Complex Network Structure Research Articles

Forecast of coal consumption in salt lake enterprises based on temporal gated recurrent unit network with squeeze-and-excitation attention

Accurate coal consumption forecasting is key to improving energy supply efficiency and strategic planning. However, current forecasting methods mainly emphasize temporal variations, ignoring key influencing factors such as seasonality and environmental changes. In addition, the complex structure of neural networks complicates the selection of ideal hyperparameters. To address these issues, this study proposes a novel squeezing-and-excitation temporal gated recurrent unit (SE-TGRU) method. By incorporating squeezing-and-excitation attention modules, this method autonomously assesses the relative significance of diverse historical time points, unveiling their correlations to capture essential information about coal consumption. Notably, a novel reverse learning-based dwarf mongoose optimization (RL-DMO) algorithm is devised to comprehensively analyze data within a specified time window. This augmentation enhances the ability of the model to discern data anomalies with heightened sensitivity, enabling corresponding adjustments during the training process. Finally, the efficacy of the proposed method in coal consumption forecasting is verified through extensive experimentation using actual data sourced from the procurement department of a salt lake enterprise. The experimental results demonstrate that the proposed method exhibits an 11 % improvement in the R-squared (R2) indicator and an 18 % reduction in the root mean square error (RMSE) compared to the baseline method.

Environmental heterogeneity caused by large-scale cultivation of Pyropia haitanensis shapes multi-group biodiversity distribution in coastal areas

The response of marine biodiversity to mariculture has long been a research focus in marine ecology. However, the effects of seaweed cultivation on biological community assembly are poorly understood, especially in diverse communities with distinct ecological characteristics. In this study, we used environmental DNA metabarcoding to investigate the spatial distribution patterns of bacterial, protistan, and metazoan diversity, aiming to reveal the mechanisms of community assembly in the Pyropia haitanensis cultivation zone along the Fujian coast, China. We found that, compared with the biological communities in control zones, those in P. haitanensis cultivation zones exhibited stronger geographic distance-decay patterns and displayed more complex and stable network structures. Deterministic processes (environmental selection) played a more important role in the assembly of bacterial, protistan, and metazoan communities in P. haitanensis cultivation zones, especially metazoan communities. Variance partitioning analysis showed that environmental variables made greater contributions to the diversity of the three types of communities within the P. haitanensis cultivation zones than in the control zones. Partial least squares path modeling analysis identified nitrate‑nitrogen (NO3-N), pH, particulate organic carbon (POC), and dissolved organic carbon (DOC) as the key environmental variables affecting biodiversity. Overall, the environmental heterogeneity caused by the large-scale cultivation of P. haitanensis could be the crucial factor influencing the composition and structure of various biological communities. Our results highlight the importance of the responses of multi-group organisms to the cultivation of seaweed, and provide insights into the coexistence patterns of biodiversity at the spatial scale.

Scaling theory of fractal complex networks

We show that fractality in complex networks arises from the geometric self-similarity of their built-in hierarchical community-like structure, which is mathematically described by the scale-invariant equation for the masses of the boxes with which we cover the network when determining its box dimension. This approach—grounded in both scaling theory of phase transitions and renormalization group theory—leads to the consistent scaling theory of fractal complex networks, which complements the collection of scaling exponents with several new ones and reveals various relationships between them. We propose the introduction of two classes of exponents: microscopic and macroscopic, characterizing the local structure of fractal complex networks and their global properties, respectively. Interestingly, exponents from both classes are related to each other and only a few of them (three out of seven) are independent, thus bridging the local self-similarity and global scale-invariance in fractal networks. We successfully verify our findings in real networks situated in various fields (information—the World Wide Web, biological—the human brain, and social—scientific collaboration networks) and in several fractal network models.

Real-time Graph Visualization with JavaFX: Exploring Large-scale Network Structures

The ability to visualize and comprehend complex network structures is crucial in various domains, including social media analysis, computer networks, and transportation systems. However, visualizing large-scale graphs poses significant challenges due to computational limitations and rendering performance constraints. This research paper presents a novel approach to real-time graph visualization using JavaFX, a powerful Java-based framework for developing rich client applications. By leveraging efficient data structures, rendering optimizations, and multithreading techniques, our proposed system achieves real-time visualization of large-scale graphs, enabling users to explore and interact with dynamic network structures seamlessly. The system incorporates advanced layout algorithms and visual encodings to enhance the clarity and interpretability of the visualizations. Extensive experiments were conducted using real-world and synthetic datasets to evaluate the system's performance, scalability, and usability. The results demonstrate the effectiveness of our approach in rendering large graphs in real-time, outperforming existing techniques. Furthermore, a user study was conducted to assess the system's usability and gather feedback on interaction and exploration features, highlighting potential applications in various domains. Keywords— Graph Visualization, Real-time Rendering, JavaFX, Large-scale Networks, Layout Algorithms, Rendering Optimizations, User Interaction, Performance Evaluation.

Modeling question difficulty for unbiased cognitive diagnosis: A causal perspective

Cognitive diagnosis is an intelligent education task that aims to learn students’ cognitive states on knowledge concepts based on historical answering logs over questions. Existing studies focus on modeling the interactions between students and questions through either manual-designed functions (e.g., logistic function) or complex neural network structures. However, such studies neglect the question difficulty bias, i.e., questions exhibit uneven distribution on the answering frequency, as simple questions are answered more times than difficult ones for the given concept. To tackle this issue, we present a Causal Cognitive Diagnosis Framework (CausalCDF), which considers the question difficulty bias and could be readily integrated with traditional diagnostic models for better cognitive diagnosis. Specifically, we first analyze the effect of question difficulty (acting as the confounder) on student performance via a causal graph. Then we eliminate the bad effect of the confounding difficulty bias via causal intervention in model training. We instantiate CausalCDF on five representative diagnostic models and perform extensive experiments on two real-world datasets. Empirical studies prove the effectiveness of CausalCDF compared to existing studies.

Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.

Identifying influential nodes based on the disassortativity and community structure of complex network

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

Enhancing Threat Prioritization In Cybersecurity Using Avian Shield Optimizer (ASO) In Graph Forge Elite (GFE) Framework

In the ever-evolving field of cybersecurity, the ability to effectively prioritize threats is essential for organizations to allocate resources wisely and proactively mitigate potential risks. Avian Shield Optimizer (ASO) is a revolutionary optimization technique specifically designed for threat prioritization. ASO utilizes evolutionary-inspired mechanisms to dynamically adjust the prioritization of threats based on a comprehensive analysis of factors such as severity, relevance, and potential impact on organizational assets. By continuously monitoring and adapting to the dynamic threat landscape, ASO enables organizations to anticipate and respond quickly to emerging threats, ensuring that resources are allocated appropriately to address the most critical vulnerabilities. Moreover, ASO seamlessly integrates with existing threat intelligence platforms, providing cybersecurity teams with actionable insights and recommendations to comprehensively strengthen their defense strategies. Concurrently, the GraphForgeElite framework enhances this approach by facilitating the analysis of complex network data connections and structures, enabling the detection of subtle irregularities that may indicate potential cyber threats. ASO collaborates synergistically with GraphForgeElite, utilizing evolutionary-inspired methodologies to dynamically adapt the framework's design and settings, automatically tuning the parameters. Through rigorous experimentation, the performance of ASO-GraphForgeElite's Network is compared to other state-of-the-art classifiers. The results demonstrate the superior performance of ASO-GraphForgeElite's Network, surpassing 99% accuracy, precision, recall, and F1-score. Additionally, the framework exhibits efficiency in handling complex network data structures, enabling the identification of subtle patterns that indicate potential threats.

