Abnormal Node Classification and Security Detection for Cross-border SME E-commerce Using Blockchain Network Topology Algorithms

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope

Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs). This method allows the model to focus on relevant spatial features while capturing sequential relationships in time-series data. The approach uses attention mechanisms to dynamically weight geographic features and LSTM layers to model temporal patterns, resulting in enhanced predictive accuracy. Evaluations using a real-world multi-modal urban transportation dataset demonstrate the performance of GT-LSTM, with significant reductions of 15% in Mean Absolute Percentage Error (MAPE) and 20% in Root Mean Square Error (RMSE) compared to traditional methods. The model also shows substantial improvements over traditional techniques, including Convolutional LSTM and Graph Convolutional Networks. The effectiveness of GT-LSTM in capturing both spatial and temporal dynamics highlights its potential for real-time urban mobility prediction and provides valuable insights for urban planners, policymakers, and transportation authorities to improve decision-making and system efficiency.

This study introduces the MSTA-GNet (Multi-Scale Spatiotemporal Attention Graph Network), a novel deep learning model which integrates spatiotemporal self-attention mechanisms to model heterogeneous dependencies in traffic networks. The primary objective of the study is to improve existing traffic flow prediction models to address the inadequacies of traditional models in complex big data environments. Key innovations of the MSTA-GNet model include positional encoding and global and local self-attention mechanisms to capture long-term and short-term dependencies. Using the PeMS (Performance Measurement System) dataset, the study conducted performance comparison experiments among various deep learning models, including LSTM (Long Short-Term Memory), GCN (Graph Convolutional Network), DCRNN (Diffusion Convolutional Recurrent Neural Network), STGCN (Spatiotemporal Graph Convolutional Network), STMetaNet (Spatiotemporal Meta Network), and MSTA-GNet. The results showed that MSTA-GNet significantly outperformed other models with improvements of 13.4%, 11.8%, and 9.7% in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics, respectively. Ablation studies further validated the significance of attention mechanisms, feature extraction, convolutional layers, and graph networks, confirming the effectiveness and practical application of MSTA-GNet in traffic flow prediction. This research provides important insights for AI-based congestion management, support for low-carbon traffic networks, and optimization of local traffic operations, demonstrating its significant practical value in intelligent transportation systems.

This work addresses the issue of spatiotemporal relationship modeling in sports dance motion recognition by proposing a spatiotemporal Biomechanics-Enhanced Hypergraph Convolutional Network (BE-HGCN). The model combines biomechanics data with a spatiotemporal refined graph convolutional network to enhance the accuracy and reliability of motion recognition tasks. Experiments are conducted on two publicly available datasets and a self-collected dataset for validation. The findings reveal that the BE-HGCN model is superior to existing classical models in all performance metrics, achieving excellent accuracy, precision, recall, and F1 score. Specifically, on the Nanyang Technological University, Red, Green, Blue + Depth (NTU RGB + D) 60 dataset, the proposed BE-HGCN model achieves an accuracy of 94.7%, precision of 93.8%, recall of 94.3%, and F1 score of 94.0%. On the NTU RGB + D 120 dataset, it demonstrates accuracies of 91.5% (Cross-Subject protocol) and 90.9% (Cross-Setup protocol), with corresponding F1 scores of 92.9% (Cross-Subject protocol) and 92.2% (Cross-Setup protocol). Furthermore, comparative analysis between expert ratings and biomechanics information-based scorings on the self-collected dataset reveals strong consistency with minimal deviations. The root mean square error between the two evaluation methods measures 1.08, while the mean absolute error is 1.01. These results indicate that biomechanics-based scoring can effectively substitute expert ratings in certain applications while maintaining high accuracy and stability. The results of this work offer a new approach to sports dance motion recognition while providing a theoretical foundation for further research on the integration of biomechanics and motion recognition.

This study integrates multi-source satellite-remote-sensed PM2.5 data and provincial vehicle ownership data in China (1998-2023) to construct a high-precision prediction model. We developed a spatiotemporally coupled hybrid model that fuses time series analysis (Prophet) with graph convolutional networks, and incorporates a reinforcement learning framework for dynamic feature optimization. The model achieves an R² of 0.95, with a mean absolute error of 1.97 g/m³ and a root mean square error of 2.67 g/m³ on the 2022-2023 test set, representing a 12% improvement over single models. Analysis reveals significant regional heterogeneity, indicating a stronger PM2.5 suppression effect from new energy vehicles in eastern coastal areas compared to inland regions. This study provides a robust quantitative tool for vehicle pollution control and offers a methodological framework for multi-scale environmental modeling.

