Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a ship trajectory clustering method based on ship trajectory resampling and enhanced BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed. The method has been tested using 764,393 AIS trajectory points of 13,845 ships in the waters of the Taiwan Strait of China, and 832 ship trajectories have been generated and clustered to obtain 172 classes of ship trajectory line clusters among 40 port pairs. The experimental results show that the proposed method has exhibited a good clustering effect on ship trajectories. Compared with the existing ship trajectory clustering methods, the proposed method can more efficiently detect and identify differences between trajectories with largely similar spatial distribution characteristics, so as to obtain legitimate clustering results. In addition, this study has constructed the main ship navigation routes between ports based on the extracted ship trajectory line clusters, and the constructed main routes are directional, refined, and rich in content compared with the existing ship routes. This research provides theoretical and technical support for ship route planning and maritime traffic management.

The serious problem of soil erosion not only has a profound impact on people’s lives but also results in a series of ecological and environmental challenges. To determine the impact of changes in land use type on soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains, this study commences by employing the InVEST-SDR (integrated valuation of ecosystem services and tradeoffs–sediment delivery ratio) model to calculate soil erosion levels spanning from 2000 to 2020. Subsequently, it forecasts land use and land cover (LULC) conditions for the year 2030 under three scenarios: Q1 (natural development), Q2 (ecological protection), and Q3 (economic priority). This projection is accomplished through the integration of a coupled Markov chain and multi-objective planning model (MOP) alongside patch-generating land use simulation (PLUS) models. Ultimately, based on these outcomes, the study predicts soil erosion levels for the year 2030. There has been a consistent decline in soil erosion from 2000 to 2020 with high-intensity erosion concentrated in the Tianshan Mountain region. Grasslands, glaciers, and permafrost are identified as the most erosion-prone land types in the study area, with forests exhibiting the highest capacity for soil retention. Converting from grassland and barren land to forest within the same area results in a substantial reduction in soil erosion, specifically by 27.3% and 46.3%, respectively. Furthermore, the transformation from barren land to grassland also leads to a noteworthy 19% decrease in soil erosion. Over the past two decades, the study area has witnessed a significant decline in the area of grasslands, with a notable shift towards barren and impervious surfaces due to economic development and mining activities. The three predicted scenarios depict significant expansion towards barren land, grassland, and impervious area, respectively. Soil erosion decreases under different shared socio-economic pathway (SSP) scenarios relative to 2020. There is an increase in soil erosion in the Q1 scenario and in the Q3 scenario, whereas the amount of soil erosion in the Q2 scenario exhibits a continued decrease when only the effect of land change on soil erosion is considered. Persistently rapid economic development can exacerbate soil erosion problems, underscoring the need to find a balance between economic growth and ecological conservation. As economic expansion slows down, greater emphasis should be placed on environmental protection to maintain ecological stability.

Organic polymer materials, as the most abundantly produced materials, possess a flammable nature, making them potential hazards to human casualties and property losses. Target polymer design is still hindered due to the lack of a scientific foundation. Herein, we present a robust, generalizable, yet intelligent polymer discovery framework, which synergizes diverse capabilities, including the in situ burning analyzer, virtual reaction generator, and material genomic model, to achieve results that surpass the sum of individual parts. Notably, the high-throughput analyzer created for the first time, grounded in multiple spectroscopic principles, enables in situ capturing of massive combustion intermediates; then, the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information; further, the proposed feature engineering tool, which embedded both polymer hierarchical structures and massive intermediate data, develops the generalizable genomic model with excellent universality (adapting over 20 kinds of polymers) and high accuracy (88.8%), succeeding discovering series of novel polymers. This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.

Industry is widely valued as an important contributor to carbon emissions. Therefore, it is of great significance to analyze the industrial carbon emissions (ICE) in Guangdong, the strongest industrial province in China. We have adopted the carbon emission accounting model and standard deviational ellipse analysis model to analyze the temporal and spatial characteristics and evolution trends of the industry carbon emission amount and intensity in Guangdong from 1998 to 2013. The study results include: (1) Due to the rapid development of industry, Guangdong’s ICE showed a steady growth trend; (2) The distribution characteristics of ICE were characterized by the trend of taking the Pearl River Delta (PRD) region as the center and gradually spreading to the surrounding areas. From the perspective of industrial sectors, it can be divided into steady growth type, fluctuant growth type, basically stable type, and decrease type; (3) The spatial pattern of the ICE in Guangdong is basically the same as that of the total industrial output value, that is, the southwest-northeast pattern. This work is helpful for China’s carbon peak, especially for the formulation of industrial carbon peak policy and the sustainable development of the environment.

The microstructure morphology, static magnetic properties, and electromagnetic absorption characteristics of nano FexCoNi alloy particles prepared by chemical liquid deposition with five different Fe content levels are investigated in this paper. The results show that spherical FexCoNi alloy particles with an average particle size of about 100–200 nm and a face-centered cubic crystal structure were obtained. All five samples exhibited soft magnetic behavior, with the saturation magnetization intensity showing an increasing-then-decreasing trend with increasing Fe content, peaking at 141.8 emu/g for Fe content x = 1.0. The dielectric constants (real and imaginary parts) of the prepared alloy particles exhibit significant differences with respect to the variation of Fe content, while the changes in the real and imaginary parts of the magnetic permeability show less pronounced effects with increasing Fe content. As the electromagnetic wave frequency increases, the real parts of the dielectric constants for all composites show minimal fluctuations, and the real parts of the magnetic permeability exhibit a decreasing trend. Moreover, the imaginary parts of the dielectric constants and magnetic permeability show an increasing followed by a decreasing trend as the frequency rises. The material with Fe content x = 1 demonstrated optimal dielectric loss performance and relatively excellent magnetic loss performance, with a sample thickness of 1.9 mm exhibiting the highest reflection loss (RLmax) of −24.2 dB and an effective absorption bandwidth of 4.48 GHz.