In the context of new quality productive forces, enterprises must leverage technological innovation and intelligent management to enhance financial risk resilience. This article proposes a financial distress prediction model based on deep learning, combined with a CNN, BiLSTM, and attention mechanism, using SMOTE for sample imbalance and Hyperband for hyperparameter optimization. Among four CNN-BiLSTM-AT model structures and seven mainstream models (CNN, BiLSTM, CNN-BiLSTM, CNN-AT, BiLSTM-AT, CNN-GRU, and Transformer), the 1CNN-1BiLSTM-AT model achieved the highest validation accuracy and relatively faster training speed. We conducted 100 repeated experiments using data from two companies, with validation on 2025 data, confirming the model’s stability and effectiveness in real-world scenarios. This article lays a solid empirical foundation for further optimization of financial distress warning models.

The objectives of this research were to develop a model to forecast and estimate the return levels for daily maximum PM2.5 concentrations in Thailand, applying Extreme Value Theory (EVT) with the Generalized Extreme Value (GEV) distribution under eight models for stationary and non-stationary process. This research utilized reanalysis data from the NASA EARTHDATA satellite, represented as grid points with a spatial resolution of 50 × 62.5 km, enabling the analysis of daily maximum PM2.5 concentrations across 176 grid points from January 1, 2009 to October 31, 2024. The analysis revealed that Model 2 (μ(t)=β₀+β₁t where σ and ξ are constants) is the most suitable model for five grid points, namely Sa Kaeo Province, Uthai Thani Province, Nakhon Ratchasima Province, Bueng Kan Province and Mae Hong Son Province, whereas Model 1 (μ, σ and ξ are constants) is suitable for the remaining 171 grid points. Estimating the return levels for return periods of 5, 10, 25, and 50 years showed that Northern Thailand had the most extreme daily PM2.5 concentrations, for all return periods especially Mae Hong Son Province. The results of this analysis can serve as valuable information to support decision-making for response planning in high-risk areas, aiding in efficient resource allocation and preventive measures.

Adaptive cluster sampling (ACS) is a sampling method commonly employed when the population is rare and exhibits clustering. However, the initial sample selection may include units that do not satisfy the specified condition. To address this, general inverse sampling is incorporated into ACS, where the initial units are selected sequentially and termination criteria are applied to regulate the number of rare elements drawn from the population. The objective of this study is to develop an estimator of the population mean by utilizing auxiliary information within the framework of general inverse adaptive cluster sampling. The proposed estimator, constructed on the basis of a regression-type estimator, is analytically examined. A simulation study was conducted to validate the theoretical results. In this study, the region of interest was divided into 400 square units (20 rows by 20 columns). The results demonstrate that the proposed estimator, which incorporates auxiliary variables, consistently yields a lower variance than the conventional mean estimator without auxiliary information. This superiority holds across all scenarios considered, specifically when the predetermined number of rare units r ranges from two to ten. Therefore, the proposed estimator is shown to be more efficient than the estimator that does not employ auxiliary information.

Adaptive cluster sampling (ACS) is a sampling technique commonly used for rare populations that exhibit spatial clustering. However, the initially selected sample units may not always satisfy the specified inclusion condition. To address these limitations, general inverse sampling has been incorporated into ACS, in which the initial units are sequentially selected, and a termination criterion is applied to control the number of rare elements drawn from the population. The objective of this study is to develop an estimator of the population mean that incorporates auxiliary information within the framework of general inverse adaptive cluster sampling (GI-ACS). The proposed estimator is constructed based on a regression-type estimator and analytically examined. A simulation study was conducted to validate the theoretical findings. Three scenarios were considered, representing low, moderate, and high correlations between the variable of interest and the auxiliary variable. The simulation results indicate that the proposed estimator achieves lower variance than the GI-ACS estimator that does not utilize auxiliary information across all examined correlation scenarios. Therefore, the proposed estimator is more efficient and preferable when auxiliary variables are available.

This paper deals with the concepts of upper $s$-$(\tau_1,\tau_2)$-continuous multifunctions and lower $s$-$(\tau_1,\tau_2)$-continuous multifunctions. Moreover, several characterizations and some properties concerning upper $s$-$(\tau_1,\tau_2)$-continuous multifunctions and lower $s$-$(\tau_1,\tau_2)$-continuous multifunctions are investigated.

In China, a major agricultural country, the agricultural economy plays a crucial role in the national economy. As a representative of the agricultural industry chain, agricultural listed companies not only bear the responsibility of support, but also face various financial risks such as market fluctuations, climate change, and policy adjustments. This research is based on agricultural listed companies on the Shanghai and Shenzhen A-shares from 2010 to 2023, taking into account both financial and non-financial indicators from three years ago. Principal Component Analysis (PCA) dimensionality reduction and oversampling techniques (SMOTE) were used to handle sample imbalance. Through the BP neural network model optimized by genetic algorithm, the financial risk of agricultural listed companies was successfully predicted, and the overall accuracy rate reached 94\(\%\). Compared with the original neural network, the optimized neural network had better performance. This research provides important decision support for agricultural enterprises and investors and establishes an empirical foundation for further optimization of financial risk warning models.