Node Degree Centrality Research Articles

Analysis of Agricultural Water Security Based on Network Invulnerability: A Case Study in China's Virtual Water Trade Networks

Abstract“Invulnerability” of complex network was firstly introduced to virtual water (VW) research, aiming to broaden the scope of studies on water use and management. Beginning with the construction of China's virtual water trade networks (VWTNs) of major grain crops, Node Degree (K) and Betweenness Centrality (B) are employed to evaluate and rank the importance of China's 31 regions. Regions with high values for both indicators are identified as playing pivotal roles in the VWTNs: Jiangsu (ranking 1st for both K and B), Hubei (2nd for K, 3rd for B), Henan (3rd for K, 6th for B), Hebei (4th for K, 4th for B), Hunan (4th for K, 5th for B). Using this ranking to simulate the invulnerability of VWTNs under random and intentional attacks. The results reveal a rapid decrease in both Network Efficiency (E) and Maximum Connectivity (C) under intentional attack. In comparison to seven random attacks, E falls below 0.1 and C drops below 0.5 after only three intentional attacks, and the network completely collapsed after 10 intentional attacks. This highlights the VWTN's vulnerability in maintaining food supply and agricultural water security when key regions are subjected to man‐made destruction, such as military blockades or occupations. Future work should include integrating climate change models, crops yield models, and water resource allocation models to protect the key areas. Furthermore, interdisciplinary approaches are crucial for overcoming the limitations of VW research and these findings will provide valuable insights to enhance the optimal regulation of VWTNs.

Comparative Transcriptome Analysis Unveils Regulatory Factors Influencing Fatty Liver Development in Lion-Head Geese under High-Intake Feeding Compared to Normal Feeding.

The lion-head goose is the only large goose species in China, and it is one of the largest goose species in the world. Lion-head geese have a strong tolerance for massive energy intake and show a priority of fat accumulation in liver tissue through special feeding. Therefore, the aim of this study was to investigate the impact of high feed intake compared to normal feeding conditions on the transcriptome changes associated with fatty liver development in lion-head geese. In this study, 20 healthy adult lion-head geese were randomly assigned to a control group (CONTROL, n = 10) and high-intake-fed group (CASE, n = 10). After 38 d of treatment, all geese were sacrificed, and liver samples were collected. Three geese were randomly selected from the CONTROL and CASE groups, respectively, to perform whole-transcriptome analysis to analyze the key regulatory genes. We identified 716 differentially expressed mRNAs, 145 differentially expressed circRNAs, and 39 differentially expressed lncRNAs, including upregulated and downregulated genes. GO enrichment analysis showed that these genes were significantly enriched in molecular function. The node degree analysis and centrality metrics of the mRNA-lncRNA-circRNA triple regulatory network indicate the presence of crucial functional nodes in the network. We identified differentially expressed genes, including HSPB9, Pgk1, Hsp70, ME2, malic enzyme, HSP90, FADS1, transferrin, FABP, PKM2, Serpin2, and PKS, and we additionally confirmed the accuracy of sequencing at the RNA level. In this study, we studied for the first time the important differential genes that regulate fatty liver in high-intake feeding of the lion-head goose. In summary, these differentially expressed genes may play important roles in fatty liver development in the lion-head goose, and the functions and mechanisms should be investigated in future studies.

Establishment and evaluation of a premenstrual syndrome rat model induced by progesterone-withdrawal and emotional-stimulation

