First-order Components Research Articles

Asymptotic Slow-Scale Stability Boundary of PFC AC–DC Power Converters: Theoretical Prediction and Experimental Validation

A unified approach for the stability analysis of both preregulator and two-stage power factor correction (PFC) ac–dc converters is presented. Practical and accurate analytical expressions for the stability boundary are obtained by studying the dynamics of the first harmonic component. The resulting model of this dynamics has an equilibrium point at the origin of the harmonic state space. In the case of a stable equilibrium point, the first-order component converges to zero, and the system oscillates at twice the line frequency. If the equilibrium point loses stability, another equilibrium point depending on high-order harmonics will be reached while the real system will exhibit a period-doubling phenomenon leading to subharmonic oscillations. It is demonstrated that the stability boundaries depend on the power level. Numerical simulations and experimental results in both a preregulator and a complete two-stage PFC converter validate the approach.

Components and Developmental Differences of Executive Functioning for School-Aged Children

This study examined the developmental differences, components, and underlying factor structure of executive functioning (EF) in school-aged children by utilizing subtests from Test of Everyday Attention for Children and some additional EF tests. The developmental differences identified across age groups between 7 to 14 years for a sample of 185 children support a multistage interpretation of EF development. Structural equation modeling was used to test models with three first-order EF components which included shifting, working memory/updating, and inhibition. Results indicated that the first-order full, three-factor model was the best model among all the alternative first-order and second-order models.

Nonlinear Optical Properties of Stacked Conjugated Systems

We study linear and nonlinear optical properties of two push-pull polyenes stacked in head to head (HtH) and head to tail (HtT) configurations, at different stacking angles within the Pariser-Parr-Pople model using exact diagonalization method. By varying the stacking angle between the polyenes, we find that the optical gap varies marginally, but transition dipoles show large variations. We find that the dominant first-order hyperpolarizability component beta(XXX) for HtH arrangement and beta(YYY) for HtT arrangement strongly depend on the distance of separation between molecules, while the other smaller component beta(XYY) for HtH arrangement and beta(XXY) for HtT arrangement) does not show this variation with distance. We find that the beta(XXX) for HtH configuration shows a maximum at an angle away from 0, in contrast with the oriented gas model. This angle varies with distance between the polyenes, and at large distance it falls to 0. The ratio of all components of beta of a dimer to monomer is less than two for HtH configuration for all angles. But for HtT configurations the ratio of the dominant beta component is greater than two at large angles. Our ZINDO study on two monomers (4-hydroxy-4'-nitroazobenzene) connected in a nonconjugative fashion shows a linear increase in vertical bar(beta) over right arrow (av)vertical bar without much red shift in optical gap. There is a linear increase in vertical bar(beta) over right arrow (av)vertical bar with increase in number of monomers connected nonconjugatively without resulting in a red shift in optical gap.

Consciousness of the first order in blindsight

At suprathreshold levels, detection and awareness of visual stimuli are typically synonymous in nonclinical populations. But following postgeniculate lesions, some patients may perform above chance in forced-choice detection paradigms, while reporting not to see the visual events presented within their blind field. This phenomenon, termed "blindsight," is intriguing because it demonstrates a dissociation between detection and perception. It is possible, however, for a blindsight patient to have some "feeling" of the occurrence of an event without seeing per se. This is termed blindsight type II to distinguish it from the type I, defined as discrimination capability in the total absence of any acknowledged awareness. Here we report on a well-studied patient, D.B., whose blindsight capabilities have been previously documented. We have found that D.B. is capable of detecting visual patterns defined by changes in luminance (first-order gratings) and those defined by contrast modulation of textured patterns (textured gratings; second-order stimuli) while being aware of the former but reporting no awareness of the latter. We have systematically investigated the parameters that could lead to visual awareness of the patterns and show that mechanisms underlying the subjective reports of visual awareness rely primarily on low spatial frequency, first-order spatial components of the image.

