Boundary-layer flow of the power-law fluid over a moving wedge: a linear stability analysis

We study the stability of the two-dimensional boundary-layer flow of a power-law (Ostwald de Waele) non-Newtonian fluid over a moving wedge. The mainstream velocity is assumed to have a power of distance from the leading boundary layer, such that the system admits to the self-similar solutions. We discuss the problem in question for both shear-thickening and shear-thinning fluids which lead to a non-uniqueness (double solutions) in the base flow solutions. We then address an issue of the stability of the non-unique solutions. A linear eigenvalue analysis of the double solution reveals that the basic flow represented by the first solution is always stable, and this flow is practically encountered. The system becomes unstable to the second solutions which have the mode-two perturbations with larger boundary-layer thickness. The first and second solutions form a tongue-like structure in the solution space. Furthermore, the modification of the viscosity for the power-law fluids reveals that the system predicts an infinite viscosity in the confinement of the boundary-layer region. Extensive comparisons of the solutions with the existing models with Newtonian fluid are made, and a physical explanation behind these solutions is proposed.

     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).     

The microbiology of sheep carcasses processed in a modern Indian abattoir

A study has been conducted on the microbiology of sheep carcasses processed in a modern abattoir. The data revealed that careful handling at the different stages of processing of sheep reduced the level of microbial contamination of carcasses. Processing steps such as evisceration and washing did not increase the microbial counts on the carcass surface. Sources of microbial contamination in the abattoir were examined. It was observed that skin, floor washings, intestinal contents and gambrels were the major sources of microbial contamination. Seasonality did not have any effect on the microbial contamination of carcasses. The study revealed that total plate counts in 86·6% of the carcasses ranged between 3·0–4·9log/cm². The counts of coliforms, staphylococci, enterococci and psychrotrophs were low. Pathogens such as Salmonella were not detected. The microbial counts were well within the generally acceptable levels. These findings demonstrated hygienic handling of carcasses. Shoulder and neck are the critical points for microbiological sampling as these sites showed higher microbial counts. Micrococcus and Staphylococcus predominated among microorganisms associated with carcasses. It was noted that differences occurred in microbial types of carcasses processed in tropical and temperate climates. The data generated in a model facility procided useful information for improving meat handling practices.     

Planning for the Subernarekha river system in eastern India

The study describes the systems studies carried out to plan a river basin in order to determine the nature and size of water storage facilities and releases for irrigation and industrial uses, and the associated cropping pattern. The model has been formulated in the framework of a linear programming model for a specific target year. This model is developed in the context of planning the Subernarekha river basin. The results are discussed and these provide information and insight suggesting the need for more disaggregated analysis of interaction between irrigation and related agroeconomic parameters.     

Surface characterization of 6H-SiC (0001) substrates in indentation and abrasive machining

Surface characterization of 6H-SiC (0001) substrates in indentation and abrasive machining was carried out to investigate microfracture, residual damage, and surface roughness associated with material removal and surface generation. Brittle versus plastic deformation was studied using Vickers indention and nano-indentation. To characterize the abrasive machining response, the 6H-SiC (0001) substrates were ground using diamond wheels with grit sizes of 25, 15 and 7 μm, and then polished with diamond suspensions of 3 and 0.05 μm. It is found that in indentation, there was a scale effect for brittle versus plastic deformation in 6H-SiC substrates. Also, in grinding, the scales of fracture and surface roughness of the substrates decreased with a decrease in diamond grit size. However, in polishing, a reduction in grit size of diamond suspensions gave no significant improvement in surface roughness. Furthermore, the results showed that fracture-free 6H-SiC (0001) surfaces were generated in polishing with the existence of the residual crystal defects, which were associated with the origin of defects in single crystal growth.     

An Overview of Next-Generation Androgen Receptor-Targeted Therapeutics in Development for the Treatment of Prostate Cancer

Traditional endocrine therapy for prostate cancer (PCa) has been directed at suppression of the androgen receptor (AR) signaling axis since Huggins et al. discovered that diethylstilbestrol (DES; an estrogen) produced chemical castration and PCa tumor regression. Androgen deprivation therapy (ADT) still remains the first-line PCa therapy. Insufficiency of ADT over time leads to castration-resistant PCa (CRPC) in which the AR axis is still active, despite castrate levels of circulating androgens. Despite the approval and use of multiple generations of competitive AR antagonists (antiandrogens), antiandrogen resistance emerges rapidly in CRPC due to several mechanisms, mostly converging in the AR axis. Recent evidence from multiple groups have defined noncompetitive or noncanonical direct binding sites on AR that can be targeted to inhibit the AR axis. This review discusses new developments in the PCa treatment paradigm that includes the next-generation molecules to noncanonical sites, proteolysis targeting chimera (PROTAC), or noncanonical N-terminal domain (NTD)-binding of selective AR degraders (SARDs). A few lead compounds targeting each of these novel noncanonical sites or with SARD activity are discussed. Many of these ligands are still in preclinical development, and a few early clinical leads have emerged, but successful late-stage clinical data are still lacking. The breadth and diversity of targets provide hope that optimized noncanonical inhibitors and/or SARDs will be able to overcome antiandrogen-resistant CRPC.     

Influence of Montmorillonite Clay Content on Thermal,Mechanical, Water Absorption and Biodegradability Properties of Treated Kenaf Fiber/ PLA-Hybrid Biocomposites

Silicon - Nowadays, the starring attentions of the polylactic acid based composites are improved due to environmental awareness and diminution of petroleum oil. The bio-plastics were restricted to...     

Influence of Ball Burnishing Process on Equal Channel Angular Pressed Mg-Zn-Si Alloy on the Evolution of Microstructure and Corrosion Properties

Silicon - In the present study, Mg-4Zn-1Si alloy was subjected to equal channel angular pressing (ECAP) up to 4 passes at 300 °C, followed by ball burnishing using 0.3 mm...