A New Extension of Power Hazard Distribution with Applications

A new lifetime distribution is suggested using the Sine function by considering power hazard distribution as baseline distribution. Some mathematical and statistical features are discussed. The Maximum likelihood method is used to estimate the parameters for proposed distribution. Three real data sets are examined to analyze the performance of proposed distribution with some other distributions. The new distribution has been shown better fit to the bladder cancer patients’ data and COVID-19 data as compared to some distributions through statistical criterion

Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor

The brushless DC motors with permanent magnets (PM-BLDC) are widely used in a miscellaneous of industrial applications. In this paper, The adaptive neuro fuzzy inference system (ANFIS) controller for Six-Step Brushless DC Motor Drive is introduced. The brushless DC motor’s dynamic characteristics such as torque , current , speed, , and inverter component voltages are showed and analysed using MATLAB simulation. The  propotional-integral (PI) and fuzzy system controllers  are developed., based on designer’s test and error process and experts. The  experimential and hardware resuts for the inverter- driver circuits are presented. The simulation results using MATLAB simulink are conducted to validate the proposed (ANFIS) controller’s robustness and high performance relative to other controllers

Improving the Performance of a Series-Parallel System Based on Lindley Distribution

In this article, the performance of a series-parallel system is improved. The system components are assumed to follows independently and identically Lindley distributed with three parameters. The system reliability for the given system will be improved by using reduction method, hot, cold and imperfect duplication method. Some reliability measures are derived. Two types of reliability equivalence factors and gamma fractiles are calculated. A numerical example is introduced to explain the theoretical results

Autocatalytic plume pinch-off

A localized source of buoyancy flux in a non-reactive fluid medium creates a plume. The flux can be provided by either heat, a compositional difference between the fluid comprising the plume and its surroundings, or a combination of both. For autocatalytic plumes produced by the iodate-arsenous acid reaction, however, buoyancy is produced along the entire reacting interface between the plume and its surroundings. Buoyancy production at the moving interface drives fluid motion, which in turn generates flow that advects the reaction front. As a consequence of this interplay between fluid flow and chemical reaction, autocatalytic plumes exhibit a rich dynamics during their ascent through the reactant medium. One of the more interesting dynamical features is the production of an accelerating vortical plume head that in certain cases pinches-off and detaches from the upwelling conduit. After pinch-off, a new plume head forms in the conduit below, and this can lead to multiple generations of plume heads for a single plume initiation. We investigated the pinch-off process using both experimentation and simulation. Experiments were performed using various concentrations of glycerol, in which it was found that repeated pinch-off occurs exclusively in a specific concentration range. Autocatalytic plume simulations revealed that pinch-off is triggered by the appearance of accelerating flow in the plume conduit.Comment: 10 figures. Accepted for publication in Phys Rev E. See also http://www.physics.utoronto.ca/nonlinear/papers_chemwave.htm

Characterization and Cytotoxicity Analysis of a Ciprofloxacin Loaded Chitosan/Bioglass Scaffold on Cultured Human Periodontal Ligament Stem Cells: a Preliminary Report

AIM: The aim of this study was to analyze the cytotoxicity of ciprofloxacin (CIP) loaded on chitosan bioactive glass scaffold on human periodontal ligament stem cells (PLSCs) in vitro.MATERIALS AND METHODS: PLSCs obtained from human third molars, cultures treated with medium containing 15 x 15 mm chitosan/bioactive glass scaffolds without/with different concentration 0, 5, 10, and 20 % of CIP. A total of 15 x 10^3 cells were plated in 6 well plates. The attached cells of each group were harvested from the plates after 1, 4 and 8 days of culture to detect the viability of cells. The cell number was determined using a hemocytometer and the trypan blue dye-exclusion assay. Data was analyzed using normality using Shapiro-Wilk test. Comparisons between groups were made using One-way ANOVA complemented by Tukey's test.RESULTS: When comparing the proliferation rate of cells in the four groups, no statistically significant difference was found (P = 0.633). With regards to cell viability, no statistical difference was found between the 0, 5, and 10 % CIP concentrations, while the 20 % CIP concentration demonstrated the least viability with a high statistically significant difference (P = 0.003).CONCLUSION: Twenty percentages CIP demonstrated the least proliferation rate and viability

Effect of a Saccharomyces cerevisiae preparation on in vitro ruminal fermentation of four fibrous substrates

Yeast cultures, especially Saccharomyces cerevisiae, are beneficial in the rumen, and have been progressively introduced into the feed industry. They can affect microbial activities, thus improving fiber digestion, modifying VFA production and increasing animal performance

The Effect of Three Different Biomaterials on Proliferation and Viability of Human Dental Pulp Stem Cells (In-vitro Study)

BACKGROUND: Biomaterial cytotoxicity on dental stem cells plays a critical role in managing the regeneration of dental tissue.AIM: The aim of the present study was to evaluate the effect of Nano-hydroxy apatite (NHA), Mineral trioxide aggregate (MTA), and Calcium-enriched mixture (CEM) on the proliferation, and viability of human dental pulp stem cells (hDPSCs) isolated from third molar teeth.METHODS: Cultured DPSCs were characterized and the tested biomaterials were shaped into cylinders then inserted directly on the DPSCs. Proliferation and viability percentage of DPSCs were evaluated at 1, 3, 5, 7, 9, 11, and 14 days of culture.RESULTS: The biomaterials supplemented DPSCs showed a significant initial decrease in cell count and viability percentage at day one. Then, a rise in cell counts and viabilities was noticed after that. There was a decrease in cell counts, and viabilities in the NHA supplemented cells in comparison to other tested biomaterials.CONCLUSIONS: All tested biomaterials maintain the proliferation of DPSCs for different durations. NHA showed less proliferative and more cytotoxic effect than other tested materials

Logic shrinkage: learned connectivity sparsification for LUT-based neural networks

FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoffs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In this article, we propose the learned optimization of such LUT-based topologies, resulting in higher-efficiency designs than via the direct use of off-the-shelf, hand-designed networks. Existing implementations of this class of architecture require the manual specification of the number of inputs per LUT, K. Choosing appropriate K a priori is challenging, and doing so at even high granularity, e.g. per layer, is a time-consuming and error-prone process that leaves FPGAs’ spatial flexibility underexploited. Furthermore, prior works see LUT inputs connected randomly, which does not guarantee a good choice of network topology. To address these issues, we propose logic shrinkage, a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference. By removing LUT inputs determined to be of low importance, our method increases the efficiency of the resultant accelerators. Our GPU-friendly solution to LUT input removal is capable of processing large topologies during their training with negligible slowdown. With logic shrinkage, we better the area and energy efficiency of the best-performing LUTNet implementation of the CNV network classifying CIFAR-10 by 1.54 × and 1.31 ×, respectively, while matching its accuracy. This implementation also reaches 2.71 × the area efficiency of an equally accurate, heavily pruned BNN. On ImageNet with the Bi-Real Net architecture, employment of logic shrinkage results in a post-synthesis area reduction of 2.67 × vs LUTNet, allowing for implementation that was previously impossible on today’s largest FPGAs. We validate the benefits of logic shrinkage in the context of real application deployment by implementing a face mask detection DNN using BNN, LUTNet and logic-shrunk layers. Our results show that logic shrinkage results in area gains versus LUTNet (up to 1.20 ×) and equally pruned BNNs (up to 1.08 ×), along with accuracy improvements