Divergence Phenomenon Research Articles

Extrapolating time development curves of clear-water scour around piles: From empirical fitting to physics-based approach

The currently available prediction formulas for the scour development around a pile predominantly rely on empirical fittings, lacking clear physical mechanisms. This study integrates the principle of sediment mass conservation, the flat-bed sediment transport model, and the phenomenological theory of turbulence to propose a semi-analytical model for predicting clear-water scour development around circular piles. This model overcomes scale-related issues and is governed by three fundamental parameters: the equilibrium scour depth, the characteristic time, and the ratio of the scour depth coefficient to the primary horseshoe vortex coefficient. The results demonstrate that the proposed model is consistent with experimental data. The proposed model offers significant improvements by addressing two critical issues observed in existing models: (i) empirical formulas expressed purely in mathematical terms exhibit low prediction accuracy for long-term scour, resulting in underestimation of the extrapolated equilibrium scour depth and premature attainment of equilibrium conditions; (ii) other models exhibit a divergence phenomenon, where the extrapolated equilibrium scour depth greatly exceeds the pile diameter, contradicting prevailing understanding. Applying the proposed model allows for a substantial reduction in test duration while accurately capturing the subsequent scour development process.

Flutter and divergence zones of perovskite solar cell-based panels of aircraft wings in subsonic airflow

Solar-powered aircraft have advantages such as quite long flight time and resource conservation. In recent years, with the improvements in solar cell efficiency, lightweight structural design, microelectronics, and automation control technology, more and more countries and organizations have conducted research on solar-powered aircraft. Solar cells are an important component of solar-powered aircraft systems. Perovskite solar cells have the advantages of high flexibility, lightweight, and high specific power, making them the ideal material for solar-powered aircraft wings. To receive as much sunlight radiation as possible and improve the lift-to-drag ratio of the entire aircraft, solar-powered aircraft have a high aspect ratio, making them more sensitive to changes in airflow. Therefore, the stability zones and vibration of perovskite solar cell (PSC) panels in subsonic airflow were studied for the first time. Additionally, to enhance the novelty of the paper and present more comprehensive results, the effect of the nanomaterial properties of one-layered perovskite solar cell substrates reinforced by carbon nanotubes and graphene platelets on the flutter and divergence phenomenon was investigated. To accurately simulate the displacement field after the composite perovskite panel is bent, Reddy's high-order shear deformation theory was adopted. The PSC panel's aerodynamic-elastic coupling motion equations were established by combining Hamilton's principle with the linear potential flow theory and the Bernoulli equation. The dynamic equations were discretized using a meshless numerical method to obtain the generalized multi-parameter characteristic equations. The obtained natural frequencies and critical divergence speeds were compared with published literature to validate the correctness of the aerodynamic model and laminated board model. The divergence instability and flutter instability of the PSC nanocomposite panel were investigated for changing the thickness, nano-fillers, porosity, laying angle, and aspect ratio of the substrate. We showed, that the PSC panel has a divergence instability state first, and then flutter instability appears. Additionally, the substrate nanomaterial parameters of the PSC panel significantly affect shifting divergence and flutter zones. Interestingly, the corresponding natural frequency increases when flutter occurs with an increase in the porosity volume.

FedBoosting: Federated learning with gradient protected boosting for text recognition

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protection. The Federated Learning (FL) framework enables the collaborative learning of a shared model without necessitating the centralization or sharing of data among the data proprietors. Nonetheless, in this paper, we demonstrate that the generalization capability of the joint model is suboptimal for Non-Independent and Non-Identically Distributed (Non-IID) data, particularly when employing the Federated Averaging (FedAvg) strategy as a result of the weight divergence phenomenon. Consequently, we present a novel boosting algorithm for FL to address both the generalization and gradient leakage challenges, as well as to facilitate accelerated convergence in gradient-based optimization. Furthermore, we introduce a secure gradient sharing protocol that incorporates Homomorphic Encryption (HE) and Differential Privacy (DP) to safeguard against gradient leakage attacks. Our empirical evaluation demonstrates that the proposed Federated Boosting (FedBoosting) technique yields significant enhancements in both prediction accuracy and computational efficiency in the visual text recognition task on publicly available benchmarks.

Numerical Methods for Time-Domain Responses of the Frequency-Independent Damped System

The complex damping model can only be used to calculate the steady-state responses, while the transient responses are divergent. Based on the complex damping model, the Hilbert transform is introduced to establish a hysteretic damping model, eliminating the divergence phenomenon. However, with the increase of the loss factor, the damped natural frequency also increases. To overcome this shortcoming, a frequency-independent damping model is proposed based on the hysteretic damping model. However, traditional time-domain methods are no longer applicable to frequency-independent damping models. Therefore, the transient response and steady-state response are separated, and the assumption of external excitation acceleration is introduced. Time-domain methods-based linear polynomial assumption, quadratic polynomial assumption and trigonometric function assumption are proposed, respectively. Numerical examples show that the time-domain methods based on linear polynomial assumption and quadratic polynomial assumption have high computational efficiency. But these two methods cannot take into account the vibration frequency of external excitation acceleration. Hence, the computational accuracy is low. Compared with them, the time-domain method based on trigonometric function assumption has the lowest computational efficiency and the highest computational accuracy.

Thermo-mechanical vibration and stability behaviors of bi-directional FG nano-pipe conveying fluid

This work focuses on the thermo-mechanical modeling of a size-dependent pipe made of bi-directional functionally graded (FG) materials and conveying fluid through its inner wall. The material properties of this FG nano-scale pipe along radial and axial directions are assumed to be separately varying according to the power-law and exponential distributions. The governing equations of the FG nano-scale pipe are derived according to nonlocal strain gradient theory and Hamilton’s principle, and interactions between the size-dependent pipe and its inner fluid are revealed via size-dependent constitutive relation. The frequency response of the pipe and its critical flow velocity are calculated via generalized differential quadrature method. After validation of the proposed model by comparing with previous works, a detailed parametric study is conducted to fully examine the coupling effect of material distribution and various parameters including small size, aspect ratio, boundary constraint, temperature change and flow velocity on the frequency response of the FG nano-pipe. Simultaneously, the critical flow velocity of inner fluid under which the divergence phenomenon appears is also predicted and physically explained in detail. Numerical results show that bi-directional FG design is especially beneficial to improve the stability of FG nano-pipe, which can provide a guide to design advanced micro/nanofluidic devices for bio-engineering applications.

Advanced Imaging for Robotic Bronchoscopy: A Review.

Recent advances in navigational platforms have led bronchoscopists to make major strides in diagnostic interventions for pulmonary parenchymal lesions. Over the last decade, multiple platforms including electromagnetic navigation and robotic bronchoscopy have allowed bronchoscopists to safely navigate farther into the lung parenchyma with increased stability and accuracy. Limitations persist, even with these newer technologies, in achieving a similar or higher diagnostic yield when compared to the transthoracic computed tomography (CT) guided needle approach. One of the major limitations to this effect is due to CT-to-body divergence. Real-time feedback that better defines the tool-lesion relationship is vital and can be obtained with additional imaging using radial endobronchial ultrasound, C-arm based tomosynthesis, cone-beam CT (fixed or mobile), and O-arm CT. Herein, we describe the role of this adjunct imaging with robotic bronchoscopy for diagnostic purposes, describe potential strategies to counteract the CT-to-body divergence phenomenon, and address the potential role of advanced imaging for lung tumor ablation.

Leader-Following Cluster Consensus of Multiagent Systems With Measurement Noise and Weighted Cooperative–Competitive Networks

Leader-following cluster consensus is investigated for multiagent systems with weighted cooperative–competitive networks and measurement noise. A stochastic approximation protocol is proposed for interactively balanced and sub-balanced networks, and pinning control is introduced to deal with the divergence phenomenon in interactively unbalanced networks. With these protocols, sufficient conditions for reaching a strong mean-square leader-following cluster consensus are established for all the three types of networks, which are also extended to the cases without measurement noise. Numerical examples illustrate the effectiveness of the proposed protocols and theoretical analysis.

