Search results

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

At present, one of the key equipment in pillar industries is a large rotating machinery. Conducting regular health monitoring is important for ensuring safe operation of the large rotating machinery. Because vibrations sensors play an important role in the workings of the rotating machinery, measuring its vibration signal is an important task in health monitoring. This paper aims to present these.

In this work, the contact vibration sensor and the non-contact vibration sensor have been discussed. These sensors consist of two types: the electric vibration sensor and the optical fiber vibration sensor. Their applications in the large rotating machinery for the purpose of health monitoring are summarized, and their advantages and disadvantages are also presented.

Compared with the electric vibration sensor, the optical fiber vibration sensor of large rotating machinery has unique advantages in health monitoring, such as provision of immunity against electromagnetic interference, requirement of less insulation and provision of long-distance signal transmission.

Both contact vibration sensor and non-contact vibration sensor have been discussed. Among them, the electric vibration sensor and the optical fiber vibration sensor are compared. Future research direction of the vibration sensors is presented.

Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process.

Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia.

Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths.

Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms.

The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence.

We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups.

This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process.

The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.

An optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.

The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.

This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

Music has a priming effect on product selection. The purpose of this paper is to extend the current understanding on this issue using an experimental design incorporating behavioural and brainwave data.

An experiment with 40 participants was conducted to explore how and why wine tasting preferences would be primed by different genres of musical stimuli. Electroencephalographic measurement was adopted to measure participant brainwave activity in two experiments, each involving two rounds of wine tasting, and the treatment was administered between the two rounds.

Significant associations between the musical stimulus genre and participant change in wine selection were found, and the musical stimuli resulted in different brainwave activities because participant β and γ wave activities significantly differed in the first and second wine tasting rounds. Correlational analyses indicated that α, β and γ wave activities generated by the musical stimuli were significantly but negatively correlated with α wave activity. α wave activity in the musical stimulus phases was significantly negatively correlated with β wave activity in the second round of wine tasting, and the other associations were significant and positive.

This study highlighted the priming effect of musical stimuli in wine tasting. Empirical evidence derived from experimental research was analysed with behavioural and brainwave data. This study’s original contribution is that it explored wine tasting preferences from a neuromarketing perspective. The results of this study can provide empirical evidence on how to effectively use music in marketing strategies.

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Parameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.

The MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.

The numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.

A novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

As humans are influenced by their environment, this study explores how different construal levels of ambient scent temperature affect consumers’ food choices.

This study employed a series of experimental methods from three studies, totalling five experiments. The experiments involved both laboratory and field settings, as well as neuroscientific techniques, thus generating empirical evidence.

Three studies were conducted to investigate how construal levels of both ambient scent temperature and tasks influenced food choice. Study 1 found that the construal level of ambient scent temperature significantly affected the type of food consumed. Study 2 included the task’s construal level as another factor to examine whether it interacted with the ambient scent temperature construal level. Both factors were significant, but only when perceived by the participants simultaneously. If the task’s construal level was manipulated before exposure to the ambient scent temperature, the latter did not have a significant effect. Study 3 employed a neuroscientific method to explore the mechanism behind the match between ambient scent temperature and food choices based on construal levels. The congruence of ambient scent temperature and food choice based on construal level enhanced positive emotions.

The sample size, although in line with other neuroscientific studies, was not sufficiently large for robust generalizability. This limitation can encourage future research to increase the number of participants and thus enhance the accountability of the findings. Another limitation is the participants’ cultural background.

This study’s practical implications are twofold. First, odour intensity was perceived to be the strongest in hot samples (Kähkönen et al., 1995), and we confirmed how ambient scent temperature can influence one’s food choice. Thus, food business operators can use warm ambient scent temperatures to promote hedonic food or snacks. Second, participants’ positive emotions were enhanced by the congruence of ambient scent temperature and food choice.

The association between ambient scent temperature and food choice has been extensively researched. However, this study provides an empirical explanation for the application of CLT. Accordingly, we performed a series of laboratory and field experiments using behavioural and neuroscientific approaches. The results confirmed that the construal level of ambient scent temperature significantly affected food choice. Moreover, the FAA revealed that one’s positive emotions would be prompted if there was congruence in the construal levels of ambient scent temperature and food choice.

This study has theoretical and managerial value because people’s poor understanding of food selection is affected by ambient scent temperature. Moreover, its novelty lies in the application of a neuroscientific approach to one experiment.

Visual stimulation affects the taste of food and beverages. This study aimed to understand how latte art affects coffee consumption by collecting participants' brainwave data and their taste responses.

Seventy subjects participated in a two-stage experiment. Electroencephalography (EEG) was employed to measure brainwave activity. With an interval of one week, each stage involved coffee consumption with and without latte art. The responses to the taste of the coffee were also collected for analysis.

Significant differences were found in the participants' alpha and beta brainwave bands. When drinking coffee with latte art, the participants' alpha bands were significantly lower, whereas the beta bands were higher. These findings were supported by Bayesian statistics. A significant increase was found in the participants' taste of sweetness and acidity with latte art, and Bayesian statistics confirmed the results for sweetness although the evidence on the increase in acidity was anecdotal. No difference was found in the taste of bitterness.

This study highlights the effect of latte art on coffee consumption. The authors analysed the empirical evidence from this two-stage experimental study in the form of the participants' brainwave data and their responses to taste. This study's original contribution is that it explored the crossmodal effects of latte art on consumers' taste of coffee from a neuroscientific perspective. The results of this study can provide empirical evidence on how to effectively use latte art in practical business environments.