Cloud storage provides scalable and low cost resources featuring economies of scale based on cross-user architecture. As the amount of data outsourced grows explosively, data deduplication, a technique that eliminates data redundancy, becomes essential. The most important cloud service is data storage. In order to protect the privacy of data owner, data are stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for data storage. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. This paper proposes a combined compressive sensing and video deduplication to maximize

The research aims at the identity of the accounting information and its characteristics, and then to study the possibility of using accounting information in rationalizing the decisions of capital expenditure. The study relied upon the descriptive analytical approach it is suitable to the nature of this study, the hypotheses of the study were tested by using a number of statistical methods by relying on statistical package program (SPSS), and the research concluded that the companies listed in Khartoum Stock Exchange using accounting information in the comparison between investment alternatives available and estimating the number of years required to recover of the investment cost, the challenges that cause weakness in using the

The systems cooling hybrid solar uses solar collector to convert solar energy into the source of heat for roasting Refrigerant outside of the compressor and this process helps in the transformation of Refrigerant from the gas to a liquid state in two-thirds the top of the condenser instead of two-thirds the bottom of the condenser as in Conventional cooling systems and this in turn reduces the energy necessary to lead the process of cooling. The system cooling hybrid use with a capacity of 1 ton and Refrigerant type R22 and the value of current drawn by the system limits (3.9-4.2A), the same value of electric current calculated by the system are  Conventional  within this atmosphere of Iraq, and after taking different readings

The purpose of this interview study was to explore teachers’ perceptions of Response to Intervention (RtI) implementation in their school. Particularly, the study explored teachers’ knowledge of RtI, teachers’ perceptions of RtI their intervention/instruction in school, and teachers’ suggestions of RtI implementation in their school. The study design was a qualitative interview in nature and data were collected from face-to-face interviews with four teachers in one school. The findings revealed that RtI means to identify students’ problems; the positive teachers’ perceptions of their implementation included: (a) students who demonstrate progress through RtI are those who receive private education services, (b) progress monito

High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,

The Aim of this paper is to investigate numerically the simulation of ice melting in one and two dimension using the cell-centered finite volume method. The mathematical model is based on the heat conduction equation associated with a fixed grid, latent heat source approach. The fully implicit time scheme is selected to represent the time discretization. The ice conductivity is chosen
to be the value of the approximated conductivity at the interface between adjacent ice and water control volumes. The predicted temperature distribution, percentage melt fraction, interface location and its velocity is compared with those obtained from the exact analytical solution. A good agreement is obtained when comparing the numerical results of one

The simulation study has been conducted for the harmonics of Nd: YAG laser, namely the second harmonic generation SHG, the third harmonic generation THG, and the fourth harmonic generation FHG. Determination of beam expander's expansion ratio for specific wavelength and given detection range is the key in beam expander design for determining minimum laser spot size at the target. Knowing optimum expansion ratio decreases receiving unit dimensions and increases its performance efficiency. Simulation of the above mentioned parameters is conducted for the two types of refractive beam expander, Keplerian and Galilean. Ideal refractive indices for the lenses are chosen adequately for Nd: YAG laser harmonics wavelengths, so that increasing transm

In this paper we used frequentist and Bayesian approaches for the linear regression model to predict future observations for unemployment rates in Iraq. Parameters are estimated using the ordinary least squares method and for the Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. Calculations are done using the R program. The analysis showed that the linear regression model using the Bayesian approach is better and can be used as an alternative to the frequentist approach. Two criteria, the root mean square error (RMSE) and the median absolute deviation (MAD) were used to compare the performance of the estimates. The results obtained showed that the unemployment rates will continue to increase in the next two decade

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall