Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

Dialogue summarization aims to distill a given conversation into a brief and focused summary. The challenge lies in the diverse perspectives of participants and the frequent shifts in topics throughout the dialogue. These factors make it difficult to extract and highlight the most significant information effectively. To tackle this challenge, we introduce a novel topic segmentation method that assigns distinct topics to dialogue segments while accounting for their importance and influence within the entire conversation. Our method's performance has been validated on two public benchmark datasets, CSDS and SAMSUM, demonstrating significant improvements in accuracy and coherence. The results show that our approach not only captures the essential content of dialogues more precisely but also enhances the overall quality and coverage of the summaries. This work provides a fresh approach to dialogue summarization and highlights its potential for practical application.

Urban space exhibits marked heterogeneity in both form and function, yet how urban functions align with multilevel street structures remains insufficiently understood. This study investigates the coupling between street hierarchy and urban functions in Shenzhen through two complementary representations: (1) the relationship between street connectivity and POIs located near each street, and (2) the relationship between street nodes and POIs contained within street-node-based hotspots. Both datasets were hierarchically partitioned using head/tail breaks to reveal intrinsic scaling structures. Power-law detection shows that natural streets and hotspot clusters follow heavy-tailed distributions, forming nested living structures. The exponents for node- and POI-based clusters fall within the typical range of 2.0–2.2, whereas exponents for street connectivity and street-based POIs are higher, indicating stronger heterogeneity. Correlation analyses reveal consistent positive associations between POIs and street connectivity across all levels, with the strongest relationships in mid- to upper-level substructures. Toward finer levels, correlations weaken and become increasingly nonlinear, reflecting growing spatial irregularity. These findings demonstrate that Shenzhen’s urban functions are coherently organized along its street hierarchy, highlighting the fundamental role of multilevel street configurations in shaping functional spatial patterns.

distribution network voltage is the main index of power quality, which has a direct impact on the safety and stability of distribution network and the safe operation of power equipment, power loss of transmission lines, industrial and agricultural safety production and people's daily electricity consumption. Based on the PSASP simulation software, the real-time simulation analysis of the voltage in the Nailin area is carried out. During the irrigation period, the simulation results show that the low voltage problem is serious, and the daily load fluctuation is obvious, so it is necessary to adopt the control measures which can adjust the voltage in both directions flexibly. After comparison, the line voltage regulator and SVG are proposed to be used. After low voltage control, the simulation results show that the low voltage problem is obviously improved . Finally, the management measures to improve the low voltage problem are put forward.