Large Parameter Research Articles

Parametric uniform numerical method for singularly perturbed differential equations having both small and large delay

Abstract In this paper, singularly perturbed differential equations having both small and large delay are considered. The considered problem contains large delay parameter on the reaction term and small delay parameter on the convection term. The solution of the problem exhibits interior layer due to the delay parameter and strong right boundary layer due to the small perturbation parameter ε. The resulting singularly perturbed problem is solved using exponential fitted operator method. The stability and parameter uniform convergence of the proposed method are proved. To validate the applicability of the scheme, one model problem is considered for numerical experimentation.

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time. Codes and models are available at https://github.com/StephenLinn/LMQFormer.

PROPERTIES OF SOLUTIONS TO ONE CLASS OF NONLINEAR SYSTEMS OF DIFFERENTIAL EQUATIONS WITH A PARAMETER

A system of nonlinear ordinary differential equations of large dimension with a parameter is considered. We investigate asymptotic properties of solutions to the system in dependence on the growth of the number of the equations or parameter. We prove that, for sufficiently large number of differential equations, the last component of the solution to the Cauchy problem is an approximate solution to an initial problem for one delay differential equation. For a fixed number of equations and a sufficiently large parameter, the solution to the Cauchy problem for the system is an approximate solution to the Cauchy problem for a simpler system.

Characteristics of Flow Hydrophysics Variables and Flood Potential Around Kuala Jengki Estuary Manado City

Flood disasters along the river resulted from damage to flood barrier construction along the river bank. The damage to the flood retaining wall is technically caused by the very large hydrophysical parameters of the river flow. In theory, the flow mass density parameter shows the concentration of sediment transported through one cross-section. As a result of changes in the volume of sediment transport, there will be an increase in flow energy, followed by changes in large water masses, and this is seen as a destructive force component in some equivalent places or riverbanks, which can physically be formulated in the form of a model function. It is important to do a map of the hydrophysical character variable flow and flood potential along Kuala Jengki estuary Manado City, it is important to do because the estuary is often flooded. Measurement of the hydrophysical variable mass flow density was carried out at a depth of 0.6 rivers at high and low tides. The mass density of the flow is the mass of water containing transport sediment divided by its volume. The mass of water (sample) is measured using a scale, while the volume is measured using a measuring cup or another container that has a volume measurement. For the presentation of density data profiles and other analytical purposes, the measured data were reproduced using linear interpolation techniques. The mass density data modeling was carried out using an experimental function iteration technique containing constants. The results of the research at high tide, the position which is approximately 275 meters at measurement points 10 to 13 at a distance of 675 meters to 950 meters becomes a location that is very prone to river lip damage if there is a rise in water level due to high rainfall, or a position where it becomes flood-prone when the flow density increases, along with an increase in the volume of river water, which is followed by changes in river flow velocity carrying various types of sediment. At a distance of 165 meters at low tide, the measurement position is between 10 and 13, there is an increase in large flow discharge accompanied by an increase in flow velocity that carries flow mass (mass density) with various types of sediment transport. In this condition, the location which is 165 meters away according to the model map becomes prone to river lip damage, which means that it is a location that has the potential for flooding. In contrast to measurement positions 5 and 6 at high tide, there is a significant increase in the low mass density, while at low tide this condition does not occur, this is because the flow velocity at low tide does not change significantly, causing the mass flow density to be relatively the same.

On the shapes of liquid curtains flowing from a non-vertical slot

A theoretical and experimental investigation of two-dimensional (2-D) liquid curtains (gravitationally thinning liquid sheets) is provided under conditions where the curtain issues from a thin slot whose centreline is inclined with respect to the vertical. This analysis is motivated in part by recent works where it has been proposed that oblique liquid curtains (those exiting a non-vertical slot) may bend upwards against gravity when the relevant Weber number at the slot is less than unity ( $We <1$ ). By contrast, Weinstein et al. (J. Fluid Mech., vol. 876, 2019, R3) have proposed that such $We<1$ curtains must be vertical and downward falling regardless of the inclination of the slot. Under low-Reynolds-number ( $Re$ ) conditions typical of liquid film coating operations, our experiments show that the curtain shape follows the classic ballistic (parabolic) trajectory in the supercritical regime ( $We>1$ ). In subcritical conditions ( $We<1$ ), experiments show that the downward-falling curtain is vertical except in a relatively small region near the slot, where the combined effects of viscosity and surface tension induce the so-called teapot effect. These experimental results are confirmed by 2-D numerical simulations, which predict the curtain behaviour ranging from highly viscous ( $Re = O(1)$ ) to nearly inviscid conditions. The one-dimensional (1-D) inviscid model of Weinstein et al. is recast in a different form to facilitate comparisons with the 2-D model, and 1-D and 2-D results agree favourably for supercritical and subcritical conditions. Despite the large parameter range explored, we have found no evidence that upward-bending curtains exist in an oblique configuration.

Joint Sub-Band and Transmission Rate Selection for Anti-Jamming Non-Contiguous Orthogonal Frequency Division Multiplexing System: An Upper Confidence Bound Based Reinforcement Learning Approach

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming.

Research on a random sampling method for bulk grain based on the M-Unet and SGBM algorithms

In response to the challenge of limited real-time capability in identifying and positioning sampling points during random sampling of bulk grains in industrial settings, this paper introduces a novel approach. It combines the utilization of a lightweight neural network model, known as M-Unet, with the Semi-Global Block Matching (SGBM) algorithm to achieve the random sampling of bulk grains. The process commences with the deployment of a binocular camera to capture image data from the grain loading area, followed by binocular correction. Recognizing the constraints associated with the traditional U-Net network model, characterized by large parameters and suboptimal real-time performance, we introduce the M-Unet network, which is a more efficient alternative. M-Unet incorporates MobileNet as a substitute for the U-Net network encoder, resulting in a significant improvement in real-time performance and segmentation quality. Subsequently, the M-Unet network is employed to partition the corrected left image into multiple regions based on predefined sampling principles, and random sampling points are generated within each region. To determine the three-dimensional spatial positioning of these sampling points, we employ the SGBM algorithm, a semi-global stereo matching technique, on the left and right images of the corrected grain loading area. Experimental results demonstrate that the proposed method exhibits robust real-time performance and effectively recognizes grain loading areas. Furthermore, this method is suitable for deployment on portable devices, offering novel insights and methodologies for automating and advancing the intelligence of grain sampling processes. This innovation effectively addresses the challenges associated with low efficiency and human-related factors in traditional grain sampling. The findings presented in this paper carry substantial significance in safeguarding the interests of grain farmers, reducing food losses, minimizing waste, and enhancing the intelligence of sampling processes.

Rational approximation for solving Fredholm integro-differential equations by new algorithm

Abstract In this article, we used a novel semi-analytical approach, named the optimal auxiliary function method (OAFM), to solve integro-differential equations (IDEs). The OAFM includes an auxiliary function and convergence control parameters, which expedite the convergence of the method. To apply the proposed method, some assumptions regarding small or large parameters in the problem are necessary. We present numerical outcomes acquired via the OAFM alongside those obtained from other numerical techniques in tables. Furthermore, we demonstrate the efficacy and ease of implementing the proposed method for various IDEs using 2D graphs.

The dependence of wave height probability distributions on physical parameters from measurements near Sakhalin Island

The data of long-term surface waves measurements with bottom sensors near Sakhalin Island were used to build instrumental probability distributions for exceedance of wave heights. Waves with heights exceeding the significant wave height by more than three times were recorded. Specific features of the observations conducted during the periods of open and ice-covered sea surfaces are discussed. The subsets of statistically homogeneous data are arranged through selection considering the natural physical dimensionless parameters of the task, that control the effects of finite depth and nonlinearity (namely, the wave steepness, the wave amplitude to water depth ratio, the Ursell parameter). The manifestation of these and composite parameters in theoretical probability distributions of wave heights is discussed. The effects of nonlinearity and the measurement point depth on probability distributions are estimated with a focus on abnormally high waves. In particular, it is shown that for the place of registration an increase in the wave amplitude to water depth ratio parameter leads to a decrease in the probability of abnormally high waves. This behavior is consistent with the Glukhovsky theoretical distribution. The waves characterized by relatively large dimensionless depth parameter exhibit a higher probability of substantial exceedance of the significant height and are better described by the Rayleigh distribution.

A new approach for sensitivity analysis of the StormWater Management Model applied in an airport.

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.

Self‐Assembled Perovskite Nanocomposite With Beneficial Lattice Tensile Strain as High Active and Durable Cathode for Low Temperature Solid Oxide Fuel Cell

AbstractThe sluggish kinetics of oxygen reduction reaction (ORR) at low temperatures and the fast degradation of the cathode are the main obstacles to the commercialization of solid oxide fuel cells (SOFCs). However, it is still very challenging to achieve both high catalytic activity and favorable stability for single‐phase materials. Herein, a highly active and durable nanocomposite cathode (Ba0.5Sr0.5)0.75Pr0.25Co0.575Fe0.3W0.125O3‐δ (BSPCFW) for low‐temperature SOFC (≤650 °C) is presented, which self‐assembles into two cubic perovskites: the simple perovskite Pr0.38Ba0.25Sr0.37Co0.62Fe0.38O3‐δ (PBSCF‐c) and the B‐site cations ordered double perovskite Ba1.30Sr0.70Co1.0Fe0.25W0.75O6‐δ (BSCFW‐c). The former PBSCF‐c serves as the highly conductive and active catalyst for ORR, while the latter BSCFW‐c with a large lattice parameter introduces a key beneficial lattice tensile strain into the PBSCF‐c phase through a coherent interface, which significantly promotes the ORR activity at low temperatures with the area specific resistance of 0.034 Ω cm2 at 650 °C, and the long‐term stability of 2 years storage and 1280 h operation in symmetrical cell. The introduction of the beneficial lattice tensile strain in self‐assembled composite catalysts is an effective way to synergistically enhance the electrochemical activity and durability of the electrode materials for electrochemical devices.

