Nodes In Network Research Articles

Connectivity reliability evaluation and most reliable shipping route choice in a seaborne crude oil network

The connectivity reliability of strait and canal nodes in seaborne crude oil networks is uncertain because of various risk factors. Existing studies have mainly focused on road networks and often ignored real-world factors by assuming fixed and identical values for the connectivity reliability of each node, leading to inaccurate estimations. Few studies have considered node reliability when identifying the most reliable routes for oil shipments in response to various external and changing risks. To address these limitations, we first establish new connectivity reliability evaluation methods for both nodes and networks. Then, we develop the α-most reliable shipping route and the very most reliable shipping route models using uncertain programming to dynamically identify the most reliable routes for crude oil, ensuring timely and safe transportation. We apply these models to China's seaborne network of imported crude oil. The results show a network connectivity reliability of 0.6228, which is impacted by unreliable origin–destination pairs in the Middle East. The risk values of the most reliable oil shipping routes vary regionally, with higher values in Africa and the Middle East than in Asia and Latin America. As node risk increases, regional disparities also increase. These findings will aid in the development of energy transportation and import strategies to enhance transportation reliability.

The causal role of the subthalamic nucleus in the inhibitory network.

The neural network mediating successful response inhibition mainly includes right hemisphere activation of the pre-supplementary motor area, inferior frontal gyrus (IFG), subthalamic nucleus (STN), and caudate nucleus. However, the causal role of these regions in the inhibitory network is undefined. Five patients with Parkinson's disease were assessed prior to and after therapeutic thermal ablation of the right STN in two separate functional magnetic resonance imaging (fMRI) sessions while performing a stop-signal task. Initiation times were faster but motor inhibition with the left hand (contralateral to the lesion) was significantly impaired as evident in prolonged stop-signal reaction times. Reduced inhibition after right subthalamotomy was associated (during successful inhibition) with the recruitment of basal ganglia regions outside the established inhibitory network. They included the putamen and caudate together with the anterior cingulate cortex and IFG of the left hemisphere. Subsequent network connectivity analysis (with the seed over the nonlesioned left STN) revealed a new inhibitory network after right subthalamotomies. Our results highlight the causal role of the right STN in the neural network for motor inhibition and the possible basal ganglia mechanisms for compensation upon losing a key node of the inhibition network.

Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning.

We present a new framework to measure the intrinsic properties of (deep) neural networks. While we focus on convolutional networks, our framework can be extrapolated to any network architecture. In particular, we evaluate two network properties, namely, capacity, which is related to expressivity, and compression, which is related to learnability. Both these properties depend only on the network structure and are independent of the network parameters. To this end, we propose two metrics: the first one, called layer complexity, captures the architectural complexity of any network layer; and, the second one, called layer intrinsic power, encodes how data are compressed along the network. The metrics are based on the concept of layer algebra, which is also introduced in this article. This concept is based on the idea that the global properties depend on the network topology, and the leaf nodes of any neural network can be approximated using local transfer functions, thereby allowing a simple computation of the global metrics. We show that our global complexity metric can be calculated and represented more conveniently than the widely used Vapnik-Chervonenkis (VC) dimension. We also compare the properties of various state-of-the-art architectures using our metrics and use the properties to analyze their accuracy on benchmark image classification datasets.

DETHACDA: A Dual-View Edge and Topology Hybrid Attention Model for CircRNA-Disease Associations Prediction.

There exists growing evidence that circRNAs are concerned with many complex diseases physiological processes and pathogenesis and may serve as critical therapeutic targets. Identifying disease-associated circRNAs through biological experiments is time-consuming, and designing an intelligent, precise calculation model is essential. Recently, many models based on graph technology have been proposed to predict circRNA-disease association. However, most existing methods only capture the neighborhood topology of the association network and ignore the complex semantic information. Therefore, we propose a Dual-view Edge and Topology Hybrid Attention model for predicting CircRNA-Disease Associations (DETHACDA), effectively capturing the neighborhood topology and various semantics of circRNA and disease nodes in a heterogeneous network. The 5-fold cross-validation experiments on circRNADisease indicate that the proposed DETHACDA achieves the area under receiver operating characteristic curve of 0.9882, better than four state-of-the-art calculation methods.

