Node-based Approach Research Articles

On Shared Leadership Modeling: Contrasting Network and Dyadic Approaches

Shared leadership is a dynamic phenomenon that has gained attention in behavioral science and management research over the last two decades. Network modeling is frequently employed to study this phenomenon, with the recent literature favoring a node-based approach over the traditional dyad-based approach. In this study, we investigate the differential impact of these approaches on shared leadership dynamics in student teams, specifically examining their effects on team task cohesion, team social cohesion, and team performance. We utilized multilevel structural equation modeling to compare node-based and dyad-based approaches in modeling shared leadership networks. Our findings indicate that increased leadership interactions positively influenced team performance and cohesion across both approaches. The dyad-based approach demonstrated a greater effect of leadership interactions on team performance, while leadership centrality significantly impacted performance exclusively in the node-based approach. This research contributes to the field by elucidating the differential impacts of node-based and dyad-based approaches, highlighting their strengths in capturing shared leadership dynamics and centrality effects. Our results underscore the critical importance of aligning theoretical foundations and research objectives with methodological choices in shared leadership studies. These insights enhance our understanding of shared leadership measurement and its implications for team outcomes, offering valuable guidance for future empirical investigations in this domain.

Locating charging infrastructure for freight transport using multiday travel data

Vehicle electrification has shown the potential to reduce environmental impacts and greenhouse gas emissions from the transport sector. As electric vehicles (EVs) become increasingly prominent, the efficient placement of charging infrastructure poses a complex challenge that demands careful consideration. This paper delves into the investigation of how travel and parking patterns, derived from empirical data on freight vehicles, influence the optimal distribution of charging infrastructure across the freight network. This paper presents a node-based approach to optimize the allocation of charging infrastructure tailored explicitly for freight transport. The study identifies optimal locations for operator-owned charging infrastructure by leveraging GPS-based data collected from a fleet of freight vehicles operating in the greater Gothenburg metropolitan area. This research aims to enhance our understanding of the charging infrastructure requirements inherent in the freight transport system and provide decision support to logistics companies contemplating the shift from conventional fossil fuel vehicles to electric freight vehicles. The proposed model holds the potential for seamless adaptation to diverse freight transport systems, offering valuable insights to expedite the transition toward fossil-free freight transport on a broader scale.

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

Improving routing performance in social internet of things with FNRA: The free node-based approach

In the distributed environment of the Social Internet of Things (SIoT), where nodes rely primarily on the “store-carry-forward” mechanism for message delivery, it has been determined that forming community structures is advantageous for message propagation. Nevertheless, due to the dynamic nature of nodal social relationships and inaccurate community division, routing algorithm performance is inferior. This paper proposes a novel SIoT routing algorithm, the Free Node-based Routing Algorithm (FNRA) to resolve these issues. The FNRA algorithm presents a comprehensive framework for message forwarding, differentiating between in-community and out-of-community scenarios. The algorithm compares the forwarding capabilities of relay and source nodes within the community. A graph-theoretic approach is used to classify source, relay, and destination nodes into four distinct structures outside the community. Considering node-node, node-community, community-node, and community-community interactions, the algorithm addresses situations where the source, destination, and relay nodes do not belong to the same community. FNRA dynamically selects appropriate forwarding strategies to expedite message delivery to the destination node by leveraging the community structure and the presence of free nodes. The FNRA algorithm exhibits superior performance through detailed experimental simulations by enhancing message delivery rate, decreasing network overhead, and reducing average latency compared to existing routing algorithms. This demonstrates the algorithm's effectiveness in optimizing overall network performance.

Set Cover Model with Path-Segment Concept to Determine the Optimum Rest Areas on Toll Road as the Location of Electric Vehicle Battery Charging Stations

The rapid adoption of electric vehicles (EVs), especially battery-based electric vehicles (BEVs), requires battery recharging facilities. Battery charging station (BCS) is an important component in EVs ecosystem. EVs requires BCS infrastructure for battery recharging. Limited battery range in one full charge is a concern for EVs users which is often called range anxiety so the number and placement of BCS must be adequate to prevent EVs from running out of battery in the middle of the trip. This study uses the real data of Trans Java toll road network, specifically the Semarang-Ngawi section for running numerical experiment to test the performance of the proposed model. Rest areas on toll roads are considered as candidate points for BCS locations. The proposed model is a set cover model formulated into binary integer programming. The Simplex algorithm in Microsoft Excel Solver is used to find the optimum solution on numerical experiment. Four locations are found as the optimum locations of BCS, i.e rest area r3, r6, r9, and rest area r12. The novelty of this study is combining the path-based approach with node-based approach to get a more compact set cover model.

COSMOS: An Innovative, Graph-based Application for the Relational Presentation and Visualization of Greek Mythology and the Associated Art

The project “COSMOS. Cultural Osmosis - Mythology & Art” covers the development of an application for the presentation of Greek Mythology and its related Art, in an innovative and interactive way, using AI technology. The COSMOS application takes one step beyond the usual practice of the linear narration of myths and the depiction of related artwork images, as found in existing applications. COSMOS is developed in two interconnected levels, Myths, and Art, each organized in three data visualization, dynamic windows. In level Myths, these windows present a) the Stories in a Character-oriented organization (e.g., Zeus Thematic), b) the participating Characters, and c) the Places where the Stories unfold. Ιn level Art, respectively, they present a) the depictions of the myths in ancient Artworks, b) the depicted Characters, and c) the Artworks place of origin if known, as well as their present location. The user can discover relations that are not obvious, such as (i) the connections and influences between different Stories, (ii) the various stories that take place in a specific location or (iii) the various stories that a specific Character participates, (iv) the Artworks exhibited in the same location, etc. To reveal the aforementioned relations, the User Interface design adopts a node-based approach, where the nodes' connections, based on shared elements, are displayed. Regarding the implementation, two systems were developed: The Knowledge Organization System and the Knowledge Presentation System. Employing cutting-edge technologies in the fields of Machine Learning, Natural Language Processing, and 3D Graphics, COSMOS visualizes Myths, Artworks, and the connections between them, engagingly and coherently. The COSMOS application was tested and evaluated in two events. The evaluation results from both events reflect the overall positive response of the audience, and validate the fulfillment of the objectives set at the beginning of the project. The final product intends to address a vast audience and it can be used a) as a study aid for anyone interested, b) for educational purposes, by teachers and students, c) as a reference tool for the production of research projects, and d) as a scientific documentation tool and artworks database, for exhibition curating purposes.

Techniques for mitigating the checkerboard formation: application in bone remodeling simulations

The checkerboard phenomenon is a numerical problem that can occur in bone remodeling simulations. This phenomenon degrades the quality of the results and emphasizes a strong mesh dependency. The node-based approach is a well-used method for solving this problem. However, this method does not provide a stable solution, and long-time simulations do not reach steady-state. This paper aims to discuss techniques for mitigating the checkerboard formation and obtaining bone characterization. For this, we apply the simple nodal average technique on stress, density, or both simultaneously. Then, we compare the element-based and node-based approaches with and without smoothing. Also, we introduced a quantification parameter called standard density variation. This parameter measures the intensity of the checkerboard formation and analyzes the evolution of the bone tissue behavior. The application of the smoothing techniques mitigates the checkerboard formation. Also, the application allows obtaining a stable behavior for long-time simulations. The standard density variation allowed visualizing this stable behavior. In particular, the stress field smoothing technique provides the best results. Besides the stabilization and checkerboard control, the technique allows obtaining a physiological density distribution.

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.

A Direct Comparison of Node and Element-Based Finite Element Modeling Approaches to Study Tissue Growth.

