Search results

The purpose of this paper is to study the microstructure and properties of Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys with and without a rotating magnetic field (RMF).

Optical microscopy, scanning electron microscopy and X-ray diffraction were used to analyze the effect of an RMF on the microstructure of the solders. Differential scanning calorimetry was used to study the influence of the RMF on the thermal characteristics of the solders. The mechanical properties of the alloys were determined by tensile measurements at different strain rates.

The ß-Sn grains and intermetallic compounds for the Sn-3.5Ag and Sn-3.5Ag-0.5Sb lead-free solder alloys were refined under an RMF, and the morphology of the ß-Sn grains changed from dendritic to equiaxed. The pasty range was significantly reduced under an RMF. The ultimate tensile strength (UTS) of Sn-3.5Ag improved under the RMF, whereas the UTS of Sn-3.5Ag-0.5Sb decreased slightly. The addition of Sb to the Sn-3.5Ag alloy significantly enhanced the UTS and elongation (El.%) of the samples. The UTS of the solder increased with increasing strain rate.

The results revealed that the application of RMF in the molten alloy had a significant effect on its microstructure and mechanical properties. The thermal characteristics of the Sn-3.5Ag and Sn-3.5Ag-0.5Sb solder alloys were improved under the RMF. This research is expected to fill a knowledge gap regarding the behaviour of Sn-Ag solder alloys under RMF.

The existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.

A three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.

The efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.

A three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

As a transition economy, China is interested in allocating its limited innovation resources economically, reasonably and efficiently to produce as many outputs as possible with its limited financial and human resources. Nonetheless, what is the efficiency of the allocation of innovative resources for civil–military integration enterprises, and what factors hinder its efficiency improvement? The purpose of this paper is to explore these problems.

The improved two-stage network data envelopment analysis (DEA) method is used to measure the overall efficiency and stage efficiency of the innovation resource allocation of 58 Chinese civil–military integration listed companies from 2010 to 2016. Tobit model is used to analyze the influencing factors of resource allocation efficiency.

The results indicate that the overall efficiency and stage efficiency of innovation resource allocation fluctuate in varying degrees during the period. The optimization of overall efficiency is restricted by lower efficiency of innovation achievement transformation. Enterprise scale was found to have a significant negative impact on both overall and two-stage efficiencies. Proportion of research and development (R&D) personnel had a positive effect on the overall and two-stage efficiency. Government support had a significant positive effect on the stage of innovation resource development and overall efficiency.

Previous research studies have used either the DEA or stochastic frontier analysis method to measure the efficiency of innovation activities as a whole and ignored the stage of initial investment to final output in innovation activities. That is, the process in which initial input of R&D resources becomes innovation output, and then becomes economic benefits. Therefore, this paper studies the efficiency of innovation resource allocation of civil–military integration listed companies. The improved two-stage chain network DEA method and Tobit model were used.

The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects of 0.5%Sb and 0.07%Ce doping on microstructure, thermal properties and mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder were investigated.

According to the mass ratio, the solder alloys were prepared from tin ingot, antimony ingot, silver ingot and copper ingot with purity of 99.99% at 400°C. X-ray diffractometer was adopted for phase analysis of the alloys. Optical microscopy, scanning electron microscopy and energy dispersive spectrometer were used to study the effect of the Sb and Ce doping on the microstructure of the solder. Then, the thermal characteristics of alloys were characterized by a differential scanning calorimeter (DSC). Finally, the ultimate tensile strength (UTS), elongation (EL.%) and yield strength (YS) of solder alloys were measured by tensile testing machine.

With the addition of Sb and Ce, the ß-Sn and intermetallic compounds of solders were refined and distributed more evenly. With the addition of Sb, the UTS, EL.% and YS of Sn-1.0Ag-0.5Cu increased by 15.3%, 46.8% and 16.5%, respectively. The EL.% of Sn-1.0Ag-0.5Cu increased by 56.5% due to Ce doping. When both Sb and Ce elements are added, the EL.% of Sn-1.0Ag-0.5Cu increased by 93.3%.

The addition of 0.5% Sb and 0.07% Ce can obtain better comprehensive performance, which provides a helpful reference for the development of Sn-Ag-Cu lead-free solder.

This paper aims to analyze the role and advantages of knowledge resources in the carbon emission reduction of the industrial chain, and how it can be used to promote the carbon emission reduction of the industrial chain, so that the industry can better achieve the saving of energy and the reduction of emission.

This paper argues that the traditional resource-plundering industrial chain production method can no longer meet the needs of sustainable development of the green and low-carbon industrial chain, and builds the coupling and coordination of knowledge technology innovation drive and industrial chain carbon emission reduction mechanism, in the four dimensions of industrial chain organization, government support, internet support and staff brainstorming, put forward suggestions for knowledge resources to drive carbon emission reduction in the industrial chain.

This paper holds that the use of knowledge resource advantages can better help industrial chain enterprises to carry out technological innovation, knowledge resource digital platform construction, knowledge resource overflow and transfer, application and management of network information technology, so as to reduce carbon emission in industrial chain.

This paper contributes to the discussion about the high-quality implementation of the revitalization strategy of the industrial chain and also deepens research on the knowledge resource-driven carbon emission reduction of the industrial chain. Further, this paper enriches the role of knowledge resources in the industrial industry, and the theoretical results support the advantages of knowledge resource in the field of chain carbon emission reduction.

Long lease real estate funds (over £15bn in Q3 2020) have emerged as an increasingly important part of UK pension fund real estate portfolios. This paper explores the reasons for their dramatic growth, their characteristics and performance.