Robustness of higher-order interdependent networks with reinforced nodes.

In reality, pairwise interactions are no longer sufficient to describe the higher-order interactions between nodes, such as brain networks, social networks, etc., which often contain groups of three or more nodes. Since the failure of one node in a high-order network can lead to the failure of all simplices in which it is located and quickly propagates to the whole system through the interdependencies between networks, multilayered high-order interdependent networks are challenged with high vulnerability risks. To increase the robustness of higher-order networks, in this paper, we proposed a theoretical model of a two-layer partial high-order interdependent network, where a proportion of reinforced nodes are introduced that can function and support their simplices and components, even losing connection with the giant component. We study the order parameter of the proposed model, including the giant component and functional components containing at least one reinforced node, via theoretical analysis and simulations. Rich phase transition phenomena can be observed by varying the density of 2-simplices and the proportion of the network's reinforced nodes. Increasing the density of 2-simplices makes a double transition appear in the network. The proportion of reinforced nodes can alter the type of second transition of the network from discontinuous to continuous or transition-free, which is verified on the double random simplicial complex, double scale-free simplicial complex, and real-world datasets, indicating that reinforced nodes can significantly enhance the robustness of the network and can prevent networks from abrupt collapse. Therefore, the proposed model provides insights for designing robust interdependent infrastructure networks.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD.

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively). EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Maximal diameter of integral circulant graphs

Integral circulant graphs are proposed as models for quantum spin networks enabling perfect state transfer. Understanding the potential information transfer between nodes in such networks involves calculating the maximal graph diameter. The integral circulant graph ICGn(D) has vertex set Zn={0,1,2,…,n−1}, with vertices a and b adjacent if gcd⁡(a−b,n)∈D, where D⊆{d:d|n,1≤d<n}. Building on the upper bound 2|D|+1 for the diameter provided by Saxena, Severini, and Shparlinski, we prove that the maximal diameter of ICGn(D) for a given order n with prime factorization p1α1⋯pkαk is r(n) or r(n)+1, where r(n)=k+|{i|αi>1,1≤i≤k}|. We show that a divisor set D with |D|≤k achieves this bound. We calculate the maximal diameter for graphs of order n and divisor set cardinality t≤k, identifying all extremal graphs and improving the previous upper bound.

Risk spillovers among oil, gold, stock, and foreign exchange markets: Evidence from G20 economies

This paper investigates the tail risk spillover effects among the stock and foreign exchange markets of G20 economies, as well as the oil and gold markets by constructing a tail event driven network. Adjacency matrices indicate time-varying connectedness between network nodes. The systemic risk decomposition results highlight the predominant contribution of stock markets to the aggregate risk level, while oil, gold, and specific currencies such as JPY, USD, and CNY contribute to diversifying systemic risk. Moreover, tail event driven network quantile regression analysis demonstrates the asymmetry and market heterogeneity of risk spillover effects. Our findings have instructive implications for financial regulators and institutional investors.

