Populus simonii is an important tree in the genus Populus, widely distributed in the Northern Hemisphere and having a long cultivation history. Although this species has ecologically and economically important values, its genome sequence is currently not available, hindering the development of new varieties with wider adaptive and commercial traits. Here, we report a chromosome-level genome assembly of P. simonii using PacBio long-read sequencing data aided by Illumina paired-end reads and related genetic linkage maps. The assembly is 441.38 Mb in length and contain 686 contigs with a contig N50 of 1.94 Mb. With the linkage maps, 336 contigs were successfully anchored into 19 pseudochromosomes, accounting for 90.2% of the assembled genome size. Genomic integrity assessment showed that 1,347 (97.9%) of the 1,375 genes conserved among all embryophytes can be found in the P. simonii assembly. Genomic repeat analysis revealed that 41.47% of the P. simonii genome is composed of repetitive elements, of which 40.17% contained interspersed repeats. A total of 45,459 genes were predicted from the P. simonii genome sequence and 39,833 (87.6%) of the genes were annotated with one or more related functions. Phylogenetic analysis indicated that P. simonii and Populus trichocarpa should be placed in different sections, contrary to the previous classification according to morphology. The genome assembly not only provides an important genetic resource for the comparative and functional genomics of different Populus species, but also furnishes one of the closest reference sequences for identifying genomic variants in an F₁ hybrid population derived by crossing P. simonii with other Populus species.

CO₂ emissions from fossil energy have caused global climate problems and threatened human survival. However, there are few studies on the spatiotemporal distribution and driving factors of carbon emissions. This paper takes the Yangtze River Delta (YRD) urban agglomeration as the research object and analyzes the spatiotemporal heterogeneity of carbon dioxide emissions and their driving factors from 2000 to 2017. First, a series of preprocessing, such as resample, interpolation, and image clipping, are conducted on the CO₂ emission data and nighttime light remote sensing images. Second, the dynamic time wrapping (DTW) and hierarchical clustering algorithms were involved in manipulating the CO₂ emission data. Consequently, the cities’ and CO₂ emissions’ time series were classified into four categories and three stages separately. Finally, the geographical detector model (GDM) and geographical and temporal weighted regression (GTWR) are coupled to evaluate the spatiotemporal heterogeneity and quantify the driving factors. The results show the following: (1) The spatiotemporal distribution of CO₂ emissions has spatial consistency from 2000 to 2017. High-emission areas are concentrated in economically developed areas such as Shanghai, Suzhou, and Wuxi. The results are consistent with previous research. (2) Regional aggregation is a revealed new trend. CO₂ emissions in the target urban areas are gradually converging into economic center cities and diverse class cities, e.g., Shanghai and Ningbo. (3) In cities of different economic development levels, the driving factors of CO₂ emissions are different. The secondary sector and urban infrastructure dominate in the early stages of developed cities. On top of that, the influence of the tertiary industry is more significant in the later development stages. According to the results, in the urban development process, humans should not only pursue the increase in speed but also pay attention to the negative impact of the economic development process on the ecological environment. Besides, since the spatiotemporal characteristics and dominant factors of urban carbon emissions are different in each stage of development, the formulation of carbon reduction policies should be associated with urban features.

Accurate detection of oil and gas (O&G) facilities in high-resolution remote sensing imagery is critical for infrastructure surveillance and sustainable resource management, yet conventional detectors struggle with severe class imbalance, extreme scale variation, and arbitrary orientation. In this work, we propose OGF Oriented R-CNN (Oil and Gas Facility Detection Oriented Region-based Convolutional Neural Network), an enhanced oriented detection model derived from Oriented R-CNN that integrates three improvements: (1) O&G Loss Function, (2) Class-Aware Hard Example Mining (CAHEM) module, and (3) Feature Pyramid Network with Feature Enhancement Attention (FPNFEA). Working in synergy, they resolve the coupled challenges more effectively than any standalone fix and do so without relying on rigid one-to-one matching between modules and individual issues. Evaluated on the O&G facility dataset comprising 3039 high-resolution images annotated with rotated bounding boxes across three classes (well sites: 3006, industrial and mining lands: 692, drilling: 244), OGF Oriented R-CNN achieves a mean average precision (mAP) of 82.9%, outperforming seven state-of-the-art (SOTA) models by margins of up to 27.6 percentage points (pp) and delivering a cumulative gain of +10.5 pp over Oriented R-CNN.