Glycated proteome: From reaction to intervention

Glycation, a nonenzymatic reaction between reducing sugars and proteins, is a proteome wide phenomenon, predominantly observed in diabetes due to hyperglycemia. Glycated proteome of plasma, kidney, lens, and brain are implicated in the pathogenesis of various diseases, including diabetic complications, neurodegenerative diseases, cancer, and aging. This review discusses the strategies to characterize protein glycation, its functional implications in different diseases, and intervention strategies to protect the deleterious effects of protein glycation.     

Entrance effects for power-law fluid

Entrance pressure losses for the creeping flow of a power-law fluid are calculated for an abrupt contraction of ratio 2, 4, 8 and ∞ for both the axisymmetric and planar cases using P₂P₁ and PP₁ finite elements. Contrary to some earlier findings in the literature, the entrance pressure loss obtained by using the two different types of finite elements, both of which satisfy the Babus?ka-Brezzi condition, are found to converge to the same results. The present results also confirm that the variational method of Duda and Vrentas gives excellent upper bounds for both the axisymmetric and planar cases with infinite contraction ratio.     

An Efficient Enhanced Full Homomorphic Encryption for Securing Video in Cloud Environment

Wireless Personal Communications - Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell (SC) deployments in heterogeneous user distribution in a...     

Support vector regression based modeling of pier scour using field data

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach.     

Application of support vector machines in scour prediction on grade-control structures

Research into the problem of predicting the maximum depth of scour on grade-control structures like sluice gates, weirs and check dams, etc., has been mainly of an experimental nature and several investigators have proposed a number of empirical relations for a particular situation. These traditional scour prediction equations, although offer some guidance on the likely magnitude of maximum scour depth, yet applicable to a limited range of the situations. It appears from the literature review that a regression mathematical model for predicting maximum depth of scour under all circumstances is not currently available. This paper explores the potential of support vector machines in modeling the scour from the available laboratory and field data obtained form the earlier published studies. To compare the results, a recently proposed empirical relation and a feed forward back propagation neural network model are also used in the present study. The outcome from the support vector machines-based modeling approach suggests a better performance in comparison to both the empirical relation and back propagation neural network approach with the laboratory data. The results also suggest an encouraging performance by the support vector machines learning technique in comparison to both empirical relation as well as neural network approach in scaling up the results from laboratory to field conditions for the purpose of scour prediction.     

Speech enhancement using Teager energy operated ERB-like perceptual wavelet packet decomposition

In recent past, wavelet packet (WP) based speech enhancement techniques have been gaining popularity due to their inherent nature of noise minimization. WP based techniques appeared as more robust and efficient than short-time Fourier transform based methods. In the present work, a speech enhancement method using Teager energy operated equal rectangular bandwidth (ERB)-like WP decomposition has been proposed. Twenty four sub-band perceptual wavelet packet decomposition (PWPD) structure is implemented according to the auditory ERB scale. ERB scale based decomposition structure is used because the central frequency of the ERB scale distribution is similar to the frequency response of the human cochlea. Teager energy operator is applied to estimate the threshold value for the PWPD coefficients. Lastly, Wiener filtering is applied to remove the low frequency noise before final reconstruction stage. The proposed method has been applied to evaluate the Hindi sentences database, corrupted with six noise conditions. The proposed method’s performance is analysed with respect to several speech quality parameters and output signal to noise ratio levels. Performance indicates that the proposed technique outperforms some traditional speech enhancement algorithms at all SNR levels.     

Wap: Transitional Technology for M-Commerce

Wireless application protocol (WAP) is the most popular Internet-enabling technology being adopted en masse by handset manufacturers and service providers alike. This article discusses the fast-growing trend for WAP tools to access the Internet versus the current predominant use of personal computers. the article also describes the importance of WAP in the fields of E-commerce and M-commerce.     

Java-MaC: A Run-Time Assurance Approach for Java Programs

We describe Java-MaC, a prototype implementation of the Monitoring and Checking (MaC) architecture for Java programs. The MaC architecture provides assurance that the target program is running correctly with respect to a formal requirements specification by monitoring and checking the execution of the target program at run-time. MaC bridges the gap between formal verification, which ensures the correctness of a design rather than an implementation, and testing, which does not provide formal guarantees about the correctness of the system.Use of formal requirement specifications in run-time monitoring and checking is the salient aspect of the MaC architecture. MaC is a lightweight formal method solution which works as a viable complement to the current heavyweight formal methods. In addition, analysis processes of the architecture including instrumentation of the target program, monitoring, and checking are performed fully automatically without human direction, which increases the accuracy of the analysis. Another important feature of the architecture is the clear separation between monitoring implementation-dependent low-level behaviors and checking high-level behaviors, which allows the reuse of a high-level requirement specification even when the target program implementation changes. Furthermore, this separation makes the architecture modular and allows the flexibility of incorporating third party tools into the architecture. The paper presents an overview of the MaC architecture and a prototype implementation Java-MaC.     

A Six-sigma approach for benchmarking of RP&M processes

This paper presents a methodology of using six-sigma quality tools for benchmarking of rapid prototyping & manufacturing (RP&M) processes. It involves the fabrication of a geometric benchmark part and a methodology to control and identify the best performance of the process to reduce the variablity in the fabricated parts. The approach is demonstrated with a case study based on the direct laser sintering (DLS) process for prototyping using plastic powder. In the case study an identified set of six-sigma/ statistical process control tools is employed to determine and best tune factors affecting the desired outcomes of the built parts.     

Carrier concentration dependence of donor activation energy in n-type GaN epilayers grown on Si (1 1 1) by plasma-assisted MBE

The n-type GaN layers were grown by plasma-assisted MBE and either intentionally doped with Si or unintentionally doped. The optical characteristics of a donor level in Si-doped, GaN were studied in terms of photoluminescence (PL) spectroscopy as a function of electron concentration. Temperature dependent PL measurements allowed us to estimate the activation energy of a Si-related donor from temperature-induced decay of PL intensity. PL peak positions, full width at half maximum of PL and activation energies are found to be proportional to the cube root of carrier density. The involvement of donor levels is supported by the temperature-dependent electron concentration measurements.