Improved Nonlinear Finite-Memory Estimation Approach for Mobile Robot Localization

In this article, we present a new mobile robot localization algorithm. The Kalman filter (KF) and particle filter (PF), which are widely used in localization problems, may show poor performance or the divergence phenomenon due to the existence of disturbances or missing measurements. This article proposes an improved nonlinear finite-memory estimation (INFME) algorithm to overcome the performance degradation problem caused by linearization errors in existing finite-memory (FM) estimation methods. To ensure robustness against noise and disturbances, the INFME algorithm was designed with an FM structure based on the minimization of an objective function, which induces reduction of adverse effects of disturbances including the linearization error. It showed superior accurate, robust, real-time performance in real mobile robot localization experiments. The accuracy and robustness of the new algorithm were verified using harsh experimental scenarios including a kidnapped robot problem and a situation in which multiple missing measurements occurred.

Modeling and Simulation of Wideband Radio Waves Traveling Through Plasma Coupling With Uniform Magnetic Fields

Propagation behaviors of radio waves through plasma are an essential issue in both science and engineering research. Many numerical methods are proposed to simulate the behaviors, in which the scattering matrix method (SMM) is one of the most popular techniques. However, the SMM available still has important shortcomings existing in the physical modeling aspect, which will result in numerical divergence in calculations. With the analysis of the scattering matrix theory, a new physical modeling method is presented to eliminate this problem. Then, based on the reconstructed model, a modified SMM is developed. Numerical examples indicate its correctness and ability to overcome the divergence phenomenon. Finally, the method is applied to investigate the propagation behaviors of radio waves in plasma boards coupling with uniform magnetic fields, and some particular behaviors are found, such as transmission stopband of the plasma plate. These results can present a new approach for plasma simulation related to the electromagnetic computation and provide further insights into plasma effects on propagations of electromagnetic waves (EMs).

PH-responsive wormlike micelles for acid stimulation in carbonate reservoirs

Stimulation is a technique used for damage removal and to increase the reservoir permeability. For carbonate reservoirs, this process is benefited by the reaction of the fluid with the matrix. However, generally the process is restricted to occurs only in regions of high permeability. In order, to solve this problem, acid diverting fluids based on viscoelastic surfactants (VES) are commonly used. The divergence in this case is caused by viscosity changes of VES triggered by chemical stimulus, such as pH variations. Formulations based in VES can be prepared to respond to [H+] and [Ca2+], tuning the rheological properties of the surfactant solution and producing polymer-like aggregates named as wormlike micelles. When increasing the viscosity due to these responses, the divergence phenomenon occurs, and the fluid changes its direction heading regions of low permeability. As consequence, desirable corrosion patterns known as wormholes are formed. A laboratory scale investigation might be required to properly adopt such special fluids, so a protocol for neutralization of acid fluids was stablished and tested by using two commercial fluids. The rheology of the fluids upon different neutralizing degrees were studied and simulates the rheological behavior when they are flowing and reacting into the carbonate matrix. Additionally, several generations of a new responsive diverting fluid based on VES, named as QuoVadis (from the Latin “Whither goest thou”), were developed and their rheological characteristics, in linear and non-linear regimes, were obtained in different H+ and Ca2+ concentrations. Such formulations are subject of a patent application filed in the National Institute of Industrial Property of Brazil (INPI patent application number BR102020006183-6). The QuoVadis samples were submitted to the neutralizing protocol and their rheological behaviors were considered to evaluate their performance in several core-flooding experiments. The statistical results demonstrate that the desirable dominant wormhole is formed, which was visualized in one of the outcrops through NMR images.

3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing.