This study uses data for the period 2004–2020 collected directly from fund managers and from AREF/MSCI and empirical analysis to explore their characteristics and performance.

Pension fund de-risking and regulatory guidance have supported the dramatic growth of long lease real estate funds. Long lease real estate funds have delivered strong risk-adjusted returns relative to both balanced property funds (with shorter lease terms) and the wider property market. This relative performance has been particularly strong when wider property market performance has been weak. Long lease funds have objectives aligned with liability matching and their performance suggests they are lower risk (more bond-like) investments. In addition, our analysis highlights they are far less responsive to the wider property market than balanced funds. However, they are not significantly different from balanced property funds in terms of their short-term relationship with gilt yield movements.

For pension funds and other investors the paper highlights that long lease real estate funds offer a different exposure than balanced property funds. Long lease funds have objectives more closely aligned to the overall objectives for pension fund investment but are not significantly more reliable than balanced property funds in the short-term as a liability hedge. For real estate fund managers, occupiers, developers and others active in the real estate market, the paper highlights why these funds have been (and are likely to remain) attractive to investors leading to substantial demand for long lease real estate investments.

This is the first study to review this increasingly important part of the UK real estate fund universe.

The purpose of this paper is to attempt the construct of a Centipede Game network model of investment in real estate market based on grey integration and forwards induction.

The investment process of decision making in the real estate market is a multi-agent-stage dynamic process. The behavior of the game are interdependent and the ultimated equilibrium is an outcome of every the game adjust themselves comprehensively in certain conditions. The paper constructs a Centipede Game network model of investment in real estate market based on grey integration and forwards induction, regulars the method framework based on grey integration and forwards induction.

The results verifies that the validity of Centipede Game network model by taking N's real estate market as an example.

Most of the existing real estate market game researches study it from the view of pairwise game or one-to-many game, in fact, the game tend to be developed in the form of a network. And the paper puts forward Centipede Game network model based on the mode of players game in real estate market.

The paper succeeds in providing a new and systematic framework of studying the real estate market game by using game theory.

Two corrosion inhibitors for closed cooling water systems, nitrite-based and mixture of nitrite and molybdate corrosion inhibitor, are often compared to each other. This study aims to optimize these two inhibitors in terms of concentration and pH for carbon steel protection, with insights into the double layer structure on surface and its impact on corrosion inhibition.

Electrochemical analysis including electrochemical impedance spectroscopy and potentiodynamic test are carried out for quick assessment of corrosion inhibition efficiency and optimization, which is confirmed by immersion test and microscopic analysis. The electronic properties of the surface film are analyzed through Mott–Schottky method which provides new insights into the inhibition mechanism and the role of each component in mixture inhibitor.

Mixture of nitrite and molybdate is shown to present higher inhibition efficiency, owning to the double layer structure. Nitrite alone can form a protective surface film, whereas molybdate leads to an n-type semiconductive film with lower donor density, hence giving rise to a better inhibition effect.

Surface after inhibitor treatment has been carefully characterized to the microscopic scale, implying the effect of micro-structure, chemical composition and electronic properties on the corrosion resistance. Inorganic corrosion inhibitors can be tuned to provide higher efficiency by careful design of surface film structure and composition.

Almost every study on corrosion inhibitor applies such method for quick assessment of corrosion inhibition effect. Mott–Schottky test is one of electrochemical methods that reveals the electronic properties of the surface film. Previous works have studied the surface layer mainly through X-ray photoelectron spectroscopy. This study provides another insight into the surface film treated by nitrite and molybdate through Mott–Schottky analysis, and relates this structure to the corrosion inhibition effect based on multiple analysis including electrochemistry, microscopic characterization, thermodynamics and interface chemistry.

Digital natives have become significant users of social network sites (SNSs); therefore, their disclosed personal information can be misused by SNS providers and/or other users. The purpose of this paper is to understand how digital natives make their self-disclosure decisions on SNSs, as well as whether the concept of culture can still be relevant to digital natives.

The hypotheses were tested with survey data collected from the USA and China.

The results show that trust in SNSs and trust in SNS users are positively related to social rewards. Social rewards are positively related to intention to self-disclose, while privacy risk is positively related to privacy concerns. Further, culture significantly moderates the relationship between trust and social rewards.

The study clarifies the effects of different types of trust on privacy in the context of SNSs. Further, the study shows the effects of culture when digital natives make self-disclosure decisions.

SNS providers also need to focus on different types of trust when operating in different cultural contexts. Further, SNS providers expanding their markets should emphasize social rewards to increase the likelihood of self-disclosure.

The results show that while culture can still be helpful to explain digital natives’ trust beliefs, digital natives have started to converge regarding their perceptions about privacy concerns and self-disclosure.

The symmetrical valve is usually used in the hydraulic servo control system to control the asymmetrical cylinder, but this system’s structure involves asymmetry, and so its dynamic characteristics are asymmetrical, which causes issues in the control system of symmetric response. The purpose of this paper is to achieve the aim of symmetric control.

In this paper, the authors proposed a method that combined wavelet neural network (WNN) and model reference adaptive control. The reference model determined the dynamic response that the system was expected to achieve, and the WNN adaptive control made the system follow the reference model to achieve the purpose of symmetric control.

The experimental results show that the method can achieve a more accurate symmetric control and position control compared with the solutions via the classical PID control.

The proposed combination of the WNN and the reference model can effectively compensate for the asymmetry of dynamic response of the asymmetric cylinder in forward and return directions, which can be extended to deal with other classes of applications.