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Copyright of Zeitschrift für Herz-, Thorax- und Gefaesschirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zeitschrift für Herz-, Thorax- und Gefaesschirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we carried out with the DTP (dithieno(3,2-b:2,3-d) pyrrole) molecule which is a member of the conductive polymer class that is one of the topics that have been studied prevalently in recent times, and the density functional theory's (DFT) calculation methods were used to shed light on the sensor mechanisms of its interactions with SOx (SO2 and SO3) sulfur oxides. The changes in the geometric and electronic characteristics of a sensor mechanism designed with the DTP molecule when it encountered sulfur oxides were examined. With these changes, the usability of DTP as a sensor material was proven. DTP sensor applications using this method were not investigated in previous studies. The B3LYP 6–31 G(d) levels of DFT were used in the calculations. In the calculations, during the interaction between analyte (SO2-SO3) and the conductive polymer DTP (dithieno(3,2-b:2,3-d) pyrrole), especially, the changes in its geometric and electronic structures were observed. With these changes that were observed in the geometric structure, as a result of the interaction between the conductive polymer and gas molecules, the resistance on the polymer's main chain decreased, and conductivity increased. Calculations on the bandgap on HOMO-LUMO energy levels were observed to decrease. Thus, the structural conductivity of the molecule increased. Additionally, the experiments showed that, as a result of interaction with gas molecules, the bandgap in the ionization potential, electron affinity, and HOMO-LUMO energy levels varied. These variations showed the detection mechanisms for sulfur oxides by the DTP (dithieno(3,2-b:2,3-d) pyrrole) molecule that may be used to design sensors. [ABSTRACT FROM AUTHOR]

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index. [ABSTRACT FROM AUTHOR]

Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the correlation of laboratory tests and sympathetic skin response parameters in patients with fibromyalgia syndrome (FMS) by using artificial neural networks. It focuses on the relationship of sympathetic skin response (SSR) parameters and laboratory tests. The study was conducted at Suleyman Demirel University's Faculty of Medicine's Department of Physical Medicine and Rehabilitation.

In this study, the points of Sympathetic skin response that can be measured from different zones on body of healthy and patient subjects are determined. The Sympathetic skin responses on these points are obtained using a measurement device that is called Grass Model 7 Polygraph 1. The database is formed in Cerrahpaşa University, Faculty of Medicine and data is taken from healthy and patient subjects who are volunteer. Some parameters of the subjects which are more effective on SSR such as height, weight, age must be chosen between the specific limits to obtain results more clearly. The symmetric points on human body are chosen for the measurement. After that, the Sympathetic skin response values which are measured from a human body are simulated and tested by using artificial neural network toolbox on Matlab software. The structure of the chosen neural network is a multilayer feedforward neural network. According to simulation results, the application method as diagnosis-purposed of the lung cancer patients is explained by using the differences related to these values on the skin. [ABSTRACT FROM AUTHOR]

In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen–Loéve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method. [ABSTRACT FROM AUTHOR]