MEDtube Science

ABSTRACT Kidney transplantation from a living donor is a method of choice in the treatment of end-stage renal disease. The programme of care for a living kidney donor should cover the assessment of health status and quality of life and an analysis of the data collected on the basis of the same criteria. The conclusions drawn should be used in the assessment of the risk associated with the kidney harvesting procedure, assessment of the health status of the living donor and an improvement of the results of kidney transplantation from a living donor

Artificial Neural Network Structure Optimisation in the Pareto Approach on the Example of Stress Prediction in the Disk-Drum Structure of an Axial Compressor

The article presents the process of selecting and optimising artificial neural networks based on the example of determining the stress distribution in a disk-drum structure compressor stage of an aircraft turbine engine. The presented algorithm allows the determination of von Mises stress values which can be part of the penalty function for further mass optimization of the structure. A method of a parametric model description of a compressor stage is presented in order to prepare a reduced stress distribution for training artificial neural networks. A comparative analysis of selected neural network training algorithms combined with the optimisation of their structure is presented. A genetic algorithm was used to determine the optimal number of hidden layers and neurons in a layer. The objective function was to minimise the absolute value of the relative error and standard deviation of stresses determined by FEM and artificial neural networks. The results are presented in the form of the Pareto front due to the stochastic optimisation process

Optimization of a Jet Engine Compressor Disc with Application of Artificial Neural Networks for Calculations Related to Time and Mass

The paper presents the results of a series of numerical research on the possibility of applying Artificial Neural Networks (ANNs) for ultimate strength calculations of selected parts of rotating machines. The layout and the principle of the algorithm operation were described, beginning from the general assumptions and then moving to the detailed description of the subsequent modules. The effects of applying the algorithm were presented on the example of the analysis of the compressor disc. The significant benefits of using it were the reduction of optimization time by about 40% and the disc weight reduction by 0.5 kg. Accuracy of ANNs was different in each iteration of a presented algorithm. Finally, high accuracy of neural networks was achieved with the following mean values of relevant indices reached in the last iteration: RMSE=0.5983, MAPA=0.0733 and R^2=0.99895. The further perspectives of undertaken research were defined at the end

Modification of Genetic Algorithm Based on Extinction Events and Migration

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc

Application of Laboratory Tests in Numerical Analysis for Exhaust Emissions in Business Jet Engines

This article deals with the exhaust emissions from aircraft turbine engines, which is related to the rapidly growing market for this type of aircraft and its contribution to toxic emissions. The test carried out was done on a business jet turbine engine exhaust pollutants. The test object was the DGEN 380 engine. In order to determine the toxic composition of the exhaust gas as a function of the engine's operating range, an experiment related to the actual engine was conducted in the first stage. The test performed on the static thrust stand of the DGEN 380 turbine engine provided the necessary data on the parameters of the working medium for further research. The actual rotational characteristics of the engine were obtained. It was also determined numerically using GasTurb software. A high correspondence between experimental and calculated parameters was obtained, which gave the possibility of using them in further analyses of the exhaust gas pollutants of the studied engine. The correspondence of the results showed the correctness of the computational model built, thus predestining it for use in further analysis. This paper presents a model of the reverse-flow combustor made for numerical thermal-fluid studies. The thermal-fluid analysis of the model was performed in the ANSYS Fluent environment. The calculations were performed for three shaft speed. The numerical analysis provided information on changes in pollutant components of the exhaust gas of the DGEN 380 aircraft turbine engine as a function of changes in the shaft speed range. The results showed that the levels of nitrogen oxides depend greatly on shaft speed. The model built and the numerical analyses conducted also provided information about the zones inside of liner casing that affect significantly the amount of pollutant compounds obtained, which can then be used in the work on improving the design in terms of reducing the engine exhaust pollutants

The affective tone of narration and posttraumatic growth in organ transplant recipients

The aim of the study was to verify the hypothesis that positive affective tone of narratives is connected to the experience of posttraumatic growth among transplant patients. Kidney transplant patients (N = 51) and liver transplant patients (N = 48) participated in the study. In the first stage, about 10 weeks after transplant, the participants told two stories about important, freely chosen events from their lives. During the second meeting 10-12 months later we measured posttraumatic growth. Results indicated that the affective tone of narratives about past events was associated with the level of post-traumatic growth measured 10-12 months later. This proves that the affective tone of narratives about life, understood as a relatively constant individual characteristic, promote posttraumatic growth