The “tragedy paradox” of music, avoiding experiencing negative emotions but enjoying the sadness portrayed in music, has attracted a great deal of academic attention in recent decades. Combining experimental psychology research methods and machine learning techniques, this study (a) investigated the effects of gender and Big Five personality factors on the preference for sad music in the Chinese social environment and (b) constructed sad music preference prediction models using audio features and individual features as inputs. Statistical analysis found that males have a greater preference for sad music than females do, and that gender and the extraversion factor are involved in significant two-way interactions. The best-performing random forest regression shows a low predictive effect on the preference for sad music ( $R^{2} =0.138$ ), providing references for music recommendation systems. Finally, the importance-based model interpretation feature reveals that, in addition to the same music inputs (audio features), the perceived relaxation and happiness of music play an important role in the prediction of sad music preferences.

A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.

Urban arterial traffic coordination control has attracted much attention in smart city construction process. To achieve optimal signal timing, many studies have attempted to adjust green splits of a cycle time according to the distance between road intersections. However, existing green wave traffic control systems usually require a sophisticated calculation that depend upon the stability of vehicle speed and traffic flow, which can lead to weak robustness. Therefore, this paper proposes two novel approaches to control arterial traffic coordination with the help of artificial intelligence: DDPG-BAND and ES-BAND. DDPG-BAND has two stages: a coarse-tuning stage reduces the blocking coefficient, and a fine-tuning stage optimizes the traffic evaluation index. ES-BAND introduces the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), a scalable alternative to reinforcement learning, into signal timing. Different traffic variables are adopted as parameters to search for the optimal value by the CMA-ES. To evaluate the feasibility and effectiveness of our approaches, we import real traffic flow data from Zhongshan Road, Ningbo, Zhejiang Province, China, into a traffic environment simulator for training and then conduct a series of experiments. The results show that ES-BAND outperforms the traditional methods in terms of better convergence, lower journey time, fewer stops, and more throughput.

Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, but previous studies have seldom characterized the heavy-tailed distribution of street connectivities from a fractal perspective. The long-tail distribution of street connectivities can be fractal under the new, third definition: a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The number of recurred scaling patterns of far more less-connected streets than well-connected ones greatly helps in measuring the scaling hierarchy of a street network. Moreover, it enables us to examine the potential fractality of urban street networks at the national scale. In this connection, the present study aims to contribute to urban morphology in China through the investigation of the ubiquity of fractal cities from the lens of street networks. To do this, we generate hundreds of thousands of natural streets from about 4.5 million street segments over 298 Chinese cities and adopted power-law detection as well as three fractal metrics that emerged from the third definition of fractal. The results show that almost all cities bear a fractal structure in terms of street connectivities. Furthermore, our multiple regression analysis suggests that the fractality of street networks is positively correlated with urban socioeconomic status and negatively correlated with energy consumption. Therefore, the fractal metrics can be a useful supplement to traditional street-network configuration measures such as street lengths.

Ground‐dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground‐dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground‐dwelling invertebrates has not been strongly considered during survey design. In this study, data from six ICTs with two shooting areas (A1, 38.48 cm²; A2, 400 cm²) were used to investigate ground‐dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20: 00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1420 h, invertebrates within 9 taxa, 2447 individuals, and 112,909 ind./m² were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground‐dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground‐dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground‐dwelling invertebrates. This study further provides an automatic tool and high‐quality data for biodiversity monitoring and protection in farmlands.

Theft is an inevitable problem in the context of urbanization and poses a challenge to people’s lives and social stability. The study of theft and criminal behavior using spatiotemporal, big, demographic, and neighborhood data is important for guiding security prevention and control. In this study, we analyzed the theft frequency and location characteristics of the study area through mathematical statistics and hot spot analysis methods to discover the spatiotemporal divergence characteristics of theft in the study area during the pre-COVID-19 and COVID-19 periods. We detected the spatial variation pattern of the regression coefficients of the local areas of thefts in Haining City by modeling the influencing factors using the geographically weighted regression (GWR) analysis method. The results explained the relationship between theft and the influencing factors and showed that the regression coefficients had both positive and negative values in the pre-COVID-19 and COVID-19 periods, indicating that the spatial distribution of theft in urban areas of Haining City was not smooth. Factors related to life and work indicated densely populated areas had increased theft, and theft was negatively correlated with factors related to COVID-19. The other influencing factors were different in terms of their spatial distributions. Therefore, in terms of police prevention and control, video surveillance and police patrols need to be deployed in a focused manner to increase their inhibiting effect on theft according to the different effects of influencing factors during the pre-COVID-19 and COVID-19 periods.