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This study investigates the significance of trade credit (TC) as an alternative source of funding in financing the growth of financially dependent firms.

Panel data analysis using the difference generalized method of moments (GMM) and fixed-effects ordinary least squares (FE-OLS) is conducted on annual data from publicly listed firms across a number of developing economies. The data cover the period from 2003 to 2019.

The findings indicate that financially dependent firms rely on TC to manage their growth, especially when they have exhausted their debt capacity. This dependence on TC displays a cyclical pattern. As firms enhance their financial position, they tend to scale back their dependence. Nevertheless, firms with significant growth opportunities continue utilizing TC for at least two years after their initial identification as financially dependent.

The author's conclusion highlights that TC can be a valuable and accessible source of funding, especially in developing economies where the real sector may require alternative financing channels. Hence, TC has the potential to play a very significant role in financing corporate growth in these economies.

The current study adds to the existing body of literature by revealing that access to alternative sources of finance is also critical for firms that are dependent on external sources and for firms that have exhausted their financial debt capacity.

The purpose of this study is to determine propeller damage based on acoustic recordings taken from unmanned aerial vehicle (UAV) propellers operated at different thrust conditions on a test bench. Propeller damage is especially critical for fixed-wing UAVs to sustain a safe flight. The acoustic characteristics of the propeller vary with different propeller damages.

For the research, feature extraction methods and machine learning techniques were used during damage detection from propeller acoustic data. First of all, sound recordings were obtained by operating five different damaged propellers and undamaged propellers under three different thrusts. Afterwards, the harmonic-to-noise ratio (HNR) feature extraction technique was applied to these audio recordings. Finally, model training and validation were performed by applying the Gaussian Naive Bayes machine learning technique to create a diagnostic approach.

A high recall value of 96.19% was obtained in the performance results of the model trained according to damaged and undamaged propeller acoustic data. The precision value was 73.92% as moderate. The overall accuracy value of the model, which can be considered as general performance, was obtained as 81.24%. The F1 score has been found as 83.76% which provides a balanced measure of the model’s precision and recall values.

This study include provides solid method to diagnose UAV propeller damage using acoustic data obtain from the microphone and allows identification of differently damaged propellers. Using that, the risk of in-flight failures can be reduced and maintenance costs can be lowered with addressing the occurred problems with UAV propeller before they worsen.

This study introduces a novel method to diagnose damaged UAV propellers using the HNR feature extraction technique and Gaussian Naive Bayes classification method. The study is a pioneer in the use of HNR and the Gaussian Naive Bayes and demonstrates its effectiveness in augmenting UAV safety by means of propeller damages. Furthermore, this approach contributes to UAV operational reliability by bridging the acoustic signal processing and machine learning.