Impulse breakdown voltages of triggered multistage vacuum gap

Several sealed-off triggered vacuum gaps are connected in series to improve hold-off voltage. The characteristics of impulse breakdown voltage of these series-connected gaps are investigated experimentally. The sum hold-off voltage of series-connected gaps decreases to a unit hold-off voltage when the maximum value of voltage division ratio across the gaps increases to unity. Self-breakdown probability of the series-connected gaps is always higher than that of a single gap under the same conditions. Hence, stage efficiency of the multistage gap decreases with increasing number of stages. Its value is 90 percent with 2-stage gap and 75 percent with 5-stage gap, respectively, under the same voltage division ratio and the same gap length (2.0 mm) in each stage. Triggered breakdown voltage of 2- or 3- stage gap is several hundred volts when all gaps are triggered simultaneously at the peak of the main impulse wave and a working voltage range is nearly 100 percent in this case. The working voltage range decreases with number of stages. Its value is 45 percent with 3-stage gap and 15 percent with 5-stage gap, respectively, when one triggered gap is fired for switching.     

Color image segmentation using adaptive unsupervised clustering approach

This paper presents the Region Splitting and Merging-Fuzzy C-means Hybrid Algorithm (RFHA), an adaptive unsupervised clustering approach for color image segmentation, which is important in image analysis and in understanding pattern recognition and computer vision field. Histogram thresholding technique is applied in the formation of all possible cells, used to split the image into multiple homogeneous regions. The merging technique is applied to merge perceptually close homogeneous regions and obtain better initialization for the Fuzzy C-means clustering approach. Experimental results have demonstrated that the proposed scheme could obtain promising segmentation results, with 12% average improvement in clustering quality and 63% reduction in classification error compared with other existing segmentation approaches.     

Summarizing consumer reviews

Journal of Intelligent Information Systems - E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While...     

Numerical method using cubic B-spline for the heat and wave equation

In the present paper, the cubic B-splines method is considered for solving one-dimensional heat and wave equations. A typical finite difference approach had been used to discretize the time derivative while the cubic B-spline is applied as an interpolation function in the space dimension. The accuracy of the method for both equations is discussed. The efficiency of the method is illustrated by some test problems. The numerical results are found to be in good agreement with the exact solution.     

Exploring the relationship between Internet ethics in university students and the big five model of personality

The widespread use of the Internet and the convenient mechanism it provides, such as easy access, easy downloads, and easy copy and paste functions have made many types of unethical behaviors easier, particularly those involving students in academic settings. Among the issues in ethics within the academic environment that can be triggered by the Internet are fraudulence, plagiarism, falsification, delinquency, unauthorized help, and facility misuse. Given these issues, the study seeks to investigate the extent to which students at a public university in Malaysia engage in such unethical behavior and their relationship with the big five personality model. This study was conducted using a survey method of 252 students in three different academic faculties. The results of factor analyses confirm and refine the reliability of the scales for both big five personality variables and unethical Internet behaviors as conceptualized through Internet triggered academic dishonesty (ITADS). The findings indicate that personality traits such as (1) agreeableness, (2) conscientiousness and (3) emotional stability are significantly and negatively correlated with unethical Internet behavior in university students. Significant differences in facility misuse are also observed between the three academic faculties investigated. This research should provide significant contributions to educators in designing the computer ethics curriculum and in allowing for educational institutions as well as other organizations in developing relevant policies and guidelines on matters pertaining to academic conduct, utilization of computers and Internet, and recruitment exercises.     

A novel distributed scheduling scheme for OFDMA uplink using channel information and probabilistic transmission

Distributed uplink scheduling in OFDMA systems is considered. In the proposed model, mobile terminals have the responsibility of making their own transmission decisions. The proposed scheme is based on two dimensional reservation in time and frequency. Terminals use channel state information in order to favor transmissions over certain subchannels, and transmission is done in a probabilistic manner. The proposed approach provides more autonomy to mobile devices in making transmission decisions. Furthermore, it allows avoiding collisions during transmission since it leads to collision detection during the resource reservation phase. The proposed approach is compared to other random access methods and shown to be superior in terms of increasing sum-rate, reducing the number of users in outage, and reducing the collision probability in the reservation phase.     

Free and forced convection boundary layer flow through a porous medium with large suction

An analytical study is made of the free and forced convection boundary layer flow past a porous medium bounded by a semi-infinite vertical porous plate. Locally similar solutions are then obtained by a perturbation method for large suction. Solutions for the velocity and temperature distributions are shown graphically for various suction velocities and values of the driving parameter G_r/R, where G_r is the Grashof number and R_e is the Reynolds number. The corresponding values of the skin friction coefficient and the Nusselt number are finally shown in tabular form.     

New Beta-lactamases in Candidate Phyla Radiation: Owning Pleiotropic Enzymes Is a Smart Paradigm for Microorganisms with a Reduced Genome

The increased exploitation of microbial sequencing methods has shed light on the high diversity of new microorganisms named Candidate Phyla Radiation (CPR). CPR are mainly detected via 16S rRNA/metabarcoding analyses or metagenomics and are found to be abundant in all environments and present in different human microbiomes. These microbes, characterized by their symbiotic/epiparasitic lifestyle with bacteria, are directly exposed to competition with other microorganisms sharing the same ecological niche. Recently, a rich repertoire of enzymes with antibiotic resistance activity has been found in CPR genomes by using an in silico adapted screening strategy. This reservoir has shown a high prevalence of putative beta-lactamase-encoding genes. We expressed and purified five putative beta-lactamase sequences having the essential domains and functional motifs from class A and class B beta-lactamase. Their enzymatic activities were tested against various beta-lactam substrates using liquid chromatography-mass spectrometry (LC-MS) and showed some beta-lactamase activity even in the presence of a beta-lactamase inhibitor. In addition, ribonuclease activity was demonstrated against RNA that was not inhibited by sulbactam and EDTA. None of these proteins could degrade single- and double-stranded-DNA. This study is the first to express and test putative CPR beta-lactamase protein sequences in vitro. Our findings highlight that the reduced genomes of CPR members harbor sequences encoding for beta-lactamases known to be multifunction hydrolase enzymes.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.