Nodes In Network Research Articles

Design of pick-up locations in the crowded road sections

The effective management of urban transport is contingent upon a profound comprehension of the dynamic nature of traffic patterns, alongside the implementation of strategies that are bespoke to the circumstances in question. The growth of urban areas and the increased mobility of individuals have resulted in significant increases in traffic demand, frequently leading to congestion within constrained infrastructure. Cram school districts are particularly susceptible to congestion due to the considerable influx of students and parents, which serves to exacerbate existing traffic issues. The efficacy of traditional methods, such as road expansion, improved public transport, and stricter parking enforcement, in addressing the core causes of congestion has been limited. The objective of this study is to mitigate congestion by identifying optimal pick-up and drop-off (PUDO) zones using the Maximal Covering Location Problem methodology, which considers both pedestrian mobility and network nodes. A social cost analysis of parking facilities and PUDO zones revealed that the latter could reduce social costs by up to 172.3 million KRW. This research is therefore anticipated to alleviate congestion, reduce social costs, and enhance transportation efficiency in cram school districts. The findings may also be applicable to other areas with high demand, thereby offering broader benefits.

Identifying influential nodes in complex networks: a semi-local centrality measure based on augmented graph and average shortest path theory

Identifying influential nodes in complex networks: a semi-local centrality measure based on augmented graph and average shortest path theory

Decentralized Energy Swapping for Sustainable Wireless Sensor Networks Using Blockchain Technology

Wireless sensor networks deployed in energy-constrained environments face critical challenges relating to sustainability and protection. This paper introduces an innovative blockchain-powered safe energy-swapping protocol that enables sensor nodes to voluntarily and securely trade excess energy, optimizing usage and prolonging lifespan. Unlike traditional centralized management schemes, the proposed approach leverages blockchain technology to generate an open, immutable ledger for transactions, guaranteeing integrity, visibility, and resistance to manipulation. Employing smart contracts and a lightweight Proof-of-Stake consensus mechanism, computational and power costs are minimized, making it suitable for WSNs with limited assets. The system is built using NS-3 to simulate node behavior, energy usage, and network dynamics, while Python manages the blockchain architecture, cryptographic security, and trading algorithms. Sensor nodes checked their power levels and broadcast requests when energy fell under a predefined threshold. Neighboring nodes with surplus power responded with offers, and intelligent contracts facilitated secure exchanges recorded on the blockchain. The Proof-of-Stake-based consensus process ensured efficient and secure validation of transactions without the energy-intensive need for Proof-of-Work schemes. The simulation results indicated that the proposed approach reduces wastage and significantly boosts network resilience by allowing nodes to remain operational longer. A 20% increase in lifespan is observed compared to traditional methods while maintaining low communication overhead and ensuring secure, tamper-proof trading of energy. This solution provides a scalable, safe, and energy-efficient answer for next-generation WSNs, especially in applications like smart cities, precision agriculture, and environmental monitoring, where autonomy of energy is paramount.

Resting-state functional connectivity between the frontoparietal network and the default mode network is aberrantly increased in ankylosing spondylitis

Lower back pain comprises the majority of the disease burden of patients with ankylosing spondylitis (AS), while the alterations of the large-scale brain networks could be implicated in the neuropathophysiology of pain. The frontoparietal network (FPN) is known as a pain modulation hub, with key nodes dorsolateral prefrontal cortex (dlPFC) and ventrolateral prefrontal cortex (vlPFC) participating in the pain modulation and reappraisal process. In this study, we adopted the analytical approaches of independent component analysis (ICA) and seed-based correlation analysis (SCA) to examine the resting-state functional connectivity (rsFC) of the large-scale brain networks, notably FPN, between 82 AS patients and 61 healthy controls (HCs). We also investigated the correlation between the rsFC and the clinical measures of AS patients. Both ICA and SCA consistently showed that the rsFC between FPN and mPFC, a key node of the default mode network (DMN), was significantly increased in AS. In addition, SCA also identified a cluster at the right posterior lobe of cerebellum which exhibited increased rsFC with the posterior cingulate cortex, and the right lateral prefrontal cortex also showed increased rsFC with the right dlPFC. Correlation analysis showed that the rsFC between mPFC and the left anterior prefrontal cortex was significantly correlated with C-reactive protein in AS. The increased FPN-DMN connectivity could contribute to the neuropathophysiology of lower back pain in AS, with potential association with faulty pain modulation and reappraisal mechanisms facilitated by the FPN.