Premenstrual syndrome(PMS) lacks a highly consistent and feasible animal model that aligns with diagnostic and therapeutic standards in both traditional Chinese medicine(TCM) and western medicine, resulting in a lack of reliable experimental carriers for studying its pathogenesis and pharmacological effects. This study aims to systematically analyze the biological implications of PMS from the perspective of the "disease-syndrome-symptom" correlation and establish preparation and evaluation methods for an improved animal model of this disease. Firstly, clinical symptom gene sets related to the Qi stagnation syndromes due to liver depression and blood stasis in PMS in both modern medicine and TCM diagnostic standards were collected through GeneCards, DisGeNET, Mala-Cards, and the System of Foundational Diagnostic Association(SoFDA) database, as well as published literature. Based on the interaction information between genes, a "disease-syndrome-symptom" correlation network of PMS was established. Based on data mining results, an improved rat model of PMS was prepared by combining chronic restraint stress with the classical progesterone-withdrawal mo-del to simulate emotional depression caused by external environmental stimuli during the clinical onset process, inducing pathological damage from both physiological and emotional dimensions. The evaluation of the improved model before and after modification included open field experiment scores, organ indices, ovarian pathological changes, serum levels of estradiol(E_2), follicle-stimulating hormone/luteinizing hormone(FSH/LH), 5-hydroxytryptamine(5-HT), dopamine(DA), norepinephrine(NE), as well as coagulation parameters and hemorheology indexes. By calculating the degree, betweenness, and closeness centrality of nodes in the "disease-syndrome-symptom" correlation network, 163 core genes with topological importance were identified. Further biological function mining results indicated that core genes in PMS mainly participated in the regulation of the "nervous-endocrine-immune" system and pathways related to circulatory disorders. Mapping analysis of clinical phenotype symptom gene sets suggested significant correlations between core genes in PMS and depressive symptoms and pain symptoms caused by blood stasis. Compared with the simple progesterone withdrawal model, rats subjected to combined injections and restraint stress showed more significant abnormalities in open field experiment scores, ovarian tissue pathology, serum neurotransmitter levels of 5-HT and DA, as well as serum hormone levels of E_2 and FSH/LH. The modified modeling conditions exacerbated the pathological changes in blood rheology, coagulation function, and red blood cell morphology in model rats, confirming that the improved rat model could characterize the "nervous-endocrine-immune" system disorder and circulatory system disorders in the occurrence and progression of PMS, consistent with the clinical diagnostic and therapeutic standards of both TCM and western medicine. The establishment of the improved rat model of PMS can provide a reliable experimental carrier for elucidating the pathogenesis of PMS and discovering and evaluating therapeutic drugs. It also provides references for objectively reflecting the clinical characteristics of PMS in TCM and western medicine and precision treatment.

Influence Maximization in Temporal Social Networks with the Mixed K-Shell Method

The study of influence maximizing in temporal social networks (IMT) is an important aspect of influence maximization (IM) research. Currently, two main types of algorithms can solve the IMT problem: greedy-based algorithms and heuristic-based algorithms. However, the greedy-based algorithm is too time-consuming to be used in practice, and most existing heuristic methods do not consider the attributes of nodes, resulting in these methods being unable to solve the IMT problem. Therefore, this paper proposes a mixed k-shell (MKS) algorithm, which considers nodes’ local and global attributes to characterize their influence and select seed nodes. At the local level, we consider the degree centrality of nodes, and at the global level, we propose the temporal k-shell decomposition (TKS) algorithm. Ultimately, the influence of a node is determined by combining the influence of itself and its neighbors. Experiments on four real temporal social networks show that MKS performs better in effectiveness than other heuristic baselines and can maintain a balance between effectiveness and efficiency, providing a useful solution for solving the IMT problem.

Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer’s disease rat model as a measure of early network alterations

This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer’s disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer’s disease.

Multiparameter neuroimaging study of neurovascular coupling changes in patients with end-stage renal disease.

