Search

As a new seawater standard, the International Thermodynamic Equation of Seawater-2010 (TEOS-10) has made it possible to accurately calculate salinity changes caused by small changes in the relative proportions of inorganic dissolved constituents in the open ocean and thus to accurately calculate the thermodynamic properties of seawater. However, offshore and semi-enclosed seas are subject to the dual influence of geo-biochemical processes and terrestrial inputs, and the spatial and temporal variations in the relative composition of their seawater are more complex, resulting in very few studies of salinity change due to changes in the relative composition in these regions, and the applicability of the TEOS-10 in these seas needs to be further evaluated. The Bohai Sea is a typical semi-enclosed sea that has accumulated a large amount of physical oceanography and marine chemistry measurements, making it an ideal area to study the effects of changes in the relative composition of seawater on salinity. Based on repeated measurements of Section B in the Bohai Sea from 1985 to 2020, complemented by quasi-synchronous large-scale measurements of the Bohai Sea from 2006 to 2007, this study found that [HCO3−] and [Ca2+] ions anomalies dominate the relative composition anomalies of dissolved substances in this sea relative to the Standard Sea Water (SSW). These additional components could increase the absolute salinity SA by up to 0.1 g·kg-1 and the practical salinity SP by 0~0.04 according to the TEOS-10 algorithm and the mathematical model Pa08. Based on the specific environmental characteristics of the Bohai Sea, the sources, spatial and temporal variations, and influencing factors of the relative compositional anomalies are analyzed, and the empirical formulas of the absolute salinity anomaly δSA and practical salinity anomaly δSP for the Bohai Sea are further fitted, with the uncertainty of 0.007~0.021 g·kg-1 for δSA and 0.003~0.008 for δSP. The above results can not only improve the accuracy of the thermodynamic properties of the Bohai Seawater, but also provide the compatibility level of the long-term salinity changes in the Bohai Sea and its adjacent waters.

After years of construction and development, China Ocean Mineral Resources Data Center of COMRA has realized the comprehensive management of ocean survey data and related research results data based on the deep-sea ocean integrated database. For the requirement of efficient, visual and online comprehensive analysis in one map, based on multi-source deep ocean data resources at home and abroad, facing the management and application needs of deep ocean spatial data, the comprehensive database is closely connected to carry out necessary spatial vectorization transformation of the existing deep ocean data. Java language, SpringBoot, Cesium, and other technologies are used. Deep ocean spatial data management system based on GIS is developed and integrated display and visual analysis system, developed based on one map of deep sea map, deep sea situational analysis spatial data resources, work planning and deployment, resource potential evaluation, equipment performance analysis and assessment project applications such as data analysis function, to construct digital deep-sea one map based on the analysis of visual system. The system has been deployed in the national marine communication network and put into operational trial operation, providing data and information technology support for China's deep-sea ocean mineral resources exploration, environmental impact assessment and related scientific research activities.

In the fields of physics and engineering, it is crucial to understand phase transition dynamics. This field involves fundamental partial differential equations (PDEs) such as the Allen–Cahn, Burgers, and two-dimensional (2D) wave equations. In alloys, the evolution of the phase transition interface is described by the Allen–Cahn equation. Vibrational and wave phenomena during phase transitions are modeled using the Burgers and 2D wave equations. The combination of these equations gives comprehensive information about the dynamic behavior during a phase transition. Numerical modeling methods such as finite difference method (FDM), finite volume method (FVM) and finite element method (FEM) are often applied to solve phase transition problems that involve many partial differential equations (PDEs). However, physical problems can lead to computational complexity, increasing computational costs dramatically. Physics-informed neural networks (PINNs), as new neural network algorithms, can integrate physical law constraints with neural network algorithms to solve partial differential equations (PDEs), providing a new way to solve PDEs in addition to the traditional numerical modeling methods. In this paper, a hard-constraint wide-body PINN (HWPINN) model based on PINN is proposed. This model improves the effectiveness of the approximation by adding a wide-body structure to the approximation neural network part of the PINN architecture. A hard constraint is used in the physically driven part instead of the traditional practice of PINN constituting a residual network with boundary or initial conditions. The high accuracy of HWPINN for solving PDEs is verified through numerical experiments. One-dimensional (1D) Allen–Cahn, one-dimensional Burgers, and two-dimensional wave equation cases are set up for numerical experiments. The properties of the HWPINN model are inferred from the experimental data. The solution predicted by the model is compared with the FDM solution for evaluating the experimental error in the numerical experiments. HWPINN shows great potential for solving the PDE forward problem and provides a new approach for solving PDEs.

Informal development, as a unique phenomenon that has become widespread in China’s urbanization process in recent years, has continued to attract the attention of both the government and academia. Existing studies focus on urban village redevelopment strategies, and little research has been conducted on informal development in urban villages under the land property approach. In particular, research needs to further explore what impact China’s collective land property rights have had on informality in urban villages. This study mainly adopts a qualitative research method, including field observation and in-depth interviews. The research was conducted in urban villages in Guangzhou. The study finds that land property rights have an important impact on urbanization and property rights arrangements have an important impact on resource allocation efficiency. Due to the ambiguity of collective land property rights in China, informal development in urban villages is the result of the collective action of villagers, government, and enterprises under the stimulation of economic development. The interaction of the stakeholders has promoted the rapid development of informal housing in urban villages.

For investigating ocean activities and comprehending the role of the oceans in global climate change, it is essential to gather high-quality ocean data. However, existing ocean observation data have deficiencies such as inconsistent spatial and temporal distribution, severe fragmentation, and restricted observation depth layers. Data assimilation is computationally intensive, and other conventional data fusion techniques offer poor fusion precision. This research proposes a novel multi-source ocean data fusion network (ODF-Net) based on deep learning as a solution for these issues. The ODF-Net comprises a number of one-dimensional residual blocks that can rapidly fuse conventional observations, satellite observations, and three-dimensional model output and reanalysis data. The model utilizes vertical ocean profile data as target constraints, integrating physics-based prior knowledge to improve the precision of the fusion. The network structure contains channel and spatial attention mechanisms that guide the network model’s attention to the most crucial features, hence enhancing model performance and interpretability. Comparing multiple global sea temperature datasets reveals that the ODF-Net achieves the highest accuracy and correlation with observations. To evaluate the feasibility of the proposed method, a global monthly three-dimensional sea temperature dataset with a spatial resolution of 0.25°×0.25° is produced by fusing ocean data from multiple sources from 1994 to 2017. The rationality tests on the fusion dataset show that ODF-Net is reliable for integrating ocean data from various sources.

In the process of globalization, the transformation of production methods has triggered a restructuring of scales, resulting in the emergence of a new spatial phenomenon known as cross-border regions. Previous studies have focused on the coordination of cross-border regions, often concentrating solely on either spatial or governance aspects, and lacking a comprehensive exploration of the underlying mechanisms linking space and governance. This study examines the evolution of cross-border governance using the Tokyo Bay Area as a case study, taking into account the dual characteristics of space and governance. The findings of this research indicate a certain correlation between spatial evolution and governance mechanisms during different stages of development in the Tokyo Bay Area. Over time, the spatial configuration of the port cluster has undergone significant changes, while the governance mechanisms of the cross-border region have transitioned from informal to formal approaches. Additionally, the study summarizes both diachronic and synchronic characteristics, confirming the effectiveness of the space governance and providing an analysis of its underlying mechanisms. These findings provide valuable insights for promoting the modernization of national governance systems and governance capabilities.

Medical ultrasound imaging plays an important role in computer-aided diagnosis systems. In many cases, it is the preferred method of doctors for diagnosing diseases. Combined with computer vision technology, segmentation of ovarian ultrasound images can help doctors accurately judge diseases, reduce doctors' workload, and improve doctors' work efficiency. However, accurate segmentation of an ovarian ultrasound image is a challenging task. On the one hand, there is a lot of speckle noise in ultrasound images; on the other hand, the edges of objects are blurred in ultrasound images. In order to segment the target accurately, we propose an ovarian follicles segmentation network combined with edge information. By adding an edge detection branch at the end of the network and taking the edge detection results as one of the losses of the network, we can accurately segment the ovarian follicles in an ultrasound image, making the segmentation results finer on the edge. Experiments show that the proposed network improves the segmentation accuracy of ovarian follicles, and that it has advantages over current algorithms.

Sperm motility is an important index to evaluate semen quality. Computer-assisted sperm analysis (CASA) is based on the sperm image, through the image-processing algorithm to detect the position of the sperm target and track tracking, so as to judge the sperm activity. Because of the small and dense sperm targets in sperm images, traditional image-processing algorithms take a long time to detect sperm targets, while target-detection algorithms based on the deep learning have a lot of missed detection problems in the process of sperm target detection. In order to accurately and efficiently analyze sperm activity in the sperm image sequence, this article proposes a sperm activity analysis method based on the deep learning. First, the sperm position is detected through the deep learning feature point detection network based on the improved SuperPoint, then the multi-sperm target tracking is carried out through SORT and the sperm motion trajectory is drawn, and at last the sperm survival is judged through the sperm trajectory to realize the analysis of sperm activity. The experimental results show that this method can effectively analyze the sperm activity in the sperm image sequence. At the same time, the average detection speed of the sperm target detection method in the detection process is 65fps, and the detection accuracy is 92%.

In order to solve the problem on the serious deformation and failure of the rock column at the intersection of large cross sections in Shiyakou coal mine, based on field engineering geomechanics analysis, theoretical analysis, and three-dimensional numerical simulation, combined with the mutual coupling between the constant resistance coupling support cable and surrounding rock, a dual-control constant-resistance coupling control technology with high prestressed constant resistance coupling support cable as the core solution is proposed. The field engineering practice and field monitoring data show that the dual-control constant resistance coupling support control technology has a good control effect on the deformation of the rock column at the large cross section intersection of the deep soft rock roadway and can be effectively used in the engineering practice.

Exposure to medium and high doses of ionizing radiation (IR) can induce long-term bone marrow (BM) suppression. We previously showed that recombinant human thrombopoietin (rhTPO) significantly promotes recovery from hematopoietic-acute radiation syndrome, but its effect on long-term BM suppression remains unknown. C57BL/6 mice were exposed to 6.5 Gy γ-rays of total body irradiation (TBI) at a dose-rate of 63.01 cGy per minute, and the mice were treated with rhTPO (100 μg; intramuscular injection) or vehicle at 2 h after TBI. All mice were killed one or two months after TBI for analysis of peripheral blood cell counts, long-term hematopoietic stem cell (HSC) frequency, and BM-derived clonogenic activity. The HSC self-renewal capacity was analyzed by BM transplantation. The levels of reactive oxygen species (ROS) production and ratios of γH2AX+ and p16, p53, and p21 mRNA in HSCs were measured by flow cytometry and real-time polymerase chain reaction, respectively. Treatment with rhTPO reduced long-term myelosuppression by improving long-term hematopoietic reconstitution (p < 0.05) after transplantation and resting state maintenance of HSCs (p < 0.05). Moreover, rhTPO treatment was associated with a sustained reduction in long-term ROS production, reduction of long-term DNA damage, diminished p53/p21 mRNA expression, and prevention of senescence after TBI. This study suggests rhTPO is an effective agent for treating IR-induced long-term BM injury because it regulates hematopoietic remodeling and HSC cycle disorder through the ROS/p53/p21/p16 pathway long term after IR.

Tourism plays a key role in sustaining economic development. Previous research has established the connections between tourism and urban growth, as well as the influence of tourism on population and social structure, and the impact of tourism on the landscape. Fewer studies have been conducted on the impact of tourism on the process of rural development change, especially how rural spatial transformation and local community development are driven through tourism. This paper investigates the link between tourism and rural spatial development in China, using in-depth interviews. The research demonstrates that the ambiguity of collective land ownership in China is a key factor in the process of village development. The transformation of villages from “production space” to “consumption space” is driven by tourism based on the land institution arrangement. It is hoped that this research will contribute to a deeper understanding that tourism should be valued in the future as part of the village development process, especially when tourism plays a long-term role in promoting village development.

Mining in close distance coal seams (CDCSs) is frequently associated with engineering disasters because of the complicated nature of stress distribution within CDCSs. In order to establish a layout of a roadway to minimize the occurrence of disasters associated with mining CDCS, here the spatial and temporal evolution of stress distribution during the multiworking face mining of a CDCS was explored through numerical simulation based on the engineering and geological conditions of the Nantun Coal Mine. The numerical simulation results indicate that, after the extraction of adjacent multiple working faces, the spatial distribution of stress can be characterized with areas of increased, reduced, and intact stress. The superposed stress of inclined seams that are very close to each other propagates through coal pillars in the bottom floor, and this propagation follows neither the line along the axis of the coal pillar nor the line perpendicular to the direction of the floor. It instead propagates along a line angled with the axis of the coal pillar. The roadway can be arranged in the area with reduced stress, to improve its the stability. Based on the computed spatial and temporal evolution of stress, an optimized layout of roadway was proposed. This layout features a reasonable interval between the mining roadway and a minimal proportion of increased stress areas along the mining roadway and is aligned with geological structures.

Urban regeneration is an important strategic choice in promoting urban development globally. Existing research on urban regeneration mainly focuses on the community’s economic benefits. However, less research concentrates on how community participation contributes to the sustainable development of communities. The aim of this study is to explore the community regeneration approach in the context of urban regeneration in a typical village community in China. This study finds that participatory planning, which is mainly characterized by public participation, can be an effective way of communication and cooperation. The collaborative workshops provide a participatory platform for stakeholders and promote sustainable community development. Therefore, traditional planning approaches may need to be changed. The contribution of this article is to develop a collaborative planning approach for sustainable community development, which can serve as a reference for community governance in China and other developing countries.

Ocean current (OC) prediction plays an important role for carrying out ocean-related activities. There are plenty of studies for OC prediction with deep learning to pursue better prediction performance, and the attention mechanism was widely used for these studies. However, the attention mechanism was usually combined with deep learning models rather than purely used to predict OC, or, if it was purely used, did not further optimize the attention weight. Therefore, a deep learning model based on weighted pure attention mechanism is proposed in this paper. This model uses the pure attention mechanism, introduces a weight parameter for the generated attention weight, and moves more attentions from other elements to the key elements based on weight parameter setting. To our knowledge, it is the first attempt to use the weighted pure attention mechanism to improve the OC prediction performance, and it is an innovation for OC prediction. The experiment results indicate that the proposed model can fully take advantage of the strengths from the pure attention mechanism; it can further optimize the pure attention mechanism and significantly improve the prediction performance, and is reliable for OC prediction with high performance for a wide time range and large spatial scope.

Informal settlements in developing countries have attracted widespread attention, and existing research is mainly concerned with the causes and evolution of informal settlements. However, few studies have focused on investigating the spatial governance strategies of informal settlements in order to understand the evolution of informal settlements in the context of institutional arrangements. The aim of this research is to develop a framework for the spatial governance of informal settlements to explain their causes. The research finds that informal settlements in China are influenced by an urban–rural dual land system in which the collective land ownership system means that villagers are ambiguous in their approach to land development. During rapid urbanization, villagers have adopted diverse land development strategies based on land rent incentives to attract capital investment and earn profits, which has led to the rapid spread of informal settlements.