Finite element analysis is a useful tool to model growth of biological tissues and predict how growth can be impacted by stimuli. Previous work has simulated growth using node-based or element-based approaches, and this implementation choice may influence predicted growth, irrespective of the applied growth model. This study directly compared node-based and element-based approaches to understand the isolated impact of implementation method on growth predictions by simulating growth of a bone rudiment geometry, and determined what conditions produce similar results between the approaches. We used a previously reported node-based approach implemented via thermal expansion and an element-based approach implemented via osmotic swelling, and we derived a mathematical relationship to relate the growth resulting from these approaches. We found that material properties (modulus) affected growth in the element-based approach, with growth completely restricted for high modulus values relative to the growth stimulus, and no restriction for low modulus values. The node-based approach was unaffected by modulus. Node- and element-based approaches matched marginally better when the conversion coefficient to relate the approaches was optimized based on the results of initial simulations, rather than using the theoretically predicted conversion coefficient (median difference in node position 0.042 cm versus 0.052 cm, respectively). In summary, we illustrate here the importance of the choice of implementation approach for modeling growth, provide a framework for converting models between implementation approaches, and highlight important considerations for comparing results in prior work and developing new models of tissue growth.

A Validated Finite Element Model of Intertrochanteric Fractures with Inhomogeneous Material Properties Extracted From CT Images

Abstract Currently available finite element (FE) models of intertrochanteric fractures cannot accurately represent the real material properties of femurs. This paper aimed to create and validate an FE model of intertrochanteric fractures with inhomogeneous material properties assigned from CT images by a node-based approach. The geometry of a femur was automatically generated from its CT images. The density and Young’s modulus were assigned to the FE model via an in-house program. The fractured femur was fixed with a dynamic hip screw (DHS) system. The resonant frequencies of the intact femur were calculated by finite element analyses (FEA) and then compared with the existing experimental results. The strains at the femoral neck and the lesser trochanter and the reaction forces under compression were also investigated by the finite element analysis. These simulation results were validated by in vitro experimental tests. In addition, the FE model with homogeneous material properties was used for comparison. Relative error analyses showed that the FE predictions matched the experimental results very well (as the errors of all the four indexes were lower than 10%), which meant that the node-based inhomogeneous material model was accurate and that the FE model could be used to investigate the intertrochanteric fractures.

Shape Optimization of a Backward Extrusion Process Using a Non-Invasive Form Finding Algorithm

The objective of our algorithm is to optimize the material configuration of a forming workpiece to ensure that the spatial configuration corresponds to a given target shape. Hence, the preform of a forming process is optimized in order to satisfy the geometric requirements at the end of the forming process. The algorithm is non-invasive, which is a unique feature as it enables commercial solvers to be used to simulate the forming process. As a result, even complex forming processes can be considered. For most of these processes the material undergoes large deformations, which in turn requires the use of remeshing strategies to ensure a high quality mesh throughout the entire simulation. The global remeshing of the system poses a particular challenge to the form finding algorithm when using a node-based approach. This problem is explicitly addressed in this contribution. The implementation is highlighted for the example of a backward extrusion process.

Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem.

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this article, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the node-based approach solves the hard instance of the cluster-based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the node-based approach for a class of Euclidean instances.

Dispersal capacity and genetic relatedness in Acropora cervicornis on the Florida Reef Tract

Sexual reproduction in scleractinian corals is a critical component of species recovery, fostering population connectivity and enhancing genetic diveristy. The relative contribution of sexual reproduction to both connectivity and diversity in Acropora cervicornis may be variable due to this species’ capacity to reproduce effectively by fragmentation. Using a biophysical model and genomic data in this threatened species, we construct potential connectivity pathways on the Florida Reef Tract (FRT) and compare them to inferred migration rates derived from next-generation sequencing, using a link and node-based approach. Larval connectivity on the FRT can be divided into two zones: the northern region, where most transport is unidirectional to the north with the Florida Current, and the southern region that is more dynamic and exhibits complex spatial patterns. These biophysical linkages are poorly correlated with genetic connectivity patterns, which resolve many reciprocal connections and suggest a less sparse network. These results are difficult to reconcile with genetic data which indicate that individual reefs are diverse, suggesting important contributions of sexual reproduction and recruitment. Larval connectivity models highlight potential resources for recovery, such as areas with high larval export like the Lower Keys, or areas that are well connected to most other regions on the FRT, such as the Dry Tortugas.

