Irregular Trajectories Research Articles

Coupled dynamics of two impurities in Bose-Einstein condensate

This paper examines the dynamics of two impurities in a quasi-one-dimensional Bose-Einstein condensate, considering the effect of interaction amplitude and the initial state of the impurity on the coupled dynamics of the impurities and condensate. We consider the interaction between impurities only through the condensate neglecting any direct interactions. By changing the initial parameters of the impurities, the contribution of the condensate density to the trajectory of the impurities is demonstrated. The results show that the trajectory of the impurities strongly depends on their initial position, leading to irregular trajectories due to the deformation of the condensate density at a sufficiently strong attractive interaction between the condensate and impurities.

A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories.

Many dedicated cone-beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibrationmodel. A high-precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high-resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometriccalibration. Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise-robust nonconvex model-based calibration method. The measurement model in the first part is achieved by extending our previous high-precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back-projection model is developed to solve the geometric parameters of eachview. The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high-accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurementsystem. The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extremecases.

Continuous irregular dynamics with multiple neutral trajectories permit species coexistence in competitive communities

The colonization model formulates competition among propagules for habitable sites to colonize, which serves as a mechanism enabling coexistence of multiple species. This model traditionally assumes that encounters between propagules and sites occur as mass action events, under which species distribution can eventually reach an equilibrium state with multiple species in a constant environment. To investigate the effects of encounter mode on species diversity, we analyzed community dynamics in the colonization model by varying encounter processes. The analysis indicated that equilibrium is approximately neutrally-stable under perfect ratio-dependent encounter, resulting in temporally continuous variation of species’ frequencies with irregular trajectories even under a constant environment. Although the trajectories significantly depend on initial conditions, they are considered to be “strange nonchaotic attractors” (SNAs) rather than chaos from the asymptotic growth rates of displacement. In addition, trajectories with different initial conditions remain different through time, indicating that the system involves an infinite number of SNAs. This analysis presents a novel mechanism for transient dynamics under competition.

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation.

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods.

Контингентность сущего и дифференциальные уравнения

The purpose of the article is to study the essence of physical law. It is assumed that the modern era gave rise to a special type of theoretical constructions that nominally inherit the notions of lawfulness known since Antiquity (“regularity,” “orderliness,” “cyclicality,” etc.), but in fact they are arranged fundamentally differently. The perspective within which the authors expect to determine its specificity is outlined in the Quentin Meillassoux’s After Finitude, where an attempt was made to combine the “contingency of beings” with the stability and immutability of physical laws discovered by the experimental science of modern time. However, the authors of the article move away from the speculative solution proposed by Meillassoux and suggest that the guarantee of the stability of physical laws is contained in the very “mathematized scientific statement,” the justification of which Meillassoux is looking for at the level of philosophical speculation. This guarantee emerges from the mid-18th century, during the so-called quiet scientific revolution, when, thanks primarily to the efforts of Leonhard Euler and Joseph Louis Lagrange, the laws of nature began to be formulated using the apparatus of differential equations. This circumstance makes it possible to explain the stability of the physical law under conditions of the contingency of beings, since the solution to the differential equation may turn out to be unstable, irregular and chaotic motion trajectories. This possibility is a consequence of the specific structure of the differential equation, which makes it possible to relate arbitrarily complex, arbitrary changes in physical quantities. As a result, the law of nature, formulated through a differential relation between physical quantities, does not describe regular, repeatable motion, but fixes arbitrary changes of being, without violating that relation. Thus, it is the mathematical form in which the law of nature is expressed that makes it stable and thereby turns it into a mechanism for expressing contingent beings.

A Behavioral Study of Different Controllers and Algorithms in Real-Time Applications

Systems, generally being nonlinear behaves unexpectedly due to external disturbances. Therefore, operation controllers are needed in tracking, chemical engineering, electrical engineering, and other areas. For continuing quality and productivity of industrial firms, the deployment of industrial robots is increasing. Different structures of fuzzy-Proportional Integral Derivative based trajectories for robot manipulators are designed, for various regular and irregular trajectories. Industrial robot’s performance is compared by Proportional Integral Derivative and Fuzzy- Proportional Integral Derivative controller. For tracking the peak power in a Photo Voltaic system, a nonlinear proportional–integral–derivative controller is applied for rapidly changing environmental conditions, particle swarm optimization based Nonlinear-PID, Maximum Power Point Tracking method offers a clean response for Maximum Power Point Tracking. To maintain constant frequency and power variations in power systems the freedom of a three-degree Proportional Integral Derivative controller was designed. For excitation control in Automatic Voltage Regulators, Proportional Integral Derivative and Proportional Integral Derivative -Acceleration controllers are used, Proportional Integral Derivative -Acceleration has more response speed and more stability and it has a good response time and response of frequency by implementing the Particle Swarms Optimization and Gravitational Search algorithm-based Proportional Integral Derivative controller Automatic Voltage Regulator’s voltage response is effective. This paper deals with studying the behavior of various controllers and algorithms in practical applications, comparative analysis of magnetic levitation system response with different controllers and algorithms, and finding the performance of magnetic levitation systems by using a Particle Swarm Optimization algorithm-based Proportional Integral Derivative controller.

