Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Optimization of desert lake information extraction from remote sensing images using cellular automata

Abstract Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintaining the regional ecological environment. However, large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes. Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification. However, the spatial resolution of these images often leads to mixed pixels near water boundaries, affecting extraction precision. Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel, making it difficult to accurately describe the spatial distribution. In this paper, the cellular automata (CA) model was adopted in order to realize lake information extraction at a sub-pixel level. A mining area in Shenmu City, Shaanxi Province, China is selected as the research region, using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference. First, water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares (FCLS) method. Second, the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation. Lastly, the process was implemented using Python and IDL, with the ArcGIS and ENVI software being used for validation. The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy. The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area, and is therefore suitable for desert lake monitoring in mining areas

Dynamic Reclamation Methods for Subsidence Land in the Mining Area with High Underground Water Level

Dynamic reclamation for subsidence land is a development trend of ecological restoration in the mining area. It emphasizes that treatments should be taken during or before land damage to control the ecological degradation. Because dynamic reclamation is a complex problem, there still exist many challenges that have to be studied for practical application. In this paper, taking subsidence land in the area with high underground water level as an example, according to the requirements of land use and economic development, key techniques for dynamic reclamation are analyzed based on mine subsidence theories. The research proposes a general mode for dynamic reclamation, and provides a method for calculating the control time of soil excavation, and puts forth an optimization thought for land use, then establishes a procedure for soil reconstruction. The results show that dynamic reclamation can reduce the cost and shorten the land waste time, and enhance the sustainable development ability of the mining area

Dynamic simulation for the process of mining subsidence based on cellular automata model

Under the background of the ecological civilization era, rapidly obtaining coal mining information, timely assessing the ecological environmental impacts, and drafting different management and protection measures in advance to enhance the capacity of green mine construction have become the urgent technical problems to be solved at present. Simulating and analyzing mining subsidence is the foundation for a land reclamation plan. The Cellular Automata (CA) model provides a new tool for simulating the evolution of mining subsidence. This paper takes a mine in East China as a research area, analyses the methods and measures for developing a model of mining subsidence based on the theories of CA and mining technology, then discusses the results of simulation from different aspects. Through comparative analysis, it can be found that the predicted result is well consonant with the observation data. The CA model can simulate complex systems. The system of mining subsidence evolution CA is developed with the support of ArcGIS and Python, which can help to realize data management, visualization, and spatial analysis. The dynamic evolution of subsidence provides a basis for constructing a reclamation program. The research results show that the research methods and techniques adopted in this paper are feasible for the dynamic mining subsidence, and the work will continue to do in the future to help the construction of ecological civilization in mining areas

Discrimination of Senescent Vegetation Cover from Landsat-8 OLI Imagery by Spectral Unmixing in the Northern Mixed Grasslands

The mixed grasslands of North America are ecosystems with a high volume of dead biomass. This characteristic underlies key ecosystem features such as the rate of carbon and nutrient uptake, heat flux exchange between the surface and the atmosphere, and wildlife habitat. Senescent vegetation is an important forage resource for grazing animals and is related to natural fire frequency and intensity. Therefore, quantitative estimation of photosynthetic vegetation (PV), senescent vegetation (NPV), and bare soil (BS) fraction is important for natural resource management. The authors propose an approach for extracting PV, NPV, and BS endmembers from the normalized difference vegetation index–dead fuel index (NDVI–DFI) plane by using the Landsat-8 imagery. The constrained linear spectral unmixing model was applied to discriminate NPV, PV, and BS using original spectral bands, NDVI–DFI indices, and original spectral plus NDVI and DFI indices. As a comparison, the traditional NDVI–SWIR32 was also investigated. Results showed that the DFI performed better than the SWIR32 to predict NPV from spectral unmixing. Index selection has a significant effect on NPV and BS cover fraction estimation. Choice of equation setup has a significant effect on the PV estimation. The methods proposed here can be applied to grassland ecosystems across the northern mixed grasslands region

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Oral pH value predicts the incidence of radiotherapy related caries in nasopharyngeal carcinoma patients

Abstract Radiotherapy-related caries is a complication of radiotherapy for nasopharyngeal carcinoma; however, factors influencing the occurrence, accurate prediction of onset, and protective factors of radiotherapy-related caries remain unclear. This study analyzed risk factors, disease predictors, and protective factors for radiotherapy-related caries in nasopharyngeal carcinoma. This prospective study included 138 nasopharyngeal carcinoma patients receiving radical radiotherapy at our hospital during June 2012–December 2016 and were followed up for dental caries. Patients’ clinical data on radiotherapy were collected, dynamic monitoring was performed to assess changes in oral pH values, and a questionnaire survey was administered to collect patients’ lifestyle habits. Time-dependent cox regression trees, event-free Kaplan–Meier curve, Mann–Whitely U test were used to analysis the results. The median follow-up time was 30 (12–60) months. Radiotherapy-related caries occurred in 28 cases (20.3%). Univariate analyses showed that radiotherapy-related caries was associated with patient’s age, oral saliva pH value, green tea consumption, and radiation dose to sublingual glands, but not with the radiation dose to the parotid and submandibular glands. Multivariate analysis showed that oral saliva pH value [hazard ratio (HR) = 0.390, 95% confidence interval = 0.204–0.746] was an independent prognostic factor for radiotherapy-related caries. Patients with oral saliva pH values ≤ 5.3 in the 9th month after radiotherapy represented a significantly higher risks for radiotherapy-related caries (p < 0.001). Green tea consumption was associated with the occurrence of radiotherapy-related caries, and oral saliva pH values could predict the occurrence of radiotherapy-related caries. Limiting radiation doses to sublingual glands can reduce the occurrence of radiotherapy-related caries

A shear-thinning, ROS-scavenging hydrogel combined with dental pulp stem cells promotes spinal cord repair by inhibiting ferroptosis

Spinal cord injury (SCI) is a serious clinical disease. Due to the deformability and fragility of the spinal cord, overly rigid hydrogels cannot be used to treat SCI. Hence, we used TPA and Laponite to develop a hydrogel with shear-thinning ability. This hydrogel exhibits good deformation, allowing it to match the physical properties of the spinal cord; additionally, this hydrogel scavenges ROS well, allowing it to inhibit the lipid peroxidation caused by ferroptosis. According to the in vivo studies, the TPA@Laponite hydrogel could synergistically inhibit ferroptosis by improving vascular function and regulating iron metabolism. In addition, dental pulp stem cells (DPSCs) were introduced into the TPA@Laponite hydrogel to regulate the ratios of excitatory and inhibitory synapses. It was shown that this combination biomaterial effectively reduced muscle spasms and promoted recovery from SCI