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The operation mode of enterprises will affect its resource commitment in the host country, involving different costs and time, as well as risks. Yet, the current state of knowledge about how the institutional environment affects the operation mode change of international construction enterprises is equivocal. This study aims to explore the impact of a host country's institutional environment on the operation mode change of international construction enterprises.

First, this study proposes a model on the impact of the institutional environment on the operation mode change of international construction enterprises. Second, this study used the Worldwide Governance Indicators (WGI) published by the World Bank and a questionnaire survey to collect data. Finally, the study employs a multiple regression methodology to test the hypothesis and discusses the results.

Results highlight that the important impact of the institutional environment on the operation mode change of international construction enterprises. The results showed that enterprises are more willing to increase resource commitments under the condition of stable institutional environment. In addition, enterprises' market-specific experience and general international experience, as moderating variables, weaken the impact of the institutional environment on the operation mode change. However, general international experience has no significant moderating effect.

The findings of this study provide practical implications for the investment risk assessment of international construction enterprises. Enterprises need to consider the change in institutional quality and institutional instability of the host country, as well as their own international experience when changing operation mode.

This study extends internationalization theory to the international construction field and provided theoretical guidance for the mechanism of operation mode change of international construction enterprises.

The proposed model can emphasize the priority of new information and can extract messages from the first pair of original data. The comparison results show that the proposed model can improve the traditional grey model.

The grey multivariate model with fractional Hausdorff derivative is firstly put forward to enhance the forecasting accuracy of traditional grey model.

The proposed model is used to predict the air quality composite index (AQCI) in ten cities respectively.

The effect of population density on AQCI in cities with poor air quality is not as significant as that of the cities with better air quality.

Electromagnetic noise of permanent magnet synchronous motor (PMSM) seriously affects the sound quality of electric vehicles (EVs). This paper aims to present a comprehensive process for the electromagnetic noise analysis and optimization of a water-cooled PMSM.

First, the noises of an eight-pole 48-slot PMSM in at speeds up to 10,000 rpm are measured. Furthermore, an electromagnetic-structural-acoustic model of the PMSM is established for multi-field coupling simulations of electromagnetic noises. Finally, the electromagnetic noise of the PMSM is optimized by using the multi-objective genetic algorithm, where a multi-objective function related to the slot width of PMSM stator is defined for radial electromagnetic force (REF) optimization.

The experimental results show that main electromagnetic noises are the 8n-order (n = 1, 2, 3, …) and 12-order noises. The simulated results show that the REFs are mainly generated by the 8n-order (n = 1, 2, 3, 4, 5, 6) vibrations, especially those of the 8th, 16th, 24th and 32th orders. The 12-order noise is a mechanical noise, which might be caused by the bearings and other structures of the PMSM. Comparing the simulated results before and after optimization, both the REFs and electromagnetic noises are effectively reduced, which suggests that an appropriate design of stator slot is important for reducing electromagnetic noise of the PMSM.

In view of applications, the methods proposed in this paper can be applied to other types of PMSM for generation mechanism analysis of electromagnetic noise, optimal design of PMSM and thereby noise improvement of EVs.

The application of ultra-high performance concrete (UHPC) in anchorage zones can significantly improve the local compression performance of structures. However, the high cost and complex preparation of UHPC make UHPC difficult to be widely used in practice. This study proposes a method to strengthen the local compression zone of structures built by normal strength concrete (NSC) by incorporating UHPC cores.

In this study, a Finite Element Model (FEM) of local compression specimens was established by ABAQUS, and the accuracy of FEM was verified by comparing the FEM calculation results with experimental results. The verified FEM was adapted to the research on the influences of affecting factors on local compression performance of structures, including NSC strength, UHPC strength, spiral steel bar strength, and UHPC core diameter.

The results show that the peak load of the strengthened specimen SC1-U + N increases by 210.2% compared to that of the SC1-NSC. Furthermore, compared to SC1, the strengthened specimen SC1-U + N can save 64.7% amount of UHPC while the peak load decreases by only 34.4%. The peak load of the strengthened specimens increases with the axial compressive strength and the diameter of UHPC cores increasing, crack load increases with increasing the compressive strength of NSC, the spiral steel bar with high strength can prevent the sharp drop of load-deflection curve and the residual bearing capacity increases accordingly. All findings indicate that increasing the diameter of UHPC cores can improve the overall performance of the specimens. Under loading, all specimens fail by following a similar pattern. The effectiveness of this new strengthen method is also verified by FEM analytical calculations.

Based on the experimental study, this study extrapolates the influence of different parameters on the local bearing capacity of the strengthened specimens by finite element simulation. This method not only ensures the accuracy of bearing capacity assessment, but also does not require many samples, which ensures the economy of the reinforcement process. The research results provide a reference for the reinforcement design of anchorage zone.

Due to the deformation between the pylon and the girder caused by single tension of cables, the previously tensioned steel strands have stress relaxation, resulting in the actual cable forces being less than the design cable forces. To compensate the stress loss caused by the single tension of cables, this paper aims to present a practical compensation algorithm of stress relaxation during the construction period.

From the perspective of the essential cause of the stress relaxation, finite element analysis is used to solve the tension control force of each steel strand after a rigorous theoretical formula derivation.

The deformation and tension control force of each steel strand decrease with the advance of the tension sequence, and the decline rate drops gradually. However, the calculated force values of the steel strand are in good agreement with the measured value as the cable length decreases.

