Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal vibration absorber with nonlinear viscous power law damping and white noise excitation

A tuned mass damper with a nonlinear power law viscous damper excited by white noise is considered. The system is analyzed by statistical linearization and stochastic simulation with the objective of minimizing the standard deviation of the response. It is shown that the optimal parameters for the tuned mass damper are unaffected by the magnitude of the structural damping in the linear case. However, in the nonlinear case the structural damping influences the equivalent parameters obtained by statistical linearization and thereby indirectly the optimal values for the damper parameters. Results from stochastic simulation show good agreement with results from statistical linearization in terms of the standard deviation of the response. It is shown that the optimal damping, which can be obtained by the passive device, is the same for the linear and nonlinear damper. However, for the nonlinear tuned mass damper the optimal parameters will depend on both structural damping and excitation intensity (or vibration amplitude). The results are presented in such a way that they can be used directly for the design of a tuned mass damper with damping governed by a nonlinear viscous power law.
Parameter Dependence of Multiscale Systems and Nanostructures on Crystals

In the first part of this article (Sections 1 and 2) we study atomic steps on surfaces of crystals. The crystals under consideration are called “unorthodox” since their atomic steps form increasingly fine nanostructures when the temperature decreases. Using a physical model for the shape of atomic steps on the surface of these crystals (the HMM-model) we perform numerical simulations for terrace- and islandlike shapes of atomic steps. In the second part of this article (Section 3) we present a method for obtaining information on the parameter dependence of multiscale systems by means of Γ-convergence. We apply this concept to derive partial information on the temperature dependence of some of the physical constants in the HMM-model and reveal a more complicated interplay of the various parameters than originally expected. Our method may prove useful also in other physical problems with multiple scales.
Space Telescope Imaging Spectrograph Observations of High-Velocity Interstellar Absorption-Line Profiles in the Carina Nebula

An atlas of ultraviolet interstellar absorption-line profiles toward four stars in the Carina Nebula is presented. The observations have been made with the Space Telescope Imaging Spectrograph on the Hubble Space Telescope, with a resolving power of 114,000. Low-ionization, high-ionization, and excited-state lines from a wide array of chemical species are included. Extensive measurements of radial velocities, velocity dispersions, and column densities of individual components in these profiles are also given. The unprecedented capabilities of STIS reveal many more velocity components than previously known; most of the high-velocity components in previous observations with the International Ultraviolet Explorer are now resolved into multiple subcomponents, and even higher velocities are seen. The great range of line strengths available permits the detection of the low-velocity components in the weakest lines, and progressively higher velocities in stronger lines (in which the low-velocity components become completely blended). The weak and high-ionization lines trace global structure in the H II region, while the strong low-ionization lines show intricate high-velocity structure that likely originates relatively near to the O stars observed. The extreme velocities found in the low-ionization lines toward these four stars are -388 and +127 km s-1, with 23-26 resolved components in each. Some components in different stars may be related, but many are different in each line of sight. A remarkably well-defined Routly-Spitzer effect is found in this region. Temporal variations toward one star observed twice have already been reported. These measurements will be used in subsequent astrophysical analyses to further constrain the origins of the phenomena.
Stochastic effects in the planet migration and orbital distribution of the Kuiper Belt

Assuming the Jovian planets experienced smooth orbital migrations, Malhotra successfully explained some important features of the Kuiper Belt by the mechanism of resonance capture. However, the migration should really be stochastic. In this paper we numerically simulate numerous orbital evolutions of test particles under such stochastic planet migrations. The main results are as follows. Given the proper parameters, the concentration of objects in the 3 : 2 resonance is distinct, while very few objects enter the 2 : 1 resonance, and consequently, the orbital distribution of Kuiper Belt objects matches the observational data very well. The stochastic effects excite the orbital eccentricities and inclinations of objects in the non-resonant regions. The introduction of stochastic effects also gives possible explanations of the mass depletion, and of the sources of the classical and scattered Kuiper Belt objects.
Pure and Randomized Equilibria in the Stochastic Von Neumann-Gale Model

The paper examines the problem of the existence of equilibrium for the stochastic analogue of the von Neumann-Gale model of economic growth. The mathematical framework of the model is a theory of set-valued random dynamical systems defined by positive stochastic operators with certain properties of convexity and homogeneity. Existence theorems for equilibria in such systems may be regarded as generalizations of the Perron-Frobenius theorem on eigenvalues and eigenvectors of positive matrices. The known results of this kind are obtained under rather restrictive assumptions. We show that these assumptions can be substantially relaxed if one allows for randomization. The main result of the paper is an existence theorem for randomized equilibria. Some special cases (models defined by positive matrices) are considered in which the existence of pure equilibria can be established.
Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like Kp, Ki, Kd , and α with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.
Switching current reduction using perpendicular anisotropy in CoFeB-MgO magnetic tunnel junctions

