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The well-known discrete methods of computational fluid dynamics (CFD), lattice Boltzmann method (LBM), cellular automata (CA), volume-of-fluid (VoF) and others rely on several parameters describing the boundary or the surface. Some of them are vector normal to the surface, coordinates of the point on the surface and the curvature. They are necessary for the reconstruction of the real surface (boundary) based on the values of the volume fractions of several cells. However, the simple methods commonly used for calculations of the vector normal to the surface are of unsatisfactory accuracy. In light of this, the purpose of this paper is to demonstrate a more accurate method for determining the vector normal to the surface.

Based on the thesis that information about the volume fractions of the 3 × 3 cell block should be enough for normal vector determination, a neural network (NN) was proposed for use in the paper. The normal vector and the volume fractions of the cells themselves can be defined on the basis of such variables as the location of the center and the radius of the circumference. Therefore, the NN is proposed to solve the inverse problem – to determine the normal vector based on known values of volume fractions. Volume fractions are inputs of NNs, while the normal vector is their output. Over a thousand variants of the surface location, orientations of the normal vector and curvatures were prepared for volume fraction calculations; their results were used for training, validating and testing the NNs.

The simplest NN with one neuron in the hidden layer shows better results than other commonly used methods, and an NN with four neurons produces results with errors below 1° relative to the orientation of the normal vector; for several cases, it proven to be more accurate by an order of magnitude.

The method can be used in the CFD, LBM, CA, VoF and other discrete computational methods. The more precise normal vector allows for a more accurate determination of the points on the surface and curvature in further calculations via the surface or interface tracking method. The paper contains the data for the practical application of developed NNs. The method is limited to regular square or cuboid lattices.

The paper presents an original implementation of NNs for normal vector calculation connected with CFD, LBM and other application for fluid flow with free surface or phase transformation.

A computational methodology, based on the coupling of the finite element and boundary element methods, is developed for the solution of magnetothermal problems. The finite element formulation and boundary element formulation, along with their coupling, are discussed. The coupling procedure is also presented, which entails the application of the LU decomposition to eliminate the need for the direct inversion of matrices resulting from FE‐BE formulation, thereby saving computation time and storage space. Corners for both FE‐BE interface and BE regions, where discontinuous fluxes exist, are treated using the double flux concept. Numerical results are presented for three different systems and compared with analytical solutions when available. Numerical experiments suggest that for magnetothermal problems involving small skin depths, a careful mesh distribution is critical for accurate prediction of the field variables of interest. It is found that the accuracy of the temperature distribution is strongly dependent upon that of the magnetic vector potential. A small error in the magnetic vector potential can produce significant errors in the subsequent temperature calculations. Thus, particular attention must be paid to the design of a suitable mesh for the accurate prediction of vector potentials. From all the cases examined, 4‐node linear elements with adequate progressive coarsening of meshes from the surface gave the results with best accuracy.

This paper is concerned with rank analysis of rectangular matrix of a homogeneous set of incremental equations regarded as an element of continuation method. The rank analysis is based on a known feature that every rectangular matrix can be transformed into the matrix of echelon form. By inspection of the rank, correct control parameters are chosen and this allows not only for rounding limit and turning points but also for branch‐switching near bifurcation points.

This paper develops a vector variation score that quantifies the change in an array of data points from period-to-period. The array could be the amounts reported on an income tax return, the closing stock prices for a set of listed companies, the monthly sales amounts for retail locations or the monthly balances in general ledger accounts.

The score is grounded in analytic geometry. The angle θ measures whether the changes were uniformly spread across the line items. The item(s) with the largest contribution(s) to the score can be identified. Line items can be weighted such that they contribute less than fully to the score.

The method can identify tax returns with large year-on-year changes. The method can identify the fact that the price movements during earnings season are less dependent than is usually the case. The method can identify anomalies in reported sales amounts. The method should be able to identify ledger accounts’ large abnormal changes.

Auditors will need to be trained to interpret the results and to reduce the number of false positives.

The score could be used in both external and internal audit applications where auditors want to quantify and rank period-on-period changes in a search for outliers.

The change score is normalized to the [0, 1] range. The results can be plotted as a polar plot for display on an auditing dashboard. The contribution of a single line item can be calculated and line items can be weighted to prevent them from having an undue influence on the results.

This paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies.

The authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty.

It provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

The future work will focus on exploring the similarity measure and combinational methods in every dimension.

This paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

The scientific literature has played an important role in the dissemination of new knowledge throughout the past century. However, the increasing numbers of scientific articles being published in recent years has intensified the perception of information overload for users attempting to find relevant scientific information. The purpose of this paper is to describe a task‐focused strategy that employs the task profiles of users to make recommendations in a digital library.

This paper combines information retrieval, common citation analysis, and coauthor relationship analysis techniques with a citation network analysis technique – the CiteRank algorithm – to find relevant and high‐quality articles. In total, nine variations of the proposed approach were tested using articles downloaded from the CiteSeerX system and usage logs collected from the authors' experimental server.

The results from the authors' experimental evaluations demonstrate that the proposed Content‐citation approach outperforms the Relevance‐CiteRank, Relevance‐citation count, and Relevance‐only approaches.

This paper describes an original study that has produced a novel way to combine information retrieval, common citation analysis, and coauthor relationship analysis techniques to find relevant and high‐quality articles for recommendation in a digital library.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

This study aims to propose an aerodynamic force decomposition which, for the first time, allows for thrust/drag bookkeeping in two-dimensional viscous and unsteady flows. Lamb vector-based far-field methods are used at the scope, and the paper starts with extending recent steady compressible formulas to the unsteady regime.

Exact vortical force formulas are derived considering inertial or non-inertial frames, viscous or inviscid flows, fixed or moving bodies. Numerical applications to a NACA0012 airfoil oscillating in pure plunging motion are illustrated, considering subsonic and transonic flow regimes. The total force accuracy and sensitivity to the control volume size is first analysed, then the axial force is decomposed and results are compared to the inviscid force (thrust) and to the steady force (drag).

Two total axial force decompositions in thrust and drag contributions are proposed, providing satisfactory results. An additional force decomposition is also formulated, which is independent of the arbitrary pole appearing in vortical formulas. Numerical inaccuracies encountered in inertial reference frames are eliminated, and the extended formulation also allows obtaining an accurate force prediction in presence of shock waves.

No thrust/drag bookkeeping methodology was actually available for oscillating airfoils in viscous and compressible flows.

The purpose of this paper is to determine the agility level of a manufacturing system along different timelines.

The fuzzy synthetic extents of agile manufacturing enablers (AMEs), on the basis of their importance, are determined. Then they are integrated with their performance ratings along different timeline for calculating the Fuzzy Agile Manufacturing Index (FAMI). Euclidean distances of FAMI from predetermined agility levels are mapped to determine the agility level of the manufacturing system along different timeline.

The proposed methodology was implemented in an Indian manufacturing organization to determine its agility level. It was concluded from the obtained results that there was significant improvement in the agility level along the timeline.

The weights of the AMEs are assumed to be constant along the timeline.

The supply chain mangers can easily apply this methodology in their respective manufacturing organizations to assess and determine the agility level. This proposed approach will show the direction to check the performance of agility and evaluate the evolution of agility in their respective manufacturing organizations.

The combination of fuzzy synthetic extent of weights and average fuzzy performance ratings of AMEs to calculate the FAMI along the timeline considering judgments of multiple experts is a unique contribution.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.