Eyyup Gulbandilar

Evaluating of traumatic brain injuries using artificial neural networks

The purpose of this study is to develop a diagnostic system to detect the severity of traumatic brain injuries using artificial neural networks. Three layered back propagation neural network with an input layer of 10 nodes whose output providing the inputs to a hidden layer was used. Thirty-two patients with traumatic brain injuries in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the data. The results obtained from the system were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild, moderate and severe electroencephalography tracing data. Getting this system in routine use will lead to make a rapid decision for the degree of trauma with electroencephalography and revised trauma score.

Prediction of Low Back Pain with Two Expert Systems

Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.

Detection of traumatic brain injuries using fuzzy logic algorithm

The aim of this study was to develop a diagnostic system for detecting the severity of traumatic brain injuries using fuzzy logic. Twenty-six traumatic brain injury patients in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the system. The results were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild and severe electroencephalography tracing data. Getting this system in routine use will facilitate to make a rapid decision for the degree of trauma with electroencephalography.

Relationship between skin resistance level and static balance in type II diabetic subjects

Diabetes mellitus is major cause leading to pathological changes in skin foot plantar area (SFPA) and affected the static standing balance duration (SSBD). Skin resistance level (SRL) is related to skin conductance which changes in the presence of sweat. This study aims to find out the relationship between the SRL and SSBD in type II diabetic patients. Sixty-eight voluntary students, 30 type II diabetic patients and 30 healthy non-diabetic subjects, were participated to the study. The SSBD was measured on dominant and non-dominant legs. SRLs were recorded with two surface electrodes over the metatarsus heads and heel. The SSBD of the healthy young group was found to be higher than the other groups (P<0.001). The SRL values of the non-dominant leg in the diabetic group was found to be lower than the others (P=0.014). For dominant and non-dominant legs within each group, only the healthy young group has statistically difference (P=0.012). A significant correlation was seen to be between the SRL and SSBD for only healthy non-diabetic group for the non-dominant leg. The relation between the SRL and SSBD is poor but very promising. Measurement of the SRL can be used in evaluating the inflammation of the diabetic foot.

Comparison of the biomechanical effects of pertrochanteric fixator and dynamic hip screw on an intertrochanteric femoral fracture using the finite element method

This study was conducted to examine and compare the pertrochanteric fixator (PTF) and dynamic hip screw (DHS).

Prediction of the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic

Fuzzy logic has recently been widely used to model in many areas of civil engineering applications. Especially as a result of the findings of experimental studies with fuzzy logic to predict good results have been obtained. In this study, Portland cement is composed of fly ash and silica fume with determined proportional. By this procedure, eight different mixtures were prepared and the effect of cement was investigated on the starting and finishing time of the setting. According to the results obtained in the setting time and finishing, all the mixing ratio of the prolonged period of time was determined. Also, by using fuzzy logic method, prediction model was formed based on the quantity of fly ash and silica fume to predict the initial and final setting times of cement, which could not be determined with experimental approaches. The experimental results are compared with the fuzzy logic results, and the correlation coefficients for the initial and final setting time are found 0.96 and 0.92, respectively. These results show that the developed model can be successfully applied in the cement industry.

Comparison of effects of different screw materials in the triangle fixation of femoral neck fractures

In this study, biomechanical behaviors of three different screw materials (stainless steel, titanium and cobalt–chromium) have analyzed to fix with triangle fixation under axial loading in femoral neck fracture and which material is best has been investigated. Point cloud obtained after scanning the human femoral model with the three dimensional (3D) scanner and this point cloud has been converted to 3D femoral model by Geomagic Studio software. Femoral neck fracture was modeled by SolidWorks software for only triangle configuration and computer-aided numerical analyses of three different materials have been carried out by AnsysWorkbench finite element analysis (FEA) software. The loading, boundary conditions and material properties have prepared for FEA and Von-Misses stress values on upper and lower proximity of the femur and screws have been calculated. At the end of numerical analyses, the best advantageous screw material has calculated as titanium because it creates minimum stress at the upper and lower proximity of the fracture line.Graphical Abstract

Determination of the lower calorific and ash values of the lignite coal by using artificial neural networks and multiple regression analysis

The calorific value of coal varies depending on type of coal and foreign matter content. The calorific value of coal from pits is determined by analyzing moisture, volatile matter, ash and sulfur content in laboratories. This analysis process imposes a burden on businesses both in terms of time and cost. However, calorific value, in particular, can be determined through simpler methods by using ash and moisture values. The aim of this study was to develop a model that reduces the time and labor costs of coal companies by determining the calorific value and ash content of coal with the back-propagation algorithm of artificial neural networks (ANN). The model design was developed based on the data that was obtained from the laboratory analyses of raw coals from the pits of Tuncbilek and Seyitomer mining areas in Turkey. The values of moisture, volatile matter, original ash and sulfur were determined as input variables, and the lower calorific values and ash content were selected as output variables. The lower calorific values (LCV) and Ash estimated by the developed model were compared with the LCV obtained in the laboratory tests and the results showed a correlation. In addition, two different ANN models and multiple regression analysis (MRA) were developed to obtain the single output of the LCV and ash parameters with similar features. As a result, the ANN model and MRA equation models proposed in this study was shown to successfully estimate the LCV and ash content of coals without performing laboratory analyses.

Response concerning ‘Letter to editor’ by Ni et al. (2017), Arch Orthop Trauma Surg. DOI 10.1007/s00402-017-2710-2

1. Gok K, Inal S, Gok A, Gulbandilar E (2017) Comparison of effects of different screw materials in the triangle fixation of femoral neck fractures. J Mater Sci Mater Med 28(5):81 2. Ni J, Wang X, Yuang Y, Liu H, Zhou L (2017) Letter to the editor concerning ‘‘Femoral neck fracture osteosynthesis by the biplane double-supported screw fixation method (BDSF) reduces the risk of fixation failure: clinical outcomes in 207 patients’’ by Filipov O, Sommer C et al (2017). Arch Orthop Trauma Surg. doi:10.1007/s00402-017-2710-2 3. Gok K, Inal S (2015) Biomechanical comparison using finite element analysis of different screw configurations in the fixation of femoral neck fractures. Mech Sci. 6(2):173–179. doi:10.5194/ ms-6-173-2015 Dear Editor,

DETERMINING OF DEFORMATION ON THE CERAMICS USING INTELLIGENT SYSTEMS

In the development and design of new products in ceramic industry unlimited number of trials has to be carried out to obtain the appropriate format due to the deformation. These trials lead to increased costs and loss of labor in the production stage. In this study, it is aimed to develop computer software to reduce these losses in the ceramic industry. In this work, in different compositions, firing temperatures and sintering times cylindrical form samples are created. These samples, after the drying process have been scanned with the 3D scanner at the same time measured using classical methods. Later on these samples are fired at different sintering times and temperatures in ceramic firing and measured using the same methods. Thus the deformations in the base, side and mouth regions of the cylindrical samples are identified. In the light of this experimental data fuzzy systems have been designed. In the fuzzy system, the firing temperature, sintering time and composition of ceramic samples have been used as the input data while the amount of deformation is used as the output data. In the fuzzy system we have developed, an accuracy of 80% for the base deformation, 77.5% for the edge deformation and 55% for the mouth deformation of the cylindrical form have been achieved. However, in determining the deformation of the square form the targeted results have not been obtained.