The Convergence Analysis of Evolutionary Dynamics for Continuous Action Iterated Dilemma in Information Loss Networks

In this article, we propose a convergence analysis method for evolution dynamics in information loss networks, which overcomes the analytical difficulties caused by complex network relationships. Evolutionary game theory is a widely-used tool for analyzing player behavior in networks, where players typically adopt binary strategies, either cooperation or defection. However, player behavior in real-world scenarios is often multidimensional and complex, and thus a dynamic model of continuous action iterated dilemma (CAID) with continuous strategy is proposed to enrich the strategies of players, allowing them to choose intermediate states between cooperation and defection, providing a more accurate representation of the evolution of cooperation than traditional dynamic models. Meanwhile, the convergence of traditional models is often analyzed using Jacobian matrices, which requires a significant amount of derivation related to the complex network structure, leading to inefficiencies. As such, a new convergence analysis method based on the Lyapunov function has been designed to circumvent these complex calculations. Additionally, as there is often noise present during the transfer of information between players, we further analyze the convergence of dynamic models in information loss networks using the Lyapunov function. Two examples based on the prisoner’s dilemma and snowdrift dilemma on networks are proposed to show the effectiveness of the designed convergence analysis.

Computing Metric Dimension of Two Types of Claw-free Cubic Graphs with Applications

Consider the simple connected graph G with vertex set V(G) and edge set E(G). A graph \(G\) can be resolved by \(R\) if each vertex’s representation of distances to the other vertices in \(R\) uniquely identifies it. The minimum cardinality of the set \(R\) is the metric dimension of \(G\). The length of the shortest path between any two vertices, x, y in V(G), is signified by the distance symbol d(x, y). An ordered k-tuple \(r(x/R)=(d(x,z_1),d(x,\ z_2),…,d(x,z_k))\) represents representation of \(x\) with respect to \(R\) for an ordered subset \(R={\{z}_1,z_2,z_3…,z_k\}\) of vertices and vertex \(x\) in a connected graph. Metric dimension is used in a wide range of contexts where connection, distance, and connectedness are essential factors. It facilitates understanding the structure and dynamics of complex networks and problems relating to robotics network design, navigation, optimization, and facility location. Robots can optimize their localization and navigation methods using a small number of reference sites due to the pertinent idea of metric dimension. As a result, many robotic applications, such as collaborative robotics, autonomous navigation, and environment mapping, are more accurate, efficient, and resilient. A claw-free cubic graph (CCG) is one in which no induced subgraph is a claw. CCG proves helpful in various fields, including optimization, network design, and algorithm development. They offer intriguing structural and algorithmic properties. Developing algorithms and results for claw-free graphs frequently has applications in solving of challenging real-world situations. The metric dimension of a couple of claw-free cubic graphs (CCG), a string of diamonds (SOD), and a ring of diamonds (ROD) will be determined in this work.

Spatial correlation network of municipal solid waste carbon emissions and its influencing factors in China

Clarifying the provincial spatial correlation network of municipal solid waste (MSW) carbon emissions and its influencing factors is significant for reducing MSW carbon emissions and for ensuring cross-regional coordinated emission reduction. This study used a modified gravity model and social network analysis to explore the structural characteristics of the spatial correlation network of MSW carbon emissions. The quadratic assignment procedure was then used to examine the influencing factors. The results are as follows: (1) From 2005 to 2020, the MSW carbon emissions spatial correlation network showed an intuitive and complex network structure. The tightness of the network increased with fluctuations but still needs improvement, while the network structure exhibited good accessibility and stability. (2) The spatial network exhibited a significant “core-edge” distribution, with Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, and Fujian being at the center of the spatial correlation network and playing leading roles. (3) The spatial correlation network of MSW carbon emissions was divided into four parts, with the four parts having few internal connections but with the relationships between them being close. (4) Geographical adjacency, economic development level, science and technology level, and MSW treatment structure significantly influenced spatial correlations, while the urbanization level and degree of government intervention had no significant influence on spatial correlations.

