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This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail.

First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency.

In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis.

The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

The purpose of this paper is to investigate the effects of parameters on the liquid phase migration (LPM) during the freeze-form extrusion fabrication (FEF) process.

To carry out this study, three factors were systematically investigated using orthogonal design of experiments. These three parameters are the extrusion velocity, the extrusion interval time and the extrusion head length. An orthogonal array with nine test units was selected for the experiments. Range analysis and analysis of variance were used to analyze the data obtained by the orthogonal experiments to identify the order of significant factors on LPM.

It was found that the LPM decreased with the increase of extrusion velocity and increased with the lengthening of extrusion interval time and the length of the extrusion nozzle. The order of significant factors for the LPM were found to be extrusion velocity > extrusion nozzle length > extrusion interval time.

Using an orthogonal design of experiments and a statistical analysis method, the liquid content of extrudate can be predicted and appropriate process parameter values can be selected. This leads to the minimization of LPM during the FEF process. Also, this analysis method could be used to study the LPM in other paste extrusion processes.

This paper suggests that the factors have significant impact on LPM during FEF process. The following analysis in this paper is useful for FEF users when prediction of LPM is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other FEF-type processes. The research can also help to get better understanding of LPM during the FEF process.

This paper aims to disclose the evolution of pendulum hardness of two-component acrylic polyurethane coatings during the cure process and attempts to describe the quantitative relationship between pendulum hardness and curing time. These findings are helpful for the study of fast curing acrylic polyurethane coatings.

The pendulum hardness method was used to monitor the hardness of two-component acrylic polyurethane coatings during curing. The quantitative relationship between pendulum hardness and curing time can be obtained with Avrami equation.

The evolution of coating pendulum hardness can be divided into three stages. By using the Avrami equation that explained the influence of both the acid value and the curing temperature on the drying speed of hydroxyl acrylic resin, the evolution of coating pendulum hardness during curing can also be accurately described.

It should be noted that the physical meaning of the Avrami exponent, n, is not yet clear.

The results are of great significance for the development of fast-curing hydroxyl-functional acrylic resins, with the potential to improve the drying speed of the coatings used in automotive refinish.

It is novel to divide the pendulum hardness into three stages, and, for the first time, the Avrami equation is utilized to describe the evolution of coating pendulum hardness during curing.

This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem.

At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers of neural networks to accomplish the path planning task.

The NAO biomimetic robot respond quickly and correctly to the dynamic environment. The simulation experiments show that the deep neural network outperforms in dynamic and static environment than the conventional method.

A new method of deep learning based biomimetic robot path planning is proposed. The authors designed a multi-layer CNN which includes max-pooling layer and convolutional kernel. Then, the first and second layers features can be extracted by these kernels. Finally, the authors use the sparse auto encoder training algorithm to train the CNN so as to accomplish the path planning task of NAO robot.

The application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud.

The authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation.

The target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state.

The research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.

Private firms in China are like the third child in a family, constantly struggling to establish their position in an environment favoring their state-owned and collective siblings. The purpose of this paper is to discover some long-term-oriented legitimacy building strategies for private firms in China.

This paper examines the effect of both internal and external institutional factors on long-term legitimacy for private enterprises. The authors integrate stakeholder perspective and institutional theory to provide a framework of building sustainable legitimacy.

The authors’ framework delineates that a private company can build sustainable legitimacy through catering long-term legitimacy conferring to constituents such as customers, social responsibility and patriotism in the external institutional environment.

The authors’ framework further indicates how private firms could leverage internal institutional environment through developing appropriate mission, culture, leadership and human resources practices in conformity to the demands of constituents for gaining long-term legitimacy.

This paper is the first to address the short-term nature of legitimacy building strategies proposed in the past literature. In addition, it is also the first attempt to explore the multiplicity in legitimacy in China in search of long-term legitimacy building approaches.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.

A structural and textural characterization study has been performed to investigate the adherence of zeolite-based catalyst washcoated onto honey-comb-type cordierite monoliths. The supports were characterized by the scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD) and Brunauer–Emmett–Teller (BET) techniques.

SEM/EDS provided quantitative estimate of the washcoated monolith as the elemental composition of catalyst coating. The XRD pattern deduced that the zeolite-based catalysts were successfully mounted on the cordierite support, showing the characteristic peaks of zeolites (Zeolite Socony Mobil–5; ZSM-5) at Braggs angles of 7.88°, 8.76°, 23.04°, 23.88° and 24.36°, whereas the characteristic peak of cordierite is seen at a Braggs angle of 10.44°.

The BET results proved that a monolayer of zeolite may serve the need for surface area and porosity. This was evident in the increase of surface area of washcoated support as against the bare support. The obtained isotherms were of Type IV, illustrating the presence of mesopores. The adsorption and desorption isotherm branches coincided over the interval 0 < P/P0 < 0.50 and 0 < P/P0 < 0.45, showing N₂ reversible adsorption for the two samples, respectively.

It was concluded that the composite materials which are ZSM-5 (Si/Al = 25) and precursors of the transition salts of copper, zinc and ceria powders were deposited on the catalyst supports, establishing the success of the coating procedure relative to the adherence of the catalyst compositions on the ceramic support.

This paper aims to investigate the effect of different doses of gamma radiation on the micromechanical response (hardness properties and creep behaviour) of 96.5Sn-3.0Ag-0.5Cu (SAC305) solder alloys.

SAC305 solder pastes deposited on printed circuit boards (PCBs) were subjected to a reflow soldering process to form soldered samples. The soldered samples were irradiated with a gamma source at different doses (5–50 Gy). Nanoindentation testing was used to determine the hardness properties and creep behaviour after gamma irradiation.

The results showed that the hardness of SAC305 solder alloys gradually increased up to 15 Gy and then gradually decreased to 50 Gy of gamma irradiation. The highest hardness value (0.37 GPa) was observed on SAC305 solder alloys exposed to 15 Gy irradiation. Hardening of SAC305 solder alloy was suggested to be due to the high defect density induced by the gamma irradiation. Meanwhile, exposure to 50 Gy irradiation resulted in the lowest hardness value, 0.13 GPa. The softening behaviour of SAC305 solder alloy was probably due to the evolution of defect size in the solder joint. In addition, the creep behaviour of the SAC305 solder alloys changed significantly with different gamma irradiation doses. The creep rates were higher at a dose of 10 Gy up to a dose of 50 Gy. Gamma irradiation caused the SAC305 solder alloy to become more ductile compared to the non-irradiated alloy. The stress exponent also showed different deformation mechanisms with varying gamma doses.

Research into the micromechanical properties of solder alloys subjected to gamma irradiation has rarely been reported, especially for Sn-Ag-Cu lead-free solder. Thus, this research provides a fundamental understanding of the micromechanical response (hardness and creep behaviour) of solder, especially lead-free solder alloy, to gamma irradiation.