Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Adaptive semi-empirical model for non-contact atomic force microscopy

Non-contact atomic force microscope is a powerful tool to investigate the surface topography with atomic resolution.Here we propose a new approach to estimate the interaction between its tips and samples,which combines a semi-empirical model with density functional theory(DFT)calculations.The generated frequency shift images are consistent with the experiment for mapping organic molecules using CuCO,Cu,CuCl,and CuO_(x)tips.This approach achieves accuracy close to DFT calculation with much lower computational cost.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Semi-empirical modeling of volumetric efficiency in engines equipped with variable valve timing system

Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines.Specifically,using three-way catalytic converter involves strict control of the air/fuel ratio around the stoichiometric point and hence requires an accurate model for air charge estimation.However,high degrees of complexity and nonlinearity of the gas flow in the internal combustion engine make air charge estimation a challenging task.This is more obvious in engines with variable valve timing systems in which gas flow is more complex and depends on more functional variables.This results in models that are either quite empirical(such as look-up tables),not having interpretability and extrapolation capability,or physically based models which are not appropriate for onboard applications.Solving these problems,a novel semi-empirical model was proposed in this work which only needed engine speed,load,and valves timings for volumetric efficiency prediction.The accuracy and generalizability of the model is shown by its test on numerical and experimental data from three distinct engines.Normalized test errors are 0.0316,0.0152 and 0.24 for the three engines,respectively.Also the performance and complexity of the model were compared with neural networks as typical black box models.While the complexity of the model is less than half of the complexity of neural networks,and its computational cost is approximately 0.12 of that of neural networks and its prediction capability in the considered case studies is usually more.These results show the superiority of the proposed model over conventional black box models such as neural networks in terms of accuracy,generalizability and computational cost.

Kinetics of zinc sulfide concentrate direct leaching in pilot plant scale and development of semi-empirical model

The direct leaching kinetics of an iron-poor zinc sulfide concentrate in the tubular reactor was examined.All tests werecarried out in the pilot plant.To allow the execution of hydrostatic pressure condition,the slurry with ferrous sulfate and sulfuric acidsolution was filled into a vertical tube(9m in height)and air was blown from the bottom of the reactor.The effects of initial acidconcentration,temperature,particle size,initial zinc sulfate concentration,pulp density and the concentration of Fe on the leachingkinetics were investigated.Results of the kinetic analysis indicate that direct leaching of zinc sulfide concentrate follows shrinkingcore model(SCM).This process was controlled by a chemical reaction with the apparent activation energy of49.7kJ/mol.Furthermore,a semi-empirical equation is obtained,showing that the order of the iron,sulfuric acid and zinc sulfate concentrationsand particle radius are0.982,0.189,-0.097and-0.992,respectively.Analysis of the unreacted and reacted sulfide particles bySEM-EDS shows that insensitive agitation in the reactor causes detachment of the sulfur layer from the particles surface in lowerthan60%Zn conversion and lixiviant in the face with sphalerite particles.

Salt-Affected Soil Mapping in an Arid Environment Using Semi-Empirical Model and Landsat-OLI Data

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca2+, Mg2+, Na+, K+, Cl- and SO42-), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab, which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline. The laboratory chemical analyses corroborate these results. Furthermore, the semi-empirical predictive model provides good global results in comparison to the ground truth and laboratory analysis (EC-Lab), with correlation coefficient (R2) of 0.97, an index of agreement (D) of 0.84 (p < 0.05), and low overall root mean square error (RMSE) of 11%. Moreover, we found that topographic attributes have a substantial impact on the spatial distribution of salinity. The areas at a relatively high altitude and with hard bedrock are less susceptible to salinity, while areas at a low altitude and slope (≤2%) composed of Quaternary soil are prone to it. In these low areas, the water table is very close to the surface (≤1 m), and the absence of an adequate drainage network contributes significantly to waterlogging. Consequently, the intrusion and emergence of seawater at the surface, coupled with high temperature and high evaporation rates, contribute extensively to the soil salinity in the study area.