Elastic-wave modulation approach to crack detection: Comparison of conventional modulation and higher-order interactions

Comparison of recent theoretical estimates with experiments has indicated that the ultimate sensitivity of the conventional modulation technique of crack detection is mainly determined by the background modulation produced by the quadratic component of the atomic nonlinearity of the matrix material. Much smaller level of masking nonlinear effects is typical of higher-order interactions due to cubic and higher-order components in the power-series expansion of the background nonlinearity of the solid. In contrast, the level of formally higher-order components originated due to nonlinearity of crack-like defects can be comparable with that of the first-order components. Such strongly increased efficiency of higher-order interactions is due to the fact that crack-like defects often demonstrate non-analytic (non power-law) nonlinearity even for moderate acoustic amplitudes. Besides the increased level, the higher-order components arisen due to non-analytic nonlinearity of cracks can demonstrate significantly different functional behavior compared to manifestations of the atomic nonlinearity. This difference can also help to discriminate the contributions of the defects and the background atomic nonlinearity. Here, we focus on the main differences between the modulation components arisen due to cubic terms in the power-series expansion of the atomic nonlinearity and similar components generated by clapping Hertzian nonlinearity of inner contacts in cracks. We also examine experimental examples of higher-order modulation interactions in damaged samples. These examples clearly indicate non-analytical character of the defects’ nonlinearity and demonstrate that the use of higher-order modulation effects can significantly improve the ultimate sensitivity and reliability of the modulation approach to detection of crack-like defects.

Closing the loop between neurons and neurotechnology

Brain-machine interfaces (BMIs, or brain-computer interfaces, BCIs) have caused a lot of excitement in the past few years; they promise to make the lame walk, the mute talk, the blind see, and perhaps even to enhance cognition (Serruya and Kahana, 2008). Already, cochlear implants have proven immensely successful at making the deaf hear: over 150,000 completely deaf people can now participate in two-way oral communication with the rest of the hearing world, thanks to multi-electrode stimulation of their cochlear nerves, controlled by compact, even stylish, miniature computers worn behind their ears (Chorost, 2006). Deep-brain stimulators have also been quite successful at modulating aberrant neural activity to alleviate Parkinsonism, chronic pain and tremor, among other disorders (Gross, 2004). Artificial retinas for the blind (Yanai et al., 2007), voices for locked-in patients (Brumberg et al., 2010), and neurally-controlled robotic limbs for amputees (Ojakangas et al., 2006) have been substantially less successful, but progress seems to be accelerating. What is holding them back? Perhaps we are only beginning to appreciate the complexity and dynamics of the neural circuits involved. Motor BMIs to date have been unidirectional, with, for example, neural recordings controlling a robotic limb using only visual feedback. Great advances in usability, dexterity, acceptance, or reduced cognitive load may occur when they include sensory feedback (tactile, temperature, proprioception) delivered directly to the nervous system via electrical stimulation. Even for sensory prostheses and deep-brain stimulators, it may prove useful to continuously monitor neural responses to stimulation, and adjust the stimulation to optimize function or therapeutic benefits. In the future, sensory, motor, and modulatory BMIs are likely to take advantage of a continuous dialog between the nervous system and artificial computational devices. Bridging the large chasm between the present and that closed-loop future will certainly require much basic research using reduced preparations. Mussa-Ivaldi and co-workers have pioneered bidirectional BMIs between simple nervous systems maintained in vitro, and artificial robotic bodies (Reger et al., 2000; Kositsky et al., 2009; Mussa-Ivaldi et al., 2010). These hybrid living/artificial robots (or hybrots, Potter, 2004) are simpler than intact animals, having fewer and more well-defined signals, which are under control of the experimenter. Mussa-Ivaldi and co-workers studied the dynamics of a vestibular circuit in the lamprey brainstem, giving it an artificial body – a small wheeled robot – that was controlled by the lamprey brain's motor output signals. The robot's light sensors were translated (in real-time) into frequency-coded electrical stimuli for the vestibular circuit. By observing the neurally-controlled robot's responses to light input, the dynamical dimension of the neural system could be estimated; that is, the number of free parameters in a set of equations that can accurately predict the system's input-output behavior. Initial attempts to model the system with linear, and then with nonlinear (e.g., 4th-order polynomial) equations proved inadequate. Models in which current output is a function of recent output, i.e., using a simple first-order dynamic component, fared much better at accurately describing the system's behavior, even with fewer free parameters. This points to the dynamical dimension as an important property of neural circuits, which can be estimated in hybrid systems as the difference between the known dimensionality of the artificial component (the robot), and the dimensionality of the whole system, which can be measured. Thanks to their controllability and relative simplicity, artificially embodied in vitro networks provide excellent test beds for studying plasticity mechanisms. Using cortical networks cultured on multi-electrode arrays, several groups have demonstrated that the input-output functions of the networks can be reliably altered by multi-electrode stimulation, to effect desired behavior or normalize aberrant activity patterns (Wagenaar et al., 2005; Novellino et al., 2007; Bakkum et al., 2008; Chiappalone et al., 2008; Marom et al., 2009). It is not hard to imagine that this electrical training and modulation of cortical tissue could form the basis of future adaptive, closed-loop BMIs. The continuous electrical dialog would take advantage of brain plasticity to enhance functionality or merely to allow the user to adjust to the neural interface more quickly and easily. The ideal system also would have “learning” on the artificial side, such as the optimization of a set of nonlinear “force fields” that most effectively map recorded neural activity onto (artificial) motor behavior, or map artificial sensory input (or desired neuromodulation) onto neural stimulation.

