Nonlinear static and dynamic analysis of circular plates and shallow spherical shells using the collocation method

The present work investigates the efficacy and applicability of interior global orthogonal point collocation method to the axisymmetric nonlinear analysis of elastic circular plates and shallow spherical shells subjected to uniformly distributed transverse load. Spacewise discretisation has been carried out using a polynomial expansion with the zeros of a Chebyshev polynomial as collocation points. Timewise integration has been carried out with Newmark k-β scheme corresponding to average acceleration method. The static response and snap-through buckling results, as well as, the dynamic response and dynamic buckling results under a uniformly distributed step load have been obtained and found to agree closely with the available results.     

Non-darcy natural convection from a heated vertical plate in saturated porous media with mass transfer

The nonsimilar and self-similar flows for the steady natural convection over a vertical heated surface in a saturated porous medium with mass transfer using non-Darcy model have been studied under boundary layer approximations. The differential equations governing both the nonsimilar and self-similar flows have been solved numerically using an implicit finite-difference scheme developed by Keller. The results indicate that both heat transfer and velocity field are appreciably affected by the modified Grashof number and mass transfer except that the effect of the modified Grashof number on the heat transfer for large suction is very small. It is found that the maximum velocity occurs at the wall and it increases as the modified Grashof number or injection increases.     

Effect of Calcium Lactate, 4-Hexyl Resorcinol and Vacuum Packing on Physico-chemical, Sensory and Microbiological Qualities of Minimally Processed Litchi (Litchi chinensis Sonn.)

Litchi (Litchi chinensis Sonn.) fruits are very susceptible to pericarp browning which adversely affects consumer acceptability even though the aril portion remains in excellent condition. Litchi arils (litchis) were treated with a solution containing 0–2% (w/v) calcium lactate (CL), 0–0.02% (w/v) 4‐hexyl resorcinol (4‐HR) and 1% potassium sorbate. The pH of solution was adjusted to 4.0 with citric acid. Treated litchis were packed in polystyrene trays, over‐wrapped with polypropylene film, vacuum‐packed (0, 47409.3, 94831.9 Pa) and stored at 4 ± 2 °C. Drip losses, pH, total soluble solids (TSS), sensory attributes and microbiological quality of stored samples were estimated. A four‐factor, three‐level experimental design (D₆ Hokes design) with 19 experiments was chosen. Mathematical models were developed to analyse and predict the effect of CL, 4‐HR, in‐package vacuum and storage time on the responses. TSS, pH and sensory scores decreased significantly (P 0.01), whereas drip losses and microbial count increased significantly (P 0.01) with time. Drip loss was significantly (P 0.1) reduced by addition of CL. 4‐HR prevented browning and changes in colour score during storage were significantly less. Vacuum in packages exerted significant (P 0.01) effect over pH, TSS, sensory and microbiological qualities of minimally processed litchis.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

Reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured P2P networks

Free riding is a major problem in peer-to-peer networks. Reputation management systems are generally employed to overcome this problem. In this paper, a new reputation based scheme called probabilistic resource allocation is proposed. This strategy probabilistically decide whether to provide the resource to requesting peer or not. Aforesaid method gives selection preference to higher reputation peers and at the same time provides some finite probability of interaction between those peers who don’t have good reputation about each other. This avoids disconnection between the aforesaid peers. The proposed scheme also introduces a new mechanism for resource distribution which not only allocates resources based on peers’ reputation but simultaneously maximizes network utility also. Algorithm for formation of interest groups based upon both similarity of interests and reputation between peers is also presented.     

Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.