Search Results (53)

Resource-exhausted cities face dual crises of economic stagnation and ecological degradation, which is primarily attributable to the inefficient use of industrial land. The redevelopment of industrial land has emerged as a crucial solution to the “resource depletion-urban decline” dilemma. The issue of inefficient industrial land use in resource-exhausted cities is of great significance as it directly impacts both economic development and ecological protection. Therefore, finding effective ways to redevelop this land is essential for the sustainable development of these cities. This research takes Hegang, a representative resource-exhausted city in China, as a case study. A multi-dimensional evaluation framework and an adaptive redevelopment strategy system are constructed in this research. By integrating data related to land use status, land use efficiency, policy constraints, and development potential, a parcel-scale assessment model is established. This model consists of 4 primary indicators and 13 secondary indicators. Through this model, 11.01 km² of inefficient industrial land in the main urban area of Hegang is identified. Standard deviation ellipse and kernel density analysis are employed to reveal the spatial pattern of inefficient land. The results show that the inefficient industrial land in Hegang exhibits a pattern of “overall dispersion with localized agglomeration”. It is found that idle and abandoned land are the dominant types of inefficient industrial land in Hegang’s main urban area, accounting for 69.7% of the total. This finding provides a clear understanding of the nature of the inefficient land use problem in resource-exhausted cities. A strategic framework is proposed, which incorporates classified governance, dynamic restoration, and multi-stakeholder collaboration. This framework offers a governance toolkit with both theoretical depth and practical value for resource-exhausted cities. Breaking the locked relationship between industrial land and resource dependence promotes the deep integration of spatial restructuring and sustainable transformation. The findings of this research provide significant scientific insights for similar cities worldwide to address the challenges they face and achieve harmony between human activities and land use. Future research could focus on further refining the evaluation framework and redevelopment strategies based on different regional characteristics and resource endowments. Full article

Toponyms reflect the multifaceted relationship between humans and nature, recording and transmitting important cultural information. A toponymic cultural landscape (TCL) is the comprehensive embodiment of the cultural connotations and landscape significance carried by toponyms, reflecting various factors such as regional culture, historical memory, and social values. Wuhan, as the hinterland of Jing-Chu culture, carries a profound geo-culture and brings together numerous toponymic cultural heritages. Studying the spatial distribution characteristics of Wuhan’s toponymic cultural heritage and their association with landscape elements is of great significance in promoting the inheritance of intangible cultural heritage and realizing the orderly continuation of local culture, and it also confers deeper cultural connotations and local characteristics to the process of landscape characterization. This study analyzes 3638 toponyms in Wuhan as the research objects, utilizing geospatial analysis methods, including kernel density analysis, standard deviation ellipse analysis, and average nearest neighbor analysis, to visualize the spatial distribution of Wuhan’s TCL. It further combines these methods with the optimal parameter geographic detector (OPGD) model to explore the influence of landscape elements on the spatial distribution of different types of toponyms and their interaction effects. The results show the following: (1) The TCL of Wuhan is divided into two basic types, the natural landscape (61.16%) and the humanistic landscape (41.37%), of which hydrological-related toponyms occupy a significant proportion, reaching 35.02% of the total number of toponyms in Wuhan. (2) The distribution of Wuhan’s TCL is characterized by aggregation, mostly in the core area of the city, with the Yangtze River as the main axis, and the density of the spatial distribution of humanistic landscape toponyms varies greatly. (3) The results of a single-factor analysis show that construction land (X2) is an important influencing factor in both natural and humanistic landscape toponyms, which indicates the central role of human activities in the formation of toponyms. (4) The explanatory power of the interaction effect of two factors on the spatial differentiation of Wuhan’s TCL is stronger than that of the single factors, which suggests that the spatial differentiation characteristics of the TCL of Wuhan are formed under the joint influence of the respective variables. Full article

Centrifugal pumps are known to efficiently transport water from a certain point. However, they developed great concerns in water supply and distribution applications regarding their operating efficiency, which were caused by the accumulated losses and sudden power consumption growth. Thus, mitigating these concerns is important to improve the performance of the centrifugal pump. This study used ANSYS 2022 R2 for the optimization design process, combining the strengths of Computational Fluid Dynamics (CFD) and Response Surface Method (RSM), to come up with an optimal design for a centrifugal water pump. Splitter blades, with a length of 80% of the main blade, were included in the design to assess their effects on the performance of the pump. Design parameters such as the placement of the splitter blades, their ellipse ratios, and the volute tongue, were also investigated for further improvement. Results indicate that finding a perfect balance between the placement of the splitter blades, the design of the volute tongue clearance and thickness, and configuring the ellipse ratio of the splitter blades improves the pump’s performance. The optimal design results in 27.35%, 15.70%, 28.18%, 16.67%, and 8.36% improvement in total efficiency, total head, static efficiency, static head, and power consumption, respectively. Full article

