Large Parameter Research Articles

Travelling Waves in the Ring of Coupled Oscillators with Delayed Feedback

We studied travelling waves in N nonlinear differential equations with a delay and large parameter. This system is important because it can be regarded as a phenomenological model of N-coupled neuron-like oscillators with delay. The problem of the existence of travelling-wave-type solutions was reduced to the study of the dynamics of an auxiliary equation with two delays. Using a special asymptotic method for the large parameter we proved that this equation has a relaxation cycle, studied its properties (amplitude, period and asymptotics) and found the sufficient stability conditions. Based on this periodic solution the travelling waves of the initial model were constructed.

UNDERGROUND UTILITY LOCATOR EQUIPPED WITH A TWO-STROKE PULSE TRANSDUCER

With detailed large-scale topographic surveys of cities, especially when photographing executive master plans of industrial facilities, geodetic services perform work on measuring not only ground sites, but also - on detecting and localizing of underground utilities: water, oil and gas metal pipelines, power and telephone cables, etc. The search for underground communications by various services is carried out at use of special electronic devices for searching of underground communications: tracers, cable detectors. A number of shortcomings have been identified when operating these devices, including difficulties in detecting and fixing of pipes or cables in conditions of increased industrial interference, insufficient efficacy and accuracy of the search process, high energy consumption, low level of ease of operation, due to relatively large weight and size parameters. A comparative analysis of currently used search devices has proven technical defects of circuit solutions, and has formed the basis for developing of an electronic circuit. A working model of an underground utility finder with a pulse converter has been produced. Main technical characteristics were obtained after testing. The technical solution has been patented in the GU of the Intellectual Property Center of the Republic of Uzbekistan.

The role of small airway function parameters in preschool asthmatic children

BackgroundSmall airways are the major sites of inflammation and airway remodeling in all severities of asthma patients. However, whether small airway function parameters could reflect the airway dysfunction feature in preschool asthmatic children remain unclear. We aim to investigate the role of small airway function parameters in evaluating airway dysfunction, airflow limitation and airway hyperresponsiveness (AHR).MethodsEight hundred and fifty-one preschool children diagnosed with asthma were enrolled retrospectively to investigate the characteristics of small airway function parameters. Curve estimation analysis was applied to clarify the correlation between small and large airway dysfunction. Spearman’s correlation and receiver-operating characteristic (ROC) curves were employed to evaluate the relationship between small airway dysfunction (SAD) and AHR.ResultsThe prevalence of SAD was 19.5% (166 of 851) in this cross-sectional cohort study. Small airway function parameters (FEF25-75%, FEF50%, FEF75%) showed strong correlations with FEV1% (r = 0.670, 0.658, 0.609, p<0.001, respectively), FEV1/FVC% (r = 0.812, 0.751, 0.871, p<0.001, respectively) and PEF% (r = 0.626, 0.635, 0.530, p<0.01, respectively). Moreover, small airway function parameters and large airway function parameters (FEV1%, FEV1/FVC%, PEF%) were curve-associated rather than linear-related (p<0.001). FEF25-75%, FEF50%, FEF75% and FEV1% demonstrated a positive correlation with PC20 (r = 0.282, 0.291, 0.251, 0.224, p<0.001, respectively). Interestingly, FEF25-75% and FEF50% exhibited a higher correlation coefficient with PC20 than FEV1% (0.282 vs. 0.224, p = 0.031 and 0.291 vs. 0.224, p = 0.014, respectively). ROC curve analysis for predicting moderate to severe AHR showed that the area under the curve (AUC) was 0.796, 0.783, 0.738, and 0.802 for FEF25-75%, FEF50%, FEF75%, and the combination of FEF25-75% and FEF75%, respectively. When Compared to children with normal lung function, patients with SAD were slightly older, more likely to have a family history of asthma and airflow obstruction with lower FEV1% and FEV1/FVC%, lower PEF% and more severe AHR with lower PC20 ( all p<0.05).ConclusionSmall airway dysfunction is highly correlated with large airway function impairment, severe airflow obstruction and AHR in preschool asthmatic children. Small airway function parameters should be utilized in the management of preschool asthma.

