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Accurately identifying the patterns of evolution in farmland plays an important role in optimizing farmland management. The aim of this study is to classify the evolution patterns of farmland in China and explore related mechanisms, providing a reference for constructing a systematic farmland management plan. Using land cover data from five periods in China, nine types of farmland evolution process are described and identified based on landscape process models. We analyzed these processes’ spatiotemporal dynamics and, by examining regional variations, achieved a zoned mapping of China’s farmland evolution. In this study, we combined natural and socioeconomic factors to analyze the mechanisms driving the evolution of farmland landscapes in China. The results indicated that from 1980 to 2020, areas of both lost and restored farmland showed a trend of first increasing and then decreasing, while the total area of farmland fluctuated. The remaining farmland types consisted mainly of core and edge. Their distribution was similar to that of the major agricultural regions in China. Expansion was the main means of farmland restoration. Farmland fragmentation was widespread, and, over time, it became increasingly severe. Shrinkage and subdivision dominated the farmland fragmentation. Altitude and slope had the greatest impact on the evolution patterns of farmland. Increasing urban industrialization and an increase in population density led to an increase in the demand for food production, which placed greater demands on the farmlands in the region. The farmland evolution pattern is a result of the interactions among multiple factors.

The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method.

Today, 7xxx aluminum alloys are widely used in aerospace and other fields due to their excellent properties such as low density, high specific strength, and good processing performance. The heat treatment process of 7xxx aluminum alloy is crucial in controlling the strengthening phases and grain size, which is a significant way to enhance the alloy’s performance. In this study, solution heat treatment tests of 7050 aluminum alloys were carried out at different temperatures, ranging from 440 °C to 470 °C, with a holding time ranging from 0.5 h to 8 h, using a DIL 805A thermomechanical test machine. The microstructural evolution during the solution heat treatment was characterized using an optical microscope (OM), a scanning electron microscope (SEM), and a transmission electron microscope (TEM). The effects of the solution parameters on the alloy’s microhardness were analyzed using a digital Vickers microhardness tester. According to the ASTM E112-13 standard, The Anelli grain growth models were established to illustrate the grain size evolution during solution heat treatment, and a modified Anelli grain growth model was established. The results indicated that the grain size significantly increases with the increase in the solution heat treatment time and temperature. The Anelli grain growth model can illustrate the phenomenon of grain growth more accurately in the solution heat treatment process of 7050 aluminum alloy. It was found that prolonging the time and elevating the temperature of the solution heat treatment reduced the microhardness of the aluminum alloy because of the dissolution of the precipitates.

This paper mainly studies fault-tolerant control for a class of semi-linear fractional-order multi-agent systems with diffusion characteristics, where the actuator fault is considered. The adaptive fault-tolerant control protocol based on the adjacency relationship of agents is firstly designed, which can adjust the coupling gain online through the adaptive mechanism. Using the Lyapunov stability theory, the adaptive fault-tolerant control protocol can drive the agents to achieve consensus for leader-following and leaderless cases. Finally, the simulation experiment is carried out, showing the effectiveness of the proposed theory.

China’s grain production has been on a pathway paralleled with urbanization in recent 40 years, from lower efficiency and productivity to the higher ones, and has met complex impact factors and contradiction between sustainable development of farming and urbanization. Spatial-temporal pattern of grain production in China, by total and per capita output, was analyzed through Local Moran’I from 1985 to 2019. Until 2019, the high-output countries were mainly aggregated in the main plains and basins on the third step of China’s terrain and the low-output areas increased in the southeastern coastal regions; the countries with high per capita output almost disappeared and the low areas increased a lot in the central and southern regions Then the impact factors, by total output in the whole country and in grain surplus-shortage regions, were detected and found out through Geodetector. In the whole country, cultivated land areas, agricultural machinery, total meat output and value-added of primary industry not only played important role independently, but also had mutual coupling effect in grain total output. And in the grain surplus regions, major factors were cultivated land areas and value-added of primary industry, while in the grain shortage regions, were labors and market as well as economic factors. Thirdly, factor mechanism of China’s grain production for four types of development orientations, i.e., orientation on production scale, production efficiency and benefit, product quality and economic coordination, were concluded, and the way of grain production in different regions with unique suitability and advantage were expounded.

