Node Connectivity Research Articles

Enhancing wireless sensor network connectivity and coverage using Hybrid GWO‐HSA algorithm

SummaryWireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains.

Analysis of Progressive Collapse Resistance in Precast Concrete Frame with a Novel Connection Method

The configuration of beam–column joints in precast concrete (PC) building structures varies widely, and different connection methods significantly affect the progressive collapse resistance of the structure. This study investigates the progressive collapse resistance of an innovative beam–column connection node frame. Finite element models of four-story, two-span space frame structures made of reinforced concrete (RC) and PC were developed using ANSYS 14.0/LS-DYNA R5.x software, employing nonlinear dynamic and static analysis to examine structural collapse behavior under bottom middle or corner column damage. Numerical results indicate that following the failure of the middle or corner column due to explosion loading, the vertical displacement and collapse rate of the PC structure with the novel connection method are less than those of the RC structure during collapse progression. Furthermore, upon removal of the middle or corner column, the residual load-carrying capacity of the PC structure with the innovative connection increased by 7% and 3.7%, respectively, compared to the RC structure. This suggests that PC structures with this type of connection demonstrate superior performance in resisting progressive collapse, offering valuable insights for future engineering applications.

A MILP-based power system parallel restoration model with the support of mobile energy storage systems

During bulk power system restoration, black-start (BS) units are used to crank non-BS units in such a way that the over system generation capability is maximized. However, traditional power system restoration methods rely on stationary BS units, whose number is limited due to high investment and geographic conditions, impeding a fast system restoration. To enhance restoration efficiency, this paper proposes an integrated power system parallel restoration method considering the support of mobile energy storage systems (MESSs), which can provide cranking power to non-BS units and transform them into MESS-assisted BS units. MESSs are routed and scheduled via transportation network, along with restoration planning in power network by integrating them into a mixed integrated linear programming model. Specifically, to describe the interactive behavior between MESSs and the power system, a mobility model for MESSs is proposed, which indicates the travel path, travel time and connection nodes. Then, an integrated optimization model involving BS unit location, zone partitioning, network reconfiguration and generator start-up is formulated. The proposed method was simulated in the modified IEEE 30-bus transmission system coupled with 11-node transportation system. The results show that, in contrast with a decoupled restoration method, the proposed method increases system generation capability by 6.3%.

Using Bibliometric Data to Define and Understand Publishing Network Equity in Anesthesiology.

Anesthesiology departments and professional organizations increasingly recognize the need to embrace diverse membership to effectively care for patients, to educate our trainees, and to contribute to innovative research. 1 Bibliometric analysis uses citation data to determine the patterns of interrelatedness within a scientific community. Social network analysis examines these patterns to elucidate the network's functional properties. Using these methodologies, an analysis of contemporary scholarly work was undertaken to outline network structure and function, with particular focus on the equity of node and graph-level connectivity patterns. Using the Web of Science, this study examines bibliographic data from 6 anesthesiology-specific journals between January 1, 2017, and August 26, 2022. The final data represent 4453 articles, 19,916 independent authors, and 4436 institutions. Analysis of coauthorship was performed using R libraries software. Collaboration patterns were assessed at the node and graph level to analyze patterns of coauthorship. Influential authors and institutions were identified using centrality metrics; author influence was also cataloged by the number of publications and highly cited papers. Independent assessors reviewed influential author photographs to classify race and gender. The Gini coefficient was applied to examine dispersion of influence across nodes. Pearson correlations were used to investigate the relationship between centrality metrics, number of publications, and National Institutes of Health (NIH) funding. The modularity of the author network is significantly higher than would be predicted by chance (0.886 vs random network mean 0.340, P < .01), signifying strong community formation. The Gini coefficient indicates inequity across both author and institution centrality metrics, representing moderate to high disparity in node influence. Identifying the top 30 authors by centrality metrics, number of published and highly cited papers, 79.0% were categorized as male; 68.1% of authors were classified as White (non-Latino) and 24.6% Asian. The highly modular network structure indicates dense author communities. Extracommunity cooperation is limited, previously demonstrated to negatively impact novel scientific work. 2 , 3 Inequitable node influence is seen at both author and institution level, notably an imbalance of information transfer and disparity in connectivity patterns. There is an association between network influence, article publication (authors), and NIH funding (institutions). Female and minority authors are inequitably represented among the most influential authors. This baseline bibliometric analysis provides an opportunity to direct future network connections to more inclusively share information and integrate diverse perspectives, properties associated with increased academic productivity. 3 , 4.

