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China intends to enhance its environmental regulations, which will affect many industries, because of the serious environmental pollution that the country faces. This study aims to investigate the influence of environmental regulations on China’s provincial tourism competitiveness.

A vertical-and-horizontal scatter degree method is used to construct provincial-level tourism competitiveness and environmental regulation indices in China. Thereafter, a spatial econometric model is established to empirically assess the influence of environmental regulations on China’s provincial tourism competitiveness and investigate the spatial spillover effects of environmental regulations.

Environmental regulations and China’s provincial tourism competitiveness exhibit a “U”-shaped relationship, mainly because of the indirect effects of environmental regulations (spatial spillover effects). The environmental regulation indices of the majority of the provinces have crossed the turning point. Thus, improving environmental regulations has a positive effect on tourism competitiveness. This effect mainly originates from the positive spatial spillover effects.

Tourism development plays an important role in promoting economic growth. However, increasing environmental pollution may constrain the development of tourism. Therefore, the possible influence of environmental regulations on tourism development should be understood.

At present, no research has explored the influence of environmental regulations on China’s tourism competitiveness. The current study considers the nonlinear effects of environmental regulations and investigates their spatial spillover effects.

The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group.

Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied.

In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group.

The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.

The dependency on human expertise for analysis and interpretation is the main reason for wear debris analysis not being used in industry to its full potential and becoming one of the most powerful machine condition monitoring strategies. The dependency on human expertise makes the interpretation and result subjective in nature, costly and time consuming. The purpose of this paper is to review work being done to develop an automatic, reliable and objective wear particle classification system as a solution to the above problem. At the same time it also aims to discuss some common off line test methods being practiced for wear debris analysis.

Computer image analysis is a solution for some of the problems associated with the conventional techniques. First it is tried to efficiently describe the characteristics of computer images of different types of wear debris using a few numerical parameters. Then using some Artificial Intelligence tools, the wear particle classification system can be developed.

Many shape, size and surface parameters are discussed in the paper. Out of these, nine numerical parameters are selected to describe and distinguish six common type of wear debris. Once the type of debris is identified, the mode of wear and hence the machine condition can be assessed.

The present process of fault and condition monitoring of an equipment by wear debris analysis involves human judgment of debris formations. A set‐up standard for comparison of debris will enable the maintenance team to diagnose faults in a comparatively better way.

The aim of this paper is to discuss the difficulties in identifying wear particles and finding out the exact health of equipment, which, due to its subjective nature, is influenced by human errors. An objective method with certain standards for classification of wear particles compatible with an artificial intelligence system will yield some flawless results of wear debris analysis, which has not been attempted in the past as per available literature.

The purpose of this paper is to reveal the strategic change-related issues by examining the dynamics between external fit and internal fit, as the success of strategic change is involved in the consideration of both internal and external fit.

This paper uses regression analysis to analyze the panel data from the electronics companies in Taiwan’s stock market between 2004 and 2011.

The empirical results show that there is a relationship between internal fit and external fit, and the momentum of internal fit was found as well. Moreover, the impact of the interaction effect of external and internal fit on performance is moderated by external fit.

This research contributes to and extends the current research in both theoretical and practical ways. From a theoretical aspect, this paper considers internal fit and external fit simultaneously and has adopted the profile deviation approach to test their impacts on performance. From a practical aspect, the empirical results have derived implications for managers as to understand the dynamics such as trade-off, momentum and synergy between the two types of strategic fit, which may be helpful for making decision on strategy change.

Understanding the fluid flow through rock masses, which commonly consist of rock matrix and fractures, is a fundamental issue in many application areas of rock engineering. As the equivalent porous medium approach is the dominant approach for engineering applications, it is of great significance to estimate the equivalent permeability tensor of rock masses. This study aims to develop a novel numerical approach to estimate the equivalent permeability tensor for fractured porous rock masses.

The radial point interpolation method (RPIM) and finite element method (FEM) are coupled to simulate the seepage flow in fractured porous rock masses. The rock matrix is modeled by the RPIM, and the fractures are modeled explicitly by the FEM. A procedure for numerical experiments is then designed to determinate the equivalent permeability tensor directly on the basis of Darcy’s law.

The coupled RPIM-FEM method is a reliable numerical method to analyze the seepage flow in fractured porous rock masses, which can consider simultaneously the influences of fractures and rock matrix. As the meshes of rock matrix and fracture network are generated separately without considering the topology relationship between them, the mesh generation process can be greatly facilitated. Using the proposed procedure for numerical experiments, which is designed directly on the basis of Darcy’s law, the representative elementary volume and equivalent permeability tensor of fractured porous rock masses can be identified conveniently.