Event-Based Control for Network Systems via Integral Quadratic Constraints

This paper investigates the event-based control for network systems. Edge-based approach is utilized to predict the value of edge state between two event instants. Different from the node-based approach, it can significantly reduce the communication burden at event instants. Based on the small gain theorem, the closed-loop system is expressed as the feedback interconnection of a linear time-invariant system and an operator. The operator describes the unstable component and has an upper bound in $L_{2}$ space and the control gain is designed, such that the feedback interconnection is stable. Furthermore, we adopt the integral quadratic constraints to describe the correlation between output and input of the operator. And the Kalman–Yakubovich–Popov Lemma is adopted to transform the infinite dimensional stability condition into one inequality. The designed event-triggering condition and distributed control law can ensure that a network system achieves synchronization. The effectiveness of the communication protocol and control strategy is illustrated by a numerical example.

A virtual replica node-based flash crowds alleviation method for sensor overlay networks

The rapid development of sensor networks has made it possible to build large-scale sensor overlay networks by integrating separated sensing resources. In our previous work, we employed a range queriable P2P (peer-to-peer) network, Skip Graph, as the overlay substrate of sensor overlay networks. Range queriable P2Ps such as Skip Graph enable retrieving sensing resources whose properties are within the specified range in an effective and scalable manner. A side effect of the range queriable property is that when flash crowds occur, it is likely that multiple nodes with successive keys/IDs will become hotspots at virtually the same time, which rarely happens in typical DHT (Distributed Hash Table)-based systems. In this work, we present a virtual replica node-based approach to protect sensor overlay networks from flash crowds. In the proposed approach, a hotspot node can request physically spare nodes located anywhere in the overlay to generate virtual nodes with its key and force the virtual replica nodes to join the overlay network as if they were normal nodes around the hotspot. The proposed method is effective for both single hotspot and hotspot zone scenarios. With theoretical analysis, simulations, and testbed experiments, we demonstrate that the proposed method is efficient, scalable and feasible.

A node-based version of the cellular Potts model

The cellular Potts model (CPM) is a lattice-based Monte Carlo method that uses an energetic formalism to describe the phenomenological mechanisms underlying the biophysical problem of interest. We here propose a CPM-derived framework that relies on a node-based representation of cell-scale elements. This feature has relevant consequences on the overall simulation environment. First, our model can be implemented on any given domain, provided a proper discretization (which can be regular or irregular, fixed or time evolving). Then, it allowed an explicit representation of cell membranes, whose displacements realistically result in cell movement. Finally, our node-based approach can be easily interfaced with continuous mechanics or fluid dynamics models. The proposed computational environment is here applied to some simple biological phenomena, such as cell sorting and chemotactic migration, also in order to achieve an analysis of the performance of the underlying algorithm. This work is finally equipped with a critical comparison between the advantages and disadvantages of our model with respect to the traditional CPM and to some similar vertex-based approaches.

The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach.

This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval.

Comparisons of node-based and element-based approaches of assigning bone material properties onto subject-specific finite element models

Subject-specific finite element (FE) models can be generated from computed tomography (CT) datasets of a bone. A key step is assigning material properties automatically onto finite element models, which remains a great challenge. This paper proposes a node-based assignment approach and also compares it with the element-based approach in the literature. Both approaches were implemented using ABAQUS. The assignment procedure is divided into two steps: generating the data file of the image intensity of a bone in a MATLAB program and reading the data file into ABAQUS via user subroutines. The node-based approach assigns the material properties to each node of the finite element mesh, while the element-based approach assigns the material properties directly to each integration point of an element. Both approaches are independent from the type of elements. A number of FE meshes are tested and both give accurate solutions; comparatively the node-based approach involves less programming effort. The node-based approach is also independent from the type of analyses; it has been tested on the nonlinear analysis of a Sawbone femur. The node-based approach substantially improves the level of automation of the assignment procedure of bone material properties. It is the simplest and most powerful approach that is applicable to many types of analyses and elements.

의미간의 유사도 연구의 패러다임 변화의 필요성-인지 의미론적 관점에서의 고찰

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called `World Knowledge.` World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human`s cultural knowledge may also change. Today`s society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.