A new Ni(II) nanostructured chelating system (DHN) was introduced for selective optical heavy-metal ion sensing in an aqueous medium. The cooperative chelating system comprising 8-hydroxyquinoline (8-HQ) and dimethylglyoxime (DMG) has been developed for the first time in association with fibre optic sensing for selective optical heavy-metal ion sensing in an aqueous medium. The Ni(II) nanocompound fluoresces upon 578 nm excitation, showing a highly sensitive optical response with a linear calibration curve in the range 0–100 ng/mL. The regression equation of the calibration curve is y = 0.0035x + 0.9990, which indicates very good linearity, implying R2 = 0.999 with high sensitivity (calibration slope of 0.0035) and low baseline noise (bla

The current paper investigates the effect of cut-out design parameters on load-bearing capacity and buckling behaviour of steel cylindrical shell using a nonlinear finite element analysis in modelling cylinder buckling under longitudinal compressive load. The effect of four geometry design parameters: shell diameter to thickness ratio, cut-out location, orientation, and size were investigated in this study. To enhance the prediction of buckling behaviour, both geometrical and material nonlinearities were considered. An ANSYS APDL code was written and tested by verifying its validity through comparison with former buckling study. The results showed that changing the cut-out location from mid-height of the cylindrical shell towards a

Background: Atherosclerosis is well known related to age and certain cardiovascular diseases. Aging is one reason of arteries function deterioration which can cause loss of compliance and plaque accumulation, this effect increases by the presence of certain diseases such as hypertension and diabetes disease. Aim: To investigate the reduction of blood supply to the brain in patients with diabetes and hypertension with age and the role of resistive index in the diagnosis of reduced blood flow. Method: Patients with both diseases diabetic and hypertension were classified according to their age to identify the progression of the disease and factors influencing the carotid artery blood flow. By using ultrasound and standard Doppler techniq

An experimental study was conducted to evaluate the effect of AL-coholic extract alkaloid of Cordia myxa leafs in fourth larval stage of lesser grain borer Rhyzopertha dominica. Using alkaline extracts of 8%, the study has been shown clear effect increased in mortality rate for fourth larval stage 93.3% and degressed to 66.6% at 4% concentrate to 13.3% with control treatment .Ahigher percentage of pupal mortality 16.6% at 4% concentrate has been observed, while no natural emergence carried out at concentrates of 4.6% comparing with control treatment of 86.66%, at the same time percentage of deformation has been increased to 16.66% at 4% of extracts and degressed to 6.66% at 6% while no deformation have been shown with control treatment .

Background: Diabetes mellitus consists of a group of diseases characterized by abnormally high blood glucose levels. Glycated haemoglobin (HbA1c) is a form of haemoglobin used to identify the average concentration of plasma glucose over prolonged periods of time. It is formed in a non-enzymatic pathway by normal exposure of hemoglobin to high levels of plasma glucose, The main alterations observed in the saliva of Type 1 diabetic patients are hyposalivation and alteration in its composition, particularly those related to the levels of glucose. The aim of the present study was to assess the effect of Glycated haemoglobin level on the level of salivary glucose which may have an effect on oral health condition. Materials and methods

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti