Effect of in Ovo injection with Nano- Selenium or Nano- Zinc on Post-Hatch Growth Performance and Physiological Traits of Broiler Chicks

The current study was aimed to investigate the effect of in ovo injection with Nano-selenium or Nano-zinc on post-hatch growth performance and physiological traits of broiler chicks under heat stress. Four hundred fertile broiler eggs from cobb500™ flock were randomly divided into four treatments (100 eggs each). First was normally without injection (control), second was injected with 15 ppm Nano-Selenium (SENPs) /egg, third treatment was injected by 15 ppm Nano-Zinc (ZnNPs)/egg and fourth treatment was injected with phosphate buffered saline (PBs) 15 ppm /egg. To study the post-hatch performance, A total number of 240 one day-old chicks were randomly distributed into 4 equal experimental treatments of 60 chicks each. Every treatment was sub-divided into three replicates (20 chicks/ each), were at lasted 5weeks. Results obtained could be summarized as follows: Nano-selenium explained higher chick\u27s weight at hatch, chick\u27s weight to egg weight ratio and hatchability % than all other treatments. At first week of age, the body weight (BW) in the nano-selenium treatment increased than the untreated (control) treatment, although the gastrointestinal tract weight was 0.44 % and the intestine weight was 0.8 %, this is explained by an augmentation in the length of both the length of the small intestine and the gastrointestinal tract by 12 % at 7 day of age. The highest live body weight and body weight gain and the best-feed conversion ratio were recorded with Nano- selenium than all other treatments at 35 day of age. In conclusion, under semi-arid conditions, USAge the Nano-selenium are not harmful to the embryo (injected with 15 ppm) and can be used to improve the post-hatch performance of broiler under semi arid condition

Comparative immunohistochemical expression of β-catenin, EGFR, ErbB2, and p63 in adamantinomatous and papillary craniopharyngiomas

AbstractCraniopharyngiomas (CPs) are rare epithelial tumors located mainly in the sellar/parasellar region. CPs have been classified into histopathologically, genetically, clinically and prognostically two distinctive subtypes: adamantinomatous and papillary variants.AimTo determine the immunohistochemical expression of β-catenin, EGFR, ErbB2, and p63 in adamantinomatous and papillary CPs.Materials and methodsβ-Catenin, EGFR, ErbB2, and p63 immunostaining was performed on paraffin embedded tissue sections of 25 CPs including 18 adamantinomatous craniopharyngioma (ACP) and 7 cases of papillary craniopharyngiomas (PCPs).Results17 cases (94%) of ACP exhibited strong nuclear/cytoplasmic expression of β-catenin. On the contrary, all cases of PCP showed exclusively membranous expression (P value<0.0001). Regarding EGFR, 15 (83%) and 5 cases (71%) of APC and PCP respectively were positive. On the other hand, only 3 cases (17%) of APC and none of PCP exhibited positivity for ErbB2. p63 over-expression was observed in 16 cases of ACP (89%) and 6 cases of PCP (86%). However, the distribution of p63 staining was diffuse in ACP, while in PCP; the staining was mainly restricted to the basal cell layer.ConclusionNuclear accumulation of β-catenin is a diagnostic hallmark of the ACP and is very helpful in the differential diagnosis between both ACP and PCP in the setting of small biopsies. Moreover, the restricted nuclear β-catenin accumulation in the cohesive cell clusters within the whorl-like areas supports that aberrant β-catenin expression may play a role in the morphogenesis of ACP

The construction of Complete (kn,n)-arcs in The Projective Plane PG(2,5) by Geometric Method, with the Related Blocking Sets and Projective Codes

A (k,n)-arc is a set of k points of PG(2,q) for some n, but not n + 1 of them, are collinear. A (k,n)-arc is complete if it is not contained in a (k + 1,n)-arc. In this paper we construct complete (kn,n)-arcs in PG(2,5), n = 2,3,4,5, by geometric method, with the related blocking sets and projective codes

Design of a modified natural egyptian solar house

The rate of increase in energy consumption and high costs in addition to the depletion of existing resources has a significant impact on our standard of living for next generations. In this case, the priority is to develop alternative cost-effective sources for powering the residential and non-residential buildings. This paper proposes and develops a design of a modified small two-story residential solar house for a medium-sized family located in Cairo, Egypt. This modified solar house meets almost all its energy demands including space heating by using solar air collector with a pebble storage unit in winter and a summer cooling system using wind catcher theory. Hot water is obtained throughout the day by using a steel sheltered water storage tank with a capacity of 1000 liter. Finally, the proposed heating system of the solar house is sized and modeled

Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators

Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement

Transformer Faults Classification Based on Convolution Neural Network

This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily