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This study aims to explore how institutional quality impacts private capital participation in large-scale infrastructure development, particularly in public–private partnership (PPP) projects, aiming to enhance incentives for private sector involvement.

Building on new institutional theory, a triangular theoretical framework was constructed to analyze the high participation of private capital in PPP projects, focusing on seven key institutional factors. Data from 1,319 PPP projects across 36 Belt and Road Initiative (BRI) countries from 2015 to 2020 were then analyzed using a combination of necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) to evaluate the combined impact and interactions of these factors.

Results indicate that high private capital participation does not hinge on a single institutional quality factor but results from the synergistic influence of multiple factors. The paths leading to high private capital participation can be categorized as regulatory-led, normative-cognitive synergistic, regulatory-normative synergistic and institutional failure-led. Among these, regulatory quality plays a central role in the regulatory-led; the synergy between political stability and voice and accountability is pivotal in the normative-cognitive synergistic, and the rule of law, in combination with voice and accountability, is essential to the regulatory-normative synergistic.

This research systematically examines the multidimensional impact of institutional quality, revealing how different institutional factors interact to influence private capital’s willingness to participate and behavior. It enriches applied research in institutional economics within PPP projects and provides a new theoretical perspective and methodological framework to the scholarly community.

This study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.

This study proposes the user profiling and role evaluation model of government microbloggers in the context of public emergencies. The indicators are designed from the four dimensions of time, content, scale and influence, and the feature labels are identified. Three different public emergencies were investigated, including the West Africa Ebola outbreak, the Middle East respiratory syndrome outbreak and the Shandong vaccine case in China.

The results found that most government microbloggers were follower responders, short-term participants, originators, occasional participants and low influencers. The role distribution of government microbloggers was highly concentrated. However, in terms of individual profiles, the role of a government microblogger varied with events.

The findings can provide a reference for the performance assessment of the government microbloggers in the context of public emergencies and help them improve their ability to communicate with the public and respond to public emergencies.

By analyzing the performance of government microbloggers from the four dimensions of time, content, scale and influence, this paper fills the gap in existing literature on designing the user profiling and role evaluation model of government microbloggers in the context of public emergencies.

The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility.

The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis.

The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper.

The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.

Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.

This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.

The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.

The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.

This paper aims to present a longitudinal and visualising study using bibliometric approaches to depict the emerging trends and research hotspots within the mobile information system domain.

Publications included in the Web of Science (WoS) database for 2001–2021 are reviewed and analysed on various aspects through coauthorship, cocitation and co-occurrence analysis. The analyses are conducted using VOSViewer, a scientific visualisation software program.

Academic publications related to mobile information systems fluctuated at a low level during the initial part of the 21st century and have grown rapidly in number in the past decade. The USA and China are the leading contributors to these publications and hold dominant positions in the obtained collaboration network. Computer science, engineering and telecommunications are the top three research areas in which mainstream mobile information system research occurs. Moreover, medical informatics and health-care science services have gradually become new research hotspots.

This study provides a systematic and holistic account of the developmental landscape of the mobile information system domain. This study provides a good basis for analysing the evolution of research in mobile information systems and may serve as a potential foundation for future research.

The purpose of this study is to effectively optimize vehicle lane-changing behavior and alleviate traffic congestion in ramp area through the study of vehicle lane-changing behaviors in upstream segment of ramp areas.

In the upstream segment of ramp areas under a connected vehicle environment, different strategies of vehicle group lane-changing behaviors are modeled to obtain the best group lane-changing strategy. The traffic capacity of roads can be improved by controlling group lane-changing behavior and continuously optimizing lane-changing strategy through connected vehicle technologies. This paper constructs vehicle group lane-changing strategies in upstream segment of ramp areas under a connected vehicle environment. The proposed strategies are simulated by VISSIM.

The results show that different lane-changing strategies are modeled through vehicle group in the upstream segment of ramp areas, which can greatly reduce the delay of ramp areas.

The simulation results verify the validity and rationality of the corresponding vehicle group lane-changing behavior model strategies, effectively standardize the driver's lane-changing behavior, and improve road safety and capacity.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

Roofing is one of the most dangerous jobs in the construction industry. Due to factors such as lack of planning, training and use of precaution, roofing contractors and workers continuously violate the fall protection standards enforced by the US Occupational Safety and Health Administration. A preferable way to alleviate this situation is automating the process of non-compliance checking of safety standards through measurements conducted in site daily accumulated videos and photos. As a key component, the purpose of this paper is to devise a method to detect roofs in site images that is indispensable for such automation process.

This method represents roof objects through image segmentation and visual feature extraction. The visual features include colour, texture, compactness, contrast and the presence of roof corner. A classification algorithm is selected to use the derived representation for statistical learning and detection.

The experiments led to detection accuracy of 97.50 per cent, with over 15 per cent improvement in comparison to conventional classifiers, signifying the effectiveness of the proposed method.

This study did not test on images of roofs in the following conditions: roofs initially built without apparent appearance (e.g. structural roof framing completed and undergoing the sheathing process) and flat, barrel and dome roofs. From a standpoint of construction safety, while the present work is vital, coupling with semantic representation and analysis is still needed to allow for risk analysis of fall violations on roof sites.

This study is the first to address roof detection in site images. Its findings provide a basis to enable semantic representation of roof site objects of interests (e.g. co-existence and correlation among roof site, roofer, guardrail and personal fall arrest system) that is needed to automate the non-compliance checking of safety standards on roof sites.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

The purpose of this paper is to respond to the existing gap in the literature and analyze empirically the mediating role of potential absorptive capacity and innovation capacity in the relationship between socio-cognitive capital and new product performance in tourism businesses.

Partial least squares structural equation modeling (PLS-SEM) was applied to measure the effect of independent variables and mediators on the results of new products through information collected from 300 companies through a structured questionnaire applied to tourism companies.

Important findings are presented demonstrating the positive and significant influence of cognitive social capital on the results of new products; however, this is not enough, so the potential absorption capacity and the capacity for innovation play a very important role in improving the effect on the results of new products. The findings suggest that organizations should direct their culture and shared goals toward assimilation and knowledge and the development of innovation capabilities in order to obtain more successful new product results.

The study adds value to the study of social capital by analyzing social cognitive capital and its impact on new product performance. In contrast to previous studies, it suggests incorporating potential absorptive capacity and innovation capacity as mediating variables in a comprehensive model that illustrates the positive spillover effect, thereby enhancing the outcomes related to new product performance.

本文旨在處理現存文獻內的研究缺口。研究人員以實證研究法、去分析於旅遊業內潛在的吸收能力和創新能力在社會認知性資本與新產品性能之間的關聯上所扮演的協調角色。

研究人員以結構型問卷向300間旅遊公司收集資料和數據,並使用偏最小平方法的結構方程模型 (PLS-SEM),去測量各自變數與協調者對新產品的成效所產生的影響。

研究結果頗為重要,因它證明了認知性社會資本,對新產品的成效會產生積極和重大的影響。唯這仍不足夠; 研究結果更確認了潛在的吸收能力和創新能力在優化新產品成效所帶來的影響方面,確扮演著極其重要的角色; 因此,研究結果建議組織應引導其文化和共同目標,走向知識同化和發展創新能力的道路上,以獲取更成功的新產品成效。

本研究分析社會認知性資本及它對新產品成效的影響,就此而言,本研究增添了研究社會資本的價值。與過去的研究相比,本研究建議設計一個顯示積極的溢出效應的全面性模型,當中包含潛在的吸收能力和創新能力,作為中介變數,因此,與新產品性能有關的成果得以提昇。