The dynamic stress analysis of a strip plate with elliptical holes under the action of an incident SH wave was performed using a complex function method and a successive mirror method. Firstly, a complex plane coordinate system of elliptic holes was established by using the complex variable function method and integral transformation method. The elliptic hole wave field and stress were established using the wave function expansion method. Then, the relation between the argument angle of any point on the edge of the ellipse hole and the angle between the vertical line and the coordinate axis was established. Using boundary conditions to solve the unknown coefficients in the equation, finally, the integral equation was simplified to a linear equation by means of the effective truncation method, and the steady-state response of dynamic stress under different parameters was analyzed. In addition, by comparing the finite element solution with the numerical solution, the accuracy of the results was effectively verified. The results show that the studied geometric model can provide solid theoretical support for the inspection of plate and shell structures, which is of great significance in practical engineering. Full article

Land-use change is an important cause of carbon emissions (CEs). In the context of achieving carbon peaking and carbon neutrality goals, understanding the coupling mechanisms between land-use change and CEs is of great significance for fostering regional low-carbon sustainable development. In this study, the land-use net carbon emissions (LCN) calculation and evaluation model was built based on the perspective of land-use change. The land-use variation matrix, standard deviation ellipse, and spatial autocorrelation analysis were used to analyze the spatio-temporal evolution of land-use and the LCN in the Chengyu urban agglomeration (CUA) from 2000 to 2020. Meanwhile, the economic contribution coefficient and ecological support coefficient were applied to evaluate the alignment among the CEs, socio-economic development, and the ecological environment. In addition, the modified Kaya and Logarithmic Mean Divisia Index (LMDI) models were used to quantitatively analyze the drivers and underlying influence mechanisms of the LCN. The results showed the following: (1) The area of built-up land and forest land expanded rapidly, mainly transforming grassland and farmland to built-up land and forest land in the CUA during the study period. The built-up land was the main source of the regional CEs. The land-use changes led to the migration of the LCN center and the variations in spatial clustering. (2) The growth rate of the LCN decreased after 2010, and the disparities in carbon productivity and the carbon compensation rate among the cities gradually narrowed from 2000 to 2020. The alignment among the regional CEs, socio-economic development, and ecological environmental governance was effectively improved. (3) The economic development level and energy consumption intensity were the primary facilitator and inhibitor of the LCN, respectively. The results could offer valuable references and insights for formulating regional carbon reduction strategies and policies. Full article

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. Full article

Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved. Full article

Food security is important to guarantee national security and people’s livelihoods, but the increasingly serious problem of non-grain production (NGP) on croplands has exacerbated the risk of food security and directly affected the sustainable development of the national economy and society. This study adopted 130 cities (states) in the Yangtze River Economic Belt as the research units and used ArcGIS10.8, GeoDA1.22, and Origin2022 software and spatial autocorrelation, standard deviational ellipse, and GeoDetector methods to conduct analyses. This study explored the spatial evolution patterns and factors influencing cropland NGP in the Yangtze River Economic Belt. The results show, firstly, that the NGP rate of cropland in the Yangtze River Economic Belt increased from 35.85% in 2006 to 38.62% in 2022. The number of cities (states) with mild and moderate NGP decreased, while the number of cities (states) with severe NGP increased significantly. Secondly, the spatial distribution of the rate of cropland NGP in the Yangtze River Economic Belt had a strong positive correlation, with “high–high agglomeration” tending to be dispersed, “low–low agglomeration” tending to be concentrated, and the overall trajectory of the center of gravity migrating from the northeast to the southwest. Thirdly, the single-factor detection found that the per capita food possession, slope, elevation, and average annual precipitation had strong explanatory power regarding the spatial difference in cropland NGP in the Yangtze River Economic Belt, and the interaction of any two influencing factors showed nonlinear enhancement. The results of this study can help to precisely identify the spatial and temporal evolution characteristics of cropland NGP in the Yangtze River Economic Belt, which is of great significance for supporting the country in controlling the risk of NGP cultivation, promoting the sustainable development of the Yangtze River Economic Belt, and guaranteeing food security. Full article

