Centrality Research Articles

Evaluating neonatal pain via fusing vision transformer and concept-cognitive computing

In clinical nursing, neonatal pain assessment is a challenging task for preventing and controlling the impact of pain on neonatal development. To reduce the adverse effects of repetitive painful treatments during hospitalization on newborns, we propose a novel method (namely pain concept-cognitive computing model, PainC3M) for evaluating facial pain in newborns. In the fusion system, we first improve the attention mechanism of vision transformer by revising the node encoding way, considering the spatial structure, edge and centrality of nodes, and then use its corresponding encoder as a feature extractor to comprehensively extract image features. Second, we introduce a concept-cognitive computing model as a classifier to evaluate the level of pain. Finally, we evaluate our PainC3M on various open pain data sets and a real clinical pain data stream, and the experimental results demonstrate that our PainC3M is very effective for dynamic classification and superior to other comparative models. It also provides a good approach for pain assessment of individuals with aphasia (or dementia).

Dynamical network-based evaluation for neuromuscular dysfunction in stroke-induced hemiplegia during standing

BackgroundA given movement requires precise coordination of multiple muscles under the control of center nervous system. However, detailed knowledge about the changing characteristics of neuromuscular control for multi-muscle coordination in post-stroke hemiplegic patients during standing is still lacking. This study aimed to investigate the hemiplegia-linked neuromuscular dysfunction during standing from the perspective of multi-muscle dynamical coordination by utilizing a novel network approach – weighted recurrence network (WRN).MethodsTen male hemiplegic patients with first-ever stroke and 10 age-matched healthy male adults were instructed to stand on a platform quietly for 30 s with eyes opened and eyes closed, respectively. The WRN was constructed based on the surface electromyography signals of 16 muscles from trunk, hips, thighs and calves. Relevant topological parameters, including clustering coefficient (C) and average shortest path length (L), were extracted to evaluate the dynamical coordination of multiple muscles. A measure of node centrality in network theory, degree of centrality (DC), was innovatively introduced to assess the contribution of single muscle in the multi-muscle dynamical coordination. The standing-related assessment metric, center of pressure (COP), was provided by the platform directly.ResultsResults showed that the post-stroke hemiplegic patients stood with remarkably higher similarity of muscle activation and more coupled intermuscular dynamics, characterized by higher C and lower L than the healthy subjects (p < 0.05). The DC values and rankings of back, hip and calf muscles on the affected side were significantly decreased, whereas those on the unaffected side were significantly increased in hemiplegia group compared with the healthy group (p < 0.05). Without visual feedback, subjects exhibited enhanced muscle coordination and increased muscle involvement (p < 0.05). A decrease in C and an increase in L of WRN were observed with decreased COP areas (p < 0.05).ConclusionsThese findings revealed that stroke-induced hemiplegia could significantly influence the neuromuscular control, which was manifested as more coupled intermuscular dynamics, abnormal deactivation of muscles on affected side and compensation of muscles on unaffected side from the perspective of multi-muscle coordination. Enhanced multi-muscle dynamical coordination was strongly associated with impaired postural control. This study provides a novel analytical tool for evaluation of neuromuscular dysfunction and specification of responsible muscles for impaired postural control in stroke-induced hemiplegic patients, and could be potentially applied in clinical practice.

Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China

Water system connectivity is an important measure to optimize the balanced spatial allocation of water resources and water security patterns. Inland river basins in arid zones are generally insufficiently connected, so the scientific evaluation of the current status of water system connectivity and the centrality of its nodes in the water system network has practical significance for the scientific construction of regional water networks. Taking the Tarim River Basin in Xinjiang, China, as an object, this study conducted a comprehensive evaluation of basin water system connectivity by constructing a water system connectivity evaluation system with a total of 12 indicators for the three aspects of pattern connectivity, structural connectivity and functional connectivity. Subsequently, the TOPSIS model, with combined weights of the analytic hierarchy process and the entropy weight method, was used to comprehensively evaluate the connectivity of the watershed’s water system. The research evaluated the node centrality of the water system network that was closely related to the basin water system connectivity by using complex network analyses. The study results indicated the following: (1) among the source streams in the Tarim River Basin, the connectivity of the Aksu Basin was the best and that of the Keriya Basin was the worst and (2) the distribution patterns of the eigenvector centrality and betweenness centrality of the basin hydrological network nodes were similar, with nodes of highest centrality concentrated in the vicinity of the mainstems and the source–mainstem intersections. This work provides a basis and reference for the construction of water system connectivity and the selection of key control sections for ecological flow in the Tarim River Basin.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

A clustering coefficient structural entropy of complex networks

The structural entropy is a quantification of the topological structural complexity of the static complex networks, which is defined based on the structural characteristics and the Shannon entropy. For instance, the ’degree structural entropy’ is based on the network’s ’first-order’ topological properties: the degree of each node. The ’betweenness structural entropy’ is based on the betweenness centrality of nodes, which is a global topological structural property of static complex networks. The two different structural entropy give two completely different views of the network’s topological structural complexity. However, a ’mesoscopic’ structural entropy is still missing in the network theory. In this work, a clustering coefficient structural entropy of complex networks is proposed to quantify the structural complexity of static networks on the mesoscopic scale. The effectivity of the proposed ’mesoscopic’ structural entropy is verified in a series of networks that grow from two different seed networks under the Barabási–Albert and Erdős–Rényi rules. We also find that the quantification of structural entropy effectively reflects the impact of structural heterogeneity on the growth rule in the early stages of seed network growth. Finally, we observe that the structural ratio of the clustering coefficient structural entropy and degree structural entropy remains stable and unchanged when network growth reaches maturity. We also note that the convergence rate of the network’s structural entropy ratio varies under different guiding rules. These findings suggest that the differences in structural entropy can serve as a novel tool for measuring the stability of complex networks and provide fresh insights into achieving a ’balanced’ state in the dynamic evolution of complex networks.

Feature analysis of 5G traffic data based on visibility graph

IntroductionAs 5G networks become widespread and their application scenarios expand, massive amounts of traffic data are continuously generated. Properly analyzing this data is crucial for enhancing 5G services.MethodsThis paper uses the visibility graph method to convert 5G traffic data into a visibility graph network, conducting a feature analysis of the 5G traffic data. Using the AfreecaTV dataset as the research object, this paper constructs visibility networks at different scales and observes the evolution of degree distribution with varying data volumes. The paper employs the Hurst index to evaluate the 5G traffic network and uses community detection to study the networks converted from 5G traffic data of different applications.ResultsExperimental results reveal significant differences in node degree distribution and topological structures of 5G traffic data across different application scenarios, such as star structures and multiple subnetwork structures. It is found that the node degree distribution of 5G traffic networks exhibits heterogeneity, reflecting the uneven growth of node degrees during network expansion. The Hurst index analysis discovers that the 5G traffic network retains the long-term dependence and trends of the original data. Through community detection, it is observed that networks converted from 5G traffic data of different applications exhibit diverse community structures, such as high centrality nodes, star-like community structures, modularity, and multilayer characteristics.DiscussionThese findings indicate that 5G traffic networks in different application scenarios exhibit complex and diverse characteristics. The heterogeneity of node degree distribution and differences in topological structures reflect the imbalance in node connection methods during network expansion. The results of the Hurst index show that the 5G traffic network inherits the long-term dependence of the original data, providing a basis for analyzing the dynamic characteristics of the network. The diverse community structures reveal the inherent modularity and hierarchy of the network, which helps to understand the performance and optimization directions of 5G networks in different applications.

Hybrid Shared-Buffer for Multi-Master Databases

Distributed shared buffer (DSB) is a well-known solution to support multi-master database systems. DSB involves controlling access to shared data among different nodes through a distributed shared buffer and lock-based cache coherence protocols. Existing DSB implementations resolve conflicts at the page level and lack the flexibility required by modern cloud database systems. Authors present HyBuffer which mixes rows and pages in the distributed shared buffer. It enables multiple masters to independently modify different rows on the same page, thereby enhancing concurrency and performance. HyBuffer adopts the hybrid design to track the location information of cached data. A centralized node maintains page locations indicating which masters have cached which pages, and the record locations of a cached page are maintained by the master which has cached this page. This design eliminates the need to redistribute metadata when dynamically adding or removing masters. Experimental results demonstrate that HyBuffer achieves better scalability and performance compared to existing approaches.

Chain-aware graph neural networks for molecular property prediction.

Predicting the properties of molecules is a fundamental problem in drug design and discovery, while how to learn effective feature representations lies at the core of modern deep-learning-based prediction methods. Recent progress shows expressive power of graph neural networks (GNNs) in capturing structural information for molecular graphs. However, we find that most molecular graphs exhibit low clustering along with dominating chains. Such topological characteristics can induce feature squashing during message passing and thus impair the expressivity of conventional GNNs. Aiming at improving node features' expressiveness, we develop a novel chain-aware graph neural network model, wherein the chain structures are captured by learning the representation of the center node along the shortest paths starting from it, and the redundancy between layers are mitigated via initial residual difference connection (IRDC). Then the molecular graph is represented by attentive pooling of all node representations. Compared to standard graph convolution, our chain-aware learning scheme offers a more straightforward feature interaction between distant nodes, thus it is able to capture the information about long-range dependency. We provide extensive empirical analysis on real-world datasets to show the outperformance of the proposed method. The MolPath code is publicly available at https://github.com/Assassinswhh/Molpath.

Coupled Analysis of Risk Factor for Tailing Pond Dam Failure Accident Based on N–K Model and SNA

The failure of tailings pond dams represents a complex coupled system involving various risk factors, including human, governance, facilities, and environmental aspects. It is crucial to identify key risk factors at the system level to enhance the safety management of tailings ponds. We analyzed 74 cases of tailings pond dam failure accidents, both domestically and internationally, from the perspectives of human, governance, facility, and environment. We employed the 2–4 Model to identify and extract the causes of dam failures, summarizing these into four primary risk factors and 40 secondary risk factors, while constructing a risk coupling mechanism model. The natural killing (N–K) model was implemented to analyze the risk coupling values of primary risk factors and quantify these couplings. The N–K model facilitated an analysis of the risk coupling values of first-level risk factors, while social network analysis (SNA) was employed to visualize the relationships among second-level risk factors and assess the centrality and accessibility of nodes within the risk factor network. The out-degree of the risk nodes was corrected by integrating the N–K model with the SNA, leading to the identification of key risk factors associated with tailings pond dam failures and the formulation of corresponding safety prevention and control strategies. The findings indicate that managing multi-risk factor coupling is an effective approach to mitigating the occurrence of tailings pond dam failure accidents. Notably, unfavorable environmental risk factors significantly contribute to the coupling of human–governance–facility–environmental risks, necessitating targeted management strategies. Furthermore, inadequate safety supervision, weak safety awareness, inadequate receipt and inspection, and irregular operation represent additional key risk factors requiring focused prevention and control efforts.

Research on energy saving effect of spatial correlation network of digital infrastructure: based on the analysis of network centrality.

Accurately portraying the spatially linked network characteristics of digital infrastructure and exploring its energy-saving effects are highly valuable for enhancing the synergy in digital infrastructure development and expanding its network spillover effects on energy conservation. This paper uses panel data at the city level in China and employs a modified gravity model to calculate the centrality of digital infrastructure spatial correlation network nodes. Based on this, an econometric model is constructed, incorporating variables such as digital infrastructure spatial correlation network node centrality and urban green total factor energy efficiency. The model is used to analyze the effects and transmission paths of digital infrastructure network node centrality on urban green total factor energy efficiency. The analysis yields the following conclusions: (1) Digital infrastructure spatial correlation network node centrality significantly improves urban green total factor energy efficiency, with considerable variability due to city geographic location, city scale, and city attributes. (2) Nonlinear testing results indicate that as digital infrastructure construction advances, its impact on urban green total factor energy efficiency shifts from inhibitory to promotional. (3) The impact mechanism shows that digital infrastructure node centrality enhances urban green total factor energy efficiency through green technology innovation. Additionally, it promotes advanced industrial structures and reduces capital mismatch, further influencing energy efficiency. (4) Digital infrastructure node centrality not only boosts urban green total factor energy efficiency but also facilitates regional convergence, increasing the convergence rate from 0.094 to 0.170%. The findings of the research offer policy guidance for the government on advancing digital transformation initiatives and enhancing energy efficiency.

Who has the greatest influence on adolescent gaming disorder: parents, teachers, or peers? An interpersonal relationships network model of gaming disorder.

Gaming disorder (GD) in adolescents is associated with impaired interpersonal relationships, including those with parents, teachers and peers. However, the interpersonal relationships most strongly associated with GD-related maladaptive behaviors are not well established. This study aimed to investigate the associations between these three types of relationships and the manifestation of GD in adolescents. In this cross-sectional study, 1920 Chinese adolescents participated in a survey that assessed interpersonal relationships (parent-child, teacher-student, and peer relationships) and demographic variables (e.g., gender, grade, duration of gaming), and 1414 participants were ultimately included. A network analysis approach was utilized to evaluate the key network metrics of edge weight and node centrality. The findings revealed that peer fear and inferiority (r = 0.12) and teacher-student conflict were most strongly correlated with GD, followed by parent-child conflict (r = 0.09). Peer fear and inferiority exhibited the highest strength centrality (0.84), followed by teacher-student conflict (0.83) and parent-child conflict (0.35). Moreover, the duration of gaming was significantly and positively correlated with GD (r = 0.19). The present study underscores the significant role of conflict and rejection within interpersonal relationships, particularly among peers, in the manifestation of GD-related behaviors in Chinese adolescents.

Construction of Metaphorical Maps of Cyberspace Resources Based on Point‐Cluster Feature Generalization

ABSTRACTIn the digital age, the expansion of cyberspace has resulted in increasing complexity, making clear cyberspace visualization crucial for effective analysis and decision‐making. Current cyberspace visualizations are overly complex and fail to accurately reflect node importance. To address the challenge of complex cyberspace visualization, this study introduces the integrated centrality metric (ICM) for constructing a metaphorical map that accurately reflects node importance. The ICM, a novel node centrality measure, demonstrates superior accuracy in identifying key nodes compared to degree centrality (DC), k‐shell centrality (KC), and PageRank values. Through community partitioning and point‐cluster feature generalization, we extract a network's hierarchical structure to intuitively represent its community and backbone topology, and we construct a metaphorical map that offers a clear visualization of cyberspace. Experiments were conducted on four original networks and their extracted backbone networks to identify core nodes. The Jaccard coefficient was calculated considering the results of the three aforementioned centrality measures, ICM, and the SIR model. The results indicate that ICM achieved the best performance in both the original networks and all extracted backbone networks. This demonstrates that ICM can more precisely evaluate node importance, thereby facilitating the construction of metaphorical maps. Moreover, the proposed metaphorical map is more convenient than traditional topological maps for quickly comprehending the complex characteristics of networks.

Using network analysis to provide evidence for brain health as a unified construct relevant to aging with HIV.

Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV. Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network. The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops," indicating strong connectivity. The most central symptoms were "How much do you enjoy life?" and "How often do you have negative feelings?". The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.

A staggered Lagrangian magnetohydrodynamics method based on subcell Riemann solver

This paper uses a general formalism to derive staggered Lagrangian method for 2D compressible magnetohydrodynamics (MHD) flows. A subcell method is introduced to discretize the MHD system and some Riemann problems over subcells are solved at the cell center and grid node respectively. In these solvers, only the fast-waves in all jumping relations are considered and thus the solution structure is simple. The discrete conservations of mass, momentum and energy are preserved naturally in the proposed numerical method. In order to meet the thermodynamic Gibbs relation in isentropic flows, an adaptive Riemann solver is implemented at the cell center, in which a criterion is proposed to reduce overheating errors in the rarefying problems and maintains the excellent shock-capturing ability simultaneously. It is worth to be noticed that the divergence-free condition is naturally satisfied in the Lagrangian method. Various numerical tests are presented to demonstrate the accuracy and robustness of the algorithm.

WITHDRAWN: Restoration of the ecological network structure based on the spatiotemporal changes of the wintering Grus japonensis habitat in the Yancheng coastal area

As an Asian endemic species, Grus japonensis (G. japonensis) reproduction and survival rely on natural wetland resources. G. japonensis is adept at long-distance flight, thereby displaying distinct network structural characteristics in its utilization of habitat within its range. Therefore, scientific restoration and management of the ecological network (ENs) are urgently needed. On the basis of the data of G. japonensis and environmental data, this study used the MaxEnt model, stepping-stone theory, and circuit models to analyze the spatiotemporal changes in the ENs structure of G. japonensis habitat. The primary factors influencing the habitat suitability of G. japonensis was the distance to Phragmites australis (P. australis) community. The area of ecological source areas decreased from 56,528.59 hm2 in 1987 to 14,040.20 hm2 in 2021, with the distribution range contracting from Beihuan (BH), Nanhuan (NH), Hexin (HX) and Tiaozini (TZN) to Hexin (HX). Between 1987 and 2007, there were dense ecological corridors along the coastal areas. However, by 2021, only BH and HX had dense ecological corridors between them. The node centrality of HX decreased from 900.78 in 1987 to 264.64 in 2021, with the most significant decrease between 2007 and 2021. On this basis, the expanded habitat area, the stepping stone area in 2021 and the ecological source area of 2007 with low development intensity were taken as restoration areas. The restoration areas were classified into two types based on their distribution: landing nodes and expansion nodes. The addition of landing nodes enhanced the overall stability of the ENs, resulting in an increase in the centrality of nodes from 264.64 to 398.08. Finally, an optimization and restoration framework for the habitat network based on identifying priority restoration areas, land use type transformation, and enhancing ENs stability for habitat restoration was proposed.

Node centrality based on its edges importance: The Position centrality

We propose a novel family of node centralities in social networks, named family of position centralities, which explicitly takes into account the importance of the links to assess the centrality of the nodes that support them through the Position value (Meessen, 1988). Our proposal shares with the family of Myerson centralities (Gómez et al., 2003) that it is a game-theoretic family of measures that allows to consider the functionality of the network modelled by a symmetric cooperative game. We prove that, like the Myerson centrality measures, every Position centrality measure also satisfies essential properties expected of a centrality measure. We analyse in detail the main differences between the Myerson and the position families of centrality measures. Specifically, we study the differences regarding the connection structures that share dividends and the fairness and stability properties. Along this analysis we consider the case of hub-and-spoke clusters, a prevalent model for studying transportation networks. Finally, a characterisation of the Position Attachment centrality is given, which is the Position centrality obtained when the functionality of the network is modelled by the attachment game. Some comparisons are made with the Attachment centrality introduced by Skibski et al. (2019), which is the analogue member of the family of Myerson centralities.

The Symptom Structure and Causal Relationships of Comorbid Anxiety and Depression Among Chinese Primary and Middle School Teachers: A Network Analysis.

In China, as educational reforms progress, the characteristics of teachers' work have undergone significant changes, resulting in extremely high levels of stress that can trigger anxiety and depression. Anxiety and depression often co-occur, with two mainstream theories explaining this co-existence: the tripartite model and the diathesis-stress model. However, systematic research focusing on this population is relatively scarce, and the applicability of these models has not been thoroughly tested. This study aims to use network analysis methods to examine the interactions between symptoms and analyze the co-existence of anxiety and depression, thereby expanding the research on teachers. Data were provided by the Science Database of People Mental Health, which includes 1670 teachers with a mean age of 30.01. The Self-Rating Anxiety Scale and Self-Rating Depression Scale were used to estimate the network structures of anxiety and depression, respectively. Shared symptoms between depression and anxiety were identified using network analysis and clique percolation methods. Bayesian Networks was used to estimate causal relationships between symptoms. Data were analyzed using R packages. Network structure was constructed with the qgraph package, node centrality and bridge symptoms were evaluated using the networktools package, and network stability was measured via the bootnet package. The Clique Percolation method was implemented with the CliqurPercolation package, and Bayesian network modeling was performed via the Bnlearn package. Dizziness and Easy Fatigability & Weakness were central symptoms in the network. Bridging strength results showed that, the important bridging symptoms included Tachycardia, Depressed Affect, Fatigue, Crying Spell, Easy Fatigability & Weakness, Nightmares, Face Flushing, and Sweating were the strong bridging symptoms. Additionally, Sleep Disturbance played a key mediating role. Depressed Affect and Dissatisfaction were activation symptoms for anxiety-depression co-existence. Using network analysis, this study elucidated core, bridging, and shared symptoms, as well as potential causal pathways between anxiety and depression. Specifically, somatic symptoms are crucial in maintaining and developing the anxiety-depression network among teachers. Sleep disturbance serves as the sole gateway for mild symptoms to develop into other communities. The Bayesian network identified two key activating symptoms within the teacher anxiety-depression network, validating the applicability of the tripartite model among teachers.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.

Graph Metrics Reveal Brain Network Topological Property in Neuropathic Pain Patients: A Systematic Review.

Neuropathic pain (NP) is a common and persistent disease that leads to immense suffering and serious social burden. Incomplete understanding of the underlying neural basis makes it difficult to achieve significant breakthroughs in the treatment of NP. We aimed to review the functional and structural brain topological properties in patients with NP and consider how graph measures reveal potential mechanisms and are applied to clinical practice. Related studies were searched in PubMed and Web of Science databases. Topological property changes in patients with NP, including small-worldness, functional separation, integration, and centrality metrics, were reviewed. The findings suggest that NP was characterized by retained but declined small-worldness, indicating an insidious imbalance between network integration and segregation. The global-level measures revealed decreased global and local efficiency in the NP, implying decreased information transfer efficiency for both long- and short-range connections. Altered nodal centrality measures involve various brain regions, mostly those associated with pain, cognition, and emotion. Graph theory is a powerful tool for identifying topological properties of patients with NP. These specific brain changes in patients with NP are very helpful in revealing the potential mechanisms of NP, developing new treatment strategies, and evaluating the efficacy and prognosis of NP.

Dodecagonal Parameter-Based Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of several sensor nodes (SN) that are dispersed geographically and interact with one another over wireless media in order to track and log physical data from their environment. At the moment, WSNs frequently use routing and clustering strategies to extend the life of their networks. This paper proposes a DODECAGONAL parameter-based Energy-Efficient Routing in WSN (DOPE-WSN) for improvement of network lifetime and saving the energy consumption. Initially the cluster is selected using Agglomerative clustering. In the second phase, Pelican optimization (PELO) based Cluster head selection (CHs) meant for considering Congestion, Node Degree, Node Density, Network area, Network Coverage, Number of clusters, Number of nodes, Communication cost, Distance, Residual Energy, Distance to neighbor, Node Centrality. Moreover, the Sooty Tern Optimization (STO) model are utilized for the determination of the best routing path for the cluster heads. Taking into account node degree, residual energy, and distance, the STO maximizes network performance. The suggested approach has undergone thorough testing for ensuring network durability and energy efficiency. The proposed model achieved a maximum 97% Packet Delivery Ratio (PDR) with 900 nodes in comparison with 91%, 89%, 83%, and 82% for CRPSH, HQCA, EACRLEACH, and BWO-IACO algorithms respectively.