Abrasion Resistance of Field-Assisted Ion-Exchanged Glass

The fracture strength of HF-etched samples was measured as a function of field-assisted K-Na ion exchange depth, as well as the severity of abrasion. As expected, the more severe the abrasion, the greater the depth required to make the strength insensitive to abrasion. An abrasion-independent weakening mechanism that increased with exchange depth was also found to be operative.     

Efficient mining of discriminative co-clusters from gene expression data

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. In certain biological applications such as gene expression analysis, it is critical to consider the discriminative patterns that are correlated only in a subset of the feature space. The objective of this paper is twofold: first, it presents an algorithm to efficiently find arbitrarily positioned co-clusters from complex data. Second, it extends this co-clustering algorithm to discover discriminative co-clusters by incorporating the class information into the co-cluster search process. In addition, we also characterize the discriminative co-clusters and propose three novel measures that can be used to evaluate the performance of any discriminative subspace pattern-mining algorithm. We evaluated the proposed algorithms on several synthetic and real gene expression datasets, and our experimental results showed that the proposed algorithms outperformed several existing algorithms available in the literature.     

Mechanical and dynamic mechanical properties of jute fibers–Novolac–epoxy composite laminates

A study was done of jute composite using a polymer matrix of epoxidized Novolac resin (ENR), diglycidyl ether of bisphenol A (DGEBA)–based epoxy, and their blends with different weight percentages of the resins. It was found that on blending ENR with DGEBA, the storage modulii at room temperature are enhanced by about 100% or more in the case of 30 and 40% ENR‐containing matrices, whereas the enhancement in the case of 20 and 12% ENR‐containing matrices is only 50% that of the pure matrix. It was also observed that the tan δ peak heights of the composites containing 30 and 40% ENR are closer to that of 20% ENR‐containing composite. The probable explanation drawn on the basis of experimental findings of DMA and mechanical analysis is that by blending ENR with DGEBA epoxy it is possible to manufacture jute composites with increased stiffness without sacrificing their ductility. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 2800–2807, 2002     

Elevated temperature instrumented Charpy impact of a sintered silicon carbide

A commercially available, densely sintered alpha silicon carbide was tested in air from room temperature to 1700°C using a modified instrumented pendulum unit and standard Charpy size test specimens. The resistance heated silicon carbide specimens exhibited only elastic behavior, even at 1700°C. A compliance analysis of the test revealed a linear elastic decrease of the absorbed impact energy and also a decrease of the fracture stress of the silicon carbide with increasing temperature.     

A Formal Methodology for Detecting Managerial Vulnerabilities and Threats in an Enterprise Information System

From information security point of view, an enterprise is considered as a collection of assets and their interrelationships. These interrelationships may be built into the enterprise information infrastructure, as in the case of connection of hardware elements in network architecture, or in the installation of software or in the information assets. As a result, access to one element may enable access to another if they are connected. An enterprise may specify conditions on the access of certain assets in certain mode (read, write etc.) as policies. The interconnection of assets, along with specified policies, may lead to managerial vulnerabilities in the enterprise information system. These vulnerabilities, if exploited by threats, may cause disruption to the normal functioning of information systems. This paper presents a formal methodology for detection of managerial vulnerabilities of, and threats to, enterprise information systems in linear time.     

A dynamic model for short-term prediction of stream attributes

Multiple data streams coming out of a complex system form the observable state of the system. The streams may correspond to various sensors attached with the system or outcome of internal processes. Such stream data may consist of multiple attributes and may differ in terms of their frequency of generation and observation. The streams may have dependency among themselves. One will have to rely on such data streams for monitoring the health of the system or to take any corrective measure. Predicting the value of certain stream data is an important task that can help one to take decision and act accordingly. In this work, a simple but generic visualization of a complex system is presented and thereafter a linear regression-based dynamic model for short-term prediction is proposed. The model is based on the past history of the attributes of multiple streams as suggested by the domain experts. But, it automatically determines the meaningful attributes and reformulates the model. The model is also re-computed if the prediction error exceeds the allowable tolerance. All these make the model dynamic. Experiment is carried out with stock market data streams to predict the close value well in advance. It is observed that in terms of quality of prediction and performance metric, the proposed model is quite effective.     

N-Linked Glycosylation in Chinese Hamster Ovary Cells Is Critical for Insulin-like Growth Factor 1 Signaling

Cell surface proteins carrying N-glycans play important roles in inter- and intracellular processes including cell adhesion, development, and cellular recognition. Dysregulation of the glycosylation machinery has been implicated in various diseases, and investigation of global differential cell surface proteome effects due to the loss of N-glycosylation will provide comprehensive insights into their pathogenesis. Cell surface proteins isolated from Parent Pro^–5 CHO cells (W5 cells), two CHO mutants with loss of N-glycosylation function derived from Pro^–5 CHO (Lec1 and Lec4 cells), were subjected to proteome analysis via high-resolution LCMS. We identified 44 and 43 differentially expressed membrane proteins in Lec1 and Lec4 cells, respectively, as compared to W5 cells. The defective N-glycosylation mutants showed increased abundance of integrin subunits in Lec1 and Lec4 cells at the cell surface. We also found significantly reduced levels of IGF-1R (Insulin like growth factor-1 receptor); a receptor tyrosine kinase; and the GTPase activating protein IQGAP1 (IQ motif-containing GTPase activating protein), a highly conserved cytoplasmic scaffold protein) in Lec1 and Lec4 cells. In silico docking studies showed that the IQ domain of IQGAP1 interacts with the kinase domain of IGF-1R. The integrin signaling and insulin growth factor receptor signaling were also enriched according to GSEA analysis and pathway analysis of differentially expressed proteins. Significant reductions of phosphorylation of ERK1 and ERK2 in Lec1 and Lec4 cells were observed upon IGF-1R ligand (IGF-1 LR3) stimulation. IGF-1 LR3, known as Long arginine3-IGF-1, is a synthetic protein and lengthened analog of insulin-like growth factor 1. The work suggests a novel mechanism for the activation of IGF-1 dependent ERK signaling in CHO cells, wherein IQGAP1 plausibly functions as an IGF-1R-associated scaffold protein. Appropriate glycosylation by the enzymes MGAT1 and MGAT5 is thus essential for processing of cell surface receptor IGF-1R, a potential binding partner in IQGAP1 and ERK signaling, the integral components of the IGF pathway.     

Study of a simulated stream machine for dataflow computation

In this paper, a stream-based dataflow architecture is proposed, and its simulation model, which has helped to evaluate the effectiveness of the proposed architectural concept, is discussed. The machine integrates the conventional Von Neumann type of control flow subsystem with a dataflow processing element of token storage type. The control flow unit tackles the dynamic nature of the stream structure including input/output whereas the dataflow unit does the computation part in an applicative style. A pipelined version of the stream machine is also discussed. The effectiveness of the machine is studied by running a few example programs in the simulated machine. The machine is expected to be useful in real time signal processing applications.