The human kidney is one of the most important organs in the human body; it performs many functions
and has a great impact on the work of the rest of the organs. Among the most important possible treatments is
dialysis, which works as an external artificial kidney, and several studies have worked to enhance the
mechanism of dialysate flow and improve the permeability of its membrane. This study introduces a new
numerical model based on previous research discussing the variations in the concentrations of sodium,
potassium, and urea in the extracellular area in the blood during hemodialysis. We simulated the differential
equations related to mass transfer diffusion and we developed the model in MATLAB Simulink environment.
A value of 700 was appeared to be the most appropriate as a mass transfer coefficient leading to the best
permeability. The suggested models enabled to track the temporal variations of urine, K and Na concentrations
in blood streamline. This also produced the time needed to reach the requested concentrations mentioned in
literature studies (960 ms). Concentrations evaluation was performed with error rates not exceeding 2% for all
ions compared to the normal values of human blood.The current work presents the first step towards combinig
the mass transfer and diffusion principles with our efforts in designing and implementing an electrophoresisbased implantable kidney.
Abstract :
The aim of the study is to diagnose the level and nature of the relationship between cognitive interactions, cognitive ignorance and the achievement of strategic excellence). The aim of this is to explore theoretical philosophy and intellectual implications of these variables, and, then test the correlation and impact relationships and their feasibility in the application environment, which was formed from the seven directorates in the Ministry of Education. The sample of the study was determined by the directors, their assistants, and the department directors. The sample number is (130). The importance of the study is to come out with a philosophical basis for the nature of the variables, based on an app
... Show MoreToday with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned
This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback
... Show More