×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerIn the realm of cryptography, the Substitution-box (S-box) is a critical component for enhancing the security of encryption algorithms. The inherent characteristics of Chaos, such as sensitivity to beginning conditions and unpredictability, make it a highly suitable choice for encryption applications. In this paper, proposed a method for generating S-Boxes using 3D chaotic maps algorithms including (Cat map, Henon map, Sine map, and Cosine map). The primary focus is on enhancing the security and efficiency of cryptographic systems by leveraging the inherent complexity and unpredictability of chaotic maps. The design methodology focuses on achieving high non-linearity, optimal avalanche effect, and Strict Avalanche Criterion ( SAC ), ensuring that minor changes in plaintext result in significant alterations in the ciphertext. Our study presents a detailed analysis of the generated S-Boxes, demonstrating their robustness against common cryptographic attacks. Key findings include significant improvements in nonlinearity, differential uniformity, and bijectivity compared to traditional methods. The test findings and performance analysis indicate that our proposed S-Box exhibits much lower values of Linear Probability ( LP ) and Differential Probability ( DP ), while maintaining a suitable average value of nonlinearity. Additionally, discussed the broader implications of our findings, emphasizing how the proposed method can be employed to produce high-quality analytical results that enhance the security measures of cryptographic applications. This work adds valuable context to existing research and highlights the potential for our model to outperform conventional S-Box generation techniques.
During the pouring of concrete deck, the installation of external bracing between the inner and outer girders may be necessary when the bridge has sharp curve in order to control the deflection and rotation of the girders. However, it is important to minimize the number of external bracing members, as they have expensive cost and they also have opposite effects for the fatigue features of the steel tub girders. The analysis of curved box girder bridges is carried out numerically by the use of finite element method through (ANSYS 19.2) software. The curved box girder with the intermediate external diaphragms was modeled and the analysis was carried out for many parameters like external bracing sections, girders with or without concrete deck, girders with end diaphragms or without them. The study concluded that ANSYS program has a good ability in evaluating the external bracing force comparing with code equations.
The reason for the widespread use of steel box girders is that they have high structural efficiency due to the high bending, high torsional stiffness and rapid erection. For bottom flange of the girders, the buckling behavior during production and erection due to compression strength can be a problem. The compression plate with longitudinal stiffeners typically renders an economic. The optimal design of longitudinal stiffeners is discussed. The results are based on 3-D FEA (ANSYS19.2) of many stiffened compression bottom flange models, the moment of inertia requirement of bottom flange longitudinal stiffener is investigated by studying the effect of many parameters as longitudinal stiffeners numbers, stiffener sections, plate aspect ratio and compression flange thickness. Also, the stiffeners effect on the compression panel plate stresses were studied by modeling girder with and without longitudinal stiffeners. The finite element method is useful as they can be used to study the plate with stiffeners in an economical way, and we don’t need experimental and laboratory tests.
Time series analysis for hydrological phenomena has an important role in water resources engineering. In this study, seven models of ARIMA family are tested for forecasting the monthly discharge at Hit station on Euphrates river in Iraq. The statistical analyses were done for models with help of IBM SPSS statistics 21 software, The number of observations used is equal to 480 reading, start from October 1932 and end at September 1972, this period represents the near-natural stream flow of the river before the construction of dams in Syria and Turkey. Statistical tests such as T-test and F-test were used to detect any change in Mean and Variance at 95% significant probability level. Results showed that the best model is (2,0,1)×(0,1,1) 12 which gives minimum error and good agreement between observed and forecast discharge.
In this paper, a Neural Network (NN) model system for self-organization fish school system is identified. Monitoring and data extraction from fish school video has been achieved by using image processing technique in order to generate the data suitable for parameter identification of NN model system. Data obtained have been used to identify the parameters of a model based on a black-box represented by nonlinear autoregressive exogenous model (NARX). The obtained results show that this system can be used for multi robot formation system.
Soil classification is a significant tool for the interpretation of soil properties and the selection of the appropriate parameters to express soil types in geotechnical engineering applications. In this paper, a comparative analysis on the prediction of soil classification using different soil parameters via Neural Network, Decision Tree, and Ensemble classifier algorithms in the machine learning tool box of MATLAB is conducted. The data is classified into 14 sets of input parameters according to the parameters of the Unified Soil Classification System (USCS), which is a standard system in practice for classifying soils based on their physical properties and their engineering behavior. The input parameters of the USCS, Passing No. 4 Sieve, Passing No. 200 Sieve, Gravel, Sand, Fines, Liquid Limit, Plastic Limit and Plasticity Index, are specified within the certain ranges to generate a synthetic data base including 478 data for the purpose of training and testing. Five Neural Network models (Medium, Trilayered, Bilayered, Narrow and Wide), three Decision Tree models (Fine, Medium, Coarse) and three Ensemble models (Boosted Trees, Bagged Trees, RUSBoosted Trees) are tested for the different test split ratios (10, 20, and 30). The results show that the Neural Network models proposed in this study have the highest prediction capability, with a classification accuracy of up to 100 % for some variable sets, and can classify soils automatically and precisely based on the soil index properties. Among the individual models, WNN and NNN generally showed the best performance for all variable sets. The Ensemble models presented high prediction capability with respect to generalization. Among the DT models, although they are fast, they presented relatively lower accuracy than the other models, especially the Coarse Tree model. This study can be used as a reference for the future development of automated soil classification systems and can offer practical suggestions for the selection of machine learning models for geotechnical engineering problems.