Computational Point Of View Research Articles

Topology and hydraulic permeability estimation of explosively created fractures through regular cylindrical pore network models

Purpose This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with their resulting hydraulic permeability. The focus of the investigation is to define a method that generates a valid geometric and topologic representation from a computational modelling point of view for explosion-generated fractures in rocks. In particular, extraction of geometries from experimentally validated Eulerian smoothed particle hydrodynamics (ESPH) approach, to avoid restrictions for image-based computational methods. Design/methodology/approach Three-dimensional stabilized ESPH solution is required to model explosively created fracture networks, and the accuracy of developed ESPH is qualitatively and quantitatively examined against experimental observations for both peak detonation pressures and crack density estimations. SPH simulation domain is segmented to void and solid spaces using a graphical user interface, and the void space of blasted rocks is represented by a regular lattice of spherical pores connected by cylindrical throats. Results produced by the RCPNMs are compared to three pore network extraction algorithms. Thereby, once the accuracy of RCPNMs is confirmed, the absolute permeability of fracture networks is calculated. Findings The results obtained with RCPNMs method were compared with three pore network extraction algorithms and computational fluid dynamics method, achieving a more computational efficiency regarding to CPU cost and a better geometry and topology relationship identification, in all the cases studied. Furthermore, a reliable topology data that does not have image-based pore network limitations, and the effect of topological disorder on the computed absolute permeability is minor. However, further research is necessary to improve the interpretation of real pore systems for explosively created fracture networks. Practical implications Although only laboratory cylindrical rock specimens were tested in the computational examples, the developed approaches are applicable for field scale and complex pore network grids with arbitrary shapes. Originality/value It is often desirable to develop an integrated computational method for hydraulic conductivity of explosively created fracture networks which segmentation of fracture networks is not restricted to X-ray images, particularly when topologic and geometric modellings are the crucial parts. This research study provides insight to the reliable computational methods and pore network extraction algorithm selection processes, as well as defining a practical framework for generating reliable topological and geometrical data in a Eulerian SPH setting.

Individual yarn fibre extraction from micro-CT: multilevel machine learning approach

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73% to 100% with processing time 2–5 s on the tested samples.

Network Coding-Based Post-Quantum Cryptography

We propose a novel hybrid universal network-coding cryptosystem (HUNCC) to obtain secure post-quantum cryptography at high communication rates. The secure network-coding scheme we offer is hybrid in the sense that it combines information-theoretic security with public-key cryptography. In addition, the scheme is general and can be applied to any communication network, and to any public-key cryptosystem. Our hybrid scheme is based on the information theoretic notion of individual secrecy, which traditionally relies on the assumption that an eavesdropper can only observe a subset of the communication links between the trusted parties - an assumption that is often challenging to enforce. For this setting, several code constructions have been developed, where the messages are linearly mixed before transmission over each of the paths in a way that guarantees that an adversary which observes only a subset has sufficient uncertainty about each individual message. Instead, in this article, we take a computational viewpoint, and construct a coding scheme in which an arbitrary secure cryptosystem is utilized on a subset of the links, while a pre-processing similar to the one in individual security is utilized. Under this scheme, we demonstrate 1) a computational security guarantee for an adversary which observes the entirety of the links 2) an information theoretic security guarantee for an adversary which observes a subset of the links, and 3) information rates which approach the capacity of the network and greatly improve upon the current solutions. A perhaps surprising consequence of our scheme is that, to guarantee a computational security level b, it is sufficient to encrypt a single link using a computational post-quantum scheme. That is, using HUNCC, we can ensure post-quantum security in networks where it is not possible to use public-key encryption over all the links in the network. In addition, the information rate approaches 1 as the number of communication links increases. As a concrete example, in a multipath network with three links, using a 128-bit computationally secure McEliece cryptosystem only over one link, we obtain a 128-bit individually computationally secure level over all paths with a total information rate of 0.91 in the network.

Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.

An approach to tackle negative effects in project management

We introduce a model to support scenario analysis while managing costs of high-tech project. If the project results from joint efforts of firms with one per single stage the model suggests budget redistribution in case of funding shortage or exchange rate volatility. Given negative outer effects for the project the model requires the project to be completed and this constraint forces firms to diminish their profit or to make a loan if needed. The model proposed suggests human-machine (expert) interaction to build scenario for analysis but is simpler compared to method of successive concessions from computational point of view.