A Semiparametric Bayesian Joint Modelling of Skewed Longitudinal and Competing Risks Failure Time Data: With Application to Chronic Kidney Disease

In clinical and epidemiological studies, when the time-to-event(s) and the longitudinal outcomes are associated, modelling them separately may give biased estimates. A joint modelling approach is required to obtain unbiased results and to evaluate their association. In the joint model, a subject may be exposed to more than one type of failure event (competing risks). Considering the competing event as an independent censoring of the time-to-event process may underestimate the true survival probability and give biased results. Within the joint model, longitudinal outcomes may have nonlinear (irregular) trajectories over time and exhibit skewness with heavy tails. Accordingly, fully parametric mixed-effect models may not be flexible enough to model this type of complex longitudinal data. In addition, assuming a Gaussian distribution for model errors may be too restrictive to adequately represent within-individual variations and may lack robustness against deviation from distributional assumptions. To simultaneously overcome these issues, in this paper, we presented semiparametric joint models for competing risks failure time and skewed-longitudinal data by using a smoothing spline approach and a multivariate skew-t distribution. We also considered different parameterization approaches in the formulation of joint models and used a Bayesian approach to make the statistical inference. We illustrated the proposed methods by analyzing real data on a chronic kidney disease. To evaluate the performance of the methods, we also carried out simulation studies. The results of both the application and simulation studies revealed that the joint modelling approach proposed in this study performed well when the semiparametric, random-effects parameterization, and skew-t distribution specifications were taken into account.

Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples

Sparse imaging is widely used in synthetic aperture radar (SAR) imaging. Compared with the traditional matched filtering (MF) methods, sparse SAR imaging can directly image the scattered points of a target and effectively reduce the sidelobes and clutter in irregular samples. However, in view of the large-scale computational complexity of sparse reconstruction with raw echo data, traditional sparse reconstruction algorithms often require huge computational expense. To solve the above problems, in this paper, we propose a 3D near-field sparse SAR direct imaging algorithm for irregular trajectories, adopting a piece of preliminary information in the SAR image to update the dictionary matrix dimension, using the Gaussian iterative method, and optimizing the signal-processing techniques, which can achieve 3D sparse reconstruction in a more direct and rapid manner. The proposed algorithm was validated through simulations and empirical study of irregular scanning scenarios and compared with traditional MF and sparse reconstruction methods, and was shown to significantly reduce the computation time and effectively preserve the complex information of the scenes to achieve high-resolution image reconstruction.

Fermiyon Benzeri İnstanton Çözümlerinin Dalgacık Entropi Analizinin İncelenmesi

Instantons are classical topological solutions, playing an important role in particle physics and cosmology. In this study, the periodicity of the orbits of the fermion-like instanton solutions in the two-dimensional Thirring model obtained with the Heisenberg ansatz is investigated. The trajectories of fermion-like instanton solutions are investigated by the Shannon wavelet entropy (WE) method. In addition, WE and WE spectrum in phase space are analyzed in order to have information about the characteristics of the trajectories of fermion-like instanton solutions. As a result of the study, it was seen that the fermion-like instanton solutions have regular trajectories around the stable point and irregular trajectories at other points. It was also compared with other known entropy methods (Renyi entropy and Tsallis entropy) and similar results were observed.

Investigation on vortex-induced motions of four-square columns in a square configuration considering galloping under different current velocity and incident angle

The study of vortex-induced motions (VIM) is practical significance for offshore oil and gas development. The VIM of multi-column structures, which has not been completely understood, is more complex compared to that of an isolated column structures. The VIM of four rigidly coupled square columns in a square configuration was investigated numerically using Reynolds-averaged Navier–Stokes (RANS) equations with the SST k–ω turbulence model for incident angles of α = 0°, 15°, 30°, and 45°. The time histories of the displacement, the motion amplitudes, the frequency characteristics, the hydrodynamic forces, the motion trajectories, and the vortex flow patterns were analyzed and discussed. The results indicate that the motion behavior of the four-square columns structure is more likely to be a combined motion of VIM and galloping. More specifically, the critical reduced velocities for occurrence of sway and surge galloping are both approximately 9.23, while yaw galloping may occur at a lower reduced velocity. It is found that the complex motion frequency components cause irregular motion trajectories and vortex flow patterns in the pre-lock-in and post-lock-in cases; but unitary motion frequency components lead to regular motions in the lock-in cases. Additionally, there is a strong coupling relationship between the sway and yaw motion, which is independent of the incident angle.

