This article employs the quantile vector autoregression (QVAR) Connectedness method to investigate the impact of geopolitical risk on major US technology firms under various market conditions.The findings reveal that: 1) the overall Connectedness index peaks at 63.76% during market uptrends and reaches a low of 38.61% at the median state; 2) Microsoft and Nvidia act as net risk transmitters, whereas Apple and the geopolitical risk index serve as net risk receivers; and 3) during the COVID-19 period in 2020, Connectedness significantly increased across all quantile levels, while a differentiated pattern emerged during the Russia-Ukraine war in 2022.The main contributions of this study include: firstly, it is the first to examine the asymmetric risk linkages between geopolitical risk and US technology firms; secondly, it enriches existing theories through both static and dynamic association analyses; and thirdly, it offers valuable risk management insights for investors.These results have important implications for portfolio management and policy formulation.

With environmental protection and energy efficiency increasingly becoming the central issues of global concern, government policies have had a significant impact on the automotive industry, particularly on corporate production methods.This paper aims to analyse the interactions and strategic choices between governments and automotive production companies through the framework of game theory, as well as how these interactions drive the evolution of production methods in the automotive industry.The research first constructs a theoretical framework, considering government regulation, corporate costs, and reputation acquisition as the main variables.Then, using game models, it analyses the strategic behaviour of different entities under the guidance of government policies during industrial transformation.The findings reveal whether automotive enterprises will transition to new energy production methods under government policies that enforce rewards or penalties.In the process of numerical simulation, we present the results of changes in choices for both sides with the modification of some key parameters, providing references for further research.

The present evaluation of visitors and the determinants that clarify the frequency of museum visits requires methodologies that exceed conventional procedures. The use of social media platforms for museum news and the development of an online presence provides valuable opportunities for visitors to plan cultural events, enhance their engagement within a specific museum, and articulate their overall perception of the experience. The aim of this research is to understand the relationship between Social Media Engagement for Museum News (SMEMN), Influence of Online Museum Presence on Visit Decision and Perception (IOMPVP), Museum Visit Decision Factors (MVDF) and Museum Visitation Frequency (MVF), to investigate the interplay and impact of these factors, and to find their direct and mediated impacts on the frequency of museum visits. The analytical methods employed were Partial Least Squares Structural Equation Modelling (PLS-SEM) and importance performance analysis (IPA). The research results indicate, contrary to common practice, thatthere may not be a clear correlation between the frequency of museum visits and the level of engagement individuals have with museum news on social media platforms.However, the online presence of a museum and the factors that influence the decision to visit a museum have a direct effect on the frequency of museum visitors.

This paper aims to address the lack of appropriate methods for analysing multivariate compositional data by introducing FAMCoDa, a factor analysis model tailored for such data, along with a two-step estimation method.First, we outline the mathematical framework of FAMCoDa and the procedures for estimating factor loadings and scores, validating its effectiveness through simulation experiments.We then apply FAMCoDa to analyse the industrial consumption structure data of 41 countries.The results reveal that FAMCoDa efficiently manages multivariate compositional data, identifying correlations between variables.Our contributions are threefold: (1) presenting a novel factor analysis model for multivariate compositional data, focusing on inter-variable correlations, unlike existing models; (2) devising a two-step estimation process, starting with multivariate CoDa PCA for initial loadings and refining them through variance rotation, followed by ilr transformation and OLS regression for factor scores; (3) ensuring the compositional structure of the extracted factors remains consistent with the original variables.This work has significant implications for economic applications.

Cloud computing enables the delivery of computing services through the Internet, allowing access to resources on demand from any device and location.Efficient resource management in cloud data centres, including CPU, RAM, storage and bandwidth, is crucial for maximising utilisation and reducing costs.This paper proposes a novel resource management model optimised using a genetic algorithm, focusing on average utilisation of resources.Two approaches are evaluated: one adjusts the number of physical and virtual machines, while the other varies the probability of selecting virtual machine requests.By implementing this model, the research aims to enhance operational efficiency and service quality, demonstrating practical applications of metaheuristic algorithms in real-world cloud computing environments.

In our world today, keeping pace with development of technologies and the expansion of non-absolute data, modelling phenomena becomes very hard and ambiguous, which makes it difficult for deciders to take the optimal decision including the assignment problem, for instance.In this case, leadership is ahead of one serious complex problem, inexact and fuzzy.Spherical fuzzy set (SFS) is disposing the indeterminacy data by the membership, the abstinence and the non-membership functions; it is a generalisation of both Pythagorean fuzzy set (PyFS) and picture fuzzy set (PFS).In this study, some results for SFSs are established first.Then; a novel score function of spherical fuzzy numbers (SFN) is proposed to avoid the comparison problem.In addition; an existing approach to the Pythagorean fuzzy assignment problem is extended to the spherical fuzzy assignment problem (SFAP) with the new proposed score function.Finally, a numerical example and a comparative study are executed to explain the method and validate its advantages.