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With the popularity of high-rise buildings, wall inspection and cleaning are becoming more difficult and associated with danger. The best solution is to replace manual work with wall-climbing robots. Therefore, this paper proposes a design method for a rolling-adsorption wall-climbing robot (RWCR) based on vacuum negative pressure adsorption of the crawler. It can improve the operation efficiency while solving the safety problems.

The pulleys and tracks are used to form a dynamic sealing chamber to improve the dynamic adsorption effect and motion flexibility of the RWCR. The mapping relationship between the critical minimum adsorption force required for RWCR downward slip, longitudinal tipping and lateral overturning conditions for tipping and the wall inclination angle is calculated using the ultimate force method. The pressure and gas flow rate distribution of the negative pressure chamber under different slit heights of the negative pressure mechanism is analysed by the fluid dynamics software to derive the minimum negative pressure value that the fan needs to provide.

Simulation and test results show that the load capacity of the RWCR can reach up to 6.2 kg on the smooth glass wall, and the maximum load in the case of lateral movement is 4.2 kg, which verifies the rationality and effectiveness of the design.

This paper presents a new design method of a RWCR for different rough wall surfaces and analyses the ultimate force state and hydrodynamic characteristics.

To study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.

With the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.

(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.

(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.

People usually try to avoid uncertainty. Recently, however, uncertainty has become an emerging marketing tool in the hedonic product industry. In the case of blind box consumption, for example, the consumers become addicted to the uncertainty created by businesses, leading to repeat purchases and even indulgences. Previous research has, yet, to focus on the impact of uncertainty on indulgence and the role of emotions.

This paper constructs and validates a chain mediation model of uncertainty triggering indulgent consumption based on the information gap theory, positive emotion theory and uncertainty resolution theory and examines the difference between resolved and unresolved uncertainty. This study also explores differences in the impact of whether uncertainty is resolved on emotions. The uncertainty-resolved group elicited a more positive emotional response than the uncertainty-unresolved group, leading to a more indulgent consumption.

The results of three studies show that uncertainty influences indulgent consumption through curiosity and positive emotion, and that curiosity and positive emotion play separate and chain mediating roles between uncertainty and indulgent consumption, respectively. We validate our central hypothesis with questionnaires among blind box consumer groups, examining the moderating role of perceived luck and risk preferences.

The findings shed new light on firms' use of uncertainty to promote consumer purchases.

As live streaming becomes a widely used online sales mode, previously content-centred anchors are attempting to switch to e-commerce live streaming. The purpose of this research was to explore the mechanisms that prompt consumers to stay or leave after content anchors transfer to live e-commerce broadcasts. In addition, we explored the factors affecting consumption from the perspectives of anchors, consumers and the external environment.

We distributed questionnaires to a group of fans who had experienced the transition of content anchors to live streaming and received back 375 valid questionnaires. Using psychological contract theory, we constructed a theoretical model for the scenario in which content anchors transition to live e-commerce broadcasting and analysed the data using partial least squares structural equation modelling (PLS-SEM).

The results show that circle culture, mainstream culture, initial trust and live streaming content all positively influenced consumers’ attitudes, whilst consumers’ past shopping experiences negatively influenced consumers’ attitudes. The personal charm of the content anchors did not have a significant effect on consumers’ attitudes. Additionally, we found that only anchors with a significant circle culture and good trust levels amongst fans were able to transition to live e-commerce streaming successfully.

This study extends the application of psychological contract theory to the field of e-commerce and describes the transformation of different types of psychological contracts. The paper’s conclusions provide a reference for decision-making and the implementation of transformation by content-based anchors to live streaming, helping them to coordinate their relationships with fans more effectively.

Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

The purpose of this paper is twofold: first, it explores the influencing factors of the government’s trust decision-making in the private sector; second, it explores how these influencing factors affect the government’s trust decisions.

A theoretical model was established, and a questionnaire survey was conducted among 152 professionals. The collected datas were analyzed by the structural equation modeling (SEM) method.

The study identified four critical factors that influence the government’s decision to trust the private sector in public-private-partnership (PPP) projects. All the four factors have a positively correlated impact on the government’s trust decision-making. The structural equation path analysis shows that the most important factor affecting the government’s trust decision-making is the trustee’s (private sector) trustworthy characteristics, and the path coefficient is 0.92. The path coefficients of risk perception and the trustor’s trust tendency are 0.83 and 0.74, respectively. The influence of the legal system environment on government trust decision-making is moderate, with a path coefficient of 0.68.

This paper contributes to the literature in two aspects. First, the factors influencing decision-making to government trust in the private sector in PPP projects have been identified. Second, a comprehensive view of the mechanism of government trust in the private sector in PPP projects has been theorized by the SEM method.

This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.