Characterizing people as non-linear, first-order components in software development, is written by Alistair A.R. Cockburn and published in Humans and Technology, HaT Technical Report 1999.03, Oct 21, 1999.

Characterizing people as non-linear, first-order components in software development, is written by Alistair A.R. Cockburn and published in Humans and Technology, HaT Technical Report 1999.03, Oct 21, 1999.

Estimation of spatial autoregressive M-way error component panel data models

This paper considers a first order spatial autoregressive panel data model with first-order spatial autoregressive disturbances, SARAR(1,1), and M-dimensional error components. We derive generalized moments (GM) estimators for the spatial autoregressive parameter of the disturbance process and the variances of the error components and define a feasible generalized two stages least squares (FG2SLS) estimator for the regression parameters of the model. Finally, we prove consistency and derive the joint asymptotic distribution of the GM and FG2SLS estimators, enabling specification tests and a proper estimation of multi-way error component models with cross-sectionally dependent observations.

Dressed electrostatic solitary waves in quantum dusty pair plasmas

Quantum-hydrodynamics model is applied to investigate the nonlinear propagation of electrostatic solitary excitations in a quantum dusty pair plasma. A Korteweg de Vries evolution equation is obtained using reductive perturbation technique and the higher-nonlinearity effects are derived by solving the linear inhomogeneous differential equation analytically using Kodama–Taniuti renormalizing method. The possibility of propagation of bright- and dark-type solitary excitations is examined. It is shown that a critical value of quantum diffraction parameter H exists, on either side of which, only one type of solitary propagation is possible. It is also found that unlike for the first-order amplitude component, the variation of H parameter dominantly affects the soliton amplitude in higher-order approximation. The effect of fractional quantum number density on compressive and rarefactive soliton dynamics is also discussed.

A theoretical model of intentional social action in online social networks

Online social networks (Facebook, MySpace, LinkedIn and the like) have become truly significant new phenomena in human communication and interaction patterns and may have a profound impact in the way people communicate and connect with each other. In this study, the decision to use an online social network is conceptualized as intentional social action and the relative impact of the three modes of social influence processes (compliance, internalization, and identification) on intentional social action to use (collective intention) is examined. An empirical study of Facebook users ( n = 389) found that collective intention to use a social networking site is determined by both subjective norm and social identity. Further, social identity is found to be a second-order latent construct comprised of cognitive, evaluative, and affective (first-order) components. Implications for research and practice are discussed.

