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The purpose of this paper was to synthesise a thermally expandable microsphere (TEMS) with fast thermal response property and small expansion temperature range, and investigate the factors affecting the expansion properties of the microspheres.

A new kind of TEMS with fast thermal response property was synthesised by suspension polymerisation method, using acrylonitrile, ethyl methacrylate and methacrylic acid as the main monomers; Mg(OH)₂ as the main dispersing agent; and isooctane or n-hexane or n-pentane as the blowing agent.

The TEMS possessed the best expansion capacity when encapsulated isooctane and n-hexane were about 18.5 Wt.%. The expansion process of the TEMS could be finished by raising the temperature to 18°C from the expansion onset, much less than the reported 30-50°C. The morphology of the TEMS turned from sphere to irregular concave shape following the content increase of the blowing agent.

A new kind of TEMS composed of acrylonitrile/ethyl methacrylate/methacrylic acid as the polymer shell was synthesised. These TEMS showed the fastest thermal response speed reported.

The construction industry is the major sector in China but it has been exposed to a series of problems including low productivity and workforce shortage. However, construction robots as an effective and sustainable approach to overcome the difficulties in construction industry have not been widely adopted. Few studies attempted to investigate on the adoption of construction robots in China. In order to fill this gap, this study aim to address the barriers to the adoption of construction robots in China.

Through literature review, semi-structured interview 24 factors hindering the adoption of construction robots are summarized. Next, a total of valid 150 questionnaires delivered to the 7 stakeholders were collected. Ranking analysis was used to identify 21 critical factors was determined by the mean score analysis and factor analysis extracted 21 critical factors into 5 clusters.

Results indicate that the “technological performance and management” cluster is the most dominant of the 5 clusters. The most important barrier is “Limited research and design input”, followed by “High purchase cost” and “Unstructured construction environment”. Construction robots are still under R&D have had limited field applications in the production and construction process.

The research findings provide a useful reference for different stakeholders to identify the critical factors appropriate strategies to promote the adoption of construction robots. Furthermore, this study provides recommendations to promote adoption of construction robots.

The purpose of this study is to investigate the enhancement and suppression of heat transfer for hybrid nanofluids (Cu–Al2O3/water) in a square enclosure containing a thermal-conductive cylinder when the Lorentz force is applied to the hybrid nanofluids.

Since the inner conductive cylinder in present research has a complex geometry, an in-house meshless method, namely, the local radial basis function (LRBF) method, is applied to solve the 2 dimensional (2D) incompressible Navier–Stokes equation in the fluid domain and Fourier heat conduction equation in solid domain. The solid–fluid interface remains the physical continuity of temperature and heat flux. Only the Lorentz force is considered for the presence of the magnetic field. The conjugate natural convection is assumed to be steady, thus only fully developed heat exchange from the nanofluids to solid or vice versa is comprehensively investigated.

It can be concluded that Lorentz force plays a more significant role than hybrid nanofluids in enhancing/suppressing heat transfer when the orientation of magnetic field is the same to the x direction. The thermal conductivity ratio can dramatically change the isotherms and streamlines as well as the mean value of the Nusselt number, resulting in totally different heat transfer phenomena. The included angle of magnetic field also has a significant effect on the heat transfer rate when it changes from horizontal to vertical.

The constant thermo-physical properties of incompressible fluid and the 2D steady flow are considered in this study.

The conjugate MHD natural convection of hybrid nanofluids is numerically investigated by an in-house meshless LRBF method. The enhancement and suppression of heat transfer under the combined influence of the volume fraction of nanoparticles, Hartmann number and the thermal conductivity ratio are comprehensively investigated.

Large gear components widely exist in the transmission system of helicopters, ships, etc. Due to the small assembly clearance of large gear components, using an automatic docking system based on position control will lead to forced assembly. The purpose of this paper is to reduce the assembly stress of large gear components by an active compliant docking technology based on distributed force sensors.

