronychiurus Pomorski, 1998, is collected and reported for the country.
Volume 5, Issue 1 (3-2019)
Abstract
In this study, the list of Collembola from west of Iran and collection information such as study site, habitats, e.g. soils, grasslands, leaf-litter, vegetation, snowfields, etc., and collectors has been presented between years 2013-3018. Moreover some samples were collected and the identified by the author during 2017-2018. In the last case, Collembolans were extracted using the respirator or Berlese funnels. When greater clearing was required, Nesbitt's solution (40g chloral hydrate, 25 mL distilled water, and 2.5 mL concentrated acetic acid) was used for heavily pigmented specimens. In the first Iranian checklist of Collembola published in 2013, 17 species, 15 genera and seven families were introduced from west of Iran. After review and analysis of the available literature and the examination of samples collected by the author from west of Iran, were listed 88 species, belonging to 42 genera, 16 families and four orders. The genus Entomobrya and Folsomia are having highest number of species for analyzed region. An up-to-date systematic checklist of Collembolans has been provided.
Volume 19, Issue 132 ( February 2023)
Abstract
Bread is one of the most important foods based on the wheat that plays a vital role in feeding the people in the world as an excellent source of energy and protein. Therefore, the development of enriched bread is one of the effective ways in providing some of the necessary nutrients of the people. this study aimed to examine the effect of sugar beet pulp and sourdough on physicochemical, textural and organoleptic properties of baguette bread. To do this, the pressed sugar beet pulp in three levels of 2, 6, and 10% and sourdough in three levels 5, 10 and 15% were added based on the weight of flour in the dough preparation stage,respectively. Color, texture, moisture, water activity, specific volume and sensory evaluation tests were performed after preparing the samples. The results showed that increasing the amount of sugar beet pulp and sourdough in baguette formulation made moisture content, specific volume and overall acceptance increase and a* decreased. Also the effect of sugar beet pulp on b* was less than sourdough and time and sourdough had more effect on flavor than sugar beet pulp and time factors. Therefore, the sample containing 6% sugar beet pulp and 10% dough as the optimal sample improved the quality characteristics of baguette bread.
Nafiseh Behradmehr, Mehdi Ahrari,
Volume 21, Issue 3 (7-2014)
Abstract
In general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. It is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. One could argue that these random changes act like noise which effects the deterministic variations in prices. In this paper, we employ the wavelet transform as a tool for smoothing and minimizing the noise presented in crude oil prices, and then investigate the effect of wavelet smoothing on oil price forecasting while using the GMDH neural network as the forecasting model. Furthermore, the Generalized Auto-Regressive Conditional Hetroscedasticity model is used for capturing time varying variance of crude oil price. In order to evaluate the proposed hybrid model, we employ crude oil spot price of New York and Los Angles markets. Results reveal that the prediction performance improves by more than 40% when the effect of noise is minimized and variance is captured by Auto-Regressive Conditional Hetroscedasticity model.
