Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

Reliability analysis of nuclear containment without metallic liners against jet aircraft crash

The present study presents a methodology for detailed reliability analysis of nuclear containment without metallic liners against aircraft crash. For this purpose, a nonlinear limit state function has been derived using violation of tolerable crack width as failure criterion. This criterion has been considered as failure criterion because radioactive radiations may come out if size of crack becomes more than the tolerable crack width. The derived limit state uses the response of containment that has been obtained from a detailed dynamic analysis of nuclear containment under an impact of a large size Boeing jet aircraft. Using this response in conjunction with limit state function, the reliabilities and probabilities of failures are obtained at a number of vulnerable locations employing an efficient first-order reliability method (FORM). These values of reliability and probability of failure at various vulnerable locations are then used for the estimation of conditional and annual reliabilities of nuclear containment as a function of its location from the airport. To study the influence of the various random variables on containment reliability the sensitivity analysis has been performed. Some parametric studies have also been included to obtain the results of field and academic interest.     

Alzheimer paired helical filaments (PHFs) studied by high-resolution TEM: what can vertical Pt-C replication tell us about the organization of the pronase-digested PHF core?

Untreated paired helical filaments (PHFs) and pronase-digested PHF-core filaments were stereoscopically imaged with a freeze-drying vertical platinum-carbon replication preparation method for TEM. The untreated PHF have an average wide region (W) = 22.8 +/- 2.4 nm, a narrow region (T) = 10.6 +/- 1.7 nm, and a helical turn period (L) = 78.6 +/- 13.4. The surfaces of the untreated PHF's fuzzy coat appears disorganized. The widths of the pronase-treated PHF-core filaments were significantly reduced (W(d) = 14.8 +/- 1.2 nm, T(d) = 5.7 +/- 1.0 nm, and L(d) = 75.4 +/- 17 nm). The surfaces of the untreated PHF contained approximately 1.1 nm strands, the same size as tau monomer ( approximately 1.0 nm). The pronase-digested PHF cores mostly contained approximately 1.6 +/- 0.3 nm strands although strand diameters ranged from 0.6-2.5 nm. The strands sometimes appear to be wrapped around the filament axis; less often, they appear to be roughly parallel to the PHF axis, and otherwise appear to be randomly oriented. Images of pronase-digested PHF core images are discussed in relation to the core's biochemical composition, its proposed beta structure, and structural subunit models. Images of the untreated and the pronase-digested PHF support a helical ribbon morphology.     

Sensitivity of CFP/YFP and GFP/mCherry pairs to donor photobleaching on FRET determination by fluorescence lifetime imaging microscopy in living cells

Fluorescent protein-based FRET is a powerful method for visualizing protein-protein interactions and biochemical reactions in living cells. It can be difficult, however, to avoid photobleaching when observing fluorescent cells under the microscope, especially those expressing CFP. We compared the sensitivity of two protein-based FRET pairs to light-induced fluorescence changes in the donor, on FRET determination by fluorescence lifetime imaging microscopy (FLIM). Thanks to the very low excitation light levels of the time- and space-correlated single photon counting (TSCSPC) method, FLIM acquisitions were achieved without donor photobleaching. Here, we show that photobleaching of CFP by a mercury lamp under the microscope induced a decrease in the mean fluorescence lifetime, which interfered with FRET determination between CFP and YFP. Importantly, the range of light-induced variation of the mean fluorescence lifetime of CFP was not proportional to the decrease in the steady state fluorescence intensity and varied from cell to cell. The choice of the CFP/YFP pair therefore requires that the cells be observed and analyzed at very low light levels during the whole FRET experiment. In contrast, the GFP/mCherry pair provided an accurate FRET measurement by FLIM, even if some GFP photobleaching took place. We thus demonstrate that CFP can be an unreliable donor for FRET determination in living cells, due to its photosensitivity properties. We demonstrate that the GFP/mCherry pair is better suited for FRET measurement by FLIM in living cells than the CFP/YFP pair.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Silk Fibroin as an Efficient Biomaterial for Drug Delivery,Gene Therapy,and Wound Healing

Silk fibroin (SF), an organic material obtained from the cocoons of a silkworm Bombyx mori, is used in several applications and has a proven track record in biomedicine owing to its superior compatibility with the human body, superb mechanical characteristics, and its controllable propensity to decay. Due to its robust biocompatibility, less immunogenic, non-toxic, non-carcinogenic, and biodegradable properties, it has been widely used in biological and biomedical fields, including wound healing. The key strategies for building diverse SF-based drug delivery systems are discussed in this review, as well as the most recent ways for developing functionalized SF for controlled or redirected medicines, gene therapy, and wound healing. Understanding the features of SF and the various ways to manipulate its physicochemical and mechanical properties enables the development of more effective drug delivery devices. Drugs are encapsulated in SF-based drug delivery systems to extend their shelf life and control their release, allowing them to travel further across the bloodstream and thus extend their range of operation. Furthermore, due to their tunable properties, SF-based drug delivery systems open up new possibilities for drug delivery, gene therapy, and wound healing.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Double Layer Coatings: A New Technique for Maintaining Physico-Chemical Characteristics and Antioxidant Properties of Dragon Fruit During Storage

A double layer coating was evaluated for maintenance of quality of dragon fruit during storage at 10?±?2 °C and 80?±?5 % RH for 28 days. Significant differences (p?<?0.05) were observed between control and the treated fruit. However, a double layer coating with 600 nm droplet size?+?1.0 % conventional chitosan showed promising results in all the tested parameters, while the fruit treated with 1,000 nm droplet size?+?1.0 % conventional chitosan showed some negative effects on fruit surface. Increase in weight loss was 12.0 % in fruit treated with 600 nm droplet size and 1.0 % conventional chitosan as compared to the control. Antioxidants and gaseous analysis also proved the efficacy of double layer coatings with 600 nm droplet size?+?1.0 % conventional chitosan. Thus it can be concluded from the present investigation that double layer coating could be used for maintaining quality in dragon fruit for up to 28 days without any off-flavours.     

Optimization of abrasive water jet cutting of ductile materials

Full factorial design of experiments was developed in order to investigate the effects of jet pressure, abrasive mixing rate, cutting feed, and plate thickness upon three response variables, surface finish of cutting wear zone, percentage proportion of striation free area, and maximum width of cut. The set of sixteen experiments was performed on each of the following two ductile materials: AISI 4340 (high strength low alloy steel, hardened to 49HRc) and Aluminum 2219. Analysis of Variance (ANOVA) was performed on experimental data in order to determine the significance of effects of different parameters on the performance measures. It was found that cutting feed and thickness were highly influential parameters, while abrasive mixing rate is influential upon surface roughness only. Strong interaction was found between jet pressure and workpiece material. Multi-criteria numerical optimization was performed in order to simultaneously maximize/minimize different combinations of performance measures.     

Phase Transition Study of Thermal Dependence of Soft Mode Frequency,Dielectric Constant and Dielectric Tangent Loss Properties in CsH2PO4 (CDP) and CsD2PO4 (DCDP) Crystals

Journal of Low Temperature Physics - Modified two-sublattice pseudospin lattice coupled-mode (PLCM) model&nbsp;Hamiltonian by adding third- and fourth-order anharmonic interactions, extra...