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The simplest facet‐shell formulation involves the combination of the constant‐strain membrane triangle with a constant‐curvature bending triangle. The paper first describes an alternative co‐rotational procedure to the one initially proposed by Peng and Crisfield in 1992. This new formulation introduces a spin matrix which allows a simpler formulation for the consistent tangent stiffness matrix. The paper then moves to the dynamics of the element. To obtain stable solutions, an energy‐conserving mid‐point time‐integration scheme is developed. This scheme exactly conserves the total energy when external forces are constant and when the physical system does not present any damping. The performance of this scheme is compared with other more conventional implicit schemes through a set of numerical examples involving large‐scale rotations.

Cognitive ability tests demonstrate strong relationships with job performance, but have several limitations; notably, subgroup differences based on race/ethnicity. As an alternative, the purpose of this paper is to develop a working memory assessment for personnel selection contexts.

The authors describe the development of Global Adaptive Memory Evaluation (G.A.M.E.) – a working memory assessment – along with three studies focused on refining and validating G.A.M.E., including examining test-taker reactions, reliability, subgroup differences, construct and criterion-related validity, and measurement equivalence across computer and mobile devices.

Evidence suggests that G.A.M.E. is a reliable and valid tool for employee selection. G.A.M.E. exhibited convergent validity with other cognitive assessments, predicted job performance, yielded smaller subgroup differences than traditional cognitive ability tests, was engaging for test-takers, and upheld equivalent measurement across computers and mobile devices.

Additional research is needed on the use of working memory assessments as an alternative to traditional cognitive ability testing, including its advantages and disadvantages, relative to other constructs and methods.

The findings illustrate working memory’s potential as an alternative to traditional cognitive ability assessments and highlight the need for cognitive ability tests that rely on modern theories of intelligence and leverage burgeoning mobile technology.

This paper highlights an alternative to traditional cognitive ability tests, namely, working memory assessments, and demonstrates how to design reliable, valid, engaging and mobile-compatible versions.

The purpose of this paper is to extend a recent unsymmetric 8-node, 24-DOF hexahedral solid element US-ATFH8 with high distortion tolerance, which uses the analytical solutions of linear elasticity governing equations as the trial functions (analytical trial function) to geometrically nonlinear analysis.

Based on the assumption that these analytical trial functions can still properly work in each increment step during the nonlinear analysis, the present work concentrates on the construction of incremental nonlinear formulations of the unsymmetric element US-ATFH8 through two different ways: the general updated Lagrangian (UL) approach and the incremental co-rotational (CR) approach. The key innovation is how to update the stresses containing the linear analytical trial functions.

Several numerical examples for 3D structures show that both resulting nonlinear elements, US-ATFH8-UL and US-ATFH8-CR, perform very well, no matter whether regular or distorted coarse mesh is used, and exhibit much better performances than those conventional symmetric nonlinear solid elements.

The success of the extension of element US-ATFH8 to geometrically nonlinear analysis again shows the merits of the unsymmetric finite element method with analytical trial functions, although these functions are the analytical solutions of linear elasticity governing equations.

The purpose of this paper is to introduce new non‐classical implementations of neural networks (NNs). The developed implementations are performed in the quantum, nano, and optical domains to perform the required neural computing. The various implementations of the new NNs utilizing the introduced architectures are presented, and their extensions for the utilization in the non‐classical neural‐systolic networks are also introduced.

The introduced neural circuits utilize recent findings in the quantum, nano, and optical fields to implement the functionality of the basic NN. This includes the techniques of many‐valued quantum computing (MVQC), carbon nanotubes (CNT), and linear optics. The extensions of implementations to non‐classical neural‐systolic networks using the introduced neural‐systolic architectures are also presented.

Novel NN implementations are introduced in this paper. NN implementation using the general scheme of MVQC is presented. The proposed method uses the many‐valued quantum orthonormal computational basis states to implement such computations. Physical implementation of quantum computing (QC) is performed by controlling the potential to yield specific wavefunction as a result of solving the Schrödinger equation that governs the dynamics in the quantum domain. The CNT‐based implementation of logic NNs is also introduced. New implementations of logic NNs are also introduced that utilize new linear optical circuits which use coherent light beams to perform the functionality of the basic logic multiplexer by utilizing the properties of frequency, polarization, and incident angle. The implementations of non‐classical neural‐systolic networks using the introduced quantum, nano, and optical neural architectures are also presented.

The introduced NN implementations form new important directions in the NN realizations using the newly emerging technologies. Since the new quantum and optical implementations have the advantages of very high‐speed and low‐power consumption, and the nano implementation exists in very compact space where CNT‐based field effect transistor switches reliably using much less power than a silicon‐based device, the introduced implementations for non‐classical neural computation are new and interesting for the design in future technologies that require the optimal design specifications of super‐high speed, minimum power consumption, and minimum size, such as in low‐power control of autonomous robots, adiabatic low‐power very‐large‐scale integration circuit design for signal processing applications, QC, and nanotechnology.

