Principles Of Graph Theory Research Articles

A Generic Particle Model with Topologic Modeling Capabilities for the Computational Form-Finding of Elastic Structures

Recent efforts on the research of bending- and tensile-active structures have shown the vast range of aesthetic and performative opportunities that elastic structures can offer for generating innovative architectural solutions. In this context, the task of conceptualizing and representing elastic structures is not trivial due to the presence of large deformations along the forming process. This condition demands to expand beyond current geometrical modeling schemes used in architecture and develop a more intuitive simulation-based design practice. Dynamic methods, such as Particle System and Dynamic Relaxation, have become an important topic on this subject with major efforts entirely focused on speeding up rates of numerical convergence. Fast simulations simplify the development of design iterations, but designers are still forced to break the integration process when topological changes need to be introduced for fulfilling a given design intention. As the exploration of elastic structures becomes more integrated within architectural design agendas, there is a growing necessity to introduce more flexible numerical models for design conceptualization. In this paper we introduce the conceptual framework for a generic Particle System model built on principles of combinatorial maps and graph theory that is designed to support topological transformations on the fly. The computational implementation of the proposed model is demonstrated with two types of data structures. The aim of this work is to enhance modeling capabilities when numerical simulations are used as design tools.

Implementing prosumers into heating networks

This article investigates possibilities for including prosumer structures in heating networks. Therefore, the performance of three different heating system scenarios for a new housing district is investigated. Low (60 °C) and ultra-low (20 °C) network temperatures are investigated. The case study includes about 100 different single-family and multi-family houses. Two scenarios contain networks with temperatures of 20 and 60 °C. The third scenario is extended with solar collectors and a supermarket as decentralised prosumers who supply heat to the network in times of surplus. The network can simulate bidirectional flows. Based on the principles of graph theory, a combined hydraulic and thermal model is developed and used for this study. The results show a heat loss reduction in ultra-low temperature networks due to lower network working temperatures (20 °C) of about 80% compared to a low-temperature network. The implementation of prosumers considerably reduces the demand from a central heat source. In this study, 20% of the total heating demand can be covered by excess heat of the prosumer supply.

Designing a Structural Equation Model for Creative Leadership in the Relief Supply Chain in Isfahan Hospitals

INTRODUCTION: Despite the fact that natural disasters are on the rise in Iran, no study has yet explained the performance indicators in the relief supply chain in crisis situations. Creative leadership in the relief supply chain can be of great help to the enhancement of internal strengths, provision of creative solutions to potential threats, and maintenance and promotion of public health. The present study aimed to provide a Structural equation model of creative hospital leadership. METHODS: The present exploratory research was conducted on 215 cases that were selected as the sample size using Cochranchr('39')s formula. The data collection tool was a standard questionnaire which was completed by managers, supervisors, and experts of hospitals in Isfahan province. The reliability coefficient of the questionnaire was assessed using SPSS software based on Cronbachchr('39')s alpha test, which indicated the high reliability of the research tool. FINDINGS: A hierarchical structure was established using DEMATEL software, which is a variety of decision-making methods based on pairwise comparisons, and benefiting from expert opinion in extracting the factors of a system and their systematical structuring based on the principles of graph theory. CONCLUSION: Finally, the effectiveness and affectability of the components were expressed as the output of DEMATEL software in a way that their interaction effect was denoted as numerical values. Thereafter, the data were analyzed in linear structural relations (LISREL) software using structural equation modeling, and the creative leadership model was designed.