To assess changes in neurovascular coupling (NVC) by evaluating the relationship between cerebral perfusion and brain connectivity in patients with end-stage renal disease (ESRD) undergoing hemodialysis versus in healthy control participants. And by exploring brain regions with abnormal NVC associated with cognitive deficits in patients, we aim to provide new insights into potential preventive and therapeutic interventions. A total of 45 patients and 40 matched healthy controls were prospectively enrolled in our study. Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. Arterial spin labeling (ASL) was used to calculate cerebral blood flow (CBF), and graph theory-based analysis of results from resting-state functional magnetic resonance imaging (rs-fMRI) was used to calculate brain network topological parameters (node betweenness centrality [BC], node efficiency [Ne], and node degree centrality [DC]). Three NVC biomarkers (CBF-BC, CBF-Ne, and CBF-DC coefficients) at the whole brain level and 3 NVC biomarkers (CBF/BC, CBF/Ne, and CBF/DC ratios) at the local brain region level were used to assess NVC. Mann-Whitney U tests were used to compare the intergroup differences in NVC parameters. Spearman's correlation analysis was used to evaluate the relationship among NVC dysfunctional pattern, cognitive impairment, and clinical characteristics multiple comparisons were corrected using a voxel-wise false-discovery rate (FDR) method (p<.05). Patients showed significantly reduced global coupling coefficients for CBF-Ne (p=.023) and CBF-BC (p=.035) compared to healthy controls. Coupling ratios at the local brain region level were significantly higher in patients in 33 brain regions (all p values<.05). Coupling ratio changes alone or accompanied by changes in CBF, node properties, or both CBF and node properties were identified. In patients, negative correlations were seen between coupling ratios and MoCA scores in many brain regions, including the left dorsolateral superior frontal gyrus, the bilateral median cingulate and paracingulate gyri, and the right superior parietal gyrus. The correlations remained even after adjusting for hemoglobin and hematocrit levels. Disrupted NVC may be one mechanism underlying cognitive impairment in dialysis patients.

Cause Analysis of Elevator Accidents Based on Complex Network

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

Multimodal knowledge graph construction for risk identification in water diversion projects

The operation risks of water diversion projects involve numerous influencing factors, complex interrelationships, and heterogeneous data from multiple sources. This study presents a multimodal knowledge graph construction approach for water diversion projects, aiming to comprehend and identify the key risks associated with engineering operation and their propagation patterns. Utilizing term-masked pre-trained language models enhances comprehension of specialized terminology and identifies risk entities within the text. Employing high-order residual convolutional neural networks improves the processing capability for complex graph data, extracting risk information from images. Aggregating multimodal knowledge graphs based on the semantic relationships among entities and conditional probability to determine the coupled features of different risks. Employing complex network theory, analyze node degree and betweenness centrality to identify the diffusion effects and propagation levels of risks. The results indicate that the knowledge extraction accuracy of our method is high (with an average F1 score of 95.85%), enabling the qualitative analysis and quantitative calculation of operational risks in engineering. Relevant studies can effectively enhance the reliability of engineering safety management and reduce the impact of engineering risks on water supply security.

Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study

Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

Regulation of soil nutrient cycling in the root zone of Pyracantha fortuneana: The role of core microbiome induced by plant species

AbstractBackgroundA host‐plant and its associated microbiota are interdependent, with the enduring root zone microbial communities evolving over an extended period for a specific plant species. However, the long‐term stability and functioning of host‐associated microbiota, and their potential to be influenced by introduced plants, remain poorly understood.AimsOur objective was to ascertain the relative contributions of core and rare microbiota in maintaining community stability and soil nutrient cycling in the presence of introduced plants.MethodsWe executed a pot experiment where four plant species at varying successional stages were planted in soil collected from the root area of Pyracantha fortuneana. Soil samples were collected 2 years post‐planting. The soil nutrients, enzyme activities, and microbial networks under different introduced plants were analyzed.ResultsThe growth of Betula luminifera significantly enhanced soil enzyme activity, multi‐nutrient cycling level, and microbial community diversity, compared to soils cultivated with Imperata cylindrica and Zanthoxylum simulans. Furthermore, the treatment involving B. luminifera planting exhibited a lower clustering coefficient and higher average path length than other treatments. Core taxa demonstrated higher node degree and betweenness centrality than rare taxa, favoring the stability of the microbial network. Importantly, the core taxa, particularly their co‐occurrence network properties, were the primary drivers for multi‐nutrient cycles of P. fortuneana root zone soils. Among the core taxa, Mortierellomycetes, Dothideomycetes, Thermoleophili, and Rubrobacteria were abundant in the treatment involving B. luminifera and were significantly positively correlated with most soil nutrient extracellular enzymes, thereby contributing to soil multi‐nutrient cycling.ConclusionCore taxa significantly influence the microbial stability in the root zone soil of P. fortuneana. The introduction of B. luminifera can enhance the stability of the microbial community structure within this soil, thereby promoting soil nutrient cycles.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