Use of Statistical Tools for Comparison between Different Analytical and Semi-Empirical Models of the Bleve Fireball

The Bleve is an explosion involving both the rapid vaporization of liquid and the rapid expansion of vapor in a vessel.The loss of containment results in a large fireball if the stored chemical is flammable.In order to predict the damage generated by a Bleve,several authors propose analytical or semi-empirical correlations,which consist in predicting the diameter and the lifetime of the fireballs according to the quantity of fuel.These models are based on previous experience,which makes their validity arbitrary in relation to the initial conditions and the nature of the product concerned.The article delves into uncertainty analysis associated with analytical and semi-empirical models of the BLEVE fireball.It could explore how uncertainties in input data,and the choice of a more or less inappropriate model,propagate into the model results.Statistical techniques such as global sensitivity analysis or uncertainty analysis are employed to quantify these uncertainties.In this paper,an attempt is made to evaluate and select reasonable models available in the literature for characterizing fireballs and their consequences.Correlations were analyzed using statistical methods and BLEVE data(experimental and estimated data by correlation)to determine the residual sum of squares(RSS)and average absolute deviation(AAD).Analysis revealed that the Center for Chemical Process Safety(CCPS),the TNO(Netherlands Organization for Applied Scientific Research),and the Gayle model revealed a high degree of satisfaction between the experimental and estimated data through correlation.

Structural optimization of acceptor molecules guided by a semi-empirical model for organic solar cells with efficiency over 15%

Despite much progress in organic solar cells(OSCs),higher efficiency is still the most desirable goal and can indeed be obtained through rational design of active layer materials and device optimization according to the theoretical prediction.Herein,under the guidance of a semi-empirical model,two new non-fullerene small molecule acceptors(NFSMAs)with an acceptor-donor-acceptor(A-D-A)architecture,namely,6 T-OFIC and 5 T-OFIC,have been designed and synthesized.6 T-OFIC exhibits wider absorption spectrum and a red-shifted absorption onset(λ_(onset))of 946 nm due to its extended conjugation central unit.In contrast,5 T-OFIC with five-thiophene-fused backbone has an absorption with theλ_(onset)of 927 nm,which is closer to the predicted absorption range for the best single junction cells based on the semiempirical model.Consequently,the device based on 5 T-OFIC yields a higher power conversion efficiency(PCE)of 13.43%compared with 12.35%of the 6 T-OFIC-based device.Furthermore,an impressive PCE of 15.45%was achieved for the5 T-OFIC-based device when using F-2 Cl as the third component.5 T-OFIC offers one of a few acceptor cases with efficiencies over 15%other than Y6 derivatives.

Vapor intrusion in buildings: Development of semi-empirical models including lateral separation between the building and the pollution source

Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion(VI).VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future indoor environments.However,the assumptions in the existing models may reduce their accuracy when they do not meet the characteristics of real situations.Wrong estimations of indoor concentration levels may lead to inappropriate solutions against VI.In this context,new semi-empirical models(SEM)are proposed in order to better specify pollution scenarios and thus increase the accuracy of VI estimations.This development is based on a parametric study(numerical CFD)and a dimensionless analysis combined to existing VI models that consider a continuous source distribution in the soil.These expressions allow to better take into account the source position in the soil(i.e.depth and lateral source/building separation),soil properties(air permeability,diffusion coefficient of the pollutant,…)and building features(building foundation,indoor pressure,air exchange rate,…)in the estimation of indoor concentration levels.The obtained results with the proposed SEM were compared with a numerical CFD model and available experimental data,showing good accuracy in the estimation of VI.Given the advantages of these new models,they can provide better precision in the health risk assessments associated with VI.Furthermore,these expressions can be easily integrated into building ventilation codes allowing to consider air exchange rate and indoor pressure variations over time.

A synthetic semi-empirical physical model of secondary electron yield of metals under E-beam irradiation

Calculations of secondary electron yield（SEY） by physical formula can hardly accord with experimental results precisely. Simplified descriptions of internal electron movements in the calculation and complex surface contamination states of real sample result in notable difference between simulations and experiments. In this paper, in order to calculate SEY of metal under complicated surface state accurately, we propose a synthetic semi-empirical physical model. The processes of excitation of internal secondary electron（SE） and movement toward surface can be simulated using this model.This model also takes into account the influences of incident angle and backscattering electrons as well as the surface gas contamination. In order to describe internal electronic states accurately, the penetration coefficient of incident electron is described as a function of material atom number. Directions of internal electrons are set to be uniform in each angle. The distribution of internal SEs is proposed by considering both the integration convergence and the cascade scattering process.In addition, according to the experiment data, relationship among desorption gas quantities, sample ultimate temperature and SEY is established. Comparing with experiment results, this synthetic semi-empirical physical model can describe the SEY of metal better than former formulas, especially in the aspect of surface contaminated states. The proposed synthetic semi-empirical physical model and presented results in this paper can be helpful for further studying SE emission, and offer an available method for estimating and taking advantage of SE emission accurately.

An improved semi-empirical friction model for gas-liquid two-phase flow in horizontal and near horizontal pipes

Pressure drop and liquid hold-up are two very important fluid flow parameters in design and control of multiphase flow pipelines.Friction factors play an important role in the accurate calculation of pressure drop.Various empirical and semi-empirical closure relations exist in the literature to calculate the liquid-wall,gas-wall and interfacial friction in two-phase pipe flow.However most of them are empirical correlations found under special experimental conditions.In this paper by modification of a friction model available in the literature,an improved semiempirical model is proposed.The proposed model is incorporated in the two-fluid correlations under equilibrium conditions and solved.Pressure gradient and velocity profiles are validated against experimental data.Using the improved model,the pressure gradient deviation from experiments diminishes by about 3%;the no-slip condition at the interface is satisfied and the velocity profile is predicted in better agreement with the experimental data.

Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

Semi-empirical Model of the White Light Flare on September 19, 1979

The intensities of the continuum emission and the Call K line of the white light flare with 3B importance on September 19, 1979 are measured and analyzed. Their variations with time are given. It is indicated that the continuum emission of this flare appeared in the early impulsive phase and lasted for about 5～6 min, with the time of maximum intensity 2～3 min earlier than that for the microwave radio burst. Based on the non-LTE theory, a semi-emplrical model at a time with the continuum emission being relatively intensive is presented. The resuhs show that the temperature in the flare photosphere is increased by 150～250 K, and that the continuum emission is produced mainly by the negative hydrogen ion. Finally, some discussions about the heating mechanism are also presented, implying that the heating energy may come from the lower atmosphere itself.

Prediction of the theoretical and semi-empirical model of ambient temperature

It is well known that the ambient temperature is a sensitive parameter which has a great effect on biology, technology, geology and even on human behavior. A prediction is a statement about an uncertain event. It is often, but not always, based upon experience or knowl- edge. Although guaranteed accurate information about the future is in many cases impossible, prediction can be useful to assist in making plans about possible developments. As a result, temperature profiles can be developed which accurately represent the expected ambient temperature exposure that this environment experiences during mea- surement. The ambient temperature over time is modeled based on the previous Train and Tmax data and using a Lagrange interpolation. To observe the comprehensive variation of ambient temperature the profile must be determined numerically. The model proposed in this paper can provide an acceptable way to measure the change in ambient temperature.