DEVELOPING ADAPTIVE LEARNING MANAGEMENT APPLICATION FOR PROJECT TEAM IN IT-INDUSTRY

Keeping employees aligned with modern trends and developments in their professional areas is the main focus of a lifelong lea rning approach. That becomes even more important for such dynamic industries like Information Technology. Therefore, it’s crucial to extend existing e-learning management system with an adaptive training component that enables the effective study of on-demand skills, leading to a broader range of candidates available for project management to select from and consequently improving the overall performance of an IT company. To improve the existing learning process according to company's and employee's needs the overview of a typical learning management system functionality is given in this paper. The main benefits from the adoption of a learning management system in small and medium-sized IT-companies were discussed, analysis of their features and problems was given. The adjustment plan for the typical learning management system to be suitable for the information technology domain including module of the adaptive learning content selection using the basic principles of graph theory was proposed to reduce the time of the learning process. LMS OpenOLAT was reworked according to the adjustment plan which is reflected by the number of diagrams such as sequence diagram, IDEF0 business process description, activity diagram that shows search algorithm steps and application component diagram. Also, GUI was adjusted to provide user with a good look and feel. The benefits of the proposed approach in business processes of IT-company are shown using “Academy – Smart”. To prove the efficiency of the proposed algorithm, real courses were used. Based on the learning material, provided by “Coursera”, a number of test cases was formed and analyzed. After applying adaptive content selection algorithm according to the models of “Academy – Smart” employees, learning time was reduced and optimized. This investigation has shown significant improvement in the resource management process and acceleration of the learning process for employees.

Global smooth leaderless consensus control of high-order nonholonomic chained systems

This paper investigates the leaderless consensus control problem of high-order nonholonomic chained systems. Based on the relationship between state and consensus error, a novel Lyapunov function is defined and is proved positive definite and radially unbounded. The first control law is constructed to make the Lyapunov function non-increasing. The Lyapunov method, graph theory and LaSalle invariance principle are applied to analyse system stability, where some skillful mathematical calculations are conducted to overcome the difficulties caused by nonholonomic constraint and high-order structure, and the explicit expression and the conditions of control parameter matrix are calculated. Then, the second control law is designed by extending the results to the input saturation case. Both of the two controllers are smooth, time-invariant, static, distributed, and able to achieve global asymptotic consensus of nonholonomic chained systems over connected undirected graph. Numerical simulation examples are implemented to demonstrate the effectiveness of the proposed control schemes.

Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality

Hospital-acquired pneumonia (HAP) bloodstream infections comprise a major cause of crude hospital mortality. This is a cross-sectional study that used claims data from the Centers for Medicare and Medicaid Services (N = 565,875). The study objective is to represent the progression of pneumonia-induced bloodstream infections using graph theory principles, where each path of the graph represents a different scenario of bloodstream-infection progression, and aims to further estimate the likelihood if hospital death for each path. To disseminate the results, the study makes available a prototype applet to navigate various paths of the graph interactively. Bayesian probabilities were calculated for each scenario, and multivariate logistic regression was conducted to estimate the adjusted OR for inpatient death after controlling for patient age, sex, and comorbidities. The mortality rate ranged from 4.99% for patients admitted with community pneumonia without bloodstream infection and reached 63.18% for cases admitted with bloodstream infection that progressed to hospital septicemia, sepsis, and septic shock. The prototype applet can be used to unfold bloodstream infection events and their associated risk for mortality and could be used in university curricula to assist educators in helping students understand the progression of pneumonia-induced bloodstream infections in a data-driven way.

Numerical simulation of T-stresses and stress biaxiality factor for a centrally cracked specimen under mixed boundary conditions

The results of the numerical calculation of T-stresses and the stress biaxiality factor in a centrally cracked tensile plate obtained by the graph method are presented. The method of analysis of a deformable solid is based on the principles of graph theory used in mechanics for constructing discrete models that allow numerical modeling of the fields of displacements, deformations, and stresses of a solid. When analyzing the stress-strain state near the crack tip, the author uses the singular element of the graph model of an elastic medium. When calculating T-stresses, stress and displacement methods are used. Calculations are performed on a coarse grid, however, it allows one to get fairly accurate results. This is because the graph laws (Kirchhoff’s vertex and cyclic laws) provide conditions for equilibrium and compatibility of deformations for the element as a whole. In addition, a special procedure for determining the coefficients of approximating polynomials leads to the implementation of equilibrium equations for the volume of the element. The state of a sample with a crack is described by two dimensionless complexes, which makes it possible to evaluate the biaxiality of the sample. One of these complexes depends on the crack length, applied load, elastic modulus of the material and crack tip opening displacement. Another complex is related to the calculation of a non-singular term in the Williams decomposition. The computational experiment allowed establishing a relationship between these dimensionless complexes. It is important to note that these parameters can be easily determined from a full-scale experiment. As a result, using several full-scale measurements, an approximate estimate for T-stresses and the stress biaxiality factor was obtained.

