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Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

Despite the widespread study and application of public-private partnerships (PPPs) since the 1980s, the field lacks a universally accepted definition that captures the concept's complexity. This study aims to offer a definition and foster a more substantive and comprehensive discourse on PPPs to improve communication and understanding between academics and practitioners from diverse disciplines and legislative backgrounds.

Grounded in the family-resemblance concept proposed by German philosopher Ludwig Wittgenstein, this study conducts a comprehensive literature review to identify core and non-core elements frequently cited in PPP descriptions. The authors used these findings to develop the PPP sunflower model as a structured framework for defining PPPs.

The analysis elucidates six core elements consistently present in PPP descriptions: clarity of roles and responsibilities, appropriate risk allocation and sharing, injection of expertise and resources, cooperation and teamwork, a bundle of services, and long-term contracts. Coupled with identified non-core elements, these core components comprise the PPP sunflower model, a structured framework for defining PPPs that accommodates their multi-faceted nature.

The PPP sunflower model distinguishes itself as a unique contribution to the PPP literature. It offers a rigorous theoretical framework that can elucidate the complexity of PPPs for various stakeholders. The model serves as a practical tool for evaluating the authenticity and viability of PPP projects. The study's novelty lies in its adoption of the family-resemblance concept, thereby providing a comprehensive, multi-dimensional framework that enhances the understanding of PPPs across different disciplines and legislative contexts.

While extensive research exists on how positive life circumstances and positive personality traits promote entrepreneurship, there is little research focusing on how negative personal circumstances may potentially breed entrepreneurship. This study aims to build upon the theory of underdog entrepreneurship to examine the effect of challenging life circumstances such as poverty on the development of entrepreneurial traits in underdog entrepreneurs. This research analyzed the effect of risk-taking (RT) on the entrepreneurial success (ES) of underdog entrepreneurs and the role of optimism (OPM) and persistence (P) as mediating variables and poverty as a moderator variable.

A cross-sectional survey was conducted to collect data from 400 micro-finance borrowers of Akhuwat Foundation Pakistan. Akhuwat provides interest-free small loans to the poor segment of society. Preliminary testing of the survey questionnaire with a sample of 35 borrowers of Akhuwat Foundation in Lahore city was conducted before data collection. Data was collected from four randomly selected branches in Lahore city.

The authors found that OPM and P significantly mediate the relationship between RT and ES. In addition, the authors analyzed the moderating effect of poverty on the relationship between OPM and ES and found a negative moderating effect. A strong positive moderating effect of poverty was found on the relationship between P and ES. The results of multi-group analysis show that successful and unsuccessful underdog entrepreneurs differ in their RT propensity, which is significantly mediated by OPM and P.

This study provides empirical evidence on entrepreneurial traits and success of poor entrepreneur in an emerging economy. This work is original and has not been submitted elsewhere.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

This study aimed to explore the spatial accessibility dynamics of urban parks and their driving forces from 1901 to 2010 in terms of the dynamic relationships between spatial morphology and road networks, taking Nanjing City as an example.

This study mapped and examined the spatiotemporal distribution of urban parks and road networks in four time points at Nanjing: the 1910s, 1930s, 1960s and 2010s, using the analysis methodology of spatial design network analysis, kernel density estimation and buffer analysis. Two approaches of spatial overlaying and data analysis were adopted to investigate the accessibility dynamics. The spatial overlaying compared the parks' layout and the road networks' core, subcore and noncore accessible areas; the data analysis clarified the average data on the city-wide and local scales of the road networks within the park buffer zone.

The analysis of the changing relationships between urban parks and the spatial morphology of road networks showed that the accessibility of urban parks has generally improved. This was influenced by six main factors: planning implementation, political policies, natural resources, historical heritage and cultural and economic levels.

The results provide a reference for achieving spatial equity, improving urban park accessibility and supporting sustainable urban park planning.

An increasing number of studies have explored the spatial accessibility of urban parks through the relationships between their spatial distribution and road networks. However, few studies have investigated the dynamic changes in accessibility over time. Discussing parks' accessibility over relatively long-time scales has practical, innovative and theoretical values; because it can reveal correlational laws and internal influences not apparent in short term and provide reference and implications for parks' spatial equity.

Stainless-steel electromagnetic shielding (EMS) fabrics are widely applied as protective materials against electromagnetic interference (EMI). However, these fabrics primarily shield electromagnetic waves through reflection, which can lead to the formation of resonance effects that severely compromise their protective capabilities and potentially cause secondary electromagnetic pollution in the external environment.

