High throughput blood plasma separation using a passive PMMA microfluidic device

Since plasma is rich in many biomarkers used in clinical diagnostic experiments, microscale blood plasma separation is a primitive step in most of microfluidic analytical chips. In this paper, a passive microfluidic device for on-chip blood plasma separation based on Zweifach–Fung effect and plasma skimming was designed and fabricated by hot embossing of microchannels on a PMMA substrate and thermal bonding process. Human blood was diluted in various times and injected into the device. The main novelty of the proposed microfluidic device is the design of diffuser-shaped daughter channels. Our results demonstrated that this design exerted a considerable positive influence on the separation efficiency of the passive separator device, and the separation efficiency of 66.6 % was achieved. The optimum purity efficiency of 70 % was achieved for 1:100 dilution times.     

Fuzzy based multi-criteria vertical handover decision modeling in heterogeneous wireless networks

Vertical handover gain significant importance due to the enhancements in mobility models by the Fourth Generation (4G) technologies. However, these enhancements are limited to specific scenarios and hence do not provide support for generic mobility. Similarly, various schemes are proposed based on these mobility models but most of them are suffered from the high packet loss, frequent handovers, too early and late handovers, inappropriate network selection, etc. To address these challenges, a generic vertical handover management scheme for heterogeneous wireless networks is proposed in this article. The proposed scheme works in three phases. In the first phase, a handover triggering approach is designed to identify the appropriate place for initiating handover based on the estimated coverage area of a WLAN access point or cellular base station. In the second phase, fuzzy rule based system is designed to eliminate the inappropriate networks before deciding an optimal network for handover. In the third phase, a network selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. The proposed scheme is tested in a mobility scenario with different speeds of a mobile node ranging from very low to very high. The simulation results are compared with the existing decision models used for network selection and handover triggering approaches. The proposed scheme outperforms these schemes in terms of energy consumption, handover delay and time, packet loss, good put, etc.     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.     

GENOTOXICITY IN CHIRONOMUS KIIENSIS (CHIRONOMIDAE: DIPTERA) AFTER EXPOSURE TO POLLUTED SEDIMENTS FROM RIVERS OF NORTH PENINSULAR MALAYSIA: IMPLICATION FOR ECOTOXICOLOGICAL MONITORING

Rapid industrialization and urbanization has led to increasing input of chemical contaminants into the aquatic environment of Malaysia. Despite the threat civilization poses to the biota, there are still very few relevant studies on ecotoxicological testing of river ecosystems. To overcome this knowledge gap, we examined lethal and genotoxic effects of sediments from different rivers of the northern Malaysia against Chironomus kiiensis, a group well represented in the aquatic fauna of this region. We exposed the larvae to sediments from Selama River (SR), Permatang Rawa River (PRR) and Kilang Ubi River (KUR) at various durations (0, 6, 12, 24 and 48 h). The larval mortality was monitored, whereas DNA damage in survivors' cells was determined using the comet assay. Pollution level indexed by the amounts of heavy metals and other organic contaminants in the sediment showed progressive increases from SR to PRR to KUR. Highly polluted sediments (PRR to KUR) were detrimental to C. kiiensis larvae, most of which did not survive following exposure for long periods. DNA analyses revealed greater damages in nuclei derived from larvae maintained on polluted sediments, in particular, those from KUR. The effects on the genomic material of C. kiiensis larvae occurred in a time‐dependent manner, with damage level increasing as exposure time progressed. Our results highlight the genotoxic properties of polluted sediments. More importantly, this study showed that C. kiiensis larvae could respond to different levels of pollution with respect to exposure time. It is concluded that C. kiiensis larvae is a potential candidate for river ecotoxicological monitoring. Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of Pipe Networks by the Finite Element Method

ABSTRACT

In this paper, the finite element technique used in the analysis and design of water distribution networks will be presented. The method was applied successfully to the solution of three different networks.

In order to demonstrate the feasibility of the approach and to show some merits and demerits of the finite element method, a comparison was made on a digital computer with the standard Hardy Cross method. In all cases the finite element method was shown to provide superior performance.

Moreover, the programmes were run on three different machines [Wang vs 100, Apple IIe, and IBM micro-computer] to show the effect of increasing storage capacity, machine accuracy, and time saving.     

