Search results

This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed.

The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism.

A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot.

This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value.

The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.

This study investigates the heterogeneous impact of R&D capital market distortions (CMDs) on the quantity and quality of innovation in Chinese firms by exploring key pathways from a dual-arbitrage perspective.

Using data from Chinese A-share listed companies (2015–2021), we employ a multidimensional fixed effects model to validate the research hypotheses. Under the Systems of National Accounts (SNA, 2008) framework, we use the Bureau of Economic Analysis method to estimate enterprise R&D capital stock and the Cobb-Douglas production function to estimate R&D CMDs.

Results show that R&D CMDs drive firms toward strategic innovation, emphasizing quantity over quality. Policy arbitrage, including policy catering and rent-seeking, emerges as a pivotal mechanism under R&D CMDs, encouraging firms to prioritize quantity over quality in innovation. High-technology firms and those in the decline stage are more inclined to spearhead strategic innovation within the context of R&D CMDs.

These findings help policymakers promote high-quality innovation in Chinese enterprises by enhancing patent review mechanisms and shifting policies from quantity-driven to quality-oriented goals.

This study enriches the research on factor market distortions and innovation in emerging markets from the perspective of R&D CMDs, based on the “emerging + transitional” comprehensive framework. Unlike previous studies, which generally use enterprise R&D expenditure flow data, we apply the theory of R&D capitalization accounting to the micro-enterprise level under the SNA (2008) framework, enhancing the accuracy of R&D CMD estimations.

This study uses data from the Chinese banking sector to explore the relationship between green credit and risk-taking in commercial banks. It also examines whether the level of regional green development acts as a moderator regarding this relationship.

Using a dataset composed of annual observations from 57 Chinese commercial banks between 2008 and 2021, this study employs both piecewise and curvilinear models.

Our results indicate that when the scale of green credit is low (<0.164), it increases the risk-taking of commercial banks. Conversely, when the scale of green credit is high (>0.164), it reduces the risk-taking of commercial banks. Moreover, this nonlinear relationship impact exhibits bank heterogeneity. Furthermore, the results show that the level of regional green development and local government policy support negatively moderate the relationship between green credit and commercial bank risk-taking. Furthermore, we find that green credit can directly enhance the net interest margin of commercial banks.

This study is the first to provide evidence of a nonlinear relationship between green credit and risk-taking in commercial banks, and it identifies the significant roles of regional green development level and local government policy support in the Chinese context.

With the wide adoption of information and communication technologies globally, digital marketing (DM) has emerged as an important and influential role in the growth and development of firms. However, it remains unclear how small and medium enterprises (SMEs) in emerging markets that normally have limited digital technology resources can gain benefits from DM. Thus, this research aims to investigate how DM benefits SMEs in emerging markets.

Drawing on the resource-based views (RBV) theory and a sample of 156,625 SMEs in Vietnam, this research uses statistical models and examines the performance of SMEs in terms of sales and productivity.

There is strong evidences that the sales and productivity performance of SMEs are positively affected by DM. Furthermore, there is evidence that corporate social responsibility plays an important role in moderating the influence of DM on SME performance.

This finding suggests that entrepreneurs should carefully consider using digital technologies to create DM strategies. This result also encourages local policymakers to invest more in digital technology and boost the growth of SMEs.

The purpose of this study is to investigate the pricing strategies for knowledge payment products, taking into account the quality level of pirated knowledge products, in the context of platform copyright supervision.

This study abstracts the knowledge payment transaction process and aims to maximize producer's revenue by constructing a pricing model for knowledge payment products. It discusses pricing strategies for knowledge payment products under two scenarios: traditional supervision and blockchain supervision. The analysis explores the impact of pirated knowledge products quality level and blockchain technology on pricing strategies and consumer surplus, while providing threshold conditions for effective strategies.

Deploying blockchain technology in platform operations can significantly reduce costs and increase efficiency. In both scenarios, knowledge producer needs to balance factors such as the quality of pirated knowledge products, the supervision level of platform, and consumer surplus to dynamically adjust pricing strategies in order to maximize his own revenue.

This study enriches the literature on the pricing models of knowledge payment products and has practical significance in guiding knowledge producer to develop effective pricing strategies under copyright supervision.

A foundation in artificial intelligence (AI) literacy among all academic staff is essential for supporting students’ AI literacy effectively. As tools like ChatGPT increasingly influence academic work, educators need to understand prompt engineering and reconsider assessment designs. However, many lack the necessary training or time to engage with courses, limiting their ability to design assessments that leverage these technologies while maintaining academic integrity. This project investigated the impact of prompt training on university academics’ abilities to craft prompts and redesign assessments within a Scottish university. A two-hour workshop on prompt engineering was conducted for academic staff, during which participants graded AI-generated content before and after they received training. Results indicated a significant improvement in the quality of prompts crafted by participants post-training. Qualitative feedback revealed mixed reactions, highlighting both the potential and limitations of AI in academic settings. The study demonstrated the need for ongoing staff development in AI literacy.

Hospitality and tourism businesses often face environmental criticism as they rely heavily on natural resources to operate. Therefore, as a recent trend, hospitality companies are trying to adopt an environmentally friendly approach. Thus, this study sought to investigate the determinants of employee intention to engage in environmentally responsible actions in the workplace, drawing on the theory of planned behavior (TPB) and the value-belief norm (VBN) theory.

This study employed the fuzzy-set qualitative comparative analysis (fsQCA) to discover sufficient configurations for predicting employees’ intentions.

The result has provided recipes with an efficient combination of factors that can influence employees’ intention to undertake environmentally responsible behaviors.

This study contributes to the body of knowledge regarding sustainable behavior among employees and sustainability in the travel and hospitality sector. The findings of this research also provide managers and operators of sustainable hospitality businesses with guidance on how to enhance their staff members' environmentally friendly behaviors at work.

The transition from fossil fuel-based energy systems to renewable energy sources, commonly referred to as the energy transition, is essential for combating climate change. However, comprehensive studies that thoroughly examine the financial mechanisms involved in this process are lacking. Despite the availability of various financial tools, there is a notable absence of extensive research that synthesizes and categorizes these mechanisms into broad groups.

A systematic literature review is used to explore a comprehensive framework for financial mechanisms related to the energy transition and their application across six stages of the process.

The framework of financial mechanisms for energy transition encompasses these six factors: public financing mechanisms, private financing mechanisms, market-based mechanisms, innovative financing mechanisms, risk mitigation instruments and institutional support and capacity building.

This is the first study that thoroughly reviewed the financial mechanisms involved in the energy transition process.

This study aims to develop analytical models that consider product quality and production volume as essential drivers for profitability in the marketplace. It also considers product demand and price dynamics to understand related nuances backed by empirical validation.

The pricing mechanism is influenced by production quality, while product demand is influenced by both price and quality. The study considers cost elements, including production cost and quality loss cost which in turn are influenced by production volume and product quality. It establishes analytical conditions for optimal product quality and applies them to numerical analyses considering four distinct industry settings.

The study reveals that unique solutions exist for optimal product quality at each production level in four industry scenarios. The optimal production volume depends on product quality, and empirical research validates these findings from analytical models and numerical analysis.

This study represents a pioneering effort to investigate operational strategies in both analytical and empirical contexts, thus contributing to the existing body of knowledge in this area.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.