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The pandemic has caused severe disruptions and significant losses in various industries. In particular, the nursing service industry has been greatly affected, leading to increased service costs and attrition of nursing service provider (NSP) residents. Although prior studies suggest that outsourcing may mitigate losses from disruptions, there still lacks a detailed analysis of whether and when to adopt such a disruption solution.

This study develops a two-period game-theoretical model to explore the impacts of demand and cost disruptions caused by the pandemic on NSPs’ operational strategies, suppliers’ strategy choices and equilibrium prices and demand.

The results present several novel managerial insights. First, we suggest that higher demand and cost disruptions decrease service demand, but do not necessarily prompt an NSP to outsource nursing services. Interestingly, we find that even when the service cost of the outsourcing strategy is low, the NSP may still insist on the in-house strategy. Additionally, the equilibrium strategy does not always result in lower prices and higher demand.

Our findings provide insightful takeaways for NSPs to cope with the pandemic in the nursing service industry. The results also offer theoretical support for other industries to recover from demand and cost disruptions.

For nanostores, striving to become the community group-buying leader is gaining prominence. This paper aims to construct Hotelling linear models to investigate whether nanostores should be registered as leaders and their decisions in a competitive environment.

This paper constructs three Hotelling linear models: neither nanostore registers as community leader, only one nanostore registers as community leader and both nanostores register as community leader. The competitive operation strategies of two general nanostores under three scenarios are solved.

The study finds that nanostores without a cost advantage may benefit from being the first leader. The nanostore's preferred decisions depend on the investment cost parameters of its own and competitors which may lead to market share competition. Furthermore, consumers' sensitivity to community group-buying service has a negative effect on nanostores' profit.

The study is one of the few to consider the competition between community leaders. Besides, the study considers that the utilities functions of consumers are concurrently impacted by the service decisions, along with the price in different nanostores. It can provide nanostores useful implications in the dynamic industry.

This paper aims to investigate how cathodic polarization behavior significantly affects the selection of cathodic protection parameters and the effectiveness of protecting underwater metal structures. Factors such as water depth and operating conditions impact seawater temperature, making it crucial to understand the effects of temperature on cathodic protection parameters for underwater pipelines.

In this paper, potentiostatic polarization was carried out by three-electrode method, and morphology, X-ray diffraction and electrochemical analysis.

It was determined that the stable current densities at the minimum negative potential (−0.8 V_SSC) for pipeline steel varied at different temperatures: 7°C, room temperature and 60°C. The cathodic protection potential corresponding to the lowest stable current density was observed to be −1.0 V_SSC at 7°C and −0.95 V_SSC at room temperature and 60°C.

This study elucidates the mechanisms by which different temperatures affect the protective performance of calcareous deposits and current densities.

This paper aims to explore the effect of different government subsidy decisions and the differences between the consequences of these decisions when supply chain members engage in cooperative green innovation through cost-sharing arrangements.

This paper investigates the optimal decisions for green supply chains under two types of subsidies, including subsidies for green innovation research and development (R&D) costs and subsidies for consumers, by integrating game theory with numerical simulation.

The optimal R&D cost-sharing ratio is found to be 2/3 for manufacturers and 1/3 for retailers. Under any subsidy policy, the supply chain can achieve maximum total profit. When the supply chain adopts the optimal R&D cost-sharing ratio, subsidies for green innovation R&D costs prove to be the most effective in increasing the supply chain’s profit. However, from the perspective of total social welfare, the analysis reveals that government subsidies to consumers are more beneficial for promoting overall social welfare.

Previous studies on green supply chain decisions have primarily focused on either government subsidies or corporate cost sharing in isolation. In contrast, this study combines both government subsidies and cost sharing within a unified framework for a more comprehensive analysis. Additionally, this paper examines the impact of government subsidies on supply chain cost-sharing decisions and their effect on overall social welfare while considering the presence of cost sharing and using the combination of theoretical modeling and simulation analysis.

This paper aims to study how the timing of these decisions affects the total profit and the individual profits of the two agents.

This paper study a supply chain for a network good where there is a manufacturer and a retailer. The manufacturer determines its wholesale price and its share in the retailer’s advertising cost while the retailer decides the retail price and the advertising cost.

This paper finds that a stronger network externality leads to higher prices and higher advertising efforts. This increases the profits of both manufacturer and retailer, but the manufacturer’s share of advertising costs depends on the order in which the supply chain enterprise make their decisions, the strength of network externality and the effect of advertising determines which decision timeline results in a higher price and greater advertising effort. The manufacturer prefers the price decision to be made before the advertising decision, while the retailer prefers these decisions to be made simultaneously.

Although this paper studies the price and advertising decision-making order preferences of channel members based on network externalities, this research can also be expanded from the following aspects based on network effects. First, network externality affects advertising cooperation between both parties in the situation such that the pricing power of retail prices is transferred from the retailer to the manufacturer and the retailer relies on revenue sharing (revenue sharing contract, nonwholesale price contract. Second, the manufacturer dominates the issues in the supply chain, but in reality, a retailer can also be the dominator or there are no dominators (Nash equilibrium). Finally, it is possible to consider pricing and advertising decisions in situations where two manufacturers or retailers compete.

