Antifungal Activity of Olive Oil and Ozonated Olive Oil Against Candida Spp. and Saprochaete Spp.

Ozonated olive oil was investigated for their capacity to inhibit growth of 38 yeast strains of Candida albicans, Candida glabrata, Candida krusei, Candida parapsilosis, and Saprochaete capitata. Two different ozonated olive oil (OZO1, OZO2) and two different olive oil (OL1, OL2) samples having different biochemical parameters were assessed in terms of their antifungal ability and comparison was made. Fluconazole was chosen as control antifungal agent. Each sample’s antifungal activity decreased in the following order: OZO1 > OZO2 > OL1 ≥ OL2. This study demonstrated that ozonated olive oil may help to control some fluconazole-resistant and dose-dependent sensitive fungal strains.     

An investigation on the mechanisms of instabilities and safe design of the south slope at a lignite pit (SW Turkey) based on a sensitivity approach

Turkey has significant lignite reserves which are generally being extracted using open pit mining methods. The Hüsamlar pit is one of the operated lignite pits in the well-known Mugla lignite province in SW Turkey. Some local failures and one large failure, which caused the evacuation of the Hüsamlar village located next to the slope crest and interruption in coal production, occurred along the south slope of this pit. This paper outlines the results of the field and laboratory geotechnical investigations associated with the causes and mechanisms of the instabilities, and assessments on the possible modifications in the current and planned final slope geometries to improve the stability of the south slope. Since no sufficient data on groundwater conditions in the pit were available, in order to reduce the uncertainty associated with groundwater, different pore pressure ratios (r _u) were considered and a sensitivity approach was used in the stability assessments. The back-analyses of the observed instabilities including one or more benches in the overburden indicated that the most critical modes of failure for the south slope are circular and composite sliding surfaces. Although kinematical analyses suggested that structurally controlled failures would not be expected, one local planar failure that occurred in the south slope emphasizes that the possibility of local planar sliding should be considered when the dip of bedding planes locally exceed 20° and pore pressure becomes high. In addition, the back-analyses revealed that r _u was probably between 0.3 and 0.4 and the residual shear strength along the bedding planes was critical when slope instabilities occurred along the south slope. The stability assessments for the current and the final south slope, which was planned by the mining organization operating the pit, indicated that some modifications in bench and slope geometries are necessary to achieve a factor of safety of 1.3, which is a commonly used value in open pit practice. In addition, these assessments also suggested that the most critical zone in the overburden was the thinly bedded marl in terms of stability, and at the thickest part of this material (30 m), the overall slope angles satisfying F = 1.3 at r _u values of 0.2, 0.3 and 0.4 should be 18°, 17° and 15°, respectively. Except those in the thinly bedded marl, bench widths in the overburden units and coal seam are reduced and steeper slopes with F ≥ 1.3 were achieved.     

Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering

This study presents the application of fuzzy c-means (FCM) clustering-based feature weighting (FCMFW) for the detection of Parkinson's disease (PD). In the classification of PD dataset taken from University of California – Irvine machine learning database, practical values of the existing traditional and non-standard measures for distinguishing healthy people from people with PD by detecting dysphonia were applied to the input of FCMFW. The main aims of FCM clustering algorithm are both to transform from a linearly non-separable dataset to a linearly separable one and to increase the distinguishing performance between classes. The weighted PD dataset is presented to k-nearest neighbour (k-NN) classifier system. In the classification of PD, the various k-values in k-NN classifier were used and compared with each other. Also, the effects of k-values in k-NN classifier on the classification of Parkinson disease datasets have been investigated and the best k-value found. The experimental results have demonstrated that the combination of the proposed weighting method called FCMFW and k-NN classifier has obtained very promising results on the classification of PD.     

Delineating compositionally different dykes in the Ulukışla basin (Central Anatolia,Turkey) using computer‐enhanced multi‐spectral remote sensing data

