Synthesis and study of organically capped ultra small clusters of cadmium sulphide

Ultra small clusters of cadmium sulphide are synthesized using non-aqueous and aqueous chemical methods. Thiophenol has been used as a capping agent for non-aqueous synthesis whereas various reagents such as mercaptoethanol, hexametaphosphate, ethylene glycol and ethanol have been used as additives for an aqueous method of synthesis. Properties of the clusters synthesized are discussed based on optical absorption, X-ray diffraction, transmission electron diffraction and photoelectron spectroscopy. Particles as small as 0.7 nm diameter could be synthesized with thiophenol and mercaptoethanol as additives. The effect of varying the molarities of the different additives on the properties of the CdS nanoclusters synthesized are discussed. Systematic ageing studies of the nanoclusters showed that larger particles age faster than the smaller clusters. Ageing also leads to better crystallization of the particles. It has been observed that the smallest particles (0.7 nm diameter) possess tetrahedrally bonded fragments of CdS and intercluster structural long range order does not exist. However, bigger particles (2.0 nm diameter) show bulk cubic structure. X-ray photoelectron spectroscopy studies have been done to study the purity and stoichiometry of the clusters synthesized and strongly support the existing proposal of the formation and stability of CdS nanoclusters.     

复合铸造与振动斜板冷却铸造SiCp增强A356复合材料显微组织与耐磨性的比较

利用复合铸造和振动斜板铸造2种方法铸造SiCp增强A356复合材料,比较2种复合材料中碳化硅含量对材料显微组织、孔隙、硬度和耐磨性的影响。在铸态条件下,振动斜板铸造和复合铸造的2种复合材料的基体分别为球形和枝晶结构。振动斜板铸造的复合材料其碳化硅颗粒分布更加均匀,并且具有更高的硬度,复合铸造的材料则具有更少的孔隙。对于这2种复合材料,碳化硅颗粒的增加(体积分数最大为20%)导致碳化硅颗粒在基体合金内更加均匀分布并且提高了其耐磨性。与复合铸造材料相比,对于振动斜板铸造的复合材料,碳化硅含量的增加,将降低球形颗粒的尺寸和形状因子,并且具有较好的耐磨性。振动斜板铸造材料比复合铸造材料具有更好的力学性能,这是因为基体中的碳化硅颗粒分布更加均匀,而且振动斜板铸造过程中形成了球形组织。     

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.     

A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem

A hybrid clustering method is proposed in this paper based on artificial immune system and simulated annealing. An integration of simulated annealing and immunity-based algorithm, combining the merits of both these approaches, is used for developing an efficient clustering method. Tuning the parameters of method is investigated using Taguchi method in order to select the optimum levels of parameters. Proposed method is implemented and tested on three real datasets. In addition, its performance is compared with other well-known meta-heuristics methods, such as ant colony optimization, genetic algorithm, simulated annealing, Tabu search, honey-bee mating optimization, and artificial immune system. Computational simulations show very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required, comparing with mentioned methods.     

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms

From a watershed management perspective, streamflow need to be predicted accurately using simple, reliable, and cost-effective tools. Present study demonstrates the first applications of a novel optimized deep-learning algorithm of a convolutional neural network (CNN) using BAT metaheuristic algorithm (i.e., CNN-BAT). Using the prediction powers of 4 well-known algorithms as benchmarks – multilayer perceptron (MLP-BAT), adaptive neuro-fuzzy inference system (ANFIS-BAT), support vector regression (SVR-BAT) and random forest (RF-BAT), the CNN-BAT model is tested for daily streamflow (Q_t) prediction in the Korkorsar catchment in northern Iran. Fifteen years of daily rainfall (R_t) and streamflow data from 1997 to 2012 were collected and used for model development and evaluation. The dataset was divided into two groups for building and testing models. The correlation coefficient (r) between rainfall and streamflow with and without antecedent events (i.e., R_t-1, R_t-2, etc.) (as the input variables) and Q_t (as the output variable) served as the basis for constructing different input scenarios. Several quantitative and visually-based evaluation metrics were used to validate and compare the model’s performance. The results indicate that R_t was the most effective input variable on Q_t prediction and the integration of R_t, R_t-1, and Q_t-1 was the optimal input combination. The evaluation metrics show that the CNN-BAT algorithm outperforms the other algorithms. The Friedman and Wilcoxon signed-rank test indicates that the prediction power of CNN-BAT algorithm is significantly/statistically different from the other developed algorithms.

     

半固态铸造A356铝合金冷却斜槽工艺参数的建模与优化(英文)

使用冷却斜槽工艺加工半固态A356铝合金。运用D-实验优化设计(DODE)设计实验并分析实验结果。采用软件抽取38个随机实验。冷却斜板长度为100、300、500 mm,斜板的斜角为30°、45°、60°,浇铸温度为660、680和700°C。将半固态铝合金浇铸到铜质冷却斜板上,然后倾入到钢模中成型。铸造完成之后,在温度590°C下,对部分样品进行5、8、12 min重熔处理。研究这些因素对初生α(Al)晶体圆整度的影响,并使用DODE进行参数优化处理。结果表明,传统铸造A356合金的初生α(Al)相为枝晶,而冷却斜槽铸造的为非枝晶。重熔后再经过冷却斜槽处理的样品呈现出球状结构。最优的浇铸温度、冷却板长度、斜角和保温时间分别为660°C、360mm、48°和9 min。在最优条件下,得到初生晶体的圆整度为0.91。所建立的模型的相关系数为0.9860。     

复合铸造工艺参数对A356-SiC_p复合材料显微组织和拉伸性能的影响(英文)

研究复合铸造工艺参数对A356-SiCp复合材料显微组织和拉伸性能的影响。在590、600和610°C的温度条件下,分别以200、400和600 r/min的速度对样品进行半固态搅拌,搅拌时间分别为10、20和30 min。分析SiC颗粒在基体材料中的分布、样品的孔隙率和拉伸性能。结果表明,通过延长搅拌时间和降低搅拌温度,可以提升颗粒分布的均匀性;然而,随着搅拌速度的提高,颗粒分布的均匀性呈先上升后下降的趋势。同时还发现,通过增大所有的工艺参数,孔隙率得到了提高。从抗拉特性来看,最佳的搅拌速度、温度和时间分别为400 r/min、590°C和30 min。与孔隙率相比,增强相分布的均匀性对拉伸性能的影响更明显。