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This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

This paper aims to investigate the corrosion kinetics and corrosion behavior of NdFeB magnets with the addition of heavy rare earth dysprosium (Dy) for its inhibitory activity on poor corrosion resistance of NdFeB magnets.

To study the effect of dysprosium addition on corrosion behavior of NdFeB magnets and investigate its mechanism, potentiodynamic polarization, scanning electron microscopy (SEM), electrochemical impedance, energy dispersion spectrum (EDS) and scanning Kelvin probe force microscopy (SKPFM) were applied in the research. Besides, microstructures were observed by SEM equipped with EDS. Atomic force microscopy was introduced to analyze the morphology, potential image as well as the contact potential difference. The SKPFM mapping scan was applied to obtain the contact potential around Nd-rich phase at 0.1 Hz. The magnets were detected via X-ray diffraction.

Substitution of Nd with Dy led to improvement of corrosion resistance and reduced the potential difference between matrix and Nd-rich phase. Corrosion resistance is Nd-rich phase < the void < metal matrix; maximum potential difference between matrix and Nd-rich phase of Dy = 0, Dy = 3 and Dy = 6 Wt.% is 411.3, 279.4 and 255.8 mV, respectively. The corrosion rate of NdFeB magnet with 6 Wt.% Dy is about 67% of that without Dy at steady corrosion stage. The addition of Dy markedly enhanced the corrosion resistance of NdFeB magnets.

This research innovatively investigates the effect of adding heavy rare earth Dy to NdFeB permanent magnets on magnetic properties, as well as their effects on microstructure, phase structure and most importantly on corrosion resistance. Most scholars are studying the effect of element addition on magnetic properties but not on corrosion resistance. This paper creatively fills this research gap. NdFeB magnets are applied in smart cars, robotics, AI intelligence, etc. The in-depth research on corrosion resistance by adding heavy rare earths has made significant and outstanding contributions to promoting the rapid development of the rare earth industry.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.

Sustainable urban rail transit requires noise barriers. However, these barriers’ durability varies due to the differing aerodynamic impacts they experience. The purpose of this paper is to investigate the aerodynamic discrepancies of trains when they meet within two types of rectangular noise barriers: fully enclosed (FERNB) and semi-enclosed with vertical plates (SERNBVB). The research also considers the sensitivity of the scale ratio in these scenarios.

A 1:16 scaled moving model test analyzed spatiotemporal patterns and discrepancies in aerodynamic pressures during train meetings. Three-dimensional computational fluid dynamics models, with scale ratios of 1:1, 1:8 and 1:16, used the improved delayed detached eddy simulation turbulence model and slip grid technique. Comparing scale ratios on aerodynamic pressure discrepancies between the two types of noise barriers and revealing the flow field mechanism were done. The goal is to establish the relationship between aerodynamic pressure at scale and in full scale.

The aerodynamic pressure on SERNBVB is influenced by the train’s head and tail waves, whereas for FERNB, it is affected by pressure wave and head-tail waves. Notably, SERNBVB's aerodynamic pressure is more sensitive to changes in scale ratio. As the scale ratio decreases, the aerodynamic pressure on the noise barrier gradually increases.

A train-meeting moving model test is conducted within the noise barrier. Comparison of aerodynamic discrepancies during train meets between two types of rectangular noise barriers and the relationship between the scale and the full scale are established considering the modeling scale ratio.

The suppliers of experimental resources required in megaprojects are driven by short-term interests, presuming that participation in the digital platform would only increase their inputs and fail to rapidly expand their revenue, resulting in their insufficient motivation to participate. This paper aims to design effective incentives for these suppliers exhibiting the aforementioned behaviour to drive them to participate and actively share their resources on the platform.

This paper develops incentives for applying the digital platform for experimental resource sharing by using a reverse induction approach to model and solve an incomplete information game. It compares the traditional experiment management mode and the new mode of applying the digital platform, taking the degree of sharing experimental resources on the platform as the variable and constructing three incentive models. By analysing these different degrees of sharing and the different experimental and informatisation capabilities of the suppliers, it could obtain the optimal incentive scheme for changes in sharing behaviour.