Performance evaluation of biodegradable polymer PHBV and PBAT blends with adjustable melt flow behaviour, heat deflection temperature, and morphological transition

AbstractMelt blending is a reliable and well‐demonstrated strategy for improving the mechanical, thermal, rheological, and surface properties of biopolymers. Poly(hydroxy‐3‐butyrate‐co‐3‐hydroxyvalerate) (PHBV) and poly(butylene adipate‐co‐terephthalate) (PBAT) are the two popular choices for blending polymers due to their diverse properties and complementary soil biodegradable behaviour. Due to their immiscibility, however, blending with the help of processing additives is necessary to reap the most significant benefits from this process and to avoid immiscibility issues. This study utilized the additives (peroxides and epoxy‐based chain extender) to compatibilize the biodegradable polymers PHBV and PBAT in a 60:40 blending ratio. The tensile strength and Young's modulus of the PHBV/PBAT(60/40) blend were improved by 32% and 64%, respectively, after adding a combination of peroxide (0.02 phr) and chain extender (0.3 phr) due to the formation of a complex network structure with increased chain length. The positive effect of an additive addition was also reflected by a 30°C increment in heat deflection temperature of biodegradable blend due to its high modulus value as supported by mechanical properties. The combined action of a peroxide and chain extender demonstrated a significantly higher complex viscosity of the PHBV/PBAT(60/40) blend due to the formation of a crosslinked polymer network as analyzed by rheological analysis. Our research demonstrated the effect of additives and their combined impact on analytical properties of PHBV/PBAT(60/40) blend to guide future work in improving their candidature to serve as a drop‐in solution in replacing non‐biodegradable petro‐based plastic products.

Application of Distributed Graphs for Facilitation of Scalable Botnet Detection and Response

Botnets pose a significant threat to modern network environments, exploiting compromised devices to carry out malicious activities such as distributed denial-of-service attacks, spam campaigns, and data theft. Traditional centralized detection systems often struggle to handle the scale and complexity of botnet attacks, leading to delays in detection and response. In response to these challenges, this paper explores the application of distributed graphs for facilitating scalable botnet detection and response. Distributed graphs offer a promising approach for modelling and analyzing complex network structures, enabling efficient detection of botnet propagation patterns and anomalous behaviour across distributed computing environments. The paper presents an overview of distributed graph-based botnet detection systems, discussing their architecture, design considerations, and key concepts such as graph partitioning, vertex-centric computation, and message passing in distributed graph algorithms. Case studies illustrate the practical application of distributed graph-based botnet detection in diverse network environments, highlighting success stories, challenges encountered, and lessons learned from deploying distributed graph systems in production cybersecurity operations. Finally, the paper discusses challenges and open research questions in the field of distributed graph-based botnet detection, addressing issues such as graph partitioning strategies, fault tolerance, privacy-preserving techniques, and integration with other security tools. It proposes potential avenues for future research and development in scalable botnet detection using distributed graphs, emphasizing the importance of adaptive threat response, collaboration with industry partners, and continuous improvement in detection algorithms for enhancing cybersecurity resilience against botnet attacks.

A comparative study of large-scale and lightweight convolutional neural networks for ImageNet classification

In the field of convolution neural networks (CNNs), many impressive architectures have been published in recent years. These can be roughly divided into two groups: large-scale models and lightweight models. These large models are characterized by many trainable weights and complex network structures, offering them strong effectiveness in various computer vision tasks, and have become essential components of many modern visual recognition systems. These lightweight CNNs are designed to maintain high performance with limited memory and computational resources. They are highly efficient in terms of inference time and resource utilization, so that particularly suitable for mobile and edge computing devices. This work focuses on some prominent models based on the ImageNet database and explores the reasons for their frameworks success. By analyzing these models, a trend could be identified in the development of CNN models: Reasonably increasing the scale of the model and utilizing suitable frameworks can both improve accuracy and efficiency.

HAIC-NET: Semi-supervised OCTA vessel segmentation with self-supervised pretext task and dual consistency training