Fast attack detection using correlation and summarizing of security alerts in grid computing networks

Due to the extensive growth of grid computing networks, security is becoming a challenge. Usual solutions are not enough to prevent sophisticated attacks fabricated by multiple users especially when the number of nodes connected to the network is changing over the time. Attackers can use multiple nodes to launch DDoS attacks which generate a large amount of security alerts. On the one hand, this large number of security alerts degrades the overall performance of the network and creates instability in the operation of the security management solutions. On the other hand, they can help in camouflaging other real attacks. To address these issues, a?correlation mechanism is proposed which reduces the security alerts and continue detecting attacks in grid computing networks. To obtain the more accurate results, a?major portion of the experiments are performed by launching DDoS and Brute Force (BF) attacks in real grid environment, i.e., the Grid??5000 (G5K) network.     

Monte Carlo simulation on tensile failure process of unidirectional carbon fiber reinforced nano-phased epoxy

This paper presents an analytical approach which combines the modified shear-lag model and Monte Carlo simulation technique to simulate the tensile failure process of unidirectional T700 carbon reinforced composite. Two kinds of matrix were investigated in the present paper, one is neat epoxy and the other one is SiC nano-particle filled epoxy. In the model, the strength of the fiber elements is randomly allocated by the Monte Carlo method, the elastic properties of the matrix elements and the friction after the interfaces breakage are definitely allocated. Using this model, the deformation, damage and failure process of the composite are simulated on the microscopic level, the tensile stress–strain relationship is well predicted. The relationship between mechanical properties of the fiber, matrix and composites was discussed. The analysis also shows that, compared with the neat system, nano-phased composite exhibits 10% improvement in tensile strength, which agrees with the experimental data.     

Efficient surface reconstruction from contours based on two‐dimensional Delaunay triangulation

This paper introduces an efficient method for surface reconstruction from sectional contours. The surface between neighbouring sections is reconstructed based on the consistent utilization of the two‐dimensional constrained Delaunay triangulation. The triangulation is used to extract the parametric domain and to solve the problems associated with correspondence, tiling and branching in a general framework. Natural distance interpolations are performed in order to complete the mapping of the added intermediate points. Surface smoothing and remeshing are conducted to optimize the initial surface triangulations. Several examples are presented to demonstrate the effectiveness and efficiency of the proposed approach. Copyright © 2005 John Wiley & Sons, Ltd.     

Performance Measurement in Construction

Performance measurement has been subject to a considerable amount of research and attention over the past 15 years. The inadequacy of traditional financially based performance measurement systems and the introduction of nonfinancial measures have been the triggers for much of this research. The purpose of this paper is to review the main performance measurement frameworks and their application by U.K. construction firms and to identify gaps in knowledge and practice that suggest future research. The contemporary performance measurement frameworks are reviewed, including the Balanced Scorecard and the European Foundation for Quality Management Excellence Model. The status of performance in the U.K. construction industry is discussed, in addition to project/operational-level performance measurement and the linkage between performance measurement and strategic management. Gaps in knowledge and practice are overviewed both in general and for the construction industry in specific, thus suggesting future research.     

Evaluation of an ultrasonic acid digestion procedure for total heavy metals determination in environmental and biological samples

In this study, a sample preparation method based on ultrasonic assisted acid digestion (UAD) has been evaluated for total heavy metals (Cd, Cr, Ni and Pb) determination in different environmental (soil, sediment and sewage sludge), and biological (fish muscles, vegetables and grains) samples, using electrothermal atomic absorption spectrometry (ETAAS). The investigated parameters influencing UAD such as presonication time, sonication time, temperature of ultrasonic bath, and different acid mixtures were fully optimized, whereas power was maintained constant at 100% of nominal power of ultrasonic bath. Six different sets of above parameters were applied on six certified reference materials (CRMs) having different matrices. The accuracy of the method was also tested by comparing the results with those obtained from conventional hot plate assisted acid digestion method on same CRMs. Analytical results for HMs by both methods showed no significant difference at 95% confidence limit (p<0.05). Recoveries of HMs ranging from 96.2% to 102% and 96.3% to 98.6% were obtained from biological and environmental samples, respectively. The average relative standard deviation of UAD method varied between 3.5% and 8.2%, depending on the analyte.