In this paper, carbon nanotube fibers are added via spacing method to replace some stainless-steel fibers to impart absorbing properties to stainless-steel EMS fabric. The shielding effectiveness (SE) of the EMS fabrics across various polarization directions is analyzed. Additionally, a spacing arrangement for the carbon nanotube fibers is designed. The EMS fabric with carbon nanotube fibers is manufactured using a semi-automatic sample loom, and its SE is tested using a small window method test box in both vertical and horizontal polarization directions.

According to the experimental data and electromagnetic theory analysis, it is determined that when the spacing between the carbon nanotube fibers is less than a specific distance, the SE of the stainless-steel EMS fabric significantly improves. The fabric exhibits stable absorbing properties within the tested frequency range, effectively addressing the issue of secondary damage that arises from relying solely on reflective shielding. Conversely, as the spacing between the carbon nanotube fibers exceeds this distance, the SE diminishes. Notably, the SE in the vertical polarization direction is substantially higher than that in the horizontal polarization direction at the same frequency.

This study provides a new path for the development of high-performance EMS fabrics with good wave-absorption characteristics and SE.

Stab-resistant body armor (SRBA) is used to protect the body from sharp knives. However, most SRBA materials currently have the disadvantages of large weight and thickness. This paper aims to prepare lightweight and high-performance SRBA by 3D printing truss structure and resin-filling method.

The stab resistance truss structure was prepared by the fused deposition modeling method, and the composite structure was formed after filling with resin for dynamic and quasi-static stab tests. The optimized structural plate can meet the standard GA68-2019. Digital image correlation technology was used to analyze the local strain changes during puncture. The puncture failure mode was summarized by the final failure morphologies. The explicit dynamics module in ANSYS Workbench was used to analyze the design of the overlapped structure stab resistance process in this paper.

The stab resistance performance of the 3D-printed structural plate is affected by the internal filling pattern. The stab resistance performance of 3D-printed structural parts was significantly improved after resin filling. The 50%-diamond-PLA-epoxy, with a thickness of only 5 mm was able to meet the stab resistance standard. Resins are used to increase the strength and hardness of the material but also to increase crack propagation and reduce the toughness of the material. The overlapping semicircular structure was inspired by the exoskeleton structure of the demon iron beetle, which improved the stab resistance between gaps. The truss structure can effectively disperse stress for toughening. The filled resin was reinforced by absorbing impact energy.

The 3D-printed resin-filled truss structure can be used to prepare high-performance stab resistance structural plates, which balance the toughness and strength of the overall structure and ultimately reduce the thickness and weight of the SRBA.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

This study aims to investigate the impact of foreign direct investment (FDI) on national digital capability, specifically differentiating the impact between FDI greenfield and mergers and acquisitions (M&A). The research also investigates factors shaping digital capabilities, encompassing government transparency and absorptive capability, while exploring the mediating influence of absorptive capability in the FDI–digital capability relationship.

An econometric model has been developed to examine the interrelationship between national digital capability, FDI inflows, national absorptive capability and government transparency. The data set encompasses 55 countries over a period of nine years (2013–2021). National digital capability data is derived from the well-established index published by the World Competitive Centre (WCC). The sources of the explanatory variables align with standard practices, drawing from reputable institutions (UNCTAD and the World Bank, among others).

The findings reveal a significant positive impact of FDI, particularly in greenfield investments, on national digital capability. Government transparency and research and development (R&D) investment are crucial factors contributing to digital capabilities. Additionally, the absorptive capacity, reflected by R&D investment, also emerges as a potential moderating factor, influencing the impact of FDI inflows on digital capabilities.

The results recommend that policymakers and stakeholders should carefully consider the role of FDI, especially in greenfield investments, as a catalyst for enhancing national digital capability. The findings also underscore the significance of promoting government transparency and directing investments towards R&D to nurture digital capabilities. Moreover, understanding the mediating role of absorptive capability can inform strategies aimed at optimizing the impact of FDI on digital capabilities.

This study contributes uniquely to the existing literature by being the first to systematically explore the influence of FDI on national digital capability. Furthermore, it presents innovative empirical findings on the role of absorptive capability in enhancing the FDI impact on national digital capability, an area that remains relatively uncharted in current literature.