This paper recasts four geodetic curves—the great ellipse, the normal section, the loxodrome, and the curve of alignment—into a parametric form of vector-algebra formula. These formulas allow these curves to be drawn using simple, efficient, and robust algorithms. The curve of alignment, which seems to be quite obscure, ought not to be. Like the great ellipse and the loxodrome, and unlike the normal section, the curve of alignment from point A to point B (both on the same ellipsoid) is the same as the curve of alignment from point B to point A. The algorithm used to draw the curve of alignment is much simpler than any of the others and its shape is quite similar to that of the geodesic, which suggests it would be a practical surrogate when drawing these curves. Full article

National Archaeological Site Parks are strategic projects in China for innovatively exploring the scientific protection and rational utilization of large heritage sites for the purpose of balancing urban development and protecting cultural heritage. Therefore, research on the spatial–temporal evolution and influencing factors of National Archaeological Site Parks can improve and optimize their management and pattern systems and is therefore of great significance for the sustainable development of large sites and their surrounding areas. Targeting the 135 National Archaeological Site Parks of China, this paper adopts the nearest-neighbor index analytical method, the kernel density estimation method, the standard deviation ellipse method, the method of constructing an indicator system, and an SPSS factor analysis method to analyze the spatial–temporal evolution and influencing factors of these parks. The findings are as follows: (1) In terms of the temporal evolution, the number of National Archaeological Site Parks increased from 2010 to 2022, and the ages and types of the large heritage sites they are built upon gradually became more balanced and diverse. (2) In terms of the spatial evolution, National Archaeological Site Parks form two high-density areas in Central China and East China. Their overall distribution is consistent with China’s “Hu Line”. (3) In terms of spatial–temporal evolution, the center of gravity of National Archaeological Site Parks’ distribution is advancing toward Southwest China, and the trend of a more balanced distribution is rising. (4) Finally, regional development, heritage tourism, historical resources, and government support were observed to be factors that affect the spatial–temporal patterns of the National Archaeological Site Parks. Based on these findings, we propose specific strategies for coordinating and linking the above four major influencing factors to promote the rational utilization of large heritage sites and the sustainable development of National Archaeological Site Parks. We aim to improve and optimize the management and pattern systems of National Archaeological Site Parks, to promote urban renewal based on large heritage sites, and to provide valuable insights for policy makers and heritage practitioners in other countries with historical resources around the world. Full article

A numerical gear rolling test (NGRT) method for actual tooth surfaces was proposed. The non-uniform rational B-spline (NURBS) fitting method was adapted to reconstruct the actual tooth surface, and the instant meshing point as well as the transmission error of two meshing gears were solved. The instant contact ellipse boundary of each meshing point can be accurately searched, and then the contact pattern formed by a series of contact ellipses on the actual tooth surface can be obtained. Finally, the rolling test experiment for a pair of hypoid gears was conducted to verify the feasibility of the proposed method. The result shows that the contact pattern obtained is consistent with the actual rolling test result, the relative errors of the transmission error amplitudes were only 4.5% and 5.3% for the drive and coast sides, respectively, and the contact ellipse of the actual tooth surface is not necessarily a standard ellipse and the instant contact path is not necessarily a straight line. This NGRT method helps to reduce equipment and time costs in closed-loop gear manufacturing, which is of great significance for mass production. Full article

China’s coastal area is an important node, carrying the connection between internal and external circulation. It is of great significance to explore the spatial and temporal evolution of economic dual circulation coordinated development and its driving factors. In this paper, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation model based on the Criteria Importance Though Intercriteria Correlation and entropy (CRITIC-entropy) weight method, coupling coordination model, standard deviation ellipse and exploratory spatial data analysis were used to analyze the spatial and temporal evolution characteristics of economic dual circulation coordinated development of China’s coastal area. In addition, the geographical detector was employed to identify its driving factors. The results showed that: (1) The development level of internal and external economic circulation in China’s coastal area was mainly stable and rising in a fluctuating manner, and the level of internal circulation was higher than that of external circulation. The overall coupling coordination degree of economic dual circulation exhibited a positive trend. (2) There was regional heterogeneity and spatial correlation in the coupling coordination degree of economic dual circulation in the coastal area of China, and the spatial distribution pattern showed the characteristic of being “strong in the internal and weak in the external”. (3) The coordinated development of economic dual circulation was driven by multiple factors. Regional technological innovation capability, per capita income level, circulation development level, marketization process, digitization level and financial development level were the core driving forces. Based on the findings of this paper, a series of policy recommendations for improving the coordination development between internal and external economic circulation was proposed. Full article

