An experiment was carried out in the vegetables field of Horticulture Department / College of Agriculture / Baghdad University , for the three seasons : spring and Autumn of 2005 , and spring of 2007 , to study the type of gene action in some traits of vegetative , flowery growth , yield and its components in summer squash crosses (4 x 3 = cross 1 , 3 x 7 = cross 2 , 3 x 4 = cross 3 , 3 x 5 = cross 4 , 5 x 1= cross 5 , 5 x 2 = cross 6). The study followed generation mean analysis method which included to each cross (P1 , P2 , F1 , F2 , Bc1P1 , Bc1P2) , and those populations obtained by hybridization during the first and second seasons. Experimental comparison was performed in the second (Two crosses only) and third seasons , (four crosses) by using RCBD with three replications. Three parameters model with chi square test and six parameters model were used to estimate the type of gene effects , in breeding depression , components of genetic variance , mean degree of dominance , heritability and expected gain due to selection. The results showed that dominance and epistatic (additive x additive) and (dominance x dominance) effects were great importance than additive effects in most traits of all crosses. In breeding depression was important except for plant length . Broad sense heritability ranged from 51.68 – 90.25% for plant length in crosses 3 , 5 and 6 while narrow – sense heritability was high (76.6 and 83.9%) for No. of leaves in crosses , 2,5 respectively and (57.13%) for No. of Nodes to first female flower in cross 6. Expected gain due to selection among F2 was agreat (29.5%) for No. of leaves in cross 5 while it was low to moderate some traits in other crosses. Also the results showed in yield and its component that dominance and epistatic (additive x additive) and (dominance x dominance) effects were great importance than additive effects in most traits of all crosses. In breeding depression was important except for fruit weight . Broad sense heritability was high in fruit weight , plant yields , and total yield (67.05 , 52.54 , 52.56) respectively for cross 1 , while it was low to moderate in some traits and crosses and narrow – sense heritability was high in fruit weight (40.82) for cross 6 and low in some traits to some crosses. Expected gain due to selection was low in plant yield and total yield for cross 4 and fruit weight for cross 6.
The acceptance sampling plans for generalized exponential distribution, when life time experiment is truncated at a pre-determined time are provided in this article. The two parameters (α, λ), (Scale parameters and Shape parameters) are estimated by LSE, WLSE and the Best Estimator’s for various samples sizes are used to find the ratio of true mean time to a pre-determined, and are used to find the smallest possible sample size required to ensure the producer’s risks, with a pre-fixed probability (1 - P*). The result of estimations and of sampling plans is provided in tables.
Key words: Generalized Exponential Distribution, Acceptance Sampling Plan, and Consumer’s and Producer Risks
... Show MoreHigher education is important because it creates and develops human capital and provides qualified human cadres, which requires restructuring government spending so that an increase in funding allocated to education is brought about. During the period 1990-2020, government spending was weak on educational institutions in Iraq, which led to a decline in The role of these institutions in the economic development of the country. The highest percentage of spending on higher education of GDP was 0.47% in 2007 and the lowest was 0.01% in 2005. The number of public universities reached 35, and the number of private universities and colleges reached 64 universities and private colleges in 2020. This was accompanied by an increase in the number of s
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... Show MoreChannel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T
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