Due to the remarkable progress in photovoltaic technology, enhancing efficiency and minimized the costs have emerged as global challenges for the solar industry. A crucial aspect of this advancement involves the creation of solar cell antireflection coating, which play a significant role in minimizing sunlight reflection on the cell surface. In this study, we report on the optimization of the characteristics of CeO2 films prepared by pulsed laser deposition through the variation of laser energy density. The deposited CeO2 nanostructure films have been used as an effective antireflection coating (ARC) and light-trapping morphology to improve the efficiency of silicon crystalline solar cell. The film’s thickness increases as laser fluence i

The atmospheric air cold plasma has been used to manufacture gold nanomaterials for treating parasitic leishmaniasis. This study experimentally assessed the treatment of Leishmania parasites (L. donovani and L. tropica) by gold nanoparticles. Specifically, atmospheric pressure nonthermal plasma was generated using different diameters (1.0, 2.8, 3.8 and 4.3 mm) of high voltage electrode. Aqueous gold tetrachloride salts (HAuCl4·4H2O) were used as precursor to produce gold nanoparticles. UV-vis spectroscopy and x-ray diffraction were conducted for characterization of the nanoparticles. The optimum condition (a diameter of 1 mm) was chosen to prepare gold nanoparticles, where the grain size was found to be 17 nm. Accordingly, the nanoparticle

To ensure fault tolerance and distributed management, distributed protocols are employed as one of the major architectural concepts underlying the Internet. However, inefficiency, instability and fragility could be potentially overcome with the help of the novel networking architecture called software-defined networking (SDN). The main property of this architecture is the separation of the control and data planes. To reduce congestion and thus improve latency and throughput, there must be homogeneous distribution of the traffic load over the different network paths. This paper presents a smart flow steering agent (SFSA) for data flow routing based on current network conditions. To enhance throughput and minimize latency, the SFSA distrib

Priority of road maintenance can be viewed as a process influenced by decision-makers with varying decision-making power. Each decision-maker may have their view and judgment depending on their function and responsibilities. Therefore, determining the priority of road maintenance can be thought of as a process of MCDM. Regarding the priority of road maintenance, this is a difficult MCDM problem involving uncertainty, qualitative criteria, and possible causal relationships between choice criteria. This paper aims to examine the applicability of multiple MCDM techniques, which are used for assessing the priority of road maintenance, by adapting them to this sector. Priority of road maintenance problems subject to internal

In this paper, the computational method (CM) based on the standard polynomials has been implemented to solve some nonlinear differential equations arising in engineering and applied sciences. Moreover, novel computational methods have been developed in this study by orthogonal base functions, namely Hermite, Legendre, and Bernstein polynomials. The nonlinear problem is successfully converted into a nonlinear algebraic system of equations, which are then solved by Mathematica®12. The developed computational methods (D-CMs) have been applied to solve three applications involving well-known nonlinear problems: the Darcy-Brinkman-Forchheimer equation, the Blasius equation, and the Falkner-Skan equation, and a comparison between the met

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l