Acceleration Model Research Articles

Spectral Energy Distribution Variability of the Blazar OJ 287 During 2009–2021

Abstract Using nearly simultaneous radio, near-infrared, optical, and ultraviolet (UV) data collected since 2009, we constructed 106 spectral energy distributions (SEDs) of the blazar OJ 287. These SEDs are well fitted by a log-parabolic model. By classifying the data into “flare” and “quiescent” segments, we find that the median flux at the peak frequency of the SEDs during the flare segments is 0.37 ± 0.22 dex higher compared to the quiescent segments, while no significant differences are observed in the median values of the curvature parameter b or the peak frequency log ν p . A significant bluer-when-brighter trend is confirmed through the relation between the V magnitude and B − V color index, with this trend being stronger in the flare segments. Additionally, a significant anticorrelation is detected between log ν p and b, with a slope of 5.79 in the relation between 1/b and log ν p , closer to the prediction from a statistical acceleration model than a stochastic acceleration interpretation, though a notable discrepancy persists. This discrepancy indicates that additional factors—such as deviations from idealized conditions or radiative contributions, such as the thermal emission from the accretion disk in the optical–UV range during quiescent states—may play a role in producing the observed steeper slope. Within the framework of the statistical acceleration mechanism, the lack of correlation between the change in the peak intensity and the change in the peak frequency suggests that the change in the electron energy distribution is unlikely to be responsible for the time-dependent SED changes. Instead, changes in Doppler boosting or magnetic fields may have a greater influence.

A Double Extended Kalman Filter Algorithm for Weakening Non-Line-of-Sight Errors in Complex Indoor Environments Based on Ultra-Wideband Technology

In complex indoor environments, target tracking performance is impacted by non-line-of sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a double extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an extended Kalman filter (EKF). Time of arrival (TOA) measurements collected by multiple stationary ultra-wideband (UWB) sensors are used. The residual errors between the measured TOA and that of the first KF are predicted, and the covariance of the first KF is adjusted correspondingly. Then, we use the estimated distance state of the first KF as a measurement vector for the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with good accuracy even without or with only one LOS TOA measurement for a period of time without prior information about the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not greatly decrease when NLOS noises exist for a long period of time. Finally, the proposed DEKF can maintain the high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model in the LOS/NLOS environment. Our simulation and experimental results show that the proposed algorithm performs much better than other algorithms in SOTA, particularly in severe mixed LOS/NLOS environments.

A Novel Non-Line-of-Sight Error Mitigation Algorithm Using Double Extended Kalman Filter for Ultra-Wide Band Ranging Technology

In complex indoor environments, target tracking performance is impacted by non-line-of-sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a Double Extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an Extended Kalman filter (EKF). The time of arrival (TOA) measurements collected by multiple stationary ultra-wide band (UWB) sensors are used. Residual errors between the measured TOA and the prediction from the first KF are used to adjust the covariance of the first KF accordingly. Then, we use the estimated distance state of the first KF as a measurement vector of the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with a good accuracy even without or with only one line-of-sight(LOS) TOA measurement for a period of time without prior information of the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not significantly decrease when NLOS noises persist for a long period of time. Finally, the proposed DEKF can maintain high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model for LOS/NLOS environments. In the case of mixed LOS/NLOS environments, the RMSE of the proposed algorithm can be kept within 5 cm, while the RMSEs of other compared algorithms are easily beyond tens of centimeters. At the same time, when the confidence of RMSE is set to 95% for 1000 MC simulations, the confidence interval of the proposed algorithm is the smallest, and the mean value is 3–5 times closer to the true value compared to other algorithms. Simulation and experimental results show that the proposed algorithm performs much better than other state-of-the-art algorithms, particularly in severe mixed LOS/NLOS environments.

A calibration method for accelerometer combination on centrifuge based on norm-observation method

Abstract To realise the overall calibration of the error model coefficients of accelerometers in an inertial combination and to improve the navigation accuracy of the inertial navigation system, a norm-observation method is applied to the calibration, especially for the quadratic coefficient of the accelerometer. The Taylor formula is used to expand the solution of the acceleration model, and the intermediate variables with error model coefficients are obtained using the least square method. The formulas for calculating the quadratic term coefficient, scale factor and bias of the accelerometer are given. A 20-position method is designed to calibrate the accelerometer combination, the effectiveness of the method is verified by simulation, and the effects of installation misalignment and rod-arm error on calibration accuracy are analysed. The results show that the installation misalignments and rod-arm errors have little influence on the coefficient calibration, less than 10−8, and can be neglected in a practical calibration process.

Revealing an unexpectedly low electron injection threshold via reinforced shock acceleration

Collisionless shock waves, found in supernova remnants, interstellar, stellar, and planetary environments, and laboratories, are one of nature’s most powerful particle accelerators. This study combines in situ satellite measurements with recent theoretical developments to establish a reinforced shock acceleration model for relativistic electrons. Our model incorporates transient structures, wave-particle interactions, and variable stellar wind conditions, operating collectively in a multiscale set of processes. We show that the electron injection threshold is on the order of suprathermal range, obtainable through multiple different phenomena abundant in various plasma environments. Our analysis demonstrates that a typical shock can consistently accelerate electrons into very high (relativistic) energy ranges, refining our comprehension of shock acceleration while providing insight on the origin of electron cosmic rays.

A uGMRT and MeerKAT Study of Radio Relics in the Low-mass Merging Cluster PSZ2 G200.95−28.16

Abstract Diffuse radio sources known as radio relics are direct tracers of shocks in the outskirts of merging galaxy clusters. PSZ2 G200.95–28.16, a low-mass merging cluster (M 500 = (2.7 ± 0.2) × 1014 M ⊙), features a prominent radio relic, first identified by R. Kale et al. We name this relic as the Seahorse. The MeerKAT Galaxy Cluster Legacy Survey has confirmed two additional radio relics, R2 and R3, in this cluster. We present new observations of this cluster with the upgraded Giant Metrewave Radio Telescope (uGMRT) at 400 and 650 MHz, paired with Chandra X-ray data. The largest linear sizes for the three relics are 1.53 Mpc, 1.12 kpc, and 340 kpc. All three radio relics are polarized at 1283 MHz. Assuming the diffusive shock acceleration model, the spectral indices of the relics imply shock Mach Numbers of 3.1 ± 0.8 and 2.8 ± 0.9 for the Seahorse and R2, respectively. The Chandra X-ray surface brightness map shows two prominent subclusters, but the relics are not perpendicular to the likely merger axis, as typically observed; no shocks are detected at the locations of the relics. We discuss possible merger scenarios in light of the low mass of the cluster and the radio and X-ray properties of the relics. The relic R2 follows the correlation known in the radio relic power and cluster-mass plane, but the Seahorse and R3 relics are outliers. We have also discovered a radio ring in our 650 MHz uGMRT image that could be an Odd Radio Circle candidate.

Compound electron acceleration at planetary foreshocks

Shock waves, the interface of supersonic and subsonic plasma flows, are the primary region for charged particle acceleration in multiple space plasma systems, including Earth’s bow shock, which is readily accessible for in-situ measurements. Spacecraft frequently observe relativistic electron populations within this region, characterized by energy levels surpassing those of solar wind electrons by a factor of 10,000 or more. However, mechanisms of such strong acceleration remain elusive. Here we use observations of electrons with energies up to 200 kiloelectron volts and a data-constrained model to reproduce the observed power-law electron spectrum and demonstrate that the acceleration by more than 4 orders of magnitude is a compound process including a complex, multi-step interaction between more commonly known mechanisms and resonant scattering by several distinct plasma wave modes. The proposed model of electron acceleration addresses a decades-long issue of the generation of energetic (and relativistic) electrons at planetary plasma shocks. This work may further guide numerical simulations of even more effective electron acceleration in astrophysical shocks.

Analysis of Causes and Intervention Design for the Papua Conflict based on Structural, Accelerator, and Trigger (SAT) Model

The Papua conflict remains unresolved and is one of Indonesia's enduring challenges. Special autonomy and development efforts by the government in the region have not succeeded in fostering peace. This study aims to explore the causes of the Papua conflict and provide insights into conflict intervention strategies using the Structural, Accelerator, and Trigger (SAT) Model. This qualitative research as defined by Sugiyono utilized secondary data analysis to gain comprehensive understanding of the conflict intervention design for Papua based on implications of Structural, Accelerator, and Trigger (SAT) Factors. Data was gathered from various sources including books, journals, government documents, and media articles related to the dynamics of the Papua conflict. The study found that by applying the Structural, Accelerator, and Trigger (SAT) Model, the underlying causes of the conflict can be delineated, paving the way for intervention strategies based on these causes. Structural causes such as trust issues with the government, economic inequality, and divergent interests need interventions such as trust building, sustainable economic policies, and negotiation. Accelerator causes such as mass media require interventions such as strengthening media ethics, while conflict triggers such as violence necessitate law enforcement through judicial measures to achieve sustainable peace in Papua.

Superdiffusion of energetic particles at shocks: A Lévy flight model for acceleration

Context. In the heliosphere, power-law particle distributions are observed, for example, upstream of interplanetary shocks, which can result from superdiffusive transport. This non-Gaussian transport regime may be due to intermittent magnetic field structures. Recently, we have shown that a Lévy flight model reproduces the observed features at shocks: power-law distributions upstream of the shock and enhanced intensities at the shock. Aims. In this work, we extend the Lévy flight model to study the impact of superdiffusive transport on particle acceleration at shocks. We compared the acceleration timescale and spectral slope to Gaussian diffusion and a Lévy walk model. Methods. We solved the fractional transport equation by sampling the number density with the corresponding stochastic differential equation that is driven by an alpha-stable Lévy distribution. For both Gaussian and superdiffusive transport, we used a modified version of the cosmic ray propagation framework CRPropa 3.2. Results. We obtained the number density and energy spectra for constant and energy-dependent anomalous diffusion, and we find, compared to the case of Gaussian diffusion, harder energy spectra at the shock as well as faster acceleration. The spectral slope is even harder than predicted for Lévy walks. Conclusions Lévy flight models of superdiffusive transport lead to observed features in the heliosphere. We further show that superdiffusive transport impacts the acceleration process by changing the probability of escaping the shock. The flexibility of the Lévy flight model allows for further studies in the future that can take the shock geometry and magnetic field structure into account.

Neural network-assisted generic predictive models of safety factor and yield acceleration for seismic slope stability and displacement assessments

As two well-recognized approaches for seismic slope stability assessment, the pseudo-static analysis estimates the factor of safety (FS) and the Newmark-type analysis estimates permanent downslope-displacement based on input yield acceleration (ky). However, FS and ky are usually obtained from non-trivial slope stability calculations, which can become computationally demanding in probabilistic analyses or regional landslide mapping. This study presents neural network-assisted predictive models for (1) seismic or static FS and the category of failure mode; and (2) ky and the thickness of failure mass. Extensive stability analyses of more than 741,000 and 123,000 slope configurations are conducted to compile datasets of FS and ky, respectively. Performance evaluations indicate that the models produce physically reasonable prediction trends and have good generalization capability with correlation coefficient higher than 0.94 in blind tests. Compared to the existing infinite slope model and predictive tools, the new models achieve improved applicability and functionality, accounting for pore-water pressure, depth to hard stratum, and various failure modes. Both the spreadsheet and MATLAB files established in this study are provided to facilitate generic applications. Therefore, this work not only demonstrates the neural network capability, but also provides useful tools for practitioners, contributing to both the pseudo-static and Newmark-type approaches.

An Elastic Filtering Algorithm with Visual Perception for Vehicle GNSS Navigation and Positioning.

Amidst the backdrop of the profound synergy between navigation and visual perception, there is an urgent demand for accurate real-time vehicle positioning in urban environments. However, the existing global navigation satellite system (GNSS) algorithms based on Kalman filters fall short of precision. In response, we introduce an elastic filtering algorithm with visual perception for vehicle GNSS navigation and positioning. Firstly, the visual perception system captures real-time environmental data around the vehicle. It utilizes the interframe differential optical flow method and vehicle state switching characteristics to assess the current driving status. Secondly, we design an elastic filtering model specifically for various vehicle states. This model enhances the precision of Kalman filter-based GNSS navigation. In urban driving, vehicles often experience frequent stationary parking. To address this, we incorporate a zero-speed constraint to further refine vehicle location data when the vehicle is stationary. This constraint matches the data with the appropriate elastic filtering model. Ultimately, we conduct simulation and real-world vehicle navigation experiments to confirm the validity and rationality of our proposed algorithm. Compared with the conventional algorithm and the existing interactive multi-model algorithm, the proposed algorithm significantly improves the navigation and positioning accuracy of vehicle GNSS in urban environments. Compared to the commonly used constant acceleration (CA) and Constant Velocity (CV) models, there has been a significant improvement in positioning accuracy. Furthermore, when benchmarked against the more advanced interactive multi-model (IMM) model, the method proposed in this paper has enhanced the positioning accuracy enhancements in three dimensions: 21.8%, 20.9%, and 31.3%, respectively.

The neuropathological basis of elevated serum neurofilament light following experimental concussion

Mild traumatic brain injury (mTBI) or concussion is a substantial health problem globally, with up to 15% of patients experiencing persisting symptoms that can significantly impact quality of life. Currently, the diagnosis of mTBI relies on clinical presentation with ancillary neuroimaging to exclude more severe forms of injury. However, identifying patients at risk for a poor outcome or protracted recovery is challenging, in part due to the lack of early objective tests that reflect the relevant underlying pathology. While the pathophysiology of mTBI is poorly understood, axonal damage caused by rotational forces is now recognized as an important consequence of injury. Moreover, serum measurement of the neurofilament light (NfL) protein has emerged as a potentially promising biomarker of injury. Understanding the pathological processes that determine serum NfL dynamics over time, and the ability of NfL to reflect underlying pathology will be critical for future clinical research aimed at reducing the burden of disability after mild TBI. Using a gyrencephalic model of head rotational acceleration scaled to human concussion, we demonstrate significant elevations in serum NfL, with a peak at 3 days post-injury. Moreover, increased serum NfL was detectable out to 2 weeks post-injury, with some evidence it follows a biphasic course. Subsequent quantitative histological examinations demonstrate that axonal pathology, including in the absence of neuronal somatic degeneration, was the likely source of elevated serum NfL. However, the extent of axonal pathology quantified via multiple markers did not correlate strongly with the extent of serum NfL. Interestingly, the extent of blood–brain barrier (BBB) permeability offered more robust correlations with serum NfL measured at multiple time points, suggesting BBB disruption is an important determinant of serum biomarker dynamics after mTBI. These data provide novel insights to the temporal course and pathological basis of serum NfL measurements that inform its utility as a biomarker in mTBI.

Innovative management of the production risks of agricultural enterprises

The article examines the innovative infrastructure in the management of production risks of agricultural enterprises, which is related to the rules for evaluating alternative scenarios for reducing threats in the production system of agricultural production entities in order to obtain the desired result. A methodical approach to the assessment of factors-tools in the innovative management of production risks, which activate the process of economic development of agricultural enterprises in an institutional environment, is presented. It is proved that the institutional determinants that manage the production risks of agricultural enterprises form a system whose effectiveness depends on the implementation of the relevant directions of economic development of agricultural institutes focused on the introduction of innovations into the production cycle. The structural dialectic connection of the concept of innovative development of agricultural enterprises with the cyclical development of the production system is presented. A structural and logical diagram of a methodical approach to the implementation of the mechanism of innovative management of production risks of agricultural enterprises has been built. A mathematical toolkit for evaluating scenarios of innovative management of production risks of agricultural enterprises is defined. Stimulating and disincentive factors-tools in the innovative management of production risks and their impact on the economic development of agricultural enterprises are determined. In order to determine the optimal scenarios for neutralizing threats to the economic development of agricultural enterprises, models of acceleration (deceleration) of the action of stimulating and disincentive factors-tools in the innovative management of production risks were built. The integral index of the economic development of agricultural enterprises of the agro-climatic regions of Ukraine in the pre-war, war-conflict and post-war periods was calculated. It has been established that due to the accumulation of a significant amount of production, financial, material, technical and innovative potential, the level of economic development of agricultural enterprises of the agro-climatic zones of the Forest-Steppe and Polissia increases, which characterizes their ability to reproduce the production system of agriculture in Ukraine.

A cosmological reconstruction of the Higgs vacuum expectation value

We present a simple toy model of cosmic acceleration driven purely by a self-interacting scalar field embedded in theory of grand unification. The scalar self-interaction is Higgs-like and provokes a spontaneous symmetry breaking. The coefficient of the quadratic term in the self-interaction potential has an evolution and it leads to a cosmic variation of proton-to-electron mass ratio, μ. We perform a cosmological reconstruction from the kinematic parameter jerk and discuss a few cosmological consequences of the theory. We also compare the theoretically calculated μ variation with the observations of molecular absorption spectra from Cesium Atomic Clock data.

Life Assessment Method And Current Situation Analysis of Initiating Explosive Device

Abstract This paper analyzes and studies the methods and current status of life assessment for initiating explosive devices in China. The main focus is on the analysis of methods and standards for evaluating the storage life of initiating explosive devices, the impact of acceleration models and coefficients on the results of life assessment, and the impact of experimental program design on the results of life assessment. The reasons for the deviation between the results of life assessment for initiating explosive devices using accelerated life testing methods and the storage life of initiating explosive devices in natural environments are discussed, and suggestions for the use of life assessment methods for initiating explosive devices are proposed.

Boundary Layer Modelling of Flow Acceleration and Energy Transfer Effects in Smart Pavement Design

The integration of remote technology into the construction industry has advanced the emergence of smart, self-learning infrastructure across all levels of land transportation design and development. However, energy harvesting capabilities for smart road ventilation purposes have not yet been fully researched as the world gravitates towards energy sustainability. This study thus presents a mathematical investigation of the self-draining design potentials of road pavements, leveraging on energy absorption and conversion to accelerate conductive and convective ventilation of these pavements during wet conditions, as a means of ensuring the skid resistance and safety of such pavements are not compromised. Applying numerical methods with the aid of commercial software code MATLAB®, the classical physics of fluid-surface interaction was used to model accelerated drainage of microflood off paved surfaces through methodical modification of the thickness of the boundary layer associated with the flow regime over the flat road surface. Results indicate significant influences of energy exchange, thermal, and viscous diffusivity on flow acceleration. Alterations in parameters such as the slip factor, thermally induced Brownian motion or phoretic characteristics of the fluid particles at the boundary induce accelerated flow at the surface of the pavement. The findings contribute to the advancement of smart infrastructure by offering practical insights into the potential benefits of integrating energy-harvesting technologies into road pavement systems. By optimizing flow acceleration mechanisms, smart pavements can play a crucial role in improving road safety and sustainability in urban environments.

Continuous near-bit inclination measurement based on installation error correction

Abstract As the petroleum industry evolves, the challenges associated with petroleum exploration and development intensify, and the continuous measurement of well inclination has grown increasingly significant. Continuous near-bit inclination measurements can improve the accuracy of calculating wellbore trajectory and drilling efficiency. However, the vibration of the bottom drill bit seriously interferes with the inclination measurement, causing serious distortion of acceleration and inaccurate inclination measurement. When the well inclination measurement module is installed eccentrically, it is easy to cause installation errors. At the same time, the drill collar rotates, and the three-axis acceleration is affected by the centrifugal acceleration. The above factors will affect the accuracy of inclination measurement. This paper derives a three-axis acceleration measurement error model caused by installation error, and proposes a continuous measurement method based on this model. First, the vibration noise is filtered out by the Kalman filter. Then a multivariate linear regression model of three-axis acceleration is established, and the true inclination is calculated based on this model. After simulation verification, the continuous inclination error measured by this method is less than 0.2°.

Multiwavelength Study of Extreme High-energy Peaked BL Lac Source 1ES 0229+200 Using Ultraviolet, X-Ray, and γ-Ray Observations

We present a comprehensive analysis of the broadband spectral energy distribution (SED) of the extreme high-energy peaked BL Lac source, 1ES 0229+200. Our study utilizes near-simultaneous data collected at various epochs between 2017 September and 2021 August (MJD 58119−59365) from different instruments, including AstroSat−UVIT, Soft X-ray focusing Telescope, LAXPC, Swift−UVOT, Fermi-LAT, and MAGIC. We investigate the one-zone synchrotron and synchrotron self-Compton (SSC) model, employing diverse particle distributions such as the log parabola, broken power law, power law with a maximum electron energy γ, energy-dependent diffusion (EDD), and energy-dependent acceleration (EDA) models to fit the broadband SED of the source. Our findings indicate that both peaks in the SED are well described by the one-zone SSC model across all particle distribution models. We estimate the jet power for different particle distributions. The estimated jet power for broken power law particle distributions is found to be on the order of 1047 (1044) erg s−1 for a minimum electron energy γmin ∼10 (104). However, for intrinsically curved particle energy distributions (e.g., log parabola, EDD, and EDA models), the estimated jet power is ∼1044 erg s−1. The SED fitting at five epochs enables us to explore the correlation between the derived spectral parameters of various particle distribution models. Notably, the observed correlations are inconsistent with the predictions in the power-law with a maximum γ model, although the EDD and EDA models yield the correlations as expected. Moreover, the estimated physical parameter values are consistent with the model assumptions.

Estimating the Total Energy Content in Escaping Accelerated Solar Electron Beams

Quantifying the energy content of accelerated electron beams during solar eruptive events is a key outstanding objective that must be constrained to refine particle acceleration models and understand the electron component of space weather. Previous estimations have used in situ measurements near the Earth, and consequently suffer from electron-beam propagation effects. In this study, we deduce properties of a rapid sequence of escaping electron beams that were accelerated during a solar flare on 2013 May 22 and produced type III radio bursts, including the first estimate of energy density from remote-sensing observations. We use extreme-ultraviolet observations to infer the magnetic structure of the source active region NOAA 11745, and Nançay Radioheliograph imaging spectroscopy to estimate the speed and origin of the escaping electron beams. Using the observationally deduced electron-beam properties from the type III bursts and cotemporal hard X-rays, we simulate electron-beam properties to estimate the electron number density and energy in the acceleration region. We find an electron density (above 30 keV) in the acceleration region of 102.5 cm−3 and an energy density of 2 × 10−5 erg cm−3. Radio observations suggest the particles travelled a very short distance before they began to produce radio emission, implying a radially narrow acceleration region. A short but plausibly wide slab-like acceleration volume of 1026–1028 cm3 atop the flaring loop arcade could contain a total energy of 1023–1025 erg (∼100 beams), which is comparable to energy estimates from previous studies.

Reliability modeling and statistical analysis of accelerated degradation process with memory effects and unit-to-unit variability

A reasonable description of the degradation process is essential for credible reliability assessment in accelerated degradation testing. Existing methods usually use Markovian stochastic processes to describe the degradation process. However, degradation processes of some products are non-Markovian due to the interaction with environments. Misinterpretation of the degradation pattern may lead to biased reliability evaluations. Besides, owing to the differences in materials and manufacturing processes, products from the same population exhibit diverse degradation paths, further increasing the difficulty of accurate reliability estimation. To address the above issues, this paper proposes an accelerated degradation model incorporating memory effects and unit-to-unit variability. The memory effect in the degradation process is captured by the fractional Brownian motion, which reflects the non-Markovian characteristic of degradation. The unit-to-unit variability is considered in the acceleration model to describe diverse degradation paths. Then, lifetime and reliability under normal operating conditions are presented. Furthermore, to give an accurate estimation of the memory effect, a new statistical analysis method based on the expectation maximization algorithm is devised. The effectiveness of the proposed method is verified by a simulation case and a real-world tuner reliability analysis case. The simulation case shows that the estimation of the memory effect obtained by the proposed statistical analysis method is much more accurate than the traditional one. Moreover, ignoring unit-to-unit variability can lead to a highly biased estimation of the memory effect and reliability. From the tuner reliability analysis case, the proposed model is superior in both deterministic degradation trend predictions and degradation boundary quantification compared to existing models, which can provide more credible reliability assessment.