Global Relationships Research Articles

Global Versus Local? A Study on the Synergistic Relationship of Ecosystem Service Trade-Offs from Multiple Perspectives Based on Ecological Restoration Zoning of National Land Space—A Case Study of Liaoning Province

Clarifying the trade-offs and synergies of ecosystem services in Liaoning’s ecological restoration zones is crucial for strengthening the positioning of ecological restoration zones and optimizing ecosystem services. This study is based on “Liaoning Provincial Land Spatial Planning (2021–2035)” and divides the area into ecological restoration zones. We utilized the InVEST model, ArcGIS Pro, and Geoda in this study to quantify five ecosystem services (Soil Conservation, Carbon Storage, Habitat Quality, Water Yield, and Food Production) and constructed an evaluation framework to assess the trade-offs and synergies of ecosystem services at both global and local levels. The conclusions are as follows: (1) The global relationships among ecosystem services in different ecological restoration zones are ranked as: strong trade-offs (35.51%) > weak trade-offs (33.17%) > low synergies (29.09%) > high synergies (2.24%); (2) The area exhibiting synergistic relationships between pairs of local ecosystem services in ecological restoration zones is larger than the area exhibiting trade-offs; (3) The strongest synergy is observed between water yield and soil conservation, while the most significant trade-off occurs between food production and soil conservation. These relationships exhibit similar spatial characteristics in the WSFR, SWCR, and WCR zones; (4) The proportion of areas showing trade-offs and synergies differs between global and local scales.

Spatio-temporal attention based collaborative local–global learning for traffic flow prediction

Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.

SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods.

The Rehabilitation of the Old Medina of Bejaad, Morocco: a Technological Approach (BIM) to Enhance the Built Heritage

Objective: This study proposes a process of revitalization and regeneration of the old medina of Bejaad, as well as a methodology for the technical analysis of its urban fabric and traditional structures, while respecting the inherent values of its constructions. Through data related to the location of each building and a more in-depth investigation, a global relationship with its environment and history is established. Theoretical Framework: The old medina of Bejaad, through its ancient fabric and its potential social and economic wealth, should be leveraged for the development of this spiritual city. This built heritage is characterized by buildings that, for the most part, have preserved an uncommon restraint, maintaining the art of past construction and the secrets of local housing. Its housing is composed of two types of built fabric: traditional and more recent structures. These two types of buildings are connected by a network of public spaces that bring the inhabitants together. Method: Creation of specific Building Information Modelling (BIM) object libraries, derived from three-dimensional models created by 3D scanning using Lidar technology. Results and Discussion: The results of this investigation highlight the need to identify new methodologies for studying the built environment, which no longer focus solely on individual building types, but encompass the broader scale of the fabric, within which it is possible to assess the constructive relationships between the elements composing the block. This is achieved through a process of adopting measures for the protection, rehabilitation, and appropriation of their living environment. Originality/Value: The study proposes a practical evaluation approach by providing precise data to improve the preservation of the built heritage of the old medina of Bejaad, with the aim of establishing an architectural, urban, and landscape charter for the city and its components.

Hyperspectral and multispectral images fusion based on pyramid swin transformer

Remote sensing image fusion aims to generate a high spatial resolution hyperspectral image (HR-HSI) by integrating a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI). While Convolutional Neural Networks (CNNs) have been widely employed in addressing the HSI-MSI fusion problem, their limited receptive field poses challenges in capturing global relationships within the feature maps. On the other hand, the computational complexity of Transformers hinders their application, especially in dealing with high-dimensional data like hyperspectral images (HSIs). To overcome this challenge, we propose an HSI-MSI fusion method based on the Pyramid Swin Transformer (PSTF). The pyramid design of the PSTF effectively extracts multi-scale information from images. The Spatial-Spectral Crossed Attention (SSCA) module, comprising the Cross Spatial Attention (CSA) and the Spectral Feature Integration (SFI) modules. The CSA module employs a cross-shaped self-attention mechanism, providing greater modeling flexibility for different spatial scales and non-local structures compared to traditional convolutional layers. Meanwhile, the SFI module introduces a global memory block (MB) to select the most relevant low-rank spectral vectors, integrating global spectral information with local spatial–spectral correlation to better extract and preserve spectral information. Additionally, the Separate Feature Extraction (SFE) module enhances the network’s ability to represent image features by independently processing positive and negative parts of shallow features, thus capturing details and structures more effectively and preventing the vanishing gradient problem. Compared with the state-of-the-art (SOTA) methods, experimental results demonstrate the effectiveness of the PSTF method.

A Gravitational-like Relationship of Dispersion Interactions is Exhibited by 40 Pairs of Molecules and Noble Gas Atoms.

We present computational results of many-body dispersion (MBD) interactions for 40 pairs of molecular and atomic species: hydrocarbons, silanes, corresponding fluorinated derivatives, pairs which have multiple H---H contacts between the molecules, as well as pairs having π-π interactions, and pairs of noble gases. The calculations reveal that the MBD stabilization energy (EDISP,MBD) obeys a global relationship, which is gravitational-like. It is proportional to the product of the masses of the two molecules (M1M2) and inversely proportional to the corresponding distances between the molecular centers-of-mass (RCOM-COM) or the H---H distances of the atoms mediating the interactions of the two molecules (RH-H). This relationship reflects the interactions of instantaneous dipoles, which are formed by the ensemble of bonds/atoms in the interacting molecules. Using the D4-corrected dispersion energy (EDISP,D4), which accounts for three-body interactions, we find that the EDISP,MBD and EDISP,D4 data sets are strongly correlated. Based on valence-bond modeling, the dispersion interactions occur primarily due to the increased contributions of the oscillating-ionic VB structures which maintain favorable electrostatic interactions; the [Sub─C+:H-+H:C-─Sub] and [Sub─C:-+H -H:C+─Sub] structures; Sub symbolizes general residues. This augmented contribution is complemented by simultaneously diminished-weights of the destabilizing pair of structures, [Sub─C+:H--H:C+─Sub] and [Sub─:C- H++H:C-─Sub]. The local charges are propagated to the entire ensemble of bonds/atoms by partially charging the Sub residues, thus bringing about the "gravitational-like" dependence of dispersion.

Enhanced Multi-Scale Attention-Driven 3D Human Reconstruction from Single Image

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details of the non-visible side remains difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles on the model’s surface. However, we have identified challenges with existing normal prediction networks when dealing with complex scenes, such as a lack of focus on local features and insufficient modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced Multi-scale Attention-Driven Single-Image 3D Human Reconstruction. This approach incorporates a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention mechanisms by adaptively adjusting the weights between features, enabling the network to more effectively leverage information across various scales. Furthermore, we have improved the feature fusion method to better integrate representations from different scales. This enhanced feature fusion allows the network to more comprehensively understand both fine details and global structures within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention mechanism and feature fusion method, optimizing the network’s training process and enhancing the quality of reconstruction results. Our network demonstrates significant improvements in performance for 3D human model reconstruction. Experimental results show that our method exhibits greater robustness to challenging poses compared to traditional single-scale approaches.

Attention Score Enhancement Model Through Pairwise Image Comparison

This study proposes the Pairwise Attention Enhancement (PAE) model to address the limitations of the Vision Transformer (ViT). While the ViT effectively models global relationships between image patches, it encounters challenges in medical image analysis where fine-grained local features are crucial. Although the ViT excels at capturing global interactions within the entire image, it may potentially underperform due to its inadequate representation of local features such as color, texture, and edges. The proposed PAE model enhances local features by calculating cosine similarity between the attention maps of training and reference images and integrating attention maps in regions with high similarity. This approach complements the ViT’s global capture capability, allowing for a more accurate reflection of subtle visual differences. Experiments using Clock Drawing Test data demonstrated that the PAE model achieved a precision of 0.9383, recall of 0.8916, F1-Score of 0.9133, and accuracy of 92.69%, showing a 12% improvement over API-Net and a 1% improvement over the ViT. This study suggests that the PAE model can enhance performance in computer vision fields where local features are crucial by overcoming the limitations of the ViT.

Trade Wars and Tariff Policies: Long-Term effects on Global Trade and Economic Relationship

Trade wars and Tariff policies have become serious concerns defining and affecting global trade and economic relations in the 21st century. These policies characterized by the use of tariffs and other trade barriers have impacted the world beyond just the economic or financial effect between countries involved. The paper focuses on the long-term effect of trade wars and tariff policies on global trade and economic relationship between countries. Using historical and modern trade war scenarios such as USA-China trade war, the study indicates that disruption of global supply chain, volatility of financial market, and impact on specific sectors of the country economy such as agriculture are some of the long-term impacts of these policies on global trade. In terms of the impact on economic relationship between countries, the study highlights creation of new trade agreements and diversification of trade partnership as possible effects on economic relationship between countries. It is therefore imperative for countries to diversify their trade relationship by adopting initiatives against the effects of trade war. The study concludes that while national measures could bring some political and economic results in the short term, it is important to note that global prosperity cannot be long-lasting without cooperative and stable international trade policies.

DPNet: Scene text detection based on dual perspective CNN-transformer.