Network analysis of caffeine use disorder, withdrawal symptoms, and psychiatric symptoms

ObjectiveCaffeine Use Disorder (CUD) is not currently recognized as a formal diagnosis in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, recent studies within the DSM-5 context have explored this issue. Also, this disorder is closely associated with caffeine withdrawal symptoms, which are formally recognized as a diagnosis in the DSM-5. Additionally, there is limited evidence regarding the connection between caffeine-related issues and psychiatric symptoms. The main aim of the present study was to determine the network structure of CUD and caffeine withdrawal symptoms among the general population. Also, the bridge symptoms among CUD, psychiatric symptoms, and caffeine withdrawal have been estimated.MethodParticipants were 1228 adults (50.3% females, Mean age (x̄±sd) 35.49 ± 11.70 years) who completed Caffeine Use Disorder Questionnaire (CUDQ), Caffeine Withdrawal Symptoms Questionnaire (CWSQ), and Symptom Checklist-25 (SCL-25). All estimations were conducted according to the Gaussian Graphical Model.Results“Excessive consumption” and “role obligations” were central symptoms in the CUD network. Difficulty in concentration was the most central node in the caffeine withdrawal network. Also, the obsessive-compulsive symptom emerged as a central and highly influential node in the relationship between caffeine-related nodes and psychiatric symptoms.ConclusionsMental health providers should target these specific symptoms in clinical interventions to mitigate caffeine-related problems among individuals in the general population effectively.

Scalable Multi-Clustering Aggregation Scheme in WSN Using Machine Learning

Due to Limited resource constraints in WSN data packets collide while being routed to sink, redundant data can be eliminated by data aggregation, which minimizes overall amount of data transferred and increases network's lifespan. Minimizing energy consumption and boost data aggregation rate is most crucial factor in WSN. A Scalable Multi-Clustering Aggregation utilizing Machine Learning (SMCA-ML) focuses on data aggregation approach of heterogeneous wireless sensor networks, using neurons as wireless sensor network nodes in a machine learning method. Machine Learning method accumulates the captured data collected by senor nodes and integrates the accumulated data with multi-clustering route. Threshold value of hidden layer and weight of input layer are randomly generated by the proposed method prior to training. This results in an unstable output that affects the efficiency of data aggregation and causes a long delay. More crucially, distinct threshold settings were made in accordance with the features of uneven energy consumption in wireless sensor networks (WSNs) allow data packets more swiftly by setting smaller threshold in far sink with enough energy. To maximize data aggregation, close sink area with tight energy employs a greater threshold. This way, the combination can result in high data fusion, efficient energy consumption, and little delay. The results of simulation suggested that SMCA-ML based data aggregation algorithm can significantly extend lifespan of network, substantially decrease energy consumption, enhance network energy, expand network performance and improve data aggregation efficiency when compared to conventional Stable Election Protocol (SEP), Back Propagation algorithm, and Extreme Learning Machine.

Optimal µPMU Placement Considering Node Importance and Multiple Deployed Monitoring Devices in Distribution Networks

The placement of micro phasor measurement units in an active distribution network can improve the monitoring performance of the system. However, the price and placement cost of micro phasor measurement units are high, so existing research mostly focuses on solving the problem of achieving global observability with the minimum number of micro phasor measurement units. However, the problems of frequent topology change in the distribution network and the increase in the number of faults that will be caused by the high proportion of distributed energy resources connected to the system may lead to the above placement scheme losing the observability of the system. The balance of maintaining the number of micro phasor measurement units placed while ensuring the monitoring stability of the system remains a crucial challenge. To address this issue, a micro phasor measurement unit placement method that considers the observability of multiple monitoring devices and node importance is proposed in this paper, which, on the one hand, fully considers the impact of the observability of existing monitoring devices on the micro phasor measurement units placement scheme and reduces the number of micro phasor measurement units that are used to achieve global observability, and, on the other hand, gives priority to placing the micro phasor measurement units at important nodes in the distribution network that are closely related to observation stability, improving the observation performance of the system. The superiority of the proposed μPMU method is verified in standard systems such as IEEE-33, IEEE-34, and P&G69, in which the number of required μPMUs is reduced by 20%, and the observation performance of the system is improved by 30% in special cases.

Database energy saving strategy using blockchain and Internet of Things

This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers’ mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method’s stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.

Enhancement of Fracture Toughness of NiTi Alloy by Controlling Grain Size Gradient.

Fracture toughness is a critical indicator for the application of NiTi alloys in medical fields. We propose to enhance the fracture toughness of NiTi alloys by controlling the spatial grain size (GS) gradient. Utilizing rolling processes and heat treatment technology, three categories of NiTi alloys with distinct spatial GS distributions were fabricated and subsequently examined through multi-field synchronous fracture tests. It is found that the one with a locally ultra-high GS gradient (20 nm-3.4 μm) has significantly enhanced fracture toughness, which is as high as 412% of that of the normally distributed nano-grains with an average GS of 8 nm and 178% of that of the coarse-grains with an average GS of 100 nm. Theoretical analysis reveals that in such a gradient structure, phase transition in the coarse-grained matrix greatly absorbs the surface energy of subcritical and stable propagation. Meanwhile, the locally non-uniform GS distribution leads to deviation and tortuosity of the crack path, increasing the critical fracture stress. Furthermore, the nanocrystalline clusters distributed in the form of network nodes reduce the stress intensity factor due to their higher elastic modulus compared to the coarse-grained matrix. This work provides guidance for developing new gradient nanostructured NiTi alloys with high fracture toughness.

MicroRNAs signatures as potential molecular markers in mild cognitive impairment: a meta-analysis

Mild cognitive impairment (MCI) is characterized by a decline in cognitive functioning without significant interference in daily activities. Its high heterogeneity and elevated conversion rate to dementia pose challenges for accurate diagnosis and monitoring, highlighting the urgent need to identify methodologies focused on the early detection and intervention of MCI. Due to their biological characteristics, microRNAs (miRNAs) are potential candidates as non-invasive molecular markers for the identification and assessment of MCI progression. Therefore, in this study, we conducted a meta-analysis to identify the miRNAs commonly deregulated in MCI, focusing on expression profiles in plasma, serum, and extracellular vesicle samples. Our analysis identified eight upregulated miRNAs, including hsa-miR-149-3p, and four downregulated miRNAs, such as Let-7f-5p. Notably, hsa-miR-149-3p emerged as a central node in interaction networks, suggesting its crucial role in regulating cellular processes relevant to MCI. Additionally, pathway analysis revealed significant enrichment in biological processes associated with transcriptional regulation and neurodegeneration. Our results underscore the potential of circulating miRNAs as non-invasive molecular markers for MCI and open the possibility for new methodologies that enable more accurate diagnosis and monitoring of disease progression. Validating the expression of miRNAs such as hsa-miR-149-3p and Let-7f-5p, along with identifying their functional role in the specific context of MCI, is essential to establish their biological relevance. This work contributes to the understanding of the miRNA profile in mild cognitive impairment using easily accessible samples, which could be useful for the development of various strategies aimed at preventing or delaying MCI in individuals at risk of developing dementia, including Alzheimer’s disease.

Event‐Triggered Integral Sliding Mode Control for Nonlinear Networked Cascade Control Systems With Uncertain Delay

ABSTRACTThis article investigates the problem of event‐triggered integral sliding mode control (ISMC) for a class of nonlinear networked cascade control systems (NCCSs). Due to the limited communication bandwidth between network nodes, uncertain delay can occur during transmission. This may result in a deterioration of the capabilities of NCCS and system instability in certain instances. Thus, an event‐triggered mechanism (ETM) and ISMC are introduced into the system, and a new model is constructed to increase the network bandwidth and reduce the effect of nonlinear perturbation. After that, the design of two controllers and ETM is accomplished through the use of the Lyapunov generalized method and linear matrix inequality (LMI) techniques. Finally, a simulation example of a power plant model is given to verify the effectiveness of the method. The results show that, for the first time, the event‐triggered ISMC is applied to NCCSs, which greatly saves the network bandwidth resources. The states of the system can reach the sliding mode surface (SMS) in finite time and remain stabilized there afterward, which ensures the stable operation of the system.

Secure quantum-enhanced measurements on a network of sensors

Two-party secure quantum remote sensing protocols enable quantum-enhanced measurements at remote locations with guaranteed security against eavesdroppers. This idea can be scaled up to networks of nodes where one party can directly measure functions of parameters at the different nodes using entangled states. However, the security on such networks decreases exponentially with the number of nodes. Here we show how this problem can be overcome in a hybrid protocol that utilizes both entangled and separable states to achieve quantum-enhanced measurement precision and security on networks of any size. Published by the American Physical Society 2025

Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions

Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience.