A novel numerical approach to estimate the equivalent permeability tensor for fractured porous rock masses is proposed. In the approach, the RPIM and FEM are coupled to simulate the seepage flow in fractured porous rock masses, and then a numerical experiment procedure directly based on Darcy’s law is introduced to estimate the equivalent permeability tensor.

This qualitative study aimed to explore how the school–university partnership (SUP) enhances the elementary teachers' professional development in a school-based setting.

By following the qualitative case study methodology, this case study employs semi-structured interviews (the authors designed) with 10 school teachers and administrators. The authors adapted the iterative process analysis (Miles and Huberman, 1984) for compiling, coding, annotating the data and interpreting the interview transcripts. The authors also used the member checking technique that establishes credibility in a qualitative study (Lincoln and Guba, 1985) with six participants.

The findings suggest that participating in a series of professional learning activities led to the enhancement of teachers' and administrators learning experiences in view of educational theories, action research abilities, teaching efficiency, teaching research capacities and improvement of school guidelines. Further, they reveal that the participants' expectations for future SUP collaborations centred on realizing the sustainability and effectiveness of collaborations, and autonomy of teachers.

The limitations of the current study include its focus on a single SUP within one school and the reliance on data collected only by interviews during the SUP process. This study offers implications for teacher learning within SUP collaborations. First, schools should consider how to involve and influence all teachers rather than SUP core members only. To this end, authentic professional learning communities need to be constructed. Second, universities should pay much more attention to the professional development of their faculty members towards integration of theoretical knowledge and practical experiences.

This original study explores practical ways of improving teachers' theoretical and practical teaching practices/experiences through SUP projects, and contributes new knowledge to the teaching professional development of school teachers and administrators.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

In real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.

The authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.

The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

The purpose of this paper is to examine the impact on stock liquidity following the reduction of minimum tick size from $0.01 to $0.005 for a selection of dual-listed and property stocks on the New Zealand Exchange (NZX) during 2011.

Various liquidity measures were examined six months either side of the change in minimum tick size for the eligible stocks and these were compared to a sample of stocks matched on similar liquidity characteristics. Liquidity measures examined in the paper include quoted and effective spread, volume, depth and binding-constraint probability.

After controlling for firms matched on similar pre-period liquidity characteristics both spread and depth decline significantly. Evidence that small firms experience significant declines in trading activity was also found, and while firms with higher binding-constraints probability have greater declines in spread, their decline in depth is greater still.

The small sample of 17 stocks eligible for the $0.005 minimum tick size potentially impacts on the strength of the statistical analysis. As such, it is harder to detect statistically significant changes in liquidity.

These findings have important implications for policymakers as the hoped for benefits of smaller tick increments may only be fully realized by larger more active stocks.

The paper examines the impact of a change in minimum tick size on eligible New Zealand Exchange (NZX) stocks to determine whether it meet the stated NZX goal of boosting liquidity.

This paper aims to study the seismic response of frame structure with friction dampers.

The state equation of the structure subjected to the earthquake is presented and solved, from which the maximum drift and the interlayer drift angle of the floors of the structure subjected to the seismic waves of four types of sites are analyzed.

The result indicates that the damping effect is significant on the floors with the friction damper but is almost little influence on the other floor.

The result indicates that the damping effect is significant on the floors with the friction damper but is almost little influence on the other floor.

Adaptive slicing is a key step in three-dimensional (3D) printing as it is closely related to the building time and the surface quality. This study aims to develop a novel adaptive slicing method based on ameliorative area ratio and accurate cusp height for 3D printing using stereolithography (STL) models.

The proposed method consists of two stages. In the first stage, the STL model is sliced with constant layer thickness, where an improved algorithm for generating active triangular patches, the list is developed to preprocess the model faster. In the second stage, the model is first divided into several blocks according to the number of contours, then an axis-aligned bounding box-based contour matching algorithm and a polygons intersection algorithm are given to compare the geometric information between several successive layers, which will determine whether these layers can be merged to one.

Several benchmarks are applied to verify this new method. Developed method has also been compared with the uniform slicing method and two existing adaptive slicing methods to demonstrate its effectiveness in slicing.

Compared with other methods, the method leads to fewer layers whilst keeping the geometric error within a given threshold. It demonstrates that the proposed slicing method can reach a trade-off between the building time and the surface quality.