Search Results (4)

This study systematically analyzes the composition and microstructure of Neolithic pottery unearthed from the Dazhuzhuang, Likou, and Biting Sites in the Yongcheng District using techniques such as X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), infrared spectroscopy (IR), and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS). The results show that although the raw materials for pottery at the three sites were likely sourced from nearby ancient soil layers, significant differences in chemical composition and manufacturing techniques are evident. Pottery from the Dazhuzhuang Site is mainly composed of argillaceous gray pottery, with relatively loose raw material selection and a wide fluctuation in SiO₂ content (64.98–71.07%), reflecting diversity in raw material sources. At the Likou Site, argillaceous black pottery predominates, characterized by higher Al₂O₃ content (17.78%) and significant fluctuations in CaO content (1.46–2.22%), suggesting the addition of calcareous fluxes and the adoption of standardized manufacturing techniques. Pottery from the Biting Site mainly consists of argillaceous gray pottery, showing higher Al₂O₃ content (17.36%), stable SiO₂ content (65.19–69.01%), and the lowest CaO content (0.84–1.81%). The microstructural analysis further reveals that the black pottery (from the Likou Site) displays dense vitrified regions and localized iron enrichment. In contrast, the gray pottery (from the Dazhuzhuang and Biting Sites) shows clay platelet structures and vessel-type-specific differences in porosity. This research provides important scientific evidence for understanding raw material selection, manufacturing techniques, and regional cultural interactions in the Yongcheng area during the Longshan Culture period. Full article

The destruction of arable land caused by coal mining in coal grain compound areas is a major bottleneck restricting grain production increase. The spatiotemporal correlation between the decline in cultivated land quality and crop growth deterioration due to mining subsidence still needs to be clarified. This study employed the CDR AVHRR NDVI dataset and applied correlation and trend analysis methods to extract vegetation cover information from 1982 to 2022. It also explored the relationships between vegetation cover and temperature and precipitation. The study found the following: (1) Over the past 41 years, the NDVI in the study area showed a significant upward trend. Specifically, the average annual NDVI growth rate in the mining area was 51.85%, while the corresponding growth rates for the 10 km buffer area, 20 km buffer area, and check area (CK) were 65.91%, 65.86%, and 68.09%, respectively. The start of the growing season (SOS) for winter wheat in the mining area and control area advanced by 49 ± 1.5 days and 65 ± 1.5 days, respectively, while the length of the growing season (LOS) extended by 59 ± 1.5 days and 72 ± 1.5 days, respectively. For summer maize, the SOS advanced by 11 ± 1.5 days and 15 ± 1.5 days, respectively, and the LOS extended by 17 ± 1.5 days and 19 ± 1.5 days, respectively. The study area exhibited a significant positive correlation between the NDVI and temperature. Specifically, the correlation coefficient for the mining area was 0.6865 (p < 0.01); for the 10 km buffer zone, it was 0.5937 (p < 0.01), for the 20 km buffer zone, it was 0.6775 (p < 0.01), and for the control check area (CK), it was 0.6591 (p < 0.01). The results of this study can provide data support for the collaborative rehabilitation of and source reduction in coal grain compound areas, as well as for the restoration of damaged farmland. Full article

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas. Full article

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing. Full article