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In this article, Marieke Napier and Ed Bremner introduce the new JISC Quality Assurance (QA) Focus post, discussing how it will operate and the benefits it will provide, the challenges it faces and how it will affect projects.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

This study aims to investigate the most effective approach for governments and enterprises to combat desertification by considering the governance cycle. The focus is on understanding how the government can incentivize enterprises to actively engage in desertification combat efforts.

Both the government and the enterprise are treated as rational entities, making strategic choices for joint participation in combating desertification. Recognizing the dynamic nature of the desertification combat area, differential game models are employed to identify the optimal mode for combating desertification.

The findings underscore the significant influence of the governance cycle duration on the selection of desertification combat modes for government and enterprise. A cooperative mode is best suited to a short governance cycle, while an ecological subsidy mode is optimal for a longer cycle. Enhancing governance technology and shortening the governance cycle are conducive to combating desertification. Reducing taxes alone may not be an effective control strategy; rather, the government can better motivate enterprises by adopting tax rate policies aligned with the chosen governance mode.

This research contributes by elucidating the impact mechanism of the government cycle’s length on the desertification combat process. The results may offer valuable insights for governments in formulating strategies to encourage corporate participation in combating desertification and provide theoretical support for selecting optimal desertification combat modes.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.

In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed.

In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.

The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given.

This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms.

Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.

The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.

Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.

The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.

The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.

It is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.

Using a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.

The results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.

By considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.

In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.