Here we report the synthesis of Pt/Ag bimetallic nanostructures with controlled number of void spaces via a tailored galvanic replacement reaction (GRR). Ag nanocubes (NCs) were employed as the template to react with Pt ions in the presence of HCl. The use of HCl in the GRR caused rapid precipitation of AgCl, which grew on the surface of Ag NCs and acted as a removable secondary template for the deposition of Pt. The number of nucleation sites for AgCl was tailored by controlling the amount of HCl added to the Ag NCs or by introducing PVP to the reaction. This strategy led to the formation of Pt/Ag hollow nanoboxes, dimers, multimers, or popcorn-shaped nanostructures consisting of one, two, or multiple hollow domains. Due to the presence of large void space and porous walls, these nanostructures exhibited high surface area and improved catalytic activity for methanol oxidation reaction.

An effective feature reduction method is a key issue to improve the detection performance of the electronic nose (e-nose). In this study, a feature reduction method coupled with a support vector machine (SVM) was proposed to enhance the detection performance of the e-nose for the quality detection of tea. Firstly, the time-domain features were extracted, which can represent the original gas information of different grades of tea. Secondly, to consider the importance of the relationship between each feature and output category, a subset of multiple features with the best variable importance of projection (VIP) score was generated to obtain the optimized feature set. Finally, kernel principal component analysis (KPCA) and kernel entropy component analysis (KECA) were performed to further reduce the correlation between features to obtain the best feature set. The results indicated that VIP-KECA can obtain the best feature set effectively, and a good classification accuracy of 98% was obtained. This study shows that the feature reduction method is effective for enhancing the detection performance of the e-nose. It also provides an effective technique to monitor the quality of tea.

To enhance the detection performance of electronic nose (e-nose), a recognition method of gas feature based on a global extended extreme learning machine (GEELM) is proposed, which combines the expansion factor and global balance coefficient to expand and balance the difference between categories, and improve the classification performance. Then this method is applied to identify the quality of tea. Firstly, the dragging factor and following matrix are introduced to increase the distance between classes. Secondly, the global identification coefficient is introduced further to increase the feature differences among different types of tea, and improve the classification stability. Finally, under different feature sets, the classification performance of multi-pattern recognition methods is compared to prove the effectiveness of GEELM in e-nose gas feature recognition. The results show that GEELM has the best classification accuracy of 98.20%, F1-score of 0.9871, and Kappa coefficient of 0.9775. In conclusion, GEELM can be an effective technique to identify gas features, and it also provides a new method for tea quality measurement.

Background: With the development of technology, the data amount has increased significantly. In data processing, multi table query is the most frequently operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper proposes a heuristic multi table join optimization method. By sharing information, the unconnected tuples are eliminated, so as to reduce the amount of data transmitting. This shortens response time and improves the execution performance. Method: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying.

Background: With the rapid development of science, more data are produced in people's life. Therefore, the storage and calculation of big data has become the focus of scientific research. MapReduce performs well in big data processing. However, it is prone to data skew. Which affects the overall efficiency of the data processing cluster. Objective: Aiming at the low efficiency of MapReduce data join, this paper proposes an intelligent data join load balancing algorithm based on dynamic programming. The algorithm introduces data sampling and partition algorithm. Due to the high performance of dynamic programming in data constraint problem, it is used to intelligently solve the data skew problem. Methods: Firstly, the causes of data skew are analyzed and the data partition method is improved. The algorithm introduces a data sampling method. In the task allocation stage, the multidimensional knapsack algorithm is used. Different key values are evenly divided to each computing node through the load cost. Finally, The performance of the improved algorithm is verified by experiments. Results: The experimental results show that compared with the traditional load balancing algorithm and the existing improved algorithm, the new algorithm improves the data processing efficiency, reduces the data skew problem and better solves the problem of data load imbalance. Conclusion: A two table equivalent join load balancing algorithm based on key cost is proposed. The algorithm creatively combines dynamic programming with intelligent data sampling, which greatly improves the efficiency and quality of data processing. The algorithm is worthy of popularization and application.