A network-based simulation framework for robustness assessment of accessibility in healthcare systems with the consideration of cascade failures

The accessibility of healthcare system is vulnerable to various types of hazards, where the failure of one system component may lead to a diffusion of the pressure and result in cascading failures. This study proposes a network-based simulation framework for robustness assessment of access to healthcare through integrating cascading failure mechanism. Weighted complex networks are constructed to model the accessible patient transfer under both general and elderly healthcare scenarios. The cascade failure mechanism is incorporated into the constructed networks, and several attack strategies (including random, initial degree (ID), initial betweenness (IB), recalculated degree (RD), and recalculated betweenness (RB) attack) are adopted to simulate the process of system robustness assessment. Results indicate that the proposed framework enables to discover the vulnerable nodes in the constructed healthcare accessibility networks, where the robustness metric combining network efficiency and relative size of the largest component acts as a benchmark; all the intentional attack strategies outperform the random attack strategy, which indicates the effectiveness of the detection of vulnerable healthcare facilities by the developed model; and the metrics of node degree and betweenness centrality make progress on identifying the vulnerable healthcare facility nodes, which should be taken heed of to optimize the management and operation of healthcare systems.

Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study.

Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

Solidarity or self-interest? Carbon footprint pressure measurement and spatial correlation in the Yangtze River Delta region

The augmentation of collaborative governance, intertwining both emission mitigation and carbon sequestration enhancement, is pivotal for urban areas to manifest a leading role within the context of the “dual carbon” objectives. Notwithstanding, extant research exhibits an insufficiency in detail and a neglect of the intricate relationship and reciprocal dynamics between urban carbon emissions and ecological carbon sinks. In response to this gap, this study constructs and gauges the carbon footprint pressure (CFP) index of the Yangtze River Delta region over the period 2000–2020. Moreover, a comprehensive approach combining a modified gravity model, social network analysis, and the STIRPAT model is employed to systematically explore the evolution characteristics and effects of spatial correlations of CFP. The results show that the overall CFP predominantly displayed an oscillatory uptrend, underscored by stark spatial non-uniformity and an annually exacerbating divergence. The CFP exhibited a complex network structure, with continually intensifying stability and incremental convergence in its hierarchical framework. Nevertheless, the core-periphery attributes within this network remained pronounced, despite the low degree of network connectivity. Furthermore, the network incorporated four dynamically transmuting blocks. With the variable extent of the block's deconstruction and restructuring, the “club” spatial dispersion characteristic became increasingly evident. The degree centrality and betweenness centrality of urban nodes rendered an adverse impact on CFP, with the latter wielding a more potent effect.

Adaptive Neural Message Passing for Inductive Learning on Hypergraphs.

Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawback is mitigated by hypergraphs, in which an edge can connect an arbitrary number of nodes. Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperMSG, a novel hypergraph learning framework that uses a modular two-level neural message passing strategy to accurately and efficiently propagate information within each hyperedge and across the hyperedges. HyperMSG adapts to the data and task by learning an attention weight associated with each node's degree centrality. Such a mechanism quantifies both local and global importance of a node, capturing the structural properties of a hypergraph. HyperMSG is inductive, allowing inference on previously unseen nodes. Further, it is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets. Finally, we demonstrate the effectiveness of HyperMSG in learning multimodal relations through detailed experimentation on a challenging multimedia dataset.