The results show that the designed incentives could increase the participation of suppliers in the platform and the number of their shared resources and make the benefits of both the supplier and the demand side reach the optimal state of a win-win situation. However, a higher degree of sharing by suppliers does not yield better results. In addition, the incentive coefficients for this degree should be set based on the suppliers’ different experimental and informatisation capabilities and the ratio of input cost-sharing, so as to avoid blind inputs from both supply and demand.

This study fills the research gap regarding incentives of the digital platform of experimental resource-sharing for megaprojects; it contributes to the body of knowledge by providing a quantitative perspective of understanding the experimental resource-sharing behaviour that motivates the usage of the digital platform. Furthermore, it reveals the incentive mechanism for application in different scenarios, and quantitative analysis is conducted to provide practical insights into promoting the new experiment management mode in megaprojects for more effective incentivisation.

This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

The purpose of this paper is to investigate the impact of various decision-making approaches and government subsidies on supply chain performance, aiming to enhance the profits of disposal firms and retailers as well as to improve social welfare.

In this paper, a two-echelon biomass supply chain composed of a disposal firm and a retailer is developed. Firstly, considering the effects of government subsidies, we analyze biofuels prices, corporate social responsibility levels, social welfare and supply chain profitability under centralized and decentralized decision-making scenarios, respectively. Furthermore, we assess how subsidies influence pricing, market participation, profitability and social welfare. Secondly, we propose a revenue sharing–cost sharing contract to enhance the profits of the disposal firm and retailer. Thirdly, we extend the supply chain to a disposal firm and two retailers and explore the impact of competition intensity on corporate decision-making behavior. Finally, numerical analysis is conducted by taking one biomass energy firm as an example to support the results.

Our research finds that (1) Equilibrium strategies under the centralized decision-making scenario are greater than those under the decentralized decision-making scenario. Centralized decision-making can increase market demand and consumer surplus. (2) Government subsidies can promote corporate social responsibility levels, despite causing a slight increase in retail price for biofuels. When market competition intensifies, companies usually reduce their investment in CSR, and this trend is particularly pronounced in the absence of subsidies. (3) In both the decentralized and the centralized decision-making scenarios, increasing conversion rates and the CSR coefficient can significantly increase the overall profitability and social welfare.

A three-echelon biomass supply chain involving collection station, disposal firm and retailer can be studied in the future.

By examining the effects of subsidies on CSR engagement and market outcomes, our study contributes valuable insights into policy design for promoting sustainable practices in biomass industries.

The purpose of this paper is to study the aerodynamic characteristics and uplift force tendencies of pantographs within the operational height span of 1,600–2,980 mm, aiming to offer valuable insights for research concerning the adaptability of pantograph-catenary systems on double-stack high container transportation lines.

Eight pantograph models were formulated based on lines with the contact wire of 6,680 mm in height. The aerodynamic calculations were carried out using the SST k-ω separated vortex model. A more improved aerodynamic uplift force method was also presented. The change rule of the aerodynamic uplift force under different working heights of the pantograph was analyzed according to the transfer coefficients of the aerodynamic forces and moments.

The results show that the absolute values of the aerodynamic forces and moments of the upper and lower frame increase with the working height, whereas those of the collector head do not change. The absolute values of the transfer coefficients of the lower frame and link arm were significantly larger than those of the upper frame. Therefore, the absolute value of the aerodynamic uplift force increased and then decreased with the working height. The maximum value occurred at a working height of 2,400 mm.

A new method for calculating the aerodynamic uplift force of pantographs is proposed. The specifical change rule of the aerodynamic uplift force of the pantograph on double-stack high container transportation lines was determined from the perspective of the transfer coefficients of the aerodynamic forces and moments.