Geometric localized graph convolutional network for multi-view semi-supervised classification

Multi-view learning has received increasing attention in recent years due to its ability to leverage valuable patterns hidden in heterogeneous data sources. While existing studies have achieved encouraging results, especially those based on graph convolutional networks, they are still limited in their ability to fully exploit the connectivity relationships between samples and are susceptible to noise. To address the aforementioned limitations, we propose a framework called geometric localized graph convolutional network for multi-view semi-supervised classification. This framework utilizes a diffusion map to obtain the geometric structure of the feature space of multiple views and constructs a stable distance matrix that considers the local connectivity of nodes on the geometric structure. Additionally, we propose a truncated diffusion correlation function that maps the distance matrix of each view into correlations between samples to obtain a reliable sparse graph. To fuse the features, we use learnable weights to concatenate the coordinates of the geometric structure. Finally, we obtain a graph embedding of the fused feature and topology by using graph convolutional networks. Comprehensive experiments demonstrate the superiority of the proposed method over other state-of-the-art methods.

Sticky Links: Encoding Quantitative Data of Graph Edges.

Visually encoding quantitative information associated with graph links is an important problem in graph visualization. A conventional approach is to vary the thickness of lines to encode the strength of connections in node-link diagrams. In this paper, we present Sticky Links, a novel visual encoding method that draws graph links with stickiness. Taking the metaphor of links with glues, sticky links represent connection strength using spiky shapes, ranging from two broken spikes for weak connections to connected lines for strong connections. We conducted a controlled user study to compare the efficiency and aesthetic appeal of stickiness with conventional thickness encoding. Our results show that stickiness enables more effective and expressive quantitative encoding while maintaining the perception of node connectivity. Participants also found sticky links to be more aesthetic and less visually cluttering than conventional thickness encoding. Overall, our findings suggest that sticky links offer a promising alternative to conventional methods for encoding quantitative information in graphs.

P‐232: Late‐News Poster: PixelAI: Designing AMOLED Pixel Circuits using Reinforcement Learning

In this paper, we propose the pixel circuit design framework of an active‐matrix organic light‐emitting diode display (AMOLED) based on reinforcement learning (RL). The efficient pixel circuit design process is accomplished by the integration of various schemes such as mask matrix, initial node connection, sub‐goals, circuit trimming, and initial state selection.

Research on seismic performance of prefabricated composite shear wall with end steel plate connection

Chinese construction industry is gradually transforming to industrialization, which promotes the development of prefabricated buildings. As an efficient lateral force resisting system, prefabricated steel-plate concrete composite shear wall structure has become a research hotspot in the field of architecture at home and abroad. Based on the mechanical characteristics of the shear wall, this paper proposes a end steel plate connection prefabricated composite shear wall structure. Through numerical simulation and analysis of the prefabricated composite shear wall with different connection areas and the cast-in-place composite shear wall, which verified the feasibility and reliability of end steel plate connection. Based on the above verification, the influence of different parameters on the seismic performance of the prefabricated composite shear wall with end steel plate connection has been further studied, and the theoretical calculation of bearing capacity is carried out. The results show that the length of the connector should be controlled within a reasonable range. The tie bars in the node connection area can improve the strength and ductility of the prefabricated composite shear wall. The aspect ratio and axial pressure ratio have obvious influence on the seismic performance of the wall, which should be reasonably selected in design.

The promotion of Shanghai’s position and function in the global container shipping network

ABSTRACT Shanghai’s container throughput has ranked first worldwide for 13 consecutive years beginning in 2010, indicating that Shanghai has built an international shipping centre capable of allocating resources for global shipping factors. This study considers global container shipping and shipping service networks as research objects to examine the node status and connection directions of Shanghai in these two networks. It also identifies the comparative advantages and weaknesses of Shanghai and proposes countermeasures and suggestions. The results show that Shanghai ranks high in the global container shipping network and is a sub-centre of the Busan system. Shanghai performs well in the mid-to-low-end shipping service industry in the shipping service network; however, a clear gap exists in the high-end service industry between Shanghai and European port cities. Thus, Shanghai should vigorously implement an international development strategy, actively promote a coordinated development strategy between ports, and integrate the construction of the shipping centre with that of financial and technological innovation centres.

Supramolecular hydrogen-bonded networks formed from copper(II) carboxylate dimers.

