Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

Fluorinated semi-interpenetrating polymer networks for enhancing the mechanical performance and storage stability of polymer-bonded explosives by controlling curing and phase separation rates

Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepared using different catalyst amounts(denoted as F23-CLF-30-D). The involved curing and phase separation processes were monitored using Fourier-transform infrared spectroscopy, differential scanning calorimetry, a haze meter and a rheometer. Curing rate constant and activation energy were calculated using a theoretical model and numerical method, respectively. Results revealed that owing to its co-continuous micro-phase separation structure, the F23-CLF-30-D3 semi-IPN exhibited considerably higher tensile strength and elongation at break than pure fluororubber F2314 and the F23-CLF-30-D0 semi-IPN because the phase separation and curing rates matched in the initial stage of curing.An arc Brazilian test revealed that F23-CLF-30-D-based composites used as mock materials for PBXs exhibited excellent mechanical performance and storage stability. Thus, the matched curing and phase separation rates play a crucial role during the fabrication of high-performance semi-IPNs;these factors can be feasibly controlled using an appropriate catalyst amount.

Variations and determinants of tissue carbon concentration of 32 sympatric temperate tree species

Plant carbon(C)concentration is a fundamental trait for estimating C storage and nutrient utilization.However,the mechanisms of C concentration variations among different tree tissues and across species remains poorly understood.In this study,we explored the variations and determinants of C concentration of nine tissues from 216 individuals of 32 tree species,with particular attention on the effect of wood porosity(i.e.,non-porous wood,diffuse-porous wood,and ring-porous wood).The inter-tissue pattern of C concentration diverged across the three porosity types;metabolically active tissues(foliage and fine roots,except for the foliage of ring-porous species)generally had higher C levels compared with inactive wood.The poor inter-correlations between tissue C concentrations indicated a necessity of measuring tissue-and specific-C concentrations.Carbon concentration for almost all tissues generally decreased from non-porous,to diffuse-porous and to ring-porous.Tissue C was often positively correlated with tissue(foliage and wood)density and tree size,while negatively correlated with growth rate,depending on wood porosity.Our results highlight the mediating effect of type of wood porosity on the variation in tissue C among temperate species.The variations among tissues were more important than that among species.These findings provided insights on tissue C concentration variability of temperate forest species.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines,the formation of hydrates is highly probable,leading to uncontrolled movement and aggregation of hydrates.The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines,resulting in production reduction,shutdowns,and pressure build-ups.Consequently,a cascade of risks is prone to occur.To address this issue,this study focuses on the operational process of natural gas gathering and transmission pipelines,where a comprehensive framework is established.This framework includes theoretical models for pipeline temperature distribution,pipeline pressure distribution,multiphase flow within the pipeline,hydrate blockage,and numerical solution methods.By analyzing the influence of inlet temperature,inlet pressure,and terminal pressure on hydrate formation within the pipeline,the sensitivity patterns of hydrate blockage risks are derived.The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate,potentially mitigating pipeline blockage during natural gas transportation.Furthermore,an increase in inlet temperature and terminal pressure,and a decrease in inlet pressure,results in a displacement of the most probable location for hydrate blockage towards the terminal station.However,it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system,contradicting the operational goal of energy efficiency and reduction of energy consumption.Consequently,for high-pressure gathering pipelines,measures such as raising the inlet temperature or employing inhibitors,electrical heat tracing,and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation.Moreover,considering abnormal conditions such as gas well production and pipeline network shutdowns,which could potentially trigger hydrate formation,the installation of methanol injection connectors remains necessary to ensure production safety.

Rate Law for Photoelectrochemical Water Splitting Over CuO

Photocatalytic splitting of water over p-type semiconductors is a promising strategy for production of hydrogen.However,the determination of rate law is rarely reported.To this purpose,copper oxide(CuO)is selected as a model photocathode in this study,and the photogenerated surface charge density,interfacial charge transfer rate constant and their relation to the water reduction rate(in terms of photocurrent)were investigated by a combination of(photo)electrochemical techniques.The results showed that the charge transfer rate constant is exponential-dependent on the surface charge density,and that the photocurrent equals to the product of the charge transfer rate constant and surface charge density.The reaction is first-order in terms of surface charge density.Such an unconventional rate law contrasts with the reports in literature.The charge density-dependent rate constant results from the Fermi level pinning(i.e.,Galvani potential is the main driving force for the reaction)due to accumulation of charge in the surface states and/or Frumkin behavior(i.e.,chemical potential is the main driving force).This study,therefore,may be helpful for further investigation on the mechanism of hydrogen evolution over a CuO photocathode and for designing more efficient CuO-based photocatalysts.

Humic Acid Effects on Reducing Corn Leaf Burn Caused by Foliar Spray of Urea-Ammonium Nitrate at Different Humic Acid/Urea-Ammonium Nitrate Ratios

Technologies for reducing corn leaf burn caused by foliar spray of urea-ammonium nitrate (UAN) during the early growing season are limited. A field experiment was carried out to evaluate the effects of humic acid on corn leaf burn caused by foliar spray of undiluted UAN solution on corn canopy at Jackson, TN in 2018. Thirteen treatments of the mixtures of UAN and humic acid were evaluated at V6 of corn with different UAN application rates and different UAN/humic acid ratios. Leaf burn during 1 2, 3, 4, 5, 6, 7, and 14 days after UAN foliar spray significantly differed between with or without humic acid addition. The addition of humic acid to UAN significantly reduced leaf burn at each UAN application rate (15, 25, and 35 gal/acre). The reduction of leaf burn was enhanced as the humic acid/UAN ratio went up from 10% to 30%. Leaf burn due to foliar application of UAN became severer with higher UAN rates. The linear regression of leaf burn 14 days after application with humic acid/UAN ratio was highly significant and negative. However, the linear regression of leaf burn 14 days after application with the UAN application rate was highly significant and positive. In conclusion, adding humic acid to foliar-applied UAN is beneficial for reducing corn leaf burn during the early growing season.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic,intricately governed by natural selection and historically garnering considerable attention.Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain,ushering in an unprecedented era of mutation rate research.This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates.It examines various types of mutations,explores the evolutionary dynamics and associated theories,and synthesizes both classical and contemporary hypotheses.Furthermore,this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies,mutational patterns,molecular mechanisms,and driving forces influencing variations in mutation rates across species and tissues.Finally,it proposes several potential research directions and pressing questions for future investigations.

Present-day Upper-crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications:Constraints from GPS Measurements with ABIC Method

The Earth’s surface kinematics and deformation are fundamental to understanding crustal evolution.An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks.If the discrete observed velocity field is obtained,the velocity related fields,such as dilatation rate and maximum shear strain rate,can be estimated by applying varied mathematical approaches.This study applied Akaike's Bayesian Information Criterion(ABIC)method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau.Comparison with results derived from other three methods revealed that our ABIC-derived strain rate fields were more precise.The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet,indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel.Additionally,distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed.The northeast region,represented by the Longmenshan area,exhibited negative dilatational anomalies;while the southwest region,represented by the Jinsha River area north of 29°N,displayed positive dilatational anomalies.This indicates compression in the former and extension in the latter.Combined with deep geophysical observations,we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state,probably caused by the escape-drag effect of material.The presence of a large,low-viscosity region south of 29°N may not enable the entire escape of the crust,but instead result in a differential escape of the lower crust faster than the upper crust.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Sm-like 5 knockdown inhibits proliferation and promotes apoptosis of colon cancer cells by upregulating p53,CDKN1A and TNFRSF10B

BACKGROUND The role of Sm-like 5(LSM5)in colon cancer has not been determined.In this study,we investigated the role of LSM5 in progression of colon cancer and the potential underlying mechanism involved.AIM To determine the role of LSM5 in the progression of colon cancer and the potential underlying mechanism involved.METHODS The Gene Expression Profiling Interactive Analysis database and the Human Protein Atlas website were used for LSM5 expression analysis and prognosis analysis.Real-time quantitative polymerase chain reaction and Western blotting were utilized to detect the expression of mRNAs and proteins.A lentivirus targeting LSM5 was constructed and transfected into colon cancer cells to silence LSM5 expression.Proliferation and apoptosis assays were also conducted to evaluate the growth of the colon cancer cells.Human GeneChip assay and bioinformatics analysis were performed to identify the potential underlying mechanism of LSM5 in colon cancer.RESULTS LSM5 was highly expressed in tumor tissue and colon cancer cells.A high expression level of LSM5 was related to poor prognosis in patients with colon cancer.Knockdown of LSM5 suppressed proliferation and promoted apoptosis in colon cancer cells.Silencing of LSM5 also facilitates the expression of p53,cyclin-dependent kinase inhibitor 1A(CDKN1A)and tumor necrosis factor receptor superfamily 10B(TNFRSF10B).The inhibitory effect of LSM5 knockdown on the growth of colon cancer cells was associated with the upregulation of p53,CDKN1A and TNFRSF10B.CONCLUSION LSM5 knockdown inhibited the proliferation and facilitated the apoptosis of colon cancer cells by upregulating p53,CDKN1A and TNFRSF10B.

A CRISPR/RfxCas13d-mediated strategy for efficient RNA knockdown in mouse embryonic development

The growing variety of RNA classes,such as mRNAs,lncRNAs,and circRNAs,plays pivotal roles in both developmental processes and various pathophysiological conditions.Nonetheless,our comprehension of RNA functions in live organisms remains limited due to the absence of durable and effective strategies for directly influencing RNA levels.In this study,we combined the CRISPR-RfxCas13d system with spermlike stem cell-mediated semi-cloning techniques,which enabled the suppressed expression of different RNA species.This approach was employed to interfere with the expression of three types of RNA molecules:Sfmbt2 mRNA,Fendrr lncRNA,and circMan1a2(2,3,4,5,6).The results confirmed the critical roles of these RNAs in embryonic development,as their loss led to observable phenotypes,including embryonic lethality,delayed embryonic development,and embryo resorption.In summary,our methodology offers a potent toolkit for silencing specific RNA targets in living organisms without introducing genetic alterations.

INTERFACE BEHAVIOR AND DECAY RATES OF COMPRESSIBLE NAVIER-STOKES SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND A VACUUM

In this paper,we study the one-dimensional motion of viscous gas near a vacuum,with the gas connecting to a vacuum state with a jump in density.The interface behavior,the pointwise decay rates of the density function and the expanding rates of the interface are obtained with the viscosity coefficientμ(ρ)=ρ^(α)for any 0<α<1;this includes the timeweighted boundedness from below and above.The smoothness of the solution is discussed.Moreover,we construct a class of self-similar classical solutions which exhibit some interesting properties,such as optimal estimates.The present paper extends the results in[Luo T,Xin Z P,Yang T.SIAM J Math Anal,2000,31(6):1175-1191]to the jump boundary conditions case with density-dependent viscosity.