Traditional digital geometry mainly focuses on local geometric features such as curvature and normal of 3D models. Although the curvature can describe the geometric curvature of the model surface, these local geometric properties cannot describe the global functional structure and associated properties of the 3D model. The purpose of this paper was to study the accurate splicing of cultural relic fragments based on the intrinsic structural features of the 3D model, such as geometry, texture, and function, whose method is the key to realizing the virtual reconstruction of damaged cultural relics. In this paper, a method for analyzing the intrinsic structure of 3D point cloud data is proposed, the feature representation method of 3D discrete curves and surfaces is studied, and a method for identifying geometric features of 3D point clouds based on the similarity measure of the principal curvature is proposed, which realizes the effective extraction of geometric features of the 3D point cloud model of cultural relics. By calculating the visual curvature distribution of the model under multiscale constraints, the effective extraction of the structural primitives of cultural relics with rich surface noise is realized. The experimental results in this paper show that the initial matching time is 1.416 seconds, the final matching time is 1.555 seconds, the average number of iterations is 13, the average stitching error is 1.7233 mm, and the standard deviation is 1.0265 mm. The experimental data show that the algorithm proposed in this chapter has good convergence characteristics and effectively avoids the divergence phenomenon that is easy to occur in the existing stitching algorithms.

Design, simulation and measurement of a single-band high-transmittance all-dielectric metamaterial filter

Metamaterial filters are widely used in various photo-electronic devices because of the rich resonance behaviors. Transmission and thermal resonance properties are important factors to measure the performance of photo-electronic devices based on metamaterial filters. It is important to accurate measure the thermal resonance performance of the metasurface to improve the thermal management of these photo-electronic devices, reduce heat loss, and delay device damage. In this paper, a metamaterial filter is proposed and verified in the 0.5–3.0 THz range. A transmission peak of 94.3% at resonance frequency 1.43 THz is achieved by the proposed filter samples at room temperature. The thermal diffusivity (8.28) and thermal resistance (7.58) of the metasurface are measured by using thermal pulses, which can be improved effectively by separating the sample and the standard substrate with a resin layer (TIM layer). The intensities of the applied electric field and heat pulse have significant influence on the thermal diffusivity and thermal resistance of the metasurface. When the electric field exceeds 42 km/mm or the heat pulse exceeds 1.3 mJ, the thermal diffusivity and resistance of the metasurface appear obvious divergence phenomenon. Moreover, the thermal resonance properties of the proposed sample is higher than that of the stacked plate structure.

Size-dependent vibrational behavior of embedded spinning tubes under gravitational load in hygro-thermo-magnetic fields

In the present investigation, with the aim of performance improvement of size-dependent bi-gyroscopic structures, the vibrational behavior of spinning small-scale tunes conveying fluid embedded in various foundations subjected to distributed tangential load and hygro-thermo-magnetic environments by including gravitational effect is investigated. The modified couple stress theory is used to study the microscale tube, and the modified nonlocal theory is utilized to model the nanoscale tubes. A parametric investigation is also conducted to highlight the impacts of various key factors such as gravity, flow profile modification factor, fluid velocity, spinning speed, substrate coefficients, boundary conditions, size-dependent parameters, and environmental attacks on divergence and flutter thresholds of the structure. The dynamical equations are solved using Laplace transformation as well as Galerkin discretization techniques, and forward and backward frequencies are identified accordingly. Meanwhile, the instability borders of the system are obtained analytically. Campbell and stability diagrams, and the time history of the system, are acquired. The results revealed that contrary to influences of gravity, magnetization, and size-dependent parameters, the compressive tangential load and humidity have decreasing effects on the vibrational frequencies and make the system prone to experience static and dynamical instabilities. Moreover, it is determined that applying the viscous foundation eliminates the re-stabilization zone in the system stability evolution, and after the occurrence of the divergence phenomenon, the system immediately undergoes the flutter instability.