The well-known copper carboxylate dimer, with four carboxylate ligands extending outwards towards the corners of a square, has been employed to generate a series of crystalline compounds. In particular, this work centres on the use of the 4-hydroxybenzoate anion (Hhba-) and its deprotonated phenolate form 4-oxidobenzoate (hba2-) to obtain complexes with the general formula [Cu2(Hhba)4-x(hba)xL2-y]x-, where L is an axial coligand (including solvent molecules), x = 0, 1 or 2, and y = 0 or 1. In some cases, short hydrogen bonds result in complexes which may be represented as [Cu2(Hhba)2(H0.5hba)2L2]-. The main focus of the investigation is on the formation of a variety of extended networks through hydrogen bonding and, in some crystals, coordinate bonds when bridging coligands (L) are employed. Crystals of [Cu2(Hhba)4(dioxane)2]·4(dioxane) consist of the expected Cu dimer with the Hhba- anions forming hydrogen bonds to 1,4-dioxane molecules which block network formation. In the case of crystals of composition [Et4N][Cu2(Hhba)2(H0.5hba)2(CH3OH)(H2O)]·2(dioxane), Li[Cu2(Hhba)2(H0.5hba)2(H2O)2]·3(dioxane)·4H2O and [Cu2(Hhba)2(H0.5hba)2(H0.5DABCO)2]·3CH3OH (DABCO is 1,4-diazabicyclo[2.2.2]octane), square-grid hydrogen-bonded networks are generated in which the complex serves as one type of 4-connecting node, whilst a second 4-connecting node is a hydrogen-bonding motif assembled from four phenol/phenolate groups. Another two-dimensional (2D) network based upon a related square-grid structure is formed in the case of [Et4N]2[Cu2(Hhba)2(hba)2(dioxane)2][Cu2(Hhba)4(dioxane)(H2O)]·CH3OH. In [Cu2(Hhba)4(H2O)2]·2(Et4NNO3), a square-grid structure is again apparent, but, in this case, a pair of nitrate anions, along with four phenolic groups and a pair of water molecules, combine to form a second type of 4-connecting node. When 1,8-bis(dimethylamino)naphthalene (bdn, `proton sponge') is used as a base, another square-grid network is generated, i.e. [Hbdn]2[Cu2(Hhba)2(hba)2(H2O)2]·3(dioxane)·H2O, but with only the copper dimer complex serving as a 4-connecting node. Complex three-dimensional networks are formed in [Cu2(Hhba)4(O-bipy)]·H2O and [Cu2(Hhba)4(O-bipy)2]·2(dioxane), where the potentially bridging 4,4'-bipyridine N,N'-dioxide (O-bipy) ligand is employed. Rare cases of mixed carboxylate copper dimer complexes were obtained in the cases of [Cu2(Hhba)3(OAc)(dioxane)]·3.5(dioxane) and [Cu2(Hhba)2(OAc)2(DABCO)2]·10(dioxane), with each structure possessing a 2D network structure. The final compound reported is a simple hydrogen-bonded chain of composition (H0.5DABCO)(H1.5hba), formed from the reaction of H2hba and DABCO.

Cluster synchronization control for the complex network with dynamic connection relations

AbstractIn this article, the cluster synchronization control for a complex dynamic network (CDN) with mutually coupled nodes subsystem (NS) and connection relations subsystem is investigated. The process of cluster synchronization is segmented into three dynamic continuous stages: first, the free movement, then the task‐oriented clustering, and finally the cluster synchronization. The control scheme, founded on the clustering nodes of CDN, is proposed, such that the cluster synchronization arises when clustering all the nodes into two different sub‐networks (SNs). This approach originates from the flight motion for a group of autonomous aircrafts. At a certain moment, free flight is interrupted, and all the aircrafts split into two SNs according to the given clustering rules. Then the two controlled SNs are required to achieve cluster synchronization, that is, every SN converges to the same in speed. In order to split all the nodes into two different SNs, a clustering algorithm according to the “central nodes” is proposed. Based on Lyapunov stability theory, by designing the suitable inner cluster links, the adaptive controller of NS for each SN is synthesized to guarantee the stability of the error system. It is shown that the eventual inner topology of each SN is displayed in the pre‐given form when the cluster synchronization achieves. Finally, the merits and effectiveness of the proposed control scheme are verified by a simulation example.

Smart EV Charger With Reactive Power Compensation and Surge Protection

The paper proposes integrating compensating functions into fast EV charging stations to enhance grid reliability and reduce reliance on costly devices like STATCOMs and Capacitor Banks used to solve voltage drop issues caused by EV adoption. The developed smart fast EV charger functions as a boost rectifier while simultaneously providing surge protection and compensating reactive power in the grid during charging. Using a cross-coupling strategy, a PID controller manages the leading current to supply reactive power to regulate voltage at the connection node. An ILPS with SPD safeguards the charger from lightning or other induced surges. The charger can operate in three modes: charging the battery at unity power factor, acting as a STATCOM without connecting the battery, or charging the battery while supplying reactive power to the grid in a bidirectional manner.