A “Hardware-Friendly” Foreign Object Identification Method for Belt Conveyors Based on Improved YOLOv8

As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system’s safe and efficient operation. This paper introduces a new ‘hardware-friendly’ method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to “hardware friendly” algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor.

Quasinormal modes of phantom Reissner-Nordström-de Sitter black holes

In this paper, we investigate some characteristics of phantom Reissner-Nordström-de Sitter (RN-dS) black holes. The peculiar features of phantom field render this kind of black holes quite different from their counterparts. We can only find at most two horizons in this spacetime, i.e. event horizon and cosmological horizon. For the black hole charge parameter, we find that it is not bounded from below. We calculate quasinormal modes (QNMs) frequencies of massless neutral scalar field perturbation in this black hole spacetime, and some properties related to the large charge parameter are disclosed.

Fast estimation of a large TVP-VAR model with score-driven volatilities

This paper proposes a fast approach to estimating a large time-varying parameter vector autoregressive (TVP-VAR) model. Based on a score-driven modeling framework, we first assume that the time-varying variances of random errors in each equation of the TVP-VAR are score-driven, and then propose filtering and smoothing procedures to estimate time-varying parameters and time-varying volatilities. We show that under the forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, significantly reducing the dimension of state space and thus delivering fast estimation. Moreover, a fast smoothing estimation can be derived, avoiding the inverse of the super-high dimensional state equation covariance matrix. We provide dynamic model averaging (selection) and maximum likelihood estimates for forecasting and inference. Our simulation study shows that the proposed method is more accurate than the popular methods and enjoys tremendous computational gain from the equation-by-equation estimator. Finally, we conduct an empirical study on the dynamic connectedness of global stock markets, demonstrating the merits of our methods in real-time and ex-post analysis.

Grasping extreme aerodynamics on a low-dimensional manifold

Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in gusty environments, encountered in urban canyons, over mountainous terrains, and in ship wakes. With extreme weather becoming ever more frequent due to global warming, it is anticipated that aircraft, especially those that are smaller in size, will encounter sizeable atmospheric disturbances and still be expected to achieve stable flight. However, there exists virtually no theoretical fluid-dynamic foundation to describe the influence of extreme vortical gusts on wings. To compound this difficulty, there is a large parameter space for gust-wing interactions. While such interactions are seemingly complex and different for each combination of gust parameters, we show that the fundamental physics behind extreme aerodynamics is far simpler and lower-rank than traditionally expected. We reveal that the nonlinear vortical flow field over time and parameter space can be compressed to only three variables with a lift-augmented autoencoder while holding the essence of the original high-dimensional physics. Extreme aerodynamic flows can be compressed through machine learning into a low-dimensional manifold, which can enable real-time sparse reconstruction, dynamical modeling, and control of extremely unsteady gusty flows. The present findings offer support for the stable flight of next-generation small air vehicles in atmosphere conditions traditionally considered unflyable.

Kilometer-precise (UII) Umbriel physical properties from the multichord stellar occultation on 2020 September 21

ABSTRACT We report the results of the stellar occultation by (UII) Umbriel on 2020 September 21. The shadow crossed the USA and Canada, and 19 positive chords were obtained. A limb parameter accounted for putative topographic features in the limb fittings. Ellipse fittings were not robust – only upper limits were derived for the true size/shape of a putative Umbriel ellipsoid. The adopted spherical solution gives radius = 582.4 ± 0.8 km, smaller/close to 584.7 ± 2.8 km from Voyager II. The apparent ellipse fit results in a true semi-major axis of 584.9 ± 3.8 km, semi-minor axis of 582.3 ± 0.6 km, and true oblateness of 0.004 ± 0.008 for a putative ellipsoid. The geometric albedo was pV = 0.26 ± 0.01. The density was ρ = 1.54 ± 0.04 g cm−3. The surface gravity was 0.251 ± 0.006 m s−2 and the escape velocity was 0.541 ± 0.006 km s−1. Upper limits of 13 and 72 nbar (at 1σ and 3σ levels, respectively) were obtained for the surface pressure of a putative isothermal CO2 atmosphere at T = 70 K. A milliarcsecond precision position was derived: α = 02h30m28${_{.}^{\rm s}}$84556 ± 0.1 mas, δ = 14o19′36″.5836 ± 0.2 mas. A large limb parameter of 4.2 km was obtained, in striking agreement with opposite Southern hemisphere measurements by Voyager II in 1986. Occultation and Voyager results indicate that the same strong topography variation in the surface of Umbriel is present on both hemispheres.