With the continuous advancement of deep learning, research in scene text detection has evolved significantly. However, complex backgrounds and various text forms complicate the task of detecting text from images. CNN is a deep learning algorithm that automatically extracts features through convolution operation. In the task of scene text detection, it can capture local text features in images, but it lacks global attributes. In recent years, inspired by the application of transformers in the field of computer vision, it can capture the global information of images and describe them intuitively. Therefore, this paper proposes scene text detection based on dual perspective CNN-transformer. The channel enhanced self-attention module (CESAM) and spatial enhanced self-attention module (SESAM) proposed in this paper are integrated into the traditional ResNet backbone network. This integration effectively facilitates the learning of global contextual information and positional relationships of text, thereby alleviating the challenge of detecting small target text. Furthermore, this paper introduces a feature decoder designed to refine the effective text information within the feature map and enhance the perception of detailed information. Experiments show that the method proposed in this paper significantly improves the robustness of the model for different types of text detection. Compared to the baseline, it achieves performance improvements of 2.51% (83.81 vs. 81.3) on the Total-Text dataset, 1.87% (86.07 vs. 84.2) on the ICDAR 2015 dataset, and 3.63% (86.72 vs. 83.09) on the MSRA-TD500 dataset, while also demonstrating better visual effects.

Waste classification strategy based on multi-scale feature fusion for intelligent waste recycling in office buildings

Waste classification is an important measure to protect the environment. Existing waste classification methods mainly focus on scientific research, but lack attention to the challenges of waste classification in actual scenarios. For example, wastes with similar contours, similar textures, or contaminated appearance are difficult to be classified in actual scenarios. To address these issues, this paper proposes an innovative multi-scale feature fusion strategy (MFFS) to improve the classification accuracy of these wastes. MFFS combines local fine-grained features with global coarse-grained features to improve the feature expression ability of waste. However, how to effectively fuse these two features is a key challenge. This paper proposes a dual-scale feature fusion strategy, first fusing fine-grained features in the first dimension, then fusing coarse-grained features in the second dimension, and introducing spatial features to further enhance feature expression capabilities. In order to reduce the interference of background information, the model in this paper models global relationships based on convolutional features. The MFFS strategy achieved a classification accuracy of 95.5% on the self-built dataset and 94.1% on the public dataset TrashNet. The number of parameters of our model is reduced by 57.2% compared with the classic VGG16 and by 34.2% compared with the Vision Transformer. In addition, we designed an intelligent waste sorting device and deployed the MFFS model on the device to implement the application. Experiments show that our model has ideal accuracy and stability and can be promoted and applied.

STAFGCN:A spatial-temporal attention-based fusion graph convolution network for pedestrian trajectory prediction

Pedestrian trajectory prediction provides crucial data support for the development of smart cities. Existing pedestrian trajectory prediction methods often overlook the different types of pedestrian interactions and the micro-level spatial-temporal relationships when handling the interaction information in spatial dimension and temporal dimension. The model employs a spatial-temporal attention-based fusion graph convolutional framework to predict future pedestrian trajectories. For the different types of local and global relationships between pedestrians, it first employs spatial-temporal attention mechanisms to capture dependencies in pedestrian sequence data, obtaining the social interactions of pedestrians in spatial contexts and the movement trends of pedestrians over time. Subsequently, a fusion graph convolutional module merges the temporal weight matrix and the spatial weight matrix into a spatial-temporal fusion feature map. Finally, a decoder section utilizes TimeStacked Convolutional Neural Networks to predict future trajectories. The final validation on the ETH and UCY datasets yielded experimental results with an Average Displacement Error(ADE) of 0.34 and an Final Displacement Error(FDE) of 0.55. The visualization results further demonstrated the rationality of the model.

CULTURAL GLOBALIZATION OF TOURISM AND ITS IMPACT ON THE BEHAVIOR OF THE LOCAL COMMUNITY IN KARBALA GOVERNORATE

The research aims to identify the extent of the impact of cultural globalization of tourism on the behavior of the local community in Karbala Governorate. It underscores the importance of knowledge exchange as a key strategy in addressing the challenges posed by cultural globalization. The study assumed a positive and significant impact relationship of cultural globalization of tourism on the behavior of the local community. To achieve its objectives, a questionnaire was designed that included (17) paragraphs to collect data from the research sample of (75) individuals. The descriptive analytical approach was adopted when the data was collected. Hypotheses were tested using a set of statistical methods with the help of the statistical program (SPSS. V.23). After conducting the statistical analysis; the research concluded that there is a significant statistical correlation between the cognitive dimension and the behavior of the local community, and accordingly, there is an impact. The most important recommendation was to work on exchanging ideas, opinions, information, and experiences to increase knowledge of the importance of tourism and its development. This call for knowledge exchange invites the audience to be part of the solution, fostering a sense of engagement and shared responsibility.

VG-CALF: A vision-guided cross-attention and late-fusion network for radiology images in Medical Visual Question Answering

Image and question matching is essential in Medical Visual Question Answering (MVQA) in order to accurately assess the visual-semantic correspondence between an image and a question. However, the recent state-of-the-art methods focus solely on the contrastive learning between an entire image and a question. Though contrastive learning successfully model the global relationship between an image and a question, it is less effective to capture the fine-grained alignments conveyed between image regions and question words. In contrast, large-scale pre-training poses significant drawbacks, including extended training times, handling substantial data volumes, and necessitating high computational power. To address these challenges, we propose the Vision-Guided Cross-Attention based Late Fusion (VG-CALF) network, which integrates image and question features into a unified deep model without relying on pre-training for MVQA tasks. In our proposed approach, we use self-attention to effectively leverage intra-modal relationships within each modality and implement vision-guided cross-attention to emphasize the inter-modal relationships between image regions and question words. By simultaneously considering intra-modal and inter-modal relationships, our proposed method significantly improves the overall performance of MVQA without the need for pre-training on extensive image-question pairs. Experimental results on benchmark datasets, such as, SLAKE and VQA-RAD demonstrate that our proposed approach performs competitively with existing state-of-the-art methods.

Non-Linear Relationship between MiRNA Regulatory Activity and Binding Site Counts on Target mRNAs

MicroRNAs (miRNA) exert regulatory actions via base pairing with their binding sites on target mRNAs. Cooperative binding, i.e., synergism, among binding sites on an mRNA is biochemically well characterized. We studied whether this synergism is reflected in the global relationship between miRNA-mediated regulatory activity and miRNA binding site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, we found that, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extraordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA-targeted mRNAs in translationally less active polysomes relative to more active polysomes. In these four studies, we qualitatively observed obvious positive correlation between the extent to which an mRNA is miRNA-regulated and its binding site count. This paper summarizes the datasets used. We also quantitatively analyzed the correlation by comparative linear and non-linear regression analyses. Non-linear relationships, i.e., accelerating rise of regulatory activity as binding site count increases, fit the data much better, conceivably a transcriptome-level reflection of cooperative binding among miRNA binding sites on a target mRNA. This observation is potentially a guide for integrative quantitative modeling of the miRNA regulatory system.

Asymmetric Network Combining CNN and Transformer for Building Extraction from Remote Sensing Images.

The accurate extraction of buildings from remote sensing images is crucial in fields such as 3D urban planning, disaster detection, and military reconnaissance. In recent years, models based on Transformer have performed well in global information processing and contextual relationship modeling, but suffer from high computational costs and insufficient ability to capture local information. In contrast, convolutional neural networks (CNNs) are very effective in extracting local features, but have a limited ability to process global information. In this paper, an asymmetric network (CTANet), which combines the advantages of CNN and Transformer, is proposed to achieve efficient extraction of buildings. Specifically, CTANet employs ConvNeXt as an encoder to extract features and combines it with an efficient bilateral hybrid attention transformer (BHAFormer) which is designed as a decoder. The BHAFormer establishes global dependencies from both texture edge features and background information perspectives to extract buildings more accurately while maintaining a low computational cost. Additionally, the multiscale mixed attention mechanism module (MSM-AMM) is introduced to learn the multiscale semantic information and channel representations of the encoder features to reduce noise interference and compensate for the loss of information in the downsampling process. Experimental results show that the proposed model achieves the best F1-score (86.7%, 95.74%, and 90.52%) and IoU (76.52%, 91.84%, and 82.68%) compared to other state-of-the-art methods on the Massachusetts building dataset, the WHU building dataset, and the Inria aerial image labeling dataset.

Power and Governance in the Context of Globalization

Introduction. Issues of power and management in the context of globalization are one of the most pressing topics in Western social sciences, affecting a wide range of branches of scientific knowledge – from sociology, political science, social and political philosophy to economics and jurisprudence. The prospect of the disappearance of the state as a sovereign subject of legal relations, declared by globalists, or a radical transformation of this established political and legal phenomenon, the rapid development of non-governmental and non-profit organizations, the digital revolution, an increase in the level of informatization of society and many other factors of reality that did not take place in previous centuries, raise natural questions about the exercise of power and control in the new prevailing conditions. The study of this issue is new for domestic legal science, since this topic has not yet been considered by Russian legal scholars. Methods. In this study, dialectical, sociological and hermeneutical methods are applied. The above methods are used in conjunction to obtain a synergistic effect. Results. As a result of a comprehensive analysis of power and management in the context of globalization, the main aspects of this concept in relation to modern times have been identified, and forecasts have been made regarding the described situation in the near future. Discussion and Conclusion. This article, unlike other articles related in topic and purpose, offers a comprehensive analysis of power and management in the context of globalization in their modern and relevant form. The author takes into account the close relationship of globalization, power and power relations with the historical heritage, regional characteristics and objective factors of legal reality.

