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Abstract This work is mainly concerned with the effect of anthropogenic activities and natural radioactivity due to the presence of highly radioactive black sand spots, factory construction, and shipping, in addition to other activities like agriculture on human beings. Forty samples were collected along Baltim–El Burullus coastal plain to detect the effect of these problems and determine the suggested solutions. The black sand of the Baltim–El Burullus coastal plain exhibits a considerable amount of economically heavy minerals, their ratio relative to the bulk composition in the investigated samples ranges from 3.18 to 10.5% with an average of 5.45%. The most important of them are magnetite, ilmenite, rutile, leucoxene, garnet, zircon and monazite. The existence of some radioactive-bearing accessory mineral deposits like zircon and monazite led to measuring the naturally occurring radionuclides 226Ra, 232Th and 40K to evaluate the excess lifetime cancer risk (ELCR). The results showed that these concentrations are 19.1 ± 9.73, 14.7 ± 9.53 and 211 ± 71.34 Bq kg−1 were lower than the corresponding reported worldwide average of 35, 45, and 412 Bq kg−1 for each radionuclide (226Ra, 232Th, and 40 K). The gamma hazard indices such as absorbed dose rate (Dair), the annual effective dose (AED), and excess lifetime cancer risk (ELCR) factor were computed in the investigated sediments and all the results were found (Dair = 26.4 nGy h−1, AED = 0.03 mSv year−1, ELCR = 0.0001) to be lower than the values suggested by the United Nations Scientific Committee on the effect of Atomic Research (59 nGy h−1, 0.07 mSv year−1 and 0.0029 for Dair, AED and ELCR, respectively). The study suggests that the black sand is safe to use in various infrastructure applications at Baltim–El Burullus coastal plain. The levels of radioactivity are not high enough to pose a risk to human health.

The objective of this study was to investigate the genetic polymorphisms in the keratin-associated protein (KAP22-1) gene in Barki (n=206), Rahmani (n=28) and Ossimi (n=28) as the three major sheep breeds in Egypt. Subsequently, the detected variants were correlated with important wool traits. The traits included greasy fleece weight (GFW, g), staple length (SL, cm), prickle factor (PF, %), medullated fiber (MF, %), fiber diameter (FD, µm), crimp percentage (CR, %) and the standard deviation of FD (SDfd, µm), as well as the subjectively assessed traits of kemp score (KS), handle grade (HG), greasy color grade (GCG), bulk grade (BG), luster grade (LG) and staple structure (SST). Animals were genotyped by polymerase chain reaction (PCR) – single strand conformation polymorphism (SSCP). Five SSCP banding patterns representing three different nucleotide variants (A, B and C) were detected. DNA sequencing confirmed three single nucleotide polymorphisms (SNPs). Animal age significantly affected GFW (P=0.007), SDfd (P=0.006), SL (P=0.002), CR (P=0.006), KS (P=0.001), LG (P=0.006) and SST (P=0.013). Likewise, the breed had a significant effect on all studied traits except HG and BG, which was not significant. Results showed significant associations between the KAP22-1 variants and CR (P=0.01), SL (P=0.012), KS (P) and GCG (P=0.01). Interestingly, animals with BB genotypes tended to produce more wool yield (1163.63±65.91 g) with high SL (8.38±0.20 cm), CR (8.38±0.21 %) and KS (1.98±1.88). Results of this study strongly recommend the KAP22-1 gene as a candidate gene for wool production traits in Egyptian sheep, with new useful insights into the visually assessed wool traits. The identified genetic markers may be incorporated into breeding strategies and genetic improvement programs of wool traits in Egyptian sheep.

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a distributed computing environment has become the most often utilized strategy for developers in order to better cope with a huge quantity of training overhead. The current deep neural network model is the stochastic gradient descent (SGD) technique. It is one of the most widely used training techniques in network models, although it is prone to gradient obsolescence during parallelization, which impacts the overall convergence. The majority of present solutions are geared at high-performance nodes with minor performance changes. Few studies have taken into account the cluster environment in high-performance computing (HPC), where the performance of each node varies substantially. A dynamic batch size stochastic gradient descent approach based on performance-aware technology is suggested to address the aforesaid difficulties (DBS-SGD). By assessing the processing capacity of each node, this method dynamically allocates the minibatch of each node, guaranteeing that the update time of each iteration between nodes is essentially the same, lowering the average gradient of the node. The suggested approach may successfully solve the asynchronous update strategy’s gradient outdated problem. The Mnist and cifar10 are two widely used image classification benchmarks, that are employed as training data sets, and the approach is compared with the asynchronous stochastic gradient descent (ASGD) technique. The experimental findings demonstrate that the proposed algorithm has better performance as compared with existing algorithms.

