This paper presents a comprehensive study leveraging Support Vector Machine (SVM) regression and Principal Component Regression (PCR) to analyze carbon dioxide emissions in a global dataset of 62 countries and their dependence on idiosyncratic, country-specific parameters. The objective is to understand the factors contributing to carbon dioxide emissions and identify the most predictive elements. The analysis provides country-specific emission estimates, highlighting diverse national trajectories and pinpointing areas for targeted interventions in climate change mitigation, sustainable development, and the growing carbon credit markets and green finance sector. The study aims to support policymaking with accurate representations of carbon dioxide emissions, offering nuanced information for formulating effective strategies to address climate change while informing initiatives related to carbon trading and environmentally sustainable investments.

Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems.

Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB.

This paper first analyzes the accident data in China's chemical industry from 2018 to 2022, revealing that although the number of accidents and the mortality rate have decreased, the accident rate remains high, highlighting the urgency of strengthening safety supervision methods. To address this, a dynamic supervision indicator system covering multiple key dimensions such as production load, alarm conditions, operation quantity, and maintenance status has been constructed. This indicator system can capture key factors related to safety risks through multidimensional monitoring of enterprises' daily work safety status. The study uses the entropy weight method to objectively weight each indicator and employs K-means and DBSCAN clustering algorithms to classify enterprise data. While the entropy weight method provides an intuitional basis for feature weighting, further optimization is needed to improve sample discrimination. The K-means algorithm effectively distinguishes different risk states of enterprises, such as shutdown and full-load production, and reasonably classifies enterprises into three categories: special control, key attention, and general supervision. In contrast, the DBSCAN algorithm performs better in identifying high-risk enterprises but is limited in finely distinguishing between special control and key attention enterprises. The final results show that the K-means algorithm performs best when combined with the indicator system for dynamic supervision, particularly in accurately identifying low-risk and generally supervised enterprises. By establishing a comprehensive dynamic supervision indicator system and applying machine learning algorithms, this paper provides a new technological path for safety supervision of hazardous chemical enterprises, demonstrating the potential of intelligent supervision in enhancing accident prevention and risk control levels.