The Riemann–Hilbert Approach to the Higher-Order Gerdjikov–Ivanov Equation on the Half Line

The Fokas method exhibits remarkable versatility in solving boundary value problems associated with both linear and nonlinear partial differential equations, particularly when conventional approaches encounter challenges in handling intricate boundary conditions. The existing literature often lacks the incorporation of unconventional boundary conditions, and this study addresses this issue by extending the application of the Fokas method to the higher-order Gerdjikov-Ivanov equation on the half line (−∞,0]. We have demonstrated the exclusive representation of the potential function u(z,t) in the higher-order Gerdjikov–Ivanov equation through the solution of a Riemann–Hilbert problem. The characteristic function is partitioned on the complex plane, and we obtain the jump matrix between each partition based on the positive and negative values of the partition as well as the spectral matrix determined by the initial data and boundary value data. The findings suggest that the spectral functions are not mutually independent; instead, they conform to a global relationship. The novel aspect of this study is the application of the Fokas method to a previously unexplored case, contributing to the theoretical and practical understanding of complex partial differential equations and filling a gap in the treatment of boundary conditions.

A link prediction method for Chinese financial event knowledge graph based on graph attention networks and convolutional neural networks

Finance is a knowledge-intensive domain in nature, with its data containing a significant amount of interconnected information. Constructing a financial knowledge graph is an important application for transforming financial text/web content into machine-readable data. However, the complexity of Chinese financial knowledge and the dynamic and evolving nature of Chinese financial data often lead to incomplete knowledge graphs. To address this challenge, we propose a novel link prediction method for Chinese financial event knowledge graph based on Graph Attention Networks and Convolutional Neural Networks. Our method begins with the construction of the foundational Chinese financial event knowledge graph using a relational triple extraction module integrated with a large language model framework, along with a Prompting with Iterative Verification (PiVe) module for validation. To enhance the completeness of the knowledge graph, we introduce an encoder-decoder framework, where a graph attention network with joint embeddings of financial event entities and relations acts as the encoder, while a Convolutional Knowledge Base embedding model (ConvKB) serves as the decoder. This framework effectively aggregates crucial neighbor information and captures global relationships among entity and relation embeddings. Extensive comparative experiments demonstrate the utility and accuracy of this method, ultimately enabling the effective completion of Chinese financial event knowledge graphs.

Effect of climate, crop, and management on soil phosphatase activity in croplands: A global investigation and relationships with crop yield

Agricultural and livestock production cover more than a third of the Earth's land surface and are crucial to food supply. Soil extracellular enzymes play an important role in the transformation of elements and compounds in soil, particularly acid (ACP) and alkaline (ALP) phosphatases (both, APases). These enzymes have a vital role in releasing phosphorus (P) from organic matter. However, the effect of climate variables and agro-ecosystem management on APase activity in croplands remains unclear, as does its eventual relationship with agricultural productivity. Therefore, we compiled a global database of APase activity in croplands (between 1977 and 2022) and we analysed 5876 observations across 474 papers to study climate variables, crop family, and management effects on ACP and ALP activity, and their relationship with yield. ACP activity is reduced by higher temperatures (p<0.001) and lower rainfall (p=0.002). There was an interaction effect of temperature and precipitation on ALP activity (p=0.046), with the negative effect of temperature being stronger with high precipitation, and low precipitation showing low ALP activity levels at any temperature. The crop family greatly influenced APase activity (p<0.001). Management practices affected ACP and ALP activity differently; ACP activity was positively influenced by organic fertilization combined with, crop rotation or irrigation by an average of 15.6 % and 30.7 %, respectively. ALP activity was mainly positively influenced by the interaction of two different factors: organic or inorganic-organic fertilization and reduced or zero tillage. Further understanding of soil enzyme mechanisms would aid global food security and yield. As ACP activity doubles from 100.0 to 200.0 mg pNP kg−1h−1, the crop yield increases by more than two-fold, an outcome not demonstrated in croplands until now. These results could enhance yield potential through the promotion of APase activity, and the consideration of climate variables and agro-ecosystem management, which could ultimately improve cost-benefit ratios for sustainable crop growth.