Convergence of residential property prices in the Szczecin agglomeration in the context of the COVID-19 pandemic

The outbreak of the COVID-19 pandemic has left a great mark on all of humanity. In addition to the immediate threat to health and life, the virus in a way also infected other spheres of human life. The lockdowns, which lasted for many weeks, had a very big impact on various sectors of the economy. Most of them experienced a severe slowdown or recession. However, there were areas that benefited from the fact that the people in many countries stayed at home and worked remotely. Examples of such areas include the computer and tablets market, online stores, and delivery companies. This proves that even in difficult circumstances like a pandemic, we are not facing a total global recession. In such a context, it is worth looking at the impact of the COVID-19 pandemic on the real estate market. This area of the economy can experience the impact of the virus in different ways. On the one hand, a general increase in global uncertainty can cause certain investment projects to freeze or be abandoned. Lockdowns cause difficulties in accessing workers at construction sites. Populations losing their jobs during a pandemic have difficulty paying rent or mortgage payments. On the other hand, rising inflation and low interest rates on bonds and bank deposits may encourage people to protect their savings by buying property. Remote working increases the demand for larger apartments that need to serve as offices in addition to residential functions. Remote work also means not having to commute to workplaces, which may encourage people to move to cheaper locations without having to give up a well-paying job in a larger but also more expensive city. These two groups of impulses are contradictory and may have a completely different impact on the shape of the real estate market understood as the structure of prices and the number of transactions concluded. The study is part of a broader research effort to assess the impact of the COVID-19 pandemic on various areas of the economy. Previous research conducted in the Szczecin agglomeration proved the existence of the price divergence phenomenon. The main city of the agglomeration showed a tendency to pull in the economic potential from the region, which translated into a faster increase in real estate prices in the capital of the region in comparison with the surrounding cities. The aim of this study is to verify whether the pandemic and the socio-economic processes triggered by it, such as the development of remote working, changed the process of divergence into a process of convergence, but whether the faster growth of residential property prices in the main city of the agglomeration, which started years ago, has not been stopped. For this purpose, α-convergence and σ-convergence models were used. The study covers the period 2015-2021. The study area is the city of Szczecin and the four largest cities adjacent to the capital of the West Pomeranian Voivodeship in Poland.

Analysis of Difference in Household Debt across Regions of Thailand

This study analyzed the determinants of household debt in Thailand at both the regional and the national levels using the panel data of 76 provinces over the years 2009–2017. The Panel Quantile Regression Model was employed to enable the analysis of the formation of household debt ranging from low to high levels. The findings indicate that household indebtedness in different regions has been shaped by a variety of factors, and that households in the same region with different levels of debt burden would experience different impacts or outcomes. We also tested the convergence of household debt, which produced the thought-provoking finding that household debt convergence failed to occur at both the national and the regional levels, while household debt divergence was found instead at the statistical significance level in some regions. The growing debt divergence phenomenon might be an outcome indicator of the unequal access to credit sources among different households in Thai society.

Repetitive Learning Control for Control-Affine Systems With Time Delays

This article presents a repetitive learning control method, which deals with both parametric and nonparametric uncertainties involved in the control-affine systems with time delays. The desired control is taken as a time-varying parameter, and a learning algorithm is applied for estimation. The time-delay nonlinearity and the state-dependent input gain are handled as the nonparametric uncertainties, for which the adaptive bounding technique is adopted such that the requirement for the knowledge about the system undertaken can be reduced, and the troublesome time delays are effectively treated. The presented control method only needs the lower bound of the input gain is to be known as a priori. Furthermore, it is shown applicable to systems with multiple time delays. The fully saturated learning algorithms are utilized to solve the positive accumulation of estimates so that the possible divergence phenomenon can be avoided. The convergence of the tracking error is established, while the boundedness of the variables in the closed-loop system is assured. The numerical results are presented to verify the effectiveness of the proposed learning control method.

