Green finance plays a crucial bridge as an intermediary between finance and the environment, facilitating resource allocation. The disclosure of environmental information (EID) is vital for promoting sustainable economic development. This study utilizes panel data covering the period from 2012 to 2019, focusing on Chinese companies listed in high-polluting industries. The findings demonstrate that green finance policies have a significant positive impact on EID, while increased managerial power has a detrimental effect. However, green finance policies can mitigate the negative consequences (...) arising from moral hazard and adverse selection resulting from managerial power. They enhance the quality of EID by restricting long-term loan resources for polluting entities. The heterogeneity test reveals that the effectiveness of green finance policies in promoting EID varies across different regions of China. In non-state-owned companies, green finance policies are more effective in constraining managerial power. The moderating effect of green finance on managerial power is weakened by improvements in internal control. Green financing can offset the adverse impact of managerial power on EID when debt levels are high. Our research plays a crucial role in advancing micro-academic studies in the field of green finance. By strengthening internal controls for corporate managers and promoting EID policies, we provide essential support and guidance to decision-makers. (shrink)

It is known that in an infinite very weakly cancellative semigroup with size $\kappa $, any central set can be partitioned into $\kappa $ central sets. Furthermore, if $\kappa $ contains $\lambda $ almost disjoint sets, then any central set contains $\lambda $ almost disjoint central sets. Similar results hold for thick sets, very thick sets and piecewise syndetic sets. In this article, we investigate three other notions of largeness: quasi-central sets, C-sets, and J-sets. We obtain that the statement applies (...) for quasi-central sets. If the semigroup is commutative, then the statement holds for C-sets. Moreover, if $\kappa ^\omega = \kappa $, then the statement holds for J-sets. (shrink)

In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial-temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single-frame detection, and finally to multiframe detection, respectively. This (...) enables the ground-based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam-parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error. (shrink)

In [3], Shi proved that there exist incomparable Zermelo degrees at γ if there exists an ω‐sequence of measurable cardinals, whose limit is γ. He asked whether there is a size antichain of Zermelo degrees. We consider this question for the ‐degree structure. We use a kind of Prikry‐type forcing to show that if there is an ω‐sequence of measurable cardinals, then there are ‐many pairwise incomparable ‐degrees, where γ is the limit of the ω‐sequence of measurable cardinals.

Parallel attribute reduction is one of the most important topics in current research on rough set theory. Although some parallel algorithms were well documented, most of them are still faced with some challenges for effectively dealing with the complex heterogeneous data including categorical and numerical attributes. Aiming at this problem, a novel attribute reduction algorithm based on neighborhood multigranulation rough sets was developed to process the massive heterogeneous data in the parallel way. The MapReduce-based parallelization method for attribute reduction was (...) proposed in the framework of neighborhood multigranulation rough sets. To improve the reduction efficiency, the hashing Map/Reduce functions were designed to speed up the positive region calculation. Thereafter, a quick parallel attribute reduction algorithm using MapReduce was developed. The effectiveness and superiority of this parallel algorithm were demonstrated by theoretical analysis and comparison experiments. (shrink)