Node Duplication Research Articles

Special care is needed in applying phylogenetic comparative methods to gene trees with speciation and duplication nodes

Special care is needed in applying phylogenetic comparative methods to gene trees with speciation and duplication nodes

Robotic Path Planning Based on a Triangular Mesh Map

Aiming at the problem of robot path planning in complex maps, an algorithm of robot path planning based on triangular grid graph is proposed. Firstly, a weighted undirected loop graph and a feasible domain of nodes are obtained by discretizing the triangular mesh map. Next, the Dijkstra search algorithm is applied to find the feasible shortest path from an initial to a final configuration. Finally, The Douglas-Peucker algorithm is applied to remove duplicate and redundant nodes in the feasible path, and the waypoint are extracted. The final path is a curve that is obtained by connecting the several extracted waypoint. The proposed algorithm is tested for various maps. Compared with probabilistic roadmap method, the experimental results show that the proposed method can overcome the shortcomings of the random sampling method. Furthermore, the experimental result of triangular mesh map method used in two labyrinth maps show that the triangular mesh map method can solve the robot path planning problem in complex map very well, and it is an excellent algorithm for robot path planning.

FDIPP: False Data Injection Prevention Protocol for Smart Grid Distribution Systems.

In this paper, a false data injection prevention protocol (FDIPP) for smart grid distribution systems is proposed. The protocol is designed to work over a novel hierarchical communication network architecture that matches the distribution system hierarchy and its vast number of entities. The proposed protocol guarantees both system and data integrity via preventing packet injection, duplication, alteration, and rogue node access. Therefore, it prevents service disruption or damaging power network assets due to drawing the wrong conclusions about the current operating status of the power grid. Moreover, the impact of the FDIPP protocol on communication network performance is studied using intensive computer simulations. The simulation study shows that the proposed communication architecture is scalable and meets the packet delay requirements of inter-substation communication as mandated by IEC 61850-90-1 with a minimal packet loss while the security overhead of FDIPP is taken into account.

Community recombination and duplication node traverse algorithm in opportunistic social networks

In social communication, mobile devices can be regarded as socialization nodes in opportunistic social networks. Furthermore, they can carry and store useful information. Mobile devices can select destination nodes and deliver messages through opportunistic social networks because messages can be securely and conveniently stored, carried, and transmitted with nodes. However, many communities in traditional methods, may deliver messages often depending on one or two nodes, such as messages exchange by two nodes in Epidemic algorithm, or messages are sprayed with node in Spray and Wait algorithm. If those nodes are not enough cache and over-flooding, data transmission in communities may wait for a long time. In this study, communities are recombined and then duplication nodes are filtered in our method. According to evaluate contribution by node and restructure community, deliver ratio and overhead can be improved in opportunistic networks. With satisfactory results from simulation and comparison with some existing algorithms, the average delivery ratio of new research method is 0.91, which is 30% higher than that of Epidemic algorithm; the end-to-end delay reduces 75% with Spray and wait algorithm.

R-эффект от декомпозиции дублированных систем на равнонадежные функциональные узлы

The work is devoted to the reliability of non-recoverable two-channel automation systems and computer equipment. As alternative options, a system with block duplication (SBD) and a system with a quasi-bridge structure (QBS) are considered. SBD in general is a two-channel system consisting of a series connection of duplicated nodes of different reliability. In case of failure of one of the functional subunits (FSU) of the duplicated node using the control and reconfiguration (SCR) scheme, it is masked, withdrawn from the computational process, and reconfigured the system structure in the “Non Stop” operating mode. A QBS system also represents a two-channel structure, but consisting of a serial connection of duplicated nodes of equal reliability, while the technical element intensity (redundancy level) and the functionality of this system are identical to SPD. The QBS system is also a fail-safe system that provides the “Non Stop” mode of operation. The probabilistic-physical calculation method (WF-method) is used as a tool for studying the reliability of systems, which is based on the diffusion distribution of mean-time-to-failure (DN-distribution), specially formalized for assessing the reliability of electronic, electrical and electromechanical elements and systems. While maintaining the redundancy level of the considered two-channel redundant systems, decomposing the channels into equally reliable duplicated nodes leads to the R-effect – an increase in the likelihood of system uptime with an increase in the number of nodes. The presence of the R-effect was established by other methods of calculation and by statistical modeling for both non-restored and restored systems.

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization.

Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

Machine Learning to Support the Presentation of Complex Pathway Graphs.

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article, we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts.

Analytical results for the in-degree and out-degree distributions of directed random networks that grow by node duplication

We present exact analytical results for the degree distribution in a directed network model that grows by node duplication (ND). Such models are useful in the study of the structure and growth dynamics of gene regulatory networks and scientific citation networks. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network and duplicates each outgoing link of the mother node with probability p . In addition, the daughter node forms a directed link to the mother node itself. Thus, the model is referred to as the corded directed-node-duplication (DND) model. The corresponding undirected ND model was studied before and was found to exhibit a power-law degree distribution. We obtain analytical results for the in-degree distribution , and for the out-degree distribution , of the corded DND network at time t. It is found that the in-degrees follow a shifted power-law distribution, so the network is asymptotically scale free. In contrast, the out-degree distribution is a narrow distribution, that converges to a Poisson distribution in the limit of and to a Gaussian distribution in the limit of . Such distinction between a broad in-degree distribution and a narrow out-degree distribution is common in empirical networks such as scientific citation networks. Using these distributions we calculate the mean degree , which converges to in the large network limit, for the whole range of 0 < p < 1. This is in contrast to the corresponding undirected network, which exhibits a phase transition at p = 1/2 such that for p > 1/2 the mean degree diverges in the large network limit. We also present analytical results for the distribution of the number of upstream nodes, , and for the distribution of the number of downstream nodes, , from a random node. We show that the mean values scale logarithmically with the network size. This means that in the large network limit only a diminishing fraction of pairs of nodes are connected by directed paths, unlike the corded undirected ND network that consists of a single connected component. Therefore, the corded DND network is not a small-world network.

A new formulation of the electric vehicle routing problem with time windows considering concave nonlinear charging function

Abstract: The electric vehicle routing problem with time window (EVRPTW) is an extension of the traditional vehicle routing problem with time window (VRPTW), where new features of electric vehicles are considered, such as limited battery capacities, lack of infrastructures, and long charging time. In this study, new technical formulations were presented for vehicle route selection and charging station visit, which reduces the formulation complexity without using duplicated dummy nodes or arcs. Besides, a new linearization method was developed that employs a set of secant lines to surrogate the concave nonlinear charging function with linear constraints. This method defines the charging time as a continuous variable and uses fewer variables than existing formulation in literature. A mixed-integer linear programming (MILP) model was developed for the EVRPTW and computational experiments on Solomon's VRPTW instances were conducted to verify the proposed model. The experimental results were compared with those obtained by traditional routing models, which showed that the proposed model can result in better EVs logistics schedules with higher charging time utilizations.

Analytical results for the distribution of shortest path lengths in directed random networks that grow by node duplication

We present exact analytical results for the distribution of shortest path lengths (DSPL) in a directed network model that grows by node duplication. Such models are useful in the study of the structure and growth dynamics of gene regulatory networks and scientific citation networks. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network and duplicates each outgoing link of the mother node with probability $p$. In addition, the daughter node forms a directed link to the mother node itself. Thus, the model is referred to as the corded directed-node-duplication (DND) model. In this network not all pairs of nodes are connected by directed paths, in spite of the fact that the corresponding undirected network consists of a single connected component. More specifically, in the large network limit only a diminishing fraction of pairs of nodes are connected by directed paths. To calculate the DSPL between those pairs of nodes that are connected by directed paths we derive a master equation for the time evolution of the probability $P_t(L=\ell)$, $\ell=1,2,\dots$, where $\ell$ is the length of the shortest directed path. Solving the master equation, we obtain a closed form expression for $P_t(L=\ell)$. It is found that the DSPL at time $t$ consists of a convolution of the initial DSPL $P_0(L=\ell)$, with a Poisson distribution and a sum of Poisson distributions. The mean distance ${\mathbb E}_t[L|L<\infty]$ between pairs of nodes which are connected by directed paths is found to depend logarithmically on the network size $N_t$. However, since in the large network limit the fraction of pairs of nodes that are connected by directed paths is diminishingly small, the corded DND network is not a small-world network, unlike the corresponding undirected network.

Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR.

BackgroundClinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important.ObjectiveThis study aimed to develop a retrieval method to identify CIMs online to represent EHR data.MethodsWe proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics.ResultsWe downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering “diagnostic reports,” “problem list,” “patients background,” “clinical decision,” etc, as well as models that other retrieval methods could not find, such as “problems and diagnoses.”ConclusionsThe graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable.

Local routing protocols performance for computer virus elimination in complex networks

Although extensive research has been devoted to treat virus propagation over decades, the effect of local routing protocols algorithms on it has been ignored. In this paper, we studied the effectiveness of local routing protocols and their additional algorithms; next-nearest neighbors (NNN) and restrictive queue- length algorithm (RQL) in term of robustness in computer virus spreading. It is found that, the local routing protocols without additional algorithms favor the virus spreading, due to the blind transmission of packets between source and destination, while in local routing protocols under (NNN) algorithm, the virus propagation is reduced remarkably. Moreover in local routing protocols under RQL, the virus propagation continues to be reduced more and more compared with NNN algorithm. Furthermore our results show that the node duplication avoidance (NDA) algorithm favors the computer virus spreading especially in sparse networks. Moreover, in comparison with shortest path (SP) and efficient path (EP) global routing strategies ,local routing protocols under the restrictive queue- length algorithm (RQL), the virus propagation is highly reduced and becomes unexpectedly comparable to the traditional shortest path strategy and overcomes the efficient path strategy which shows a high vulnerability and sensitivity to computer virus propagation. These results could be very helpful for network routing protocols designers to give more attention to local routing strategies and their additional algorithms in order to benefit from high capacity and high level of security plus the adaptability with the large scale free networks as the internet.

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

Secure Key Distribution in Wireless Sensor Networks

Key distribution is an inevitable part of the wireless networks and is important for node identification and authentication and also is important for maintaining data confidentiality and integrity of the communication system. In this paper, the numerous key distribution algorithms already implemented in the wireless sensor networks have been studied along with their architecture and their efficiency based on the various hardware constraints of WSN and the various attacks affecting the wireless networks like that of the DOS and node duplication attacks. On the basis of the study, a new key distribution algorithm have been suggested to overcome the drawbacks of the other methods and has focused on the same in ad hoc networks so that it can be implemented in critical situations requiring random deployment of sensors in a dynamic environment. The authors have suggested a symmetric key algorithm based on relative counters and timers which will be used to create exclusive pair keys from a master key provided by a trusted base station.

Master equation for the degree distribution of a Duplication and Divergence network

Network growth as described by the Duplication–Divergence model proposes a simple general idea for the evolution dynamics of natural networks. In particular it is an alternative to the well known Barabási–Albert model when applied to protein–protein interaction networks. In this work we derive a master equation for the node degree distribution of networks growing via Duplication and Divergence and we obtain an expression for the total number of links and for the degree distribution as a function of the number of nodes. Using algebra tools we investigate the degree distribution asymptotic behavior. Analytic results show that the network nodes average degree converges if the total mutation rate is greater than 0.5 and diverges otherwise. Treating original and duplicated node mutation rates as independent parameters has no effect on this result. However, difference in these parameters results in a slower rate of convergence and in different degree distributions. The more different these parameters are, the denser the tail of the distribution. We compare the solutions obtained with simulated networks. These results are in good agreement with the expected values from the derived expressions. The method developed is a robust tool to investigate other models for network growing dynamics.

A note on the interpretation of the efficiency centrality

The interpretation of efficiency centrality is extended including nodes for which this metric acquires negative values. It is demonstrated that it happens either for nodes that are in shortest paths in which the nodes are the terminal nodes or if there are alternate shortest paths with similar length. It is shown that duplication of a node in a network leads to increase of the global efficiency, yet the efficiency centrality of the duplicated node is reduced by a factor of ½ with each subsequent replication.

Investigations on a FEM‐sub‐model for “Die‐Less‐Hydroforming” weld seams

AbstractA global forming model for the special and unconventional forming process called “Die‐Less‐Hydroforming” that can be used for the inflation of arbitrary double‐layered or multi‐layered seal‐welded blanks was already presented by the authors in recent publications. While looking on the modeling of the specific seal‐weld in this global model, as a special modeling step the elements at the edges of the blanks are folded towards each other and after that the seal‐weld is modeled by merging the resulting duplicated nodes in the welded joining zone. This paper focuses on the investigation whether this modeling strategy for the seal‐weld is appropriate, i.e. whether the load‐displacement‐performance and stiffness behavior correspond to reality. This was investigated with a FEM‐sub‐model within LS‐DYNA in combination with a specially developed experimental benchmark‐test for the investigation of “Die‐Less‐Hydroforming” weld seams. (© 2017 Wiley‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

The effects of data quality on the analysis of corporate board interlock networks

Nowadays, social network data of ever increasing size is gathered, stored and analyzed by researchers from a range of disciplines. This data is often automatically gathered from API’s, websites or existing databases. As a result, the quality of this data is typically not manually validated, and the resulting social networks may be based on false, biased or incomplete data. In this paper, we investigate the effect of data quality issues on the analysis of large networks. We focus on the global board interlock network, in which nodes represent firms across the globe, and edges model social ties between firms – shared board members holding a position at both firms. First, we demonstrate how we can automatically assess the completeness of a large dataset of 160 million firms, in which data is missing not at random. Second, we present a novel method to increase the accuracy of the entries in our data. By comparing the expected and empirical characteristics of the resulting network topology, we develop a technique that automatically prunes and merges duplicate nodes and edges. Third, we use a case study of the board interlock network of Sweden to show how poor quality data results in distorted network topologies, incorrect community division, biased centrality values and abnormal influence spread under a well-known diffusion model. Finally, we demonstrate how the proposed data quality assessment methods help restore the network structure, ultimately allowing us to derive meaningful and correct results from the analysis of the network.

Distribution of shortest path lengths in a class of node duplication network models.

We present analytical results for the distribution of shortest path lengths (DSPL) in a network growth model which evolves by node duplication (ND). The model captures essential properties of the structure and growth dynamics of social networks, acquaintance networks, and scientific citation networks, where duplication mechanisms play a major role. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network, forming a link to the mother node, and with probability p to each one of its neighbors. The degree distribution of the resulting network turns out to follow a power-law distribution, thus the ND network is a scale-free network. To calculate the DSPL we derive a master equation for the time evolution of the probability P_{t}(L=ℓ), ℓ=1,2,⋯, where L is the distance between a pair of nodes and t is the time. Finding an exact analytical solution of the master equation, we obtain a closed form expression for P_{t}(L=ℓ). The mean distance 〈L〉_{t} and the diameter Δ_{t} are found to scale like lnt, namely, the ND network is a small-world network. The variance of the DSPL is also found to scale like lnt. Interestingly, the mean distance and the diameter exhibit properties of a small-world network, rather than the ultrasmall-world network behavior observed in other scale-free networks, in which 〈L〉_{t}∼lnlnt.

Gene Tree Construction and Correction Using SuperTree and Reconciliation.

The supertree problem asking for a tree displaying a set of consistent input trees has been largely considered for the reconstruction of species trees. Here, we rather explore this framework for the sake of reconstructing a gene tree from a set of input gene trees on partial data. In this perspective, the phylogenetic tree for the species containing the genes of interest can be used to choose among the many possible compatible "supergenetrees", the most natural criteria being to minimize a reconciliation cost. We develop a variety of algorithmic solutions for the construction and correction of gene trees using the supertree framework. A dynamic programming supertree algorithm for constructing or correcting gene trees, exponential in the number of input trees, is first developed for the less constrained version of the problem. It is then adapted to gene trees with nodes labeled as duplication or speciation, the additional constraint being to preserve the orthology and paralogy relations between genes. Then, a quadratic time algorithm is developed for efficiently correcting an initial gene tree while preserving a set of "trusted" subtrees, as well as the relative phylogenetic distance between them, in both cases of labeled or unlabeled input trees. By applying these algorithms to the set of Ensembl gene trees, we show that this new correction framework is particularly useful to correct weakly-supported duplication nodes. The C++ source code for the algorithms and simulations described in the paper are available at https://github.com/UdeM-LBIT/SuGeT.