Combination Of Graph Theory Research Articles

Optimizing Disaster Response through Efficient Path Planning of Mobile Aerial Base Station with Genetic Algorithm

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS.

Functions and architecture of the expert-analytical system in the field of educational and personnel processes

The research is aimed at solving the problem of increasing the efficiency of integrating the scientific and technical potential of educational institutions with existing enterprises in Russia. A combination of graph theory and fuzzy set theory is proposed as a mathematical apparatus. Goal – to develop the functions and architecture of an expert-analytical system in the field of educational and personnel processes. In order to select relevant vacancies, taking into account the length of service and academic performance of graduates, an expert recommendation system was developed that provides a list of relevant vacancies sorted in descending order of the estimated salary, taking into account the length of service and the level of knowledge of the graduate. Tools have been developed for analyzing and visualizing graphs of current vacancies and areas of graduate training for research and comparison of skills, requirements for applicants, geography and other information collected from open sources. The algorithm of a fuzzy recommendation system is described, which takes into account the length of service and the level of knowledge of the graduate (test scores or grades in specialized subjects). The developed analytics and visualization tools contribute to the effective management of integration processes in the interests of universities and enterprises.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor.

Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.

PRIVACY-PRESERVING REAL TIME TRACING SYSTEM FOR COVID-19 PATIENT USING GPS TECHNOLOGY

The new normal condition in Indonesia does not mean that Indonesia is completely free from infection with the Covid 19 virus. Individuals exposed to the Covid 19 virus have symptoms like mild, moderate, to severe condition. Most individuals who have mild symptoms are self isolating at their home until tested negative for the Covid 19 virus. The impact of Covid 19 has led to an increase in the use of gadgets to access all the information needed. The purpose of this study is to provide information regarding patients infected with Covid 19 in a certain area through a tracing application. The application can help public to find out how many individuals are infected with Covid 19 in the surrounding environment by prioritizing privacy-preserving in a real time. The method used in this study is a combination of graph theory and GPS tracing system on a gadget. The initial stage of this study was carried out through tracing Covid 19 patients based on their position of residence. The final stage of the study was carried out using a graph approach based on distance and percentage of transmission. The result of this study obtained privacy-preserving real-time tracing with the predicted precentage of Covid 19 transmission susceptibility within the scope of danger or vulnerability, quite safe, and secure. Furthermore, individuals can take precautions by maintaining a safe distance.

A Combined Graph Theory–Machine Learning Strategy for Planning Optimal Radial Topology of Distribution Networks

A mesh type distribution network is often configured into a radial structure to offer a variety of practical benefits, such as lower short-circuit current, low operating costs, and low construction costs. This paper presented a novel technique for optimal planning of radial distribution networks based upon a combination of Graph Theory and Machine Learning (ML) approaches. A network comprises several sectionalizing and tie-type switches. The conversion of a mesh type system into a radial one involves the selection of the best set of switches on the feeder lines that are to be opened or closed. Out of all switching combinations, we have provided the optimal radial combination, consequently the maximum voltage stability of the network with less active and reactive power losses. The proposed system also preserves the system’s radial nature, with out islanding any load by a simple coding scheme. To test the capabilities of the proposed technique, we carried out simulations on the 30-node test distribution system and the results are encouraging.

The Power Graph of a Dihedral Group

Graph theory is one of the topics in mathematics that is quite interesting to study because it is applicable and can be combined with other mathematical topics such as group theory. The combination of graph theory and group theory is that graphs can be used to represent a group. An example of a graph is a power graph. A power graph of the group is defined as a graph whose vertex set is all elements of and two distinct vertices and are connected if and only if or for a positive integer x and y. In this study, the author discusses the power graph of the dihedral group The results obtained from this study are the power graph of the dihedral group where with prime numbers and an natural number is a graph consisting of two non-disjoint subgraphs, namely complete subgraphs and star subgraphs. And we find that its radius and diameter are 1 and 2.

