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This paper aims to study the use of electrochemical noise (EN) technology in the corrosion continuous monitoring of stainless steel (SS) in an atmospheric environment.

An EN electrode was designed and fabricated to acquire the EN of 304 SS in the atmospheric environment. The statistical analysis and shot noise analysis were used to analyze the EN, and the surface morphology analysis of 304 SS was used to verify the EN analysis results.

The activation state, passive film formation and pitting corrosion of 304 SS can be clearly distinguished by the amplitude and frequency change of EN. The metastable pitting corrosion and steady-state pitting corrosion can be identified with the shot noise parameters q and fn. Under the existence of chloride ion, the stability of 304 SS passive film decreases and the steady-state corrosion pits of 304 SS are more likely to form with the reduction of thin electronic layer (TEL) pH. The critical TEL pH of 304 SS corrosion is a pH between 3 and 4.

In an atmospheric environment, the EN technology was used in the corrosion continuous monitoring of SS.

With the rapid development of the economy in China, the wealth of residents has continued to increase, and most families have gradually been aware of the importance of commercial insurance. The family purchase of insurance in China was still not optimistic. Many scholars focus on wealth allocation, but the attention to the commercial insurance market was still less. Based on previous research studies, this study aims to investigate the impact of education and financial literacy on the commercial insurance purchase in China.

China Household Finance Survey data was used to investigate the purchase of commercial insurance in Mainland Chinese families. Factor analysis was used to construct financial literacy, and the education data were combined to analyze the commercial insurance purchase using the Probit model and the Tobit model. Finally, the contributions of education and financial literacy to commercial insurance purchases were analyzed.

Both education and financial literacy exerted a positive impact on the purchase of commercial insurance in China. Individual characteristics such as gender, age, marital status, risk attitude, purchase of social insurance and consultation with a financial advisor possessed significant effects; household factors like household size and assets, macro factors such as the density of financial institutions and the density of financial industry staff, and regional factors as local unemployment rate excreted influences on the commercial insurance purchase.

Based on the current economic development in China, this study investigated and expressed opinions on the public and insurance companies regarding commercial insurance purchases. It accentuated financial literacy and education as factors that facilitated commercial insurance development.

This paper aims to address the limitations of current deep learning algorithms for sound source localization (SSL), which focus on a single feature and frequency scale, neglecting the integration of multi-scale information. The method developed in this study enhances localization accuracy by effectively using the spatial information and spectral diversity provided by microphone arrays.

The method is based on a multi-scale cross-short-time Fourier transform (STFT) complex-valued convolutional neural network (CCNN). It uses cross-STFT spectra at different scales to capture detailed acoustic information across various frequencies. The effectiveness of the algorithm was validated through both simulations and experimental studies.

Experimental results demonstrate that the proposed multi-scale cross-STFT CCNN not only outperforms the single-scale cross-STFT model but also delivers superior localization performance compared to other advanced methods, achieving consistently higher accuracy. The method shows excellent robustness across various signal-to-noise ratio (SNR) conditions and performs well even on imbalanced datasets, confirming its strong generalization capabilities.

This paper introduces a novel approach to SSL that integrates multi-scale information, addressing a key limitation of existing methods. The findings offer significant value to researchers and practitioners in the field of acoustic signal processing, particularly those focused on deep learning-based localization techniques.