A comparative study of seismic tomography models of Southwest China

The margin of the Tibetan Plateau of Southwest China is one of the most seismically active regions of China and is the location of the China Seismic Experimental Site(CSES).Many studies have developed seismic velocity models of Southwest China,but few have compared and evaluated these models which is important for further model improvement.Thus,we compared six published seismic shear-wave velocity models of Southwest China on absolute velocity and velocity perturbation patterns.The models are derived from different types of data(e.g.,surface waves from ambient noise and earthquakes,body-wave travel times,receiver functions) and inversion methods.We interpolated the models into a uniform horizontal grid(0.5° × 0.5°) and vertically sampled them at 5,10,20,30,40,and 60 km depths.We found significant differences between the six models.Then,we selected three of them that showed greater consistency for further comparison.Our further comparisons revealed systematic biases between models in absolute velocity that may be related to different data types.The perturbation pattern of the model is especially divergent in the shallow part,but more consistent in the deep part.We conducted synthetic and inversion tests to explore possible causes and our results imply that systematic differences between the data,differences in methods,and other factors may directly affect the model.Therefore,the Southwest China velocity model still has considerable room for improvement,and the impact of inconsistency between different data types on the model needs further research.Finally,we proposed a new reference shear-wave velocity model of Southwest China(SwCM-S1.0) based on the three selected models with high consistency.We believe that this model is a better representation of more robust features of the models that are based on different data sets.

Accuracy comparison of dry matter intake prediction models evaluated by a feeding trial of lactating dairy cows fed two total mixed rations with different forage source

Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source. Thirty-two cows were randomly assigned to one of two total mixed ration groups： a ration containing a mixed forage （MF） of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover （CS） as unique forage source. The actual DMI was greater in MF group than in CS group （P=0.064）. The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups （1.09 kg d-1 vs. 1.28 kg d-1） and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet （0.89 vs. 0.87）. Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values （P〈0.001）. The DMI variation in MF was caused by week of lactation （55.6%）, milk yield （13.9%）, milk fat percentage （7.1%） and dietary neutral detergent fiber （13.3%）, while the variation in CS was caused by week of lactation （50.9%）, live body weight （28.2%）, milk yield （8.4%）, milk fat percentage （5.2%） and dietary neutral detergent fibre （3.8%）. In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.

Comparison of three mathematical prediction models in patients with a solitary pulmonary nodule