A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems

In this work, we present an extension of genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a lower-dimensional space. In many cases, GA requires in fact more function evaluations than other optimization methods to converge to the global optimum. Thus, complex and high-dimensional functions can end up extremely demanding (from the computational point of view) to be optimized with the standard algorithm. To address this issue, we propose to linearly map the input parameter space of the original function onto its AS before the evolution, performing the mutation and mate processes in a lower-dimensional space. In this contribution, we describe the novel method called ASGA, presenting differences and similarities with the standard GA method. We test the proposed method over $n$-dimensional benchmark functions---Rosenbrock, Ackley, Bohachevsky, Rastrigin, Schaffer N. 7, and Zakharov---and finally we apply it to an aeronautical shape optimization problem.

A Multiscale Approach to Axon and Nerve Stimulation Modeling: A Review.

Electrical nerve fiber stimulation is a technique widely used in prosthetics and rehabilitation, and its study from a computational point of view can be a useful instrument to support experimental tests. In the last years, there was an increasing interest in computational modeling of neural cells and numerical simulations on nerve fibers stimulation because of its usefulness in forecasting the effect of electrical current stimuli delivered to tissues through implanted electrodes, in the design of optimal stimulus waveforms based on the specific application (i.e., inducing limb movements, sensory feedback or physiological function restoring), and in the evaluation of the current stimuli properties according to the characteristics of the nerves surrounding tissue. Therefore, a review study on the main modeling and computational frameworks adopted to investigate peripheral nerve stimulation is an important instrument to support and drive future research works. To this aim, this paper deals with mathematical models of neural cells with a detailed description of ion channels and numerical simulations using finite element methods to describe the dynamics of electrical stimulation by implanted electrodes in peripheral nerve fibers. In particular, we evaluate different nerve cell models considering different ion channels present in neurons and provide a guideline on multiscale numerical simulations of electrical nerve fibers stimulation.

Simplified micro-modelling of masonry cross vaults: stereotomy and interface issues

The importance and the relevance of masonry cross vaults is evident in churches, palaces and cloisters of almost all European countries. However, despite their relevance and long-lasting history, which prove an accurate design with respect to gravitational loads, their seismic behaviour is still poorly understood. In this regard, the present work deals with the finite element micro-modelling analysis of masonry cross vaults subjected to horizontal loads. Assuming the blocks rigid-infinitely resistant, the interface represents the only source of physical nonlinearities. Consequently, block interlocking assumes a capital importance, here discussed in the light of the ancient rules of thumb. From the computational point of view, the mechanical properties of the interface are assessed by means of a sensitivity analysis. Comparisons between numerical and experimental results available in literature are presented in terms of ultimate strength capacity and failure mechanisms.

АНАЛИЗ СОСТОЯНИЯ УСТРОЙСТВ ИНТЕРНЕТА ВЕЩЕЙ НА ОСНОВЕ БЭГГИНГА

The development of IoT concept makes it necessary to search and improve models and methods for analyzing the state of remote autonomous devices. Due to the fact that some devices are located outside the controlled area, it becomes necessary to develop universal models and methods for identifying the state of low-power devices from a computational point of view, using complex approaches to analyzing data coming from various information channels. The article discusses an approach to identifying IoT devices state, based on parallel functioning classifiers that process time series received from elements in various states and modes of operation. The aim of the work is to develop an approach for identifying the state of IoT devices based on time series recorded during the execution of various processes. The proposed solution is based on methods of parallel classification and statistical analysis, requires an initial labeled sample. The use of several classifiers that give an answer “independently” from each other makes it possible to average the error by “collective” voting. The developed approach is tested on a sequence of classifying algorithms, to the input of which the time series obtained experimentally under various operating conditions were fed. Results are presented for a naive Bayesian classifier, decision trees, discriminant analysis, and the k nearest neighbors method. The use of a sequence of classification algorithms operating in parallel allows scaling by adding new classifiers without losing processing speed. The method makes it possible to identify the state of the Internet of Things device with relatively small requirements for computing resources, ease of implementation, and scalability by adding new classifying algorithms.

Floorplanning for Area Optimization Using Parallel Particle Swarm Optimization and Sequence Pair

In the integrated circuit (IC) designing floorplanning is an important phase in the process of obtaining the layout of the circuit to be designed. The floorplanning determines the performance, size, yield, and reliability of VLSI ICs. The obtained results in this step are necessary for the other consecutive process of the chip designing. VLSI floorplanning from the computational point of view is a non-polynomial hard (NP-hard problem), and hence cannot be efficiently solved by the classical optimization techniques. In this paper, we have proposed a metaheuristic approach to address the problem by using the parallel particle swarm optimization (P-PSO) technique. The P-PSO uses a new greedy operation on the sequence pair (SP) to explore the search space to find an optimal solution. Experimental results on the Microelectronic Centre of North Carolina and Gigascale Systems Research Center benchmark shows that the applied parallel PSO (P-PSO) may be used to produce an optimal solution.

Catalytic role of amines in activation of PhICl2 from a computational point of view.

We thoroughly investigated mechanistic features of dichlorination of diazoacetates using PhICl2 catalysed by pyridine. We found that the pyridine serves as a catalyst for decomposition of PhICl2 to PhI and Cl2, leading to the dichlorination step being driven by the in situ generated Cl2. This type of activation was found to be applicable to other amine-catalysed chlorination reactions using PhICl2.

Nanopore RNA Sequencing Analysis.

The recent advent of Nanopore sequencing allows for the sequencing of full-length RNA or cDNA molecules. This new type of data introduces new challenges from the computational point of view, and requires new software as well as dedicated analysis pipelines. In this chapter, we guide the reader through the typical analysis steps required to process the raw data produced by the instrument into a table of counts suitable for downstream analyses. We first describe the procedure to convert raw direct RNA-Seq and cDNA-Seq data into sequences in fastq format. We then outline how to perform quality control and filtering steps and how to map the filtered long reads to a reference transcriptome or genome.

A V(iii)-induced metallogel with solvent stimuli-responsive properties: structural proof-of-concept with MD simulations.

A new solvent stimuli-responsive metallogel (VGel) was synthesized through the introduction of vanadium ions into an adenine (Ade) and 1,3,5-benzene tricarboxylic acid (BTC) organogel, and its supramolecular self-assembly was investigated from a computational viewpoint. A relationship between the synthesized VGel integrity and the self-assembly of its components is demonstrated by a broad range of molecular dynamics (MD) simulations, an aspect that has not yet been explored for such a complex metallogel in particular. MD simulations and Voronoi tessellation assessments, both in agreement with experimental data, confirm the gel formation. Based on excellent water stability and the ethanol/methanol stimuli-responsive feature of the VGel an easy-to-use visualization assay for the detection of counterfeit liquor with a 6% (v/v) methanol limit of detection in 40% (v/v) ethanol is reported. These observations provide a cheap and technically simple method and are a step towards the immersible screening of similar molecules in methanol-spiked beverages.

О ГЕНЕРИЧЕСКОЙ СЛОЖНОСТИ ПРОБЛЕМЫ ИЗОМОРФИЗМА КОНЕЧНЫХ ПОЛУГРУПП

Generic-case approach to algorithmic problems was suggested by A. Miasnikov, V. Kapovich, P. Schupp, and V. Shpilrain in 2003. This approach studies behavior of an algorithm on typical (almost all) inputs and ignores the rest of inputs. In this paper, we study the generic complexity of the isomorphism problem for finite semigroups. In this problem, for any two semigroups of the same order, given by their multiplication tables, it is required to determine whether they are isomorphic. V. Zemlyachenko, N. Korneenko, and R. Tyshkevich in 1982 proved that the graph isomorphism problem polynomially reduces to this problem. The graph isomorphism problem is a well-known algorithmic problem that has been actively studied since the 1970s, and for which polynomial algorithms are still unknown. So from a computational point of view the studied problem is no simpler than the graph isomorphism problem. We present a generic polynomial algorithm for the isomorphism problem of finite semigroups. It is based on the characterization of almost all finite semigroups as 3-nilpotent semigroups of a special form, established by D. Kleitman, B. Rothschild, and J. Spencer, as well as the Bollobas polynomial algorithm, which solves the isomorphism problem for almost all strongly sparse graphs.