Evaluation and optimization of urban hydrological connectivity based on graph theory: A case study in Chengdu, China

The standardization of river system structures has reduced hydrological connectivity, exerting significant pressure on both river ecology and urban development. The precise diagnosis and proposal of optimization strategies remain key challenges. This study utilizes graph theory to delineate the river network topology in Chengdu city and establishes an evaluation index system (α, β, and γ) for hydrological connectivity. Spatiotemporal changes in Chengdu's hydrological connectivity from 2000 to 2020 are analyzed, alongside a validated optimization scheme focusing on “pressure dispersion” among river network nodes. The findings reveal a simplification of Chengdu's river network structure over the past two decades, marked by a reduction of 72 nodes and 81 river segments. Particularly in the city center (region II), these changes have significantly diminished overall connectivity. Following the implementation of the “pressure dispersion” optimization method, the distribution of critical nodes in Region II has been more equitably, enhancing resilience against disasters like floods by diversifying the burden on key nodes. Furthermore, the hydrological connectivity indices in Region II showed improvements post-optimization, with the α, β, and γ indices rising by 75%, 7.26%, and 7.26%, respectively, thereby validating the efficacy of the approach. Analyzing variations in hydrological connectivity parameters across river systems identifies vulnerable links and fundamental issues, laying groundwork for future improvements. The proposed optimization methodology offers insights applicable to cities worldwide grappling with similar connectivity challenges.

GTR: GAN-Based Trusted Routing Algorithm for Underwater Wireless Sensor Networks.

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%.

IS-GNN: Graph neural network enhanced by aggregating influential and structurally similar nodes

Nowadays, it has been demonstrated that graph neural networks (GNNs) are a powerful tool for graph representation learning. However, most GNN models are shallow ones because stacking more layers may cause the over-smoothing problem. In addition, conventional neighborhood aggregation GNNs have an implicit homophily assumption, leading to poor performance when dealing with networks with heterophily. To address the aforementioned issues, we present a new GNN model, called IS-GNN, to effectively enhance the representation quality of nodes in general networks. IS-GNN learns the representation of each node using three aspects of information: local structural information from its neighborhood, global topological information from influential nodes, and homophilous information from its structurally similar nodes. Consequently, the representations of nodes are more powerful and discriminative. The effectiveness of IS-GNN is validated on both homophilous and heterophilous networks. The experimental results of semi-supervised node classification manifest that the accuracy of IS-GNN is superior to that of baselines.

PPSO and Bayesian game for intrusion detection in WSN from a macro perspective

The security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Community detection based on influential nodes in dynamic networks

Community detection based on influential nodes in dynamic networks

A Fault Recovery Method for Distribution Networks With Distributed Power Sources Based on 5G Smart Terminal

Firstly, a smart terminal is designed based on 5G communication technology, which is embedded with various protection measurement devices to form a 5G smart terminal. A distributed control model for the distribution network is constructed based on 5G smart terminals, and an objective function is designed taking into account factors such as restoring important loads, surplus power supply in the distribution network, network losses, and the number of switch actions. Finally, the Sparrow Search Algorithm (SSA) is improved using elite reverse learning strategy and dynamic sine perturbation strategy, and it is used to solve the objective function. Based on the IEEE33 distribution network node system, experimental tests are conducted, and the results show that the average delay of 5G smart terminals is about 30ms. Taking 14:00 as an example, the proposed method achieves network loss, number of switch actions, recovered important load power, and power supply surplus of 46.39 kW, 3 times, 185.65 kW, 240.28 kW, respectively. The fault recovery effect of the distribution network is ideal.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Legal Aspects of Using Blockchain Technologies in the Field of E-Commerce on SDG'S

Objective: The objective of this study is legal aspects of blockchain. Theoretical Framework: Blockchain is one of the most prominent technologies and it has become a driving factor in business transformation. Method: Methods of historical and logical analysis and comparison, expert opinions, system analysis and forecasting were used. The research materials included legal aspects, in particular, in the development of blockchain technology. Results and Discussion: Blockchain, like any electronic technology, can have different forms of social implementation. Their openness is manifested in the fact that anyone can become a network node participating in the consensus procedure. Information in public blockchains is actually and legally publicly available. The listed qualities ensure a high level of security of public blockchains and a high level of users’ trust toward them. Research Implications: The legal regulation of digital technologies is becoming an important tool for ensuring a balance between public and private interests, benefits and risks of the digital age. Originality/Value: The global blockchain market will grow significantly in the coming years. The new vector of blockchain technology will be aimed at accelerating the pace of digitalization, financial inclusion and enhancing security. There is no doubt that blockchain is a disruptive technology and will continue to dominate the business world in the coming years.