Recently, Code Quality (CQ) has become critical in a wide range of organizations and in many areas from academia to industry. CQ, in terms of readability, security, and testability, is a major goal throughout the software development process because it affects overall Software Quality (SQ) in terms of subsequent releases, maintenance, and updates. It is particularly important for the development of safety critical systems. Existing studies on CQ have several shortcomings in that they are based on incomplete information about the source code, and tend to focus on only one feature, which is likely to determine the performance of the model. Moreover, these considerations often limit obtaining high accuracy because there is no strong relationship between the input data and the output data. Thus, it is necessary to design an effective and efficient SQ measurement system for measuring multiple quality factors. To that end, we propose a deep learning framework that employed a Latent Dirichlet Allocation (LDA) with Convolutional Neural Networks (CNN), called CNN-LDA, to classify input data into topics that are related to CQ features and to identify hidden patterns and correlations in programming data. Three SQ metrics (i.e., readability, security, and testability) and machine learning techniques (e.g., random forest (RF) and support vector machine (SVM)) are taken into account to validate the proposed model. The proposed CNN-LDA outperformed its peers across the vast majority of datasets examined. The average overall F-measure for readability, security, and testability are 94%,94% and 93%. The average overall accuracy for readability, security, and testability are 93%,93% and 92%. The superiority of LDA-CNN over the other classifiers was very clear based on a Wilcoxon’s non-parametric statistical test (α=0.05).

Concrete containing wastes from the demolition of old deteriorated buildings are produced enormously. Concrete is a brittle matrix that is usually reinforced by ductile reinforcement such as steel bars. However, due to the susceptibility of steel to corrosion, fiber-reinforced polymers (FRP) bars are used as an alternative reinforcement. The main drawback of FRP bars is their brittleness. These two types of reinforcements, i.e., steel and glass FRP (GFRP) bars, have been used in the present work. The flexural behavior of twelve RC beams reinforced with different ratios of GFRP or steel areas containing recycled aggregate has been experimentally studied and compared with beams without recycled aggregate. The present results show that beams reinforced with GFRP and containing recycled aggregate exhibit a lower load-carrying capacity, lower the first crack, and the highest deflection. All GFRP RC beams exhibited brittle failure, i.e., concrete crushing in the compression zone, except one beam, with 2(16 bars and concrete without recycled aggregate, which ruptured GFRP bars. However, ductile failure modes are observed for all beams reinforced with steel bars, i.e., yielding in steel bars followed by concrete failure. The novelty and advantage of the present results are that the large deflection of GFRP RC beams represents enough warning before failure, as found in ductile RC beams

In the present work, thirteen reinforced concrete beams with openings at different locations were tested under four-point bending to investigate the efficiency of strengthening techniques of such openings. Different strengthening techniques around the opening were used before casting as internally reinforced by diagonal or upper and lower additional steel bars or after casting as externally bonded sheets of carbon fiber reinforced polymers (CFRP). The stiffness, mid-span deflection, maximum load, and failure mode of the strengthened beams were discussed. Experimental results showed that the best opening location is in the constant moment region around the neutral axis. The strengthening of such beam by internal reinforcements exhibited a higher ultimate load than that of the beam without opening. At the same time, the strengthening by external bonded CFRP failed to store the strength of such beam. The upper and lower additional steel bars technique is the best technique to restore the beam with the opening in mid-span or beneath the applied load. The worst opening location is in shear span. The beam's strength with the opening in shear span was restored using CFRP, while internal reinforcement�s techniques failed. It can be concluded that the strengthening technique type depends on the opening location.

Data of 651 lambs (68 Romanov, 49 Rahmani, 151 [♀1/2 Rahmani X ♂1/2 Romanov) and 383 (♀3/4 Rahmani and 1/4♂ Romanov]) were collected from Mehalet Mousa Farm, belonging to Animal Production Research Institute from the period of 2009 to 2016 to estimate phenotypic and genetic parameters. The traits studied were birth weight (BW), body weight at four week (BW4), body weight at eight weeks (BW8) and body weight at twelve weeks (BW12) or weaning weight. Least squares analysis of variance shows significance of the effects of breed groups, gender of lambs, birth type; month of birth and year of birth on all traits studied. Rahmani lambs had heavier BW, BW4, BW8 and BW12 while Romanov lambs had the lowest ones. The first generation (♀1/2 Rhamani X ♂1/2 Romanov) had heavier body weights than Romanov and the second generation (♀3/4 Rahmani X ♂1/4 Roamnov). Gender of lambs had highly significant effect on body weights. Males were significantly (p < 0.01) heavier than females for all traits studied. Least square means of BW, BW4, BW8 and BW12 for single lambs were 2.69, 10.43, 13.53 and 16.10 kg, respectively. Least square means of BW, BW4, BW8 and BW12 for twin lambs were 2.50, 9.37, 12.5 and 15.16 kg, respectively, while least square means of BW, BW4, BW8 and BW12 for triple lambs were 2.09, 7.86, 10.83 and 13.67 kg, respectively. Estimates of direct heritability measured by single trait animal model were 0.14, 0.23, 0.25 and 0.26 for BW, BW4, BW8 and BW12, respectively, and the corresponding measured by multi trait animal model were 0.17, 0.24, 0.32 and 0.36 for the same traits, respectively. All genetic and phenotypic correlations among different traits studied are positive and significant.

Using time-scaled phylogenies of several endemic clades of small Afro-Arabian mammals, Dorien de Vries et al. infer a steep drop in species numbers around the Eocene-Oligocene transition. This dramatic decline coincides with a global cooling trend as well as the onset of widespread flood basalt volcanism in East Africa, and is found to be correlated with a major loss in dietary diversity.