In this research, we study the classical continuous Mixed optimal control vector problem dominated by couple nonlinear elliptic PDEs. The existence theorem for the unique state vector solution of the considered couple nonlinear elliptic PDEs for a given continuous classical mixed control vector is stated and proved by applying the Minty-Browder theorem under suitable conditions.  Under suitable conditions, the existence theorem of a classical continuous mixed optimal control vector associated with the considered couple nonlinear elliptic PDEs  is stated and proved.

The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estim

this paper presents a novel method for solving nonlinear optimal conrol problems of regular type via its equivalent two points boundary value problems using the non-classical

     This paper considers the maximum number of weekly cases and deaths caused by the COVID-19 pandemic in Iraq from its outbreak in February 2020 until the first of July 2022. Some probability distributions were fitted to the data. Maximum likelihood estimates were obtained and the goodness of fit tests were performed. Results revealed that the maximum weekly cases were best fitted by the Dagum distribution, which was accepted by three goodness of fit tests. The generalized Pareto distribution best fitted the maximum weekly deaths, which was also accepted by the goodness of fit tests. The statistical analysis was carried out using the Easy-Fit software and Microsoft Excel 2019.

     Detection moving car in front view is difficult operation because of the dynamic background due to the movement of moving car and the complex environment that surround the car, to solve that, this paper proposed new method based on linear equation to determine the region of interest by building more effective background model to deal with dynamic background scenes. This method exploited the permitted region between cars according to traffic law to determine the region (road) that in front the moving car which the moving cars move on. The experimental results show that the proposed method can define the region that represents the lane in front of moving car successfully with precision over 94%and detection rate 86

Shallow foundations have been commonly used to transfer load to soil layer within the permissible limits of settlement based on the bearing capacity of the soil. For most practical cases, the shape of the shallow foundation is of slight significance. Also, friction resistance forces in the first layers of soils are negligible due to non-sufficient surrounding surface area and compaction conditions. However, the bearing capacity of a shallow foundation can be increased by several techniques. Geocell is one of the geosynthetic tool applied mainly to reinforce soil. This study presents a numerical approach of honeycombed geocell steel panels reinforcing the sandy soil under shallow foundation, and several parameters are investigated such as th

Recently, the theory of Complex Networks gives a modern insight into a variety of applications in our life. Complex Networks are used to form complex phenomena into graph-based models that include nodes and edges connecting them. This representation can be analyzed by using network metrics such as node degree, clustering coefficient, path length, closeness, betweenness, density, and diameter, to mention a few. The topology of the complex interconnections of power grids is considered one of the challenges that can be faced in terms of understanding and analyzing them. Therefore, some countries use Complex Networks concepts to model their power grid networks. In this work, the Iraqi Power Grid network (IPG) has been modeled, visua

Localized surface plasmons (LSPs) are a potentially valuable property for the practical use of small size metallic particles. Exploiting the LSPs in metallic nanoparticle (NP)-based solar cells was shown to increase the efficiency of solar panels. A large extinction cross section of NPs allows for high scattering of light at the surface of the panel, which reduces the panel thickness, allowing for small size and low-cost solar cells. In this paper, the extinction cross-section of spherical nanoparticles is studied and simulated numerically. Surface plasmons were first modeled using the Drude’s model then the scattering and absorption cross-sections were derived. Commercial3D simulation software was used to model the near field dis

n this work, it have earthquakes data been analytized as a result of ground water injection attendant to dig wells and oil, which affects the pressure pores in the rock and is according to cause a medium-sized earthquake that have already occurred more than 20 times. according to the publication of the Department of Attenba, the dangers of earthquakes and movement flooring in Guy-Greenbrie-United area of one of United States of America cities, where these data represent a monitor magnitudes of earthquakes from September 2008 the September 2011, we conducted statistical analysis and create a model for earthquakes flooring that was chosen for four dis-tributions and results i the optima reveal the best distribution is the Burr X11 then Gen

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi