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This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios.

Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm.

Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm.

This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

This paper aims to focus on the environmental perception of unmanned platform under complex street scenes. Unmanned platform has a strict requirement both on accuracy and inference speed. So how to make a trade-off between accuracy and inference speed during the extraction of environmental information becomes a challenge.

In this paper, a novel multi-scale depth-wise residual (MDR) module is proposed. This module makes full use of depth-wise separable convolution, dilated convolution and 1-dimensional (1-D) convolution, which is able to extract local information and contextual information jointly while keeping this module small-scale and shallow. Then, based on MDR module, a novel network named multi-scale depth-wise residual network (MDRNet) is designed for fast semantic segmentation. This network could extract multi-scale information and maintain feature maps with high spatial resolution to mitigate the existence of objects at multiple scales.

Experiments on Camvid data set and Cityscapes data set reveal that the proposed MDRNet produces competitive results both in terms of computational time and accuracy during inference. Specially, the authors got 67.47 and 68.7% Mean Intersection over Union (MIoU) on Camvid data set and Cityscapes data set, respectively, with only 0.84 million parameters and quicker speed on a single GTX 1070Ti card.

This research can provide the theoretical and engineering basis for environmental perception on the unmanned platform. In addition, it provides environmental information to support the subsequent works.

Effective knowledge management has played a crucial role in propelling the green transformation of organisations and industries. Nevertheless, its underutilisation in the real estate sector has impeded the progress of green transformation. Therefore, the purpose of this paper is to offer a theoretical and practical analysis of the green transformation of the real estate industry through the lens of knowledge management and to provide a valuable reference to facilitate the industry’s green transformation.

This study entailed applying induction and deduction method, using China’s real estate industry as a typical case, and collecting and analysing the public data, corporate reports and literature of China’s real estate industry. On this basis, the authors conducted an in-depth analysis of the mechanisms through which the green transformation has empowered the sustainable development of China’s real estate industry, as well as the critical role of knowledge management.

The study has revealed that the primary challenges encountered by China’s real estate industry during the green transformation have stemmed from significant disparities in the knowledge base among different industry entities, the complexities related to knowledge integration and the “difficulty” of applying green knowledge across the entire life cycle. To address these issues, the authors recommend several strategic actions, including creating a dedicated green knowledge platform for the real estate industry, establishing a knowledge-sharing mechanism, enhancing knowledge acquisition on both the supply and demand sides and intensifying the focus on the application of green knowledge within the real estate industry.

This research holds considerable theoretical and practical significance concerning the comprehension and promotion of knowledge management’s role in the green transformation of China’s real estate industry. These insights can be applied to significantly enhance the theoretical framework of knowledge management, and the research outcomes provide substantial support for propelling the green transformation in China’s real estate industry and contributing to the sustainable development of the overall economy in China.

From a knowledge management perspective, this study introduces a series of solutions and recommendations, presenting new research ideas and pathways for advancing the green transformation of the real estate industry. In addition to guiding the industry’s sustainable development, it also significantly contributes to enhancing the theoretical framework of knowledge management.

The aim of this study is to first investigate the surface integrity of cylindrical rollers under grinding process and then design a reasonable superfinishing process that improve the anti-fatigue performance of cylindrical rollers by optimization of the surface integrity.

First, the white and dark layers produced by the grinding process is analyzed by microscope. Then, the influence of oilstone pressure on the stock removal, surface precision and crowned profile are explored. Finally, an optimal superfinishing process and a novel turnaround device are designed to improve surface integrity.

The experimental results show that as the oilstone pressure increases, the stock removal first increases and then remains stable. This hints that the stock removal of a single-time superfinishing process has an upper limit. In the current conditions, the maximum stock removal is 6 µm. Double-time superfinishing process and the turnover device can effectively eliminate the white and dark layers and improve the symmetric of roller profile. In addition, the surface precision is also improved.

The surface integrity of bearing rollers is very important to the application of industry field. The findings and the methods in the study can be helpful to improve the surface integrity of the bearing rollers.

The purpose of this paper is to identify dominant scams against domestic tourists in popular tourism cities in China. There are two questions of concern: what types of scams do domestic tourists experience and are the patterns of scams different between the capital and regional cities? The social situation framework was employed to interpret the outcomes.

A content analysis facilitated by Leximancer software was applied to 102 Chinese travel blogs reporting experiences of being scammed in Beijing, Hangzhou, Xi’an, Sanya and Guilin. Clear themes and concepts emerged from the analysis of these travel reviews and differences in scamming patterns between Beijing and regional cities were identified.

The most frequently reported scams in the capital Beijing were linked to the chaotic environment at tourist attractions and the misbehaviours of tour agents. By way of contrast scams involving manipulating the weight and quality of products purchased were more common in regional cities. The differences between Beijing and other locations may lie in the greater monitoring of fraudulent practices in the capital. Additionally, the role of shills (confederates of the scammer) was highlighted in many of the scams studied.

Scams include a slightly less serious but still troublesome set of problems accompanying major crimes and assaults. Rare research specifically focussed on tourist scams despite substantive work discussing crimes against tourists as general. Implications of the present study lie in enriching the literature on scams against tourists. The analysis of scams as a special type of social situation proved to be insightful in directing attention to facets of the interaction thus providing connections to previous work and directions for further study. It is also promising to be developed to inform strategic approaches to creating a safer tourism environment in cities.

This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge.

A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed.

Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability.

This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

The purpose of this study is to investigate the multi-dimensional structure of the Airbnb customer experience and to examine the influences of this experience on behavioral outcomes.

A multi-phase methodology was adopted using a survey questionnaire to explore the dimensions. Data were collected from a sample of 561 Airbnb users in the USA. Exploratory factor analysis and confirmed factor analysis were conducted to evaluate the reliability and validity of the scale.

First, the results support the hypothesis that the Airbnb customer experience comprises four dimensions: home benefits, personalized services, authenticity and social connection. Second, the study demonstrates that these dimensions significantly influence customers’ behavioral intentions.

The use of a US Airbnb users sample may affect the generalizability of the results.

The findings of this study provide insights for Airbnb hosts and hotel managers. More specifically, this study offers suggestions to Airbnb hosts about how to enhance their services to customers based on the four experience dimensions and to hotels about how they can compete with Airbnb on the four experience dimensions.

This study provides an important theoretical framework for measuring the Airbnb customer experience through an empirical examination.

Based on the resource orchestration perspective, this study aims to explore whether and how strategic supply management (SSM) affects firms’ operational performance (OP) and innovation performance (IP).

Survey data comprising 404 valid responses are collected from traditional manufacturing firms in China. Confirmatory factor analysis confirms the reliability and validity of the measures. Structural equation modeling and bootstrapping are used to test all hypotheses.

SSM improves firms’ OP and IP. Furthermore, supply base resource mobilization (SBRM) and supply market resource mobilization (SMRM) have partial mediating effects on the relationships. SBRM has a greater effect on OP, while SMRM has a greater effect on IP. In addition, these two types of resource mobilization form different mediating paths between SSM and firm performance, and environmental uncertainty positively moderates this relationship.

With the development of national innovation strategies such as the “Made in China 2025” plan, the Chinese manufacturing industry aims to move from low-cost manufacturing to innovative and high-quality manufacturing. The study’s findings further emphasize the role of purchasing and supply management in external resource management. In addition to demonstrating the differential effects of heterogeneous resource mobilization on OP and IP, different mediation pathways through external resources mobilization are identified in the relationship between SSM and firm performance.

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.