The cGMP-dependent protein kinase gene can regulate trail-following behaviour and locomotion in the termite Reticulitermes chinensis Snyder.

Social behaviours in termites are closely related to the chemical communication between individuals. It is well known that foraging worker termites can use trail pheromones to orient and locomote along trails so as to take food resources back to the nest. However, it is still unclear how termites recognize trail pheromones. Here, we cloned and sequenced the cGMP-dependent protein kinase (PKG) gene from the termite Reticulitermes chinensis Snyder, and then examined the response of termites to trail pheromones after silencing PKG through RNA interference. We found that PKG knockdown impaired termite ability to follow trail pheromones accurately and exhibited irregular behavioural trajectories in response to the trail pheromone in the termite R. chinensis. Our locomotion assays further showed that PKG knockdown significantly increased the turn angle and angular velocity in the termite R. chinensis. These findings help us better understanding the molecular regulatory mechanism of foraging communications in termites.

A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

Predictive modeling of clinical data is fraught with challenges arising from the manner in which events are recorded. Patients typically fall ill at irregular intervals and experience dissimilar intervention trajectories. This results in irregularly sampled and uneven length data which poses a problem for standard multivariate tools. The alternative of feature extraction into equal-length vectors via methods like Bag-of-Words (BoW) potentially discards useful information. We propose an approach based on a kernel framework in which data is maintained in its native form: discrete sequences of symbols. Kernel functions derived from the edit distance between pairs of sequences may then be utilized in conjunction with support vector machines to classify the data. Our method is evaluated in the context of the prediction task of determining patients likely to develop type 2 diabetes following an earlier episode of elevated blood pressure of 130/80 mmHg. Kernels combined via multi kernel learning achieved an F1-score of 0.96, outperforming classification with SVM 0.63, logistic regression 0.63, Long Short Term Memory 0.61 and Multi-Layer Perceptron 0.54 applied to a BoW representation of the data. We achieved an F1-score of 0.97 on MKL on external dataset. The proposed approach is consequently able to overcome limitations associated with feature-based classification in the context of clinical data.

Formation and Structures of Horizontal Submarine Fluid Conduit and Venting Systems Associated With Marine Seeps

AbstractMethane‐rich water moves through conduits beneath the seafloor whose surfaces are formed through precipitation reactions. To understand how such submarine fluid conduit and venting systems form and grow, we develop a detailed mathematical model for this reaction‐advection system and we quantify the evolution of an ensemble of similar filaments. We show that this growth can be described by a superposition of advection and dispersion. We analyze analog laboratory experiments of chemical‐garden type to study the growth of a single filament undergoing a precipitation reaction with the surrounding environment. We apply these findings to geological fluid conduit and venting systems, showing that their irregular trajectories can lead to very effective spreading within the surrounding seabed, thus enhancing contact and exchanges of chemicals between the conduit and external fluids. We discuss how this methane venting leads to the formation of marine authigenic carbonate rocks, and for confirmation, we analyze two field samples from the Gulf of Cadiz for composition and mineralogy of the precipitates. We note the implications of this work for hydrate melting and methane escape from the seabed.

Coupling response of flow-induced oscillating cylinder with a pair of flow-induced rotating impellers

An innovative device transforming the active control of rotating rods to passive control with a pair of impellers is proposed and numerically examined in this paper. The coupling response of a vortex-induced vibrating (VIV) circular cylinder symmetrically equipped with two impellers that are free to rotate is analyzed based on the results of computations that carried out for a reduced velocity range of Ur = 2–14 at a low Reynolds number of 150. In comparison with the bare cylinder, both the in-line and cross-flow responses are significantly augmented in the VIV initial branch with the introduction of a pair of passively rotating impellers, which is mainly attributed to the unstable rotation response in both direction and speed and the wake adjustment including the reduction in vortex formation length and broadening of flow wake. In the VIV lower branch, although the response amplitude is close to that of a bare cylinder, the strong interaction between two directional responses occurs with the same dominant frequency locking on the natural one. Nevertheless, the coexistence of multiple vibration frequencies leads to irregular oscillation trajectories and irregular vortex shedding. Moreover, the secondary vortex street is observed in the whole Ur range, but the number of merged vortices for the formation of secondary vortex street varies with Ur, depending on the response amplitude and the interaction between the shear layers of the main cylinder and impellers. In terms of time-averaged rotation, the symmetrical inward counter-rotating pattern is achieved despite the intermittent alteration of rotation direction. Furthermore, the vibration–rotation coupling is demonstrated from the variation of time-averaged rotation speed that closely follows the variation of vibration amplitude against Ur.

