Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

Establishment and evaluation of a risk prediction model for gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.

Assessing the Performance of CMIP6 Models in Simulating Droughts across Global Drylands

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However,reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6(CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed.While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ~33%(with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration(PET) by ~32%(17.5%–47.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50%(29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation(water deficit) associated with dryland droughts is overestimated by 28%(24%) compared to observations. The observed increasing trends in drought fractional area,occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s,especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs.

A Dynamic Bayesian-Based Comprehensive Trust Evaluation Model for Dispersed Computing Environment

Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.

Quantitative evaluation model of shale oil adsorption:A case study of the first member of Cretaceous Qingshankou Formation in northern Songliao Basin,NE China

A quantitative evaluation model that integrates kerogen adsorption and clay pore adsorption of shale oil was proposed,and the evaluation charts of adsorption-swelling capacity of kerogen(Mk)and adsorbed oil capacity of clay minerals(Mc)were established,taking the 1st member of Cretaceous Qingshankou Formation in the northern Songliao Basin as an example.The model and charts were derived from swelling oil experiments performed on naturally evolved kerogens and adsorbed oil experiments on clays(separated from shale core samples).They were constructed on the basis of clarifying the control law of kerogen maturity evolution on its adsorption-swelling capacity,and considering the effect of both the clay pore surface area that occupied by adsorbed oil and formation temperature.The results are obtained in four aspects:(1)For the Qing 1 Member shale,with the increase of maturity,Mk decreases.Given Ro of 0.83%–1.65%,Mk is about 50–250 mg/g.(2)The clay in shale adsorbs asphaltene.Mc is 0.63 mg/m^(2),and about 15%of the clay pore surface is occupied by adsorbed oil.(3)In the low to medium maturity stages,the shale oil adsorption is controlled by organic matter.When Ro>1.3%,the shale oil adsorption capacity is contributed by clay pores.(4)The oil adsorption capacity evaluated on the surface at room temperature is 8%–22%(avg.15%)higher than that is held in the formations.The proposed evaluation model reveals the occurrence mechanisms of shale oils with different maturities,and provides a new insight for estimating the reserves of shale oil under formation temperature conditions.

Employment Quality EvaluationModel Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzy C-means(FCM)is proposed.Firstly,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzy cluster analysis algorithm,and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.

Calculation and Evaluation of Ecological Flow of Hydropower Station Based on Fuzzy Evaluation Model

The reasonable determination of ecological flow is of great significance for the efforts to promote the transformation of water ecological environmental protection from pollution management to synergistic management of water resources,water ecology and water environment,and to promote them in an integrated manner.This paper analyzed and calculated the ecological flow process of the Bangsha River diversion power station using the minimum ecological flow method,the annual spreading method,the improved annual spreading method,the NGPRP method,and the month-by-month frequency method,and evaluated the reasonableness of the process and results of the ecological flow calculations by using the fuzzy evaluation model established.The study showed that the minimum ecological flow rate determined by improving the coupling of the spreading method and the NGPRP method was the best,and the suitable ecological flow rate determined by the month-by-month frequency method was the best;the minimum ecological flow rate of the Bangsha River diversion power station was at 0.43-4.21 m 3/s,and the suitable ecological flow rate was at 0.56-4.94 m 3/s,and the trend of its change showed the trend of first increasing and then decreasing,and the trend of change from January to July showed the trend of first increasing and then decreasing.Its trend of change showed an increasing and then decreasing trend,from January to July showed a gradually increasing trend,from August to December showed a gradually decreasing trend.It aimed to provide a theoretical basis for the reasonable determination of the ecological flow of the river hydropower station.

Evaluation of Water Resources Carrying Capacity in Gansu Section of Yellow River Basin Based on Fuzzy Comprehensive Evaluation Model

As a basic natural resource and strategic economic resource,the development and utilization of water resources is an important issue related to the national economy and people's livelihood.How to scientifically evaluate the water resources carrying capacity is the premise to improve the regional water resources carrying capacity and ensure the regional water security.The Gansu section of the Yellow River basin is an important water conservation and recharge area.Whether the water resources in this area can ensure the normal operation of the ecosystem and whether it can carry the sustainable development of social economy is the key to realize the high-quality development of the Yellow River basin.In this study,from the three dimensions of water consumption per capita,water consumption of 10000 yuan GDP and ecological water use rate,by constructing the evaluation index system and index grading standard of water resources carrying capacity,the fuzzy comprehensive evaluation model was used to evaluate the water resources carrying capacity of Gansu section of the Yellow River Basin,in order to provide theoretical decision-making basis for the comprehensive development,utilization and planning management of water resources in Gansu section of the Yellow River basin and even the whole basin,and help the high-quality development of the Yellow River basin.

Research on the Construction of Evaluation Model for the Development of Biopharmaceutical Park in China

Objective To study the influencing factors on the development of biopharmaceutical park,and to construct an evaluation model of the influencing factors for biopharmaceutical park in China.Methods By analyzing various factors affecting biopharmaceutical parks,an evaluation index system of biopharmaceutical parks and an evaluation model of influencing factors of biopharmaceutical park development based on fuzzy group decision making were established.Results and Conclusion Factors such as research and development(R&D)funding investment,incentive for transformation of scientific and technological achievements,and industrial clusters have a greater impact on the development of biopharmaceutical industrial parks in China.Local governments should increase the investment in R&D funding.Besides,they should pay attention to the incentive of transformation of scientific and technological achievements to improve the innovation ability of enterprises.Meanwhile,they should promote the clustering of high-tech enterprises to comprehensively enhance the healthy development of biopharmaceutical parks in China.

Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform,this paper designs and implements a highly efficient and accurate scoring system for subjective questions,focusing primarily on questions in the field of computer network technology.The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques,such as supervised fine-tuning.In the data preparation phase,a comprehensive collection of subjective data related to computer network technology is gathered,cleaned,and labeled.During model training and evaluation,optimal hyperparameters and tuning strategies are applied,resulting in a model capable of scoring with high accuracy.Evaluation results demonstrate that the proposed model performs well across multiple dimensions-content,expression,and development scores-yielding results comparable to those of manual scoring.

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.

Evaluation of NASA GISS Post-CMIP5 Single Column Model Simulated Clouds and Precipitation Using ARM Southern Great Plains Observations

The planetary boundary layer turbulence and moist convection parameterizations have been modified recently in the NASA Goddard Institute for Space Studies （GISS） Model E2 atmospheric general circulation model （GCM; post-CMIP5, hereafter P5）. In this study, single column model （SCM_P5） simulated cloud fractions （CFs）, cloud liquid water paths （LWPs） and precipitation were compared with Atmospheric Radiation Measurement （ARM） Southern Great Plains （SGP） groundbased observations made during the period 2002-08. CMIP5 SCM simulations and GCM outputs over the ARM SGP region were also used in the comparison to identify whether the causes of cloud and precipitation biases resulted from either the physical parameterization or the dynamic scheme. The comparison showed that the CMIP5 SCM has difficulties in simulating the vertical structure and seasonal variation of low-level clouds. The new scheme implemented in the turbulence parameterization led to significantly improved cloud simulations in P5. It was found that the SCM is sensitive to the relaxation time scale. When the relaxation time increased from 3 to 24 h, SCM_P5-simulated CFs and LWPs showed a moderate increase （10%-20%） but precipitation increased significantly （56%）, which agreed better with observations despite the less accurate atmospheric state. Annual averages among the GCM and SCM simulations were almost the same, but their respective seasonal variations were out of phase. This suggests that the same physical cloud parameterization can generate similar statistical results over a long time period, but different dynamics drive the differences in seasonal variations. This study can potentially provide guidance for the further development of the GISS model.

