Crack Stability and Its Effect on Fracture Toughness of Hot-Pressed Silicon Nitride Beam Specimens

The effect of stable crack extension on fracture toughness test results was determined using single-edge precracked beam specimens. Crack growth stability was examined theoretically for bars loaded in three-point bending under displacement control. The calculations took into account the stiffness of both the specimen and the loading system. The results indicated that the stiffness of the testing system played a major role in crack growth stability. Accordingly, a test system and specimen dimensions were selected which would result in unstable or stable crack extension during the fracture toughness test, depending on the exact test conditions. Hot-pressed silicon nitride bend bars (NC132) were prepared with precracks of different lengths, resulting in specimens with different stiffnesses. The specimens with the shorter precracks and thus higher stiffness broke without stable crack extension, while those with longer cracks, and lower stiffness, broke after some stable crack extension. The fracture toughness values from the unstable tests were 10% higher than those from the stable tests. This difference, albeit small, is systematic and is not considered to be due to material or specimen-to-specimen variation. It is concluded that instability due to the stiffness of test system and specimen must be minimized to ensure some stable crack extension in a fracture toughness test of brittle materials in order to avoid inflated fracture toughness values.     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Extreme learning machine assessment for estimating sediment transport in open channels

The minimum velocity required to prevent sediment deposition in open channels is examined in this study. The parameters affecting transport are first determined and then categorized into different dimensionless groups, including “movement,” “transport,” “sediment,” “transport mode,” and “flow resistance.” Six different models are presented to identify the effect of each of these parameters. The feed-forward neural network (FFNN) is used to predict the densimetric Froude number (Fr) and the extreme learning machine (ELM) algorithm is utilized to train it. The results of this algorithm are compared with back propagation (BP), genetic programming (GP) and existing sediment transport equations. The results indicate that FFNN-ELM produced better results than FNN-BP, GP and existing sediment transport methods in both training (RMSE = 0.26 and MARE = 0.052) and testing (RMSE = 0.121 and MARE = 0.023). Moreover, the performance of FFNN-ELM is examined for different pipe diameters.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Electrochemical degradation of petroleum hydrocarbons (PAHS) from synthetic aqueous solutions

Water separated from crude oil and wastewater discharge from petroleum oil refineries contains significant quantity of dissolved hydrocarbons. Polycyclic aromatic hydrocarbons (PAHs) are major toxicants in wastewater of refineries. It is difficult to treat wastewater containing PAHs due to their recalcitrant property and low solubility. Conventional techniques for the treatment of wastewater are still a concern of toxicity. Electrochemical oxidation process has been found to be a favorable for treating wastewater. Electrodes with high stability and electrocatalytic activity are important factors for a successful electrochemical oxidation of toxic organics in wastewater. In this study titanium anodes were coated with tin, antimony and iridium oxide mixture from their respective salts by thermal decomposition method. FESEM and XRD used for surface characterization of Ti/SnO₂–Sb₂O₅–IrO₂ anode. Quantification of PAHs was done using GC–MS. Results confirm the presence of respective oxides on anode surface. Their electrocatalytic capability was tested for degradation of 16 priority PAHs in aqueous solution. Results reveal the complete degradation of naphthalene, acenaphthylene, acenaphthene and fluorene without using NaCl electrolyte. While in the presence of NaCl naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene and pyrene were completely removed. About 98% of total PAHs removal was found at all initial pH values 3, 6, and 9 in the presence of electrolyte. Current study will be helpful in improving quality of petroleum industry wastewater containing PAHs.     

Enhancing Post-Stroke Rehabilitation and Preventing Exo-Focal Dopaminergic Degeneration in Rats—A Role for Substance P

Dopaminergic signaling is a prerequisite for motor learning. Delayed degeneration of dopaminergic neurons after stroke is linked to motor learning deficits impairing motor rehabilitation. This study investigates safety and efficacy of substance P (SP) treatment on post-stroke rehabilitation, as this neuropeptide combines neuroprotective and plasticity-promoting properties. Male Sprague Dawley rats received a photothrombotic stroke within the primary motor cortex (M1) after which a previously acquired skilled reaching task was rehabilitated. Rats were treated with intraperitoneal saline (control group, n = 7) or SP-injections (250 µg/kg) 30 min before (SP-pre; n = 7) or 16 h (SP-post; n = 6) after rehabilitation training. Dopaminergic neurodegeneration, microglial activation and substance P-immunoreactivity (IR) were analyzed immunohistochemically. Systemic SP significantly facilitated motor rehabilitation. This effect was more pronounced in SP-pre compared to SP-post animals. SP prevented dopaminergic cell loss after stroke, particularly in the SP-pre condition. Despite its proinflammatory propensity, SP administration did not increase stroke volumes, post-stroke deficits or activation of microglia in the midbrain. Finally, SP administration prevented ipsilesional hypertrophy of striatal SPergic innervation, particularly in the SP-post condition. Mechanistically, SP-pre likely involved plasticity-promoting effects in the early phase of rehabilitation, whereas preservation of dopaminergic signaling may have ameliorated rehabilitative success in both SP groups during later stages of training. Our results demonstrate the facilitating effect of SP treatment on motor rehabilitation after stroke, especially if administered prior to training. SP furthermore prevented delayed dopaminergic degeneration and preserved physiological endogenous SPergic innervation.     

Numerical modelling of the carrier gas phase in a laboratory-scale coal classifier model

The pneumatic transport of fine ideally combustible coal dust to the burner furnace is an important process in coal fired power plants. The strongly swirling air phase responsible for the particle separation and transport in a coal pulverising mill was characterised experimentally and numerically. Measurements of the swirl velocity component were taken in a scaled laboratory model of the device and compared to CFD model. In particular, an evaluation of the turbulence models used to describe the flow was performed. The modified isotropic k-epsilon turbulence models (RNG k-ε and Realizable k-ε) were compared to the anisotropic Reynolds stress model (RSM) and their ability to predict the bulk flow structure present in the classifier was assessed. The experiments showed that the swirling flow structure, responsible for coarse-fine particle classification, has several flow regimes which are governed by the areas it is bounded by. The numerical model predictions generally corroborate the results. However, a distinction in performance between the three models can be made based on accuracy, solution generation time and numerical stability. The RSM model predicted both the trends and magnitude the most accurately when compared to the isotropic models. However, the Realizable k-epsilon model, with its relatively low solution generation time, shows potential when using CFD as a classifier design optimization tool. The investigation has given some insight on single phase classifier flow and suggests a design improvement based on the results.