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This paper aims to establish a multi-input equilibrium manifold expansion (EME) model for gas turbine (GT). It proposes that the extension of model input dimension is realized based on similarity theory and affine structure in the framework of single-input EME model. The study aims to expand the scope of application of the EME model so that it can be used for the control or fault diagnosis of GTs.

In this paper, the concepts of corrected equilibrium manifold expansion (CEME) model and multi-cell equilibrium manifold expansion (MEME) model are first proposed. This paper uses theoretical analysis and simulation experiments to demonstrate the effectiveness of the bilayer equilibrium manifold expansion (BEME) model, which is a combination of the CEME and the MEME models. Simulation experiments include confirmatory experiments and comparative experiments.

The paper provides a new sight into building a multiple-input EME (MI-EME) model for GTs. The proposed method can build an accurate and robust MI-EME model that has superior performance compared with widely used nonlinear models including Wiener model (WM), Hammerstein model (HM), Hammerstein–Wiener model (HWM) and nonlinear autoregressive with exogenous inputs (NARX) network model. In terms of accuracy, the maximum error percentage of the proposed model is just 1.309%, far less than WM, HM and HWM. In terms of the stability of model calculation, the range of the mean error percentage of the proposed model is just a quarter of that of NARX network model.

The paper fulfills the construction of a novel multi-input nonlinear model, which has laid a foundation for the follow-up research of model-based GT fault detection and isolation or GT control.

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

Under the carbon tax policy, the authors examine the operational decisions of the low-carbon supply chain with the triple bottom line.

This paper uses the Stackelberg game theory to obtain the optimal wholesale prices, retail prices, sales quantities and carbon emissions in different cases, and investigates the effect of the carbon tax policy.

This study’s main results are as follows: (1) the optimal retail price of the centralized supply chain is the lowest, while that of the decentralized supply chain where the manufacturer undertakes the carbon emission reduction (CER) responsibility and the corporate social responsibility (CSR) is the highest under certain conditions. (2) The sales quantity when the retailer undertakes the CER responsibility and the CSR is the largest. (3) The supply chain obtains the highest profits when the retailer undertakes the CER responsibility and the CSR. (4) The environmental performance impact decreases with the carbon tax.

The results of this study can provide decision-making suggestions for low-carbon supply chains. Besides, this paper provides implications for the government to promote the low-carbon market.

Most of the existing studies only consider economic responsibility and social responsibility or only consider economic responsibility and environmental responsibility. This paper is the first study that examines the operational decisions of low-carbon supply chains with the triple bottom line under the carbon tax policy.

College badminton gymnasium is an important place where students take physical exercise. Student satisfaction index for badminton gymnasium is an important factor which influences the design of badminton gymnasium. To improve the design and construction level of college badminton gymnasium, student satisfaction index evaluation system for the design of college badminton gymnasium was constructed, and empirical study of the system was carried out. Delphi method was applied to screen evaluation indicators, and analytical hierarchy process (AHP) was used to confirm the weight of evaluation indicators. Finally, student satisfaction index evaluation system for the design of college badminton gymnasium was constructed, including 3 first-level indicators (external form design, internal layout design and application function design) and 12 second-level indicators. Hunan University of Finance and Economics was taken for example to demonstrate student satisfaction index evaluation system for the design of college badminton gymnasium. Besides, comprehensive evaluation score of the badminton gymnasium was gained. The student satisfaction index evaluation system is an effective tool to evaluate the design of college badminton gymnasium. During the design of college badminton gymnasium in China, the application function and internal layout should mainly be taken into account.

Uncertain level discount (ULD) is a type of promotion combining regular discount (RD) with uncertainty. The purpose of this paper is to explore the impact of ULD on consumers’ perceived quality compared with RD and to identify the relevant influencing mechanism and boundary for the effectiveness of ULD.

Three online experiments were conducted with 445 participants from China. First, experiment 1 compares the attractiveness of ULD and RD. Second, experiment 2 evaluates the impacts of ULD and RD on consumers’ perceived quality and clarifies the mechanism in this process. Finally, experiment 3 examines the moderating effect of product knowledge.

ULD has the same level of attractiveness as RD with equivalent expected discount value for consumers. Besides, consumers in ULD give higher ratings to product quality compared with those in RD, and the lower diagnosticity of price cues in ULD underlies the differential effects of ULD vs RD. Furthermore, product knowledge moderates the relationship between the two promotions and perceived quality.

The findings provide valuable guidance for managers to conduct promotional campaigns. ULD is an effective promotion to attract consumers to purchase with keeping consumers’ perceived quality high, and such effectiveness will rise for products that consumers are unfamiliar with. Managers can make rational use of ULD to achieve positive promotion results in both the short and long term.

