The combination of high protein content and a soft seed coat makes the wheat-rye hybrid Triticale (Triticosecale) vulnerable to attack by rice weevils. Drying triticale grain to moisture contents safe for storage can prevent infestation by rice weevils, but if grain is being stored for seed, high drying temperatures can affect seed germination. Grain can be effectively dried at low temperatures, but low-temperature drying is difficult in hot, humid regions such as the Gulf Coast. This study nvestigated the effects of drying temperatures from 35°C to 45°C on triticale seed germination and found no statistical differences between the germination rates of the seed at any of the drying temperatures and the germination rates of controls. Final

This paper considers the nonlinear homogeneous fractional Burger's equation as a type of nonlinear fractional partial differential equations (FPDE). Our goal in this paper is to show that an initial value problem (IVP) can be modified with a second initial condition when (α ∈ ( 1,2 ]) as the velocity of the movement, and the obtained solution agrees with the nature of the wave with space and time for the problem. The Caputo fractional derivative is used in all the fractional derivatives. Also, the algorithm of the Laplace transform decomposition method (LTDM) for fractional PDEs is presented. The approximate solution converges to the exact solution in Theorem 1. Also, a numerical simulation is made to confirm the theoretical resu

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator

The analysis of the classic principal components are sensitive to the outliers where they are calculated from the characteristic values and characteristic vectors of correlation matrix or variance Non-Robust, which yields an incorrect results in the case of these data contains the outliers values. In order to treat this problem, we resort to use the robust methods where there are many robust methods Will be touched to some of them.

   The robust measurement estimators include the measurement of direct robust estimators for characteristic values by using characteristic vectors without relying on robust estimators for the   variance and covariance matrices. Also the analysis of the princ

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord