Electric-field induced fluctuations in laser generated plasma plume

The effect of an external electric field on laser-generated plasma has been studied. It is observed that the laser-generated plasma can be used for the ignition of a spark in the presence of a low voltage external electric field. An eight-fold emission intensity enhancement in Cu Ⅰ spectral lines are measured as compared to the signal intensity in the absence of an external electric field.The plasma parameters remain the same initially, up to a few microseconds after the generation of plasma, and this feature makes it more interesting for the quantitative analysis of any sample using laser induced breakdown spectroscopy(LIBS). In the presence of an external electric field,fluctuations(contraction and expansion) in the laser-generated plasma are observed which increase the plasma decay time and consequently result in enhanced signal intensity.     

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

The maximum exponentially weighted moving average (MaxEWMA) control charts have gained considerable attention for simultaneously detecting both increases and decreases in the mean and/or dispersion of a process. In this paper, we propose a new auxiliary information‐based (AIB) MaxEWMA control chart, called the AIB‐MaxEWMA chart. The AIB‐MaxEWMA chart encompasses the existing MaxEWMA chart. Extensive Monte Carlo simulations are performed to evaluate the average run length, standard deviation of the run length, and diagnostic abilities of the AIB‐MaxEWMA chart. An extensive comparison reveals that the AIB‐MaxEWMA chart performs uniformly better than the MaxEWMA chart. An example is also used to explain the implementation and working of the AIB‐MaxEWMA chart. Copyright © 2017 John Wiley & Sons, Ltd.     

Synthesis and Physical Properties of Mn Doped ZnO Dilute Magnetic Semiconductor Nanostructures

Mn doped ZnO nanostructures have been prepared using low temperature simple, quick, and versatile synthesis approach. The structural, microstructural, and vibrational investigations reveal that as prepared nanostructures with low Mn doping concentration have single hexagonal phase and are grown along the preferred c-axis. The X-rays photoelectron spectroscopy demonstrates that the Mn ions are in mixed oxidation states for high doping concentration of Mn, while are in 2+ oxidation state for low concentration into ZnO lattice. The photoluminescence spectrum (PL) exhibits a significant red-shift of 22 nm in the optical band gap of doped ZnO and shows the improved luminescence properties, which makes it potential for its use in the photocatalyst, optoelectronics and solar cell nanodevices. Furthermore, the magnetic measurement of Mn doped ZnO nanostructures exhibits the ferromagnetism at room temperature.     

Improving fairness in IEEE 802.11 networks using MAC layer opportunistic retransmission

This paper introduces DAFMAC (Decode And Forward MAC), a scalable opportunistic cooperative retransmission enhancement for the IEEE 802.11 MAC protocol which operates without the need for additional explicit control signalling. Distributed opportunistic retransmission algorithms rely on selecting a single suitable relay without direct arbitration between nodes. Simulations show that DAFMAC offers a significant improvement in fairness for both throughput and jitter, giving multiple parallel data flows a more equal opportunity to utilise the channel. DAFMAC cooperative retransmissions are shown to reduce node energy consumption for a given throughput. Further, the DAFMAC relay selection algorithm is shown to scale very well in terms of complexity and memory requirements in comparison to other cooperative retransmission schemes.     

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.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Learning multiple experiences useful visual features for active maps localization in crowded environments

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of-words model to retrieve images and accurately localize the robot. The pose of the robot is the one retrieved from the AM that has maximum a posteriori. Experiments have been conducted on a unique highly crowded data-set collected from Indian roads. The results support the proposed method due to speed and localization accuracy.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

The effect of some alloying elements on the corrosion resistance of lead-antimony alloys — II. Silver

The effect of Ag on the stationary and non-stationary anodic corrosion rates of PbSbCd and PbSb alloys in H₂SO₄ has been studied. Anodic polarization curves were constructed under galvanostatic and potentiostatic conditions. Optical microscopic examination and microprobe analysis of the alloys were conducted. The beneficial effect of Ag was ascribed to a delay in closure of pores in the initial PbSO₄ film. The leaching out of Sb from the outermost layers and the simultaneous nucleation of PbSO₄ and Ag₂SO₄ from supersaturated solutions in the pores is thus made possible.     

Atomic-Resolution Structures and Mode of Action of Clinically Relevant Antimicrobial Peptides

Global rise of infections and deaths caused by drug-resistant bacterial pathogens are among the unmet medical needs. In an age of drying pipeline of novel antibiotics to treat bacterial infections, antimicrobial peptides (AMPs) are proven to be valid therapeutics modalities. Direct in vivo applications of many AMPs could be challenging; however, works are demonstrating encouraging results for some of them. In this review article, we discussed 3-D structures of potent AMPs e.g., polymyxin, thanatin, MSI, protegrin, OMPTA in complex with bacterial targets and their mode of actions. Studies on human peptide LL37 and de novo-designed peptides are also discussed. We have focused on AMPs which are effective against drug-resistant Gram-negative bacteria. Since treatment options for the infections caused by super bugs of Gram-negative bacteria are now extremely limited. We also summarize some of the pertinent challenges in the field of clinical trials of AMPs.