Altered small-world and disrupted topological properties of functional connectivity networks in patients with nonarteritic anterior ischemic optic neuropathy

BackgroundNonarteritic anterior ischemic optic neuropathy (NAION) is a disease of the optic nerve, but its effect on brain network topology is still unclear.This study aimed to investigate brain network alterations in NAION patients and to explore their relationship with functional impairment. MethodsResting-state functional MRI data were collected from 23 NAION patients and 23 matched healthy control subjects.We used graph theory analysis to investigate the global and nodal network topological properties,and network-based statistical (NBS) methods were used to explore intergroup differences in functional connectivity (FC) strength. ResultsCompared to the control group, NAION patients had lower global efficiency, normalized clustering coefficient and small-world values and higher characteristic path length (P < 0.05). In the hub distributions of functional networks, the NAION group had one hub region disappearing and four hub regions appearing in nodal degree centrality (Dc), and two hubs disappearing and one hub region appearing in nodal betweenness centrality (Bc). The NAION group also had enhanced brain FC primarily associated with the frontal, prefrontal, parietal lobes and cerebellum. Furthermore, the right temporal pole, superior temporal gyrus (r = –0.424), the right inferior temporal gyrus (r = –0.414), the right cerebellar lobule Ⅵ (r = 0.450), and the left cerebellar lobule crus Ⅰ (r = 0.584) were significantly correlated with clinical severity. ConclusionNAION patients show disruption and redistribution of FC in specific regions of the brain network, which may be associated with visual impairment.

Construction of vaccination network and influencing factors: a case study of Chongqing, China

Vaccination is a fundamental tool in preventing infectious diseases. However, due to the wide array of vaccines available, comprehending the entire vaccine landscape can be a daunting task. To tackle this complexity, this study employs advanced network analysis methods capable of capturing the intricate relationships within multivariate datasets. The objective is to investigate how the vaccination landscape has evolved both before and after the COVID-19 pandemic. This study examined vaccination data in the Nanan District of Chongqing, China, spanning from 2016 to 2022. Additionally, the network topological characteristics were computed and scrutinized across 2326 sliding windows. The investigation focused on assessing alterations in the topological structure of the vaccination network before and after the COVID-19 pandemic, encompassing analyses at both macro and mesoscale levels. Furthermore, at the micro level, this study delved into the correlation degrees of selected vaccine nodes within the vaccination network. The analysis unveiled that the correlation and activity within the vaccination network showed a noticeable enhancement in strength in the wake of the COVID-19 pandemic, especially during the spring and winter months. However, the community structure and the average interactions between vaccines displayed a diminishing trend post-pandemic. Among the analyzed vaccines, the HepA vaccine emerged as the one with the highest average node-degree centrality rank. A closer examination of the node-degree centrality ranking chart within the vaccination network disclosed fluctuations in the rankings of various vaccine types across different time periods and seasons. In contrast, vaccines incorporated into NIP exhibited a remarkable degree of consistency, which is attributed to children adhering to a fixed vaccination schedule, rendering NIP vaccines less susceptible to disruptions and enhancing their stability within the vaccination network. This study offers valuable insights into the dynamics of the vaccination network, shedding light on the impact of the COVID-19 pandemic, seasonal variations, and the ever-shifting correlation patterns among different vaccine types. These discoveries enrich our comprehension of vaccination trends and have the potential to guide forthcoming endeavors aimed at refining vaccination strategies and enhancing public health outcomes, not only in the Nanan District but also in analogous settings.

Land reclamation increased organic P fractions and phosphatase activities, and strengthened the co-occurrence networks of phoD community in calcareous soils

