Classification of BMD and ADHD patients using their EEG signals

Bipolar Mood Disorder (BMD) and Attention Deficit Hyperactivity Disorder (ADHD) patients mostly share clinical signs and symptoms in children; therefore, accurate distinction of these two mental disorders is a challenging issue among the psychiatric society. In this study, 43 subjects are participated including 21 patients with ADHD and 22 subjects with BMD. Their electroencephalogram (EEG) signals are recorded by 22 electrodes in two eyes-open and eyes-closed resting conditions. After a preprocessing step, several features such as band power, fractal dimension, AR model coefficients and wavelet coefficients are extracted from recorded signals. This paper is aimed to achieve a high classification rate between ADHD and BMD patients using a suitable classifier to their EEG features. In this way, we consider a piece wise linear classifier which is designed based on XCSF. Experimental results of XCSF-LDA showed a significant improvement (86.44% accuracy) compare to that of standard XCSF (78.55%). To have a fair comparison, the other state-of-art classifiers such as LDA, Direct LDA, boosted JD-LDA (BJDLDA), and XCSF are assessed with the same feature set that finally the proposed method provided a better results in comparison with the other rival classifiers. To show the robustness of our method, additive white noise with different amplitude is added to the raw signals but the results achieved by the proposed classifier empirically confirmed a higher robustness against noise compare to the other classifiers. Consequently, the proposed classifier can be considered as an effective method to classify EEG features of BMD and ADHD patients.     

利用不稳定压力数据建立碳酸盐岩储集层模型

地质模型通常是根据地质信息,利用地球物理和岩石物理资料来建立.为了建立准确的碳酸盐岩油藏(伊朗海上油田)模型,不仅利用了地震和测井数据,也结合了该油田现有99井次的试井数据.在试井解释过程中观察到了许多特征,如封闭断层、水层、裂缝和分层系统,其中某些特征只能利用压力瞬态数据来识别,如地震数据中观察不到的一条封闭断层在试井曲线上则可识别.除了识别构造特征外,试井解释得到的渗透率数据也可用于绘制渗透率等值线图,而不再采用岩心分析得到的渗透率数据.试井得到的渗透率是泄油区域的有效渗透率,而岩心分析得到的渗透率并不能代表非均质油藏的渗透性.利用非稳态压力分析结果建立油藏模型,可以提高碳酸盐岩油藏建模的准确性.图6表1参8     

Rapid synthesis of nanocrystalline YAG via microwave‐assisted solvothermal process

A rapid synthesis procedure for the preparation of nanocrystalline yttrium aluminium garnet (nYAG) particles has been developed, for the first time, using the combination of subcritical conditions and microwave irradiation. It is believed that the use of butanediol delayed the onset of pressure rise due to its low partial pressure, while the microwaves facilitated a selective crystallization of nanocrystalline YAG particles in the solvent medium. This methodology was found to encourage rapid bulk nucleation with minimal particle growth or agglomeration of nYAG particles. The resultant powder characteristics, examined using XRD and TEM analyses, revealed that a narrow window of pressure and temperature conditions needed to be maintained to achieve spherical particles in the size range 60‐80 nm without any intermediate phases being formed. The STEM/EDX and FTIR results obtained suggested that the nucleated YAG particles were masked by carbon clouds until they were completely crystallized into single phase YAG particles; this allowed them to be dispersed in water with little agglomeration.     

Presenting a new search strategy to select synchronization values for classifying bipolar mood disorders from schizophrenic patients

There is a growing interest to employ synchronization methods to reveal natural connections among the brain lobes by measuring co-activations among EEG channels. Regarding high number of EEG channels, lots of synchronization indexes are determined between two by two channels leading to construct a high dimensional feature vector for each time frame. The objective of this paper is to propose an effective feature selection method to find discriminative synchronization indexes in order to classify patients with schizophrenia from those with bipolar mood disorder (BMD). The state-of-art synchronization methods from various domains such as phase-locking value (PLV), robust synchronization (RS), and synchronization likelihood (SL), were implemented to provide a rich feature set in order to classify the two groups. To increase the classification accuracy, a capable feature selection scheme is proposed entitled greedy overall relevancy (GOR) to select discriminative synchronization indexes. The elicited synchronization vectors of 53 subjects imposed to support vector machine (SVM) classifier and the classification result with and without applying GOR, provided 92.45% and 88.68% accuracy, respectively. Across-group variance (AGV) is chosen as a rival method to GOR; the selected features by AGV entered to the classifier resulting in 91.34% accuracy. Using pair T-test exhibits the significant superiority of GOR to AGV such that P-value determined less than 0.05. To the best of authors' knowledge, this is the first attempt to utilize the selected synchronization indexes as informative features applying to a classifier for diagnosing the psychiatric patients.     

Using the mixture design approach to predict the rheological properties of low-calorie dairy desserts containing gum tragacanth exuded by three Iranian Astragalus species

Food Science and Biotechnology - In this study, the flow behavior and creep parameters of saffron desserts containing gum tragacanth combinations of three species were modeled by the mixture design...     

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.     

Analysis of sulfur removal in gasoil hydrodesulfurization process by application of response surface methodology

To investigate the efficiency of a Co-Mo catalyst in HDS process, a set of experiments were designed and carried out based on central composite design (CCD) methodology in an HDS pilot plant. The designed variables included temperature, LHSV and pressure. However, the hydrogen over fresh feed ratio remained constant. The ranges of these variables were, respectively, equal to 335?C361 ??C, 1.06?C1.8 1/hr and 46.8?C53.2 bar. The outcomes of experiments were employed to determine the coefficients of statistical models. For the clarification of the accuracy of the model, several statistical tests like ANOVA (Analysis of Variance), Lack-of-Fit test and residual squares were carried out. To optimize the operating conditions to achieve maximum sulfur removal, an optimization algorithm was employed. The outcomes revealed that the minimum sulfur content, which is 23.65 ppm in the final product, is attained at 355 ??C, 1.2 1/hr and 49.2 bar.