Few studies pay attention to the long-term effects of the uncertain promotion. This research profoundly investigates the impact of ULD on perceived quality, which complements existing studies from a more integrated perspective that combines short- and long-term effects. Also, this research identifies the mechanism based on the cue diagnosticity theory and puts forward a new explanation for positive uncertainty in uncertain promotions. Finally, this research applies the impact of product knowledge on information process strategies into the uncertain promotion, which clarifies the utility boundary of ULD from a new perspective and offers a more comprehensive understanding for this promotion.

This study examines the effects of scent arousal on consumers' time perception in retail service environments and further explores how the effect is moderated by consumer-perceived stress.

A laboratory experiment (Study 1) and a field experiment (Study 2) were conducted to examine the relationship between scent arousal and time perception and the mediating effect between scent arousal and consumers' store evaluations. Another laboratory experiment (Study 3) was conducted to explore how consumers' stress modifies the scent arousal effect.

Consumers in a low-arousal scent condition perceived a shorter duration of time than those in a high-arousal scent condition. This finding was verified in a field experiment, whereas scent arousal affects consumers' store evaluations through the mediating effects of time perception. However, the impact of scent arousal on time perception was attenuated in high-stress conditions.

Time duration perception is an important indicator in the retail service marketing process. Evidence shows that underestimating time duration in the shopping process represents positive responses. This study extends prior research by examining how scent arousal influences time perception and how consumers' stress moderates scent arousal’s effect.

The boom in livestreaming commerce (LSC) has brought significant changes to social interaction methods. Understanding customer engagement in LSC is critical for online sellers who try to enhance the social influence and improve marketing effectiveness of LSC. Based on the stimulus–organism–response (S–O–R) paradigm, this study aims to develop a model to investigate the effects of perceived attributes of LSC (real-time interaction, perceived proximity and perceived authenticity) on social support (informational and emotional support) and subsequent engagement.

An online survey is conducted to collect data from LSC customers, and data are analyzed using SPSS and SmartPLS.

The results indicate that informational and emotional support are positively affected by real-time interaction, perceived proximity and perceived authenticity. In turn, informational and emotional support enable and mediate the prediction of customer engagement intention in LSC.

Prior LSC studies tend to focus on the motivation influencing LSC engagement from the perspective of perceived value. This study confirms the importance of perceived attributes of LSC in driving customer engagement from the perspective of social support.

Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.

This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.

This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.

This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.

Social network analysis (SNA) seeks to manage the connections between entities through investigating and understanding behaviours and relationships. This study demonstrates the increasing relevance of social network approaches to solving contemporary and looming operations management (OM) and supply chain management (SCM) problems; including the coordination operations challenges raised by increased connectivity.

The systematic literature review approach adopted here examines 63 papers in OM and SCM published between 2000 and 2019. To-date OM reviews on SNA have focussed on discussing archetypal supply chains, what differentiates this study is the focus on how value was created in other forms of chains and operations.

This study reveals that current SNA adoption in OM is dominated by a manufacturing style focus on linear, sequential value creation, with a resulting focus only on sequential interdependence. SNA studies on reciprocally co-ordinated value creation (e.g. many service and network operations) are shown to have been neglected and are linked to a new agenda on contemporary management issues.

Beyond encouraging the use of SNA, this study seeks to re-orient SNA approaches towards how contemporary services and networks create value.

Through adopting a unique combination of approaches and frameworks, this study challenges extant work to offer a substantially revised agenda for SNA use in Operations and Supply Chain Management.

Entrepreneurial competence plays a decisive role in entrepreneurship success. To promote the employment of college students and accelerate economic growth through entrepreneurial activities, the Chinese government and universities encourage the use of multiple inputs to boost holistic entrepreneurship education and training. This study aims to assesses the entrepreneurial competence of college students by analysing the effects of entrepreneurship education and policy implementation in China.

The quantitative approach considered a large sample of 12,269 students, who participated in entrepreneurship education in Chinese “double-first-class” universities, to construct a theoretical model of their entrepreneurial competence. Entrepreneurship competition was introduced as a mediating variable in this model.

This study revealed that college students develop entrepreneurial competence by participating in entrepreneurship competitions unlike students who participate in regular entrepreneurship education. Additionally, there was a significant difference in the measured impact of entrepreneurship policy between students who participated in entrepreneurship competitions and those who did not.

The effects of the implementation of entrepreneurship education and policy were studied using a quantitative design. Additionally, this study highlights the effect of entrepreneurship competitions with empirical evidence from China, and contributes to the discussion of entrepreneurship education at schools and entrepreneurship policy for policymakers.