The stochastic relaxion

We revisit the original proposal of cosmological relaxation of the electroweak scale by Graham, Kaplan and Rajendran in which the Higgs mass is scanned during inflation by an axion field, the relaxion. We investigate the regime where the relaxion is subject to large fluctuations during inflation. The stochastic dynamics of the relaxion is described by means of the Fokker-Planck formalism. We derive a new stopping condition for the relaxion taking into account transitions between the neighboring local minima of its potential. Relaxion fluctuations have important consequences even in the “classical-beats-quantum” regime. We determine that for a large Hubble parameter during inflation, the random walk prevents the relaxion from getting trapped at the first minimum. The relaxion stops much further away, where the potential is less shallow. Interestingly, this essentially jeopardises the “runaway relaxion” threat from finite-density effects, restoring most of the relaxion parameter space. We also explore the “quantum-beats-classical” regime, opening large new regions of parameter space. We investigate the consequences for both the QCD and the non-QCD relaxion. The misalignment of the relaxion due to fluctuations around its local minimum opens new phenomenological opportunities.

A secure and efficient authenticated key exchange scheme for smart grid

Smart grid provides convenience for power generation, consumption and distribution. Authenticated key exchange (AKE) is a fundamental technique to protect data transmission from interception and tampering in smart grid. However, since the smart meters only have limited resources in computation and communication, most of the existing AKE schemes are inefficient for smart grid. First, many schemes have to use large security parameters to compensate the loose reduction in their security proofs. Second, most of these schemes require at least three-round of communication to negotiate a secret session key with explicit key confirmation. To alleviate these issues, we propose a novel two-round AKE scheme with tight security for smart grid. Our proposed scheme integrates Diffie-Hellman key exchange and a tightly secure digital signature, in which not only mutual authentication can be realized but also the communicating parties can confirm that session keys are negotiated between them explicitly. Compared with the existing AKE schemes, the overheads in both communication and computation are lighter in our proposed scheme, because fewer rounds of communication are required and smaller security parameters can be used to achieve the same security level. Therefore, our scheme contributes to a more practical solution for secure key establishment in smart grid.

Reordering-based Schur complement preconditioners for a class of two-by-two block complex linear systems

This paper presents two efficient Schur complement preconditioners for a class of two-by-two block complex linear systems arising from optimal control with time-periodic parabolic equation. The proposed preconditioners are obtained after reordering the original problem into equivalent 4-by-4 block real systems. The resulting two preconditions can be used to accelerate the convergence rate of Krylov subspace methods (such as GMRES) with large regularization parameters (equal to or greater than 1e−6) and small regularization parameters (less than 1e−6), respectively. The spectral properties of the associated preconditioned matrices for the proposed Schur complement preconditioners are studied. Numerical experiments are presented to show the effectiveness of the proposed preconditioners.

Turbulence and added drag over acoustic liners

We present pore-resolved compressible direct numerical simulations of turbulent flows grazing over perforated plates, that closely resemble the acoustic liners found on aircraft engines. Our direct numerical simulations explore a large parameter space including the effects of porosity, thickness and viscous-scaled diameter of the perforated plates, at friction Reynolds numbers $\textit {Re}_\tau = 500\unicode{x2013}2000$ , which allows us to develop a robust theory for estimating the added drag induced by acoustic liners. We find that acoustic liners can be regarded as porous surfaces with a wall-normal permeability and that the relevant length scale characterizing their added drag is the inverse of the wall-normal Forchheimer coefficient. Unlike other types of porous surfaces featuring Darcian velocities inside the pores, the flow inside the orifices of acoustic liners is fully turbulent, with a magnitude of the wall-normal velocity fluctuations comparable to the peak in the near-wall cycle. We provide clear evidence of a fully rough regime for acoustic liners, also confirmed by the increasing relevance of pressure drag. Once the fully rough asymptote is reached, canonical acoustic liners provide an added drag comparable to that of sand-grain roughness with viscous-scaled height matching the inverse of the viscous-scaled Forchheimer permeability of the plate.

Long time gyrokinetic equations

The aim of this text is to elucidate the oscillating patterns (see C. Cheverry [Res. Rep. Math. (2018)]) which are generated in a toroidal plasma by a strong external magnetic field and a nonzero electric field. It is also to justify and then study new modulation equations which are valid for longer times than before. Oscillating coherent structures are induced by the collective motions of charged particles which satisfy a system of ODEs implying a large parameter, the gyrofrequency ε − 1 ≫ 1 \varepsilon ^{-1} \gg 1 . By exploiting the properties of underlying integrable systems, we can complement the KAM picture (see G. Benettin and P. Sempio [Nonlinearity 7 (1994), pp. 281–303]; M. Braun [SIAM Rev. 23 (1981), pp. 61–93]) and go beyond the classical results about gyrokinetics (see M. Bostan [Multiscale Model. Simul. 8 (2010), pp. 1923–1957]; A. J. Brizard and T. S. Hahm [Rev. Modern Phys. 79 (2007), pp. 421–468]). The purely magnetic situation was addressed by C. Cheverry [Comm. Math. Phys. 338 (2015), pp. 641–703; J. Differential Equations 262 (2017), pp. 2987–3033]. We are concerned here with the numerous additional difficulties due to the influence of a nonzero electric field.

DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.

SheepFaceNet: A Speed-Accuracy Balanced Model for Sheep Face Recognition.

The recognition of sheep faces based on computer vision has improved the efficiency and effectiveness of individual sheep identification, providing technical support for the development of smart farming. However, current recognition models have problems such as large parameter sizes, slow recognition speed, and difficult deployment. Therefore, this paper proposes an efficient and fast basic module called Eblock and uses it to build a lightweight sheep face recognition model called SheepFaceNet, which achieves the best balance between speed and accuracy. SheepFaceNet includes two modules: SheepFaceNetDet for detection and SheepFaceNetRec for recognition. SheepFaceNetDet uses Eblock to construct the backbone network to enhance feature extraction capability and efficiency, designs a bidirectional FPN layer (BiFPN) to enhance geometric location ability, and optimizes the network structure, which affects inference speed, to achieve fast and accurate sheep face detection. SheepFaceNetRec uses Eblock to construct the feature extraction network, uses ECA channel attention to improve the effectiveness of feature extraction, and uses multi-scale feature fusion to achieve fast and accurate sheep face recognition. On our self-built sheep face dataset, SheepFaceNet recognized 387 sheep face images per second with an accuracy rate of 97.75%, achieving an advanced balance between speed and accuracy. This research is expected to further promote the application of deep-learning-based sheep face recognition methods in production.

Structural-Phase Change of Multilayer Ceramics Zr-Y-O/Si-Al-N under High Temperature

To increase the thermocyclic resistance of material, multilayer coatings with alternating layers of Zr-Y-O and Si-Al-N were obtained via magnetron sputtering. It was established that a coating layer based on Zr-Y-O has a columnar structure; the height of the columns is determined by the thickness of the layer. The Si-Al-N-based layer is amorphous. There were monoclinic and tetragonal phases with a large lattice parameter in the composition of the Zr-Y-O-based coating layer. After high-temperature annealing, a tetragonal phase with a small lattice parameter appeared in the microscope column. In the “in situ” mode, a change in the structural state of the Zr-Y-O coating layer was detected in the temperature range of 450–500 °C; namely, a change in the grain size and coherent scattering regions, and an increase in internal elastic stresses. It was found that the thermocyclic resistance increased by more than two times for multilayer samples compared to the single-layer ones we studied earlier.

Giant low surface brightness galaxies in TNG100

ABSTRACT Giant low surface brightness (GLSB) galaxies, such as Malin 1 and UGC 1382, contain the largest stellar discs known. GLSB galaxies also often contain large masses of neutral hydrogen (H i). However, these extreme galaxies’ origin and properties remain poorly understood. Using the cosmological simulation IllustrisTNG 100, we identify and select a sample of ∼200 galaxies with extended ($R_{\rm {\rm H\,{\small I}}}\,\gt\, 50$ kpc) and well-defined H i discs, ∼6 per cent of the total galaxies in the same stellar mass range (10.2 &amp;lt; log (M*/M⊙) &amp;lt; 11.6). This GLSB sample is heterogeneous, with mixed galaxy morphologies ranging from the most disc-dominated systems to massive ellipticals. These simulated GLSB galaxies are located in massive haloes ($V_{\max }\, \gt \, 150\ \rm {km\ s^{-1}}$) and their properties, such as total H i content, stellar disc parameters, star formation rate, and rotation curves, agree with observed GLSB galaxies. We construct a paired control sample to contrast with the GLSB galaxies. The GLSB galaxies tend to have large galaxy spin parameters ($40{{\ \rm per\ cent}}$ larger) and larger ex situ stellar mass fractions than the paired control. We find evidence that aligned mergers promote the formation of extended discs and that isolated environments help the survival of those discs across cosmic time.

