This paper interest to estimation the unknown parameters for generalized Rayleigh distribution model based on censored samples of singly type one . In this paper the probability density function for generalized Rayleigh is defined with its properties . The maximum likelihood estimator method is used to derive the point estimation for all unknown parameters based on iterative method , as Newton – Raphson method , then derive confidence interval estimation which based on Fisher information matrix . Finally , testing whether the current model ( GRD ) fits to a set of real data , then compute the survival function and hazard function for this real data.

Three Saccharomyces cerevisiae isolates from different sources (China, Turkey and Egypt) were screened by culturing on solid state fermentation to select the most efficient isolate for invertase production. S. cerevisiae from China was high specific activity 34.7 U/mg. The optimum conditions for enzyme production from this isolate were determined by using a medium composed of wheat bran moisten with 1:0.5 (v:w) corn steep liquor as nitrogen source at initial pH 5.0 for 5 days at 30OC.

Permanent deformation in asphalt concrete pavements is pervasive distress [1], influenced by various factors such as environmental conditions, traffic loading, and mixture properties. A meticulous investigation into these factors has been conducted, yielding a robust dataset from uniaxial repeated load tests on 108 asphalt concrete samples. Each sample underwent systematic evaluation under varied test temperatures, loading conditions, and mixture properties, ensuring the data’s comprehensiveness and reliability. The materials used, sourced locally, were selected to enhance the study ʼs relevance to pavement constructions in hot climate areas, considering different asphalt cement grades and con- tents to understand material variability ef

The choice of binary Pseudonoise (PN) sequences with specific properties, having long period high complexity, randomness, minimum cross and auto- correlation which are essential for some communication systems. In this research a nonlinear PN generator is introduced . It consists of a combination of basic components like Linear Feedback Shift Register (LFSR), ?-element which is a type of RxR crossbar switches. The period and complexity of a sequence which are generated by the proposed generator are computed and the randomness properties of these sequences are measured by well-known randomness tests.

An interpretive (structural and stratigraphic) study of the two,-dimensional seismic, data of East Nasiriya area (30 km to the south east of Nasiriya oil field within Thi-Qar province, southeastern Iraq) was carried out using Petrel 2017 program. The study area has an importance due to its location between many oil fields, but still without exploration of oil wells. Twenty five seismic lines were used, date back to different types of seismic surveys conducted in the region at different time periods.  Also, the seismic velocity surveys of the nearest wells to oil fields, such as Nasiriya-1 and Subba-8, in addition to their sonic and density logs were used. A synthetic seismogram with a good matching with the seismic section was achie

This research considers the preservation of environment through recycling old toys. This is achieved by transforming the old toys into educational clothing accessories for kindergarten stages. The research methodology adapts both descriptive and applied approaches. The research questionnaire targeted a sample of 35 teachers to collect information about the waste toys in kindergarten. Also, another sample of 30 teachers and mothers were targeted to measure the suitability of the clothing designs for the early childhood stages. The results shows that both teachers and mothers were well satisfied with clothing accessories designed with the toys waste. This concept contributes to limiting the pollution caused by toys and could save time, eff

    Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l