The particle-hole state densities have been calculated for 232Th in
the case of incident neutron with  ,  1 Z Z T T T T and   2 Z T T .
The finite well depth, surface effect, isospin and Pauli correction are
considered in the calculation of the state densities and then the
transition rates. The isospin correction function ( ) iso f has been
examined for different exciton configurations and at different
excitation energies up to 100 MeV. The present results are indicated
that the included corrections have more affected on transition rates
behavior for        , , and    above 30MeV excitation energy

In the present study, the physical characteristics of elastomer (EL) blend with natural polymers such as polyvinyl alcohol (PVA), Dexrin (D), Arabic gum (AG), and corn starch (CS) based on high-density fiberboard wood adhesives were investigated. The EL blends were prepared by dissolving AG, D, PVA, and CS in deionized water at 70 °C for 1 h under magnetic stirring continuously until the solution was clear, and blends were made with a weight of 60/40 (w/w); then were cast into a mold with a 20 cm diameter and left at room temperature for 24 h to ensure complete water removal and drying of the samples. The prepared EL and EL blend structures, adhesion strengths, roughness, wettings, and dielectric strengths, were investigated. The modifi

In this paper an improved weighted 0-1 knapsack method (WKM) is proposed to optimize the resource allocation process when the sum of items' weight exceeds the knapsack total capacity .The improved method depends on a modified weight for each item to ensure the allocation of the required resources for all the involved items. The results of the improved WKM are compared to the traditional 0-1 Knapsack Problem (KP). The proposed method dominates on the other one in term of the total optimal solution value of the knapsack .

In cognitive radio system, the spectrum sensing has a major challenge in needing a sensing method, which has a high detection capability with reduced complexity. In this paper, a low-cost hybrid spectrum sensing method with an optimized detection performance based on energy and cyclostationary detectors is proposed. The method is designed such that at high signal-to-noise ratio SNR values, energy detector is used alone to perform the detection. At low SNR values, cyclostationary detector with reduced complexity may be employed to support the accurate detection. The complexity reduction is done in two ways: through reducing the number of sensing samples used in the autocorrelation process in the time domain and through using the Slid

    Tissue culture of Catharanthus  roseus  was established under many parameters to insure good results for detection of the alkaloids present in this plant . It was found that NItsch and Nitsch medium containing 8µM Benzyladeninpurine plus Naphalene acetic acid were the best and the callus of C.roseus left to grow in the dark and had much better influence for the production of Alkloids. The precursor phenylalanine showed a better result than other precursor( tryptophan ) . Abscisic acid has an inhibitory effect on the production of Alkaloid

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit