Node Identification Research Articles

Identification of Critical Nodes for Delay Propagation in Susceptible-Exposed-Infected-Recovered (SEIR) and Genetic Algorithm (GA) Route Networks

In response to the challenges associated with forecasting the trajectory of flight delay propagation, pinpointing pivotal nodes within the route network, and the substantial costs involved in enhancing operational efficiency, this study introduces an innovative approach to identifying critical nodes that influence delay propagation across route networks. The methodology commences by establishing a route network model for East China, leveraging the principles of complex network theory. It then incorporates the SEIR (Susceptible-Exposed-Infected-Recovered) model, typically used for analyzing the dynamics of infectious disease spread, to examine the propagation of delays between routes. Subsequently, the approach employs a GA to identify key nodes, which are then compared against those identified by network topology indices. The simulation outcomes demonstrate that the GA’s identification of key nodes offers superior insights into the overall network’s susceptibility to infection, thereby presenting operational managers with novel perspectives for analyzing the spread of flight delays.

Magnetic particle imaging enables non-radioactive quantitative sentinel lymph node identification: feasibility proof in murine models

Abstract Background Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic-staging procedure. Conventional SLNB procedures with 99mTc radiotracers and scintigraphy are constrained by tracer half-life and in some cases insufficient image resolution. Here we explore an alternative magnetic (non-radioactive) image guided SLNB procedure. Purpose To demonstrate that magnetic particle imaging (MPI) lymphography can sensitively, specifically, and quantitatively identify and map SLNs in murine models in multiple regional lymphatic basins. Materials and Methods Iron oxide nanoparticles were administered intradermally to healthy C57BL/6 mice (male, 12-week-old, n = 5). The nanoparticles (0.675 mg Fe/kg) were injected into the tongue, forepaw, base of tail, or hind footpad, then detected by 3D MPI at multiple timepoints between 1 hour and 4-6 days. In this mouse model, the SLN is represented by the first lymph node draining from the injection site. SLNs were extracted to verify the MPI signal ex vivo and processed using Perl’s Prussian iron staining. Paired t-test was conducted to compare MPI signal from SLNs in vivo vs. ex vivo and considered significant if p < .05. Results MPI lymphography identified SLNs in multiple lymphatic pathways, including the cervical SLN draining the tongue, axillary SLN draining the forepaw, inguinal SLN draining the tail, and popliteal SLN draining the footpad. MPI signal in LNs was present after 1 hour and stable for the duration of the study (4-6 days). Perl’s Prussian iron staining was identified in the subcapsular space of excised SLNs. Conclusion Our data supports the use of MPI lymphography to specifically detect SLN(s) using a magnetic tracer for a minimum of 4-6 days, thereby providing information required to plan the SLN approach in cancer surgery. As clinical-scale MPI is developed, translation will benefit from a history of using iron-oxide nanoparticles in human imaging and recent regulatory-approvals for use in SLNB.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Identifying influential nodes in social networks via improved Laplacian centrality

Identifying influential nodes in social networks has significant applications in terms of social analysis and information dissemination. How to capture the crucial features of influential nodes without increasing the computational complexity is an urgent issue to be solved in the context of big data. Laplacian centrality (LC) measures nodal influence by computing nodes' degree, making it extremely low complexity. However, there is still significant room for improvement. Consequently, we propose the improved Laplacian centrality (ILC) to identify influential nodes based on the concept of self-consistent. Identifying results on 9 real networks prove that ILC is superior to LC and other 6 classical measures in terms of ranking accuracy, top-k nodes identification and discrimination capability. Moreover, the computational complexity of ILC has not significantly increased compared to LC, and remains the linear order of magnitude O(m). Additionally, ILC has excellent robustness and universality such that there is no need to adjust parameters according to different network structures.

In silico identification of switching nodes in metabolic networks

In silico identification of switching nodes in metabolic networks

Effect of Fluorescence Lymph Node Mapping on Improving Diagnostic Values of CT D3 Lymph Node Staging for Right-Sided Colon Cancer

Background/Objectives: This study evaluated the impact of fluorescence lymph node mapping (FLNM) using indocyanine green (ICG) on the diagnostic accuracy of preoperative computed tomography (CT) in right-sided colon cancer. Methods: A total of 218 patients who underwent laparoscopic right hemicolectomy with D3 lymph node dissection (LND) were analyzed: 86 patients in the FLNM group and 132 in the conventional surgery group. The FLNM technique allowed for enhanced intraoperative visualization of lymph node (LN) and more precise dissection, improving the identification of metastatic LNs. The diagnostic value of preoperative CT staging was assessed in both the FLNM and control groups by calculating the apparent prevalence, true prevalence, sensitivity, specificity, positive predictive value (PPV), negative predictive value, positive likelihood ratio (PLR), negative likelihood ratio, false positive and false negative proportions, and accuracy. Results: FLNM increased the accuracy of CT staging for detecting D3 LN metastasis in advanced cancer cases, with a higher PPV, PLR, and accuracy. In the FLNM group, the false-positive rate was significantly reduced, and the specificity was higher compared to the control group. Multivariate analysis identified FLNM as an independent factor associated with improved D3 LN metastasis detection. These findings suggest that incorporating FLNM into surgical procedures enhances the diagnostic value of preoperative CT by improving the precision of LND, particularly in patients with advanced colon cancer. Conclusions: The use of FLNM for D3 LND enhances the diagnostic accuracy of cN staging in right-sided colon cancer by improving surgical precision.

Predictive value of methylene blue combined with indocyanine green in sentinel lymph node metastasis in breast cancer: a prospective pilot cohort study.

The status of sentinel lymph nodes is crucial for prognosis and treatment decisions in breast cancer patients. This study aimed to evaluate the predictive value of combined methylene blue and indocyanine green for sentinel lymph node metastasis in breast cancer. This prospective cohort study enrolled 90 clinically node-negative breast cancer patients. Methylene blue and indocyanine green were injected locally before surgery. Sentinel lymph nodes were grouped based on fluorescence intensity and methylene blue staining. A binary logistic regression model was established using 285 lymph node groups to predict metastatic risk. A total of 475 lymph nodes were identified, with 33 being metastatic. The metastatic risk reached 70% for partially blue-stained and weakly fluorescent lymph nodes between 1-2 cm. The model revealed associations between lymph node size, dye staining patterns, and metastatic risks (P<0.05). The AUC of the ROC curve was 0.855. The staining pattern of combined methylene blue and indocyanine green could predict risks of sentinel lymph node metastasis and facilitate rapid intraoperative identification of high-risk lymph nodes.

Multi Attribute Trust Aware Routing for Adversary Detection in Wireless Sensor Networks

Security attacks has become major obstacles in Wireless Sensor Networks (WSN) and Trust Aware Routing is second line of defense. With an aim to improve on the existing routing mechanisms, in this paper, we propose Interactive, Onlooker and Capability Trust Aware Routing (IOC-TAR), a multi-trust attribute framework for trust management in WSNs. IOC-TAR employs three trust features to establish a trustworthy relationship between sensor nodes for their cooperation. Interactive trust uses communication interactions, onlooker trust uses neighbor node’s opinions and capability trust uses stability and fault tolerance for trust assessment. For, each node, one composite trust factor is formulated and decides its trustworthiness. Extensive simulation experiments are conducted to evaluate the effectiveness and efficiency of proposed IOC-TAR in the identification of malicious nodes and the provision of attack resilience. The results declare that the IOC-TAR enhances the attack resilience by improving Malicious Detection rate and reducing False Positive Rate.

In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, accounting for over 90% of pancreatic cancers, and is characterized by limited treatment options and poor survival rates. Systems biology provides in-depth insights into the molecular mechanisms of PDAC. In this context, novel algorithms and comprehensive strategies are essential for advancing the identification of critical network nodes and therapeutic targets within disease-related protein-protein interaction networks. This study employed a comprehensive computational strategy using the metaheuristic algorithm Trader to enhance the identification of potential therapeutic targets. Analysis of the expression data from the PDAC dataset (GSE132956) involved co-expression analysis and clustering of differentially expressed genes to identify key disease-associated modules. The STRING database was used to construct a network of differentially expressed genes, and the Trader algorithm pinpointed the top 30 DEGs whose removal caused the most significant network disconnections. Enriched gene ontology terms included “Signaling by Rho GTPases,” “Signaling by receptor tyrosine kinases,” and “immune system.” Additionally, nine hub genes—FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1—were identified as central to PDAC pathogenesis. This integrated approach, combining co-expression analysis with protein-protein interaction network analysis using a metaheuristic algorithm, provides valuable insights into PDAC mechanisms and highlights several hub genes as potential therapeutic targets.

Activity centrality-based critical node identification in complex systems against cascade failure

Identifying critical nodes in the network has been a concern permanently. Cascading failure would cause catastrophic events, and in the field of cascading failure in complex networks, the structure and dynamics are considered as the key in the process of cascading failure. It is vital to have an applicable centrality to find critical nodes that could control and prevent the cascading failure. In this paper, we propose a steady-state activity centrality to evaluate the importance of each node, and the proposed centrality is related to the degree of each node and the activity of its neighbor nodes. The giant component, the average activity, and the tipping point under different attack strategies are introduced to compare the attack effect of these three centralities including steady-state activity centrality, betweenness centrality and closeness centrality. The results show that the attack effect under the proposed centrality is better than the effect under the other two centralities. In particular, for the network with the SIS and gene regulation dynamic, the attack effect under the steady-state activity centrality driven strategy is obviously better than the effect under the betweenness centrality driven strategy when the network is heterogeneous.

