Ning Zhao, Hai‐tao Ma and Lai Wang
The paper aims to investigate the interfacial reactions between two Sn‐Cu based multicomponent Pb‐free solders, Sn‐2Cu‐0.5Ni and Sn‐2Cu‐0.5Ni‐0.5Au (wt per cent), and Ni…
Abstract
Purpose
The paper aims to investigate the interfacial reactions between two Sn‐Cu based multicomponent Pb‐free solders, Sn‐2Cu‐0.5Ni and Sn‐2Cu‐0.5Ni‐0.5Au (wt per cent), and Ni substrates during soldering and aging.
Design/methodology/approach
Differential scanning calorimetry (DSC) was performed to measure the melting behaviors of the solders and determine the temperature of soldering. DSC tests showed that the onset temperature were 227.47 and 224.787°C for Sn‐2Cu‐0.5Ni and Sn‐2Cu‐0.5Ni‐0.5Au, respectively. Two intermetallic compounds (IMCs), Cu6Sn5 and (Cu,Ni)6Sn5, were formed in Sn‐2Cu‐0.5Ni solder. While the IMCs detected in Sn‐2Cu‐0.5Ni‐0.5Au matrix were (Cu,Ni)6Sn5, (Cu,Au)6Sn5 and (Cu,Ni)6Sn5. The IMC layer formed at the both solder/Ni interfaces was (Cu,Ni)6Sn5 with stick‐lick morphology after soldering at 260°C.
Findings
The interfacial IMC layers became planar when aged at 170°C for 500 h. However, cracks were found in the IMC layers at both joints when the aging time reached 1,000 h, that implies reliability problem may exist in the joints. Moreover, Au‐containing IMCs were found on the top of the IMC layer in Sn‐2Cu‐0.5Ni‐0.5Au/Ni joint after for 1,000 h.
Originality/value
This study focuses on the interfacial reactions of Sn‐2Cu‐0.5Ni/Ni and Sn‐2Cu‐0.5Au/Ni during soldering and isothermal aging.
Details
Keywords
Chen Hao and Chen Hai-tao
The purpose of this paper is to examine and explore the factors that drive users to gift through social network services (SNS).
Abstract
Purpose
The purpose of this paper is to examine and explore the factors that drive users to gift through social network services (SNS).
Design/methodology/approach
A questionnaire method was applied to collect data from the sample of the WeChat users who have used mini-program. This paper employed the partial least squares method and used SmartPLS2.0 to analysis sample data, which examined the validity as well as reliability of the sample and further tested the hypotheses by the path coefficients.
Findings
The empirical results showed that pleasure, social relationship maintenance, convenience and comprehensiveness are significantly related to SNS gifting behavior, and conscientiousness moderates the relationship between intention and behavior in the context of SNS gifting. However, this study cannot find the effect of symbolic representation, impersonality and gift reciprocity motivations.
Research limitations/implications
Theoretically, this study perfects the research of SNS gifting on the lack of exploring characteristics of comprehensiveness. Practically, this paper lends insights on how SNS providers attract users to adopt gifting.
Originality/value
SNS gifting lacks a complete and effective promotion strategy, resulting in a small number of users as well as low profit. Besides, prior studies have focused on tradition gifting and online gifting. Little research talks about gifting on SNS phenomena, and thus it is necessary to perfect the theory of SNS gifting.
Details
Keywords
This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P…
Abstract
Purpose
This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.
Design/methodology/approach
In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.
Findings
The authors got very satisfactory classification results.
Originality/value
DDPML system is specially designed to smoothly handle big data mining classification.