Background： Effective methods for managing patients with solitary pulmonary nodules（SPNs） depend critically on the predictive probability of malignancy.Methods： Between July 2009 and June 2011, data on gender, age, cancer history, tumor familial history, smoking status, tumor location, nodule size, spiculation, calcification, the tumor border, and the final pathological diagnosis were collected retrospectively from 154 surgical patients with an SPN measuring 3-30 mm. Each final diagnosis was compared with the probability calculated by three predicted models—the Mayo, VA, and Peking University（PU） models. The accuracy of each model was assessed using area under the receiver operating characteristics（ROC） and calibration curves.Results： The area under the ROC curve of the PU model [0.800; 95% confidence interval（CI）： 0.708-0.891] was higher than that of the Mayo model（0.753; 95% CI： 0.650-0.857） or VA model（0.728; 95% CI： 0.623-0.833）; however, this finding was not statistically significant. To varying degrees, calibration curves showed that all three models overestimated malignancy.Conclusions： The three predicted models have similar accuracy for prediction of SPN malignancy, although the accuracy is not sufficient. For Chinese patients, the PU model may has greater predictive power.Background： Here, we introduced our short experience on the application of a new CUSA Excel ultrasonic aspiration system, which was provided by Integra Lifesciences corporation, in skull base meningiomas resection.Methods： Ten patients with anterior, middle skull base and sphenoid ridge meningioma were operated using the CUSA Excel ultrasonic aspiration system at the Neurosurgery Department of Shanghai Huashan Hospital from August 2014 to October 2014. There were six male and four female patients, aged from 38 to 61 years old（the mean age was 48.5 years old）. Five cases with tumor located at anterior skull base, three cases with tumor on middle skull base, and two cases with tumor on sphenoid ridge.Results： All the patents received total resection of meningiomas with the help of this new tool, and the critical brain vessels and nerves were preserved during operations. All the patients recovered well after operation.Conclusions： This new CUSA Excel ultrasonic aspiration system has the advantage of preserving vital brain arteries and cranial nerves during skull base meningioma resection, which is very important for skull base tumor operations. This key step would ensure a well prognosis for patients. We hope the neurosurgeons would benefit from this kind of technique.Background： The purposes of this study were to explore the effects of high mobility group protein box 1（HMGB1） gene on the growth, proliferation, apoptosis, invasion, and metastasis of glioma cells, with an attempt to provide potential therapeutic targets for the treatment of glioma. Methods： The expressions of HMGB1 in glioma cells（U251, U-87 MG and LN-18） and one control cell line（SVG p12） were detected by real time PCR and Western blotting, respectively. Then, the effects of HMGB1 on the biological behaviors of glioma cells were detected： the expression of HMGB1 in human glioma cell lines U251 and U-87 MG were suppressed using RNAi technique, then the influences of HMGB1 on the viability, cycle, apoptosis, and invasion abilities of U251 and U-87 MG cells were analyzed using in a Transwell invasion chamber. Also, the effects of HMGB1 on the expressions of cyclin D1, Bax, Bcl-2, and MMP 9 were detected. Results： As shown by real-time PCR and Western blotting, the expression of HMGB1 significantly increased in glioma cells（U251, U-87 MG, and LN-18） in comparison with the control cell line（SVG p12）; the vitality, proliferation and invasive capabilities of U251 and U-87 MG cells in the HMGB1 siR NA-transfected group were significantly lower than those in the blank control group and negative control（NC） siR NA group（P〈0.05） but showed no significant difference between the blank control group and NC siR NA group. The percentage of apoptotic U251 and U-87 MG cells was significantly higher in the HMGB1 siR NA-transfected group than in the blank control group and NC siR NA group（P〈0.05） but was similar between the latter two groups. The HMGB1 siR NA-transfected group had significantly lower expression levels of Cyclin D1, Bcl-2, and MMP-9 protein in U251 and U-87 MG cells and significantly higher expression of Bax protein than in the blank control group and NC siR NA group（P〈0.05）; the expression profiles of cyclin D1, Bax, Bcl-2, and MMP 9 showed no significant change in both blank control group and NC siR NA group. Conclusions： HMGB1 gene may promote the proliferation and migration of glioma cells and suppress its effects of apoptosis. Inhibition of the expression of HMGB1 gene can suppress the proliferation and migration of glioma cells and promote their apoptosis. Our observations provided a new target for intervention and treatment of glioma.

Comparison of Ten Potential Evapotranspiration Models and Their Attribution Analyses for Ten Chinese Drainage Basins

Potential evapotranspiration(EPET)is usually calculated by empirical methods from surface meteorological variables,such as temperature,radiation and wind speed.The in-situ measured pan evaporation(ETpan)can also be used as a proxy for EPET.In this study,EPET values computed from ten models are compared with observed ETpan data in ten Chinese river basins for the period 1961−2013.The daily observed meteorological variables at 2267 stations are used as the input to those models,and a ranking scheme is applied to rank the statistical quantities(ratio of standard deviations,correlation coefficient,and ratio of trends)between ETpan and modeled EPET in different river basins.There are large deviations between the modeled EPET and the ETpan in both the magnitude and the annual trend at most stations.In eight of the basins(except for Southeast and Southwest China),ETpan shows decreasing trends with magnitudes ranging between−0.01 mm d−1 yr−1 and−0.03 mm d−1 yr−1,while the decreasing trends in modeled EPET are less than−0.01 mm d−1 yr−1.Inter comparisons among different models in different river basins suggest that PETHam1 is the best model in the Pearl River basin,PETHam2 outperforms other models in the Huaihe River,Yangtze River and Yellow River basins,and PETFAO is the best model for the remaining basins.Sensitivity analyses reveal that wind speed and sunshine duration are two important factors for decreasing EPET in most basins except in Southeast and Southwest China.The increasing EPET trend in Southeast China is mainly attributed to the reduced relative humidity.

A comparative study of seismic tomography models of the Chinese continental lithosphere

