Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

 In this study, we derived the estimation for Reliability of the Exponential distribution based on the Bayesian approach. In the Bayesian approach, the parameter of the Exponential distribution is assumed to be random variable .We  derived  posterior distribution the parameter of the Exponential distribution under four types priors distributions for the scale parameter of the Exponential distribution is: Inverse Chi-square distribution, Inverted Gamma distribution, improper distribution, Non-informative distribution. And the estimators for Reliability is obtained using the two proposed loss function in this study which is based on the natural logarithm for Reliability function .We used simulation technique, to compare the

This study included collecting of 150 specimens of the Iraqi Bird Bulbule Pycnonotus lecuotis mesoptamiae (Pycnotidae , Passeriformes), from the 1st of November 2004 to 30th of October 2005 in the city of Baghdad and its suburbs, in concern of identifying the internal parasite of the digestive tract of these birds and studying the diseases resulted from the infection. The study showed that the bird Bulbule is Parasitized by Four types of Cestodes ,two of them are recorded in this kind of bird in Iraq for the first time ,Paradicranotinae anormalis and Haploparaxis sp. , and the other two are Raillietina tetragona and Allohymenolepsis sp. ,and by two types of Pin Worm(Trematode) that are identified for the first time in the Bird Bulb

Different formula of bioagents (Rhizobium cicceri cp-93, Azospirillum sp.,
Pseudomonas fluorescence, Trichoderma harzianum ) used in this study as a
biofertilizer on wheat crop with two level of chemical fertilizer (0 and 12.5
kg/donm Dap) compared to 50kg/donm Dap (standard amount).the study carried out
in Iraq/Diyala –Alkhales during November 2014,results showed significant increase
in no. of spikes, no. of spikelet’s, length of spike ,Weight of 1000 seed and yield of
one m2 when adding (Rhizobium cicceri cp-93,Azospirillumsp+ Trichoderma
harzianum +12.5 kg/donm Dap) in comparison with the 50kg/donm Dap. Other
formulas recorded same results with the treatment 50kg/Donm Dap with not
significant differences

In this research the Empirical Bayes method is used to Estimate the affiliation parameter in the clinical trials and then we compare this with the Moment Estimates for this parameter using Monte Carlo stimulation , we assumed that the distribution of the observation is binomial distribution while the distribution with the unknown random parameters is beta distribution ,finally we conclude that the Empirical bayes method for the random affiliation parameter is efficient using Mean Squares Error (MSE) and for different Sample size .

The aim of this research is to estimate the area unit function of productivity for the potato crop in Anbar province for the autumn season (2008 / 2009) Anbar province has been chosen as an applied model for the study due to its well known in cultivating potato crop , and the data were collected through a random sample about (10%) from the study society with a (150) farmers,  The results indicated that the double logarithmic formula was the best representative of the relationship between crop productivity and independent variables (quantity of potato tubers , quantity of herbicides stuffs, quantity of fertilizer , hours of mechanical labour