With the development of medium- and high-resolution satellites, successfully segmenting differently sized geo-objects remains a challenging issue for geographic object-based image analysis (GEOBIA). The hybrid image segmentation method is a good alternative to produce good segmentation that best matched the different sizes of geo-objects. However, the existing methods almost use segmentation parameters (SPs), such as scale, to control the sizes and shapes of segments. This will lead to two issues: (1) one single scale is impossible to segment every geo-object well due to the land cover complexity within remote-sensing imageries; (2) over- and under-segmented regions still occur in the segmentation results, whatever using any advanced segmentation methods. To solve the above problems, this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing. First, the primitive segmentation was produced and then stratified into layers with different land covers. Then, the local scale was calculated for a more objective merging process in the separating layers. Third, the over- and under-segmentation at separating layers was recognized and re-processed for achieving a fine segmentation. To validate the proposed method, it was applied to three test images of gaofen-1 satellite with different land cover types, and ten competing methods were compared. The visual and quantitative results indicated the advantage of our method in segmenting out different sizes of geo-objects, which can effectively reduce the over- and under-segmentation error.

The unsupervised segmentation evaluation (USE) method has been commonly used for remote sensing segmentation parameter (SP) determinations to produce good segmentation results, due to its objectiveness and high efficiency. Existing studies have used different criteria to measure homogeneity and heterogeneity and have used certain combination strategies to form overall evaluations. However, different criteria have unique statistical characteristics. The differentiated statistical characteristics maintained in homogeneity and heterogeneity calculations may result in inherent instability in the USE results, leading to unsuitable SP selections. Moreover, few studies have focused on the simultaneous determination of a single optimal SP and multiple optimal SPs. In this article, double-variance (DV) measures were proposed for recognizing more suitable SPs. Then, two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively. The multiresolution segmentation algorithm and Gaofen-1 data were used to test the proposed method. The comparative results indicated that the DV is a more promising internal homogeneity and external heterogeneity metric for segmentation evaluation and optimal SP determination compared to conventional methods. The F-measure-based DV method could produce better overall goodness of segmentation for differently sized natural geo-objects, compared with the competing methods. The LP-based DV method could obtain multiple optimal scales that produced better segments for the identification of small, natural geo-objects to large, semantic geo-objects, compared to the competitive methods.

Rhenium (Re) is widely used in the diagnosis and treatment of cancer due to its unique physical and chemical properties. Re has more valence electrons in its outer shell, allowing it to exist in a variety of oxidation states and to form different geometric configurations with many different ligands. The luminescence properties, lipophilicity, and cytotoxicity of complexes can be adjusted by changing the ligand of Re. This article mainly reviews the development of radionuclide 188Re in radiotherapy and some innovative applications of Re as well as the different therapeutic approaches and imaging techniques used in cancer therapy. In addition, the current application and future challenges and opportunities of Re are also discussed.

Ozone pollution is a prominent public health issue, but there are few studies on the effect of ozone on the ultrastructure of respiratory system; we conducted this research. Exposed to 1.1 ppm O3 4 h per day, the mice lungs and bronchi were taken on the 15th or 30th day. The sections stained with HE and immunohistochemical streptavidin–peroxidase methods for NQO1, Nrf2, and Keap1 were observed and measured under the optical microscope. TEM was used for ultrastructure observation. The animals’ serums were detected for CRP and IL-6 levels. The HE-stained sections showed no obvious micromorphological changes in the O3 exposure, but the NQO1 average optical density was higher than the control on the 15th day (p < 0.05). The ultrastructural changes were found in the O3 exposure group, such as bulges and vacuoles in type I alveolar cells, the increased evacuation of substance from lamellar bodies in the type II alveolar cells, the increased space around the goblet nucleus, binuclear goblet, and columnar cells. CRP and IL-6 levels increased compared with the control (p < 0.05). Although inhaling 1.1 ppm O3 had no significant effect on the micromorphology of the mice lungs and bronchi, it did affect the ultrastructure with oxidative stress and inflammatory responses.