The previous rough calculation methods for the tension force of steel strands cannot meet the accuracy, and the accurate calculation methods often include the solution of nonlinear equations, which complicate the calculating process. Otherwise, there are few studies on the compensation of stress loss by calculating the deformation of the steel strand during the tension process. So, it developed an accurate and efficient algorithm to determine the tension control forces.

The purpose of this paper is to improve performance for predicting the life spans of complex equipment systems.

The gray system model with fractional order accumulation (FGM(1,1)) is used to predict the life spans of complex equipment systems using small samples.

FGM(1,1) yielded a lower mean absolute percentage error (MAPE) for an in-sample and a much lower MAPE for an out-of-sample forecast, which means that FGM(1,1) can predict memory processes.

FGM(1,1) can predict the life spans of other complex equipment.

FGM(1,1) yielded a lower MAPE for an in-sample and a much lower MAPE for out-of-sample forecasts, which means that FGM(1,1) can predict memory processes.

It is widely known that fast-fashion retailers are struggling to keep up with consumer attention for quick responses within the fashion industry. With the advance of Internet and e-commerce, consumers prefer to purchase online. Online platform information has become an essential source for exploring consumer attention. However, there is often a mismatch between the information provided by retailers and the feedback received from consumers, leading to an imbalance between the supply side and demand side of online information. The purpose of this study is therefore to provide a unified approach to discover consumer attention from the design topic aspect by revealing the information imbalance between supply side and demand side.

To address the issue of online information imbalance and discover consumer attention, this study proposed an approach that focuses on the design topic perspective. The design topic is a collection of design elements that represent a clothing-design feature more comprehensively and accurately compared to a single design element. The proposed approach begins with generating design topics through topic modeling based on online information provided by retailers on e-commerce platforms. Two indicators, influence degree and attention degree, are then used to quantify the intensity of supply information and consumer attention related to design topics. Finally, design topic strategy diagrams are constructed to reveal information imbalance and discover consumer attention.

The experimental case demonstrates the existence of information imbalance, indicating that the intensity of supply information and consumer attention from the perspective of design topics is not uniform, although both follow the Pareto principle. The results of consumer attention distribution with heavy power-law tails are consistent with current research findings. This further demonstrates that the proposed approach is capable of discovering consumer attention in the design topic strategy diagrams.

The issue of information imbalance between retailers and consumers poses a challenge in keeping up with customer attention. The proposed approach offers a practical solution by visually identifying the symptoms of information imbalance and discovering consumer attention through design topic strategy diagrams. This approach provides fast-fashion retailers with a valuable reference to seize market opportunities, improve product design and adjust marketing or management strategies.

This study proposes a novel approach to disclose the issue of information imbalance between supply side and demand side and therefore to discover consumer attention from the perspective of design topics. In addition, guidelines for applying the proposed approach for fast-fashion marketing and management are presented.

The purpose of this paper is to develop a potential nanoparticles lubricant additive for solving the problem of the insolubility and stable dispersity; the complex nanoparticles with core-shell structures are less studied in the field. Therefore, this paper determines novel complex nanoparticles and their tribological properties.

According to the conventional preparation method, the complex nanoparticle styrene/calcium borate (PS/O-CaB) was synthesized. The microstructures of the as-obtained samples were characterized by X-ray diffraction infrared spectroscopy and thermogravimetric analysis. Tribological properties of PS/O-CaB used as lubricating oil additive were evaluated on four-ball tribometer. The worn surface of the steel ball was investigated by a three-dimensional non-contact surface profilometer and X-ray photoelectron spectroscopy (XPS).

The results of the structure characteristic indicate two different crystalline forms, namely, Ca₂B₆O₁₁ and Ca₂B₂O₅, and the average size of calcium borate nanoparticles in PS/O-CaB is about 20-40 nm. Moreover, the good tribological properties are due to a wear resistance film containing both depositions and the tribochemical reaction products which comprise B2O3, FeB and Fe2O3.

Novel complex nanoparticles with core-shell structure (PS/O-CaB) were successfully prepared. Moreover, the PS/O-CaB shows excellent tribological capacity such as load-carrying, friction-reducing and antiwear property.

The promotion of new energy vehicles (EVs) is an effective way to achieve low carbon emission reduction. This paper aims to investigate the optimal pricing of automotive supply chain members in the context of dual policy implementation while considering consumers' low-carbon preferences.

This article takes manufacturers, retailers and consumers in a main three-level supply chain as the research object. Stackelberg game theory is used as the theoretical guidance. A game model in which the manufacturer is the leader and the retailer is the follower is established. The author also considered the impact of carbon tax policies, subsidy policies and consumer preferences on the results. Furthermore, the author investigates the optimal decision-making problem under the profit maximization model.

Through model solving, it is found that the pricing of EVs is positively correlated with the unit price of carbon and the amount of subsidies. The following conclusions can be obtained by numerical analysis of each parameter. Changes in carbon prices have a greater impact on conventional gasoline vehicles. Based on the numerical analysis of parameter β, it is also found that when the government subsidizes consumers, supply chain members will increase their prices to obtain partial subsidies. Compared with retailers, low-carbon preferences have a greater impact on manufacturers.

The new energy automobile industry involves many policies, including tax cuts, tax exemptions and subsidies. The policy environment faced by the members of a supply chain is complex and diverse. Therefore, the analysis in this article is based only on partial policies.

The authors innovatively combine the three factors of subsidy policy, carbon tax policy and consumer low-carbon preference, with research on the pricing of EVs. The influence of policy factors and consumer preferences on the pricing of EVs is studied.

The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities.

Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm.

The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature.

For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life.

In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.