Sub-picosecond tunable mid-infrared light source for driving high-efficiency optical rectification.

Optical rectification (OR) is a popular way to generate coherent terahertz radiation. Here, we develop a sub-picosecond mid-infrared (mid-IR) light source with a tailored wavelength and pulse duration for enhancing the OR efficiency. Numerical simulations for a LiNbO3-based OR with tilted pulse-front excitation are first conducted to determine the optimal parameters of pump wavelength and pulse duration, demonstrating that the OR efficiency pumped by 4-µm sub-picosecond (0.5-0.6 ps) pulses is approximately twice the value with 0.8-µm pump at the same conditions. Guided by the simulation results, we build a BaGa4Se7-based optical parametric chirped-pulse amplification system with 1030-nm thin-disk pump and broadband mid-IR seeds. The output performances of >200-µJ pulse energy, ∼600-fs pulse duration and 1-kHz pulse repetition rate are achieved in a spectral range tunable from 3.5 to 5 µm. The large energy scalability and high parameter tunability make the light source attractive to high-efficiency OR in various materials.

Flow characteristics of radial inflow in impeller rear cavity with baffle plate

The research purpose of this paper is to explore the influence of the baffle plate on the airflow in the rear cavity of the centrifugal impeller and optimize the performance of the secondary air system's air bleed section. In this paper, a comprehensive experimental study was carried out on the flow characteristics in the impeller rear cavity with baffle plate. The windage torque, flow structure and pressure drop between inlet and outlet were measured respectively. The experiment was carried out with the condition that the range of rotational Reynolds number was from 8.33 × 105 to 22.2 × 105 and the range of mass flow rate coefficient was from 0.92 × 104 to 2.92 × 104. The results show that the static cavity and the narrow stator-rotor cavity formed by the baffle plate effectively suppress the overall swirl coefficient in the cavity. Thus, the static pressure and total pressure drop in the rotor–stator cavity were reduced. The influence of the baffle plate on the windage torque of the rotary disk is related to the turbulence parameters. Under large turbulence parameters, the windage torque would be reduced with baffle plate, while under small turbulence parameters, the baffle plate would increase with baffle plate. In general, the baffle plate can improve the flow capacity and optimize the bleed air performance with proper structure and operation conditions.

Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study

A German dataset with soil-plant transfer factors for radiocaesium including many co-variables was analysed and prepared for the application of the Random Forest (RF) algorithm using the R libraries 'party', and 'caret'.A RF predictive model for soil-plant transfer factor was created based on 10 co-variables. These are, for example, taxonomic plant family, plant part, soil type and the exchangeable potassium concentration in the soil. The RF model results were compared with the results of two (semi-)mechanistic models. Of the more than 3000 entries in the original dataset, only about 1200 could be used, as this was the largest complete dataset with the largest number of co-variables available.The obtained RF predictive model can reproduce the experimental observations better than the two (semi)-mechanistic models, which are based on many assumptions and fixed parameter values. Model performance was quantified using the metrics of Root Mean Square Error (rmse) and Mean Absolute Error (mae). The RF model was able to reproduce the variability of the data by up to 6 orders of magnitude. The categorical co-predictors, especially taxonomic plant family and plant part, have a greater influence than the numerical co-predictors, such as pH and exchangeable soil potassium concentration.This feasibility study shows that RF is a promising tool to obtain predictive models for transfer factors. However, to build a widely applicable predictive model, a dataset is needed that contains at least thousands of entries for transfer factors and for the most important co-variables and considers a large parameter space.

Massive sensitisation of Eu3+ photoluminescence in NaMgF3:Eu nanoparticles by 2-thenoyltrifluoroacetone ligand exchange

NaMgF3:0.1%Eu nanocuboids capped with oleic acid were synthesised via a hydrothermal synthesis technique. Subsequent ligand exchange using 2-thenoyltrifluoroacetone resulted in a massive sensitisation of the Eu3+ photoluminescence where the intensity of the 5D0 → 7F2 transition increased by over 40 000×, relative to the oleic acid-capped nanoparticles. Large Judd-Ofelt Ω2 parameters indicated that the sensitised Eu3+ emissions originated primarily from distorted Eu3+ sites near the nanoparticle surfaces. The massive enhancement of the Eu3+ photoluminescence in 2-thenoyltrifluoroacetone sensitised NaMgF3:Eu nanoparticles encourages applications that include ultra-high resolution optical temperature sensors and dosimeters.