Weakly coupled Majorana wire arrays under tilted magnetic fields

An array of Rashba-coupled semiconducting nanowires lying in proximity to an s-wave superconducting substrate, with weak inter-wire coupling, in the presence of an external magnetic field shows even–odd effect in the differential conductance over a chosen parameter space. Such an effect is a direct consequence of end Majoranas in each nanowire hybridizing into bonding and anti-bonding states. In the present work, we study in detail the impact of tilting of external magnetic field on the differential conductance of an array of both uncoupled and weakly coupled wires. The phase diagram evolution with various control parameters including the tilt angle of the magnetic field has also been presented. From detailed analysis of the field-angle dependence of the odd–even effect, and the evolution of the same over a large parameter space we summarize that the results can be used to exploit magnetic-field angle in an array of Rashba-coupled semiconducting nanowires on a superconducting substrate as an important tuning parameter to investigate zero-bias conductance peak arising from Majorana edge modes vis-a-vis that arising from a non-topological origin.

Dynamic modelling and control system design of micro-high-temperature gas-cooled reactor with helium brayton cycle

It is possible to use the micro-high-temperature gas-cooled reactor with helium Brayton cycle (Mi-HTR) as a mobile micro-nuclear power source to provide a stable power supply. Because the Mi-HTR is a highly integrated system, the devices involved are highly coupled and mutually constrained. Therefore, the dynamic model of 5 MW Mi-HTR was established through theoretical derivation. Compared with the design value, the maximum error of steady-state calculation results is less than 2%, and the temperature error is less than 1 K. The transient behaviour is consistent with the experimental trend. The maximum relative error of flow is less than 1.5%, and the maximum relative error of pressure is less than 2.8%. On this basis, the characteristics of Mi-HTR such as large time delay, nonlinear, strong coupling and parameter time-varying are obtained by analysing the dynamic characteristics. The steady-state operation scheme of Mi-HTR is proposed. The reactor outlet helium temperature control, the electric power/rotor speed control, and the precooler/intercooler outlet helium temperature control are designed. A dynamic control model for Mi-HTR is established. The designed control system can cope with the 33% step change of external load.

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

Fast convergence strategy for ambiguous inverse problems based on hierarchical regularization

Mathematical modelling allows to predict the state of a physical system based on a set of parameters. The corresponding inverse problem, i.e., when parameter values are inferred based on constraints imposed on the system state, is commonly ill-posed. Here, we consider consistent underdetermined problems, where infinitely many combinations of possible parameter values achieve a state that accurately matches all constraints. For such problems a unique regularization strategy can be used, where the ambiguity of the solution space is reduced by sequentially incorporating additional constraints. As this regularization approach always yields a consistent problem by construction, we now show that the Gauss-Newton method is the prime choice to achieve fast convergence. Moreover, using the adjoint method allows to efficiently compute the required Jacobian matrix, which makes the overall solution approach ideally suited for large test cases with many constraints and unknown parameters. We present results for several illustrative examples related to network flow, for which we visualize the solution manifolds of the regularized inverse problem. This provides an intuitive explanation of the hierarchical approach to reduce the ambiguity of the solution. Furthermore, we confirm that combining our regularization strategy with a Gauss-Newton method results in an order of magnitude lower computational cost compared to a gradient descent algorithm. This highlights the potential of our regularization strategy in combination with the Gauss-Newton method, which likely is beneficial for many comparable inverse problems, especially with large parameter spaces.

On the constant roll complex scalar field inflationary models

In this paper we wish to point out the possibility of using a complex scalar field in a constant roll inflationary model, as needed for observational viability. We extend the idea of real field inflaton with constant rate of roll to a complex field, showing the feasibility of solving Einstein Klein-Gordon equations constrained by an appropriate form of constant roll definition. As compared to the well known (two-parametric class of) real field models, there is one more degree of flexibility in constant roll inflationary solutions which is represented by an arbitrary function of time, γ(t). We work with an arbitrary but constant function γ (where γ = 0 refers to the corresponding real field model) and find new inflationary class of potentials. In this class of models, the behavior of real and complex field models are similar in some aspects, for example the solutions with large constant roll parameter are not stable and should be considered as early time transients. These field solutions relax at late time on a dual attractor trajectory. However, complex fields phase space trajectories reach this stable regime after real fields. We performed the stability analysis on γ function space solutions and found that dynamically stable trajectories in phase space are stable under γ variations. We extended this study by considering multifield models of constant roll inflation with non-canonical kinetic terms. By enlarging the size of field space, we showed that a multifield constant roll model is dynamically a single field effective theory. If field space is parametrized by N non-canonical fields, there will be N free parameters in the potential that can be attributed to the interaction between the fields.

Double grazing bifurcation route in a quasiperiodically driven piecewise linear oscillator.