We present in-plane CoFeB–MgO magnetic tunnel junctions with perpendicular magnetic anisotropy in the free layer to reduce the spin transfer induced switching current. The tunneling magnetoresistance ratio, resistance-area product, and switching current densities are compared in magnetic tunnel junctions with different CoFeB compositions. The effects of CoFeB free layer thickness on its magnetic anisotropy and current-induced switching characteristics are studied by vibrating sample magnetometry and electrical transport measurements on patterned elliptical nanopillar devices. Switching current densities ∼4 MA/cm2 are obtained at 10 ns write times.
Influence measures for logistic regression: Another point of view

SUMMARY Several measures of influence for logistic regression have been suggested. These measures have been developed for the purpose of identifying observations which are influential relative to the estimation of the regression coefficients vector and the deviance. We propose measures for detecting influence relative to the determination of probabilities and the classification of future observations. The relationships among measures are indicated.
The Old English relative þe

In current accounts the Old English relative þe is analysed in two radically different ways. The traditional view, inherited from the nineteenth-century grammarians, views the word as a relative pronoun, while the generative analysis, derived from some remarks of Jespersen on the ModE relative that, takes it to be a subordinating particle. The generativist view is based on the word's lack of morphological variation, whereas the older approach examines more generally the grammar of the word, noting that the invariable þe shares the typical nominal categories of number and case, functioning both as a singular and a plural and representing all the four cases of OE nominal elements. A further indication of the word's nominal status is its referential function, distinguishing between specific and generic reference. Against these clear facts, the lack of overt inflection is a minor idiosyncrasy, paralleled by the OE generic man/mon, whose pronominal status is widely agreed. Þe may have been a subordinating particle in origin, but by historical OE times it retained this function in relative clauses only after relative adverbs, having been reanalysed elsewhere either as a relative adverb itself, or, in its most frequent relative use, as a pronoun.
Fourier transforms with only real zeros

The class of even, nonnegative, finite measures p on the real line such that for any b > 0 the Fourier transform of exp(bt2) dp(t) has only real zeros is completely determined. This result is then applied to the Riemann hypothesis. 1. Main results. The problem of determining whether a Fourier transform has only real zeros arises in two rather disparate areas of mathematics: number theory and mathematical physics. In number theory, the problem is intimately associated with the Riemann hypothesis [T, Chapter 10], while in mathematical physics it is closely connected with the Lee-Yang theorem of statistical mechanics and quantum field theory [SGj, [NI], [N3]; see Kac's remarks in [P, pp. 424-426] for a discussion of the historical connection between these two topics. The results of this paper developed out of the study of certain quantum field theoretic problems, but for pedagogical reasons, we present them in the context of the Riemann hypothesis. Following standard practice, we define the Riemann xi function as (1.1) _(z) = s(s 1)7T`s/2r(S/2) (s). /2; s = iz + 2 where t (s) is the Riemann zeta function. _ is the Fourier transform of the strictly positive, even function,
Revisiting binomial order in English: ordering constraints and reversibility1

The factors governing word order in binomials, i.e. coordinated word pairs of the same word class, have been the subject of discussion for a long time in linguistics. For example, why do we say law and order but not order and law? The article tests seventeen different potential ordering constraints that have previously been suggested, from the areas of semantics, phonology and word frequency, by checking over 500 high-frequency binomials extracted from the BNC against them. A clear hierarchy of constraints is suggested following the analysis of their success in predicting binomial order. In addition, however, attention is drawn to the reversibility status of binomials. The vast majority of English binomials is reversible to a smaller or larger degree. Reversibility scores were computed for all binomials in the sample so that the relationship between reversibility on the one hand and the adherence to the ordering constraints on the other could be analysed, finding that a number of semantic and metrical ordering constraints indeed increase their predictive success towards the frozen end of the reversibility cline. Complying with these constraints, then, increases the likelihood of a binomial to be less reversible. Claims for the influence of certain factors on the freezing process are thus substantiated for the first time.
A modified confidence set for the structural break date in linear regression models

ABSTRACT Elliott and Müller (EM) (2007) provide a method for constructing a confidence set for the structural break date by inverting a variant of the locally best test statistic. Previous studies have shown that the EM method produces a set with an accurate coverage ratio even for a small break; however, the set is often overly lengthy. This study proposes a simple modification to rehabilitate their method through the long-run variance estimation. Following the literature, we provide an asymptotic justification for the improvement of the modified method over the original method under a nonlocal asymptotic framework. A Monte Carlo simulation shows that the modified method achieves a shorter confidence set than the EM method, especially when the break is large or the HAC correction is conducted. The modified method may exhibit minor errors in the coverage rate when the break is small; however, the coverage is more stable than alternative methods when the break is large. We apply our method to a level shift in post-1980s Japanese inflation data.
Cavity Oscillation Mechanisms in High-Speed Flows

An experimental study was conducted to investigate the physics underlying the oscillation cycle of high-Mach- number, turbulent, open-cavity flows. The specific aim was to investigate how the interaction between the cavity acoustics and the shear-layer dynamics is affected by increasing Mach number. In the experiments the freestream Mach numbers tested were 2 and 5, the cavity length-to-depth ratio was 6, and the primary measurements made were of fluctuating surface pressures. The shear-layer-acoustics coupling was investigated by testing the cavity both with and without a plate covering over 80% of the cavity. The cover plate isolated the cavity from the free shear layer above it. In the Mach 2 cavity flow, the uncovered-cavity resonance frequencies agree well with those predicted by Rossiter's model. On the other hand, the resonance frequencies measured in the covered cavity do not agree with Rossiter's model but instead are consistent with a model that is based on closed-box acoustics. At Mach 5, both the uncovered and covered cavities have resonance frequencies that agree equally well with both Rossiter's model and closed-box acoustics. The success of a pure acoustics model suggests that the coupling between the shear-layer dynamics and the cavity acoustics is greatly reduced at high Mach numbers. Based on a consideration of the physical mechanisms implicit in Rossiter's model, it is argued that its successful prediction of the resonance frequencies in high-Mach-number cavity flows is largely coincidental and likely does not reflect the correct modeling of the flow physics.
A Low-power Column-parallel ∑Δ ADC with Shared OTAs for CMOS Image Sensor in 40-nm Process

This paper presents a low-power column-parallel ∑Δ analog-to-digital converter (ADC) with shared operational transconductance amplifier (OTA) for CMOS image sensors (CISs). Through the proposed time division multiplexing of OTAs, the power consumption of the modulator achieves a reduction of about 40%. Besides, the proposed structure alleviates the layout requirement of OTAs, which becomes appropriate to implement a high-resolution low-power image sensor in advanced CMOS technology nodes. Since the coupled noise introduced by the proposed column-shared scheme is suppressed to a lower level than the inherent crosstalk of adjacent pixels, the proposed structure only contributes an ignorable inter-column coupled noise in ADCs. The prototype sensor is implemented in a 40-nm standard CMOS technology with 256 × 256 pixel array and 256 proposed column-parallel ∑Δ ADCs. Each ∑Δ ADC occupies a core area of 4.5 μm × 310 μm, while consumes a power of 58.8 μW.
General Transit Feed Specification Assisted Effective Traffic Congestion Prediction Using Decision Trees and Recurrent Neural Networks

Traffic congestion prediction is an open-ended critical problem highlighted by the rapid growth in intra-city transit mobility in recent years fuelling the necessity for an intelligent traffic management system in metropolitan areas. The majority of this research continues to rely on data from electronic devices and mobile signals, which can sometimes be manipulated to mislead the public. Modern state-of-the-art models of predictive analysis of Graph Neural Networks (GNNs), AutoRegressive Integrated Moving Average (ARIMA) and other Hybrid Deep Neural Networks have yielded positive results. However, determining which artificial intelligence model would be able to best address the issue of traffic congestion in densely populated areas. Based on this premise, we focus on using GTFS(General Transit Feed Specification) data and have constructed a meticulous and reflective dataset. We also postulate a study of multifarious models in comparison as well as a novel approach that maps traffic congestion as a classification problem rather than a regression-prediction problem to address the shortcomings of the issue. The highest accuracy metric for the optimised models was using the Decision Tree Classifier which yielded an accuracy of 81%. In this research article, we offer an overview of predicting traffic congestion whilst focusing on the G TFS dataset.
Hidden and Face-Like Object Detection Using Deep Learning Techniques – An Empirical Study