Comparative analysis of art therapy in education: China vs. foreign countries using CiteSpace

Comparative research on art therapy in education is limited, both in China and internationally. This study examines the potential of art therapy to transform art education, focusing on improving students' physical and mental health and aesthetic abilities, both in China and other countries. This study provides a comprehensive comparative analysis of art therapy in education within a visual context. Using bibliometric analysis of publications from the CNKI and WOS databases over the past 20 years, and the CiteSpace visualization tool, research hotspots and frontiers in art therapy within education were identified. Clustering research topics were explored, and the influence of art therapy on current art education was discussed. The study also predicts the future development trend of art therapy in both Chinese and international art education. Bibliometric analysis of CNKI and WOS databases, coupled with the CiteSpace visualization tool, facilitated the identification of key nodes in the keyword network, highlighting research hotspots and frontiers. The development of art therapy has shown an upward trend over the past two decades, particularly in the last ten years. Comparative analysis revealed ongoing progress of art therapy in both China and other countries, with untapped potential in China. Future trends indicate a complex upward trajectory, emphasizing the integration of localized art therapy methods and educational approaches. This comparative research paper explores art therapy in education, focusing on its potential to improve physical and mental health, as well as aesthetic abilities, in students from China and other countries. Using bibliometric analysis and the CiteSpace visualization tool, research hotspots and frontiers in art therapy within education were identified. The findings reveal a growing trend in art therapy over the past two decades, particularly in China. The study highlights untapped potential and predicts a complex upward trajectory, emphasizing the integration of localized art therapy methods and educational approaches. These insights contribute to the advancement of art education practices in China and internationally, underscoring the need for further exploration and integration of art therapy methods.

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

A novel method for identifying key nodes in multi-layer networks based on dynamic influence range and community importance

Identifying key nodes in multi-layer networks is a hot research topic in complex network science and has broad application prospects, such as in mining enterprises that significantly affecting multi-layer industrial chains. Unlike single-layer networks, nodes in multi-layer networks exhibit heterogeneity due to varying connections and locations. There are also correlations between different network layers, which is particularly evident in industrial chains where companies operate across multiple layers of production, supply and distribution. It is necessary to consider the impact of these layers on the global performance of key node identification. In addition, due to changes in connections, the community structure of each network layer should be different, reflecting the dynamic nature of industrial collaborations and partnerships. However, existing research lacks the model that addresses the above problems. Therefore, this paper proposes a key node identification method based on Dynamic Influence Range and Community Importance (DIRCI), using both local and global information of the multi-layer network simultaneously. DIRCI determines the importance of nodes through three centrality measures: dynamic influence range-based centrality, network layer centrality and community-based centrality. Dynamic influence range-based centrality models node heterogeneity by combining the influence range of nodes and their neighbors with lower computational costs. Network layer centrality captures the corresponding importance for different network layers. Community-based centrality comprehensively considers the importance of community, the importance of each node within the community and between different communities. Experimental results for nineteen multi-layer networks show that DIRCI achieves better performance of key node identification than the latest algorithms.

A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks.

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications.

Utility of a breast biopsy clip and a point marker system in tailored axillary surgery for patients with breast cancer after neoadjuvant chemotherapy.

Tailored axillary surgery (TAS) is a new approach for selective removal of metastatic lymph nodes. This study evaluated the safety and utility of TAS using a breast biopsy clip inserted into a metastatic lymph node and a point marker consisting of a short hook wire and nylon thread to remove the clipped lymph node. Patients with breast cancer and clinically confirmed metastases to one-to-three axillary lymph nodes were included in this study. A breast biopsy clip was inserted into the metastatic lymph nodes before neoadjuvant chemotherapy. TAS was performed in patients with ycN0 disease after neoadjuvant chemotherapy. The lymph nodes containing the clips were removed using a point marker. The success criteria for TAS were the removal of the lymph node into which the clip was inserted using a point marker and the identification of the sentinel lymph node. The false-negative rate was calculated for cases in which TAS and axillary lymph node dissection were performed. Thirty individuals from two institutions were enrolled between May 2021 and November 2022, of whom 20 underwent TAS. Ten patients had clinically positive axillary lymph nodes and underwent axillary lymph node dissection. No adverse events were observed in any patient using the clips or point markers. TAS was successful in 18 of the 20 patients (90%). Seven patients underwent TAS and axillary lymph node dissection with a false-negative rate of 0%. The use of clips and point markers to perform TAS is clinically feasible.

Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes

Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022.

