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With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

In manufacturing environments, mobile radio frequency identification (RFID) robots need to quickly identify and collect various types of passive tag and active tag sensor data. The purpose of this paper is to design a robot system compatible with ultra high frequency (UHF) band passive and active RFID applications and to propose a new anti-collision protocol to improve identification efficiency for active tag data collection.

A new UHF RFID robot system based on a cloud platform is designed and verified. For the active RFID system, a grouping reservation–based anti-collision algorithm is proposed in which an inventory round is divided into reservation period and polling period. The reservation period is divided into multiple sub-slots. Grouped tags complete sub-slot by randomly transmitting a short reservation frame. Then, in the polling period, the reader accesses each tag by polling. When tags’ reply collision occurs, the reader tries to re-query collided tags once, and the pre-reply tags avoid collisions through random back-off and channel activity detection.

The proposed algorithm achieves a maximum theoretical system throughput of about 0.94, and very few tag data frame transmissions overhead. The capture effect and channel activity detection in physical layer can effectively improve system throughput and reduce tag data transmission.

In this paper, the authors design and verify the UHF band passive and active hybrid RFID robot architecture based on cloud collaboration. And, the proposed anti-collision algorithm would improve active tag data collection speed and reduce tag transmission overhead in complex manufacturing environments.

The purpose of this paper is to study how to improve the performance of RFID robot system by anti-collision algorithms. For radio frequency identification (RFID) robots operating in mobile scenes, effective anti-collision algorithm not only reduces missed reading but also enhances the speed of RFID robots movement.

An effective anti-collision algorithm is proposed to accelerate tag identification in RFID robots systems in this paper. The tag collisions in the current time slot are detected by a new method, and then further resolve each small tag collision to improve system throughput, rather than the total tags number estimation. After the reader detected the collision, three different collision resolution methods were described and studied, and the situation of missing tag caused by reader moving is also discussed.

The proposed algorithm achieves theoretical system throughput of about 0.48, 0.50 and 0.61 and simulates to show that the proposed algorithm performance is significantly improved compared with the existing ALOHA-based algorithm.

The proposed RFID anti-collision algorithm is beneficial to improve the moving speed and identification reliability of the RFID robots in complex environments.

China has long been dedicated to introducing the Western management ideas to the local enterprise practice. But the situation has changed since the financial crisis, and the China model has become a hot word with its fast recovery from crisis. Moreover, Chinese traditional culture has become increasingly popular. Yin and yang are the most familiar Chinese philosophical terms to Western minds, and also the core concept of Chinese Taoist philosophy. The purpose of this paper is to analyze how the yin yang or lao‐tzu influences a firm's core competence and performance, and to demonstrate that the Taoist‐oriented Chinese culture remains meaningful and in many situations powerful in enterprise practice.

A case study is carried out from Town Star Ltd, established in 2001, and located in Linfen city, Shanxi province, China, a city which topped the list of the world's most polluted cities. It is an integrated joint‐stock company, specializing in coal coking chemical industry, gardening, logistics and technology development. Based on the humanized management doctrine, the company has formed the 5S management model which boosts its rapid and sustainable development. It coped well in major junctures such as industry restructuring and transferring, financial crisis, etc. Five key dimensions of the yin and yang, which were applied to the leader's daily behaviors and management style, are: Wu‐wei (inaction), Wu (emptiness), hardness with softness, moderation and He‐xie (harmony). First, this paper built a dynamic company growth model based on this philosophy which enhanced the core competitiveness effectively by emotional management innovation and personalized HRM, among which its pivotal regulator of variables is just yin yang conversion degree. Then an approach is provided to validate the model with empirical data collected from the years 2001‐2010 (2001‐2005, 2006‐2008, 2009‐2010) of Town Star Ltd.

It is demonstrated that yin yang ideology embedded in the company growth model organically is an important means to increase marginal benefit. In practice, the firm with yin yang philosophical ideology would shape a management paradigm which combined rules and human nature appropriately to a family‐oriented business atmosphere. Based on the above philosophical wisdom, Town Star Ltd has harvested good economic and social benefits, strong core competence through the elaborately constructed unique management model and harmonious humanity environment.

This paper opens an avenue for indigenous firms, as a framework for guiding management research and practice in coping with the post‐crisis era. In addition, it is expected to attract more attention from scholars, entrepreneurs both in China and in the rest of the world, to contribute to knowledge creation in Chinese management studies.

This study aims to develop a collaborative governance framework (CGF) to systematically investigate the impeding factors (IFs) in terms of the operational sustainability of PPPs. It examines the transmission pattern (i.e. the way in which network members react to each other) of the IFs network.

Literature review and interview were adopted to identify the IFs. Then, with the data collected from the interview in China, the social network analysis and interpretive structure model were synergised to examine the chain reaction, driving and dependent powers, and hierarchical structure of the identified IFs.