A diagnostic system of an intelligent component based on Bayesian accurate inference networks

The article discusses the features of building an intelligent diagnostic system for technical systems based on Bayesian networks, which are a new method of probabilistic-statistical modeling based on a combination of graph theory, probability theory and methods of applied statistics. An example of a Bayesian network structure for diagnosing and predicting an accident in unmanned vessels is presented. Special attention is paid to the stages of building a Bayesian network for diagnosing technical systems using statistical data. Particular emphasis is placed on solving the problem of forming the Bayesian network output under conditions of decreasing uncertainty when new information arrives within the Bayesian network. The features of constructing an accurate probabilistic inference using the clustering algorithm are also considered. A number of experiments were carried out to compare the computational complexity of the clustering algorithm, which was used in the process of constructing the exact probabilistic inference in the Bayesian network, and the stochastic sampling algorithm to obtain an approximation. To simulate the structure of an intelligent system for technical diagnostics, it is proposed to divide the Bayesian network into subnets, the graphs of which will have a relatively small number of vertices. As an example, the following classes of diagnostic parameters of a technical system are proposed, for each of which it is further recommended to build a Bayesian network: probability of system uptime, system failure rate, average system (element) operation time to failure, average service life, gamma-percentage resource, average shelf life, etc.

Structural equation modeling of a winnowed soil microbiome identifies how invasive plants re-structure microbial networks.

The development of microbial networks is central to ecosystem functioning and is the hallmark of complex natural systems. Characterizing network development over time and across environmental gradients is hindered by the millions of potential interactions among community members, limiting interpretations of network evolution. We developed a feature selection approach using data winnowing that identifies the most ecologically influential microorganisms within a network undergoing change. Using a combination of graph theory, leave-one-out analysis, and statistical inference, complex microbial communities are winnowed to identify the core organisms responding to external gradients or functionality, and then network development is evaluated against these externalities. In a plant invasion case study, the winnowed microbial network became more influential as the plant invasion progressed as a result of direct plant-microbe links rather than the expected indirect plant-soil-microbe links. This represents the first use of structural equation modeling to predict microbial network evolution, which requires identification of keystone taxa and quantification of the ecological processes underpinning community structure and function patterns.

Extended Configurational Polyhedra Based on Graph Representation for Crystalline Solids

We propose theoretical approach based on combination of graph theory and generalized Ising model (GIM), which enables systematic determination of extremal structures for crystalline solids without any information about interactions or constituent elements. The conventional approach to find such set of structure typically employs configurational polyhedra (CP) on configuration space based on GIM description, whose vertices can always be candidates to exhibit maximum or minimum physical quantities. We demonstrate that the present approach can construct extended CP whose vertices not only include those found in conventional CP, but also include other topologically and/or configurationally characteristic structures on the same dimensional configuration space with the same set of figures composed of underlying lattice points, which therefore has significant advantage over the conventional approach.

Fault-Tolerant Containment Control of Multiple Unmanned Aerial Vehicles Based on Distributed Sliding-Mode Observer

This paper investigates the distributed fault-tolerant containment control (FTCC) of multiple unmanned aerial vehicles (multi-UAVs) when a subset of multi-UAVs is encountered by actuator faults and input saturation. The topology involving multiple follower UAVs and leader UAVs is an undirected, fixed communication network and only a subset of follower UAVs has access to the leader UAVs. By the combination of graph theory and sliding-mode observer (SMO), the desired reference of each follower is first estimated in a distributed manner. Then, by utilizing the estimated knowledge, a set of distributed control laws is iteratively designed to steer follower UAVs into the convex hull spanned by the leader UAVs. In the distributed control scheme, disturbance observer (DO) technique is used to estimate unknown lumped uncertainty including external disturbances and actuator faults. An auxiliary dynamic system is constructed to compensate the input saturation. Moreover, to eliminate the “explosion of complexity” in traditional backstepping architecture, high-gain observer (HGO) technique is integrated into the backstepping architecture to estimate the virtual control signals and their first derivatives. Furthermore, by using graph theory and Lyapunov-based approach, it is shown that the distributed fault-tolerant containment controller can guarantee all follower UAVs to converge into the convex hull spanned by all leader UAVs. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed distributed control scheme.