The Chinese mainland is subject to complicated plate interactions that give rise to its complex structure and tectonics. While several seismic velocity models have been developed for the Chinese mainland, apparent discrepancies exist and, so far, little effort has been made to evaluate their reliability and consistency. Such evaluations are important not only for the application and interpretation of model results but also for future model improvement. To address this problem, here we compare five published shear-wave velocity models with a focus on model consistency. The five models were derived from different datasets and methods (i.e., body waves, surface waves from earthquakes, surface waves from noise interferometry, and full waves) and interpolated into uniform horizontal grids (0.5° × 0.5°) with vertical sampling points at 5 km, 10 km, and then 20 km intervals to a depth of 160 km below the surface, from which we constructed an averaged model (AM) as a common reference for comparative study. We compare both the absolute velocity values and perturbation patterns of these models. Our comparisons show that the models have large (> 4%) differences in absolute values, and these differences are independent of data coverage and model resolution. The perturbation patterns of the models also show large differences, although some of the models show a high degree of consistency within certain depth ranges. The observed inconsistencies may reflect limited model resolution but, more importantly, systematic differences in the datasets and methods employed. Thus, despite several seismic models being published for this region, there is significant room for improvement. In particular, the inconsistencies in both data and methodologies need to be resolved in future research. Finally, we constructed a merged model (ChinaM-S1.0) that incorporates the more robust features of the five published models. As the existing models are constrained by different datasets and methods, the merged model serves as a new type of reference model that incorporates the common features from the joint datasets and methods for the shear-wave velocity structure of the Chinese mainland lithosphere.

A Model Comparison Study of Fragment Production in 140 A MeV 58,64Ni+9Be Reactions

The cross sections of fragments produced in the 140 A MeV58,64Ni＋9 Be projectile fragmentation reactions are calculated by using the antisymmetrized molecular dynamics （AMD） model, the modified statistical abrasion- ablation （SAA） model, and the empirical EPAX2/EPAX3 formulae. The Gogny-gO interaction is taken as the effective nucleon-nucleon interaction in the AMD calculation, and the decays of fragments obtained from the AMD results are calculated by using the GEMINI code. The calculated cross sections of fragments are compared.

Comparison of coupled and uncoupled models in simulating Monsoon Intraseasonal Oscillation from CMIP6

The monsoon intraseasonal oscillation(MISO)is the dominant variability over the Indian Ocean during the Indian summer monsoon(ISM)season and is characterized by pronounced northward propagation.Previous studies have shown that general circulation models(GCMs)still have difficulty in simulating the northwardpropagating MISO,and that the role of air-sea interaction in MISO is unclear.In this study,14 atmosphere-ocean coupled GCMs(CGCMs)and the corresponding atmosphere-only GCMs(AGCMs)are selected from Phase 6 of the Coupled Model Intercomparison Project(CMIP6)to assess their performance in reproducing MISO and the associated vortex tilting mechanism.The results show that both CGCMs and AGCMs are able to well simulate the significant relationship between MISO and vortex tilting.However,80%of CGCMs show better simulation skills for MISO than AGCMs in CMIP6.In AGCMs,the poor model fidelity in MISO is due to the failure simulation of vortex tilting.Moreover,it is found that failure to simulate the downward motion to the north of convection is responsible for the poor simulation of vortex tilting in AGCMs.In addition,it is observed that there is a significant relationship between the simulated sea surface temperature gradient and simulated vertical velocity shear in the meridional direction.These findings indicate that air-sea interaction may play a vital role in simulating vertical motions in tilting and MISO processes.This work offers us a specific target to improve the MISO simulation and further studies are needed to elucidate the physical processes of this air-sea interaction coupling with vortex tilting.

Software Testing Method Based on Model Comparison

A model comparison based software testing method （MCST） is proposed. In this method, the requirements aria programs or software under test are transformed into the ones in the same form, and described by the same model describe language （MDL）. Then, the requirements are transformed into a specification model and the programs into an implementation model. Thus, the elements and structures of the two models are compared, and the differences between them are obtained. Based on the diffrences, a test suite is generated. Different MDLs can be chosen for the software under test. The usages of two classical MDLs in MCST, the equivalence classes model and the extended finite state machine （EFSM） model, are described with example applications. The results show that the test suites generated by MCST are more efficient and smaller than some other testing methods, such as the pathcoverage testing method, the object state diagram testing method, etc.

Analysis of the Resolution of Crime Using Predictive Modeling

There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.

Classification of Acupuncture Points Based on the Bert Model*

In this paper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the best main acupuncture point for treating the disease by classifying and predicting the main acupuncture point for the disease, and further explore its acupuncture point grouping to provide the medical practitioner with the optimal solution for treating the disease and improving the clinical decision-making ability. The Bert-Chinese-Acupoint model was constructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were added to the acupuncture point corpus in the fine-tuning process to increase the semantic features in terms of acupuncture points, and compared with the machine learning method. The results show that the Bert-Chinese Acupoint model proposed in this paper has a 3% improvement in accuracy compared to the best performing model in the machine learning approach.

