High-resolution modeling and evaluation of ozone air quality of Osaka using MM5-CMAQ system

High-resolution modeling approach is increasingly being considered as a necessary step for improving the monitoring and predictions of regional air quality. This is especially true for highly urbanized region with complex terrain and land-use. This study uses Community Multiscale Air Quality （CMAQ） model coupled with MM5 mesoscale model for a comprehensive analysis to assess the suitability of such high-resolution modeling system in predicting ozone air quality in the complex terrains of Osaka, Japan. The 1-km and 3-kin grid domains were nested inside a 9-km domain and the domain with 1-km grid covered the Osaka region. High-resolution Grid Point Value-Mesoscale Model （GPV-MSM） data were used after suitable validation. The simulated ozone concentrations were validated and evaluated using statistical metrics using performance criteria set for ozone. Daily maxima of ozone were found better simulated by the 1-krn grid domain than the coarser 9-km and 3-km domains, with the maximum improvement in the mean absolute gross error about 3 ppbv. In addition, 1-km grid results fared better than other grids at most of the observation stations that showed noticeable differences in gross error as well as correlation. These results amply justify the use of the integrated high-resolution MM5-CMAQ modeling system in the highly urbanized region, such as the Osaka region, which has complex terrain and land-use.

A High-Resolution Modeling Strategy to Assess Impacts of Climate Change for Mesoamerica and the Caribbean

Mesoamerica and the Caribbean are low-latitude regions at risk for the effects of climate change. Global climate models provide large-scale assessment of climate drivers, but, at a horizontal resolution of 100 km, cannot resolve the effects of topography and land use as they impact the local temperature and precipitation that are keys to climate impacts. We developed a robust dynamical downscaling strategy that used the WRF regional climate model to downscale at 4 - 12 km resolution GCM results. Model verification demonstrates the need for such resolution of topography in order to properly simulate temperatures. Precipitation is more difficult to evaluate, being highly variable in time and space. Overall, a 36 km resolution is inadequate;12 km appears reasonable, especially in regions of low topography, but the 4 km resolution provides the best match with observations. This represents a tradeoff between model resolution and the computational effort needed to make simulations. A key goal is to provide climate change specialists in each country with the information they need to evaluate possible future climate change impacts.

Migration images guided high-resolution velocity modeling based on fully convolutional neural network

Current data-driven deep learning(DL)methods typically reconstruct subsurface velocity models directly from pre-stack seismic records.However,these purely data-driven methods are often less robust and produce results that are less physically interpretative.Here,the authors propose a new method that uses migration images as input,combined with convolutional neural networks to construct high-resolution velocity models.Compared to directly using pre-stack seismic records as input,the nonlinearity between migration images and velocity models is significantly reduced.Additionally,the advantage of using migration images lies in its ability to more comprehensively capture the reflective properties of the subsurface medium,including amplitude and phase information,thereby to provide richer physical information in guiding the reconstruction of the velocity model.This approach not only improves the accuracy and resolution of the reconstructed velocity models,but also enhances the physical interpretability and robustness.Numerical experiments on synthetic data show that the proposed method has superior reconstruction performance and strong generalization capability when dealing with complex geological structures,and shows great potential in providing efficient solutions for the task of reconstructing high-wavenumber components.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

High-resolution neutronics model for ^(238)Pu production in high-flux reactors

We proposed and compared three methods(filter burnup,single energy burnup,and burnup extremum analysis)to build a high-resolution neutronics model for 238Pu production in high-flux reactors.The filter burnup and single energy burnup methods have no theoretical approximation and can achieve a spectrum resolution of up to~1 eV,thereby constructing the importance curve and yield curve of the full energy range.The burnup extreme analysis method combines the importance and yield curves to consider the influence of irradiation time on production efficiency,thereby constructing extreme curves.The three curves,which quantify the transmutation rate of the nuclei in each energy region,are of physical significance because they have similar distributions.A high-resolution neutronics model for ^(238)Pu production was established based on these three curves,and its universality and feasibility were proven.The neutronics model can guide the neutron spectrum optimization and improve the yield of ^(238)Pu by up to 18.81%.The neutronics model revealed the law of nuclei transmutation in all energy regions with high spectrum resolution,thus providing theoretical support for high-flux reactor design and irradiation production of ^(238)Pu.