Optically stimulated luminescence from BeO ceramics: An LM-OSL study

Optically Stimulated Luminescence (OSL) signals of BeO ceramics were investigated using continuous wave (CW) OSL and Linearly Modulated (LM) OSL. It was found that both curves can be approximated using a linear combination of two first-order components. Experiments on the measurement temperature dependence have shown that these two components have nearly the same thermal quenching energies around 0.57 eV. Dependences of the OSL signal on preheat temperature and radiation dose were also examined. Thermal annealing experiments have shown that OSL signals originate from traps which are unstable near 340 °C, thus proving the suitability of the signals for dosimetric purposes. Dose response was found to be linear and a minimum detectable dose of ∼10 μGy was found.

Point: The kinetics of oxygen uptake during muscular exercise do manifest time-delayed phases

Modeling a physiological system's response to a stressor, such as muscular exercise, demands more than a mathematically adequate characterization of its transient response profile: adequacy and appropriateness are not synonymous. The model constituents should be reflective of the system's

Organization of multisynaptic inputs from LGN to MT and V4 of macaques

Temporal analysis of the multifocal cortical visual evoked potential (VEP) was studied using pseudo-random (m-sequence) achromatic stimulation. The effects of variation of luminance contrast on the first-order response were complex. At low to mid contrasts (<60%), a wave doublet (P100-N115) predominated. A second wave complex (N100-P120-N160) dominated at high contrasts. The second-order responses, however, showed an extremely simple variation with luminance contrast. Intrinsic differences in the adaptation time of the generators of these two components caused a distinct separation in the slices of the second-order response. A rapidly adapting nonlinearity saturating at low contrasts was only observable when measuring the responses from two consecutive flashes. Its latency coincided with the contrast saturating first-order response component. By comparison, the nonlinearity derived from the responses to the stimuli with longer interstimulus intervals (second and third slices) yielded a much more linear contrast response-function with lower contrast gain and latencies, which clearly corresponded to the longer latency component of the first-order response. Thus, the second-order responses show a first slice which is predominantly driven by neural elements that have a latency and contrast function that mimic those of the magnocellular neurons of the primate LGN and a second slice which is dominated by a generator whose properties resemble primate parvocellular function. This division into magno and parvocellular contribution to the VEP is based on function (interaction time) as distinct from other currently available analyses, with potential for neural analysis of visual disease.

Probabilistic Analysis Using High Dimensional Model Representation and Fast Fourier Transform

This paper presents an efficient probabilistic analysis method for predicting component reliability of structural/mechanical systems subject to random loads, material properties, and geometry. The proposed method involves High Dimensional Model Representation (HDMR) for the limit state/performance function approximation and fast Fourier transform for solving the convolution integral. The limit state/performance function approximation is obtained by linear and quadratic approximations of the first-order HDMR component functions at most probable point. In the proposed method, efforts are required in evaluating conditional responses at a selected input determined by sample points, as compared to full-scale simulation methods. Therefore, the proposed technique estimates the failure probability accurately with significantly less computational effort compared to the direct Monte Carlo simulation. The methodology developed is applicable for structural reliability estimation involving any number of random variables with any kind of distribution. The accuracy and efficiency of the proposed method is demonstrated through five examples involving explicit/implicit performance functions.

Structural Failure Probability Estimation Using HDMR and FFT

This paper presents a new and alternative method based on High Dimensional Model Representation (HDMR) and fast Fourier transform (FFT) to estimate the structural failure probability of structural systems subject to random loads, material properties and geometry. The proposed methodology is based on the limit state/performance function approximation and the convolution theorem to estimate the structural failure probability. The limit-state function is obtained by linear approximation of the first-order HDMR component functions at the most probable failure point, and the convolution integral is solved efficiently using the FFT technique. The proposed technique estimates the failure probability accurately with significantly less computational effort compared to the direct Monte Carlo simulation. The accuracy and efficiency of the proposed method is demonstrated through numerical examples involving implicit performance functions.

