Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error

For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.

Storage reliability assessment model based on competition failure of multi-components in missile

The degradation data of multi-components in missile is derived by periodical testing. How to use these data to assess the storage reliability (SR) of the whole missile is a difficult problem in current research. An SR assessment model based on competition failure of multi-components in missile is proposed. By analyzing the missile life profile and its storage failure feature, the key components in missile are obtained and the characteristics voltage is assumed to be its key performance parameter. When the voltage testing data of key components in missile are available, a state space model (SSM) is applied to obtain the whole missile degradation state, which is defined as the missile degradation degree (DD). A Wiener process with the time-scale model (TSM) is applied to build the degradation failure model with individual variability and nonlinearity. The Weibull distribution and proportional risk model are applied to build an outburst failure model with performance degradation effect. Furthermore, a competition failure model with the correlation between degradation failure and outburst failure is proposed. A numerical example with a set of missiles in storage is analyzed to demonstrate the accuracy and superiority of the proposed model.

CT and multimodal imaging findings of primary orbital Ewing＇s sarcoma involving the middle cranial fossa： a case report

This report details the CT and MR imaging findings of a primary orbital Ewing＇s sarcoma case involving the middle cranial fossa in a 13-year old boy. CT showed an ill-circumscribed homogeneous soft-tissue density mass with needle-like bone reaction. On MRI, the mass showed homogeneous iso-intensity on T1WI, heterogeneous hyper- intensity on T2WI, and marked homogeneous enhancement. Meanwhile, the mass was hyper-intense on DWI, and ADC alue was 575x10-6 mm2/s. The mass showed as high rCBV and rCBF, prolonged MTT based on DCS- PWI, and wash-out pattern of TIC derived from the DCE-MRI. Our case suggests that functional MRI modalities, including DWI, DSC-PWI or DCE-MR, could provide additional information for differential diagnoses. Both CT and MRI should be performed and comprehensively analyzed for limiting differential diagnoses, determining lesion extension and facilitating operative approach.

A machine-learning-based electron density (MLED) model in the inner magnetosphere

Plasma density is an important factor in determining wave-particle interactions in the magnetosphere.We develop a machine-learning-based electron density(MLED)model in the inner magnetosphere using electron density data from Van Allen Probes between September 25,2012 and August 30,2019.This MLED model is a physics-based nonlinear network that employs fundamental physical principles to describe variations of electron density.It predicts the plasmapause location under different geomagnetic conditions,and models separately the electron densities of the plasmasphere and of the trough.We train the model using gradient descent and backpropagation algorithms,which are widely used to deal effectively with nonlinear relationships among physical quantities in space plasma environments.The model gives explicit expressions with few parameters and describes the associations of electron density with geomagnetic activity,solar cycle,and seasonal effects.Under various geomagnetic conditions,the electron densities calculated by this model agree well with empirical observations and provide a good description of plasmapause movement.This MLED model,which can be easily incorporated into previously developed radiation belt models,promises to be very helpful in modeling and improving forecasting of radiation belt electron dynamics.

TiO_(2)-carbon porous nanostructures for immobilization and conversion of polysulfides

Lithium-sulfur batteries(LSBs)are among the most promising series of next-generation high density energy storage systems.However,the problem of“shuttle effect”caused by dissolution and migration of polysulfide intermediates has severely inhibited their practical applications.Herein,TiO_(2)-carbon nanocomposites embedded hierarchical porous carbon(T-hPC)interlayers are fabricated via Ti_(3)C_(2)MXene assisted phase separation and annealing method.The T-hPC processes micro-to macro-scale multipores along with highly adsorptive and catalytic carbon supported TiO_(2) nanoparticles,which significantly enhances the polysulfides immobilization and improves the redox reaction kinetics when applied as lithium-sulfur battery interlayers.An initial discharge specific capacity of 1551.1 m Ah/g and a stable capacity of 893.8 m Ah/g after 200 cycles at 0.5 C are obtained,corresponding to a capacity decay rate of only 0.04%per cycle.The investigations in this paper can provide a simple and effective strategy to enhance the electrochemical properties for lithium-sulfur batteries.