Investigating the relationship between intracranial atherosclerotic plaque remodelling and diabetes using high-resolution vessel wall imaging

BACKGROUND Intracranial atherosclerosis,a leading cause of stroke,involves arterial plaque formation.This study explores the link between plaque remodelling patterns and diabetes using high-resolution vessel wall imaging(HR-VWI).AIM To investigate the factors of intracranial atherosclerotic remodelling patterns and the relationship between intracranial atherosclerotic remodelling and diabetes mellitus using HR-VWI.METHODS Ninety-four patients diagnosed with middle cerebral artery or basilar artery INTRODUCTION Intracranial atherosclerotic disease is one of the main causes of ischaemic stroke in the world,accounting for approx-imately 10%of transient ischaemic attacks and 30%-50%of ischaemic strokes[1].It is the most common factor among Asian people[2].The adaptive changes in the structure and function of blood vessels that can adapt to changes in the internal and external environment are called vascular remodelling,which is a common and important pathological mechanism in atherosclerotic diseases,and the remodelling mode of atherosclerotic plaques is closely related to the occurrence of stroke.Positive remodelling(PR)is an outwards compensatory remodelling where the arterial wall grows outwards in an attempt to maintain a constant lumen diameter.For a long time,it was believed that the degree of stenosis can accurately reflect the risk of ischaemic stroke[3-5].Previous studies have revealed that lesions without significant luminal stenosis can also lead to acute events[6,7],as summarized in a recent meta-analysis study in which approximately 50%of acute/subacute ischaemic events were due to this type of lesion[6].Research[8,9]has pointed out that the PR of plaques is more dangerous and more likely to cause acute ischaemic stroke.Previous studies[10-13]have found that there are specific vascular remodelling phenomena in the coronary and carotid arteries of diabetic patients.However,due to the deep location and small lumen of intracranial arteries and limitations of imaging techniques,the relationship between intracranial arterial remodelling and diabetes is still unclear.In recent years,with the development of magnetic resonance technology and the emergence of high-resolution(HR)vascular wall imaging,a clear and multidimensional display of the intracranial vascular wall has been achieved.Therefore,in this study,HR wall imaging(HR-VWI)was used to display the remodelling characteristics of bilateral middle cerebral arteries and basilar arteries and to explore the factors of intracranial vascular remodelling and its relationship with diabetes.

Down-scaled regional ocean modeling system (ROMS) for high-resolution coastal hydrodynamics in Korea

A down-scaled operational oceanographic system is developed for the coastal waters of Korea using a re- gional ocean modeling system （ROMS）. The operational oceanographic modeling system consists of at- mospheric and hydrodynamic models. The hydrodynamic model, ROMS, is coupled with wave, sediment transport, and water quality modules. The system forecasts the predicted results twice a day on a 72 h basis, including sea surface elevation, currents, temperature, salinity, storm surge height, and wave information for the coastal waters of Korea. The predicted results are exported to the web-GIS-based coastal informa- tion system for real-time dissemination to the public and validation with real-time monitoring data using visualization technologies. The ROMS is two-way coupled with a simulating waves nearshore model, SWAN, for the hydrodynamics and waves, nested with the meteorological model, WRE for the atmospheric surface forcing, and externally nested with the eutrophication model, CE-QUAL-ICM, for the water quality. The op- erational model, ROMS, was calibrated with the tidal surface observed with a tide-gage and verified with current data observed by bottom-mounted ADCP or AWAC near the coastal waters of Korea. To validate the predicted results, we used real-time monitoring data derived from remote buoy system, HF-radar, and geostationary ocean color imager （GOCI）. This down-scaled operational coastal forecasting system will be used as a part of the Korea operational oceanographic system （KOOS） with other operational oceanographic systems.

Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

TOPMODEL Hydrometeorological Modeling with Rain Gauge Data Integrated by High-Resolution Satellite Estimates. A Case Study in MuriaéRiver Basin, Brazil

This study consists of hydrological simulations of the Muriaé river watershed with the topography-based hydrological model (TOPMODEL) and available stream gauge and rain measurements between 2009 and 2013 for two subbasins, namely Carangola and Patrocínio do Muriaé. The simulations were carried out with the Climate Prediction Center morphing method (CMORPH) precipitation estimates and rain gauge measurements integrated into CM- ORPH by the Statistical Objective Analysis Scheme (SOAS). TOPMODEL calibration was performed with the shuffled complex evolution (SCE-UA) method with Nash-Sutcliffe efficiency (NSE). The best overall results were obtained with CMORPH (NSE ~ 0.6) for both subbasins. The simulations with SOAS resulted in an NSE ~ 0.2. However, in an analysis of days with high- level stages, SOAS simulations resulted in a better hit rate (23%) compared to CMORPH (10%). CMORPH simulations underestimated the flows at the flood periods, which indicates the importance to use multi-sensor precipitation data. The results with TOPMODEL allow an estimate of future discharges, which allows for better planning of a flood warning system and discharge measurement schedule.