A New Activity-Based Cost (ABC) Mathematical Model

Along with the product price competition growing intensely, it is apparently important for reasonably distributing and counting cost. But, in sharing indirect cost, traditional cost accounting unveils the limitations increasingly, especially in authenticity of cost information. And the accounting theory circles and industry circles begin seeking one kind of new accurate cost calculation method, and the activity based cost (ABC) method emerges as the times require. In this paper, we will build its mathematical model by the basic principle of ABC, and will improve its mathematical model further. We will establish its comparison mathematical model and make the ABC method go a step further to its practical application.

Evaluation of Spring Persistent Rainfall over East Asia in CMIP3/CMIP5 AGCM Simulations

The progress made fi＇om Phase 3 to Phase 5 of the Coupled Model Intercomparison Project （CMIP3 to CMIP5） in simulating spring persistent rainfall （SPR） over East Asia was examined from the outputs of nine atmospheric general circulation models （AGCMs）. The majority of the models overestimated the precipitation over the SPR domain, with the mean latitude of the SPR belt shifting to the north. The overestimation was about 1mm d-1 in the CMIP3 ensemble, and the northward displacement was about 3°, while in the CMIP5 ensemble the overestimation was suppressed to 0.7 mm d-i and the northward shift decreased to 2.5°. The SPR features a northeast-southwest extended rain belt with a slope of 0.4°N/°E. The CMIP5 ensemble yielded a smaller slope （0.2°N/°E）, whereas the CMIP3 ensemble featured an unre- alistic zonally-distributed slope. The CMIP5 models also showed better skill in simulating the interannual variability of SPR. Previous studies have suggested that the zonal land-sea thermal contrast and sensible heat flux over the southeastern Tibetan Plateau are important for the existence of SPR. These two ther- mal factors were captured well in the CMIP5 ensemble, but underestimated in the CMIP3 ensemble. The variability of zonal land-sea thermal contrast is positively correlated with the rainfall amount over the main SPR center, but it was found that an overestimated thermal contrast between East Asia and South China Sea is a common problem in most of the CMIP3 and CMIP5 models. Simulation of the meridional thermal contrast is therefore important for the future improvement of current AGCMs.

Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease

Cardiovascular disease (CVD) is a leading cause of death across the globe. Approximately 17.9 million of people die globally each year due to CVD, which comprises 31% of all death. Coronary Artery Disease (CAD) is a common type of CVD and is considered fatal. Predictive models that use machine learning algorithms may assist health workers in timely detection of CAD which ultimately reduces the mortality. The main purpose of this study is to build a predictive model that provides doctors and health care providers with personalized information to implement better and more personalized treatments for their patients. In this study, we use the publicly available Z-Alizadeh Sani dataset which contains random samples of 216 cases with CAD and 87 normal controls with 56 different features. The binary variable “Cath” which represents case-control status, is used the target variable. We study its relationship with other predictors and develop classification models using the five different supervised classification machine learning algorithms: Logistic Regression (LR), Classification Tree with Bagging (Bagging CART), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These five classification models are used to investigate the detection of CAD. Finally, the performance of the machine learning algorithms is compared, and the best model is selected. Our results indicate that the SVM model is able to predict the presence of CAD more effectively and accurately than other models with an accuracy of 0.8947, sensitivity of 0.9434, specificity of 0.7826, and AUC of 0.8868.

Flux Footprint Climatology Estimated by Three Analytical Models over a Subtropical Coniferous Plantation in Southeast China

Spatial heterogeneity poses a major challenge for the appropriate interpretation of eddy covariance data. The quantification of footprint climatology is fundamental to improving our understanding of carbon budgets, assessing the quality of eddy covariance data, and upscaling the representativeness of a tower flux to regional or global scales. In this study, we elucidated the seasonal variation of flux footprint climatologies and the major factors that influence them using the analytical FSAM （Flux Source Area Model）, KM （Kormann and Meixner, 2001）, and H （Hsieh et al., 2000） models based on eddy covariance measurements at two and three times the canopy height at the Qianyanzhou site of ChinaFLUX in 2003. The differences in footprints among the three models resulted from different underlying theories used to construct the models. A comparison demonstrated that atmospheric stability was the main factor leading to differences among the three models. In neutral and stable conditions,the KM and FSAM values agreed with each other, but they were both lower than the H values. In unstable conditions, the agreement among the three models for rough surfaces was better than that for smooth surfaces, and the models showed greater agreement for a low measurement height than for a high measurement height. The seasonal flux footprint climatologies were asymmetrically distributed around the tower and corresponded well to the prevailing wind direction, which was north-northwest in winter and south-southeast in summer. The average sizes of the 90% flux footprint climatologies were 0.36 0.74 and 1.5-3.2 kin2 at altitudes of two and three times the canopy height, respectively. The average sizes were ranked by season as follows： spring 〉 summer 〉 winter 〉 autumn. The footprint climatology depended more on atmospheric stability on daily scale than on seasonal scale, and it increased with the increasing standard deviation of the lateral wind fluctuations.