Intergenerational effects of racism on amygdala and hippocampus resting state functional connectivity

Racism is an insidious problem with far-reaching effects on the lives of Black, Indigenous, and People of Color (BIPOC). The pervasive negative impact of racism on mental health is well documented. However, less is known about the potential downstream impacts of maternal experiences of racism on offspring neurodevelopment. This study sought to examine evidence for a biological pathway of intergenerational transmission of racism-related trauma. This study examined the effects of self-reported maternal experiences of racism on resting state functional connectivity (rsFC) in n = 25 neonates (13 female, 12 male) birthed by BIPOC mothers. Amygdala and hippocampus are brain regions involved in fear, memory, and anxiety, and are central nodes in brain networks associated with trauma-related change. We used average scores on the Experiences of Racism Scale as a continuous, voxel-wise regressor in seed-based, whole-brain connectivity analysis of anatomically defined amygdala and hippocampus seed regions of interest. All analyses controlled for infant sex and gestational age at the 2-week scanning session. More maternal racism-related experiences were associated with (1) stronger right amygdala rsFC with visual cortex and thalamus; and (2) stronger hippocampus rsFC with visual cortex and a temporo-parietal network, in neonates. The results of this research have implications for understanding how maternal experiences of racism may alter neurodevelopment, and for related social policy.

Evaluation of small-molecule modulators of the circadian clock: promising therapeutic approach to cancer.

The circadian clock is an important regulator of human homeostasis. Circadian rhythms are closely related to cell fate because they are necessary for regulating the cell cycle, cellular proliferation, and apoptosis. Clock dysfunction can result in the development of diseases such as cancer. Although certain tumors have been shown to have a malfunctioning clock, which may affect prognosis or treatment, this has been postulated but not proven in many types of cancer. Recently, important information has emerged about the basic characteristics that underpin the overt circadian rhythm and its influence on physiological outputs. This information implies that the circadian rhythm may be managed by using particular small molecules. Small-molecule clock modulators target clock components or different physiological pathways that influence the clock. Identifying new small-molecule modulators will improve our understanding of critical regulatory nodes in the circadian network and cancer. Pharmacological manipulation of the clock may be valuable for treating cancer. The discoveries of small-molecule clock modulators and their possible application in cancer treatment are examined in this review.

Dynamic Cluster Head Selection in WSN

A Wireless Sensor Network (WSN) comprises an ad-hoc network of nodes laden with sensors that are used to monitor a region mostly in the outdoors and often not easily accessible. Despite exceptions, several deployments of WSN continue to grapple with the limitation of finite energy derived through batteries. Thus, it is imperative that the energy of a WSN be conserved and its life prolonged. An important direction of work to this end is towards the transmission of data between nodes in a manner that minimum energy is expended. One approach to doing this is cluster-based routing, wherein nodes in a WSN are organised into clusters, and transmission of data from the node is through a representative node called a cluster-head. Forming optimal clusters and choosing an optimal cluster-head is an NP-Hard problem. Significant work is done towards devising mechanisms to form clusters and choosing cluster heads to reduce the transmission overhead to a minimum. In this article, an approach is proposed to create clusters and identify cluster heads that are near optimal. The approach involves two-stage clustering, with the clustering algorithm for each stage chosen through an exhaustive search. Furthermore, unlike existing approaches that choose a cluster-head on the basis of the residual energy of nodes, the proposed approach utilises three factors in addition to the residual energy, namely the distance of a node from the cluster centroid, the distance of a node from the final destination (base-station), and the connectivity of the node. The approach is shown to be effective and economical through extensive validation via simulations and through a real-world prototypical implementation.