Sands modeling constrained by high-resolution seismic data

In the phase of field evaluation, the changing of interwell reservoir may be out of control if the geological model was built only on well data due to few existing wells. The uncertainty of the interwell reservoir interpolation based only on well data can be decreased by comprehensive utilization of geological, logging and seismic data, especially by using highly relative seismic properties from 3D seismic data adjusted by well point data to restrict interpolation of geological properties. A 3D-geological model which takes the sand body as the direct modeling object was built through stacking the structure, reservoir and water/oil/gas properties together in 3D space.

Overview on high-resolution ocean modeling in JAMSTEC

In view of the importance of ocean component for representing climate change,efforts are underway to implement a high-resolution nesting model system in Model for Interdisciplinary Research on Climate(MIROC) for the North Pacific using the same ocean model as used in the coupled model MIROC5. By comparing double(10 km for the northwestern Pacific,50 km for the rest of the Pacific) and triple(double nesting plus 2 km resolution near Japan) nesting,it turns out that relative vorticity is drastically enhanced near Japan with 2 km resolution. It is hoped that such an elaborated nesting system will reveal detailed processes for the ocean heat uptake by,e.g.,intermediate water and mode water formation for which the"perturbed region"near Japan is the key region.

An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.

Case study on the three-dimensional structure of meso-scale eddy in the South China Sea based on a high-resolution model

Meso-scale eddies are important features in the South China Sea（SCS）. The eddies with diameters of 50–200 km can greatly impact the transport of heat, momentum, and tracers. A high-resolution wave-tide-circulation coupled model was developed to simulate the meso-scale eddy in the SCS in this study. The aim of this study is to examine the model ability to simulate the meso-scale eddy in the SCS without data assimilations The simulated Sea Surface Height（SSH） anomalies agree with the observed the AVISO SSH anomalies well. The simulated subsurface temperature profiles agree with the CTD observation data from the ROSE（Responses of Marine Hazards to climate change in the Western Pacific） project. The simulated upper-ocean currents also agree with the main circulation based on observations. A warm eddy is identified in winter in the northern SCS. The position and domain of the simulated eddy are confirmed by the observed sea surface height data from the AVISO. The result shows that the model has the ability to simulate the meso-scale eddy in the SCS without data assimilation.The three-dimensional structure of the meso-scale eddy in the SCS is analyzed using the model result. It is found that the eddy center is tilted vertically, which agrees with the observation. It is also found that the velocity center of the eddy does not coincide with the temperature center of the eddy. The result shows that the model has the ability to simulate the meso-scale eddy in the SCS without data assimilations. Further study on the forming mechanism and the three-dimensional structure of the meso-scale eddies will be carried out using the model result and cruise observation data in the near future.

Projection of Future Precipitation Change over China with a High-Resolution Global Atmospheric Model

Projections of future precipitation change over China are studied based on the output of a global AGCM, ECHAM5, with a high resolution of T319 （equivalent to 40 km）. Evaluation of the model’s performance in simulating present-day precipitation shows encouraging results. The spatial distributions of both mean and extreme precipitation, especially the locations of main precipitation centers, are reproduced reasonably. The simulated annual cycle of precipitation is close to the observed. The performance of the model over eastern China is generally better than that over western China. A weakness of the model is the overestimation of precipitation over northern and western China. Analyses on the potential change in precipitation projected under the A1B scenario show that both annual mean precipitation intensity and extreme precipitation would increase significantly over southeastern China. The percentage increase in extreme precipitation is larger than that of mean precipitation. Meanwhile, decreases in mean and extreme precipitation are evident over the southern Tibetan Plateau. For precipitation days, extreme precipitation days are projected to increase over all of China. Both consecutive dry days over northern China and consecutive wet days over southern China would decrease.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Computational Performance of the High-Resolution Atmospheric Model FAMIL

This paper describes the model speed and model In/Out （I/O） efficiency of the high-resolution atmospheric general circulation model FAMIL （Finite- volume Atmospheric Model of IAP/LASG） at the National Supercomputer Center in Tianjin, China, on its Tianhe-lA supercomputer platform. A series of three- model-day simulations were carried out with standard Aqua Planet Experiment （APE） designed within FAMIL to obtain the time stamp for the calculation of model speed, simulation cost, and model 1/O efficiency. The results of the simulation demonstrate that FAMIL has remarkable scalability below 3456 and 6144 cores, and the lowest simulation costs are 1536 and 3456 cores for 12.5 km and 6.25 krn resolutions, respectively. Furthermore, FAMIL has excellent I/O scalability and an efficiency of more than 80% on 6 I/Os and more than 99% on 1536 I/Os.