To conduct a bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies. Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved, covering the period from January 1, 2013, to December 31, 2022. In order to assess the contributions and co-occurrence relationships among different countries/regions, institutions, authors, and journals, CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified. A total of 750 English articles published between 2013 and 2022 were collected, and the number of publications exhibited an overall increasing trend. The majority of the articles were from China, followed by the United States and India. National University of Singapore, Chinese Academy of Sciences, and Sun Yat-sen University made significant contributions to the published works. Weinreb RN and Fu HZ ranked first among authors and cited authors. American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma. The disciplinary scope of this field includes ophthalmology, computer science, mathematics, molecular biology, genetics, and other related disciplines. The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma. Initially, the hot topics in this field were primarily "segmentation", "classification" and "diagnosis". However, in recent years, the focus has shifted to "deep learning", "convolutional neural network" and "artificial intelligence". With the rapid development of AI technology, scholars have shown increasing interest in its application in the field of glaucoma. Moreover, the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot. However, the reliability and interpretability of AI data remain pressing issues that require resolution.

Can we apply the concept of sentinel lymph node in rectal cancer surgery?

Introduction Colorectal cancer remains one of the most common causes of cancer-related mortality worldwide, and lymph node staging is crucial in the diagnostic and therapeutic process. Sentinel lymph nodes are the first involved in this process, but their validity in colorectal surgery has not yet been established. Following the emergence of new imaging instrumentation, some authors have attempted to propose different techniques for lymph node identification. However, a clear pattern of mesorectal lymph node distribution relative to the primary lesion site has yet to be defined. Material and methods Our analysis retrospectively reviewed suspicious mesorectal pathological lymph nodes on pre-operative magnetic resonance imaging (MRI) of rectal cancer patients, in order to assess the distribution patterns of possible tumour-related rectal lymph nodes. Mesorectal space was subdivided into quadrants and levels, and morphological features and distances from the lymph node to the primary rectal tumour were recorded. Results Two hundred and fifty-five mesorectal lymph nodes distributed among 60 patients were collected. Results show that in 92.1% of cases, nodes were distributed in the same mesorectal quadrant as the rectal primary tumour, and in 88.5% of cases, they were found at the same level as the rectal primary tumour. Conclusions Although a clear node distribution pattern was not established, these results may suggest at least a lymphatic drainage preference lane, worthy of further investigation.

Scientific Production on Entrepreneurial Skills in Young Students: Bibliometric Analysis

Entrepreneurship is considered a key element for economic and social development, requiring cognitive, attitudinal, and procedural competencies for its realization. This study focuses on a documentary review aimed at answering the following questions: How is research on entrepreneurial capabilities approached in different countries? What is the volume of scientific production on this topic? In light of this, the present work is developed within the inclusion criteria: publications between 2012 and 2022, various types of documents, written in English, and included in the SCOPUS database. To conduct the analysis, a combination of tools such as VOSviewer, Biblioshiny, and Excel was used. These tools allowed for the identification of clusters, co-occurrence frequencies, and network nodes. Based on this, the behavior of authors, countries, journals, and the evolution of the topic over time were examined. The results reveal that the United States is the leading country in publishing research on entrepreneurship. There is an increase in scientific production related to this topic, with the majority of publications concentrated in a limited number of journals (53). Limited collaboration among authors and institutions is also evident. The co-authorship network is divided into 47 clusters, with weak grouping among them.

A learning-based influence maximization framework for complex networks via K-core hierarchies and reinforcement learning

Influence maximization (IM) is a critical aspect of complex network analysis, as it holds considerable commercial value across various domains such as recommendation systems and viral marketing. Its objective is to identify a set of seed nodes that can initiate the most extensive cascade of influence within the network. However, IM encounters significant challenges due to its NP-hard nature of accurately identifying the optimal seed nodes and estimating the spread of influence. Although recent learning-based IM methods have shown promising results, there are still challenges related to scalability, efficiency, and avoiding influence overlap that need to be addressed. This paper proposes CoreQ, a novel reinforcement learning-based framework that leverages K-core hierarchies to address IM challenges and make advised decisions to learn the optimal policy to select seed nodes. CoreQ significantly enhances IM by reducing computational costs and effectively addressing the influence overlap problem, making it a scalable and efficient solution. CoreQ first utilizes K-core decomposition to analyze the network structure and extract hierarchical features that guide the process of seed node identification. Then, it employs a novel maximum likelihood-based approach to identify a sufficient number of candidate seed nodes from K-core hierarchies. Finally, it leverages Q-learning’s decision-making capabilities to optimize a policy for learning the most effective seed selection strategy. The experimental results demonstrate that CoreQ not only outperforms state-of-the-art IM methods in terms of influence spread under the weighted independent cascade model but also surpasses other competing learning-based IM methods in terms of time efficiency.