Improvement of Bare Soil Semi-Empirical Radar Backscattering Models (Oh and Dubois) with SAR Multi-Spectral Satellite Data (X-, C- and L-Bands)

The objective of this study is to improve the performance of semi-empirical radar backscatter models, which are mainly used in microwave remote sensing (Oh 1992, Oh 2004 and Dubois). The study is based on satellite and ground data collected on bare soil surfaces during the Multispectral Crop Monitoring experimental campaign of the CESBIO laboratory in 2010 over an agricultural region in southwestern France. The dataset covers a wide range of soil (viewing top soil moisture, surface roughness and texture) and satellite (at different frequencies: X-, C- and L-bands, and different incidence angles: 24.3° to 53.3°) configurations. The proposed methodology consists in identifying and correcting the residues of the models, depending on the surface properties (roughness, moisture, texture) and/or sensor characteristics (frequency, incidence angle). Finally, one model has been retained for each frequency domain. Results show that the enhancements of the models significantly increase the simulation performances. The coefficient of correlation increases of 23% in mean and the simulation errors (RMSE) are reduced to below 2 dB (at the X and C-bands) and to 1 dB at the L-band, compared to the initial models. At the X- and C-bands, the best performances of the modified models are provided by Dubois, whereas Oh 2004 is more suitable for the L-band (r is equal to 0.69, 0.65 and 0.85). Moreover, the modified models of Oh 1992 and 2004 and Dubois, developed in this study, offer a wider domain of validity than the initial formalism and increase the capabilities of retrieving the backscattering signal in view of applications of such approaches to stronglycontrasted agricultural surface states.

Simulation of a DI Diesel Engine Performance Fuelled on Biodiesel Using a Semi-Empirical 0D Model

Diesel engines have proven over the years important in terms of efficiency and fuel consumption to power generation ratio. Many research works show the potential of biodiesel as a substitute for conventional gasoil. Mainly, previous and recent researches have focused on experimental investigation of diesel engine performance fuelled by biodiesel. Researches on the mathematical description of diesel engine process running on biodiesel are scarce, and mostly about chemical and thermodynamic description of the combustion process of biodiesel rather than performance studies. This work describes a numerical investigation on the performance analysis of a diesel engine fuelled by palm oil biodiesel. The numerical investigation was made using a semi empirical 0D model based on Wiebe’s and Watson’s model which was implemented via the open access numerical calculation software Scilab. The model was validated first by comparing with experimental pressure and performance data of a one cylinder engine at rated speed and secondly by comparing with a six cylinders engine performance data at various crankshaft rotational speeds. Simulations were then made to analyze the engine performance when running on biodiesel. The calculations were made at constant combustion duration and constant coefficient of excess air. Results showed that the model matches the overall experimental data, such as the power output and peak cylinder pressure. The ignition delay was somehow underestimated by the model for the first experiment, which caused a slight gap on in cylinder pressure curve, whereas it predicted the average ignition delay fairly well for the second set of validation. The simulations of engine performance when running on biodiesel confirmed results obtained in previous experimental researches on biodiesel. The model will be further investigated for engine control when shifting to biodiesel fuel.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Dynamics of a Stochastic Epidemic Model with Age-group

A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.

Improved Semi-empirical Model of Proton Exchange Membrane Fuel Cell Incorporating Fault Diagnostic Feature

The membrane water content of the proton exchange membrane fuel cell(PEMFC)is the most important feature required for water management of the PEMFC system.Any improper management of water in the fuel cell may lead to system faults.Among various faults,flooding and drying faults are the most frequent in the PEMFC systems.This paper presents a new dynamic semi-empirical model which requires only the load current and temperature of the PEMFC system as the input while providing output voltage and membrane water content as its major outputs.Unlike other PEMFC systems,the proposed dynamic model calculates the internal partial pressure of oxygen and hydrogen rather than using special internal sensors.Moreover,the membrane water content and internal resistances of PEMFC are modelled by incorporating the load current condition and temperature of the PEMFC system.The model parameters have been extracted by using a quantum lightening search algorithm as an optimization technique,and the performance is validated with experimental data obtained from the NEXA 1.2 k W PEMFC system.To further demonstrate the capability of the model in fault detection,the variation in membrane water content has been studied via the simulation.The proposed model could be efficiently used in prognostic and diagnosis systems of PEMFC fault.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.