A crucial area of research in nanotechnology is the formation of environmentally benign nanoparticles. Both unicellular and multicellular play an important role in synthesis nanoparticles through the production of inorganic materials either intracellularly or extracellularly. The agents (pigments, siderophores, cell extracted metabolites and reducing compounds) were used to prepare silver nanparticles with different sizes and shapes. The color variations (dark yellow, slightly dark yellow and golden yellow) arising from changes in the composition, size, and shape of nanoparticles, surrounding medium can be monitored using UV-visible spectrophotometer. These effects are due to the phenomena called surface plasmon resonance. The silver nanopa

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce

     The problem in the design of a cam is the analyzing of the mechanisms and dynamic forces that effect on the family of parametric polynomials for describing the motion curve. In present method, two ways have been taken for optimization of the cam size, first the high dynamic loading (such that impact and elastic stress waves propagation) from marine machine tool which translate by the roller follower to the cam surface and varies with time causes large contact loads and second it must include the factors of kinematics features including the acceleration, velocity, boundary condition and the unsymmetrical curvature of the cam profile for the motion curve.

     In the theoretical solution

     Bimetallic Au –Pt catalysts supporting TiO₂ were synthesised using two methods; sol immobilization and impregnation methods. The prepared catalyst underwent a thermal treatment process at 400^◦ C, while the reduction reaction under the same condition was done and the obtained catalysts were identified with transmission electron microscopy (TEM) and energy-dispersive spectroscopy (EDS). It has been found that the prepared catalysts have a dimension around 2.5 nm and the particles have uniform orders leading to high dispersion of platinum molecules .The prepared catalysts have been examined as efficient photocatalysts to degrade the Crystal violet dye under UV-light. The optimum values of Bimetallic Au –

The current research aims to analyze the mathematics book for the first intermediate grade according to the dimensions of mathematical power by answering the following question: What is the percentage of the availability of the dimensions of mathematical power included in the content of the mathematics textbook for first-grade students, average, in its first, and second parts for the academic year 2020-2021, approved by the Iraqi Ministry of Education / General Directorate of Curricula? The research community was determined by middle school mathematics books, and middle school students for middle ,and high school day schools affiliated to the Directorate of Education in Dhi Qar, and by the intentional test, the research sample was s

Solid dispersion (SD) formulation has attracted much attention due to its potential in enhancing dissolution performances of poorly soluble active pharmaceutical ingredients (API). Recently, a review on dissolution performances of SDs classifies the improvement into 3 categories, where 82 % of the studies showed improved bioavailability, 8 % showed reduced bioavailability and 10 % revealed similar bioavailability as compared to pure APIs. This indicates the inconsistent degrees of dissolution improvement of poorly soluble APIs in SD. Although a few factors related to the choice of carriers have been suggested to contribute to the dissolution improvement, however, the underlying factor determining the discrepancy in the degree of dissolution

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

 A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed