Influence Of Perturbations Research Articles

The Chemistry of Phytoplankton.

Phytoplankton have a high potential for CO2 capture and conversion. Besides being a vital food source at the base of oceanic and freshwater food webs, microalgae provide a critical platform for producing chemicals and consumer products. Enhanced nutrient levels, elevated CO2, and rising temperatures increase the frequency of algal blooms, which often have negative effects such as fish mortalities, loss of flora and fauna, and the production of algal toxins. Harmful algal blooms (HABs) produce toxins that pose major challenges to water quality, ecosystem function, human health, tourism, and the food web. These toxins have complex chemical structures and possess a wide range of biological properties with potential applications as new therapeutics. This review presents a balanced and comprehensive assessment of the roles of algal blooms in generating fixed carbon for the food chain, sequestering carbon, and their unique secondary metabolites. The structural complexity of these metabolites has had an unprecedented impact on structure elucidation technologies and total synthesis, which are highlighted throughout this review. In addition, the influence of biogeochemical environmental perturbations on algal blooms and their influence on biospheric environments is discussed. Lastly, we summarize work on management strategies and technologies for the control and treatment of HABs.

Frequency-locked Si3N4 microring for Doppler frequency shift detection

In this paper, we propose to use a homemade all-fiber Si3N4 microring as the frequency discriminator for Doppler frequency shift detection. The full width at half maximum of the microring is 361.83 MHz, which covers the dynamic range of ± 70 m/s for wind measurement applications. By introducing a time-division multiplexing method, we have achieved the frequency locking of the microring to the central frequency of the laser source, which effectively stabilizes the measurement accuracy against perturbations such as temperature fluctuations. By alternatively upshifting and downshifting the pulses, a dual-frequency lasing scheme has been designed to realize the double-edge technique for frequency shift detection. A commercial single-photon detector with 25% quantum efficiency and approximately 1300 Hz dark count rate is used to detect the backscattered signals, which circumvents the need for bulky and expensive superconducting single-photon detectors. The proposed system is validated through an outdoor wind speed detection experiment using a reference anemometer. The experiment results demonstrate the feasibility of using microring as the frequency discriminator and that the precise frequency locking control is able to improve the measurement accuracy to the state-of-the-art level under the influence of perturbations, which highlights the potential for highly integrated direct detection Doppler wind lidar design.

Transmission characteristics of vortex light superposition in atmospheric turbulence disturbed by plane acoustic waves

Herein, we derive the expression for the atmospheric refractive index structure constant under the influence of planar acoustic wave perturbations under the influence of the acoustic field on the refractive index and energy of the atmosphere. Utilizing the low-frequency compensated power spectrum inversion technique, we simulate the refractive index power spectrum of atmospheric turbulence perturbed by a planar acoustic wave. Numerical analysis is conducted on the transmission characteristics of the vortex light superposition states in atmospheric turbulence perturbed by a plane acoustic wave under different acoustic wave transmission heights, acoustic pressure amplitudes, and frequencies. Results indicate that introducing an acoustic field induces fluctuations in the atmospheric refractive index structure constant, with a more pronounced impact on the refractive index than on energy. Compared with the sole consideration of the impact of the acoustic field on the atmospheric refractive index, incorporating its effect on atmospheric energy results in a decrease in the atmospheric refractive index structure constant. The impact of the acoustic field on atmospheric turbulence is directly proportional to both acoustic pressure amplitude and frequency. The influence of the acoustic field on the transmission properties of the vortex optical superposition state varies with different acoustic transmission distances. Consequently, the transmission characteristics of the vortex light superposition state can be actively modulated according to varying acoustic wave transmission distances. This study offers a theoretical basis for modulating the optical field transmission characteristics via acoustic fields.

The missing rings around Solar System moons

Context. Rings are complex structures that surround various bodies within the Solar System, such as giant planets and certain minor bodies. While some formation mechanisms could also potentially promote their existence around (regular or irregular) satellites, none of these bodies currently bear these structures. Aims. We aim to understand the underlying mechanisms that govern the potential formation, stability, and/or decay of hypothetical circumsatellital rings (CSRs) orbiting the largest moons in the Solar System. This extends to the exploration of short-term morphological features within these rings, providing insights into the ring survival timescales and the interactions that drive their evolution. Methods. To conduct this study, we used numerical N-body simulations under the perturbing influence of the host planet and other moon companions. Results. We found that, as suspected, moons with a lower Roche-to-Hill radius can preserve their rings over extended periods. Moreover, the gravitational environment in which these rings are immersed influences the morphological evolution of the system (e.g. ring size), inducing gaps through the excitation of eccentricity and inclination of constituent particles. Specifically, our results show that the rings of Iapetus and Rhea experience minimal variations in their orbital parameters, enhancing their long-term stability. This agrees with the hypothesis that some of the features of Iapetus and Rhea were produced by ancient ring systems, for example, the huge ridge in the Iapetus equator as a result of a decaying ring. Conclusions. From a dynamical perspective, we found that there are no mechanisms that preclude the existence of CSRs, and we attribute their current absence to non-gravitational phenomena. Effects such as stellar radiation, magnetic fields, and the influence of magnetospheric plasma can significantly impact the dynamics of constituent particles and trigger their decay. This highlights the importance of future studies of these effects.

Amplitude variation with azimuth direct inversion for effective pressure-sensitivity parameters in horizontal transversely isotropic media

Effective pressure prediction is of great significance for fracturability evaluation in unconventional reservoirs. Quantitative characterization of effective pressure remains a great challenge in the inversion prediction of subsurface media. Our goal is to develop a novel Bayesian amplitude varying with azimuth (AVAZ) inversion method to estimate fracture weaknesses and effective pressure for a horizontal transversely isotropic (HTI) medium. Using Schoenberg’s linear slip model and pore space compressibility theory, an anisotropic saturated stiffness matrix directly characterized by effective pressure is derived based on a weak-contrast interface separating two weakly anisotropic half-spaces of HTI medium. Based on the perturbation stiffness matrix and scattering theory, we derive a PP-wave reflection coefficient approximation directly characterized by fracture weaknesses and effective pressure-sensitive parameters, which establishes a quantitative relationship between seismic reflection coefficient and effective pressure. The analysis of accuracy and sensitivity is performed to verify the reliability and applicability of the constructed reflection coefficient. And then, the azimuth-amplitude-difference forward solver is constructed based on seismic reflection coefficients to eliminate the influence of isotropic background perturbations on seismic reflection coefficients and reduce the condition number of the forward solver. Finally, we present a novel Bayesian azimuth-amplitude-difference AVAZ inversion method for transversely isotropic media constrained by Cauchy and Gaussian regularization to directly estimate the fracture weaknesses and effective pressure-sensitive parameters. The application of synthetic data and field data verifies that the inversion results are in good agreement with the actual data, indicating that the method can effectively realize the stable prediction of anisotropic parameters and effective pressure-sensitive parameters of complex media.

Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

AbstractThis study investigated changes in motivation, self‐efficacy beliefs, and a range of emotions, including enjoyment, hope, pride, curiosity, anxiety, boredom, apathy, confusion, and shame, from a complex dynamic systems theory (CDST) perspective over a 2‐year period in the Hungarian English as a Foreign Language (EFL) context. Using the same questionnaire, we collected data four times throughout 4 semesters from 101 participants studying English in two Hungarian high schools. For data analysis, we used latent growth curve modeling (LGCM) to detect the group‐level changes in learners’ motivation, self‐efficacy, and emotions. We also employed dynamic cluster analysis to identify trends in learners’ trajectories regarding these variables. In our panel data, linear models described the data well concerning the ought‐to second language (L2) self, language learning experience, boredom, apathy, and confusion, and for enjoyment, curiosity, anxiety, and shame, nonlinear models had the best fit. We could also identify trajectories depicting attractor states and learner paths that featured influences of perturbations.

Excitation of high-quality quasi-BIC toroidal mode in a lattice perturbed terahertz metasurface

The bound state in continuum (BIC) is a phenomenon that describes the existence of nonradiative modes (dark modes) embedded in the continuum frequency range. However, an ideal BIC cannot be detected experimentally. The BIC can be transformed into a quasi-BIC by establishing a leaky channel to the radiation continuum. In this study, instead of the conventional asymmetric split ring resonator structure, a sharp quasi-BIC mode is excited in a symmetric split ring resonator (SRR) metasurface by the perturbation of the lattice constant of the unit cell via changing the interspacing distance between two adjacent SRRs. The quality factor of the quasi-BIC mode can be tuned by varying the interspacing of two SRRs, while the resonance frequency of the quasi-BIC mode remains stable. An eigenmode analysis confirms the presence of the quasi-BIC mode, while the ab initio Fano theory and a coupled oscillator model elucidate the radiative and nonradiative coupling mechanisms. The influence of geometric perturbations on the quasi-BIC mode is quantitatively assessed through the extracted fitting parameters, providing insights into the transition from the dark mode (ideal BIC) to the quasi-BIC mode. The terahertz time domain spectroscopy measurement demonstrates a signature of the quasi-BIC resonance mode as a result of the band folding in the first Brillouin zone induced by the doubling of the lattice constant.

VUV imaging of type-I ELM filamentary structures and their temporal characteristics on EAST

In the H-mode experiments conducted on the Experimental Advanced Superconducting Tokamak (EAST), fluctuations induced by the so-called edge localized modes (ELMs) are captured by a high-speed vacuum ultraviolet (VUV) imaging system. Clear field line-aligned filamentary structures are analyzed in this work. Ion transport induced by ELM filaments in the scrape-off layer (SOL) under different discharge conditions is analyzed by comparing the VUV signals with the divertor probe signals. It is found that convective transport along open field lines towards the divertor target dominates the parallel ion particle transport mechanism during ELMs. The toroidal mode number of the filamentary structure derived from the VUV images increases with the electron density pedestal height. The analysis of the toroidal distribution characteristics during ELM bursts reveals toroidal asymmetry. The influence of resonance magnetic perturbation (RMP) on the ELM size is also analyzed using VUV imaging data. When the phase difference of the coil changes periodically, the widths of the filaments change as well. Additionally, the temporal evolution of the ELMs on the VUV signals provides rise time and decay time for each single ELM event, and the results indicate a negative correlation trend between these two times.

Average bit-error rate analysis of an inter-satellite optical communication system under the effect of perturbations.

The benefits of higher data transmission rates, extensive communication frequency bandwidths, reduced power consumption, and enhanced anti-interference capabilities make inter-satellite optical communication (ISOC) a promising technology for the future. However, during orbital motion, satellites are subjected to external perturbation forces, which affect the performance of the ISOC system. This paper establishes an ISOC system consisting of two co-orbital satellites orbiting the Earth under the influence of perturbations. For what we believe to be the first time, the probability density function (PDF) of the radial displacement caused by perturbations is introduced. Subsequently, a PDF detailing the inter-satellite pointing errors, while accounting for the effects of perturbations and platform vibrations, is presented. Moreover, pointing errors and plasma absorption are considered in the PDF derivation process for the end-to-end ISOC system. The closed-form expression for the average bit-error rate (BER) of the proposed system is derived using Meijer's G function. Simulation results are provided to validate the theoretical expression. The findings show that key parameters associated with perturbations significantly influence the PDF of inter-satellite pointing errors and the average BER of the proposed ISOC system.

Rethinking the validity of perturbation in single-step adversarial training

The neural network model has the drawback of making incorrect predictions under the influence of slight adversarial perturbations. Single-step adversarial training (AT) is an effective tool to bring adversarial robustness for the model to resist such attack. From the perspective of perturbation setting in training, we identify a conflict between the pursuit of greater robustness and the need to prevent catastrophic overfitting within the AT framework. To get out of this dilemma, we delve into the impact of perturbations on human visual perception. Our analysis reveals that examples containing more misleading features should be assigned a smaller perturbation magnitude to preserve subtle yet significant features. Conversely, examples encompassing more relevant features should be assigned a larger perturbation magnitude, enabling the model to adapt to stronger attacks effectively. Motivated by these insights, we propose a concise refinement to the AT framework to unleash its full potential for single-step AT. Instead of employing a fixed perturbation magnitude, we introduce a “band” of magnitudes, allowing each example to select an appropriate magnitude based on its visual characteristic. Through extensive experiments conducted on three datasets, we demonstrate the efficacy of our proposed strategy. Our approach not only improves the model’s robustness and prevents catastrophic overfitting but also effectively mitigates robust overfitting—an issue that has remained unresolved in the context of single-step AT, marking a significant advancement in the field.

Influence of Large Perturbations on Pattern Formation in Phase Separation by Directional Quenching

Influence of Large Perturbations on Pattern Formation in Phase Separation by Directional Quenching

A physical‒data-driven combined strategy for load identification of tire type rail transit vehicle

Tire load is one of the basic input parameters required for vehicle design and safety evaluation. Identifying the tire load with high accuracy is of great significance. However, the direct measurement of tire load is often high-cost and complex. Meanwhile, load identification solely based on physical measurement or data has great limitations of low accuracy and low robustness. This paper proposes a physical‒data-driven combined load identification strategy that includes an extended Kalman filter (EKF) and a data-driven correction model. These two models are configured in series. Derived from a physical state-space model of the vehicle dynamics, the EKF is used for the preliminary identification of the load. The data-driven model extracts the spatial and temporal characteristics of signals through the convolutional neural network and bidirectional gated recurrent unit, and then predicts the errors of the extended Kalman filter and corrects the identified results. The presented strategy is applied to an APM300 rubber wheel vehicle for load identification. The results have shown that the physical‒data-driven combined strategy can reduce the influence of parameter perturbation and improve the identification accuracy (6.4%). Thanks to organically combining the physical- and data-driven methods and integrating the rules and experience of the entire system, the strategy has strong generalization performance. Compared with traditional algorithms, the presented strategy could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.

Thermal convection subjected to perturbations from the bottom of a top open cavity

Perturbations are very common in the transition and heat transfer of thermal convection in nature and industry. Accordingly, thermal convection on a top-open cavity subjected to periodic and random perturbations is investigated using three-dimensional numerical simulation. A great number of numerical experiments are performed at various Rayleigh numbers and a fixed Prandtl number of 0.71 by introducing periodic and random numerical perturbations. Numerical results demonstrate that there exists the effect of periodic perturbations on the transition route over 3.5 × 103 ≤ Ra ≤ 8.5 × 104. That is, the transition route to chaos is sensitive to the amplitude of random perturbations for, e.g., 0.01 ≤ Ar ≤ 0.05, which is also characterized. Furthermore, heat transfer enhancement under periodic and random perturbations is quantified with the scaling law. This study sheds new light on the influence of periodic and random perturbations on thermal convection on the top-open cavity below heating. The possibility to control heat transfer is revealed by introducing random perturbations on the bottom of the top-open cavity.

Distributed control of spacecraft formation under [formula omitted] perturbation in the port-Hamiltonian framework

To control the relative position and relative velocity of spacecraft formation, a time-varying nonlinear relative motion model under J2 perturbation in an elliptical orbit is established in the port-Hamiltonian (PH) framework, and a distributed control law for spacecraft formation is developed using the consensus algorithm for parameter estimation and the passivity-based control (PBC) method based on the state-error interconnection and damping assignment (IDA) technique. First, the influence of J2 perturbation on the potential energy and orbit parameters in the model is considered when the relative motion model is built in the PH frame, and the expression of the relative motion acceleration under J2 perturbation is given. Second, to solve the problem that the state of the chief spacecraft cannot be directly obtained from the deputy spacecraft under distributed communication, a consensus-based parameter estimation method is introduced, the estimated parameter of the chief spacecraft is applied to the relative motion model in the PH frame, and a state error model with the estimated parameters derived from the consistency algorithm is established. Then, after the stability analysis is conducted on the desired PH system containing the estimated parameter values, the desired Hamiltonian energy function with the estimated parameters is designed according to the time-varying errors of the relative equilibrium states of the deputy spacecraft and the errors between deputy spacecraft, and the distributed control law of spacecraft formation is derived based on the state-error IDA-PBC method. Finally, the expected relative motion trajectory designed based on the TH equation is used to simulate a spacecraft formation under J2 perturbation, and the results indicate that the spacecraft can quickly converge to the expected formation under the influence of the control law.

INeAT: an artifact-suppressed and resolution-enhanced computed tomography through iterative neural adaptive tomography.

Computed tomography (CT) with its remarkable capability for three-dimensional imaging from multiple projections, enjoys a broad range of applications in clinical diagnosis, scientific observation, and industrial detection. Neural adaptive tomography (NeAT) is a recently proposed 3D rendering method based on neural radiance field for CT, and it demonstrates superior performance compared to traditional methods. However, it still faces challenges when dealing with the substantial perturbations and pose shifts encountered in CT scanning processes. Here, we propose a neural rendering method for CT reconstruction, named iterative neural adaptive tomography (INeAT), which incorporates iterative posture optimization to effectively counteract the influence of posture perturbations in data, particularly in cases involving significant posture variations. Through the implementation of a posture feedback optimization strategy, INeAT iteratively refines the posture corresponding to the input images based on the reconstructed 3D volume. We demonstrate that INeAT achieves artifact-suppressed and resolution-enhanced reconstruction in scenarios with significant pose disturbances. Furthermore, we show that our INeAT maintains comparable reconstruction performance to stable-state acquisitions even using data from unstable-state acquisitions, which significantly reduces the time required for CT scanning and relaxes the stringent requirements on imaging hardware systems, underscoring its immense potential for applications in short-time and low-cost CT technology.

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Security in Transformer Visual Trackers: A Case Study on the Adversarial Robustness of Two Models.

Visual object tracking is an important technology in camera-based sensor networks, which has a wide range of practicability in auto-drive systems. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differentially weights the significance of each part of the input data. It has been widely applied in the field of visual tracking. Unfortunately, the security of the transformer model is unclear. It causes such transformer-based applications to be exposed to security threats. In this work, the security of the transformer model was investigated with an important component of autonomous driving, i.e., visual tracking. Such deep-learning-based visual tracking is vulnerable to adversarial attacks, and thus, adversarial attacks were implemented as the security threats to conduct the investigation. First, adversarial examples were generated on top of video sequences to degrade the tracking performance, and the frame-by-frame temporal motion was taken into consideration when generating perturbations over the depicted tracking results. Then, the influence of perturbations on performance was sequentially investigated and analyzed. Finally, numerous experiments on OTB100, VOT2018, and GOT-10k data sets demonstrated that the executed adversarial examples were effective on the performance drops of the transformer-based visual tracking. White-box attacks showed the highest effectiveness, where the attack success rates exceeded 90% against transformer-based trackers.

Fault diagnosis method for lithium-ion batteries based on the combination of voltage prediction and Z-score

ABSTRACT Safety accidents in new energy electric vehicles caused by lithium-ion battery failures occur frequently, and the timely and accurate diagnosis of failures in battery packs is crucial. Voltage, as one of the primary characterization parameters of lithium-ion battery malfunctions, is widely utilized in fault diagnosis. This article proposes a lithium-ion battery fault diagnosis method Fault diagnosis method based on the combination of voltage prediction and Z-score. Firstly, the stable trend component is extracted from the battery voltage data using variational mode decomposition, which avoids the influence of noisy signals and random perturbations to the greatest extent. Subsequently, a TCN-BiLSTM-attention model is designed to estimate the average voltage of the battery under normal conditions. Finally, the residuals between the estimated and individual cell voltages are calculated, and the Z-score is utilized to locate and judge whether the battery is caused by the occurrence of a fault. Through verification with real vehicle data and experimental data, the proposed method effectively identifies abnormal battery cells. Compared to the correlation coefficient method, this approach exhibits superior applicability.

An Experimental Investigation On The Effect Of Laser Energy Deposition In An Over-Expanded Jet

The shock train structure of a supersonic over-expanded jet is the product of the interaction between the jet flow at the nozzle exit and the ambient environment. Although the effect of flow disturbances on the performance of the supersonic inlets has been studied in detail, a limited understanding of the shock train dynamics subject to flow disturbances is available for over-expanded jets. This experimental investigation focuses on understanding the influence of perturbations due to short-duration energy deposition on the shock train structure and its dynamics in an axisymmetric over-expanded Mach 2.52 jet. The flow is perturbed by laser-induced breakdown, creating a shock wave and a high-temperature plasma zone in the shock train. A high-speed self-aligning focusing schlieren system is used to visualize the flow structure variations by measuring the shock angles and Mach disk height and width to characterize the shock train dynamics and the flow structure recovery process. The response of jet flow to the perturbation at the nozzle exit and the pre-reflection point is found to be similar, however, the response of the supersonic jet to the post-reflection energy deposition is significantly different. The damping ratio for the pre-reflection point and nozzle exit cases found are nearly constant for all scenarios whereas it is linearly dependent on the jet chamber pressure for the post-reflection cases. The frequency of the oscillations is found to be the same for all cases and irrespective of the chamber pressure, laser energy, and deposition location, approximately 10 kHz. The transition from Mach to regular reflection and vice versa are observed at a chamber pressure of Pc = 689 kPa. Any perturbation to the shock structure of a supersonic over-expanding jet at close to the steady-state bifurcation condition between Mach and regular reflection will exhibit Mach-to-regular transition or vice versa.

WITHDRAWN: Robust voltage regulation of DC-DC buck converter with ZIP load via an energy shaping control approach

ZIP loads (the parallel combination of constant impedance loads, constant current loads and constant power loads) exist widely in power system. In order to stabilize buck converter based DC distributed system with ZIP load, an adaptive energy shaping controller (AESC) is devised in this paper. Firstly, based on the assumption that the lumped disturbances are known, a full information controller is designed in the framework of the port Hamiltonian system via energy shaping technique. An obvious merit is that it is PI-type controller, which is easy to implement in practical application. Besides, using mathematical deductive method, an estimation of the domain of attraction is given to ensure the strict stability. Furthermore, to eliminate the influence of parameter perturbations on the system, a disturbance observer is proposed to reconstruct the lumped disturbances and then the estimated terms are introduced to above controller to form an adaptive control scheme. In addition, the strict stability analysis of the closed-loop system is given. Lastly, the simulation and experiment results are presented for assessing the designed controller.