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The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

The sustainable development of the platform economy has been hindered by the absence and alienation of platform corporate social responsibility. Previous studies have mainly focused on the contents and governance models for platform corporate social responsibility. This study seeks to explore which strategy participants choose in the governance of platform corporate social responsibility and their influencing factors.

Using a platform ecosystem approach, a quadrilateral evolutionary game model was developed, and the stabilities of subjects’ behavioral strategies and their combinations in various scenarios were analyzed. Additionally, the effects of key parameters on the system’s evolutionary path were simulated.

The ideal steady state system is achieved when platform enterprises, complementors and consumers adopt positive strategies while the government adopts lax regulation. Moreover, the evolutionary strategies of the subjects are influenced by several factors, including the participation costs of governance, the rewards and punishments imposed by platform enterprises, as well as the reputational losses of platform enterprises and complementors due to media coverage.

This study offers insights into improving the governance effectiveness of platform corporate social responsibility for managers and practitioners.

This study contributes to existing literature by considering the rational orientation of platform ecosystem members and revealing the interaction mechanisms among members. Furthermore, this study combines collective action theory and reputation theory to clarify the influencing factors on members’ behaviors.

The purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.

In terms of theoretical calculation, the contact pairs wear of HD are calculated based on Archard wear formula and the relative motion characteristics of contact pairs. According to the motion trajectory of flexspline teeth, the teeth backlash and the number of meshing teeth, the meshing stiffness and overall stiffness of HD are analyzed considering the wear and its randomness of contact pairs. Combined with Monte Carlo Simulation, the stiffness reliability evaluation method considering contact pairs wear is proposed, and the result of this method is verified by the stiffness reliability result deduced from the stiffness degradation measurement data.

Considering contact pairs wear, during operation, the teeth backlash increases, the number of meshing teeth decreases, the meshing stiffness decreases, ultimately leading to a gradual decrease in the overall stiffness of HD. When only one type of contact pair wear is considered, the influence of flexspline and circular spline contact pair wear on HD stiffness reliability is greater. Compared with the stiffness reliability evaluation results obtained from the stiffness degradation data in the literature, the mathematic expectation of stiffness degradation failure life distribution obtained from the proposed method is relatively bigger.

The stiffness reliability evaluation method of HD considering contact pairs wear is firstly proposed. The stiffness reliability evaluation result from theoretical calculation is verified by the stiffness reliability results deduced from HD stiffness degradation measurement.

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.

Doctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.

The authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.

The results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.

The findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

The aerodynamic differences between the head car (HC) and tail car (TC) of a high-speed maglev train are significant, resulting in control difficulties and safety challenges in operation. The arch structure has a significant effect on the improvement of the aerodynamic lift of the HC and TC of the maglev train. Therefore, this study aims to investigate the effect of a streamlined arch structure on the aerodynamic performance of a 600 km/h maglev train.

Three typical streamlined arch structures for maglev trains are selected, i.e. single-arch, double-arch and triple-arch maglev trains. The vortex structure, pressure of train surface, boundary layer, slipstream and aerodynamic forces of the maglev trains with different arch structures are compared by adopting improved delayed detached eddy simulation numerical calculation method. The effects of the arch structures on the aerodynamic performance of the maglev train are analyzed.

The dynamic topological structure of the wake flow shows that a change in arch structure can reduce the vortex size in the wake region; the vortex size with double-arch and triple-arch maglev trains is reduced by 15.9% and 23%, respectively, compared with a single-arch maglev train. The peak slipstream decreases with an increase in arch structures; double-arch and triple-arch maglev trains reduce it by 8.89% and 16.67%, respectively, compared with a single-arch maglev train. The aerodynamic force indicates that arch structures improve the lift imbalance between the HC and TC of a maglev train; double-arch and triple-arch maglev trains improve it by 22.4% and 36.8%, respectively, compared to a single-arch maglev train.

