Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Realizing high thermoelectric performance in p-type RbZn4P3 Zintl compound: a first-principles investigation

Journal of Materials Science - This work presents structural, elastic, electronic, and thermoelectric properties of a tetragonal Zintl compound RbZn4P3, using the Density Functional Theory and...     

Top Indian scientists as public communicators: a survey of their perceptions,attitudes and communication behaviors

Scientometrics - Research on science communication, especially from scientists’ point of view, is rare in the Indian context. This first of its kind study in India explores the perceptions...     

A machine learning approach for feature selection traffic classification using security analysis

Class imbalance has become a big problem that leads to inaccurate traffic classification. Accurate traffic classification of traffic flows helps us in security monitoring, IP management, intrusion detection, etc. To address the traffic classification problem, in literature, machine learning (ML) approaches are widely used. Therefore, in this paper, we also proposed an ML-based hybrid feature selection algorithm named WMI_AUC that make use of two metrics: weighted mutual information (WMI) metric and area under ROC curve (AUC). These metrics select effective features from a traffic flow. However, in order to select robust features from the selected features, we proposed robust features selection algorithm. The proposed approach increases the accuracy of ML classifiers and helps in detecting malicious traffic. We evaluate our work using 11 well-known ML classifiers on the different network environment traces datasets. Experimental results showed that our algorithms achieve more than 95% flow accuracy results.     

Preparative chromatographic isolation of hydroxy acids fromLesquerella fendleri andL. gordonii seed oils

To conduct product development research onLesquerella seed oils, we explored methods to obtain >100 g quantities of lesquerolic (14-hydroxy-cis-11-eicosenoic) acid. Preliminary experiments with open-column silica gel chromatography showed thatL. fendleri oil could be separated into 3 triglyceride (TG) fractions. The first (10%) contained nonhydroxy 16-(13%) and 18-carbon acids (65% 18∶1,2,3). The second fraction (15%) contained monolesquerolins (39% lesquerolic acid). The major TG fraction (73%) was mainly dilesquerolins (66% lesquerolic acid) showing that a hydroxy acid-enriched TG oil was obtainable by this procedure. Silica gel chromatography easily separatedL. fendleri fatty acid methyl esters (FAME) into a hydroxy-free ester fraction (40–44%) consisting largely of 18∶1 (39%), 18∶2 (19%) and 18∶3 (31%), and a hydroxy ester fraction (56–60%) that was largely methyl lesquerolate (94%) with small amounts of auricolate (5%) (14-hydroxy-cis-11,cis-17-eicosadienoate) and traces of 18-carbon hydroxy esters. This process for isolating the hydroxy FAME ofLesquerella oil was scaled up 15-to 100-fold with a preparative high performance liquid chromatograph. Thirty-gram samples ofL. gordonii FAME were dissolved in eluting solvent, pumped onto the high performance liquid chromatography (HPLC) silica column and eluted with 97∶3 hexane/ethyl acetate. In an 8-hr period, up to 200 g of methyl lesquerolate could be obtained with a purity >98%, the only contaminants being methyl auricolate and methyl ricinoleate. Presented at the AOCS meeting in Phoenix, AZ, May 1988. The mention of firm names or trade products does not imply that they are endorsed or recommended by the U.S. Department of Agriculture over other firms or similar products not mentioned.     

Studies on molecular transport of n‐alkanes through poly(tetrafluoroethylene‐co‐propylene) elastomeric membrane

Molecular transport of a series of n‐alkanes through commercial TFE elastomer (FA 150L) has been studied in the temperature range 30–50 °C using sorption‐gravimetric method. The Fickian diffusion equation was used to calculate the diffusion coefficients, which were dependent on the size of the alkanes and temperature. The diffusion coefficients at 30°C varied from 4.53 × 10^?8 cm²/s (n‐heptane) to 0.18 × 10^?8 cm²/s (n‐hexadecane). The liquid concentration profiles have also been computed using analytical solution of Fick's equation with the appropriate initial and boundary conditions and these were presented as a function of penetration depth of molecular migration and time of immersion. These results have been discussed in terms of molecular size of alkanes as well as temperature. In all the liquid penetrants, the transport phenomenon was found to follow the anomalous behavior. From the temperature dependence of diffusion and permeation coefficients, the Arrhenius activation parameters have been estimated. These parameters do not exhibit any systematic variation with the size of the penetrants. The resulting low diffusion coefficients, contribute to the superior barrier performance of the membrane, is due, in part, to the high glass transition temperature of Aflas? TFE elastomer. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2228–2235, 2006     

Examining the anomalous electrical characteristics observed in InN nanowires

Research interest in InN has intensified in recent years because of its unique material properties and promising applications in electronic and photonic devices. Measurements on InN nanowires presented by Chang et al., [J. Electron. Mater. 35, 738 (2006)] showed an anomalous resistance behavior in InN nanowires with diameters less than 90 nm. We examine possible theories presented in literature to explain this intriguing observation. We propose that the presence of a high density electron accumulation layer at the surface of thin InN nanowires is the most probable cause for the uncharacteristic relationship between the total measured resistance and the ratio of length-to-area. High density surface electron accumulation layer, characteristic of InN films and nanowire, promotes a surface conduction path distinct from the bulk conduction. For large diameter nanowires, bulk conduction is likely to be the dominant mechanism while surface conduction is proposed to play a major role for small diameter InN nanowires.     

Application-controlled handover for heterogeneous multiple radios over fibre networks

The wired-wireless integration network can be categorised as fixed mobile convergence (FMC). FMC means the convergence of the existing wired network and wireless network. Therefore a mobile device needs the function of connection and control to the FMC infrastructure. An application-controlled handover is developed, which keeps channel continuity in the wired-wireless synergy network environment that consists of 3G (UMTS) t WLAN t WPAN (UWB) and optical fibre network. A handover mechanism transmits and receives data by using the proposed application selection criteria. It maintains the channel and the seamless transmission from mobile device to the remote optical fibre network, to provide real-time service continuity for multimedia traffic. The results show that application-controlled handover has a reduction up to 83% in packet drop, 74% reduction in bit error rate, 85% reduction in power consumption and 100% enhancement in application response time (delay) as compared with the network without handover technique.     

Sustained‐Release Synthetic Biomarkers for Monitoring Thrombosis and Inflammation Using Point‐of‐Care Compatible Readouts

Postoperative infection and thromboembolism represent significant sources of morbidity and mortality but cannot be easily tracked after hospital discharge. Therefore, a molecular test that could be performed at home would significantly impact disease management. The laboratory has previously developed intravenously delivered “synthetic biomarkers” that respond to dysregulated proteases to produce a urinary signal. These assays, however, have been limited to chronic diseases or acute diseases initiated at the time of diagnostic administration. Here, a subcutaneously administered sustained‐release system, using small poly(ethylene glycol) scaffolds (<10 nm) to promote diffusion into the bloodstream over a day, is formulated. The utility of a thrombin sensor to identify thrombosis and an Matrix metalloproteinase (MMP) sensor to measure inflammation is demonstrated. Finally, a companion paper ELISA (Enzyme‐linked immunosorbent sssay), using printed wax barriers, with nanomolar sensitivity for urinary reporters for point‐of‐care detection is developed. The approach for subcutaneous delivery of nanosensors combined with urinary paper analysis may enable facile monitoring of at‐risk patients.