When the price is reasonable, advertising investment is the main determinant of product sales. The greater the intensity of network externalities the more retailers will be willing to invest in advertising. An increase in the intensity of network externalities may not necessarily enhance manufacturers’ motivation or cooperative advertising, but it depends on the decision-making sequence. The strength of network externalities determines the decision-making sequence preferences of supply chain channel members whose preferences vary leading to conflicts of interest.

The impact of cooperative advertising or decision sequence on corporate decision-making has not been considered. To fill this gap, the paper integrates network externality and supply chain cooperative advertising models, focusing on the impact of network externality on pricing and advertising decisions, as well as on the sequence of decisions.

Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded polytetrafluoroethylene (ePTFE) bonding.

A series of silicon-containing fluorinated acrylic copolymers were synthesized through free radical solution polymerization with vinyloxy trimethylsilane, allyltrimethylsilane, 3-(trimethoxysilyl)propyl methacrylate or 1,3,5-tris(3,3,3-trifluoropropyl) methylcyclotrisiloxane as silicon monomers, and comprehensive performance of the copolymers was evaluated based on Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography, glass transition temperatures (T_g), differential scanning calorimetry, thermogravimetric analysis, water contact angle, the track, 180° peel strength, and shear holding power.

Based on the FTIR and XPS results, it is confirmed that the silicon monomers were successfully introduced into the fluorinated acrylate copolymer. XPS analysis indicated that the silicon groups had the tendency to enrich on the surface of the film, thereby reducing the F content on the film surface. The glass transition temperatures (T_g) of the PSAs increased when silicon monomers were introduced, while the thermal stability declined. The contact angles of the acrylic PSA films were increased with the introduction of silicon monomers. From the perspective of bonding performance, the track, 180° peel strength and shear holding power decreased to varying degrees compared to silicon-free PSA, except significantly elevated holding power with MPS as the silicon monomer.

Silicon-containing fluorinated acrylic copolymers were synthesized, and the comprehensive performance was evaluated as PSAs of ePTFE for the first time.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.

Adding Pd element to Au wire can improve the reliability of Au-Al bonding, but the mechanism of Pd element has not been well revealed so far. The purpose of this study is to reveal in more detail the mechanism of the role of Pd elements in Au/Al bonding.

In this paper, the microstructure changes and tensile data of 99.99% (4N) gold wire and 99% (2N) gold wire with 1 at % Pd were compared through high-temperature thermal aging treatment of the specimens, so as to explore the influence mechanism of Pd element on Au-Al bonding reliability.

The addition of Pd element effectively reduces the thickness of intermetallic compounds (IMCs) layer and strengthens the fracture tension and reliability. Compared with the 4N specimen, the average thickness of the IMCs layer of the 2N specimen under the same conditions is reduced by 1 µm, and the tensile value of the 2N specimen is increased by 1−3 g. Stored at 200°C for 200 h, the failure rate of bond point of 4N specimen reached 94.64%, and that of 2N specimen was 20%, with a difference of 4.73 times.

Through comparative analysis of the data, this study found that the doping of Pd element in Au-Al IMCs in the early stage slowed down the growth rate of the IMCs, and the precipitation of Pd element in the late stage to form a better Pd-rich layer hindered the element mutual diffusion behavior between Au and Al.

Prefabricated building has emerged as a hallmark of modern construction industrialization and a pivotal driver of industrial upgrading. In this new building type, the supply of high-quality prefabricated components plays a crucial role in ensuring project quality, cost-effectiveness and on-time completion. Consequently, selecting the optimum suppliers for these components is vital. This study provides valuable insights for construction enterprises, guiding them in the optimal selection of prefabricated component suppliers and thereby contributing to the sustainable development of the construction industry.

The entropy weight method is used to integrate and rank 19 commonly used evaluation indices, forming a supplier evaluation system from the enterprises perspective. Subsequently, the VIKOR multi-attribute decision model, combined with a comprehensive evaluation method based on cloud modeling, is applied to identify the most suitable suppliers through case study.

The findings emphasized that product quality, particularly the component compliance rate, is paramount in supplier selection. Additionally, companies should prioritize cost management and fundamental supplier capabilities, such as transportation efficiency and operational flexibility, while fostering strong partnerships with high-quality suppliers. Furthermore, all stakeholders need to enhance the supply chain’s responsiveness and adaptability, ensuring these improvements are achieved without strict cost controls.

This study minimizes the influence of subjective biases from decision-makers’ by integrating quantitative and qualitative analysis methods, thereby enhancing the comprehensiveness and accuracy of evaluations. By effectively addressing the fuzziness and uncertainty inherent in evaluation data, it establishes a robust system for selecting prefabricated building suppliers. This approach offers reliable and practical decision support, providing theoretical backing for enterprises in choosing prefabricated component suppliers and promoting the sustainable development of the prefabricated construction industry.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.