Graph Theory and Ion and Molecular Aggregation in Aqueous Solutions

In molecular and cellular biology, dissolved ions and molecules have decisive effects on chemical and biological reactions, conformational stabilities, and functions of small to large biomolecules. Despite major efforts, the current state of understanding of the effects of specific ions, osmolytes, and bioprotecting sugars on the structure and dynamics of water H-bonding networks and proteins is not yet satisfactory. Recently, to gain deeper insight into this subject, we studied various aggregation processes of ions and molecules in high-concentration salt, osmolyte, and sugar solutions with time-resolved vibrational spectroscopy and molecular dynamics simulation methods. It turns out that ions (or solute molecules) have a strong propensity to self-assemble into large and polydisperse aggregates that affect both local and long-range water H-bonding structures. In particular, we have shown that graph-theoretical approaches can be used to elucidate morphological characteristics of large aggregates in various aqueous salt, osmolyte, and sugar solutions. When ion and molecular aggregates in such aqueous solutions are treated as graphs, a variety of graph-theoretical properties, such as graph spectrum, degree distribution, clustering coefficient, minimum path length, and graph entropy, can be directly calculated by considering an ensemble of configurations taken from molecular dynamics trajectories. Here we show percolating behavior exhibited by ion and molecular aggregates upon increase in solute concentration in high solute concentrations and discuss compelling evidence of the isomorphic relation between percolation transitions of ion and molecular aggregates and water H-bonding networks. We anticipate that the combination of graph theory and molecular dynamics simulation methods will be of exceptional use in achieving a deeper understanding of the fundamental physical chemistry of dissolution and in describing the interplay between the self-aggregation of solute molecules and the structure and dynamics of water.

Modelación con teoría de grafos para la unidimensionalidad de un instrumento de evaluación

Modelación con teoría de grafos para la unidimensionalidad de un instrumento de evaluación

Molecular Face-Rotating Cube with Emergent Chiral and Fluorescence Properties.

Chiral cage compounds are mainly constructed from chiral precursors or based on the symmetry breaking during coordination-driven self-assembly. Herein, we present a strategy to construct chiral organic cages by restricting the P or M rotational configuration of tetraphenylethylene (TPE) faces through dynamic covalent chemistry. The combination of graph theory, experimental characterizations and theoretical calculations suggests emergent chirality of cages is originated from complex arrangements of TPE faces with different orientational and rotational configurations. Accompanied by the generation of chirality, strong fluorescence also emerged during cage formation, even in dilute solutions with various solvents. In addition, the circularly polarized luminescence of the cages is realized as a synergy of their dual chiral and fluorescence properties. Chirality and fluorescence of cages are remarkably stable, because intramolecular flipping of phenyl rings in TPE faces is restricted, as indicated by calculations. This study provides insight into construct chiral cages by the rational design through graph theory, and might facilitate further design of cages and other supramolecular assemblies from aggregation-induced emission active building blocks.

Analytical Models of Diffusive-Reactive Flow in 3-D Microstructural Networks to Guide the Design of Electrochemical Materials

In this work, we present a fully analytical, non-dimensional model to describe steady-state diffusive flow through a 3-D network. Through a combination of graph theory, linear algebra, and geometry, the model explicitly relates the network’s topology and the morphology of its channels to the material’s effective conductivity. The effective conductivity estimates calculated by the model exhibit good agreement with those from electrochemical fin theory and finite element analysis, but are computed 1.5 and 6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the conductivity, whereby the distributions that characterize the structure are readily available for further analysis, e.g., using network science. We then present an approach for extending the model to account for (1) n interacting flows and (2) for the single-flow case we demonstrate algorithmic approaches for (a) accounting for the influence of reactions at steady state and (b) examining the transient flow properties of the network. The non-dimensional metrics that follow from each model provide a means of comparing different materials and, furthermore, each extension, to a large extent, preserves the relative speed and ease-of-use inherent in the initial steady-state model, thus making them attractive for guiding transport-relevant hypotheses in materials design efforts.

Acercamiento a la deserción estudiantil desde la integración social y académica

Colombian public policies related to dropouts in higher education urge universities to formulate and monitor their institutional retention plans. In order to analyze these government guidelines, we propose a novel combination of Graph Theory and Decision Trees to model the spontaneous interaction of students in some social networks. Our goal is to estimate the contribution of this interaction to social and academic integration. The proposal was validated with a case study showing the algorithms implemented with computational tools.

