Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

THE EFFICIENCY CHALLENGES OF RESOURCE DISCOVERY IN GRID ENVIRONMENTS

Resource discovery is one of the most important services that significantly affects the efficiency of grid computing systems. The inherent dynamic and large-scale characteristics of grid environments make their resource discovery a challenging task. In recent years, different approaches have been proposed for resource discovery, attempting to tackle the challenges of grid environments and improve the efficiency. Being aware of these challenges and approaches is worthwhile in order to choose an appropriate approach according to the application in different organizations. This study reviews the most important factors that should be considered and challenges to be tackled in order to develop an efficient grid resource discovery system.     

An efficient parallel algorithm for the longest path problem in meshes

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine.     

Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data

Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials.     

Principal component analysis with optimum order sample correlation coefficient for image enhancement

Principal component analysis (PCA) has been commonly used and has played an important role in remote sensing for information extraction. However, the ordinary PCA based on second‐order covariance or correlation is capable of forming components on the basis of the statistical properties of a majority of pixel values – pixel values around mean values. For many applications, principal components should be constructed on the basis of optimum correlation coefficients so that the components can represent low or high values of minority pixels of interest. A new version of the PCA has been proposed on the basis of an optimum order sample correlation coefficient for enhancing the contribution of the image bands including the low or high minority pixel values that can assist in extracting weak information for image classification and pattern recognition. The ordinary PCA becomes the special case of the new version of the PCA introduced in this paper. The new method was validated with a case study of identification of Au/Cu‐associated alteration zones from a Landsat Thematic Mapper (TM) image in the Mitchell‐Sulphurets district, Canada.     

A supervisory control policy over an acoustic communication network

This paper presents a supervisory multi-agent control policy over an acoustic communication network subject to imperfections (packet dropout and transmission delay) for localisation of an underwater flow source (e.g., source of chemical pollution, fresh water, etc.) with an unknown location at the bottom of the ocean. A two-loop control policy combined with a coding strategy for reliable communication is presented to perform the above task. A simulator is developed and used to evaluate the trade-offs between quality of communication, transmission delay and control for a fleet of autonomous underwater vehicles supervised over a noisy acoustic communication network by an autonomous surface vessel. It is illustrated that without compensation of the effects of severe random packet dropout, localisation of an unknown underwater flow source is not possible for the condition simulated just by implementing a two-loop control policy. But a two-loop control policy combined with a strategy for reliable communication locates the unknown location of flow source.     

Comparative Numerical Investigation on TiO2/Water Nanofluid Turbulent Flow by Implementation of Single Phase and Two Phase Approaches

In this article, turbulent TiO₂/water nanofluid flow and convective heat transfer in a horizontal tube is numerically investigated. Four computer codes were developed for each model (single phase, volume of fluid, mixture, and Eulerian) to simulate this problem. The Finite volume method is applied to solve the two-dimensional steady state governing equations. The results are compared with each other and an experimental work. A model with the most similar results to those of the experimental data and less amount of time for the CPU usage is chosen to develop two correlations for Nusselt number and friction factor based on dimensionless numbers.     

New Approach to High-Pressure Nuclear Magnetic Resonance with Anvil Cells

A novel approach that uses radio-frequency microcoils in the high-pressure region of anvil cells with Nuclear Magnetic Resonance (NMR) experiments is described. High-sensitivity Al NMR data at 70 kbar for Al metal are presented for the first time. An expected decrease in the Al Knight shift at 70 kbar is observed, as well as an unexpected change in the local charge symmetry at the Al nucleus. The latter is not predicted by chemical structure analysis under high pressure.     

Healing of double oxide film defects in A356 aluminium melt

The possibility of the bonding of the two layers of a double oxide film defect when held in liquid A356 Al alloy was investigated. The defect was modelled experimentally by maintaining two aluminium oxide layers in contact with each other and the atmosphere trapped between them in the A356 liquid alloy for varying lengths of time of between 30 s and 48 h. Any changes in the composition and morphology of these layers were studied by SEM, EDX and XRD. The results showed that the two layers of a double oxide film defect might bond to each other by two different mechanisms, one during the transformation of Al₂O₃ to MgAl₂O₄ spinel in relatively short holding times, which would cause the layers to bond at several points and the other during the gradual transformation of spinel to MgO in longer times, which would cause strong bonding between the layers. It was shown that any bonding can take place essentially only after the oxygen and nitrogen of the atmosphere within the defect are consumed.