Advantages of the latest Los Alamos Sea-Ice Model(CICE):evaluation of the simulated spatiotemporal variation of Arctic sea ice

The Los Alamos Sea-Ice Model(CICE)is one of the most popular sea-ice models.All versions of it have been the main sea-ice module coupled to climate system models.Therefore,evaluating their simulation capability is an important step in developing climate system models.Compared with observations and previous versions(CICE4.0 and CICE5.0),the advantages of CICE6.0(the latest version)are analyzed in this paper.It is found that CICE6.0 has the minimum interannual errors,and the seasonal cycle it simulates is the most consistent with observations.CICE4.0 overestimates winter sea-ice and underestimates summer sea-ice severely.Meanwhile,the errors of CICE5.0 in winter are larger than for the other versions.The main attention is paid to the perennial ice and the seasonal ice.The spatial distribution of root-mean-square errors indicates that the simulated errors are distributed in the Atlantic sector and the outer Arctic.Both CICE4.0 and CICE5.0 underestimate the concentration of the perennial ice and overestimate that of the seasonal ice in these areas.Meanwhile,CICE6.0 solves this problem commendably.Moreover,the decadal trends it simulates are comparatively the best,especially in the central Arctic sea.The other versions underestimate the decadal trend of the perennial ice and overestimate that of the seasonal ice.In addition,an index used to objectively describe the difference in the spatial distribution between the simulation and observation shows that CICE6.0 produces the best simulated spatial distribution.

Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China

The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979-2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6)are examined by comparison with observational and reanalysis datasets.Most of the models reasonably represent the Beaufort Gyre(BG)and Transpolar Drift Stream(TDS)in the spatial patterns of their long-term mean sea ice drift,while the detailed location,extent,and strength of the BG and TDS vary among the models.About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern.About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern.In the observation/reanalysis,however,the sea ice drift pattern does not match well with the surface ocean current pattern.All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic.For the Arctic basin-wide spatial average,five of the nine models overestimate the Arctic long-term(1979-2014)mean sea ice drift speed in all months.Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn.The increases are weaker than those in the observation.This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China.

Evaluation of direct intramural injection to the bladder wall as a method for developing orthotopic tumor models

Background:Bladder cancer poses a great burden on society and its high rate of recurrence and treatment failure necessitates use of appropriate animal models to study its pathogenesis and test novel treatments.Orthotopic models are superior to other types since they provide a normal microenvironment.Four methods are described for developing bladder cancer models inside the animal’s bladder.Direct intramural injection is one of these methods and is widely used.However,its efficacy in model development has not yet been studied.We aimed to evaluate the efficacy and success rate of the direct intramural injection method of developing an orthotopic model for the study of bladder cancer.Method:Tumor cell lines were prepared in four microtubes.Aliquots of 200×10^(3) cells were injected through a 27 gauge needle into the ventral wall of the bladders of 4male and 4 female BALB/c mice following a midline 1 cm laparotomy incision.In addition,1 million cells from each microtube were injected into the flanks of control mice.To prevent infection and alleviate pain,5 mg/kg enrofloxacin and 2.5 mg/kg flunixin meglumine,respectively,were injected subcutaneously.Results:Tumors formed in all mice,resulting in 100% take rate and zero post-operation mortality.Surgery time was≤15 min per mouse.In two mice,tumors were found in the peritoneal space as well.Conclusion:Direct intramural injection is a rapid,reliable,and reproducible method for developing orthotopic models of bladder cancer.It can be done on both male and female mice and only requires readily available surgical tools.However,needle track can result in cell spillage and peritoneal tumors.

Evaluation of CMIP5 Climate Models in Simulating 1979–2005 Oceanic Latent Heat Flux over the Pacific

The climatological mean state, seasonal variation and long-term upward trend of 1979-2005 latent heat flux （LHF） in historical runs of 14 coupled general circulation models from CMIP5 （Coupled Model Intercomparison Project Phase 5） are evaluated against OAFlux （Objectively Analyzed air-sea Fluxes） data. Inter-model diversity of these models in simulating the annual mean climatological LHF is discussed. Results show that the models can capture the climatological LHF fairly well, but the amplitudes are generally overestimated. Model-simulated seasonal variations of LHF match well with observations with overestimated amplitudes. The possible origins of these biases are wind speed biases in the CMIP5 models. Inter-model diversity analysis shows that the overall stronger or weaker LHF over the tropical and subtropical Pacific region, and the meridional variability of LHF, are the two most notable diversities of the CMIP5 models. Regression analysis indicates that the inter-model diversity may come from the diversity of simulated SST and near-surface atmospheric specific humidity. Comparing the observed long-term upward trend, the trends of LHF and wind speed are largely underestimated, while trends of SST and air specific humidity are grossly overestimated, which may be the origins of the model biases in reproducing the trend of LHF.

