Lagrangian systems subject to fractional damping can be incorporated into a variational framework by doubling the state variables and introducing fractional derivatives. Fractional variational integrators based on backward-differentiation convolution quadrature (BD-FCQ), combined with higher-order Galerkin methods, saturate at second-order accuracy because the multistep structure of BDFCQ does not take into account the internal stages of the Galerkin discretization. The main objective of this paper is to develop fractional variational integrators (FVIs) by combining Runge-Kutta convolution quadrature (RKCQ) for the approximation of fractional derivatives with higher-order Galerkin methods. The RKCQ approach is naturally compatible with such stage-based discretizations and is therefore better suited for the construction of higher-order schemes. We are particularly interested in the CQ based on Lobatto IIIC. Preservation properties such as energy decay, as well as convergence properties, are investigated numerically and proved for second-order schemes. The presented schemes reach 2nd, 4th and 6th order of accuracy. A brief discussion on the midpoint fractional integrator is also included.
PurposeThis study aims to examine the effect of responsible governance on tax avoidance and the moderating effect of corporate social responsibility (CSR), business ethics and green innovation on this relationship.Design/methodology/approachThe authors relied on a sample of 475 ESG index companies from 2013 to 2022, using the feasible generalized least squares (FGLS) method. To test robustness, the authors included an alternative measure of tax avoidance, applied the generalized method of moments (GMM) to address endogeneity, and accounted for the specific impact of the COVID-19 pandemic.FindingsThe results demonstrate that responsible governance significantly reduces tax avoidance. Furthermore, CSR, business ethics and green innovation reinforce this effect, confirming their moderating role.Practical implicationsThe study provides practical advice to managers and policymakers on integrating governance, CSR, business ethics and green innovation to combat tax avoidance.Originality/valueThis study sheds light on the impact of responsible governance on corporate tax avoidance by highlighting the moderating role of CSR and business ethics and green innovation. Although these factors are interrelated, they capture distinct dimensions of corporate responsibility, offering a more comprehensive understanding of governance mechanisms. It also makes a unique contribution by showing that green innovation strengthens this link, which has not been previously studied.
This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.
In a context of environment threatening factors, managing all waste types is an earnest need. The present study aims to explore the advantages of incorporating rising amounts of human hair fibers in concrete and to propose the optimal dosages. The focus was on performances relevant to both civilian and military applications. Concrete was elaborated by partially substituting aggregate volume by short hair fibers HF, with lengths ranging from 2 to 8 mm and dosages varying between 0 and 20 kg/m3. Compared with plain concrete, HF content rise resulted in reductions in both slump and density, with a maximum density decrease of 32%. Peak enhancements in compressive and tensile strengths of 12.56% and 8.73% respectively, were recorded at a dosage of 15 kg/m3, beyond which both resistances decreased. Scanning electron microscopy revealed that these trends arise from antagonist evolutions within the concrete matrix: higher HF contents increase total porosity but also provide a crack bridging action, elucidating the existence of an optimal HF dosage. Furthermore, elevating HF contents enhanced thermal, ballistic and blast load resistances. At a dosage of 20 kg/m3, thermal conductivity was reduced by 50% and concrete slabs exhibited the highest ballistic performance as evidenced by decreases in crater area, penetration depth and lost concrete mass by 65.27%, 11.54% and 70% respectively. A preliminary qualitative assessment of HF inclusive concrete under shock wave loading also demonstrated a significant reduction in crack number and widths. Based on the findings, the present work proposes a recycling procedure that imparts concrete additional protective efficiencies: thermal, anti-ballistic and anti-shock wave. It fosters circular economy practices across both civilian and military sectors.
BACKGROUND AND OBJECTIVES: The global move toward sustainability, supported by national plans like Saudi Vision 2030, requires the combination of new technologies with environmental planning. The analysis of big data has the potential to enhance environmental performance; however, the precise methods by which it contributes to establishing a green competitive advantage remain largely unclear. This is especially true for the renewable energy sector during changing market conditions. The study objectives were to explain the connections between big data analytics and green competitive advantage. This study investigates the direct impact of big data analytics on achieving a green competitive advantage, while also exploring the contributions of green supply chain management and green human resource management in this relationship. It also tests how market turbulence changes these relationships within Saudi Arabia's renewable energy industry. METHODS: A quantitative method was used. Data were collected via questionnaire given to 420 employees working in Saudi renewable energy companies. The conceptual framework for the study is established utilizing the resource-based view in conjunction with capabilities theory. Partial least squares structural equation modeling was applied to assess direct, mediating, and moderating effects simultaneously. FINDING: The analysis indicate that big data analytics have a substantial positive direct influence on competitive green advantage, while also enhancing both green human resource management and green supply chain management. Green human resource management significantly mediates the relationship between big data analytics and green competitive advantage, whereas green supply chain management exhibits a negative mediating effect, suggesting implementation-related challenges in the studied context. Furthermore, market turbulence strengthens the relationship between green supply chain management and green competitive advantage, while weakening the relationship between green human resource management and green competitive advantage. CONCLUSION: The findings confirm that big data analytics represents a critical organizational capability for achieving a competitive green advantage; however, its effectiveness depends on the nature of internal green management practices and external market conditions. The study highlights the importance of aligning digital investments with robust green human resource strategies while carefully reassessing green supply chain initiatives. These insights provide practical guidance for managers and policymakers seeking to leverage data-driven technologies to support sustainability objectives in line with Saudi Vision 2030.