A Robust Scheme to Improving Security of Data using Graph Theory

With the incredible growth of using internet and other new telecommunication technologies, cryptography has be-come an absolute necessity for securing communications between two or more entities, particularly in the case of transferring confi-dential data. In the literature, many encryption systems have been proposed against attack threats. These schemes should normally overcome the concerns by ensuring confidentiality, integrity and authenticity of transmitted data. However, several of them have shown weaknesses in terms of security and complexity. Hence the need for a robust and powerful non-standard encryption algorithm to prevent any traditional opportunity to sniff data. In this work, we propose a new encryption system that perfectly meets the security requirements. The scheme is based essentially on the principles of graph theory which are very promising at plain text representations. Our approach proposes another use of the concept of Hamiltonian circuit and adjacency matrix using a shared key and a pseudo-random generator. After analysis of the experimental results, which were very promising, the technique was found to be both efficient and robust.

Indexed Metrics for Link Prediction in Graph Analytics

With the explosive growth of the Internet and the desire to harness the value of the information it contains, the prediction of possible links (relationships) between key players in social networks based on graph-theory principles has garnered great attention in recent years. Consequently, many fields of scientific research have converged in the development of graph analysis techniques to examine the structure of social networks with a very large number of users. However, the relationship between persons within the social network may not be evident when the data-capture process is incomplete or a relationship may have not yet developed between participants who will establish some form of actual interaction in the future. As such, the link-prediction metrics for certain social networks such as criminal networks, which tend to have highly inaccurate data records, may need to incorporate additional circumstantial factors (metadata) to improve their predictive accuracy. One of the key difficulties in link-prediction methods is extracting the structural attributes necessary for the classification of links. In this research, we analysed a few key structural attributes of a network-oriented dataset based on proposed social network analysis (SNA) metrics for the development of link-prediction models. By combining structural features and metadata, the objective of this research was to develop a prediction model that leverages the deep reinforcement learning (DRL) classification technique to predict links/edges even on relatively small-scale datasets, which can constrain the ability to train supervised machine-learning models that have adequate predictive accuracy.

Opioids Propagation Model and Strategy Analysis Based on SEIR

Through the analysis of the underlying data and the creation of the model, we have specific strategies for the transmission of opioids. To begin with, we process and analyse the data by graph theory and integrity principles. Then we find that the relevant variables of drug propagation law. After that, by modelling the results, we continue using the mechanism of the infectious disease SEIR model to analyse and construct the opioid drug propagation SEIR model [ S(Susceptible nodes)-General opioid users; E(Exposed node)-Opioid addicts; I(Infected nodes)-Drug users; R(Recovered nodes)-Drug abusers ]. By setting the corresponding parameters in the differential equations, we get the transmission speed of opioid abuse is positively correlated with the degree of initial infected node. The easier the node is infected, the faster the spread of opioids will be, the earlier it will stabilize, and the more nodes will eventually recover. We analyse the constructive ways to control the spread of opioids in the end, and propose the most valuable coping strategies.

Cognitive profiles and associated structural brain networks in a multimorbid sample of marginalized adults.