In the Uluk??la basin (Central Anatolia, Turkey) several geological mapping campaigns were carried out using conventional field methods to delineate compositionally different Middle–Upper Eocene dykes. However, complete and correct mapping of these dykes was hampered by rugged terrain, lack of road access, wide spatial dyke distributions with small exposures and diverse weathering of these dykes. For these reasons, Landsat‐5 Thematic Mapper (TM) satellite image of the study area was used to facilitate delineation of the exact boundaries of gabbroic, dioritic and trachytic dykes found in the area. Remotely sensed data were analysed using several image enhancement procedures, including colour composites, band ratios, principal components analysis (PCA), and Crosta technique. Results obtained from all the processes were examined, and it was found that dyke boundaries are best visible in the PCA123 image; RGB 731 colour composite; TM band ratio 5/7, 5/1, 4 combination; and 1457‐PC4 image obtained by Crosta technique. The alteration differences of three dyke groups are enhanced much better in the 1457‐PC4 image obtained by Crosta technique, which highlights the hydroxyl‐bearing minerals as white‐coloured pixels. Using computer‐enhanced multi‐spectral remote sensing data, we were able to map the boundaries and spatial distributions of compositionally different dykes, which otherwise is an overwhelmingly difficult task to achieve using conventional field methods. In similar settings, remote sensing techniques applied in this study may provide an efficient and low‐cost alternative to time‐consuming and physically demanding field‐mapping campaigns.     

Subtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case study

A case study including the discrimination of traffic accidents as accident free and accident cases on Konya-Afyonkarahisar highway in Turkey using the proposed hybrid method based on combining of a new data preprocessing method called subtractive clustering attribute weighting (SCAW) and classifier algorithms with the help of Geographical Information System (GIS) technology has been conducted. In order to improve the discrimination of classifier algorithms including artificial neural network (ANN), adaptive network based fuzzy inference system (ANFIS), support vector machine, and decision tree, using data preprocessing need in solution of these kinds of problems (traffic accident case study). So, we have proposed a novel data preprocessing method called subtractive clustering attribute weighting (SCAW) and combined with classifier algorithms. In this study, the experimental data has been obtained by means of using GIS. The obtained GIS attributes are day, temperature, humidity, weather conditions, and month of occurred accident. To evaluate the performance of the proposed hybrid method, the classification accuracy, sensitivity and specificity values have been used. The experimental obtained results are 53.93%, 52.25%, and 38.76% classification successes using alone ANN, ANFIS, and SVM with RBF kernel type, respectively. As for the proposed hybrid method, the classification accuracies of 67.98%, 70.22%, and 61.24% have been obtained using the combination of SCAW with ANN, the combination of SCAW with SVM (radial basis function (RBF) kernel type), and the combination of SCAW with ANFIS, respectively. The proposed SCAW method with the combination of classifier algorithms has been achieved the very promising results in the discrimination of traffic accidents.     

Cash-back rewards versus equity-based electronic loyalty programs in e-commerce

Loyalty is a crucial part of today’s business because retaining a customer is generally less expensive than attracting a new one. This relationship also holds true in e-commerce. Most of the e-loyalty programs available on the Internet utilize cash-back rewards. A new type of e-loyalty program in which customers are offered a fraction of merchant firm’s equity is emerging recently. The profitability of this approach versus cash-back reward programs is still an open question. In this paper, we first survey current e-loyalty programs, and then develop a two-period duopoly model in which one of the firms gives customers a small fraction of its equity and the other offers cash-back reward for a purchase. We derive analytical conditions to compare the total profits generated through each loyalty program. In particular, we find that equity-based e-loyalty programs provide higher total profits than those of cash-back programs in markets where it is difficult for customers to switch between firms. We are grateful for the valuable comments and suggestions by the participants of the AMCIS 2004 Doctoral Consortium, New York City; the Big Ten IS Research Consortium 2004 at the Michigan State University; EURO/INFORMS 2003, Istanbul, Turkey; WISE 2002, Barcelona, Spain; and ICTEC 2002, Montreal, Canada. We are also indebted to the faculty at the Krannert Graduate School of Management for their indispensable inputs.     

Utilization of Discretization method on the diagnosis of optic nerve disease

The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.     

A hybrid expert system approach for telemonitoring of vocal fold pathology

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.     

Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Obstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann–Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.

     

Analysis of wave gaits for energy efficiency

In this paper an energy efficiency analysis of wave gaits is performed for a six-legged walking robot. A simulation model of the robot is used to obtain the data demonstrating the energy consumption while walking in different modes and with varying parameters. Based on the analysis of this data some strategies are derived in order to minimize the search effort for determining the parameters of the gaits for an energy efficient walk. Then, similar data is obtained from an actual experimental setup, in which the Robot-EA308 is used as the walking machine. The strategies are justified based on this realistic data. The analysis concludes the following: a phase modified version of wave gaits is more efficient than the (conventional) wave gaits, using the possible minimum protraction time results in more energy efficient gaits and higher velocity results in less energy consumption per traveled distance. A stability analysis is performed for the phase modification of the wave gaits, and the stability loss due to the modification is calculated. It is concluded that the loss in stability is insignificant.