Study on photocatalysis of TiO 2 nanotubes prepared by methanol-thermal synthesis at low temperature

TiO₂ nanotubes were synthesized by the solvothermal process at low temperature in a highly alkaline water–methanol mixed solution. Their characteristics were identified by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), specific surface area (BET), Fourier transform infrared spectroscopy (FTIR) and UV–Vis absorption spectroscopy. The as-prepared samples were tested by the photodegradation reaction of methylene blue (MB) dye under visible-light irradiation. The ratios of methanol and water, as well as calcination temperature, affected the morphology, nanostructure and photocatalytic performance. The methanol solvent plays an important role in improving crystallization of the anatase phase, which affects the photocatalytic reaction. Titanate nanotubes were synthesized in methanol–water volume ratios of 10:90, 20:80 and 30:70 which still had high absorbability. Titania nanotubes formed at a calcination temperature of 300 °C using methanol–water volume ratio of 30:70 showed highest photocatalytic performance, much higher than that using water solvent and TiO₂–P25 powder.     

Numerical modelling of a low power non-transferred arc plasma reactor for methane conversion

Thermal flow characteristics and the methane conversion reaction in a low power arc plasma reactor for efficient storage and transport of methane, which is the main component of shale gas, were simulated. The temperature and velocity distributions were calculated according to the type of discharge gases and arc current level by a self-developed magnetohydrodynamics (MHD) code and a commercial ANSYS-FLUENT code; the transport of chemical species was analyzed as including the chemical reactions of methane conversion. The simulated results were verified by the comparison of calculated and measured arc voltages with permissible low error as under 4%. Three C₂ hydrocarbon gases with ethane (C₂H₆), ethylene (C₂H₄), and acetylene (C₂H₂) were selected as the converted species of methane from experimental data. The mass fraction of C₂ hydrocarbons and hydrogen as the product of the conversion reaction at the reactor was also calculated. Those values show good agreement with the actual experimental results in that the major conversion reaction occurred in C₂H₂ and hydrogen, and the conversions to C₂H₆, C₂H₄, and hydrogen were minor reactions of methane pyrolysis conversion.     

非球面超精密抛光技术研究现状

非球面应用范围的拓展以及应用精度要求的不断提高对非球面元件的尺寸精度与表面质量提出了更高要求;作为终加工手段,非球面超精密抛光技术也越来越受到世界各国的重视。掌握非球面超精密抛光过程中的材料去除机理以及由此导致的亚表面损伤等特性对提高非球面抛光的精度与效率十分重要。对非球面抛光技术的发展进行回顾,并根据非球面超精密抛光技术的发展脉络,阐述当前主要非球面超精密抛光技术的加工原理及加工实例,随后从亚表面损伤、边缘效应等方面对几种非球面超精密抛光技术进行比较,并以提高抛光精度与效率为目标,对非球面超精密抛光技术的发展趋势进行预测。     

Assessment of early-age cracking of high-performance concrete in restrained ring specimens

High-performance concrete(HPC)is stronger and more durable than conventional concrete.However,shrinkage and shrinkage cracking are common phenomena in HPC,especially early-age cracking.This study assessed early-age cracking of HPC for two mixtures using restrained ring tests.The two mixtures were produced with water/binder mass ratio(mW/mB)of 0.22 and 0.40,respectively.The results show that,with greater steel thickness,the higher degree of restraint resulted in a higher interface pressure and earlier cracking.With steel thickness of 6 mm,19 mm,and 30 mm,the age of cracking were,respectively,12 days,8 days,and 5.4 days with the mW/mB=0.22 mixture;and 22.5 days,12.6 days,and 7.1 days with the mW/mB= 0.40 mixture.Cases of the same steel thickness show that the ring specimens with a thicker concrete wall crack later.With the mW/mB = 0.22 mixture,concrete walls with thicknesses of 37.5 mm,75 mm,and 112.5 mm cracked at 3.4 days,8.0 days,and 9.8 days,respectively;with the mW/mB= 0.40 mixture,the ages of cracking were 7.1 days,12.6 days,and 16.0 days,respectively.     

Low complexity probability-based piecewise linear approximation of the sigmoid function

In order to improve the network recognition accuracy in the low complexity condition, a piecewise linear sigmoid function approximation based on the distribution probability of the neurons’ values is proposed only with one addition circuit. The sigmoid function is first divided into three fixed regions. Second, according to the neurons’ values distribution probability, the curve in each region is segmented into sub-regions to reduce the approximation error and improve the recognition accuracy. The slope of the piecewise linear function is set as 2-n, effectively reducing the hardware implementation complexity. Experiments performed on Xilinx’s FPGA-XC7A200T implement the MNIST handwritten digits recognition. The results show that the proposed method achieves a 97.45% recognition accuracy in a deep neural network and 98.42% in a convolutional neural network, up to 0.84% and 0.57% higher than other approximation methods only with one addition circuit.     

Arbitrary eigenvalue assignments for linear time-varying multivariable control systems

The problem of eigenvalue assignments for a class of linear time-varying multi-variable systems is considered. Using matrix operators and canonical transformations, it is shown that a time-varying system that is ‘lexicography-fixedly controllable’ can be made via state feedback to be equivalent to a time-invariant system whose eigenvalues are arbitrarily assignable. A simple algorithm for the design of the state feedback is provided.     

Design of reduced-order state estimators for linear time-varying multivariable systems

The design of reduced-order state estimators for linear time-varying multivariable systems is considered. Employing the concepts of matrix operators and the method of canonical transformations, this paper shows that there exists a reduced-order state estimator for linear time-varying systems that are ‘lexicography-fixedly observable’. In addition, the eigenvalues of the estimator can be arbitrarily assigned. A simple algorithm is proposed for the design of the state estimator.     

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R² = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.     

CLASSIFICATION OF ON-LINE POULTRY CARCASSES WITH BACKPROPAGATION NEURAL NETWORKS1

A transportable system equipped with an overhead shackle conveying line and a visible/near-infrared (Vis/NIR) spectrophotometer system was assembled and used at a poultry slaughter plant. The reflectance spectra of each poultry carcass hung on the moving shackle was measured with a stationary fiber optic probe, which was set 2 to 5 cm away from the carcass, depending on the size. Reflectance spectra of wholesome and unwholesome poultry carcasses on the moving shackle, set at 60 or 90 birds/min, were measured, either under room light or in a dark environment. The scanning time for each carcass was 0.32 s. Most of the unwholesome poultry carcasses for this study were septicemic and air-sacculitic. The average accuracy in classifying wholesome and unwholesome carcasses was above 94%. All the misclassified carcasses were air-sacculitic. With a shackle speed of 90 birds/min, the highest average accuracy was obtained when the reflectance was measured in the dark (97.5%). The results showed that the accuracy of classification could be improved with the maintenance of a consistent lighting environment. All results indicated the Vis/NIR spectrophotometer system would be a highly accurate, robust tool for on-line, real-time classification of wholesome and unwholesome carcasses.