Electromigration (EM) is a critical reliability issue in integrated circuits and is becoming increasingly significant as fabrication technology nodes continue to advance. The analysis of the hydrostatic stress, which is paramount in electromigration studies, typically involves solving complex physical equations (partial differential equations, or PDEs in this case), which is time consuming, inefficient and not practical for full-chip EM analysis. In this paper, a novel approach is proposed, conceptualizing circuit interconnect trees as a graph within a graph neural network framework. Using finite element solution software, ground truth hydrostatic stress values were obtained to construct a dataset of interconnected trees with hydrostatic stress values for each node. An improved Graph Convolutional Network (GCN) augmented with edge feature aggregation and attention mechanism was then trained employing the dataset, yielding a model capable of predicting hydrostatic stress values for nodes in an interconnect tree. The results show that our model demonstrated a 15% improvement in the Root Mean Square Error (RMSE) compared to the original GCN model and improved the solution speed greatly compared to traditional finite element software.

With the rapid advancement of technology and profound changes in the economic landscape, artificial intelligence is integrating into the social fabric at an unprecedented speed, greatly promoting a leap in work efficiency and upgrading the quality of life. In particular, the global spread of the COVID-19, instead of curbing the booming trend of China’s crossborder e-commerce, has become a catalyst for its accelerated development, pushed it to the forefront of the global foreign trade arena, and become an indispensable bridge to connect the world market. In this process, the deep integration of artificial intelligence technology has equipped China’s cross-border e-commerce industry with intelligent services, not only optimizing supply chain management, precision marketing, and personalized services, but also greatly improving the efficiency and security of cross-border transactions. This paper attempts to provide some references for the development of China’s cross-border e-commerce market through the impact of COVID-19 on China’s cross-border e-commerce industry, the application of existing AI technology in cross-border e-commerce and its shortcomings.

Many real-world systems can be expressed in temporal networks with nodes playing different roles in structure and function, and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinions or epidemics, predict leading figures in academia, conduct advertisements for various commodities and so on. However, it is rather difficult to identify critical nodes, because the network structure changes over time in temporal networks. In this paper, considering the sequence topological information of temporal networks, a novel and effective learning framework based on the combination of special graph convolutional and long short-term memory network (LSTM) is proposed to identify nodes with the best spreading ability. The special graph convolutional network can embed nodes in each sequential weighted snapshot and LSTM is used to predict the future importance of timing-embedded features. The effectiveness of the approach is evaluated by a weighted Susceptible-Infected-Recovered model. Experimental results on four real-world temporal networks demonstrate that the proposed method outperforms both traditional and deep learning benchmark methods in terms of the Kendall τ coefficient and top k hit rate.

Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency.

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

Instability of the financial system is very bad impact is the loss of public confidence and declining economic growth and community. Besides the cost of economic recovery particularly the financial sector is very large due to the crisis. While the recovery process also runs less in line with expectations. Therefore, the stability of the financial system must be maintained to ensure the public interest. Research methods used is the method of content analysis by describing and analyzing the existing sources to record these data relate to each other the problem studied. Role of Bank Indonesia in maintaining the the stability of the financial system, among others maintain monetary stability, creating a healthy performance of financial institutions, organize and maintain smooth operation of payment systems, research and monitoring and financial system safety net. strategy of Bank Indonesia in maintaining the the stability of the financial system, among others coordination and cooperation, monitoring, crisis prevention and crisis management. There are three reasons for the importance of the stability of the financial system, namely: monetary stability can only be realized with the financial stability, a stable financial system will creating trust and supportive environment for depositors and investors, encourage efficient financial intermediation, encourage the operation of the market and improve the allocation of economic resources. Bank Indonesia and government has implemented its role and strategy in maintaining the stability of the financial system in Indonesia. Keywords : Role, Strategy, Stability of Financial System

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Radar antennas in the open air are affected by dynamic wind loading during azimuthal scanning. In this state, the acquisition and prediction of the aerodynamic load is the focus of the structure design for the antenna. We performed wind tunnel force tests on flat plate antennas with different aspect ratios. The changes in mean, maximum, and root mean square values of the aerodynamic coefficients relative to the antenna aspect ratio and reduced frequency are given for the first time. The effects of the antenna aspect ratio and reduced frequency on aerodynamic characteristics are analyzed. The experimental results indicate that the magnitude of the azimuthal moment is related to the antenna aspect ratio and the reduced frequency. It is worth noting that the antenna aspect ratio has a significant impact on the mean, maximum, and root mean square values of dynamic aerodynamic coefficients. The mean, maximum, and root mean square values of the rolling moment coefficient, azimuth moment coefficient, and pitching moment coefficient monotonously decrease with the increase in aspect ratio. In addition, the influence of reduced frequency on the dynamic aerodynamic coefficient is closely related to the antenna aspect ratio. Under the same antenna aspect ratio, increasing the reduced frequency will increase the mean, maximum, and root mean square values of the aerodynamic coefficient. For flat plate antennas with different aspect ratios, at the condition of increasing the aspect ratios, the mean, maximum, and root mean square values of aerodynamic coefficients do not change significantly with the change of reduced frequency.