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This research aims to explore the impact of augmented reality (AR), the digital technology that superimposes virtual elements in a real environment, on consumers in the context of experiential marketing. Specifically, this study proposes a research model based on the stimulus-organism-response model, which considers AR media characteristics as external stimuli, consumers’ value perceptions as the organisms, and purchase intentions as the responses. The research model was tested with 248 consumers using structural equation modelling. The results show that informativeness, ease of use, and telepresence have positive effects on consumers’ utilitarian value perception and that telepresence and interactivity have positive effects on hedonic value perception. Overall, this study contributes to the growing body of knowledge on AR and provides actionable insights for managers implementing digital transformation strategies and AR applications in marketing practices.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

This paper aims at fabricating large metallic components with high deposition rates, low equipment costs through wire and wire and arc additive manufacturing (WAAM) method, in order to achieve the morphology and mechanical properties of manufacturing process, a bead morphology prediction model with high precision for ideal deposition of every pass was established.

The dynamic response of the process parameters on the bead width and bead height of cold metal transfer (CMT)-based AM was analyzed. A laser profile scanner was used to continuously capture the morphology variation. A prediction model of the deposition bead morphology was established using response surface optimization. Moreover, the validity of the model was examined using 15 groups of quadratic regression analyzes.

The relative errors of the predicted bead width and height were all less than 5% compared with the experimental measurements. The model was then preliminarily used with necessary modifications, such as further considering the interlayer process parameters, to guide the fabrication of complex three-dimensional components.

The morphology prediction of WAAMed bead is a critical issue. Most research has focused on the formability and defects in CMT-based WAAM and little research on the effect of process parameters on the morphology of the deposited layer in CMT-based WAAM has been conducted. To test the sensitivities of the processing parameters to bead size, the dynamic response of key parameters was investigated. A regression model was established to guide the process parameter optimization for subsequent multi-layer or component deposition.

This paper aims to propose a new transformer-less inverter structure to reduce the common-mode leakage current in grid-connected photovoltaic (PV) systems.

The proposed circuit structure is the same as the conventional full-bridge inverter with three additional power switches in a triangular structure. These three power switches are between the bridge and the output filter, and they mitigate the common-mode leakage current flowing toward the PV panels’ capacitors. The common-mode leakage current mitigation is done through the three-direction clamping cell (TDCC) concept. By clamping the common-mode voltage to the middle voltage of the DC-link capacitors, the leakage current and the total harmonic distortion (THD) of the injected current to the grid is effectively reduced. Therefore, the efficiency is improved.

The switching modes and the control method are introduced. A comparison is carried out between the proposed structure and other solutions in the literature. The proposed topology and its respective control method are simulated by PSCAD/EMTDC software. The simulation results validate the advantages of the presented structure such as clamping the common-mode voltage and reducing leakage current and THD of injected current to the grid.

Presenting a single phase-improved inverter structure with low-leakage current for grid-connected PV power systems represents a significant original contribution to this work. The proposed structure can inject a sinusoidal current with low THD to the AC grid, and the power factor is unity on the AC side. In the half positive cycle, one of the switches in the TDCC is turned off under zero current. Besides, one of the other switches in TDCC is turned on with zero voltage and, therefore, its turn-on switching losses are zero. The efficiency of the proposed topology is high because of the reduction of leakage current and power losses. Accordingly, the presented topology can be a good solution to the leakage current elimination.

Effective performance management (PM) in public–private partnership (PPP) projects is critical to realizing value for money (VFM). This study aims to provide an in-depth understanding of problems existing in PPP PM and possible avenues for improvement, presenting an experimental system to verify that building information modeling (BIM) and other information communication technologies can improve PPP PM.

The mixed research method adopted in this study combined empirical research with experimental research. Semistructured interviews were used to ascertain the current situation of PPP PM with the help of Nvivo software. A BIM-based performance management system (BPMS), which combines BIM with Web and Cloud technology, was then constructed to achieve performance monitoring, performance measurement, and performance-based payment. Finally, a case study was introduced to explain the function application of the proposed system.

The case demonstration verified is found to verify that the developed BIM-based execution framework for PPP PM can effectively guide stakeholders toward achieving mixed PM, promote effective PM, and improve work efficiency with the support of BIM and other information and communication technologies.

Through the development of a BPMS for PPP projects, the effectiveness and efficiency of PM are improved. Practical PM applications are also provided to different stakeholders, through which the key performance indicators and the behaviors of the government and private-sector partners can be monitored to form a more comprehensive and reasonable PM mechanism and promote the realization of VFM in PPP projects.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

The purpose of this paper is to elucidate the influences of the animal fat and fatty acid type on the formation of polycyclic aromatic hydrocarbons (PAHs) and to propose a formation mechanism of PAHs in fat during electric roasting, which is a method of non-direct-contact-flame heating.

The effects of animal fats and model fat on the formation of PAHs were valued on the basis of the ultra high-performance liquid chromatography data. The corresponding products of the FAME pyrolysis were detected by TG-FTIR. The proposal formation mechanism of PAHs was based on the summary of the literature.

Contrary to the International Agency for Research on Cancer, DF had higher risk with 280.53 ng/g of concentration after being roasted than the others animal fats of red meat in terms of PAHs formation. This research also ensured the importance of fat on PAHs formation, the concentration of PAHs in pure fats was higher after being electric roasted than that in meat patties and juice which made from corresponding animal fat. What is more, during pure animal fats and meat products being processed, less PAHs formed in the fat with lower extent of unsaturation and lower content of linolenate. In the same way, methyl linolenate demonstrated the significant increasement to PAHs formation compared to the other fatty acids. And, the number of carbon atom and the extent of unsaturation in fatty acid affects the formation of PAHs during roasting. The detection of alkene and alkane allows to propose a formation mechanism of PAHs during model fat being heated. Further study is required to elucidate the confirm moleculars during the formation of PAHs.

This work studied the effect of the carbon atom number and the unsaturation extent of fats and model fats on the formation of PAHs. This work also assure the important of alkene and alkane on the pyrolysis of model fats. This study also researched the formation and distribution of PAHs in pure fats and meat products after being heated.