The state and partial level densities were calculated using the corresponding formulas that are obtained in the frame work of the exciton model with equidistant spacing model (ESM) and non-ESM (NESM). Different corrections have been considered, which are obtained from other nuclear principles or models. These corrections are Pauli Exclusion Principle, surface effect, pairing effect, back shift due to shell effect and bound state effect . They are combined together in a composite formula with the intention to reach the final formula. One-component system at energies less than 100 MeV and mass number range (50-200) is assumed in the present work. It was found that Williams, plus spin formula is the most effective approach to the composite

This paper studies a novel technique based on the use of two effective methods like modified Laplace- variational method (MLVIM) and a new Variational method (MVIM)to solve PDEs with variable coefficients. The current modification for the (MLVIM) is based on coupling of the Variational method (VIM) and Laplace- method (LT). In our proposal there is no need to calculate Lagrange multiplier. We applied Laplace method to the problem .Furthermore, the nonlinear terms for this problem is solved using homotopy method (HPM). Some examples are taken to compare results between two methods and to verify the reliability of our present methods.

The process of controlling a Flexible Joint Robot Manipulator (FJRM) requires additional sensors for measuring the state variables of flexible joints. Therefore, taking the elasticity into account adds a lot of complexity as all the additional sensors must be taken into account during the control process. This paper proposes a nonlinear observer that controls FJRM, without requiring equipment sensors for measuring the states. The nonlinear state equations are derived in detail for the FJRM where nonlinearity, of order three, is considered. The Takagi–Sugeno Fuzzy Model (T-SFM) technique is applied to linearize the FJRM system. The Luenberger observer is designed to estimate the unmeasured states using error correction. The develop

Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demon

Hiding technique for dynamic encryption text using encoding table and symmetric encryption method (AES algorithm) is presented in this paper. The encoding table is generated dynamically from MSB of the cover image points that used as the first phase of encryption. The Harris corner point algorithm is applied on cover image to generate the corner points which are used to generate dynamic AES key to second phase of text encryption. The embedded process in the LSB for the image pixels except the Harris corner points for more robust. Experimental results have demonstrated that the proposed scheme have embedding quality, error-free text recovery, and high value in PSNR.

Wastewater projects are one of the most important infrastructure projects, which require developing strategic plans to manage these projects. Most of the wastewater projects in Iraq don’t have a maintenance plan. This research aims to prepare the maintenance management plan (MMP) for wastewater projects. The objective of the research is to predict the cost and time of maintenance projects by building a model using ANN. The research sample included (15) completed projects in Wasit Governorate, where the researcher was able to obtain the data of these projects through the historical information of the Wasit Sewage Directorate. In this research artificial neural networks (ANN) technique was used to build two models (cost

The global food supply heavily depends on utilizing fertilizers to meet production goals. The adverse impacts of traditional fertilization practices on the environment have necessitated the exploration of new alternatives in the form of smart fertilizer technologies (SFTs). This review seeks to categorize SFTs, which are slow and controlled-release Fertilizers (SCRFs), nano fertilizers, and biological fertilizers, and describes their operational principles. It examines the environmental implications of conventional fertilizers and outlines the attributes of SFTs that effectively address these concerns. The findings demonstrate a pronounced environmental advantage of SFTs, including enhanced crop yields, minimized nutrient loss, improved nut