IntroductionCognition is impaired in homeless and vulnerably housed persons. Within this heterogeneous and multimorbid group, distinct profiles of cognitive dysfunction are evident. However, little is known about the underlying neurobiological substrates. Imaging structural covariance networks provides a novel investigative strategy to characterizing relationships between brain structure and function within these different cognitive subgroups.MethodParticipants were 208 homeless and vulnerably housed persons. Cluster analysis was used to group individuals on the basis of similarities in cognitive functioning in the areas of attention, memory, and executive functioning. The principles of graph theory were applied to construct two brain networks for each cognitive group, using measures of cortical thickness and gyrification. Global and regional network properties were compared across networks for each of the three cognitive clusters.ResultsThree cognitive groups were defined by: higher cognitive functioning across domains (Cluster 1); lower cognitive functioning with a decision-making strength (Cluster 3); and an intermediate group with a relative executive functioning weakness (Cluster 2). Between-group differences were observed for cortical thickness, but not gyrification networks. The lower functioning cognitive group exhibited higher segregation and reduced integration, higher centrality in select nodes, and less spatially compact modules compared with the two other groups.ConclusionsThe cortical thickness network differences of Cluster 3 suggest that major disruptions in structural connectivity underlie cognitive dysfunction in a subgroup of people who have a high multimorbid illness burden and who are vulnerably housed or homeless. The origins, and possible plasticity of these structure-function relationships identified with network analysis warrant further study.

CytoNet: Network Analysis of Cell Communities

We introduce cytoNet, a method to characterize multicellular topology from microscopy images. Accessible through a web-based interface, cytoNet quantifies the spatial relationships in cell communities using principles of graph theory, and evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet’s capabilities in two applications relevant to regenerative medicine: quantifying the morphological response of endothelial cells to neurotrophic factors present in the brain after injury, and characterizing cell cycle dynamics of differentiating neural progenitor cells. The framework introduced here can be used to study complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior.

The Optimum Priorities In Improving The Infrastructure Of Al-Anbar Governorate Road Network Using R.S. Data, GIS And Graph Theory

The status of the infrastructure of the transport system and then mobility in the governorate of Anbar is deplorable. Therefore, it requires two types of solutions in two phases. This study concerned with the first phase, which is represented by solving the problem of the inadequacy infrastructure in terms of availability between the cities, and work to develop it toward being maximally connected. So, generally speaking this study aimed to facilitate mobility through this network, by improving the accessibility in term of connectivity. The analysis process in this study, have twin objectives: first, to determine how much new linkages we need for our network to be maximally connected as a first stage? Second, Building a legislative framework lends the weight for decision makers in transport agency to take tough decision built up on ranking the new proposed linkages according to their relative values in providing access to the network, and the increment in comparable nodal accessibility due to the new additions. So, there is need for more sensible decisions based on more accurate analysis for deciding the optimum priorities for the new linkages to take place in the stage of development implementation via legislative framework. Therefore, the analysis will deal with topological characteristics for a number of aspects by expressing the simple graph of the network in a matrix format. These aspects are simulated and measured through the matrices powering process and the principles of graph theory. However, in addition to reducing the time the vehicles stays on the road, the study results will assist to divert a large proportion of the traffic volumes concurrently with the implementation process, and this in turn will pave the way to precede the solution of the second phase inside the cities. Not to mention, the legislative framework will bases for the financial framework of the transport agency. Keywords: infrastructure inadequacy& development, accessibility and connectivity, graph theory, matrix representation &powering, new linkage, nodal accessibility , relative value ,optimum priorities (ranking) and Decision making(legislation).

Cube-Rhombellane Related Structures: A Drug Perspective.

Rhombellanes represent a new class of structures, of which homeomorphs may be synthesized as real molecules. Cube-rhombellane is a double-shell structure, with vertices of degree 3 and 6, respectively. Several hypothetical structures/molecules were proposed and computed using molecular graph theory and coordination chemistry principles. Some geometries were optimized at the B3LYP/6-31G (d, p) level of theory, followed by harmonic vibrational frequency analysis at the same level of theory, single point data were collected in view of molecular stability evaluation. Some of the bioactive functionalized structures were also proposed and explored by molecular mechanics (MM)-based conformational analysis, to check their internal mobility. Drug-like properties of the proposed molecular structures were compared with some existing nano-molecules (fullerenes, nanotubes). ADME and other physico-chemical characteristics were computed using commercial software. Substructures of the proposed molecules, useful in a future synthesis, were provided by retro combinatorial synthesis (RECAP). Computational results obtained are promising regarding ADME properties, drug-likeness and nano-properties.

