Microfluidic continuous magnetophoretic protein separation using nanoparticle aggregates

We demonstrate a microfluidic continuous-flow protein separation process in which silica-coated superparamagnetic nanoparticles interact preferentially with hemoglobin in a mixture with bovine serum albumin, and the resulting hemoglobin-nanoparticle aggregates are recovered online using magnetophoresis. We present detailed modeling and analysis of this process yielding quantitative estimates of the recovery of both proteins, validated by experiments. While several previous studies utilize an average particle size in modeling magnetophoretic particle trajectories or process design, in this study we emphasize the importance of accounting for particle size distributions in calculating particle recovery, and therefore in estimating separation efficiency. We combine experimentally measured size distributions of protein-nanoparticle aggregates with simulations of particle trajectories and provide a simple analytical method to calculate the efficiency of separation at various flow speeds, which fully accounts for heterogeneity in particle sizes. Our method can potentially be used for affinity based biomolecular separations at both analytical and preparative scales by exploiting well-established techniques to functionalize nanoparticle surfaces with selective ligands. Further, the modeling methodology presented here may be applied to provide better estimates of particle recovery in a broad range of magnetophoretic separation processes involving heterogeneity in particle sizes.     

Determining malicious executable distinguishing attributes and low-complexity detection

Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing attributes that are considerably divergent between benign and malware executables and therefore can be used for detection. We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably high detection accuracy, while having significantly lower complexity than existing detectors.     

Attention,polychronicity, and expertise in prospective memory performance: Programmers' vulnerability to habit intrusion error in multitasking

The paper examines prospective memory (ProM) in programmer multitasking and reports administration of a naturalistic atypical action. The study emphasizes on how attention, time orientation, and expertise affect ProM performance in multitasking among a group of computer science and engineering students (N=108). The results suggest that attention play a crucial role in multitasking and ProM performance with respect to whether or not a word display requires more attention to be devoted in monitoring and identifying it for an appropriate action. Polychrons exhibit lesser degree of ProM performance failure than monochrons whereas expertise does not have an effect. Finally, results show that out of overall ProM performance failure, habit intrusion errors comprise of 16.22% occurring 1.75 times in every 10 valid click responses of ProM task. Moreover, experts demonstrate a superior performance over novices in programming.     

Harmonic modification and data adaptive filtering based approach to robust pitch estimation

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (PFS). The dominant harmonic (DH) of the PFS is determined and enhanced its amplitude. Normalized autocorrelation function (NACF) is applied to that modified signal. Then empirical mode decomposition (EMD) based data adaptive time domain filtering is applied to the NACF signal. Partial reconstruction is performed in EMD domain. The pitch period is determined from the partially reconstructed signal. The experimental results show that the proposed method performs better than the other recently developed methods for noisy and clean speech signals in terms of gross and fine pitch errors.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

The performance of multiwavelets based OFDM system under different channel conditions

In wireless communication reception, the reliability of orthogonal frequency division multiplexing (OFDM) is limited because of the time-varying nature of the channel. This causes inter-carrier interference (ICI) and increases inaccuracies in channel tracking. This can effectively be avoided at the cost of power loss and bandwidth expansion by inserting a cyclic prefix guard interval before each block of parallel data symbols. However, this guard interval decreases the spectral efficiency of the OFDM system as the corresponding amount. Recently, it was found that based on Haar-orthonormal wavelets, discrete wavelet-based OFDM (DWT-OFDM) is capable of reducing the inter symbol interference (ISI) and ICI, which are caused by the loss in orthogonality between the carriers. DWT-OFDM can also support much higher spectrum efficiency than discrete Fourier-based OFDM (DFT-OFDM). In this paper the DFT-OFDM is replaced by Multiwavelets OFDM (DMWT-OFDM) in order to further reduce the level of interference and increase spectral efficiency. It is found that proposed Multiwavelet design achieves much lower bit error rates, increases signal to noise power ratio (SNR), and can be used as an alternative to the conventional OFDM. The proposed OFDM system was modeled tested, and its performance was found under different channel conditions.     

Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation

With growing interest in extending GIS to support multi-criteria decision-making (MCDM) methods, enhancing GIS-based MCDM with sensitivity analysis (SA) procedures is crucial to understand the model behavior and its limitations. This paper presents a novel approach of examining multi-criteria weight sensitivity of a GIS-based MCDM model. It explores the dependency of model output on the weights of input parameters, identifying criteria that are especially sensitive to weight changes and to show the impacts of changing criteria weights on the model outcomes in spatial dimension. A methodology was developed to perform simulations where the weights associated with all criteria used for suitability modelling were varied one-at-a-time (OAT) to investigate their relative impacts on the final evaluation results. A tool which incorporates the OAT method with the Analytical Hierarchy Process (AHP) within the ArcGIS environment was implemented. It permits a range of user defined simulations to be performed to quantitatively evaluate model dynamic changes, measures the stability of results with respect to the variation of different parameter weights, and displays spatial change dynamics. A case study of irrigated cropland suitability assessment addressing the application of the new GIS-based AHP-SA tool is described. It demonstrates that the tool is spatial, simple and flexible.     

A new mathematical programming approach to multi-group classification problems

In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.     

Effects of combined wrist deviation and forearm rotation on discomfort score

The aim of the study was to examine the pattern of the change in discomfort for combined wrist deviation and forearm rotation as joint angles increased away from neutral in a repetitive task. There were five levels of wrist deviation (neutral, 35% and 55% of the range of motion (ROM) in radial and ulnar deviation) and five levels of forearm rotation (neutral, 30% and 60% of the ROM in pronation and supination). Twenty-five participants performed a repetitive flexion task with a force of 10 N +/- 1 N at a frequency of 15 exertions per min, with replication after 1 week for six of the participants. A visual analogue scale was used for recording the discomfort scores. Repeated measures analysis of covariance with the Greenhouse-Geisser correction, where necessary, was used on transformed values of the discomfort scores. Grip test endurance time at 50% of maximum voluntary contraction was included as a covariate. Wrist deviation (p = 0.007) and forearm rotation (p = 0.001) were found to have significant effects. Interactions of the main factors were not significant and nor was the covariate. Quadratic regression equations were derived and were used to generate iso-discomfort contours, which show a useful area of low discomfort around the central neutral zone of wrist postures, but with steep increases in discomfort at the extreme combinations of wrist ulnar/radial deviation with forearm pronation/supination. Discomfort equations and contours, showing wrist and forearm postures, which are either acceptable or potentially injurious, are useful for the design of industrial tools, machine controls and workspaces. Reference to these can help to reduce the risk of musculoskeletal injury associated with the tasks or tools by avoiding poor postures with unacceptable deviations from neutral posture.     

A comprehensive solution for application‐level adaptation

Driven by the emergence of mobile and pervasive computing there is a growing demand for context‐aware software systems that can dynamically adapt to their run‐time environment. We present the results of project MADAM that has delivered a comprehensive solution for the development and operation of context‐aware, self‐adaptive applications. The main contributions of MADAM are (a) a sophisticated middleware that supports the dynamic adaptation of component‐based applications, and (b) an innovative model‐driven development methodology that is based on abstract adaptation models and corresponding model‐to‐code transformations. MADAM has demonstrated the viability of a general, integrated approach to application‐level adaptation. We discuss our experiences with two real‐world case studies that were built using the MADAM approach. Copyright © 2008 John Wiley & Sons, Ltd.