改良森吉米尔法退火炉中带钢的氧化研究

总结了带钢经过改良森吉米尔退火炉后的4种氧化状态,通过现场实际生产经验的总结和数据积累,分析了带钢产生表面氧化的关键因素,指出了实际操作中应该注意的事项,为以后的设计调试及生产提供了参考。     

Effect of microstructure on discharge performance of Al–0.8Sn–0.05Ga–0.9Mg–1.0Zn (wt%) alloy as anode for seawater-activated battery

The electrochemical performance and discharge behavior of Al–0.8Sn–0.05Ga–0.9Mg–1.0Zn (wt%) alloys in as-cast, homogenized, and annealed states were investigated through electrochemical means, corrosion rate test, and discharge test in a 3.5 wt% NaCl solution. Results suggest that the discharge performance of this alloy is enhanced by rolling and subsequent annealing treatment. This is attributed to the fact that the microstructure of the alloy is greatly improved through rolling and subsequent annealing treatment. The 400°C-annealed alloy exhibits the most excellent discharge activity than alloys in other states, which is due to more regions being activated by a finer and more uniform Sn-rich phase. Furthermore, the anode efficiency of the 400°C-annealed alloy is higher than that of as-cast and homogeneous alloys due to the more uniform distribution of Sn in the aluminum matrix.     

Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Classification is one of the important tasks in data mining. The probabilistic neural network (PNN) is a well-known and efficient approach for classification. The objective of the work presented in this paper is to build on this approach to develop an effective method for classification problems that can find high-quality solutions (with respect to classification accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridizes the firefly algorithm with simulated annealing (denoted as SFA), where simulated annealing is applied to control the randomness step inside the firefly algorithm while optimizing the weights of the standard PNN model. We also extend our work by investigating the effectiveness of using Lévy flight within the firefly algorithm (denoted as LFA) to better explore the search space and by integrating SFA with Lévy flight (denoted as LSFA) in order to improve the performance of the PNN. The algorithms were tested on 11 standard benchmark datasets. Experimental results indicate that the LSFA shows better performance than the SFA and LFA. Moreover, when compared with other algorithms in the literature, the LSFA is able to obtain better results in terms of classification accuracy.     

Texture homogeneity and stability in severely warm-rolled and annealed ultrafine pearlite

The evolution of microstructure and texture was investigated in a severely warm-rolled ultrafine pearlitic steel. The steel was 95% warm-rolled at 600°C and annealed at 700°C for different time intervals. The spheroidisation of cementite initiated after 30% reduction and completed beyond 70% reduction. The 95% warm-rolled steel showed elongated as well as ultrafine equiaxed ferrite grains. Texture inhomogeneities were evidenced by the presence of Goss component ({011} <100>) on the surface originating due to surface shear and diffuse texture at the interior. Formation of equiaxed microstructure after annealing through continuous recrystallisation resulted in the retention of the surface Goss component. However, the Goss component was destroyed beyond annealing for 180?minutes due to the abnormal growth of other grains.     

A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.     

Structure and properties of bio‐based polyamide 109 treated with superheated water

The structure and properties of bio‐based polyamide 109 (PA109) after treatment with superheated water (140 °C ≤ T ≤ 280 °C) were investigated and characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, wide‐angle X‐ray diffraction, scanning electron microscopy and small‐angle X‐ray scattering. Below 170 °C, the hydrothermal treatment was considered to be a physical process, which exerted an annealing effect on PA109. It led to an increase in melting temperature, lamellar thickness and crystallinity, while the macromolecular structure, crystal structure and the order of crystalline regions were not affected. Above 170 °C, complete melting/dissolution of PA109 occurred with partial hydrolysis. Due to the high temperature and long reaction time, the hydrolysis reaction became more and more prominent, and the resin was completely hydrolyzed into oligomers at 280 °C. Also, above 170 °C, the hydrothermal treatment was accompanied by a chemical process and the melting temperature and molecular weight decreased progressively. Notably, the crystal structure was not altered, but the degree of perfection of crystals and the order of crystalline regions were broken, especially above 200 °C. The hydrolytic degradation reaction was significantly affected by temperature, while both time and the water to polyamide ratio were secondary factors which influenced it to a minor extent. The process could be considered as a typical nucleophilic substitution reaction which takes place step by step inducing the molecular weight to decrease gradually. Overall, this study provides a ‘green’ route for the processing, recycling and treatment of environmentally friendly polyamides based on hydrothermal treatment technology. © 2019 Society of Chemical Industry     

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

基于EEMD能量熵和混合算法的电机轴承故障诊断

针对提升机电机轴承振动信号的非平稳特性和单一粒子群算法(PSO) 优化径向基函数（RBF）神经网络时存在网络收敛速度慢和适应度值易陷入局部最小的缺点，提出基于集合经验模态分解（EEMD）能量熵和模拟退火粒子群混合算法（SAPSO）优化RBF神经网络的提升机电机轴承故障诊断方法。基于EEMD求取振动信号各固有模态函数分量的能量熵，并使用相关性分析方法剔除虚假的分量，把筛选后的有效数据作为故障识别的特征向量；利用模拟退火（SA）算法具有局部概率突跳的特性，将SA算法和PSO算法相结合，在优化RBF诊断模型隐含层参数时以实现不同算法间的优劣互补。仿真结果表明，使用SAPSO算法优化后的RBF神经网络模型在提升机电机轴承故障诊断中能够加快网络收敛速度和提升故障识别精度。