Asynchronous Communication under Reliable and Unreliable Network Topologies in Distributed Multiagent Systems: A Robust Technique for Computing Average Consensus

Nearly all applications in multiagent systems demand precision, robustness, consistency, and rapid convergence in designing of distributed consensus algorithms. Keeping this thing in our sight, this research suggests a robust consensus protocol for distributed multiagent networks, continuing asynchronous communications, where agent’s states values are updated at diverse interval of time. This paper presents an asynchronous communication for both reliable and unreliable network topologies. The primary goal is to delineate local control inputs to attain time synchronization by processing the update information received by the agents associated in a communication topology. Additionally in order to accomplish the robust convergence, modelling of convergence analysis is conceded by commissioning the basic principles of graph and matrix theory alongside the suitable lemmas. Moreover, statistical examples presenting four diverse scenarios are provided in the end; produced results are the recognisable indicator to authenticate the robust effectiveness of the proposed algorithm. Likewise, a simulation comparison of the projected algorithm with the other existing approaches is conducted, considering different performance parameters are being carried out to support our claim.

Hypergraph matching for MU-MIMO user grouping in wireless LANs

This paper investigates the user grouping problem of downlink wireless local area networks (WLANs) with multi-user MIMO (MU-MIMO). Particularly, we focus on the problem of whether single user transmit beamforming (SU-TxBF) or MU-MIMO should be utilized, and how many users and which users should be in a multi-user (MU) group. We formulate the problem for maximizing the system throughput subject to the multi-user air time fairness (MU-ATF) criterion. We show that hypergraphs provide a suitable mathematical model and effective tool for finding the optimal or close to optimal solution. We show that the optimal grouping problem can be solved efficiently for the case where only SU-TxBF and 2-user MU groups are allowed in the system. For the general case, where any number of users can be assigned to groups of different sizes, we develop an efficient graph matching algorithm (GMA) based on graph theory principles. We evaluate the proposed algorithm in terms of system throughput using an 802.11ac emulator, which is created using collected channel measurements from an indoor environment and simulated channel samples for outdoor scenarios. We show that our GMA achieves at least 93% of the optimal system throughput in all considered test cases.

Human Connectome as a big-data problem: New approaches for analysis and visualization.