Research on Kalman Filter for One-dimensional Discrete Data

Kalman filter processes the input and observation signals with noise on the basis of linear state space representation to obtain the system state or real signal. In the one-dimensional model, due to the lack of multi-dimensional description of the target, the prior estimate of the state is often the measured value of the previous moment. When the target state changes, the divergence phenomenon will occur. Aiming at the problem that the one-dimensional traditional Kalman filter lacks the target observation dimension, which leads to the divergence or imprecision of the filter, this paper focuses on improving the estimation method of the target state, and proposes a real-time prediction model based on the cascade structure. This model can improve the response of Kalman filter to the change of target state and dynamically adjust the Kalman iterative domain to improve the measurement accuracy. The digital signal filtering simulation is carried out and the performance of the filter is verified based on LabVIEW. Experimental results show that the algorithm can maintain the accuracy and real-time performance of filtering when only one dimension observation results are obtained.

Holocene landscape evolution in the Baza Basin (SE-Spain) as indicated by fluvial dynamics of the Galera River

The concrete relationships between fluvial system behavior and potential influencing factors that are, among others, climate forcing, tectonics, and human activity are a key issue in geomorphological research. In this regard, especially the Iberian Peninsula is an area of great interest because its landscapes are highly sensitive towards climate changes and anthropogenic impact. Nowadays, the Iberian Peninsula reveals a strongly heterogeneous and spatially fragmented climate configuration. This should give rise to disparate behavior of fluvial geomorphic systems considering that climate is generally assumed the most important trigger of fluvial dynamics. In fact, river systems located in more humid and more arid regions in Iberia often reveal deviating patterns of Holocene floodplain evolution. This raises the question of whether these patterns were actually caused by a different climate history or if, alternatively, other factors might have been responsible. In this study, we investigated the Holocene floodplain evolution of the Galera River that is located in the upland of Eastern Andalucía (SE-Spain) named Baza Basin. A combination of detailed stratigraphic profile logging and close-meshed radiocarbon dating revealed that Holocene river dynamics generally followed the regional climatic development, which proves the Galera floodplain record to be a valuable archive of Holocene landscape evolution. However, we demonstrate that fluvial dynamics of the Galera system are hardly comparable to other river systems in Iberia even if the climate evolution was not so different. Our results suggest that in river systems with different basic conditions and catchment-specific configurations, similar climatic influences may lead to deviating fluvial process regimes (divergence phenomenon) because of substantial imprints of other parameters such as geological substratum, relief composition, tectonics, or human interventions.

Innovation Feedback Kalman Filter

The conventional Kalman filter demands complete knowledge of the actual filter model, which is usually inaccessible in practical problems. When dealing with the filtering problem with uncertainty, any improper prediction of the uncertainty may degrade the filtering performance and even lead to divergence phenomenon. In this paper, the innovation feedback Kalman filter, which introduces the innovation feedback controller to the Kalman filter equations, is newly proposed on the basis of automatic control theory to address the filtering problem with uncertainty and is different from other filtering methods. By studying the estimate error equations, the estimate bias is extracted and its propagation mechanism is formulated. The estimate bias propagation equations reveal that eliminating the estimate bias essentially equivalents to an output regulation problem with uncertain exosystem. And a concise yet effective innovation feedback controller is subsequently given as an example. The proposed method is applied to one-dimension target tracking scenario and its high estimation accuracy performance and good stability are simulated through a comparative analysis with the ideal filtering results.

Adaptive filter based on Monte Carlo method to improve the non-linear target tracking in the radar system

This article deals with the problem of degraded tracking performance of a high non-linear target in a radar system, well known by the divergence phenomenon. In our study, we aim to improve the target state estimation to imitate the tracking scenario as well as avoid the last cited undesirable phenomenon, generated during the non-linear measurements filtering, once using extended KALMAN filter. To overcome this issue, we have implemented a new approach based on the adaptive Monte Carlo (AMC) algorithm to replace the traditional method as is known by the extended KALMAN filter (EKF). The obtained experimental results showed a challenging remediation. Where, the AMC converges towards the accurate state estimation. Thus, more efficient than extended KALMAN filter. The experimental results prove that the designed system meets the objectives set for AMC referring to an experimental database obtained by a radar system, using MATLAB software development framework.