The results reveal that the cognition, institutional, financial and participation aspects are key barriers confronted by PPP sustainability, and the government plays a leading role in controlling factors causing sustainability-related problems in PPPs. Weak government leadership and institutional environment were identified as the most fundamental reasons triggering a chain of IFs, while project governance and management activities act as bridge nodes that play an intermediary role in the IFs network.

This research contributes to the literature on PPP governance by (1) bridging the literature gap through the development of CGF for explaining the governance of PPP sustainability with a holistic view that considers both macro environment and operational project processes; and (2) identifying the transmission pattern of IFs network which uncovers the underlying dynamics causing the unsustainable operation of PPPs.

This research provides practitioners with a list of key checkpoints for preventing failure escalation, enables decision-makers to prioritise obstacle-mitigation efforts and develop a feasible process to control PPP operation, and offers management countermeasures to remove the key barriers impeding PPP sustainability.

This study is novel for adopting network-oriented techniques to quantify the relative importance of the IFs and examine the transmission pattern of the IFs system. Therefore, it visualises the complex underlying dynamics causing unsustainable PPP operation, identifies root and direct causes of PPP failures, and provides decision-makers with insights into sustaining PPP sustainability from a network-oriented perspective.

Promoting low-carbon in the construction industry is important for achieving the overall low-carbon goals. Public–private partnership is very popular in public infrastructure projects. However, different perceptions of low-carbon and behaviors of public and private sectors can hinder the realization of low-carbon in these projects. In order to analyze the willingness of each stakeholder to cooperate towards low-carbon goals, an evolutionary game model is constructed.

An evolutionary game model that considers the opportunistic behavior of the participants is developed. The evolutionary stable strategies (ESSs) under different scenarios are examined, and the factors that influence the willingness to cooperate between the government and private investors are investigated.

The results illustrate that a well-designed system of profit distribution and subsidies can enhance collaboration. Excessive subsidies have negative impact on cooperation between the two sides, because these two sides can weaken income distribution and lead to the free-riding behavior of the government. Under the situation of two ESSs, there is also an optimal revenue distribution coefficient that maximizes the probability of cooperation. With the introduction of supervision and punishment mechanism, the opportunistic behavior of private investors is effectively constrained.

An evolutionary game model is developed to explore the cooperation between the public sector and the private sector in the field of low-carbon construction. Based on the analysis of the model, this paper summarizes the conditions and strategies that can enable the two sectors to cooperate.

The information used for supervision by regulatory departments in public-private partnership (PPP) projects is primarily transmitted and processed by the PPP implementation department, which negatively impacts the information quality, leading to information asymmetry and undermining the overall effectiveness of supervision. This study aims to explore how to use blockchain to anchor the information used for supervision in PPP projects to the original information, to strengthen the oversight.

This paper adopts the principles of design science research (DSR) to design a conceptual framework that systematically organizes information along the information dissemination chain, ensuring the reliable anchoring of original information. Two-stage interviews involving experts from academia and industry are conducted, serving as formative and summative evaluations to guide the design.

The framework establishes a weak-centralized information organizing mode, including the design of governance community and on-chain and off-chain governance mechanisms. Feedback from experts is collected via interviews and the designed framework is thought to improve information used for supervision. Constructive suggestions are also collected and analyzed for further development.

This paper provides a novel example exploring the inspirations blockchain can bring to project governance, like exercising caution regarding the disorderly expansion of public sector authority in addressing information disadvantages and how to leverage blockchain to achieve this. Technical details conveyed by the framework deepen understanding of how blockchain benefits and the challenges faced in successful implementation for practitioners and policymakers. The targeted evaluation serves as rigorous validation, guiding experts to provide reliable feedback and richer insights by offering them a more cognitively convenient scenario.

The construction and real estate sectors are vital to national economies, but traditional construction methods often lead to challenges such as safety risks, noise and environmental pollution. While intelligent construction is believed to mitigate these issues, there is a lack of solid empirical evidence on whether it truly benefits the general public. This paper seeks to explore the societal benefits of intelligent construction from the public’s perspective, addressing this research gap.

The research adopts a two-step approach. First, topic mining is conducted to identify topics closely related to the public’s daily life, such as environmental impact, construction traffic management and construction technologies. These topics are then analyzed through sentiment analysis using a bidirectional long short-term memory model with attention mechanism to determine whether the public has a favorable view of these aspects of intelligent construction, indirectly demonstrating the benefits to the public.

The primary topics identified include “industry development,” “technology enterprise,” “construction equipment,” “intelligent technology,” “environmental protection,” “robots” and “construction traffic management.” Sentiment analysis shows that public sentiment is overwhelmingly positive across all topics and regions, with “environmental protection,” “construction traffic management” and “robots” receiving the most favorable reactions.

This study provides empirical evidence of the societal benefits of intelligent construction from the public’s viewpoint using social media data. The results highlight the need for continued promotion and adoption of intelligent construction due to its positive impact on society.