Although alpine grassland net primary productivity (NPP) plays an important role in balancing the carbon cycle and is extremely vulnerable to climate factors, on the Tibetan Plateau, the generalized effect of climate factors on the NPP in areas with humid and arid conditions is still unknown. Hence, we determined the effects of precipitation and temperature on the MODIS NPP in alpine grassland areas from 2001 to 2019 according to information from humid and arid climatic regions. On a spatial scale, we found that temperature generated a larger effect on the NPP than precipitation did in humid regions, but as a primary factor, precipitation had an impact on the NPP in arid regions. These results suggest that temperature and precipitation are the primary limiting factors for plant growth in humid and arid regions. We also found that temperature produced a greater effect on the NPP in humid regions than in arid regions, but no significant differences were observed in the effects of precipitation on the NPP in humid and arid regions. In a time series (2001–2019), the effects of precipitation and temperature on the NPP presented fluctuating decrease (R2 = 0.28, p < 0.05) and increase (R2 = 0.24, p < 0.05) trends in arid regions. However, the effect of the climate on the NPP remained stable in humid regions. In both humid and arid regions, the dynamics of the NPP from 2001 to 2019 were mediated by an increase in temperature. Specifically, 35.9% and 2.57% of the dynamic NPP in humid regions and 45.1 and 7.53% of the dynamic NPP in arid regions were explained by variations in the temperature and precipitation, respectively. Our findings highlighted that grassland areas in humid regions can adapt to dynamic climates, but plants in arid regions are sensitive to changes in the climate. These findings can increase our understanding of climate and ecological responses and provide a framework for adapting management practices.

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 × 400 m2 super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.

The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110).

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

Limestone Karst areas possess high levels of biodiversity and endemism. Primulina is a typical component of Karst endemic floras. The high species richness and wide distribution in various Karst microenvironments make the genus an idea model for studying speciation and local adaptation. In this study, we obtained 10 full-length sequences of the phytochrome PHYE from available transcriptome resources of Primulina and amplified partial sequences of PHYE from the genomic DNA of 74 Primulina species. Then, we used maximum-likelihood approaches to explore molecular evolution of PHYE in this Karst cave plant. The results showed that PHYE was dominated by purifying selection in both data sets, and two sites were identified as potentially under positive selection. Furthermore, the ω ratio varies greatly among different functional domains of PHYE and among different species lineages. These results suggest that potential positive selection in PHYE might have played an important role in the adaption of Primulina to heterogeneous light environments in Karst regions, and different species lineages might have been subjected to different selective pressures.

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

Air pollution caused by atmospheric particulate and gaseous pollutants has drawn broad public concern globally. In this paper, the spatial-temporal distributions of major air pollutants in Shenzhen from March 2013 to February 2014 are discussed. In this study, ground-site monitoring data from 19 monitoring sites was used and spatial interpolation and spatial autocorrelation methods were applied to analyze both spatial and temporal characteristics of air pollutants in Shenzhen City. During the study period, the daily average concentrations of Particulate Matter (PM10 and PM2.5) ranged from 16–189 μg/m3 and 10–136 μg/m3, respectively, with 13 and 44 over-limit days, indicating that particulate matter was the primary air pollutant in Shenzhen. The highest PM occupation in the polluted air was observed in winter, indicating that fine particulate pollution was most serious in winter. Meanwhile, seasonal agglomeration patterns for six kinds of air pollutants showed that Guangming, Baoan, Nanshan, and the northern part of Longgang were the most polluted areas and PMs were their primary air pollutants. In addition, wind scale and rainfall played an important role in dissipating air pollutant in Shenzhen. The wind direction impacted the air pollution level in Shenzhen in multiple ways: the highest concentrations for all air pollutants all occurred on days with a northeast wind; the second highest ones appeared on the days with no wind. The concentrations on days with north-related winds are higher on average than those of days with south-related winds.

Endocrine and exocrine functions of the pancreas control nutritional absorption, utilisation and systemic metabolic homeostasis. Under basal conditions, the lysosome is pivotal in regulating intracellular organelles and metabolite turnover. In response to acute or chronic stress, the lysosome senses metabolic flux and inflammatory challenges, thereby initiating the adaptive programme to re-establish cellular homeostasis. A growing body of evidence has demonstrated the pathophysiological relevance of the lysosomal stress response in metabolic diseases in diverse sets of tissues/organs, such as the liver and the heart. In this review, we discuss the pathological relevance of pancreatic lysosome stress in diabetes mellitus. We begin by summarising lysosomal biology, followed by exploring the immune and metabolic functions of lysosomes and finally discussing the interplay between lysosomal stress and the pathogenesis of pancreatic diseases. Ultimately, our review aims to enhance our understanding of lysosomal stress in disease pathogenesis, which could potentially lead to the discovery of innovative treatment methods for these conditions.