Maximizing UAV fog deployment efficiency for critical rescue operations: A multi-objective optimization approach

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supporting the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the networks lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, we decomposed our problem into two subproblems. Connectivity, coverage subproblem and network lifespan optimization subproblem. For the Connectivity and coverage subproblem, the optimal connectivity and coverage are optimized by strategically deploying UAV fog nodes in a manner that maximizes network coverage, thoughtfully considering the distinctive constraints that emerge in these high-pressure situations where lives are at stake. We shape our UAV fog deployment problem as a multi-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. For the network lifespan subproblem, after determining the optimal connectivity and coverage of UAV nodes and users within the entire network, the network lifespan optimization subproblem is greatly simplified and efficiently solved via a one-dimensional swapping method. After conducting thorough experiments using our proposed architecture across diverse scenarios, our method consistently surpasses existing approaches. It effectively tackles issues like restricted connectivity and potential node failures. This advancement in deployment efficiency notably enhances rescue operations, enabling us to aid the maximum number of affected individuals swiftly during critical and time-pressing situations.

Flexible and Advanced DBS: A distributed control strategy for a DC[sbnd]Nanogrid

Nowadays, it is well known that the energy system is going towards a clear and necessary energy transition. New enabling technologies are needed to accelerate this transition, like the dc nanogrids. The dc nanogrids are designed for a bottom-up approach and play an important role due to their scalability and modularity features. To take advantage of these features, in this paper, the authors propose a flexible and advanced DBS distributed control strategy so to be independent from the dc nanogrid configuration and operating both in stand-alone and in the grid-connected mode. At this scope the dc nanogrid is modelled as the connection of multiple generic nodes and a hierarchical control architecture is applied as a single control structure. It facilitates the seamless transition between the operating modes, independently from the dc nanogrid configuration. The effectiveness of the proposed flexible and advanced DBS is validated by some experiments performed on a testbed integrating a dc nanogrid laboratory prototype.

Exploring scale-free networks: Survey on autonomous system dynamics and connectivity analysis

The global Internet is continuously developing; however, it is becoming more complex with time owing to the introduction of new nodes and links among autonomous systems (ASes). The network links among ASes appear and disappear, especially when the shortest path is required or a low-cost upstream Internet service provider (ISP) is a prerequisite. Understanding the Internet dynamics is critical for improving Internet characteristic analysis. In this work, we reviewed the existing studies from the last two decades on AS network topology as well as relations and routing among ASes. Similarly, we measured the link value differences within Russia and Central Asian ASes using the Barabasi–Albert (BA) model. Our analysis results show that in the last two years, the number of newly added links among 5377 ASes is 65,782. Further, the results show that the ASes with a high network degree in terms of high connectivity or preferential attachment approach are those used by the largest telecom companies in Russia and Central Asian regions. This survey paper aimed to determine whether the Internet AS graphs in these regions adhere to scale-free network properties, and we further examined the growth patterns of node connectivity within these regions.

Navigating the Web: A Study on Network Topology and Information Flow

The internet, as a complex network of interconnected nodes, presents a dynamic landscape for the dissemination and flow of information. This study delves into the intricate interplay between network topology and information propagation, aiming to elucidate the mechanisms underlying navigation through the web. By analyzing various network models and employing techniques from graph theory, we investigate how the structural characteristics of the web influence the spread of information. Furthermore, we explore the role of key factors such as node centrality, clustering, and connectivity patterns in shaping information diffusion dynamics. Through empirical analysis and computational simulations, our findings shed light on the mechanisms governing the flow of information across the web, offering insights into strategies for effective navigation and content dissemination in the digital age.

ISSUES OF DESIGNING INDUSTRIAL BUILDINGS CONSIDERING THE RISK OF LOCAL DESTRUCTION OF THEIR BEARING STRUCTURES

Problem statement. The present-day realities show that the design of buildings and structures with regard to possible local collapses of their bearing structures is becoming more and more realistic and thus an increasingly urgent challenge. The need to solve this issue is an important component in ensuring the safety of buildings and structures under operational and emergency loads. Local destruction is interpreted as a global result that can lead to the destruction of the entire frame or a significant part of it and cause loss of life. The peculiarity of calculations for progressive collapse of steel-framed buildings is that, unlike monolithic reinforced concrete, in a steel frame, the nodes of connection of elements are usually hinged, and this can lead to transformation of the structural scheme into a geometrically variable structural scheme. In this case, structural failure may occur not due to loss of material strength, but due to loss of load-bearing capacity of the structural connection joints. The purpose of the article is to analyse the main specific peculiarities occurring in the design (reinforcement) of industrial buildings with steel frames, considering the possibility of their local destruction. Conclusion. The article considers some problems that need to be solved during the designing or reconstructioning industrial buildings with a steel frame, the operation of which may be associated with some local destruction of the bearing structures. Prospective constructive ways of effectively solving these problems are presented.

DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network

Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles (e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also shows DP-GCN’s effectiveness in defending against real risks from transaction networks.

Identifying influential nodes based on the disassortativity and community structure of complex network

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

Application of Graph Theory for Blockchain Technologies

Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.