A combination of Monte Carlo Temperature Basin Paving and Graph theory: Water cluster low energy structures and completeness of search

The knowledge of degree of completeness of energy landscape search by stochastic algorithms is often lacking. A graph theory based method is used to investigate the completeness of search performed by Monte Carlo Temperature Basin Paving (MCTBP) algorithm for (H2O)n, (n =6, 7, and 20). In the second part of the work, a combination of MCTBP and graph theory was used to devise a new algorithm for finding low energy structures of (H2O)n, (n =21-25), where input structures for (H2O)n comes from the graphs of (H2O)n−1. The new algorithm can be a complementary tool to the MCTBP method. The completeness of search of the energy landscape of water clusters has been investigated by a combination of graph theory and Monte Carlo approach. It was found that the combined technique gives more insight to the low energy structures than Monte Carlo based method.

A Graph Theoretical Sectorization Approach for Energy Reduction in Water Distribution Networks

Most water supply utilities in the world need to deal with daily operations of their systems while at the same time they are required to increase their efficiency. One way to increase the efficiency of the system is to divide the water distribution network into smaller subsystems called sectors. This article presents a new sectorization approach, which has been developed as part of the European FP7 research project ICeWater, focused on increasing energy efficiency. The approach is based on the combination of graph theory and multi-objective optimization applied to the system of Milan (Italy). Most existing approaches for sectorization deal with gravity sources. The methodology presented here has been applied to a system with 26 pump stations, which is an innovation and a step forward for other utilities facing similar challenges. The application of sectorization to the network of Milan has the advantage of allowing to fix the number of sectors and enabling the exploration of multiple scenarios of system operation.

Optimal PMU placement considering topology constraints

Abstract A new optimization algorithm for optimal PMU configuration based on combination of graph theory and genetic algorithm is proposed. According to four topology reconstruction rules and three PMU configuration rules based on the graphic relationships between PMUs, constraints of PMU placement are put forward through topology constraint analysis, which dramatically limits the feasible solution space, thereby enhancing the algorithm speed. Meanwhile, an improved genetic algorithm based on serial number coding is presented to avoid infeasible solutions and improve the overall optimization performance. New corresponding crossover and mutation operator is also created. Examples show that the proposed algorithm performs very well and is highly valuable to large-scale networks.

Neuroblastoma tyrosine kinase signaling networks involve FYN and LYN in endosomes and lipid rafts.

Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment. Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development. It is known that defects in these mechanisms cause neuroblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endosomes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half of the receptor tyrosine kinases in the human genome. Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data. Clusters of proteins in these networks are indicative of functional signaling pathways. The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases, especially FYN and LYN. Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases, ALK and KIT. The results suggest a mechanism to distinguish signaling responses to activation of different receptors, or combinations of receptors, that govern the behavior of the neural crest, which gives rise to neuroblastoma.

Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China

Geographical detector models provide a quantitative approach for evaluating spatial correlations among ecological factors, population density and landscape connectivity. Here, we used a geographical model to assess the influence of different gradients of urbanization, human activities and various environmental factors on the connectivity of urban forest landscapes in Xiamen, China from 1996 to 2006. Our overarching hypothesis is that human activity has modified certain ecological factors in a way that has affected the connectivity of urban forest landscapes. Therefore, spatiotemporal distributions of landscape connectivity should be similar to those of ecological factors and can be represented quantitatively. Integral indices of connectivity and population density were employed to represent urban forest landscape connectivity and human activity, respectively. We then simulated the spatial relationship between forest patches and population density with Conefor 2.6 software. A geographical detector model was used to identify the dominant factors that affect urban forest landscape connectivity. The results showed that a distance of 600 m was the threshold of node importance. Mean annual temperature, mean annual precipitation, elevation, patch area, population density and dominant species had significant effects on the node importance. Mean annual temperature was more significant than population density in controlling the spatial pattern of the delta of the integral index of connectivity (dIIC). The spatial interaction between population density and various ecological factors as well as their linearly enhanced or nonlinearity enhanced urban forest landscape connectivity. In conclusion, a combination of graph theory and geographical detector models is effective for quantitatively evaluating interactive relationships among ecological factors, population density and landscape connectivity.