This paper presents a study on the sensing properties of a conductive polymer composite (CPC) that is processed by an electrospinning technique. The CPC is obtained by mixing multi-walled carbon nanotubes (MWNT) with a poly (ethylene oxide) (PEO) matrix. Sensors made of this composite are characterised by measuring their electrical properties as a function of external stimuli. In particular, their responses to vapours of toluene, methanol and dioxan are investigated. As studied, the PEO/MWNT material shows high and stable sensitivity over three testing cycles for the selected vapours. An increase in electrical resistance is observed under the influence of chemical substances. This paper supports the concept that penetration of molecules of selected chemical substances leads to the partial disorder of contact between neighboured nanotubes located in the polymer matrix. The electro-spun non-woven fabric with a low amount of MWNT (3 wt.%) in the PEO matrix seems to be a good textile product for application as sensing membranes in personal protective clothing.

The slow convergence of the incomplete Cholesky preconditioned conjugate gradient (CG) method, applied to solve the system representing a magnetostatic finite element model, is caused by the presence of a few little eigenvalues in the spectrum of the system matrix. The corresponding eigenvectors reflect large relative differences in permeability. A significant convergence improvement is achieved by supplying vectors that span approximately the partial eigenspace formed by the slowly converging eigenmodes, to a deflated version of the CG algorithm. The numerical experiments show that even roughly determined eigenvectors already bring a significant convergence improvement. The deflating technique is embedded in the simulation procedure for a permanent magnet DC machine.

The purpose of this paper is to provide a general approach to compute, determine, and characterize the connectivity of the end‐effector of a robotic manipulator of arbitrary architecture, in any of the postures that it can reach.

The types of motion of this link, i.e. translational, screw motions, combinations thereof, and self‐motions, are first defined and determined, simplifying the understanding of the instantaneous behaviour of the manipulator, aided by the definition of an alternative input basis.

The characterization provided by this paper simplifies the understanding of the instantaneous behaviour of the manipulator. The mobility of the end‐effector is completely characterized by the principal screws of its motion, which can be obtained from a generalized eigenproblem. In the process, alternative demonstrations of well‐known properties of the principal screws are provided.

The approach presented is focused on the kinetostatic analysis of manipulators, and therefore, subjected to rigid body assumption.

This paper proposes effective approaches for engineering analysis of robotic manipulators.

This approach is based on a pure theoretical kinematic analysis that can characterize computationally the motion that the end‐effector of an industrial robot of general morphology (i.e. serial, parallel, hybrid manipulators, complex mechanisms, redundant or non‐redundantly actuated). Also, being implemented on a general‐purpose software for the kinematic analysis of mechanisms, it provides visual information of the motion capabilities of the manipulator, highly valuable on its design stages.

The purpose of this paper is to present extended least squares support vector machines (LS‐SVM) where data selection methods are used to get sparse LS‐SVM solution, and to overview and compare the most important data selection approaches.

The selection methods are compared based on their theoretical background and using extensive simulations.

The paper shows that partial reduction is an efficient way of getting a reduced complexity sparse LS‐SVM solution, while partial reduction exploits full knowledge contained in the whole training data set. It also shows that the reduction technique based on reduced row echelon form (RREF) of the kernel matrix is superior when compared to other data selection approaches.

Data selection for getting a sparse LS‐SVM solution can be done in the different representations of the training data: in the input space, in the intermediate feature space, and in the kernel space. Selection in the kernel space can be obtained by finding an approximate basis of the kernel matrix.

The RREF‐based method is a data selection approach with a favorable property: there is a trade‐off tolerance parameter that can be used for balancing complexity and accuracy.

The paper gives contributions to the construction of high‐performance and moderate complexity LS‐SVMs.

This paper aims to present a dynamically adjusted deflated restarting procedure for the generalized conjugate residual method with an inner orthogonalization (GCRO) method.

The proposed method uses a GCR solver for the outer iteration and the generalized minimal residual (GMRES) with deflated restarting in the inner iteration. Approximate eigenpairs are evaluated at the end of each inner GMRES restart cycle. The approach determines the number of vectors to be deflated from the spectrum based on the number of negative Ritz values, k∗.

The authors show that the approach restores convergence to cases where GMRES with restart failed and compare the approach against standard GMRES with restarts and deflated restarting. Efficiency is demonstrated for a 2D NACA 0012 airfoil and a 3D common research model wing. In addition, numerical experiments confirm the scalability of the solver.

This paper proposes an extension of dynamic deflated restarting into the traditional GCRO method to improve convergence performance with a significant reduction in the memory usage. The novel deflation strategy involves selecting the number of deflated vectors per restart cycle based on the number of negative harmonic Ritz eigenpairs and defaulting to standard restarted GMRES within the inner loop if none, and restricts the deflated vectors to the smallest eigenvalues present in the modified Hessenberg matrix.

The purpose of this paper is to get a more consistent finite element description for three-dimensional (3D) Timoshenko beam elements. It extends the common description of beam elements by modifying the shape functions and considers the warping of the cross-section due to torsion.

The paper builds mainly on a finite element description of 3D Timoshenko beam elements. The implementation of high-order shape functions for torsion is done by adding a seventh degree of freedom to the system.

The results reveal that for some beams, depending on their physical dimensions, the warping of the cross-section has large influence. In comparison to a conventional FE program, the extended finite element description considers the warping and yields more accurate results.

An application of the extended finite element description is done with an implementation of the code in MATLAB. The static and dynamic behavior of a rotor in an electrical machine is investigated.

This paper presents a more consistent finite element description of 3D Timoshenko beam elements considering the warping. A comparison to conventional finite element descriptions is given.