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The main purpose of this article is to characterize a novel neutrosophic hypersoft set hybrid called possibility single-valued neutrosophic hypersoft set (psv-NHSS) for evaluation of investment projects by using its aggregation operations and decision-support system.

Two approaches are employed in this article: set-theoretic approach and algorithmic approach. The former one is used to characterize the novel notion of psv-NHSS and its aggregations. The later one is used to construct a decision-support system by using the aggregations like core matrix, maximum-valued decision, minimum-valued decision and scoring-valued decision of psv-NHSS. The adopted algorithm is implemented in real-world scenario of hydroelectric power station project evaluation for investment purpose.

The proposed model is more flexible and reliable as it addresses the limitations of literature on neutrosophic set, neutrosophic soft set and other fuzzy set-like models by considering possibility degree, hypersoft setting and neutrosophic setting collectively.

It has limitations for decision-making situations where selection of parameters is of uncertain nature.

The scope of this study may cover a wide range of applications in many fields of mathematical sciences like artificial intelligence, optimization, multiple-criteria decision-making (MCDM), theoretical computer science, soft computing, mathematical statistics, etc.

The proposed model bears the characteristics of most of the relevant existing models collectively and fulfills their insufficiencies by introducing a novel approximate mapping.

This chapter elaborates the peculiarities for the formation of the circular economy, substantiates its current contribution and future potential as to economic growth, and elaborates the concept and the implementation of the circular economy in Russia. Russian projects in the sphere of sustainable development and “ecologization” (reduction in energy, resource consumption, and polluting emissions) of industrial productions, mitigation, and processing of waste, protection of water resources, and restoration of forests are built into the linear model of production. The performed calculations on the basis of the existing statistical data allow evaluating the scale of the circular economy in Russia at $ 755.05 billion (as GDP in current market prices is 30.97% provided by the volume of used and decontaminated waste of production and consumption). In future, it is highly probable that the scale of the circular economy in Russia will reach 50% of GDP – $ 950.95 billion (as of 2018). To accelerate the rate and quality of economic growth, positive results from the implementation of these projects must be achieved. Complete transition to the model of the circular economy can provide new possibilities for long-term growth and the development of the Russian economy.

The unprecedented SARS-CoV-2 (COVID-19) pandemic has further constrained the budgets of governments worldwide for delivering their much-needed infrastructure. Consequently, public-private partnerships (PPPs), with the private sector's investment and ingenuity, would appear to be an increasingly popular alternative. Value for money (VfM) has become the major criterion for evaluating PPPs against the traditional public sector procurement and, however, is plagued with controversy. Hence, it is important that governments compare and contrast their practice with similar and disparate bodies to engender best practice. This paper, therefore, aims to understand governments' assessment context and provide a cross-continental comparison of their VfM assessment.

Faced with different domestic contexts (e.g. aging infrastructure, population growth, and competing demands on finance), governments tend to place different emphases when undertaking the VfM assessment. In line with the theory of boundary spanning, a cross-continental comparison is conducted between three of the most noticeable PPP markets (i.e. the United Kingdom, Australia and China) about their VfM assessment. The institutional level is interpreted by a social, economic and political framework, and the methodological level is elucidated through a qualitative and quantitative VfM assessment.

There are individual institutional characteristics that have shaped the way each country assesses VfM. For the methodological level, we identify that: (1) these global markets use a public sector comparator as the benchmark in VfM assessment; (2) ambiguous qualitative assessment is conducted only against PPPs to strengthen their policy development; (3) Australia's priority is in service provision whereas that of the UK and China is project finance and production; and (4) all markets are seeking an amelioration of existing controversial VfM assessments so that purported VfM relates to project lifecycles. As such, an option framework is proposed to make headway towards a sensible selection of infrastructure procurement approaches in the post COVID-19 era.

This study addresses a current void of enhancing the decision-making process for using PPPs within today's changing environment and then opens up an avenue for future empirical research to examine the option framework and ensuing VfM decisions. Practically, it presents a holistic VfM landscape for public sector procurers that aim to engage with PPPs for their infrastructure interventions.

There is often a lack of accurate performance evaluation in Public–Private Partnership (PPP) projects. It is a challenging issue to effectively use Building Information Modeling (BIM) for PPP project performance evaluation. The objective of this study is to develop a PPP project performance evaluation model based on Industry Foundation Classes (IFC) and an enhanced matter-element method to more precisely evaluate PPP project performance.

The performance evaluation of PPP projects in the construction and operation period was explored. The PPP project performance evaluation indicator system was first established based on a literature review and PPP project practice. Then, the evaluation indicator information was expressed through IFC mapping and extension. After that, an IFC-based PPP project performance evaluation model was developed, and a case study was provided to validate the use of the proposed performance evaluation model.

The results of the case study show that the proposed approach can accurately and efficiently evaluate PPP projects, and it could favorably contribute to performance evaluation in PPP projects.

This study only concerns the performance evaluation of one type of PPP project. Further research is required to study different types of PPP projects; the model needs to be more efficient and intelligent.

The performance evaluation of PPP projects utilizing IFC extension and the enhanced matter-element method provides guidance for the government and private parties to accurately and efficiently evaluate PPP project performance.

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.

Effective performance management (PM) in public–private partnership (PPP) projects is critical to realizing value for money (VFM). This study aims to provide an in-depth understanding of problems existing in PPP PM and possible avenues for improvement, presenting an experimental system to verify that building information modeling (BIM) and other information communication technologies can improve PPP PM.

The mixed research method adopted in this study combined empirical research with experimental research. Semistructured interviews were used to ascertain the current situation of PPP PM with the help of Nvivo software. A BIM-based performance management system (BPMS), which combines BIM with Web and Cloud technology, was then constructed to achieve performance monitoring, performance measurement, and performance-based payment. Finally, a case study was introduced to explain the function application of the proposed system.

The case demonstration verified is found to verify that the developed BIM-based execution framework for PPP PM can effectively guide stakeholders toward achieving mixed PM, promote effective PM, and improve work efficiency with the support of BIM and other information and communication technologies.

Through the development of a BPMS for PPP projects, the effectiveness and efficiency of PM are improved. Practical PM applications are also provided to different stakeholders, through which the key performance indicators and the behaviors of the government and private-sector partners can be monitored to form a more comprehensive and reasonable PM mechanism and promote the realization of VFM in PPP projects.

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.

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.

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.

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.