Firstly, aiming at the noise interference in three-dimensional force sensor (TDFS), Kalman filter and Savitzky–Golay filter are used to process the sensor’s output signal. Secondly, the active compliant docking control model is constructed according to the principle of impedance control. Thirdly, the contact force is calculated based on the Euler equation, and the impedance control parameters are tuned by the particle swarm optimization algorithm. Finally, an active compliant docking system of a large gear structure based on distributed force sensor is built in the laboratory to verify the proposed method.

The experimental results show that the contact force and contact torque gradually decrease in all directions and are always in the safe range during the docking process. The feasibility of this method in practical application is preliminarily demonstrated.

The distributed TDFSs are used to replace the traditional six-dimensional force sensor in the active compliant docking system of gear components, which solves the problem of the small bearing capacity of the conventional active compliant docking system. This method can also be used for the docking of other large components.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

Many announced cross-border mergers and acquisitions (M&As) are never brought to completion despite potential negative consequences to acquirers and targets. This paper presented evidence on the dynamic effects of spatial distance and two industry-level characteristics, namely industry relatedness between the two firms and technological intensity, on the completion likelihood of cross-border M&A deals. Based on a sample of 8,489 M&A transactions we found that the completion likelihood of cross-border M&As increases with spatial distance. The effect is more pronounced for deals across technology-based industries, evidence for related deals is inconclusive.

Systemic risk has been one of the most interesting issues in banking and financial literature during the last years, particularly in evaluating its effects on the stability of the whole financial system during crises. Differently from other studies which analyze systemic risk focusing on European countries, we explore the determinant of systemic risk in other regional or continental banking systems, as Latin America. Using the CoVaR approach proposed by Adrian and Brunnermeier (2016), we study the impact of corporate variables on systemic risk on a sample of 30 Latin American banks belonging to seven countries, continuously listed from 2002Q1 to 2015Q4. We investigate the contribution of the corporate variables over different economic periods: the Subprime crisis (2007Q3–2008Q3), the European Great Financial Depression (2008Q4–2010Q2), and the Sovereign debt crisis (2010Q3–2012Q3).

With the improvement of modern aircraft requirements for safety, long life and economy, higher quality aircraft assembly is needed. However, due to the manufacturing and assembly errors of the posture adjustment mechanism (PAM) used in the digital assembly of aircraft large component (ALC), the posture alignment accuracy of ALC is difficult to be guaranteed, and the posture adjustment stress is easy to be generated. Aiming at these problems, this paper aims to propose a calibration method of redundant actuated parallel mechanism (RAPM) for posture adjustment.

First, the kinematics model of the PAM is established, and the influence of the coupling relationship between the axes of the numerical control locators (NCL) is analyzed. Second, the calibration method based on force closed-loop feedback is used to calibrate each branch chain (BC) of the PAM, and the solution of kinematic parameters is optimized by Random Sample Consensus (RANSAC). Third, the uncertainty of kinematic calibration is analyzed by Monte Carlo method. Finally, a simulated posture adjustment system was built to calibrate the kinematics parameters of PAM, and the posture adjustment experiment was carried out according to the calibration results.

The experiment results show that the proposed calibration method can significantly improve the posture adjustment accuracy and greatly reduce the posture adjustment stress.

In this paper, a calibration method based on force feedback is proposed to avoid the deformation of NCL and bracket caused by redundant driving during the calibration process, and RANSAC method is used to reduce the influence of large random error on the calibration accuracy.

Scholars mainly propose and establish theoretical models of cumulative fatigue damage for their research fields. This review aims to select the applicable model from many fatigue damage models according to the actual situation. However, relatively few models can be generally accepted and widely used.

This review introduces the development of cumulative damage theory. Then, several typical models are selected from linear and nonlinear cumulative damage models to perform data analyses and obtain the fatigue life for the metal.

Considering the energy law and strength degradation, the nonlinear fatigue cumulative damage model can better reflect the fatigue damage under constant and multi-stage variable amplitude loading. In the following research, the complex uncertainty of the model in the fatigue damage process can be considered, as well as the combination of advanced machine learning techniques to reduce the prediction error.

This review compares the advantages and disadvantages of various mainstream cumulative damage research methods. It provides a reference for further research into the theories of cumulative fatigue damage.