With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source. In our work, we used RGB frames from the NTU RGB+D dataset as the sole data source. From these frames, we extracted 2D skeleton coordinates and optical flow frames using pre-trained models. We evaluated our multi-level fusion approach with EfficientNet-B7 as a case study, and our methods demonstrated significant improvement, achieving 91.5% in NTU RGB+D 60 dataset accuracy compared to single-modality and single-view models. Despite their simplicity, our methods are also comparable to other state-of-the-art approaches.

Purpose: This research aims to examine how food streamer professionalism and social presence impact the trust of consumers and buying intention in food live-streaming e-commerce according to the stimulus-organism-response model. Furthermore, it explores the mediating role of trust in these relationships and the moderating role of flow experience in the relationship between trust and buying intention. Design/methodology/approach: This study collected 231 valid responses from food live streaming viewers in China. The analytical procedures were performed using PLS-SEM. Findings: The findings indicate that social presence and professionalism both significantly enhance consumer trust, which in turn mediates their effects on purchase intention. Surprisingly, flow experience negatively moderates the relationship between trust and buying intention. Practical implications: These findings offer valuable insights for food live streamers, organizations, platforms and policymakers to enhance trust-building strategies, ensure authenticity and optimize their operations in food live streaming e-commerce. Originality/value: This study offers new theoretical perspectives by integrating multiple disciplines to explore trust mechanisms in food live streaming e-commerce. This study reveals how social presence and professionalism build trust and demonstrates the moderating role of flow experience in buying decisions.

Hubs are strategic locations that function as central nodes within clusters of cities, playing a pivotal role in the distribution of goods, services, and connectivity. Identifying these vital hubs—through analyzing influential locations within transportation networks—is essential for effective urban planning, logistics optimization, and enhancing infrastructure resilience. This task becomes even more crucial in developing and less-developed countries, where such hubs can significantly accelerate urban growth and drive economic development. However, existing hub identification approaches face notable limitations. Traditional centrality measures often yield low variance in node scores, making it difficult to distinguish truly influential nodes. Moreover, these methods typically rely solely on either local metrics or global network structures, limiting their effectiveness. To address these challenges, we propose a novel method called Hybrid Community-based Gravity Centrality (HCGC), which integrates local influence measures, community detection, and gravity-based modeling to more effectively identify influential nodes in complex networks. Through extensive experiments, we demonstrate that HCGC consistently outperforms existing methods in terms of spreading ability across varying truncation radii. To further validate our approach, we introduce ThaiNet, a newly constructed real-world transportation network dataset. The results show that HCGC not only preserves the strengths of traditional local approaches but also captures broader structural patterns, making it a powerful and practical tool for real-world network analysis.