Rare earth elements (REEs) are considered critical elements in modern society due to their irreplaceable role in new innovative and energy technologies. The giant carbonatite-related Bayan Obo deposit contributes most REE resources in the world’s market, while its origin is still unclear because of the complicated and diverse REE ore mineralogy and texture. Thescanning electron mircroscopy SEM)-based automated mineralogy allows for the numeric assessment of rocks and ores’ compositional and textural properties. Here, we use TIMA (TESCAN Integrated Mineral Analyzer) to quantitatively characterize REE ore mineralogy from the deep drill core within the H8 unit (“dolomite marble”) to better understand the deposit. The mineral composition, occurrence, and Ce elemental deportment of the borehole ores at different depths (i.e., 1107 m, 1246 m, 1406 m, 1546 m, and 1682 m) were obtained. The results show that the main types of ores in the investigated samples can be divided into banded REE-Fe ores, banded REE ores, disseminated REE-Fe ores, and veined REE ores. REE and gangue minerals vary significantly in abundance and occurrence. Monazite-(Ce) and bastnäsite-(Ce) are the primary REE host minerals, and both contribute the most to the REE budget. Other REE minerals, such as parisite-(Ce)/synchysite-(Ce), cerite-(Ce), huanghoite-(Ce)/cebaite-(Ce), and aeschynite-(Ce), are significant contributors. The gangue minerals generally include fluorite, barite, magnetite, pyrite, quartz, magnesio-arfvedsonite, and minerals of the biotite and apatite groups, among others. Combined with the newly published mineral-scale chronological and isotopic geochemical analyses, it is reasonable to conclude that the later hydrothermal fluids remobilized and redistributed the original Mesoproterozoic carbonatitic REE minerals and formed a high variable ore mineral assemblage. Furthermore, it is demonstrated that the mineralogical study using TIMA can provide accurate and reliable mineralogy data for the comprehensive interpretation of the complex REE ores, and extend our understanding of the deposit.

The prediction of marine elements has become increasingly important in the field of marine research. However, time series data in a complex environment vary significantly because they are composed of dynamic changes with multiple mechanisms, causes, and laws. For example, sea surface temperature (SST) can be influenced by ocean currents. Conventional models often focus on capturing the impact of historical data but ignore the spatio–temporal relationships in sea areas, and they cannot predict such widely varying data effectively. In this work, we propose a cyclic evolutionary network model (CENS), an error-driven network group, which is composed of multiple network node units. Different regions of data can be automatically matched to a suitable network node unit for prediction so that the model can cluster the data based on their characteristics and, therefore, be more practical. Experiments were performed on the Bohai Sea and the South China Sea. Firstly, we performed an ablation experiment to verify the effectiveness of the framework of the model. Secondly, we tested the model to predict sea surface temperature, and the results verified the accuracy of CENS. Lastly, there was a meaningful finding that the clustering results of the model in the South China Sea matched the actual characteristics of the continental shelf of the South China Sea, and the cluster had spatial continuity.

The existing literature explains well how dual urbanization promotes land development and spatial expansion in China, but few studies have examined the emergence of state-led and village-led land development by measuring the urban expansion ratio from the perspective of institutional arrangements. To fill this gap, this study examines the spatial pattern and evolution of urban expansion and then explains the institutional dynamics in evolving different spatial forms of land development in Guangzhou. Among the findings of our research, two important points merit special attention. First, this study examines the emergence of collective-led and state-led development in Chinese cities from the perspective of institutions with the support of empirical evidence from Guangzhou. Herein, urban expansion intensity (UEI) was used to improve urban expansion as an evaluation index of urbanization speed. Second, the present study developed a theoretical framework based on land property rights from an institutional perspective to explain the two forms of land development in the dynamic urbanization process. From a broader theoretical perspective, diverse spatial forms of land development in Chinese cities contribute to the understanding of urban land expansion based on an institutional approach. The policy implication of this study is that the speed of urban expansion will gradually slow with the advancement of new urbanization, and growth in collective land in the urban area will gradually decrease. Land redevelopment policies and regulations should be taken into account for collective land in urban areas, which is necessary for sustainable urban development in China.

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions