Copper (Cu) Zinc (Zn) and Magnesium (Mg) in serum, RBC, urine and dialyzate fluids were
studied in 39 patients, who have been undergoing chronic haemodialysis treatment. They were
divided in to polyuric , oliguric and anuric depending on their urinary output. Elevated serum and
RBC Mg was observed before dialysis, while decreased serum and RBC level was noticed except
serum Mg of polyuric patients. Before dialysis elevated serum and RBC Zn were observed. While
after dialysis these parameters were increased. Normal RBC Cu value before dialysis was observed.
While low serum Cu was noticed. After dialysis serum Cu showed raised value, while RBC level
decreased in oliguric and increased in polyuric patients. Zn / Cu ratio found to be high in those
patients. All these results were discussed in relation to urine content and also to the dialyzate fluid.
Key words: Trace elements, Haemodialysis, Renal failure
The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow
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