Optical Coherence Tomography Angiography(OCTA) vessel segmentation is a challenging task. On the one hand, the complex structure of the capillary networks presents significant obstacles to achieving accurate vessel segmentation. On the other hand, current research on OCTA vessel segmentation heavily relies on high-quality manual annotations, especially in fully-supervised approaches. In contrast, pixel-level annotation of OCTA vessels is time-consuming and labor-intensive. To address these issues, we propose a semi-supervised method called HAIC-Net, which integrates self-supervised learning with a homologous augmented image classification pretext task and dual consistency training with data perturbation consistency and topological connectivity consistency. Firstly, we design a self-supervised homologous augmented image classification pretext task that directs the model’s attention to similar vascular features in homologous augmented images, thereby extracting rich vessel information from unlabeled images and reducing the dependence on manual annotations. Secondly, we introduce a dual-consistency structure with topological connectivity consistency to provide constraint from a topological perspective, which is consistent with the topological characteristics of the vascular network, to enhance the segmentation network’s sensitivity to vessel connectivity and decrease the topological errors in segmentation results. We conduct experiments on two publicly available datasets and one private dataset and validate the state-of-the-art performance of the proposed method. On the ROSE-1 dataset, our method achieves 0.9143 accuracy and 0.7658 dice coefficient, surpassing other current semi-supervised methods and approaching the performance of state-of-the-art fully supervised methods. The same result can also be observed on OCTA500 and our private dataset, demonstrating the effectiveness and superiority of our approach.

Maintaining the native gut microbiota of bharal (Pseudois nayaur) is crucial in ex situ conservation.

As wildlife protection continue to strengthen, research on the gut microbiota of wildlife is increasing. Carrying out conservation and research on endangered species in the Qinghai Tibet Plateau plays an important role in global biodiversity conservation. This study utilized 16S rRNA sequencing of fecal samples to investigate the composition, function, and changes of the gut microbiota of bharal in different environments, seasons, and genders. The results showed that Firmicutes and Bacteroidota were the dominant phyla and UCG-005, Bacteroides, UCG-010 were the dominant genera of bharal. In the wild, the abundance of Firmicutes increased which was conducive to the decomposition and utilization of cellulose, hemicellulose, and carbohydrate. Due to the variety of food types and nutrition in different seasons, the composition and function of gut microbiota were obviously different between genders. Compared with zoo, higher alpha diversity, a more complex gut microbiota network structure, and stronger metabolic function were conducive bharal to adapting to the wild environment. In the zoo, captive bharals were fed foods rich in high fat and protein, which increased the abundance of Bacteroidota and reduced the alpha diversity of gut microbiota. A fixed diet unified the gut microbiota between genders of bharal. It is very important to pay attention to the impact of captive environments and maintain the native gut microbiota of wildlife.

GIS-BASED REAL-TIME GAS LEAK DETECTION SYSTEM AT BASKENT NATURAL GAS DISTRIBUTION COMPANY

Abstract. Natural gas has been used for heating and production purposes in Turkey since 1987, and every passing year, the natural gas pipeline network continues to expand with investments. The distribution of natural gas within urban areas is achieved through a complex network structure formed by the joining of Polyethylene (PE) and Steel (ST) pipes using various connection elements. This complex structure is susceptible to deformation over time due to various reasons such as corrosion in steel lines, manufacturing errors in connection elements in PE lines, and other factors. As a result, natural gas leaks occasionally occur in these pipelines. The leakage control process in natural gas pipelines is carried out using specially designed equipment and vehicles equipped for this purpose. However, while these systems may be sufficient for collecting the necessary data for leak detection, the complex structure of the distribution network and the necessary data for leak detection, the complex structure of the distribution network and the necessity of performing this process with multiple teams bring about many challenges in terms of tracking, management, and reliability of the operation. At this point, the advancement of GIS (Geographic Information System), mobile, and communication technologies has made it possible to overcome these challenges. In this study, a real-time GIS-supported leak detection system developed using these technologies for Başkent Natural Gas Distrubition Inc. (Başkentgaz), which provides natural gas distribution services in Ankara, has been described. The study discusses the benefits brought by this system to the natural gas leak detection process.