New Numerical Method for Solving Nonlinear Stochastic Integral Equations

The purpose of this paper is to propose the Chebyshev cardinal functions for solving Volterra stochastic integral equations. The method is based on expanding the required approximate solution as the element of Chebyshev cardinal functions. Though the way, a new operational matrix of integration is derived for the mentioned basis functions. More precisely, the unknown solution is expanded in terms of the Chebyshev cardinal functions including undetermined coefficients. By substituting the mentioned expansion in the original problem, the operational matrix reducing the stochastic integral equation to system of algebraic equations. The convergence and error analysis of the etablished method are investigated in Sobolev space. The method is numerically evaluated by solving test problems caught from the literature by which the computational efficiency of the method is demonstrated. From the computational point of view, the solution obtained by this method is in excellent agreement with those obtained by other works and it is efficient to use for different problems.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Fast and accurate approximation of the angle-averaged redistribution function for polarized radiation

Context. Modeling spectral line profiles taking frequency redistribution effects into account is a notoriously challenging problem from the computational point of view, especially when polarization phenomena (atomic polarization and polarized radiation) are taken into account. Frequency redistribution effects are conveniently described through the redistribution function formalism, and the angle-averaged approximation is often introduced to simplify the problem. Even in this case, the evaluation of the emission coefficient for polarized radiation remains computationally costly, especially when magnetic fields are present or complex atomic models are considered. Aims. We aim to develop an efficient algorithm to numerically evaluate the angle-averaged redistribution function for polarized radiation. Methods. The proposed approach is based on a low-rank approximation via trivariate polynomials whose univariate components are represented in the Chebyshev basis. Results. The resulting algorithm is significantly faster than standard quadrature-based schemes for any target accuracy in the range [10−6, 10−2].

Comparison of semiclassical transfer to continuum model with Ichimura-Austern-Vincent model in medium energy knockout reactions

The full quantum mechanical (QM) model of inclusive breakup of Ichimura-Austern-Vincent (IAV) is implemented in this paper to calculate breakup from heavy radioactive nuclei on a 9Be target at intermediate energies. So far it had been implemented and applied only to low energy reactions with light projectiles. The IAV model is successful in predicting absolute cross sections among other observables. In order to get insight on the content of the model in the case of the complicated heavy-ion reactions, results are compared with those of the semiclassical transfer to the continuum (TC) model. Because the TC is based on analytical formulae the dynamics of the breakup as it is contained in the rather involved IAV formalism will become more transparent. Heavy-ion reactions at high energies (>50 A.MeV) are demanding from the computational point of view because of the high number of partial waves involved, typically around 100. The TC constitutes a useful alternative to the full QM calculations whenever predictions and/or estimates are necessary. It allows also for a systematic, fast evaluation of breakup observables. In the applications of both methods we use state-of-the art optical potentials and structure information. Excellent agreement is found between the calculated results of both methods and with available experimental data which shows that the qualitative and quantitative understanding of most aspects of one nucleon breakup is well under control.

An inner–outer iterative method for edge preservation in image restoration and reconstruction * *SG’s effort for this paper is supported in part by the EPSRC under Grants EP/P005985/1 and EP/T001593/1, and the NSF under Grant DMS-1906664. JGN’s effort for this paper is supported in part by the US National Science Foundation under Grant DMS-1819042 and the NIH under Grant 1R13EB028700-01.

We present a new inner–outer iterative algorithm for edge enhancement in imaging problems. At each outer iteration, we formulate a Tikhonov-regularized problem where the penalization is expressed in the two-norm and involves a regularization operator designed to improve edge resolution as the outer iterations progress, through an adaptive process. An efficient hybrid regularization method is used to project the Tikhonov-regularized problem onto approximation subspaces of increasing dimensions (inner iterations), while conveniently choosing the regularization parameter (by applying well-known techniques, such as the discrepancy principle or the -curve criterion, to the projected problem). This procedure results in an automated algorithm for edge recovery that does not involve regularization parameter tuning by the user, nor repeated calls to sophisticated optimization algorithms, and is therefore particularly attractive from a computational point of view. A key to the success of the new algorithm is the design of the regularization operator through the use of an adaptive diagonal weighting matrix that effectively enforces smoothness only where needed. We demonstrate the value of our approach on applications in x-ray CT image reconstruction and in image deblurring, and show that it can be computationally much more attractive than other well-known strategies for edge preservation, while providing solutions of greater or equal quality.

JGraphs: A Toolset to Work with Monte-Carlo Tree Search-Based Algorithms

Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree Search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree Search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.