Organic phosphorus (Po) is an important constituent of P pool. Soil phoD community is primarily involved Po transformation. However, influences of agriculturally-driven land-use change on different Po fractions, phosphatase activities and phoD community are largely unknown. Here, an investigation was carried out containing three treatments: vegetable field (VF), cropland (CF) and uncultivated land (UL). Various Po fractions were measured by Hedley’s sequential extraction and 31P-NMR methods. The activities of alkaline phosphomonoesterase (ALP), acid phosphomonoesterase (ACP), phosphodiesterase activity (PD) and phytase were assayed; the abundance, and diversity of phoD community and its co-occurrence networks were analyzed. Compared with the UL treatment, soil total organic P (total-Po) in the VF and CF treatments significantly increased by 75.9% and 53.0%, respectively, mainly attributed to the increases in NaHCO3-Po, NaOH-Po and C.HCl-Po. 31P-NMR spectra showed that, when land was reclaimed from uncultivated land into agriculture fields (i.e., VF and CF), myo-inositol hexaphosphate (myo-IHP), α-glycerophosphate (α-glyc) and β-glycerophosphate (β-glyc) were increased by 9.5, 3.9 and 2.0 times, respectively. The activities of ACP, ALP and PD in the VF and CF treatments were higher than in the UL treatment. The α-diversity of phoD community was also significantly increased by land reclamation. Moreover, the linkage between soil Po and Po-cycling-related biological parameters (ACP, ALP and PD and phoD community) was more pronounced by NaHCO3-Po and NaOH-Po than the Po fractions measured by 31P-NMR method. Topological parameters (edges, node degree, and betweenness centrality) of phoD community’s network in the VF and CF networks were higher than in the UL network, implying that land reclamation favored to construct a more cooperative network of phoD community. Collectively, our findings demonstrated that when uncultivated land was converted to cropland, soil Po was notably increased, and phoD community’s co-occurrence network was also strengthened. The outcomes of this study emphasize that when land was reclaimed from nature (uncultivated land) into agriculture fields, the fertilization strategy of organic combined with chemical fertilizers application was beneficial for improving P fertility and assembling organic P-cycling related community.

Robustness analysis of edge-coupled interdependent networks under different attack strategies

The robustness of interdependent networks has been a focus of research on complex networks, and the issue of attacks has become a popular research topic. However, some existing works have revealed significant vulnerabilities in interdependent networks, and the research on its robustness has been limited to node-coupled networks. While in reality, many networks are edge-coupled, and their robustness analysis has been overlooked. This paper constructs edge-coupled networks with positive, negative and random coupling based on the characteristics of edge-coupled interdependent networks. The sublayers use Erdös–Rényi (ER) random networks and scale-free (SF) networks, and four attack strategies, which includes intentional node/edge attack and random node/edge attack, are used to analyze the robustness of different edge-coupled interdependent networks. Seven edge/node importance indicators are proposed by considering node betweenness centrality, degree and eigenvector centrality, and these indicators are applied to attack strategies for result analysis, corresponding methods for enhancing robustness are proposed. The analysis results indicate that under intentional attacks, networks with negative coupling exhibit the strongest robustness, and its robustness can be influenced by their sublayers. In an environment with 500 nodes and an average degree of 4, when there is an ER network in the sublayer, protecting nodes/edges with high degree can improve the robustness of networks. If the sublayers are only composed of many SF networks, the betweenness centrality will have a greater impact when attacking most edges. While under random attacks, the networks with positive coupling exhibit the strongest robustness.

The recoverability of network controllability with respect to node additions

Network controllability is a critical attribute of dynamic networked systems. Investigating methods to restore network controllability after network degradation is crucial for enhancing system resilience. In this study, we develop an analytical method based on degree distributions to estimate the minimum fraction of required driver nodes for network controllability under random node additions after the random removal of a subset of nodes. The outcomes of our method closely align with numerical simulation results for both synthetic and real-world networks. Additionally, we compare the efficacy of various node recovery strategies across directed Erdös–Rényi (ER) networks, swarm signaling networks (SSNs), and directed Barabàsi Albert (BA) networks. Our findings indicate that the most efficient recovery strategy for directed ER networks and SSNs is the greedy strategy, which considers node betweenness centrality. Similarly, for directed BA networks, the greedy strategy focusing on node degree centrality emerges as the most efficient. These strategies outperform recovery approaches based on degree centrality or betweenness centrality, as well as the strategy involving random node additions.