Determination of dominant loop in dual loop atrial tachycardia with direct graph mapping: an alternative for entrainment mapping?

Funding Acknowledgements Type of funding sources: None. Background Entrainment mapping (EM) is an important tool to determine the mechanism of complex reentrant atrial tachycardias (ATs), mostly to distinguish dominant from bystander reentrant loops. However, entrainment maneuvers are challenging, time consuming and risk to end the tachycardia. Purpose Recently, we developed a novel method Directed Graph Mapping (DGM), using concepts of network theory, allowing to automatically determine AT reentry loops from the local activation times (LAT) of any clinical mapping system. DGM showed good performance: it correctly finds ablation target (100 % success rate) on simple AT cases and could automatically determine reentry loops confirmed by the expert electrophysiologist with EM in complex AT cases. Out of 32 single loop cases, 62.5 % was identified correctly with automated DGM and out of 6 true double loop cases, 83.3 %. Lower performance for single reentry complex cases compared to EM was mainly because DGM could not distinguish the dominant loop from additional bystander loops found by DGM. Hence, the purpose of this work was to develop additional algorithms which in case of multiple found DGM loops could automatically find the dominant loop and compare it with the results of EM. Methods We performed multiple simulations of various types of double loop reentry on a patient specific model of the left atrium. Based on a clinical case, double loops were simulated around a scar at the anterior wall (localized reentry) and the mitral valve (MV). LAT maps were determined similar as in the clinic. By varying the size of the scar in multiple steps, we obtained a transition from a regime of a dominant loop around the scar (small scar), to a true double loop and further to a regime of a dominant loop around the MV (large scar). We developed a novel DGM algorithm to determine the dominant loop from the region of collision (ROC) found from the vector field of the wavefront graph. The developed method was also tested on 8 clinical cases of double loop ATs with EM measurements. Results Our algorithm found the location of the ROC and determined the correct dominant loop in 100% of the simulated data. We tested this on 8 clinical cases of AT, and accuracy of the method was 75 %. Conclusions Determining the ROC in case of multiple loops in AT could correctly determine the dominant versus bystander loop, leading to the correct ablation target, without the need for further EM.

Reducing inconsistency in AHP by combining Delphi and Nudge theory and network analysis of the judgements: an application to future scenarios

The Analytic Hierarchy Process (AHP) is a Multi-Criteria method in which a number of decision factors (typically criteria and alternatives) are compared pairwise by one or more experts, using the Saaty scale, with the goal of sorting the alternatives (Saaty, 1977; 1980). For group AHP the Delphi method can be used in parallel with the AHP (Di Zio and Maretti, 2014), and this allows the search for a consensus on each pairwise judgement. A big issue of the AHP regards the inconsistency of the pairwise comparison matrices and here we propose a new method to reduce the inconsistency. As a solution we exploit the Nudge theory (Thaler and Sunstein, 2008) and from the second round of the Delphi survey, we calculate and circulate a Nudge to “gentle push” the experts towards more consistent evaluations. Furthermore, we propose the representation of the AHP matrices through graphs. In a direct graph two nodes are linked with two direct and weighted edges (or one edge with the direction based on the weights), where the weights indicate the evaluation given by an expert or, for a group, the geometric mean of the judgements. This type of visualization facilitates the reading of the results and could also be used as real-time feedback in the Delphi process, by displaying on the edges also a measure of variability. An application is proposed, on the evaluation of four future scenarios on the regulation of genetic modification experiments, assessed by a panel of 27 experts according to different criteria (plausibility, consistency and simplicity). The application demonstrated that it is possible to: a) reduce the inconsistency; b) collect useful textual material which enrich the AHP itself; c) use the inconsistency index as a stopping criterion for the Delphi rounds; d) display the pairwise comparison matrices with graphs.

Intelligence system of artificial vision for unmanned aerial vehicle

The article considers the method of factor cluster analysis which allows automatically retrain the onboard recognition system of an unmanned aerial system. The task of informational synthesis of an on-board system for identifying frames is solved within the information-extreme intellectual technology of data analysis, based on maxi- mizing the informational ability of the system during machine learning. Based on the functional approach to modeling cognitive processes inherent to humans during forming and making classification decisions, it was proposed a categorical model in the form of a direct graph. According to this model, the algorithmic support of the information extreme factor cluster analysis is developed. It allows automatically retrain the system when expanding the alphabet of recognition classes. According to this algorithm, the on-board recognition system preliminarily carries out the information-extremal machine learning of recognition classes of relatively low power. When new classes appear, their unclassified structured recognition attribute vectors form additional learning matrixes. After reaching a representational volume, additional learning matrix joins the input learning matrix and the on-board recognition system is retrained. Forming additional learning matrixes of new recognition classes is carried out by the agglomerative algorithm of cluster analysis of unclassified vectors by k-means clustering. As a criterion of optimizing machine-learning parameters, we used the modified Kullback criterion which is a functional of the exact characteristics of classification solutions. To increase the functional efficiency of factor cluster analysis, it is proposed to increase the depth of machine learning by optimizing the parameters of image processing frames.

Linear Cryptanalysis of S-Box BRADG

Block cipher reaction automata direct graph (BRADG) is used in protection wireless networks. BRADG processes data blocks of B bits with key length of B bits and given ciphertext of size B bits, where B is 64,128,512,...bits. BRADG is a faster and more efficient way to encrypt large data compared with the previous design of block cipher. In this thesis, the researcher presented a study to attack for BRADG algorithm by using linear cryptanalysis technique, by known-plaintexts and corresponding of the ciphertexts. As the results, it's possible to break: one bit in subkey 1-state BRADG cipher with at least 2 to power 3known-plaintext, and 16 bits subkey 1-state BRADG cipher with 2 to power 24 known plaintexts. The success rate of each attack is 84% , 92% ,97% and 99% depending on the number of known plaintext and the probability that the equation of the best approximation holds.

The role of public goods in planetary evolution

Biological public goods are broadly shared within an ecosystem and readily available. They appear to be widespread and may have played important roles in the history of life on Earth. Of particular importance to events in the early history of life are the roles of public goods in the merging of genomes, protein domains and even cells. We suggest that public goods facilitated the origin of the eukaryotic cell, a classic major evolutionary transition. The recognition of genomic public goods challenges advocates of a direct graph view of phylogeny, and those who deny that any useful phylogenetic signal persists in modern genomes. Ecological spillovers generate public goods that provide new ecological opportunities. This article is part of the themed issue ‘Reconceptualizing the origins of life’.

Optimised assignment of airport gate configurations using an immune genetic algorithm

The function of airport gate assignment is to assign appropriate gates for arrival and departure flights and to ensure the flights are on schedule. A key task of airport ground operations is assigning the airport gate with high efficiency and a reasonable arrangement. In this paper we establish an optimised model based on the characteristics of both the flights (the flight type, flight down time and the number of passengers) and the airport gates (the ease of access of the airport gate). We give a representation of the solution space and a direct graph model of the airport gate configuration based on dynamic scheduling parallel machines. We design a method for solving the airport gate configuration based on an immune genetic algorithm. The simulation results show the effectiveness of this model and algorithm.

Browsing Large Concept Lattices through Tree Extraction and Reduction Methods

Browsing concept lattices from Formal Concept Analysis (FCA) becomes a problem as the number of concepts can grow significantly with the number of objects and attributes. Interpreting the lattice through direct graph-based visualisation of the Hasse diagram rapidly becomes difficult and more synthetic representations are needed. In this work the authors propose an approach to simplify concept lattices by extracting and visualising trees derived from them. The authors further simplify the browse-able trees with two reduction methods: fault-tolerance and concept clustering.