This study compares the effects of a streamlined arch structure on a maglev train and its surrounding flow field. The results of the study provide data support for the design and safe operation of high-speed maglev trains.

With the global market for advanced technology-driven customer service set to soar, understanding the complicated relationship between advanced technology and customer purchase behaviour is paramount. While prior research has touched upon the impact of technology on purchase processes in some aspects, this study investigates the specific features of advanced technology that shape customer purchase intention in greater depth. By investigating when and under what conditions customers choose advanced technology-based purchases, this research sheds light on the evolving landscape of consumer decision-making and it seeks to quantify the transformative power of advanced technology in driving customer purchase intentions.

The objective is to address financing challenges with an innovative technical approach and provide financial support to facilitate the sustainable development of characteristic tourist towns.

In this study, a novel decision model is proposed, which utilizes the CRITIC improved G1 weighting method to analyze financing factors and applies GRA to enhance the TOPSIS model under a Z-Number fuzzy environment. Finally, sensitivity analysis and comparative assessment were conducted to validate the findings and the model.

The findings indicate that equity financing is the optimal mode of financing for characteristic tourist towns, with bond financing serving as a viable alternative. Key factors influencing financing include economic benefit, social benefit and policy risk. Managers should carefully consider these factors when selecting financing methods in order to enhance efficiency and mitigate risks.

As a new business model and value creation method of cultural and tourism integration, the financing decision of a characteristic tourism town has always been the key to the overall promotion and operation. The research constructs a financing index system for characteristic tourism towns based on benefit and risk considerations, using the hot spring town in Anhui Province, China, as a case study to evaluate eight financing models.

The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.

A kind of micro-nano hydrophobic ternary microparticles was fabricated from SiO₂/halloysite nanotubes (HNTs) and recycled concrete powders (RCPs), which was then mixed with sodium silicate and silane to form an inorganic slurry. The slurry was further sprayed on the concrete surface to construct a superhydrophobic coating (SHC). Transmission electron microscopy and energy-dispersive X-ray spectroscopy mappings demonstrate that the nano-sized SiO₂ has been grafted on the sub-micron HNTs and then further adhered to the surface of micro-sized RCP, forming a kind of superhydrophobic particles (SiO₂/HNTs@RCP) featured of abundant micro-nano hierarchical structures.

The SHC surface presents excellent superhydrophobicity with the water contact angle >156°. Electrochemical tests indicate that the corrosion rate of mild steel rebar in coated concrete reduces three-order magnitudes relative to the uncoated one in 3.5% NaCl solution. Water uptake and chloride ion (Cl^-) diffusion tests show that the SHC exhibits high H₂O and Cl^- ions barrier properties thanks to the pore-sealing and water-repellence properties of SiO₂/HNTs@RCP particles. Furthermore, the SHC possesses considerable mechanical durability and outstanding self-cleaning ability.

SHC inhibits water uptake, Cl^- diffusion and rebar corrosion of concrete, which will promote the sustainable application of concrete waste in anti-corrosive concrete projects.

The purpose of this paper is to optimize the degradation test for products subject to multiple types of inherent stresses and external random shocks. The mechanism that shows how the variables to be optimized influence the considered multiple objectives is also aimed to be explored by using the grey incidence analysis (GIA) model.

The Gamma process is employed to model the influences of different types of stresses and external random shocks. The GIA model is introduced to transfer multiple considered objectives as a comprehensive degree of grey incidence. The particle swarm optimization is integrated to search the globally optimal value of the characteristic variables to be optimized.

The acceleration of tested stresses and external random shocks both make the engineering systems become more vulnerable to the inherent degradation. And, the Kriging model can provide guidance of searching the optimal values of test characteristic variables and mitigate the computation burden. The grey incidence model can make the optimization focused and improve the optimality of objective values.

The proposed method can effectively overcome the drawbacks brought by the limitation of test data and can specify the dependence strength between the inherent degradation and external random shock. The computation cost and accuracy of optimization can be simultaneously ensured by the proposed model.