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This paper aims to obtain fire resistance of semi-rigid joints for concrete-filled steel tubular (CFST) composite frames and temperature filed distribution of composite joints in fire.

The temperature filed model of semi-rigid joints to CFST columns with slabs was made by using ABAQUS finite element (FE) software, in considering temperature heating-up stage of fire modelling. The effects of composite slab, fire type and construction location were discussed, and the model was verified by the test results. The temperature distribution of composite joint under three-side or four-side fire condition was studied by the sequentially coupled thermal analysis method. The temperature versus time curves and temperature distribution of various construction and location were analyzed.

The paper provides FE analysis and numerical simulation on temperature field of semi-rigid joints for CFST composite frames in fire. The effects of composite slab, fire type and construction location were discussed, and the model was verified by the test results. It suggests that the temperature distribution of composite joint in three- or four-side fire condition showed a different development trend.

Because of the chosen FE analysis approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The research results will become the scientific foundation of mechanical behavior and design method of semi-rigid CFST composite frames in fire.

This paper fulfils an identified need to study the temperature field distribution of the semi-rigid joints to CFST columns and investigate the mechanical behavior of the semi-rigid CFST joints in fire.

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.

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.

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.

This study focuses on the interfacial reactions of Sn‐2Cu‐0.5Ni/Ni and Sn‐2Cu‐0.5Au/Ni during soldering and isothermal aging.

The purpose of this paper is to examine and explore the factors that drive users to gift through social network services (SNS).

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.

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.

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.

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.

Social media have become famous platform to search and share the COVID-19-related information. The objective of this research is to bridge the gap by proposing the effects of network cluster and transmitter activity on information sharing process.

Data were collected by using Facebook application, which was available for 14 days (May 1–14) in 2020. These data were analyzed to determine the influence of the network cluster and transmitter activity.

The results showed that network cluster is positively related to transmitter activity on social media. In addition, transmitter activity partially mediated the effect of network cluster on the extent of information liked and shared. That is, transmitter activity can affect COVID-19-related information sharing on Facebook, and the activity effect is plausible and should become stronger as social network become denser.

This study has contributed to the knowledge of health information sharing in social media and has generated new opportunities for research into the role of network cluster. As social media is firmly entrenched in society, researches that improve the experience or quality for users is potentially impactful.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

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.

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.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.