Understanding the spatial distribution pattern and evolution characteristics of the elderly population in urban areas is of great significance for the development of urban planning and the implementation of public management policies in the context of rapid aging. Accurately identifying the spatial distribution and evolution characteristics of the elderly population in a city requires a comprehensive analysis of multiple indicators and large-scale data. Taking Wuhan City as an example, this article measures the spatial distribution characteristics and evolution trend of the elderly population from 2000 to 2020 at the street/township level based on data from the fifth, sixth, and seventh censuses, using methods such as kernel density hotspot detection, spatial clustering analysis, and standard deviation ellipse analysis. The results show that (1) there are significant differences in the aging spatial pattern between the central area and the suburban areas of Wuhan; (2) overall, Wuhan’s aging rate shows a typical “core–periphery” growth mode in space, while the density of the elderly population has significant spatial aggregation characteristics and shows an evolution trend of “centralized concentration, peripheral outliers, axial development, and near-field growth”; (3) the center of gravity of the elderly population remains relatively stable over time. Full article

This study addresses the knowledge gap regarding the spatiotemporal evolution of Chinese urban agglomerations using long time series of luminescence remote sensing data. The evolution of urban agglomerations is of great significance for the future development and planning of cities. Nighttime light data provide a window for observing urban agglomerations’ characteristics on a large spatial scale, but they are affected by temporal discontinuity. To solve this problem, this study proposes a ridge-sampling regression-based Hadamard matrix correction method and constructs consistent long-term nighttime light sequences for China’s four major urban agglomerations from 1992 to 2018. Using the Getis-Ord Gi* hot-cold spot, standard deviation ellipse method, and Baidu search index, we comprehensively analyze the directional evolution of urban agglomerations and the correlations between cities. The results show that, after correction, the correlation coefficient between nighttime light intensity and gross domestic product increased from 0.30 to 0.43. Furthermore, this study identifies unique features of each urban agglomeration. The Yangtze River Delta urban agglomeration achieved balanced development by shifting from coastal to inland areas. The Guangdong-Hong Kong-Macao urban agglomeration developed earlier and grew more slowly in the north due to topographical barriers. The Beijing-Tianjin-Hebei urban agglomeration in the north has Beijing and Tianjin as its core, and the southeastern region has developed rapidly, showing an obvious imbalance in development. The Chengdu-Chongqing urban agglomeration in the inland area has Chengdu and Chongqing as its dual core, and its development has been significantly slower than that of the other three agglomerations due to the influence of topography, but it has great potential. Overall, this study provides a research framework for urban agglomerations based on four major urban agglomerations to explore their spatiotemporal characteristics and offers insights for government urban planning. Full article

Dragon fruit is one of the most popular fruits in China and Southeast Asia. It, however, is mainly picked manually, imposing high labor intensity on farmers. The hard branches and complex postures of dragon fruit make it difficult to achieve automated picking. For picking dragon fruits with diverse postures, this paper proposes a new dragon fruit detection method, not only to identify and locate the dragon fruit, but also to detect the endpoints that are at the head and root of the dragon fruit, which can provide more visual information for the dragon fruit picking robot. First, YOLOv7 is used to locate and classify the dragon fruit. Then, we propose a PSP-Ellipse method to further detect the endpoints of the dragon fruit, including dragon fruit segmentation via PSPNet, endpoints positioning via an ellipse fitting algorithm and endpoints classification via ResNet. To test the proposed method, some experiments are conducted. In dragon fruit detection, the precision, recall and average precision of YOLOv7 are 0.844, 0.924 and 0.932, respectively. YOLOv7 also performs better compared with some other models. In dragon fruit segmentation, the segmentation performance of PSPNet on dragon fruit is better than some other commonly used semantic segmentation models, with the segmentation precision, recall and mean intersection over union being 0.959, 0.943 and 0.906, respectively. In endpoints detection, the distance error and angle error of endpoints positioning based on ellipse fitting are 39.8 pixels and 4.3°, and the classification accuracy of endpoints based on ResNet is 0.92. The proposed PSP-Ellipse method makes a great improvement compared with two kinds of keypoint regression method based on ResNet and UNet. Orchard picking experiments verified that the method proposed in this paper is effective. The detection method proposed in this paper not only promotes the progress of the automatic picking of dragon fruit, but it also provides a reference for other fruit detection. Full article