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The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

To test a social-psychological model of public acceptance of highway infrastructure projects in the Chinese architecture/engineering/construction industry.

Through a comprehensive literature review, we established a social-psychological model of public acceptance related to benefit perception, risk perception and public trust. We empirically validated our model by using structural equation model analysis based on a questionnaire survey in the S35 Yongjin Highway Infrastructure Project in Yunnan Province, China.

Benefit, trust and risk perception had a significant influence on local residents' public acceptance of highway infrastructure projects; benefit perception and trust perception had a greater influence than risk perception. Public acceptance among local male residents over the age of 35 or those with higher education levels was more likely to be determined by the relative dominance of risk and benefit perceived.

This study contributes empirical evidence to the theoretical literature related to locally unwanted land use (LULU) siting and stakeholders in the field of project management from the public perspective. This study also suggests valuable practical implications to authorities, project managers and the public in decision-making and risk communication.

Although previous studies addressed factors affecting public acceptance towards potentially hazardous facilities, understanding of the implications of these social-psychological factors and their effects are still far from sufficient. This study bridges this gap by exploring the determinants of public acceptance towards highway infrastructure projects based on a selected case in China.

Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

This paper aims to analyze the role and advantages of knowledge resources in the carbon emission reduction of the industrial chain, and how it can be used to promote the carbon emission reduction of the industrial chain, so that the industry can better achieve the saving of energy and the reduction of emission.

This paper argues that the traditional resource-plundering industrial chain production method can no longer meet the needs of sustainable development of the green and low-carbon industrial chain, and builds the coupling and coordination of knowledge technology innovation drive and industrial chain carbon emission reduction mechanism, in the four dimensions of industrial chain organization, government support, internet support and staff brainstorming, put forward suggestions for knowledge resources to drive carbon emission reduction in the industrial chain.

This paper holds that the use of knowledge resource advantages can better help industrial chain enterprises to carry out technological innovation, knowledge resource digital platform construction, knowledge resource overflow and transfer, application and management of network information technology, so as to reduce carbon emission in industrial chain.

This paper contributes to the discussion about the high-quality implementation of the revitalization strategy of the industrial chain and also deepens research on the knowledge resource-driven carbon emission reduction of the industrial chain. Further, this paper enriches the role of knowledge resources in the industrial industry, and the theoretical results support the advantages of knowledge resource in the field of chain carbon emission reduction.

Outlines the development of globalization and related research; and takes the Middle East as a basis for examining regionalization in more depth. Discusses the definition of boundaries in economic, geographic and political terms; the impact of various types of regional trade associations and trade and investment; and five factors affecting regionalization in the Middle East; peace, political will, economic compatibility, socio‐cultural similarity and geographical proximity. Considers the implications for the corporate strategy of multinationals, e.g. market segmentation, integration, strategic sourcing etc.

Geographical Indication (GI) products represent a distinct association between a product and its specific geographic origin. While scholars have acknowledged the impact of GI products on destination marketing, there exists a dearth of research concerning the underlying mechanism through which the perceived value of GI products influences consumers' intention to visit the region of origin.

This study utilizes the cognitive appraisal theory as a basis for constructing a conceptual framework to investigate the association between the perceived value of GI products, emotional reactions and destination brand awareness.

By placing particular emphasis on the diverse impacts of perceived value on tourists' affective responses and travel intentions, the results of this research offer significant contributions to the field of destination marketing.

A deeper understanding of the perceived value of GI products can enable destination marketers to effectively attract prospective tourists and foster a stronger connection between tourists and the regions of origin.

Post-disaster population resettlement is a complicated process, during which the restoration of livelihood and lifestyle plays a critical role in achieving a successful resettlement outcome. This paper attempts to examine how recovery policies and relocation approaches influence people's livelihood recovery and perception of wellbeing. It specifically investigates the role of farmland in producing a livelihood and maintaining a rural lifestyle among displaced people.

Through face-to-face questionnaire surveys and in-depth interviews with rural residents displaced from their villages after the Wenchuan earthquake in Sichuan, China, this study presents both quantitative and qualitative evidence to investigate how post-disaster policies and particularly the availability of farmland influence people's recovery and their satisfaction with the post-resettlement life.

Data suggest that availability of farmland, in spite of the size, makes big differences in post-disaster recovery because farmland provides resettled people with not only a livelihood to secure basic living but also a guarantee to maintain a rural lifestyle.

More samples are needed for analyzing factors that significantly influence disaster-displaced farmers' recovery and wellbeing post resettlement.

This study can be used as an important reference for making plans for post-disaster recovery and population resettlement programs in other disaster-prone countries across the world.

Land-based relocation is proposed as a desirable approach to addressing challenges of livelihood restoration amongst the resettled population in rural areas of developing countries.

The purpose of this paper is to provide the extensive review on dynamic monitoring of forestry area in China.

Countermeasure and suggestions were proposed for three aspects including the establishment of data sets with unified standards, top-level design of monitoring and assessment and analysis models, and establishment of the decision support platform with multiple scenario simulation.

Finally, the authors proposed key research area in this field, i.e., improving the systematic and optimal forest management through integrating and improving the data, models and simulation platforms and coupling the data integration system, assessment system and decision support system.

The authors explored the limitation of dynamic monitoring and state of the art research on data accumulation, professional model development and the analytical platform.

Explores the influence of Japan’s culture on its innovative strengths and weaknesses. Indicates that Japan is good at evolutionary and process innovation but not so hot on inventing. Links this to Hofstede’s cultural dimensions, comparing Japanese with US results. Attempts to link Japanese cultural attributes to rice and its consequent agricultural system and associated human relations. Devotes a section each to Japanese collectivism, power, uncertainty avoidance, masculinity/femininity, and Confucianism. Finds that Japanese culture does not promote individuality or risk‐taking (unlike the US), but does excel at process technology.

Discusses the effectiveness of different training approaches in other cultures. Highlights differing learning preferences in different societies. Proposes that traineers in nations which contain a lack of individuality or confucianism prefer more teacher centred methods to those with an emphasis on individualism who require a more hands on approach.