An essential aspect of artificial intelligence is how closely machines can mimic humans. One of the motivations for developing intelligent systems is human vision. While trying to recognise a class of images, it is as vital to distinguish the class of images from similar-looking objects and identify them in hidden places as it is to create bounding boxes and learn to localize the position of the object. Traditionally, deep learning models have performed exceptionally well in image classification and object detection tasks. In this work, we perform four experiments to train machines to distinguish between real faces and face-like objects and to recognise them. Nine state-of-the-art deep learning-based classifiers have been chosen to perform a comparative study on the designed experiments. Using these experiments, we establish that training models on real faces does not prepare them to identify face-like objects, and at the same time, training on face-like objects enables the models to detect face-like images even while hidden amongst other images. Despite work being done in the fields of camouflage detection and optical illusion detection, to the best of our knowledge, no work has been done in training and testing machines to distinguish between face and face-like objects with deep learning methods. This work could help researchers make better camouflage detection systems, perform context sensitive studies, understand the biases that various models possess towards certain classes of images, and have applications in real life such as military and self-driving cars.
Design and Simulation of Double gate Junctionless Field Effect Transistor for Ammonia Gas Sensing

In this work 2-D Junctionless symmetric double gate field effect transistor(DGJL FET) using catalytic metals as gate electrode is designed for ammonia (NH3) gas detection. Two different catalytic metals Cobalt and Molybdenum are used as gate contact because of their selectivity towards NH3 . When the gas molecules of NH3 are adsorbed over the surface of the catalytic metal gate, it causes considerable variation in electrical characteristics of Double gate Junctionless FET.The proposed device has been simulated using 2-D Sentaurus TCAD device Simulation tool. Simulation results of DGJL FET shows that drain current sensitivity(ON & OFF current), threshold voltage and IDON/IDOFF change significantly as the work function of Cobalt and Molybdenum gate changes from 0meV(without gas molecule) to 50meV and upto 200meV. Thus changes in these electrical characteristics have been used to investigate the response of DGJL FET as NH3 gas sensor.
A Theory for Strong, Long-Lived Squall Lines

Abstract We study herein the mechanics of long-lived, line-oriented, precipitating cumulus convection (squall lines) using two- and three-dimensional numerical models of moist convection. These models, used in juxtaposition, enable us to address the important theoretical issue of whether a squall line is a system of special, long-lived cells, or whether it is a long-lived system of ordinary, short-lived cells. Our review of the observational literature indicates that the latter is the most consistent paradigm for the vast majority of cases but, on occasion, a squall line may be composed of essentially steady, supercell thunderstorms. The numerical experiments presented herein show that either type of squall line may develop from an initial line-like disturbance depending on the magnitude and orientation of the environmental shear with respect to the line. With shallow shear, oriented perpendicular to the line, a long-lived line evolves containing individually short-lived cells. Our analysis of this type o.
Hybrid finite element solutions of time dependent Maxwell's curl equations

Hybrid finite element (FE) solutions of transient electromagnetic field are proposed. Time dependent Maxwell's curl equations are solved separately using edge elements for the electric field approximation and nodal-based elements for the magnetic field approximation. Two different formulations are presented: the first adopts a unique mesh, the second two complementary FE meshes. The solution of the resulting equations systems is performed by explicit methods. >
A Filtering Method for SIFT based Palm Vein Recognition

A key issue with palm vein images is that slight movements of fingers and the thumb or changes in the hand pose can stretch the skin in different areas and alter the vein patterns. This can produce palm vein images with an infinite number of variations for a given subject. This paper presents a novel filtering method for SIFT based feature matching referred to as the Median Distance (MMD) Filter, which checks the difference of keypoint coordinates and calculates the mean and the median in each direction, and uses a set of rules to determine the correct matches. Our experiments conducted with the 850nm subset of the CASIA dataset show that the MMD filter can detect and filter false positives that were not detected by other filtering methods. Comparison against existing SIFT based palm vein recognition systems demonstrates that the proposed MMD filter produces excellent performance with lower EER values.
Second term of asymptotics for KdVB equation with large initial data

We consider the Cauchy problem for the Korteweg-de Vries-Burgers equation. Global existence and smoothing effect for the Cauchy problem to the Kortewegde Vries-Burgers equation was proved previously for the case of large initial data. Recently the first term of the large time asymptotics was obta ined without restriction on the size of the initial data. The aim of the present paper is to obtain the second term of the large time asymptotic behavior of solutions to the Cauchy problem for the Korteweg-de Vries-Burgers equation in the case of the initial data of arbitrary size.