By definition, the detection of protein complexes that form protein-protein interaction networks (PPINs) is an NP-hard problem. Evolutionary algorithms (EAs), as global search methods, are proven in the literature to be more successful than greedy methods in detecting protein complexes. However, the design of most of these EA-based approaches relies on the topological information of the proteins in the PPIN. Biological information, as a key resource for molecular profiles, on the other hand, acquired a little interest in the design of the components in these EA-based methods. The main aim of this paper is to redesign two operators in the EA based on the functional domain rather than the graph topological domain. The perturb

In this paper, the impact of magnetic force, rotation, and nonlinear heat radiation on the peristaltic flow of a hybrid bio -nanofluids through a symmetric channel are investigated. Under the assumption of a low Reynolds number and a long wavelength, the exact solution of the expression for stream function, velocity, heat transfer coefficient, induced magnetic field, magnetic force, and temperature are obtained by using the Adomian decomposition method. The findings show that the magnetic force contours improve when the magnitude of the Hartmann number M is high and decreases when rotation increases. Lastly, the effects of essential parameters that appear in the problem are analyzed through a graph. Plotting all figures is done using the

Baghdad Metro is a vital project to fulfill the rapidly increased traffic volume requirements. The proposed metro will connect both sides of Baghdad City, passing under the Tigris River. This study is employed finite elements software (PLAXIS 3D) to evaluate the seepage force developed around the sub-river segment during different construction stages and for other water levels of Tigris. The study found that when the water level changes from maximum to minimum, the developed seepage force decreases by (8 to 13%) and (22 to 27%) respectively. The seepage forces were found to be maximum during the excavation stage. The concrete lining process led to a noticeable reduction in seepage forces at all locations. The study also

Background: Bone mineral density has been assessed using Dual-Energy X-Ray Absorptiometry. Bone mineral density is measured according to the results of the Dual-Energy X-Ray Absorptiometry examination of the vertebral column and pelvis. Although diabetes mellitus type II (DM) is known to affect bone mineral density, at the present time this particular relationship is not clear. Objective: The aim of current study was to evaluate the effects of type II diabetes mellitus on bone mineral density of the upper and lower limbs as well as gender differences. Patients and Methods: This study involved 165 patients complaining of bone pain (85 males and 80 females), 85 patients of who suffered from diabetes, involving both genders. In addition,

Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the te

LED is an ultra-lightweight block cipher that is mainly used in devices with limited resources. Currently, the software and hardware structure of this cipher utilize a complex logic operation to generate a sequence of random numbers called round constant and this causes the algorithm to slow down and record low throughput. To improve the speed and throughput of the original algorithm, the Fast Lightweight Encryption Device (FLED) has been proposed in this paper. The key size of the currently existing LED algorithm is in 64-bit & 128-bit but this article focused mainly on the 64-bit key (block size=64-bit). In the proposed FLED design, complex operations have been replaced by LFSR left feedback technology to make the algorithm perform more e

3D models delivered from digital photogrammetric techniques have massively increased and developed to meet the requirements of many applications. The reliability of these models is basically dependent on the data processing cycle and the adopted tool solution in addition to data quality. Agisoft PhotoScan is a professional image-based 3D modelling software, which seeks to create orderly, precise n 3D content from fixed images. It works with arbitrary images those qualified in both controlled and uncontrolled conditions. Following the recommendations of many users all around the globe, Agisoft PhotoScan, has become an important source to generate precise 3D data for different applications. How reliable is this data for accurate 3D mo

This research theme of the pressures of work , which is one of the important topics in order to recognize the reality of( influencing the pressures of work in the performance of employees in the General Company for Vegetable Oil Industry in Baghdad )through the statement of the existence of the correlation and influence whether or not the statement of the strength of this relationship and its impact in the case of its existence has been provided as part of my Search for variables and their removal in front of the Sub- scientific aspect has been the distribution of the questionnaire on a sample of( 62) people working in the company Mint distributors on several sections where.

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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod