Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency

Utilising the methodology of content analysis, this study uses a multidisciplinary approach to define public e-procurement. Various aspects of e-procurement have been discussed from information systems, supply chain management, electronic commerce/electronic government, and public procurement to come up with an integrated definition of public e-procurement. Following this, e-procurement assimilation has been defined and its impact on procurement efficiency has been evaluated. Following the confirmatory factor analysis in structural equation modelling, dimensional level analysis in ANOVA has been undertaken for the three forms of e-procurement technologies namely e-tendering, e-catalogue management systems, and e-marketplace. The results show the positive and significant impact of the assimilation process on procurement efficiency.     

Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard.

Nonlinear predictive control for a NNARX hydro plant model

A neural network (NN)-based nonlinear predictive control (NPC) is described for control of turbine power with variation in gate position. The studied plant includes the tunnel, surge tank and penstock effect dynamics. Multilayer perceptron neural network is chosen to represent a neural network nonlinear autoregressive with exogenous signal model of hydro power plant. With the said NN model configuration, quasi-Newton and Levenberg–Marquardt iterative optimization algorithms are applied in order to determine optimal predictive control parameters. The controlled response is simulated on different amplitude step function and trapezoidal shape reference signal. The study also discusses comparison with an approximate predictive control approach, being linearized around operating points. It is shown that NPC strategy gives impressive results in comparison to the approximated one.     

Fighting Bugs: Remove, Retry, Replicate, and Rejuvenate

Even if software developers don't fully understand the faults or know their location in the code, software rejuvenation can help avoid failures in the presence of aging-related bugs. This is good news because reproducing and isolating an aging-related bug can be quite involved, similar to other Mandelbugs. Moreover, monitoring for signs of software aging can even help detect software faults that were missed during the development and testing phases. If, on the other hand, a developer can detect a specific aging-related bug in the code, fixing it and distributing a software update might be worthwhile. In the case of the Patriot missile-defense system, a modified version of the software was indeed prepared and deployed to users. It arrived at Dhahran on 26 February 1991- a day after the fatal incident     

SOFT COMPUTING TECHNIQUES FOR PREDICTION OF BOILING HEAT TRANSFER COEFFICIENT OF LIQUIDS ON COPPER-COATED TUBES

In this article pool boiling heat transfer coefficient (HTC) of liquids (isopropanol, methanol, and distilled water) on copper-coated heating tubes over a wide range of pressure conditions is computed experimentally. The objective is to find the applicability of soft computing techniques, swarm-intelligence based neural network, and adaptive fuzzy models in the prediction of boiling HTC. The results are compared with those computed experimentally. The performance of models for prediction of HTC is analyzed in terms of root mean square of prediction error. The minimum/maximum value obtained by zero-order fuzzy model with six membership function is 0.0023/3.4383 among all the liquids considered. The model is found to predict HTC with a maximum error of ±0.5% for boiling of liquids over all the coated tubes with pressure varying from atmospheric to subatmospheric levels. The study shows an excellent agreement between the experimental and predicted data.     

On the dynamic loading of gear teeth as affected by journal motion in a hydrodynamic bearing-rotor-gear pair system

The paper is an attempt to include the effect of journal motion and the oil film forces on the determination of dynamic gear tooth loading in a hydrodynamic bearing-rotor-gear pair system. A mathematical model has been developed which relates dynamic loads on the teeth of an involute spur gear pair to the instantaneous state of the pinion journal centre in the oil film space and various bearing parameters. The direction of instantaneous line of action is determined from kinematic considerations. effects of both the full-film (360 degrees) and the cavitated π-film models of the journal bearing are used to arrive at the aforementioned relationships. Dynamic loads on gear teeth as well as on bearing film have been calculated when the journal traces certain specified paths in the clearance space. Some of the results emphasize the importance of coupling gear analysis with bearing analysis.