KINEMATIC OSCILLATIONS OF POST-CME BLOBS DETECTED BY K-COR ON 2017 SEPTEMBER 10

We investigate 20 post-coronal mass ejection (CME) blobs formed in the post-CME current sheet (CS) that were observed by K-Cor on 2017 September 10. By visual inspection of the trajectories and projected speed variations of each blob, we find that all blobs except one show irregular zigzag trajectories resembling transverse oscillatory motions along the CS, and have at least one oscillatory pattern in their instantaneous radial speeds. Their oscillation periods are ranging from 30 to 91 s and their speed amplitudes from 128 to 902 km s-1. Among 19 blobs, 10 blobs have experienced at least two cycles of radial speed oscillations with different speed amplitudes and periods, while 9 blobs undergo one oscillation cycle. To examine whether or not the apparent speed oscillations can be explained by vortex shedding, we estimate the quantitative parameter of vortex shedding, the Strouhal number, by using the observed lateral widths, linear speeds, and oscillation periods of the blobs. We then compare our estimates with theoretical and experimental results from MHD simulations and fluid dynamic experiments. We find that the observed Strouhal numbers range from 0.2 to 2.1, consistent with those (0.15-3.0) from fluid dynamic experiments of bluff spheres, while they are higher than those (0.15-0.25) from MHD simulations of cylindrical shapes. We thus find that blobs formed in a post-CME CS undergo kinematic oscillations caused by fluid dynamic vortex shedding. The vortex shedding is driven by the interaction of the outward-moving blob having a bluff spherical shape with the background plasma in the post-CME CS.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT).

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems. Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner. The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data. Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

The nonlinearities of the robotic manipulators and the uncertainties of their parameters represent big challenges against the controller design. Moreover, the tracking of regular and irregular trajectories with fewer overshoots, short settling time, and small steady-state error is the main target for the robotic response. The model predictive control (MPC) is an efficient controller to handle the performance requirements. However, the conventional MPC requires the linearization of the system model. The linearization of the model does not cover all dynamics of the robotic system. Thus, this paper introduces the nonlinear MPC (NLMPC) as a proper control method for the nonlinear systems instead of the conventional MPC. Specifically, this work proposes the use of NLMPC for controlling robotic manipulators. However, the NLMPC gains need proper tuning to attain good performance rather than the conventional methods. The neural network algorithm (NNA) considers a sufficient adaptive intelligent technique that can be utilized for this purpose. The restriction in a local optimum reveals the main issue versus artificial intelligence techniques. This paper suggests a new improvement to reinforce the exploration behavior of the NNA to overcome the local restriction issue. This modification is carried out by utilizing the polynomial mutation as an effective method to promise the exploration manner of the intelligence techniques. The proposed system can estimate all states from only the output to reduce the cost of the required sensors to measure all states. The results confirm the superiority of the proposed systems with the estimator with negligible change in the output response. The proposed modified NNA (MNNA) is evaluated with the main NNA, genetic algorithm-based PID control scheme, besides the cuckoo search algorithm-based PID control scheme from other works. The results confirm the robustness and effectiveness of the suggested MNNA-based NLMPC to track regular and irregular trajectories compared with other techniques.

Infinite dimensional pathwise Volterra processes driven by Gaussian noise – Probabilistic properties and applications –

We investigate the probabilistic and analytic properties of Volterra processes constructed as pathwise integrals of deterministic kernels with respect to the Holder continuous trajectories of Hilbert-valued Gaussian processes. To this end, we extend the Volterra sewing lemma from [18] to the two dimensional case, in order to construct two dimensional operator-valued Volterra integrals of Young type. We prove that the covariance operator associated to infinite dimensional Volterra processes can be represented by such a two dimensional integral, which extends the current notion of representation for such covariance operators. We then discuss a series of applications of these results, including the construction of a rough path associated to a Volterra process driven by Gaussian noise with possibly irregular covariance structures, as well as a description of the irregular covariance structure arising from Gaussian processes time-shifted along irregular trajectories. Furthermore, we consider an infinite dimensional fractional Ornstein-Uhlenbeck process driven by Gaussian noise, which can be seen as an extension of the volatility model proposed by Rosenbaum et al. in [13].

Signal Processing Options for High Resolution SAR Tomography of Natural Scenarios

Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.

Trayectorias lectoras discontinuas: prácticas letradas durante la transición a la universidad

The study of the transition to university attempts to provide insights into the increase in school failure during the first year of university. Transition is problematized as a standardized phenomenon that ignores students' irregular literate trajectories. This study aims to contrast academic and non-academic literate practices to account for the process of appropriation of literate culture in transition, its disruptions and emerging literate identities. A qualitative descriptive study is proposed, with a phenomenological approach to account for hierarchically structured categories of description. It is based on two series of focus groups (70 students) and eight interviews. The most relevant results indicate that literate practices coexist with the deficit discourse. The need to value reading identities from vernacular contexts and at the same time provide progressive access to dominant forms of literate practice is considered.