Simulating urban scaling with a term linkages network of a university

In this paper, we make an attempt to increase our understanding of the urban scaling phenomenon. The aim is to investigate how superlinear scaling emerges if a network increases in size and how this scaling depends on the occurrence of elements that constitute the network. To this end, we consider a city as a complex network structure and simulate this structure by the network of all publications of a research intensive university. In this simulation, the publications take the role of the city inhabitants and the concepts (terms and keywords) in the publications represent all kinds of abilities and qualities of the inhabitants. We use in this experiment all author- and database-given terms of the scientific publications of Leiden University from 2022. We calculate the co-occurrence of terms, and on the basis of these connections, we create a network and let this network grow by successively adding publications from the total set of publications. In this way, we get a series of networks with different sizes and this simulates a series of cities with different number of inhabitants. This procedure is performed for different values of the term occurrence threshold. We then analyze how four important network parameters, namely, number of terms, number of clusters, number of links, and total link strength increase with increasing size of the network. Particularly the number of network links and the total network linkage strength are in our opinion the parameters that dominate the scaling phenomenon and can be considered as a simulation of the socioeconomic strength of a city, that is, its gross urban product. We find a significant power law dependence of these network parameters on network size and the power law exponents for the lowest occurrence threshold are within the range that is typical for urban scaling. In our approach, the number of clusters can be interpreted as a measure of complexity within the network. Since the occurrence threshold determines the diversity of terms, we may expect a special relation between the occurrence threshold and the number of clusters. This is indeed the case: whereas for the three other network parameters the scaling exponent increases with increasing occurrence threshold, the number of clusters is the only network parameter of which the scaling exponent decreases with increasing occurrence threshold. Finally, we discuss how our publication term network approach relates to scaling phenomena in cities.

Afforestation-Induced Shifts in Soil Bacterial Diversity and Community Structure in the Saihanba Region.

Afforestation plays a pivotal role in ecosystem restoration, exemplified by the Saihanba Mechanized Forest Farm, the world's largest planted forest; however, the assembly mechanisms and interactions of soil microbial communities in such forests remain inadequately understood. This study aimed to elucidate the impact of different afforestation tree species, namely Larix gmelinii var. principis-rupprechtii, Picea asperata, and Pinus sylvestris var. mongolica, on soil bacterial diversity and community structure in comparison to grassland. Sixty soil samples were collected at a 20 cm depth, and high-throughput sequencing was employed to identify bacterial communities and assess their interactions with environmental factors. A total of 6528 operational taxonomic units (OTUs) were identified, with Solirubrobacter, Conexibacter, Bacillus, Massilia, Gaiella, Acidibacter, and Vicinamibacter being the dominant genera. Afforestation significantly impacted soil bacterial alpha diversity, with notable influence from key soil chemical properties, including available phosphorus (AP), cation exchange capacity (CEC), and the carbon-to-nitrogen ratio of soil organic matter (SOM-C/N). The Mantel test highlighted pH, the Normalized Difference Vegetation Index (NDVI), and spatial variable (dbMEM) as primary environmental factors influencing dominant bacterial genera. The bacterial community structure demonstrated deterministic homogeneous selection, wherein SOM-C/N emerged as a significant factor influencing the dissimilarity of soil bacterial communities. Furthermore, plantation soils exhibited a more complex network structure than grassland soil, highlighting the crucial role of bacterial communities in vegetation changes and providing valuable insights into their response to environmental factors during the reforestation process.

Exploring global embodied carbon emissions transfer network—An analysis based on national responsibility

This paper explores the global embodied carbon emissions transfer network. To clarify the carbon emissions reduction responsibilities of each country globally, a complex interdependent network structure based on the 2016 World Multi-Regional Input-Output (MRIO) table and the transfer of production factors between industries is constructed. Using input-output and social network analyses, the structural features of the global carbon emissions flow network is uncovered, and then carbon inequality among countries from a multi-regional perspective is revealed. The research findings show that the scale of global embodied carbon transfer expands continuously. The direction of transfer exhibits asymmetry, regionalism, and complexity. Hungary, Romania, Slovenia, Iran, Ethiopia, and Iran are the main carbon-exporting countries, while the United States, Germany, the United Kingdom, Japan, Canada, Spain are the main carbon-importing countries. Furthermore, the block model analysis reveals that the carbon transfer and transmission effects between global regions tend to be centralized clusters, from “demand-driven” to “internalized supply-demand coexistence”. There is significant uneven exchange between wealth benefits and environmental burdens among blocks.