At a temperature of 300 K, a prepared thin film of Ag doped with different ratios of CdO (0.1, 0.3, 0.5) % were observed using pulse laser deposition (PLD). The laser, an Nd:YAG in ?=1064 nm, used a pulse, constant energy of 600 mJ ,with a repetition rate of 6 Hz and 400 pulses. The effect of CdO on the structural and optical properties of these films was studied. The structural tests showed that these films are of a polycrystalline structure with a preferred orientation in the (002) direction for Ag. The grain size is positively correlated with the concentration of CdO. The optical properties of the Ag :CdO thin film we observed included transmittance, absorption coefficient, and the energy gap in the wavelength range of 300-1100

The current study investigated the stability and the extraction efficiency of emulsion liquid membrane (ELM) for Abamectin pesticide removal from aqueous solution. The stability was investigated in terms of droplet emulsion size distribution and emulsion breakage percent. The proposed ELM included a mixture of corn oil and kerosene (1:1) as a diluent, Span 80 (sorbitan monooleate) as a surfactant and hydrochloric acid (HCl) as a stripping agent without utilizing a carrier agent. Parameters such as homogenizer speed, surfactant concentration, emulsification time and internal to organic volume ratio (I/O) were evaluated. Results show that the lower droplet size of 0.9 µm and higher stable emulsion in terms of breakage percent of 1.12 % were

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in

Background Parkinson’s disease (PD) is currently the fastest-growing neurological disorder in the world. Patients with PD face numerous challenges in managing their chronic condition, particularly in countries with scarce healthcare infrastructure. Objective This qualitative study aimed to delve into neurologists’ perspectives on challenges and gaps in the Iraqi healthcare system that influence the management of PD, as well as strategies to mitigate these obstacles. Method Semi-structured interviews were conducted with neurologists from five different Iraqi provinces, working in both hospitals and private neurology clinics, between November 2024 and January 2025. A thematic analysis approach was employed to identify the main challenge

        In this paper, double Sumudu and double Elzaki transforms methods are used to compute the numerical solutions for some types of fractional order partial differential equations with constant coefficients and explaining the efficiently of the method by illustrating some numerical examples that are computed by using  Mathcad 15.and graphic in Matlab R2015a.

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s