Considering a piecewise linear oscillator with quasiperiodic excitation, we uncover the route of double grazing bifurcation of quasiperiodic torus to strange nonchaotic attractors (i.e., SNAs). The maximum displacement for double grazing bifurcation of the quasiperiodic torus can be obtained analytically. After double grazing of quasiperiodic orbits, the smooth quasiperiodic torus wrinkles increasingly with the continuous change of the parameter. Subsequently, the whole quasiperiodic torus loses the smoothness by becoming everywhere non-differentiable, which indicates the birth of SNAs. The Lyapunov exponent is adopted to verify the nonchaotic property of the SNA. The strange property of SNAs can be characterized by the phase sensitivity, the power spectrum, the singular continuous spectrum, and the fractal structure. Our detailed analysis shows that the SNAs induced by double grazing may exist in a short parameter interval between 1 T quasiperiodic orbit and 2 T quasiperiodic orbit or between 1 T quasiperiodic orbit and 4 T quasiperiodic orbit or between 1 T quasiperiodic orbit and chaotic motion. Noteworthy, SNAs may also exist in a large parameter interval after double grazing, which does not lead to any quasiperiodic or chaotic orbits.

GaiaData Release 3

Context.GaiaData Release 3 (DR3) provides a wealth of new data products for the astronomical community to exploit, including astrophysical parameters for half a billion stars. In this work, we demonstrate the high quality of these data products and illustrate their use in different astrophysical contexts.Aims.We produce homogeneous samples of stars with high-quality astrophysical parameters by exploitingGaiaDR3, while focusing on many regimes across the Hertzsprung-Russell (HR) diagram; spectral types OBA, FGKM, and ultracool dwarfs (UCDs). We also focus on specific subsamples of particular interest to the community: solar analogues, carbon stars, and theGaiaspectrophotometric standard stars (SPSS).Methods.We query the astrophysical parameter tables along with other tables inGaiaDR3 to derive the samples of the stars of interest. We validate our results using theGaiacatalogue itself and by comparison with external data.Results.We produced six homogeneous samples of stars with high-quality astrophysical parameters across the HR diagram for the community to exploit. We first focus on three samples that span a large parameter space: young massive disc stars (OBA; about 3 Million), FGKM spectral type stars (about 3 Million), and UCDs (about 20 000). We provide these sources along with additional information (either a flag or complementary parameters) as tables that are made available in theGaiaarchive. We also identify 15 740 bone fide carbon stars and 5863 solar analogues, and provide the first homogeneous set of stellar parameters of the SPSS sample. We demonstrate some applications of these samples in different astrophysical contexts. We use a subset of the OBA sample to illustrate its usefulness in analysing the Milky Way rotation curve. We then use the properties of the FGKM stars to analyse known exoplanet systems. We also analyse the ages of some unseen UCD-companions to the FGKM stars. We additionally predict the colours of the Sun in various passbands (Gaia, 2MASS, WISE) using the solar-analogue sample.Conclusions.GaiaDR3 contains a wealth of new high-quality astrophysical parameters for the community to exploit.

Cascading, interactive, and indirect effects of climate change on aquatic communities, habitats, and ecosystems

AbstractClimate‐change is rapidly and intensively altering aquatic communities and habitats. While previous work has focused on direct effects of potential drivers, indirect and interactive effects on organisms and ecosystems have received less attention. Here, we give an overview of contributions to a special issue in Limnology and Oceanography that addresses this knowledge gap. Contributions covered diverse habitats, from polar to tropical regions, alpine streams to coral reefs. Several studies relied on time‐series to identify indirect effects, thus emphasizing our need to maintain high‐quality time‐series data. Time‐series are particularly crucial now that the pace of climate‐change on aquatic‐ecosystems is accelerating. Another common theme is the role of species‐specific characteristics in physiology, behavior or genetics in aquatic ecosystem function. The addition of inter‐ and intra‐specific variability to investigations of climate‐change may be challenging particularly since ecosystem studies typically involve a large parameter space of environmental and biological variables across spatial and temporal scales. However, the results demonstrate that inclusion of species‐specific dynamics, although challenging, can deliver mechanistic insights into aquatic ecosystem patterns and processes. Some contributions leverage habitat changes from disturbances or climate shifts to document capacity for resilience or recovery of pelagic and benthic communities. Jointly, the results in this special issue document fruitful approaches and provide urgent information needed for deciphering aquatic ecosystem responses to climate forcings. This information is foundational if we wish to tackle the combined effects of climate change and other human impacts with maximum efficacy and minimize unintended consequences for biodiversity and ecosystem functioning.