Polarization-sensitive swept-source optical coherence tomography with continuous source polarization modulation

We present fiber-based polarization-sensitive swept-source optical coherence tomography (SS-OCT) based on continuous source polarization modulation. The light source is a frequency swept laser centered at 1.31 microm with a scanning rate of 20 kHz. The incident polarization is modulated by a resonant electro-optic modulator at 33.3 MHz, which is one-third of the data acquisition frequency. The zeroth- and first-order harmonic components of the OCT signals with respect to the polarization modulation frequency have the polarimetric information of the sample. By algebraic and matrix calculations of the signals, this system can measure the depth-resolved Jones matrices of the sample with a single wavelength scan. The phase fluctuations of the starting trigger of wavelength scan and the polarization modulation are cancelled by monitoring the OCT phase of a calibration mirror inserted into the sample arm. We demonstrate the potential of the system by the measurement of chicken breast muscle and the volumetric measurement of an in vivo human anterior eye segment. The phase retardation image shows an additional contrast in the fibrous tissue such as the collagen fiber in the trabecular meshwork and sclera.

An analytic perturbation approach for classical spinning particle dynamics

A perturbation method to analytically describe the dynamics of a classical spinning particle, based on the Mathisson-Papapetrou-Dixon (MPD) equations of motion, is presented. By a power series expansion with respect to the particle's spin magnitude, it is shown how to obtain in general form an analytic representation of the particle's kinematic and dynamical degrees of freedom that is formally applicable to infinite order in the expansion. Within this formalism, it is possible to identify a classical analogue of radiative corrections to the particle's mass and spin due to spin-gravity interaction. The robustness of this approach is demonstrated by showing how to explicitly compute the first-order momentum and spin tensor components for arbitrary particle motion in a general space-time background. Potentially interesting applications based on this perturbation approach are outlined.

The influence of trap coupling effect on the shape of optically stimulated luminescence decay

The decomposition of optically stimulated luminescence (OSL) curves into first-order components is a common practice of researchers. The first-order kinetic model can be valid for the simple energetic level set—single OSL trap and single luminescence centre, so an appearance of any additional level can produce a deformation of OSL curve. A strong deformation, which makes impossible to deconvolute the OSL curve into first-order components, informs about the incorrectness of the kinetic model assumption, but deformation, which does not lead to erroneous fitting procedure results, can be unrecognized. Two cases of basic model widening are considered—additional TL trap and additional luminescence centre. The results of simulation and curve deconvolution are presented for broad ranges of centre parameters and for different experimental conditions. The appearance of an additional recombination centre is shown as a particular source of OSL curve deformation.

Performance characteristics and weight distribution analysis of turbo product code with Reed–Muller component codes

In this paper, we present simulation results for Reed–Muller (RM) turbo codes over AWGN and Rayleigh fading channels. We also compare the simulation results of turbo product codes and block turbo codes with RM component codes over an AWGN channel. We show that minimum distance is not important as far as the BER performance of long codes is concerned. The weight distribution of RM codes of different lengths and turbo product codes with first-order RM component codes are obtained and analyzed for their good performance. We show that the weight distribution asymptotically approaches that of random coding as the code length increases.

The asymptotic behaviour of second-order stochastic Volterra series models of slow drift response

The paper presents a detailed study of the structure and asymptotic behaviour of a second-order stochastic Volterra series model of the slow drift response of large volume compliant offshore structures subjected to random seas. A long standing challenge has been to develop efficient and accurate methods for calculating the response statistics of compliant offshore structures to random seas. Recent work has revealed that the statistical properties of the response process, which consist of a linear, first-order component and a nonlinear, second-order component, is surprisingly complex. The goal of the research work presented here is to complement efforts to develop numerical procedures to calculate the statistics of the response process.