Graph Theory Model Research Articles

A mathematical modelling to detect sickle cell anemia using Quantum graph theory and Aquila optimization classifier

Recently genetic disorders are the most common reason for human fatality. Sickle Cell anemia is a monogenic disorder caused by A-to-T point mutations in the β-globin gene which produces abnormal hemoglobin S (Hgb S) that polymerizes at the state of deoxygenation thus resulting in the physical deformation or erythrocytes sickling. This shortens the expectancy of human life. Thus, the early diagnosis and identification of sickle cell will aid the people in recognizing signs and to take treatments. The manual identification is a time consuming one and might outcome in the misclassification of count as there is millions of red blood cells in one spell. So as to overcome this, data mining approaches like Quantum graph theory model and classifier is effective in detecting sickle cell anemia with high precision rate. The proposed work aims at presenting a mathematical modeling using Quantum graph theory to extract elasticity properties and to distinguish them as normal cells and sickle cell anemia (SCA) in red blood cells. Initially, input DNA sequence is taken and the elasticity property features are extracted by using Quantum graph theory model at which the formation of spanning tree is made followed by graph construction and Hemoglobin quantization. After which, the extracted properties are optimized using Aquila optimization and classified using cascaded Long Short-Term memory (LSTM) to attain the classified outcome of sickle cell and normal cells. Finally, the performance assessment is made and the outcomes attained in terms of accuracy, precision, sensitivity, specificity, and AUC are compared with existing classifier to validate the proposed system effectiveness.

Synthesis of multi-input multi-degree-of-freedom fixed shaft gear transmission configuration for vehicles

The rapid development of dual-clutch transmission (DCT) and hybrid and purely electric vehicles stimulate the researches on multi-input fixed shaft gear transmission mechanisms. The multiple degree-of-freedom (DOF) transmission mechanism can achieve a wider range of speed ratio adjustment and more modes under certain space and weight constraints, which is an ideal choice for multi gear transmission. In this paper, a synthesis method of fixed shaft gear transmission configuration is proposed. Based on the established graph theory model, kinematic characteristics of the fixed shaft gear transmission configuration are studied and the shift sequence is determined. Using these characteristics, a derivation method from the basic configuration to the comprehensive gears and shafts configuration is proposed. Multiple parameters such as the synchronizer arrangement and speed ratios are optimized. Finally, the proposed method is vigorously verified by a seven-speed DCT with four DOFs. This method is comprehensive and systematic in designing fixed shaft gear transmission configurations, which can be applied to DCT, transmissions for parallel hybrid electric vehicles, and to some novel multi-input transmissions.

Graph Theory and Matrix Approach (GTMA) Model for the Selection of the Femoral-Component of Total Knee Joint Replacement

Total Knee Replacement (TKR) is the increasing trend now a day, in revision surgery which is associated with aseptic loosening, which is a challenging research for the TKR component. The selection of optimal material loosening can be controlled at some limits. This paper is going to consider the best material selected among a number of alternative materials for the femoral component (FC) by using Graph Theory. Here GTMA process used for optimization of material and a systematic technique introduced through sensitivity analysis to find out the more reliable result. Obtained ranking suggests the use of optimized material over the other existing material. By following GTMA Co_Cr-alloys (wrought-Co-Ni-CrMo) and Co_Cr-alloys (cast-able-Co-Cr-Mo)are on the 1st and 2nd position respectively.

Implementation and quality measures of graph theory model based image segmentation process in medical application

This research work simplified the representation of an image into more significant and easier way to analyse the image segmentation process by applying graph theory using color spatial clustering with consensus region merging. The color spatial clustering with consensus region merging is compared with other traditional and graph theory model to analyse the various quality measures calculated for the input of magnetic resonance imaging (MRI) scan and X-Ray images which will be useful in medical imaging for better analysis during diagnosis. From quality measures, the proposed method shows good quality image parameters as it has lower MSE, NAE values.

Mechanism and Model for Optimizing Polytetrafluoroethylene Distribution to Improve the Electrical and Thermal Conductivity of Treated Carbon Fiber Paper in Fuel Cells.

Employing polytetrafluoroethylene (PTFE)-treated carbon fiber paper (CFP) as the substrate of the gas diffusion layer (GDL) is a common practice to improve water management in proton exchange membrane fuel cells (PEMFCs), but the resulting increase in electrical and thermal resistance is a critical problem that restricts the performance output of PEMFCs. Hence, studying the mechanism and prediction model for both the electrical and thermal conductivity in CFP is essential. This work established a mathematical graph theory model for CFP electrical and thermal conductivity prediction based on the observation and abstraction of the CFP characteristic structures. For the PTFE-treated CFP, the electrical and thermal conductivity of CFP can be effectively increased by optimizing the PTFE distribution in CFP. A "filter net effect" mechanism was proposed to reasonably explain PTFE distribution's influence on the CFP performance. Finally, the equivalent effect of multiple factors on conductivity was revealed using contour maps, which provides inspiration for further reducing the electrical and thermal resistance in CFP.

Evolutionary graph theory derived from eco-evolutionary dynamics

A biologically motivated individual-based framework for evolution in network-structured populations is developed that can accommodate eco-evolutionary dynamics. This framework is used to construct a network birth and death model. The evolutionary graph theory model, which considers evolutionary dynamics only, is derived as a special case, highlighting additional assumptions that diverge from real biological processes. This is achieved by introducing a negative ecological feedback loop that suppresses ecological dynamics by forcing births and deaths to be coupled. We also investigate how fitness, a measure of reproductive success used in evolutionary graph theory, is related to the life-history of individuals in terms of their birth and death rates. In simple networks, these ecologically motivated dynamics are used to provide new insight into the spread of adaptive mutations, both with and without clonal interference. For example, the star network, which is known to be an amplifier of selection in evolutionary graph theory, can inhibit the spread of adaptive mutations when individuals can die naturally.

Graph theory model and mechanism analysis of carbon fiber paper conductivity in fuel cell based on physical structure

The gas diffusion layer (GDL) is considered one of the most significant components in the proton exchange membrane fuel cell (PEMFC). Its electrical impedance is crucial to the performance of power generation for PEMFC. The study of the conduction mechanism of the GDL substrate-carbon fiber paper(CFP) treated with PTFE is still a considerable challenge. This work reveals that the type and number of contact points are direct factors that affect CFP through-plane resistance. The CFP resistance along the through-plane direction is a function of the contact point density inside CFP, rather than traditional loading pressure. Based on the structural characteristics of CFP, a graph theory model that can express the complex three-dimensional structure and anisotropy of CFP is constructed instead of the finite element model or formula model—using the characterization of the distribution of PTFE in the CFP section. The accuracy of model prediction for the CFP resistance of 0 wt% and 20 wt% PTFE can reach 95% and 90%, respectively. This study provides a fundamental basis for the design and processing of CFP with lower electrical loss and is of great significance for further improving the power generation efficiency of PEMFC.

A Proof of the Algebraic Tractability Conjecture for Monotone Monadic SNP

The logic MMSNP is a restricted fragment of existential second-order logic which can express many interesting queries in graph theory and finite model theory. The logic was introduced by Feder and ...

Cascadability analysis of WDM recirculating loop buffer-based switch in optical data networks

Abstract In recent years the development of cloud computing, big data, cell phone application increases day by day and this will increase the work load on the server centers. Once the network traffic increases the power consumption also increases. In recent past, for reducing this power consumption arrayed waveguide grating (AWG) based optical packet switches are proposed. In these switches the physical and network layer analysis has been presented in isolated switch. In the core network to achieve low packet loss probability an effective buffering technique is required into the design of switch. In this paper a recirculating loop buffer based switch is presented, in this switch design for sharing the recirculating buffer, WDM technology is used by the packets. The main advantage of recirculating buffer is that it allows packet priority routing and by using this technique longer duration buffering of packet is achieved. This feature provides the quality of service in optical networks. In this paper, switch is deployed in the network and the performance of switch is measured when multiple switches are cascaded in the network simultaneously. The power analysis of switch is also performed for isolated and cascaded switches. For fixing the size of the switch into the network the concept of graph theory and random graph model is used.

Anti Tracking Technology of Urban Water Supply Pollution Source Based on Water Quality Monitoring

Xue, T.L.; Wang, S.Z.; Han, F., and Sun, D.H., 2020. Anti tracking technology of urban water supply pollution source based on water quality monitoring. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 58–61. Coconut Creek (Florida), ISSN 0749-0208.In view of the poor accuracy and robustness of pollution source tracking in water supply network due to exogenous sudden pollution accidents, this paper proposes the research on anti tracking technology of urban water supply pollution sources based on water quality monitoring. The auxiliary graph theory model of the pipe network is constructed, the shortest flow path between any two nodes is calculated, the pollution matrix is constructed, the minimum coverage monitoring point set under multiple coverage is obtained, the minimum coverage node number is obtained by greedy algorithm, and the optimal layout model of online water quality monitoring points is established. By constructing a simulation-optimized pollution source anti-tracking model, using the PSO-ACO fusion algorithm to search for thresholds, solving the simulation-optimizing pollution source antitracking model, and realizing water supply pollution source antitracking. Experimental results show that the researched pollution source anti-tracking model has high robustness, and can track pollution sources quickly and accurately under adverse factors such as hydraulic errors and experimental errors.

Graph Theory Model of Dry Reforming of Methane Using Rh(111).

The adsorption energies of intermediates of the dry reforming of methane reaction (DRM CH4 + CO2 ⇔ 2CO + 2H2) using Rh(111) are approximated. Graph theory creates descriptors of the intermediates. The information recorded in these descriptors includes the elemental identities of each atom, its neighbors, and its next-nearest neighbors. Graph theory is employed because it is a rapid approximation of more expensive density functional theory (DFT) calculations and because the descriptors created by graph theory are both human and machine interpretable. DRM contains a significant number of adsorbates, and side reactions, including reverse water-gas shift, may occur simultaneously. Therefore, DRM is well-poised for analysis by a graph theory model to predict large numbers of adsorption energies. A portion of adsorbates were calculated with DFT. Then, predictions were reported for the remaining adsorption energies not calculated with DFT.

The reliability analysis of k-ary n-cube networks

With the development of scalability and application of multiprocessor system, the components of the system possibly become faulty. It is desirable to know the reliability of the system. However, the exact reliability of a complicated network system is usually difficult to determine. A typical approach is to decompose the system into smaller ones based on a graph-theory model in which nodes are assumed to fail independently with known probabilities to measure the subsystem-based reliability, which is defined as the probability that a fault-free subsystem of a specific size is still available when the system has faults. In this paper, we use the probability fault model to establish upper and lower bounds on the subsystem-based reliability of k-ary n-cube networks, by taking into account the intersection of no more than five or four subgraphs. In addition, some numerical simulations of the subsystem-based reliability of k-ary n-cube networks are conducted.

Multi-cognitive network resource allocation based on improved artificial bee colony algorithm

This paper introduces a graph theory model for resource allocation in multi-cognitive wireless network scenarios. Aiming at the problems of low search accuracy and slow convergence speed of the basic artificial bee colony algorithm, an improved bee colony algorithm is proposed. The improved algorithm introduces an adaptive t-distribution mutation strategy. Compared with the original algorithm, the performance of the improved algorithm has greatly improved. At the same time, the improved bee colony algorithm is applied to multi-cognitive wireless network scenarios for resource allocation in the model. The experimental results show that the improved artificial bee colony algorithm can obtain greater system throughput and effectively reduce the performance of multi-cognitive wireless network systems, and can allocate resources more reasonably.

Estimating maintenance budget using Monte Carlo simulation

Asset maintenance is driven by allocation of yearly maintenance budget (YMB). Lack of scientific methods for its evaluation has led to allocations that are subjective and erroneous causing wastage or lack of resources. The objective of the work is to estimate yearly budget for plant maintenance. In this work, the constraint of subjective quantification of budget influencing parameters proposed in the literature is removed. Suggested methodology is more realistic in a real world context and results into more rational estimation of maintenance budget. Graph theory and matrix approach model along with Monte Carlo simulations are deployed to evaluate maintenance budget by assigning random values to the budget parameters within specified range. MatLab code is deployed for its implementation. The budget influencing parameters are simulated for pre-defined maintenance levels. Inferential statistics is applied to find the appropriate distribution and evaluate the interval estimation of maintenance budget. The maintenance budget so estimated is validated by comparing it with world-class and best practices values reported in the literature. The Monte Carlo simulation results for different maintenance levels are presented with mean, variance and interval estimation of maintenance budget. The methodology suggested in this work will be a useful aid to maintenance managers in assigning YMB to their plant systems and provide indications for maintenance system corrections. This will avoid using historic data or subjective expert judgments.

A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.

Randomized Parameterized Algorithms for the Kidney Exchange Problem

In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically 2 ≤ L ≤ 5), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the parameterized complexity and randomized algorithms for the kidney exchange problem without chains from theory. We construct two different parameterized models of the kidney exchange problem for two cases L = 3 and L ≥ 3, and propose two randomized parameterized algorithms based on the random partitioning technique and the randomized algebraic technique, respectively.

A study on multi-ASC scheduling method of automated container terminals based on graph theory

Compared with traditional terminals, two automated stacking cranes (ASCs) are configured for each container block of automated container terminal (ACT), which interact with automated guided vehicles (AGVs) and container trucks at the two ends of a container block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal’s shoreline are considered. To utilize the yard spaces, twin ASCs are set to share transport tracks installed at the two sides of a block, while interferences between the ASCs causes the routing and sequencing operations. In order to control the scheduling of twin ASCs, the interference model is established by analyzing the time overlap between tasks. Considering the influence of AGV transportation time, the model are then established to sequence the container handling tasks under the minimization of waiting time and makespan. A particle swarm optimization algorithm (PSO) based on graph theory model is design to solve the problem. Numerical experiments show that the algorithm is more competitive than traditional algorithm. Based on the model and experimental result, the practical significance of applying the algorithm to the actual situation is discussed.

Research on Gesture Recognition Based on Improved GBMR Segmentation and Multiple Feature Fusion

Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel. By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened. In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted. To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale. Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition. Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment. Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.

Optimal Coordination Path Selecting Method for Conduction Transformation Based on Floyd Algorithm

Adaptive modification design occupies a large proportion in modern product design, which is essentially a coordination process. Due to the conduction phenomenon, it often leads to the problem that the solution space is too large or even unsolvable. Aiming at the problems caused by the conduction phenomenon, an optimal coordination path selecting method for conduction transformation based Floyd algorithm is proposed. The product conduction transformation coordination process is considered as a path by combining the product graph theory model. The selection criteria of the start and end nodes is given. The functional contribution index, modified adaptation index and conduction impact index are described in detail. The calculation of the comprehensive coordination factor composed of them is given and used as the evaluation index of the conduction transformation coordination path. Floyd algorithm is adopted to coordination path optimization selection model, case study and system development, in order to verify the method proposed in this paper.

Modeling and Optimization of Traffic Sensor Layout Based on Landslide Risk

The layout of traffic sensor network nodes is related to the economic benefits of intelligent transportation systems. In this paper, we use the information model of landslide hazard susceptibility as the new measurement method, and adapt the landslide disaster susceptibility zoning result as the constraint condition of the maximum comprehensive value optimization model. The relevant parameters of the model are analysed, and the mathematics of each parameter is given. Through the example of sensor layout of Chongqing Yuxiang Expressway, it is determined that the number of optimal section sensors is 142 and the optimal layout spacing is about 676m. The results show that compared with the widely used graph theory model, the model can not only determine the optimal number of sensors but also give the best position of each sensor. The parameters of the model are calibrated for different situations. The results show that the model is easy to extend to different cities, different road conditions and different sensor types, and has good universality.