Volume-averaged modeling of multiphase solidification with equiaxed crystal sedimentation in a steel ingot

Macrosegregation is a critical factor that limits the mechanical properties of materials.The impact of equiaxed crystal sedimentation on macrosegregation has been extensively studied,as it plays a significant role in determining the distribution of alloying elements and impurities within a material.To improve macrosegregation in steel connecting shafts,a multiphase solidification model that couples melt flow,heat transfer,microstructure evolution,and solute transport was established based on the volume-averaged Eulerian-Eulerian approach.In this model,the effects of liquid phase,equiaxed crystals,columnar dendrites,and columnar-to-equiaxed transition(CET)during solidification and evolution of microstructure can be considered simultaneously.The sedimentation of equiaxed crystals contributes to negative macrosegregation,where regions between columnar dendrites and equiaxed crystals undergo significant A-type positive macrosegregation due to the CET.Additionally,noticeable positive macrosegregation occurs in the area of final solidification in the ingot.The improvement in macrosegregation is beneficial for enhancing the mechanical properties of connecting shafts.To mitigate the thermal convection of molten steel resulting from excessive superheating,reducing the superheating during casting without employing external fields or altering the design of the ingot mold is indeed an effective approach to control macrosegregation.

Predicting the probability distribution of Martian rocks mechanical property based on microscale rock mechanical experiments and accurate grain-based modeling

The exploration of Mars would heavily rely on Martian rocks mechanics and engineering technology.As the mechanical property of Martian rocks is uncertain,it is of utmost importance to predict the probability distribution of Martian rocks mechanical property for the success of Mars exploration.In this paper,a fast and accurate probability distribution method for predicting the macroscale elastic modulus of Martian rocks was proposed by integrating the microscale rock mechanical experiments(micro-RME),accurate grain-based modeling(AGBM)and upscaling methods based on reliability principles.Firstly,the microstructure of NWA12564 Martian sample and elastic modulus of each mineral were obtained by micro-RME with TESCAN integrated mineral analyzer(TIMA)and nanoindentation.The best probability distribution function of the minerals was determined by Kolmogorov-Smirnov(K-S)test.Secondly,based on best distribution function of each mineral,the Monte Carlo simulations(MCS)and upscaling methods were implemented to obtain the probability distribution of upscaled elastic modulus.Thirdly,the correlation between the upscaled elastic modulus and macroscale elastic modulus obtained by AGBM was established.The accurate probability distribution of the macroscale elastic modulus was obtained by this correlation relationship.The proposed method can predict the probability distribution of Martian rocks mechanical property with any size and shape samples.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Surrogate modeling for unsaturated infiltration via the physics and equality-constrained artificial neural networks

Machine learning(ML)provides a new surrogate method for investigating groundwater flow dynamics in unsaturated soils.Traditional pure data-driven methods(e.g.deep neural network,DNN)can provide rapid predictions,but they do require sufficient on-site data for accurate training,and lack interpretability to the physical processes within the data.In this paper,we provide a physics and equalityconstrained artificial neural network(PECANN),to derive unsaturated infiltration solutions with a small amount of initial and boundary data.PECANN takes the physics-informed neural network(PINN)as a foundation,encodes the unsaturated infiltration physical laws(i.e.Richards equation,RE)into the loss function,and uses the augmented Lagrangian method to constrain the learning process of the solutions of RE by adding stronger penalty for the initial and boundary conditions.Four unsaturated infiltration cases are designed to test the training performance of PECANN,i.e.one-dimensional(1D)steady-state unsaturated infiltration,1D transient-state infiltration,two-dimensional(2D)transient-state infiltration,and 1D coupled unsaturated infiltration and deformation.The predicted results of PECANN are compared with the finite difference solutions or analytical solutions.The results indicate that PECANN can accurately capture the variations of pressure head during the unsaturated infiltration,and present higher precision and robustness than DNN and PINN.It is also revealed that PECANN can achieve the same accuracy as the finite difference method with fewer initial and boundary training data.Additionally,we investigate the effect of the hyperparameters of PECANN on solving RE problem.PECANN provides an effective tool for simulating unsaturated infiltration.