A traceability analysis system for model evaluation on land carbon dynamics: design and applications

Background:An increasing number of ecological processes have been incorporated into Earth system models.However,model evaluations usually lag behind the fast development of models,leading to a pervasive simulation uncertainty in key ecological processes,especially the terrestrial carbon(C)cycle.Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models.Thus,a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods:A new cloud-based model evaluation platform,i.e.,the online traceability analysis system for model evaluation(TraceME v1.0),was established.The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project(CMIP6).Results:The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models.For example,the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models.Among all models,IPSL-CM6A-LR simulated the lowest land C storage,which mainly resulted from its shortest baseline C residence time.Over the historical period of 1850–2014,gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion:TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.

An evaluation of the simulations of the Arctic Intermediate Water in climate models and reanalyses

The simulations of the Arctic Intermediate Water in four datasets of climate models and reanalyses, CCSM3, CCSM4, SODA and GLORYS, are analyzed and evaluated. The climatological core temperatures and depths in both CCSM models exhibit deviations over 0.5°C and 200 m from the PHC. SODA reanalysis reproduces relatively reasonable spatial patterns of core temperature and depth, while GLORYS, another reanalysis, shows a remarkable cooling and deepening drift compared with the result at the beginning of the dataset especially in the Eurasian Basin （about 2°C）. The heat contents at the depth of intermediate water in the CCSM models are overestimated with large positive errors nearly twice of that in the PHC. To the contrary, the GLORYS in 2009 show a negative error with a similar magnitude, which means the characteristic of the water mass is totally lost. The circulations in the two reanalyses at the depth of intermediate water are more energetic and realistic than those in the CCSMs, which is attributed to the horizontal eddy-permitting reso-lution. The velocity fields and the transports in the Fram Strait are also investigated. The necessity of finer horizontal resolution is concluded again. The northward volume transports are much larger in the two re-analyses, although they are still weak comparing with mooring observations. Finally, an investigation of the impact of assimilation is done with an evidence of the heat input from assimilation. It is thought to be a reason for the good performance in the SODA, while the GLORYS drifts dramatically without assimilation data in the Arctic Ocean.

Evaluation Model of Carrying Capacity of Water Resources Based on Standardized Indices of Radial Basis Function

[Objective] The aim was to study on RBF model about evaluation on carrying capacity of water resources based on standardized indices. [Method] The indices were transformed and the averages of standard values in different levels were taken as the standardized values of components of central vectors for basic functions of RBF hidden nodes. Hence, the basic functions are suitable for most indices, simplifying expression and calculation of basic functions. [Result] RBF models concluded through Monkey-king Genetic Algorithm with weights optimization are used in evaluation on water carrying capacity in three districts in Changwu County in Shaanxi Province, which were in consistent with that through fuzzy evaluation. [Conclusion] RBF, simple and practical, is universal and popular.

VERIFICATION OF EXTREME RAINFALL IN TYPHOON SOUDELOR(1513) WITH THE MODEL EVALUATION TOOLS(MET)

The extreme rainfall forecast performances of both of ECMWF-IFS and ECMWF-EPS with MET version 5.1 were examined in landing Typhoon Soudelor(1513) with 60 h lead times. In this study the programs for converting ECMWF's forecast data(both of ECMWF-IFS and ECMWF-EPS) format into that needed by MET were developed. Also, during landfall, the observed maximum 6-hour accumulated rainfall was investigated, and then the verification of extreme rainfall in Soudelor was carried out. Results showed that while traditional verification gives relatively low scores, by the method for object-based diagnostic evaluation(MODE) the significant rainy areas were well predicted in this case study.