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This paper aims to study passive control techniques for transonic flow over a backward-facing step (BFS) using square-lobed trailing edges. The study investigates the efficacy of upward and downward lobe patterns, different lobe widths and deflection angles on flow separation, aiming for a deeper understanding of the flow physics behind the passive flow control system.

Large Eddy Simulation and Reynolds-averaged Navier–Stokes were used to evaluate the results of the study. The research explores the impact of upward and downward patterns of lobes on flow separation through the effects of different lobe widths and deflection angles. Numerical methods are used to analyse the behaviour of transonic flow over BFS and compared it to existing experimental results.

The square-lobed trailing edges significantly enhance the reduction of mean reattachment length by up to 80%. At Ma = 0.8, the up-downward configuration demonstrates increased effectiveness in reducing the root mean square of pressure fluctuations at a proximity of 5-step height in the wake region, with a reduction of 50%, while the flat-downward configuration proves to be more efficient in reducing the root mean square of pressure fluctuations at a proximity of 1-step height in the near wake region, achieving a reduction of 71%. Furthermore, the study shows that the up-downward configuration triggers early spanwise velocity fluctuations, whereas the standalone flat-downward configuration displays less intense crosswise velocity fluctuations within the wake region.

The findings demonstrate the effectiveness of square-lobed trailing edges as passive control techniques, showing significant implications for improving efficiency, performance and safety of the design in aerospace and industrial systems.

This paper demonstrates that the square-lobed trailing edges are effective in reducing the mean reattachment length and pressure fluctuations in transonic conditions. The study evaluates the efficacy of different configurations, deflection angles and lobe widths on flow and provides insights into the flow physics of passive flow control systems.

This study examines the direct and indirect effects of organizational environmental orientation (EO)/culture, quality management practices (QMP) and sustainability experience (SE) on the relationship between organizational green practices (GP) and the triple bottom line (TBL) of sustainability performance (SuP).

To test the seven hypotheses, a structured questionnaire was used to collect data. The responses of 365 managers from various USA businesses in the service industries were analyzed using IBM SPSS and structural equation modeling (SEM)-AMOS.

The empirical results indicate that positive SuP in the economic, environmental and social dimensions and organizational GP can be improved by a strong culture of EO, effective QMP and substantial SE.

This research fills the gap in existing research between important organizational and environmental priorities and SuP. Consequently, the study provides managers with important strategic guidance: for environmental practices to achieve profitability and sustainability success, companies must promote an environmental-mindful culture and strategically invest in integrated QM systems.

This research is one of the first that explores how organizational environmental culture and QMP affect directly and indirectly the relationship between GP and SuP. These results provide empirical evidence to support the claim that environmental culture and QMP have significant direct and indirect effects on the relationship between GP and SuP dimensions.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

The environmental deterioration has become one of the most economically consequential and charged topics. Numerous scholars have examined the driving factors failing to consider the structural breaks. This study aims to explore sustainability using the per capita ecological footprints (EF) as an indicator of environmental adversities and controlling the resources rent [(natural resources (NR)], labor capital (LC), urbanization (UR) and per capita economic growth [gross domestic product (GDP)] of China.

Through the analysis of the long- and short-run effects with an autoregressive distributed lag model (ARDL), structural break based on BP test and Granger causality test based on vector error correction model (VECM), empirical evidence is provided for the policies formulation of sustainable development.

The long-run equilibrium between the EF and GDP, NR, UR and LC is proved. In the long run, an environmental Kuznets curve (EKC) relationship existed, but China is still in the rising stage of the curve; there is a positive relationship between the EF and NR, indicating a resource curse; the UR is also unsustainable. The LC is the most favorable factor for sustainable development. In the short term, only the lagged GDP has an inhibitory effect on the EF. Besides, all explanatory variables are Granger causes of the EF.

