Rain gauge stations that measure fine scale rainfall are mainly limited in number or in the length of the recorded data. Therefore, temporal disaggregation models have been considered because of their ability in generating fine scale data from coarse scale measurements of rainfall. In this study, Microcanonical cascade model, which is based on the scaling properties of rainfall and constant volume of rainfall was used for the disaggregation purposes of rainfall. To this end, a software package was primarily developed based on the existing theory and then applied to rainfall data from two weather stations located in the Zanjan Province. In order to evaluate the performance of the Microcanonical cascade model, comparisons were conducted between the observations and model results using some statistics including percentage of zero-values, volume of individual values, duration of events, volume of events and length of dry periods between events of rainfall. It was concluded that the use of Microcanonical cascade model for disaggregation of daily rainfall to 12, 6, 3 and 1. 5-hour rainfall will bring favorable results.

Amid the previous three decades, the topic of image processing has gained vital name and recognition among researchers because of their frequent look in varied and widespread applications within the field of various branches of science and engineering. As an example, image processing is helpful to issues in signature recognition, digital video processing, remote sensing and finance. Image processing models are used for detecting the face. The aim of this thesis is to solve the face-detection in the first attempt using the Haar-cascade classifier from images containing simple and complex backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a new method to deal with the frontal face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect the image as well as the coordinates. The main attraction of this paper is to solve different types of images having one object, two objects, and three objects which can’ t be solved by any of the existing methods but can be solved by our proposed method.

The study of intelligent design methods is becoming a hot topic for the design of turbine cascades. This paper proposes a data-based policy model to achieve intelligent design. To gain a high-quality policy model, empirical equations and "space extending + elitism" are adopted to dynamically optimize database. This guarantees the quality of the model. Compared to traditional optimization design approaches, the proposed method relies on less human experience to design a turbine cascade. Ten different turbine cascades are used to verify this method. Results show that, aerodynamic performance of the cascades redesigned is either the same as or better than that of the traditional cascades. The computing time is reduced by more than one order of magnitude compared to a "CFD + optimization algorithm" or "surrogate model + optimization algorithm" method. With the advantages in computing time and intelligence, the proposed novel method shows the possibility of replacing traditional design methods.

The development of nanotechnology has been a boon to mankind as its significance paved the way for several applications in therapeutics. This study was to evaluate the an ti-arthritic capability of nano Selenium-lovastatin mixture (Lov-Se) against inflammatory cascade in arthritic irradiated rats.Materials and Methods: Animal model of Arthritis was organized by subcutaneous injection of Complete Freund’s adjuvant; CFA. Rats were exposed to g-radiation (2Gy every 3 days up to total dose of 8 Gy). Lov-Se (1ml ≈20 mg kg−1 day-1 Lov and 0.1 mg kg−1 day−1 Se) was administrated by daily oral injection. The an tioxidant parameters (heart glutathione peroxides; GSH-Px, catalase; CAT, superoxide dismutase; SOD, xanthine dehydrogenase; XDH, reduced glutathione; GSH and blood selenium; Se), oxidant markers (heart Xanthine oxidase; XO, Nitric oxide; NO, protein carbonyls and thiobarbituric acid reactive substances; TBARS) and the inflammatory molecules (serum tumor necrosis factor-alpha TNF-a, C-reactive protein; CRP and rheumatoid factor; RF) were determined.Results: The arthritic and arthritic irradiated rats were displayed augment oxidative stress, inflammatory cascade and impaired anoxidant status compared to control.Conclusion: The data revealed that Lov-Se administration ameliorate oxidative, antioxidants parameters as well as inflammatory factors. The prolonged administration of Lov-Se mixture in appropriate concentration could exert a considerable systemic anti-inflammatory action through adjustment of red-ox tone and the integration of XDH/XO ratio.

Nowadays, facial plastic surgeries are performed for the correction of congenital anomalies, correction of injuries caused by accidents, treatment of burns, and the creation of a beautiful structure in the face. Therefore, surgeons need to analyze and measure quantitative facial metrics. Facial landmark detection plays an important role in the measurement of the facial golden ratio. In this study, 33 landmarks from the frontal view were detected using the cascade Regression model (CRM). Face detection is the first step in facial landmark detection to put the initial shape on the face. Therefore, the fuzzy c-means algorithm optimized by Harris Hawk Optimization (HHO) has been used for facial segmentation. In CRM, the regression function is used for mapping input face images to the desired output. For feature extraction, the Histogram Orientation Gradient (HOG) method was applied around the defined landmarks on three channels of RGB color space. Experiment results showed that feature extraction in three channels of RGB color space is more accurate than feature extraction in the gray scale. Also, results confirmed that the proposed method has a high accuracy in facial landmark detection and can be used as an effective method in the Cephalometry analysis of facial soft tissue.

Short time interval rainfall data (e.g. hourly data) serve as inputs to a range of flood and pollution transport models. However, they are not usually available. Due to availability of daily data as measured values, in this study an attempt is made to examine the suitability of a random cascade model for transformation of observed daily rainfall data measured at the rain gauge station situated on the Amameh catchment, north of Tehran, Iran. A random cascade model was proposed for the first time by Olsson (1998). The cascade model presented here is based on exact conservation of rainfall volume together with the rainfall seasonality. Having applied this model to the data measured with the mentioned rain gauge, 45-minute data were simulated and compared with its corresponding measured data for the same period of daily data. It was observed that the modelling procedure employed and used in this study can simulate the 45-minute peak values much better that the previous models such as the Olsson one. The potential for the better estimation of peak discharges is, therefore, has the potentiality to be used in storm design purpose as far as the application of rainfall-runoff models for flood estimation are concerned.