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This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle lateral motion.

In this paper the vehicle presighting and kinematic models were established, and a new sliding mode control isokinetic convergence law was devised based on the fractional order calculus to make the front wheel turning angle approach the desired value quickly. On this basis, a fractional gradient descent algorithm was proposed to adjust the radial basis function (RBF) neuron parameter update rules to improve the compensation speed of the neural network.

The simulation results revealed that, compared to the traditional sliding mode control strategy, the designed controller eliminated the jitter of the sliding mode control, sped up the response of the controller, reduced the overshoot of the system parameters and facilitated accurate and fast tracking of the desired path when the vehicle changed lanes at low speeds.

This paper combines the idea of fractional order calculus with gradient descent algorithm, proposed a fractional-order gradient descent method applied to RBF neural network and fast adjustment the position and width of neurons.

We examine whether the social capital of the area where a firm’s headquarters is located affects that firm’s credit rating. Given that credit rating agencies only infrequently visit a firm’s headquarters, it is pertinent to investigate whether this soft information is considered.

In order to test whether social capital affects firms’ credit ratings, we estimate the following model using an ordinary least squares regression: Rating_it = β₀ + β₁ Social Capital_it + ∑ Controls_it + Industry fixed Effects_i + State−year fixed effects_it + ε_it. We follow recent accounting and finance research and measure societal-level social capital at the county level (Jha & Chen, 2015; Cheng et al., 2017; Hasan et al., 2017a, b; Jha, 2017; Hossain et al., 2023). We use four inputs to calculate social capital: (1) voter turnout in presidential elections, (2) the census response rate, (3) the number of social and civic associations and (4) the number of nongovernmental organizations in each county.

W provide evidence that social capital has a causal effect on credit ratings. Interesting is that this effect is not merely localized to firms near credit rating agencies. We also find that the effect of social capital on credit ratings is concentrated among firms with moderate levels of default risk. For firms with extremely low or extremely high default risk, social capital appears irrelevant to credit ratings, suggesting that social capital plays a larger role in more ambiguous contexts or when greater judgment is required. We demonstrate that the effect of social capital on credit ratings disappears when the rating agency has extensive experience in a particular region. This result is consistent with rating agencies stereotyping certain regions of the USA and using that information to inform their ratings when they have less experience in the region. Finally, we find that while social capital is associated with credit ratings, it has no association with future defaults.

Though we cautiously followed prior studies and were confident in our data construction process, it is possible that we are measuring social capital with error.

Our findings suggest that credit rating agencies could benefit from reevaluating how they incorporate non-financial information, such as social capital, into their assessment processes, potentially leading to more nuanced and equitable credit ratings. Additionally, firms could use these insights to bolster their engagement with local communities and stakeholders, thereby enhancing their creditworthiness and attractiveness to investors as part of a broader corporate strategy. The findings also underline the need for regulatory frameworks that foster transparency and the inclusion of social factors in credit evaluations, which could lead to more comprehensive and fair financial reporting and rating systems.

Recognizing that social capital can influence economic outcomes like credit ratings may encourage both communities and firms to invest more in building and maintaining social networks, trust and civic engagement. By demonstrating how social capital impacts credit ratings, our research highlights the potential to address inequalities faced by regions with lower social capital, guiding targeted social and economic development initiatives. Moreover, understanding that regional social capital can influence credit ratings might affect public perception and trust in the impartiality and accuracy of these ratings, which is essential for maintaining market stability and integrity.

Our research provides fresh insights into how social capital, an intangible asset, influences credit ratings – a topic not extensively explored in existing literature. This sheds light on the dynamics between social structures and financial outcomes. Methodologically, our use of the 9/11 attacks as an exogenous shock to measure changes in social capital introduces a novel approach to study similar phenomena. Additionally, our findings contrast with prior studies such as Jha and Chen (2015) and Hossain et al. (2023), by delving deeper into how proximity and familiarity impact financial assessments differently, enriching academic discourse and refining existing theories on the role of local knowledge in financial decisions.