High-Tc Superconducting Memory Cell

Journal of Superconductivity and Novel Magnetism - In this paper, operational principles of a cryogenic memory cell that utilizes high-temperature superconductors (high-Tc) are presented. Such a...     

斜铁路钢桥的高等动力有限元分析

介绍斜铁路钢桥动态分析的模型。给出一个有限元分析的案例及其与实际测量结果的对比。首先,通过不同模型的特征值分析,得到基础模型选型和桥梁频率,以及评估每个模型分析桥梁动态性能的能力。通过壳单元、梁单元以及组合单元对单跨、三跨和全比例桥梁建模。结果显示,桥梁基本动态特征数值分析结果与试验结果吻合。全比例桥梁模型用于分析特征值和动力时程分析。对不同列车时速和应变记录的分析与实际测量进行比较。疲劳评估通过选取桥梁某一位置的平均应力值也进行了比较研究。结果表明,采用梁单元和壳单元的组合单元的全比例尺模型,可以高效准确地预测桥梁的动力性能和疲劳破坏的平均应力值的范围。     

Ultrasonic and high-temperature pretreatment,enzymatic hydrolysis and fermentation of lignocellulosic sweet sorghum to bio-ethanol

This study investigated the conversion of lignocellulosic biomass to hexose and pentose sugars through ultrasonic and hot water pretreatment, followed by hydrolysis with a mixture of enzymes and fermentation by Saccharomyces cerevisiae to bio-ethanol. Lignin concentration decreased by 52%, cellulose and hemicellulose concentrations increased by 49% and 25%, respectively, after the ultrasonic plus hot water pretreatment. Cellulose conversion to glucose was the highest when Accellerase 1500?+?XC enzyme was used (89%), followed by Accellerase 1500?+?BG (84%), Accellerase 1500?+?XY (83%) and Accellerase 1500 (82%). Hemicellulose conversion to xylose and arabinose was the highest when Accellerase 1500?+?XC enzyme was used (48%), followed by Accellerase 1500?+?XY (40%). Optimum yield of bioconversion of the cellulose was 3.2–4.2?g ethanol/100?g of sweet sorghum biomass and ethanol concentration was 8–10?g/L based on cellulose conversions. The results in this study can be used to optimise processing conditions for bioethanol production from lignocellulose.     

Comparative study of two new commercial echelle spectrometers equipped with intensified CCD for analysis of laser-induced breakdown spectroscopy

The purpose of this paper is to provide the reader with comparative information about two new commercial echelle spectrometers equipped with intensified CCD (ICCD) detectors for laser-induced breakdown spectroscopy analysis. We carried out a performance comparison between two commercial ICCD/echelle spectrometers [ESA 3000 (LLA Instruments GmbH, Berlin-Adlershof, Germany) and a Mechelle 7500 (Multichannel Instruments, Stockholm, Sweden)] for the determination of the concentrations of Be, Mg, Si, Mn, Fe, and Cu in the same Al alloy samples adopting the same experimental conditions. The results show that both systems, despite their differences in terms of resolution, have similar performance in terms of sensitivity and precision of measurements for these elements in an Al alloy matrix at least for the range of wavelength 280-400 nm studied in this work.     

A general method of kineto-elastodynamic design of high speed mechanisms

Kineto-elastodynamics is the kinematic and dynamic study of mechanisms including the effects of elastic deformation and mass distribution of the links. A new method of kineto-elastodynamic design is developed and illustrated with examples. With this method mechanisms with elastic links can be designed in a systematic way for a desired performance at high speeds. This is achieved by first performing the kinematic synthesis of the mechanism considering its links to be rigid and then proportioning the areas of cross-section of the links optimally to account for the kineto-elastodynamic effects.

In optimization of the design, the objective function to be minimized is taken to be the mass of the mechanism and constraints are imposed on deflections and stresses. The optimization problem is formulated in terms of stepwise linear programming. Using the simplex method, a design with minimum mass is obtained subject to linearized constraints on deflections and stresses. The design is used as an initial design for the next iteration, thus leading to an optimum design in a finite number of steps. Move limits in the form of side constraints are introduced to reduce the error incurred due to linearization of the constraint equations.     

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.     

Effects of calcium on setting mechanism of metakaolin‐based geopolymer

Geopolymer setting is seen to be substantially accelerated by addition of calcium and the objective of this study was to determine the mechanism for this effect by examining metakaolin geopolymers with and without calcium. Solid‐state ²⁷Al NMR tests were used to examine the dissolution extent both qualitatively and quantitatively. Solid‐state ²⁹Si NMR tests were conducted to determine the amount and structure of each phase. Prior to the quantitative tests, chemical extractions were used to facilitate assignment of peaks in each spectrum. On addition of calcium, it was found that both the rate and the extent of metakaolin dissolution were enhanced. Accelerating dissolution increases the Al concentration in solution, thus reducing Si/Al available for geopolymer gel formation and further accelerating the gel formation to cause faster setting. Although C‐A‐S‐H was observed in the calcium mix, no evidence indicated that it is directly involved in setting.