Calculating so the functional as the structural relations between different cerebral areas and align those links to the personal differences in cognitive, behavioral and affective domains seems to be a crucial step for the understanding of human connectome [1]. As the resolution of the accurate brain imaging increases, the number of these links also increases, generating more complex networks, elevating the connectivity research to a big data problem. This alongside with the restricted computational resources indicates the urgent need for developing new models and new approaches in order to solve and visualize this big-data problem. Graph theory is widely used for mathematical modeling of complex networks. According to graph theory, a graph is defined as a pair between a set of vertices and a set of edges. Graphs can be described so by the existence of directionality in their edges (directed/undirected), as by their weight (weighted/unweighted-binary). As Although undirected binary graphs can be used to reduce the complexity of the networks, much of the information is lost due to arbitrary thresholding [2,3] leading to the disconnection syndrome [4], with negative implications so for the computation of several graph theoretical parameters as for the cohesion of the network [5], while there is also an introduction of several biases [6]. One way to reduce the complexity of the network overcoming the aforementioned biases [7] is by modeling our weighted graphs using Minimum Spanning Tree (MST) methodology. A MST is a loopless subgraph of the original graph connecting all nodes [8], and it can be used to reduce the dimensionality of the graph, because by it’s definition it has n-1 edges, while the initial graph has n*(n-1)/2 edges. The goal of the MST is to minimize the cost (increase the weights) of the graph and preserve only the most important edges [9], and it is assumed to be the backbone of the initial graph. Another approach to reduce the complexity of the brain connectome, is by employing linear or non-linear dimension reduction techniques. One non-linear technique, called diffusion mapping [10], which has recently draw the attention of the neuroscientific community [11], is based on the principles of spectral graph theory. A typical example of the aligned mean diffusion map extracted by the resting state fMRI correlation networks (468 subjects) is depicted in Figure 1. Each node is colored according to it’s connectivity profile, meaning that two nodes with the same color (red, green, blue, etc.) are connected similarly to the rest of the brain, while they are also strongly interconnected. On the other hand, as far the difference of two nodes in diffusion spectrum increases, the dissimilarity of these nodes also increases, and moreover the transition from one node to another becomes more difficult. Another important application of diffusion mapping is the visualization of functional connectivity in a much easier and more interpretable way. Indeed, a great challenge for neuroscientists today is to visualize connectivity networks incorporating so the functional as the anatomical information, and produce easily interpretable images. Currently the most common approach is the graph-like visualization overlaid in head/cortex. In this context, the nodes bear all the anatomical information by denoting a point in an anatomical space (e.g. MNI, IS 10/20, etc.), so the graph is aligned to head’s/cortex’s anatomy. Although this approach seems as a straightforward solution for the visualization of functional networks, it only works with a low number of edges. When the number of edges increases dramatically, this methodology will lead to the obfuscation of the underlying anatomical space [12]. Several other methods have been proposed so far for the illustration of human connectome [12], however still a lot of work should be done in order to visualize dense connectivity matrices. Concluding, we should again remark that as the neuroimaging resolution increases, and the human connectome becomes even more computationally demanding, either in terms of analysis or for visualization purposes, we need to pay more attention in dimensionality reduction techniques, so as to ensure that we maintain the greatest possible portion of the information hidden in our connectome.

Consensus problems for multi-agent systems with nonlinear algorithms

The paper investigates consensus problems for multi-agent systems with nonlinear algorithms. Group consensus algorithms with actuator saturation for the first-order and second-order multi-agent systems are proposed. In addition, the adaptive consensus algorithm with nonlinear dynamic is also given. By applying the graph theory, Lyapunov function, and LaSalle's invariance principle, consensus conditions for multi-agent systems are derived. Finally, three simulation examples are provided to denote the effectiveness of obtained theoretical results.

A method for development of software packages for mathematical simulation of electric power systems

Methods for development of program simulator complexes for mathematical simulation of power systems based on graph theory and structural simulation principles are developed and discussed. The form of equations to describe the dynamic interaction model for the structural elements of electric power systems is suggested and proven. The proposed form provides a model-independent and universal mathematical description. Simulator complexes that are based on the developed technique are discussed. Their characteristics and purpose are discussed. Simulator complexes provide additional possibilities beyond actual simulation, including the possibility of making rapid changes in the structural topology of the system by the user, automatic calculation of parameters for multivariate tasks, accounting protection algorithms, development of simplified models for a variety of settings of automatic control systems, and automatic tuning and optimization of control algorithms.

Automatic Reconfiguration of a Ship's Power System Using Graph Theory Principles

Shipboard control systems equip an expert operator with the ability to safely open and close breakers to facilitate electric plant operations. In more advanced systems, ship integrated power systems are equipped with automatic control systems that provide power management by automatically connecting and disconnecting power generation sources based on plant's present load and online generation capacity. What is generally lacking, however, is an effective manner to provide autonomous reconfiguration of the power system in response to electric plant casualties and ship's mission changes when the ship's electric plant is in a degraded condition. This paper explores the use of graph theory principles as they apply to developing automation control system algorithms to provide electric plant operations full autonomous control in the event of electric plant casualties and/or ship's mission changes.