ON THE RELATIONSHIP BETWEEN PLANETARY AND SURFACE ALBEDO:MODEL'S COMPARISON AND VALIDATION

The simple linear relationship between clear-sky planetary and surface albedo can be adopted for certain accuracy.There are different parameterization schemes of atmospheric correction for different retrieval models.In this paper,several representative retrieval models are compared and tested with observational data from HEIhe basin Field Experiment (HEIFE) in western China.Some evaluations and suggestions on improvement are proposed for models which would be more applicable to plateau and arid areas.

Review of onsite temperature and solar forecasting models to enable better building design and operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.

DIAGNOSABILITY OF CAYLEY GRAPH NETWORKS GENERATED BY TRANSPOSITION TREES UNDER THE COMPARISON DIAGNOSIS MODEL

Diagnosability of a multiprocessor system is one important study topic.Cayley graph network Cay（Tn,Sn） generated by transposition trees Tnis one of the attractive underlying topologies for the multiprocessor system.In this paper,it is proved that diagnosability of Cay（Tn,Sn） is n-1 under the comparison diagnosis model for n ≥ 4.

Viscoelastic Models for Passive Arterial Wall Dynamics

This paper compares two models predicting elastic and viscoelastic properties of large arteries.Models compared include a Kelvin(standard linear)model and an extended 2-term exponential linear viscoelastic model.Models were validated against in-vitro data from the ovine thoracic descending aorta and the carotid artery.Measurements of blood pressure data were used as an input to predict vessel cross-sectional area.Material properties were predicted by estimating a set of model parameters that minimize the difference between computed and measured values of the cross-sectional area.The model comparison was carried out using generalized analysis of variance type statistical tests.For the thoracic descending aorta,results suggest that the extended 2-term exponential model does not improve the ability to predict the observed cross-sectional area data,while for the carotid artery the extended model does statistically provide an improved fit to the data.This is in agreement with the fact that the aorta displays more complex nonlinear viscoelastic dynamics,while the stiffer carotid artery mainly displays simpler linear viscoelastic dynamics.

Numerical tests for tropical cyclone track prediction by the global WRF model

This work use the global WRF model containing quadruply nesting with which the highest resolution reached 10 km to simulate the typhoons landed on the coast of China in 2011.The model is driven by the reanalysis data fnl with the resolution of 1°x 1°.The study assess the feasibility and applicability of the global WRF model in the 1-7 days prediction of Tropical Cyclone(TC)track by comparing it with the regional WRF model containing the same setting(physical scheme,dynamical frame,model resolution and nesting grid domain).The global model obtain a similar forecast accuracy to the regional model in 1-7 days,with a difference less than 50 km.The forecast accuracy of the global model for 1,2,3,4,5,6 and 7 days is about 70 km,120 km,180 km,240 km,320 km,400 km,and 500 km,respectively.The reason of the significant TC track errors in the forecast more than 3 or 4 days is analyzed,it is due to the poor representation of typhoon and its steering flow under the situation of binary typhoon system.The study show that the global WRF model can be exploited to proceed the high resolution TC simulation and make the TC track forecast up to 7 days but not in the case of multiple typhoon.

A Comparative Analysis of Performance-Based Resilience Metrics via a Quantitative-Qualitative Combined Approach: Are We Measuring the Same Thing?

Since the proposal of the pioneering“resilience triangle”paradigm,various time-series performance-based metrics have been devised for resilience quantification.The numerous choices diversify the toolbox for measuring this compound system concept;however,this multiplicity causes intractable questions for applications,including“Do these metrics measure the same resilience?”and“Which one to pick under what circumstance?”In this study,we attempted to address these two fundamental issues using a comprehensive comparative investigation.Through a quantitative-qualitative combined approach,12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19.Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes;qualitative results indicate that the majority of the metrics are based on different definition interpretations,basic components,and expression forms,and thus essentially measure different resilience.The advantages and disadvantages of each metric are comparatively discussed,and a“how to choose”guideline for metric users is proposed.This study is an introspective investigation of resilience quantification studies,aiming to offer a new perspective to scrutinize those benchmarking metrics.