M/M/1 retrial queue with working vacations

In this paper we introduce the new M/M/1 retrial queue with working vacations which is motivated by the performance analysis of a Media Access Control function in wireless systems. We give a condition for the stability of the model, which has an important impact on setting the retrial rate for such systems. We derive the closed form solution in equilibrium for the retrial M/M/1 queue with working vacations, and we also show that the conditional stochastic decomposition holds for this model as well.     

The musculoskeletal system complaints of office workers at a vehicle production factory

This study aimed to determine the prevalence of and factors contributing to musculoskeletal system complaints (MSC) in office workers of a vehicle production factory and absenteeism due to these complaints. A total of 86.2% (n = 333) of the workers completed the survey. Of the workers, 73 (21.9%) were women, and 260 (78.1%) were men, and the mean age was 36.9 ± 8.75 years. The majority of the personnel had more than one symptom with a prevalence of 80.8%. The most frequently observed discomfort was tension and pain in the back muscles (58.6%) and in the neck muscles (58.3%), followed by low back pain (41%) and shoulder pain (30.3%). Pain in the shoulders, neck, and the back and pain and numbness in the fingers were statistically significantly more prevalent in employees who spent more than 5 hours per day performing desk work. Furthermore, all of the complaints, excluding pain in the elbows and knees, were statistically significantly more common in the women. When each complaint and the effects of contributing risk factors were analyzed by logistic regression analysis, pain in the shoulders, neck, and back; pain that spread to the arms; and pain in the fingers were correlated with longer work hours and the female gender (p < 0.05). The number of employees who had used sick leave within the past year due to MSCs was 11.7%. In conclusion, duration of work hours, frequency of breaks, and gender were determined as the most important risk factors in computer users regarding MSCs. © 2011 Wiley Periodicals, Inc.     

Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation

This article addresses the issue of visual landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human visual system and its navigational ability. A feedforward–feedbackward architecture has been developed for recognising visual landmarks in real time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches towards object recognition in computer vision. The architecture mimics the pre-attentive and attentive stages in the context of object recognition, embedding neural network processing paradigm into a computational template-matching approach in computer vision. The real-time landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for optimal computational resource allocation, focusing only on the regions of interest to address the computational restrictive nature of current computer processing power. Similarly, the recognition of visual landmarks in both clean and cluttered backgrounds is implemented in the attentive stage by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the architecture. Furthermore, it also incorporates both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D landmark, which is partially occluded by adjacent features in the surroundings. The results show that the architecture is able to recognise objects in cluttered backgrounds using real-images in both indoor and outdoor scenes. Furthermore, the architecture application in autonomous robot navigation has been demonstrated through a number of real-time trials in both indoor and outdoor environments.     

Optimization of forging processes using Finite Element simulations

During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate Optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm—which differ by their sequential improvement strategies—have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.     

Maskless writing of microfluidics: Rapid prototyping of 3D microfluidics using scratch on a polymer substrate

We present a new rapid prototyping method designed for simple fabrication of 3D microfluidics using a maskless direct writing technique on polymer substrates. The entire process is enabled by a commercial cutter plotter with 10 μm resolution precision and high speed. A CAD design of top and bottom microstructures is directly written on a polymer substrate using a cutter plotter after setting up the suitable force. The smallest channel width of 20 μm was obtained with the minimum force and 100 μm from the maximum. Also the written depth increased linearly with force from 30 to 130 μm. Several 3D microfluidic devices are demonstrated using a maskless writing technique. The entire fabrication process from CAD layout to a final 3D device can be completed in 30 min outside the clean room facilities.     

Multiwalled carbon nanotubes grafted chitosan nanobiocomposite: A prosperous functional nanomaterials for glucose biosensor application

Multiwalled carbon nanotubes (MWNTs) grafted chitosan (CS) nanowire (NW) was prepared by phase separation method. Glucose oxidase (GOx) was sequentially immobilized into MWNT-CS-NW to obtain MWNT-CS-NW/GOx biosensor. Field emission scanning electron microscopy (FESEM) images of MWNT-CS-NW/GOx reveals the existence of MWNT and CS. Cyclic voltammetry and amperometry were used to evaluate the electrochemical determination of glucose. The MWNT-CS-NW/GOx biosensor shows an excellent performance for glucose at +0.34 V with a high sensitivity (5.03 μA/mM) and lower response time (3 s) in a wide concentration range of 1-10 mM (correlation coefficient of 0.9988). In addition, MWNT-CS-NW/GOx biosensor possesses better reproducibility, storage stability and there is negligible interference from other electroactive components.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.