Identifying influential nodes in complex networks based on improved local gravity model

Identifying influential nodes in complex networks based on improved local gravity model

A high-resolution global time series of street-network sprawl

Systems of street networks form a backbone for many aspects of human life and, once laid down, urban streets represent a nearly immutable influence on future urban form and concomitant travel, energy, and social outcomes. Moreover, as humanity is currently passing through its peak urbanization rate, decisions about how to design such networks at the local scale are being made faster than ever before. In this work, we quantify local street connectivity and provide a global, high-resolution time series of our Street Network Disconnectedness Index (SNDi) as an open data set. We derive a stylized version of the actual geographic road network from the 2023 vintage of OpenStreetMap by simplifying complex intersections, divided roads, and offset intersections. Using this functional representation of the network corrects systematic biases in derived properties of the network. We couple this simplified network with a newly available time series of urbanization in order to compute SNDi and provide a dynamic analysis to the year 2019 and a cross-sectional analysis for 2023. We release our data as the raw network of edges and nodes and as aggregates to a 1 km grid, to countries, and to five subnational administrative levels. We also provide interactive visualizations at sprawlmap.org . Overall, our findings present a picture of rapidly worsening street-network connectivity in many regions of the world.

A novel voting measure for identifying influential nodes in complex networks based on local structure

Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy.

Evolutionary Game Analysis of Complex Networks in Enterprise Green Technology Innovation From a Prospect Theory Perspective

ABSTRACTGreen technological innovation in enterprises is a key driving force for achieving sustainable development and industrial transformation. However, how enterprises formulate effective innovation strategies by integrating subjective factors with objective market conditions within complex industrial networks remains an area requiring further exploration. This study aims to construct a complex network evolution model based on prospect theory and examines how subjective factors, such as reference points, risk preferences, and loss aversion, influence the adoption of green technology innovation in different network environments. It further explores how network characteristics, including topology, size, and node degree, affect the diffusion of innovation. Numerical analysis results indicate that in scale‐free networks, lowering reference points for enterprise gains, reducing loss aversion, increasing risk preference, expanding network size, and raising average node degree generally promote higher adoption of green technology innovation. In small‐world networks, the dependence on reference points is relatively lower, risk preference and average node degree demonstrate more complex impacts. Additionally, a moderate rewiring probability can enhance green technology innovation adoption in small‐world networks. These findings provide new insights and practical implications for understanding the driving mechanisms of green technological innovation in enterprises. They further emphasize the importance of government interventions tailored to the specific characteristics of industrial networks to effectively facilitate the diffusion of green technological innovation.

Efficient State Synchronization in Distributed Electrical Grid Systems Using Conflict-Free Replicated Data Types

Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems (DEGSs). We present a general structure for DEGSs based on CRDTs, focusing on the Convergent Replicated Data Type (CvRDT) model with delta state propagation to optimize the communication overhead. The Observed Remove Set (ORSet) and Last-Writer-Wins Register (LWW-Register) are utilized to handle concurrent updates and ensure that only the most recent state changes are retained. An actor-based framework, “Vigilant Hawk”, leveraging the Akka toolkit, was developed to simulate the asynchronous and concurrent nature of DEGSs. Each electrical grid node is modelled as an independent actor with isolated state management, facilitating scalability and fault tolerance. Through a series of experiments involving 100 nodes under varying latency degradation coefficients (LDK), we examined the impact of network conditions on the state synchronization efficiency. The simulation results demonstrate that CRDTs effectively maintain consistency and deterministic behavior in DEGSs, even with increased network latency and node disturbances. An effective LDK range was identified (LDK effective = 2 or 4), where the network remains stable without significant delays in state propagation. The linear relationship between the full state distribution time (FSDT) and LDK indicates that the system can scale horizontally without introducing complex communication overhead. The findings affirm that using CRDTs for state synchronization enhances the resilience and operational efficiency of distributed electrical grids. The deterministic and conflict-free properties of CRDTs eliminate the need for complex concurrency control mechanisms, making them suitable for real-time monitoring and control applications. Future work will focus on addressing identified limitations, such as optimizing message routing based on the network topology and incorporating security measures to protect state information in critical infrastructure systems.

Layered Centrality Tournament Ranking: to rank nodes in multilayer networks

Layered Centrality Tournament Ranking: to rank nodes in multilayer networks