Taiyuan Normal University

With the rapid advancement and widespread deployment of artificial intelligence , issues related to ethics, trust, and security have become increasingly prominent, posing significant challenges to its sustainable development. This paper aims to systematically investigate these intertwined challenges by constructing a structured analytical framework encompassing three core dimensions: ethics, trust, and security.Methodologically, the study analyzes the evolutionary trajectory and application contexts of AI, and synthesizes key risk patterns and governance concerns. It examines major ethical issues, including algorithmic bias, data privacy, responsibility attribution, and value alignment, and further explores the underlying tension between technological rationality and social values.In addition, the paper proposes a multi-level trust formation mechanism based on technological reliability, institutional assurance, and public cognition, emphasizing that trust emerges from the joint interaction of technical systems and governance structures. At the security level, it highlights the complexity and cross-domain nature of AI risks, and advocates for a full life-cycle governance framework integrating risk assessment, technical auditing, and continuous supervision.The study’s key contribution lies in integrating ethical, trust, and security perspectives into a unified analytical framework, and in proposing a collaborative governance approach involving governments, enterprises, and international stakeholders.

This study analyzes ground-based observations and multi-source remote sensing data from eight dust storm events in 2024 at two sites: the Tazhong (TZ) station in the Taklamakan Desert interior and the Xiaotang (XT) station on its northern margin, systematically investigates the interrelationships among dust particle size, friction velocity (U*), and dust flux, and evaluates the applicability of remote sensing data in dust monitoring. The results indicate that particle size significantly influences both horizontal fluxes (Q) and vertical dust fluxes (F). Fine particles (d[0.5]) enhance surface dust flux, while coarse particles (D[4,3]-due to their greater gravitational settling-are less capable of sustained suspension, limiting their long-distance transport. A positive correlation exists between friction velocity (U*) and Q, whereas its impact on F is weaker, suggesting that vertical transport is regulated primarily by particle size, gravitational settling, and turbulent structures. Regarding remote sensing data, MODIS Aerosol Optical Depth (AOD) shows strong consistency with ground-based dust flux measurements, especially at the Xiaotang (XT) station, where AOD closely follows the variation trends of both Q and F. This reflects the effectiveness of remote sensing data in capturing changes in dust activity. Additionally, the Aerosol Absorbing Index (AAI) from Sentinel-5P exhibits a highly significant positive correlation with ground-level dust concentrations, effectively reflecting the vertical structure of dust events. This research provides valuable data support and theoretical foundation for dust warning systems and desertification control projects.

This paper investigates the strong convergence of jump-diffusion processes with Markovian switching using the truncated Euler–Maruyama (TEM) method. Under the assumption that the drift and diffusion coefficients satisfy a Khasminskii-type condition and the jump coefficient meets a linear growth condition, we derive the convergence rate. Furthermore, we demonstrate that the TEM method effectively preserves both the mean square stability and the asymptotic boundedness of the underlying jump-diffusion process. A case study involving music signals is provided to illustrate the theoretical findings.

To address the critical industry pain points in streaming data scenarios across industrial production, intelligent transportation and other related fields—namely continuous performance degradation of classification models induced by concept drift, the absence of dedicated closed-loop drift adaptation processing capability in mainstream stream processing systems, poor engineering implementability of specialized tools, and the inherent difficulty in balancing real-time performance and classification accuracy—this paper designs and implements a Dynamic Adaptive Concept Drift Processing System (DACPS) for streaming data classification. The system adopts a hierarchical modular architecture, integrating five core functional modules: streaming data preprocessing, real-time concept drift detection, dynamic sample optimization, incremental model training, and full-process management and control. This architecture enables fully automated and adaptive processing for the end-to-end workflow of streaming data classification and prediction. Extensive multi-dimensional functional tests, specialized performance tests, and scenario-based validation demonstrate that the system can stably adapt to diverse types of concept drift scenarios, and meets all design specifications in terms of classification accuracy, convergence speed, operating efficiency, and resource occupation control. Meanwhile, the system is equipped with an accessible visual interactive interface, making it widely adaptable to the streaming data classification and processing requirements across multiple application domains.

Cardiovascular disease has become a major global public health concern due to its high mortality rate. As a core non-invasive diagnostic tool, electrocardiogram (ECG) automatic classification is critical for the early screening of heart diseases. To address the problems of low efficiency in manual diagnosis, the inability of single deep learning models to simultaneously capture morphological and temporal features in ECG classification, and insufficient robustness under class-imbalanced scenarios, this paper proposes a deep ensemble learning algorithm named CBR-Stacking. The algorithm uses 1D-CNN, CNN-BiLSTM, and ResNet1D as heterogeneous base models to capture local morphological features, local-temporal fused features, and deep residual features of ECG signals, respectively. Following the Stacking ensemble strategy, the outputs of base models are taken as meta-features and fed into a logistic regression meta-classifier to obtain the final classification decision. Meanwhile, Z-score normalization and class weight method are adopted for data preprocessing to alleviate amplitude differences and class imbalance. Experimental results on the PTB myocardial infarction dataset and MIT-BIH arrhythmia dataset show that the CBR-Stacking model achieves accuracies of 99.55% and 98.86% in binary classification and five-class classification tasks, respectively, outperforming single base models and traditional machine learning methods in all evaluation metrics. Moreover, the time consumption of the model ensemble stage accounts for less than 0.1%, ensuring stable running efficiency. The proposed model effectively integrates the complementary advantages of multi-architecture deep learning models, improving the accuracy, robustness, and generalization ability of ECG classification, and provides an efficient and reliable solution for intelligent auxiliary diagnosis of electrocardiograms.

The Practical Byzantine Fault-Tolerant (PBFT) consensus algorithm faces challenges such as random master node selection, high communication overhead, and a lack of incentive and penalty mechanisms, which undermine its efficiency and security in large-scale network environments. To address these issues, this paper proposes an improved PBFT consensus algorithm, RF-PBFT, which integrates a random forest-based node grouping mechanism with a dynamic reputation system. The algorithm leverages key behavioral data of nodes during the consensus process to train a random forest model, partitioning nodes into a consensus set and a candidate set. Nodes with superior predictive performance are selected to participate in the consensus process, thereby reducing redundant communication overhead. Furthermore, a differentiated reputation mechanism is established for the consensus group and the candidate group to encourage positive behavior and deter malicious actions. The master node is elected through a combination of prediction results and voting, enhancing system reliability and security. Simulation results demonstrate that, compared with traditional PBFT and other state-of-the-art variants, RF-PBFT significantly reduces communication overhead, decreases consensus latency, and improves throughput, validating its effectiveness in multi-node blockchain systems.