A novel attempt is made to examine the long-term equilibrium and short-term dynamics under the prerequisites that the structural break points with its time and frequencies were examined by BP test and ARDL and VECM framework and the validity of the EKC hypothesis is tested.

The construction industry and its activities harmfully affect the environment. Hence, adopting green building (GRB) practices can be helpful in achieving sustainable development goals. Therefore, this study aims to identify the factors affecting the intention to adopt GRB practices by extending theory of planned behavior (TPB).

Using non-probability purposive sampling technique, data was gathered from consultant and contractor engineers in the construction industry through a questionnaire. The analysis was done using partial least square-structural equation modeling technique on a useful sample of 290.

Findings revealed that the core constructs of TPB [i.e. attitude (AT), subjective norms (SUBN) and perceived behavioral control (PBC)] significantly affect the intention to adopt GRB practices. Moreover, government support and knowledge of green practices (KNGP) were found to be critical influencing factors on AT, SUBNs and PBC. Lastly, the findings confirmed that environmental concerns (ENC) play as a moderating between SUBN and intention to adopt GRB practices, as well as AT and intention to adopt GRB practices.

This study contributes to existing knowledge on GRB, offering evidence base for policy choices regarding climate change adaptation and mitigation in the construction industry.

This study provides insights from the perspective of a developing economy and confirms the applicability of TPB in the adoption of GRB practices. Moreover, this study confirms the moderation role of ENC in between TPB constructs and intention to GRB that is not tested earlier in the context of GRB. This study also confirms that government sustainable support positively affects PBC, and KNGP significantly affects SUBNs.

This study investigated consumers’ willingness to pay (WTP) for a local, organic chicken breed “Taphao Thong-Kasetsart” and the drivers that shape consumers’ WTP across different meat preferences in Thailand.

Face-to-face interviewing was used to collect data at food-service outlets in Bangkok and Nakhon Pathom provinces, Thailand. Data analysis used the double-bounded dichotomous choice model across different consumers’ meat preferences for preferred chicken and non-preferred chicken consumers.

The results showed that there were different WTP amounts for local organic chicken across consumers’ meat preferences, with a higher WTP among non-preferred chicken consumers. This indicated that local organic chicken may attract more consumers in the alternative market. Consumers’ values and attitudes to taste drove chicken-preferred consumers to pay a premium for local organic chicken; health concerns mattered for non-preferred chicken consumers. These findings should provide useful information for food marketing campaigns based on consumers’ preferences.

The study contributed to understanding consumer heterogeneous preferences toward WTP for local organic chicken. The findings indicated that analyzing WTP across different meat preferences highlighted more effective marketing strategies to achieve the premium that consumers would pay. These strategies could help farmers to enlarge their local organic market share, leading to increased revenue and farmers’ well-being.

Human capital is acquired not only through formal education (e.g. general skills) but also through training at the workplace. Prior studies have ignored the role of government subsidies explicitly for on-the-job training, which may influence firm training decisions and firm innovation performance. Hence, the authors establish a comprehensive theoretical framework to consider these issues and fill these gaps.

Considering the Chinese manufacturing firms listed in the Shanghai and Shenzhen Stock Exchange from 2010 to 2017, the authors investigate the influence of training investment on innovation performance by illustrating the role of human capital updating in enhancing firm innovation. The authors also explore serval mechanisms on how training investment influences innovation performance.

The authors propose that training investment promotes firm innovation performance, whereas government training subsidies negatively moderate this relationship. The authors also reveal how technicists' involvement and corporate culture mediate the relationship between training investment and innovation performance.

This study provides policy implications for stimulating firm innovation by improving learning and absorption ability, strengthening cultural identity and implementing system norms. Effective policies should be adopted to provide subsidies for on-the-job training of enterprises, particularly for firms with technical executives and firms in diversified life-cycle.

This work contributes to the literature on the role of on-the-job training in promoting firm innovation and reveals the crowding-out effect of subsidies. This study also shows the heterogeneous effects of training investment on firm innovation.