Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance to...Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance tomography enables real-time monitoring of changes in cerebral blood perfusion within the ischemic brain,but investigating the feasibility of using this method to assess post-stroke rehabilitation in vivo remains critical.In this study,ischemic stroke was induced in rats through middle cerebral artery occlusion surgery.Transcranial focused ultrasound stimulation was used to treat the rat model of ischemia,and electrical impedance tomography was used to measure impedance during both the acute stage of ischemia and the rehabilitation stage following the stimulation.Electrical impedance tomography results indicated that cerebral impedance increased after the onset of ischemia and decreased following transcranial focused ultrasound stimulation.Furthermore,the stimulation promoted motor function recovery,reduced cerebral infarction volume in the rat model of ischemic stroke,and induced the expression of brain-derived neurotrophic factor in the ischemic brain.Our results also revealed a significant correlation between the impedance of the ischemic brain post-intervention and improvements in behavioral scores and infarct volume.This study shows that daily administration of transcranial focused ultrasound stimulation for 20 minutes to the ischemic hemisphere 24 hours after cerebral ischemia enhanced motor recovery in a rat model of ischemia.Additionally,our findings indicate that electrical impedance tomography can serve as a valuable tool for quantitatively evaluating rehabilitation after ischemic stroke in vivo.These findings suggest the feasibility of using impedance data collected via electrical impedance tomography to clinically assess the effects of rehabilitatory interventions for patients with ischemic stroke.展开更多
Exercise is a highly proven and beneficial health promotion modality, But it is very difficult to determine whether the person during exercise is safe. A unique and comprehensive approach is proposed to perform physic...Exercise is a highly proven and beneficial health promotion modality, But it is very difficult to determine whether the person during exercise is safe. A unique and comprehensive approach is proposed to perform physical exercise risk evaluation （PERE）, in which personalized factors are deterrrdned basing on grey correlation analysis, analytic hierarchy process （AHP） method is used to structure the large numbers of risk factors, and fuzzy comprehensive evaluation （FCE） is applied to fuzzify the factors and compute the exercise risk level. Finally, an actual calculation example is used to verify the feasibility of the method.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in...In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.展开更多
BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth o...BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.展开更多
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical...In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.展开更多
Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering w...Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.展开更多
Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the su...Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.展开更多
Purpose–The study aims to build a high-precision longitudinal dynamics model for heavy-haul trains and validate it with line test data,present an optimization method for multi-stage cyclic brakes based on the model a...Purpose–The study aims to build a high-precision longitudinal dynamics model for heavy-haul trains and validate it with line test data,present an optimization method for multi-stage cyclic brakes based on the model and conduct a multi-objective detailed evaluation of the driver’s manipulation during cyclic braking.Design/methodology/approach–The high-precision longitudinal train dynamics model was established and verified by the cyclic braking test data of the 20,000 t heavy-haul combination train on the long and steep downgrade.Then the genetic algorithm is employed for optimization subsequent to decoupling multiple cyclic braking procedures,with due consideration of driver operation rules.For evaluation,key manipulation assessments in the scenario are prioritized,supplemented by multi-objective evaluation requirements,and the computational model is employed for detailed evaluation analysis.Findings–Based on the model,experimental data reveal that the probability of longitudinal force error being less than 64.6 kN is approximately 68%,95%for less than 129.2 kN and 99.7%for less than 193.8 kN.Upon optimizing manipulations during the cyclic braking,the maximum reduction in coupler force spans from 21%∼23.9%.Andtheevaluation scoresimply that a proper elevationof the releasingspeed favorssafety.A high electric braking force,although beneficial to some extent for energy-saving,is detrimental to reducing coupler force.Originality/value–The results will provide a theoretical basis and practical guidance for further ensuring the safety and energy-efficient operation of heavy haul trains on long downhill sections and improving the operational quality of heavy-haul trains.展开更多
China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict ...China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.展开更多
OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHO...OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
基金supported by the Fundamental Research Funds for the Central Universities,Nos.G2021KY05107,G2021KY05101the National Natural Science Foundation of China,Nos.32071316,32211530049+1 种基金the Natural Science Foundation of Shaanxi Province,No.2022-JM482the Education and Teaching Reform Funds for the Central Universities,No.23GZ230102(all to LL and HH).
文摘Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance tomography enables real-time monitoring of changes in cerebral blood perfusion within the ischemic brain,but investigating the feasibility of using this method to assess post-stroke rehabilitation in vivo remains critical.In this study,ischemic stroke was induced in rats through middle cerebral artery occlusion surgery.Transcranial focused ultrasound stimulation was used to treat the rat model of ischemia,and electrical impedance tomography was used to measure impedance during both the acute stage of ischemia and the rehabilitation stage following the stimulation.Electrical impedance tomography results indicated that cerebral impedance increased after the onset of ischemia and decreased following transcranial focused ultrasound stimulation.Furthermore,the stimulation promoted motor function recovery,reduced cerebral infarction volume in the rat model of ischemic stroke,and induced the expression of brain-derived neurotrophic factor in the ischemic brain.Our results also revealed a significant correlation between the impedance of the ischemic brain post-intervention and improvements in behavioral scores and infarct volume.This study shows that daily administration of transcranial focused ultrasound stimulation for 20 minutes to the ischemic hemisphere 24 hours after cerebral ischemia enhanced motor recovery in a rat model of ischemia.Additionally,our findings indicate that electrical impedance tomography can serve as a valuable tool for quantitatively evaluating rehabilitation after ischemic stroke in vivo.These findings suggest the feasibility of using impedance data collected via electrical impedance tomography to clinically assess the effects of rehabilitatory interventions for patients with ischemic stroke.
基金The Cultivation Fund of the Key Scientific and Technical Innovation Project from Ministry of Education of China(No706024)International Science Cooperation Foundation of Shanghai ( No061307041)Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (No20060255006)
文摘Exercise is a highly proven and beneficial health promotion modality, But it is very difficult to determine whether the person during exercise is safe. A unique and comprehensive approach is proposed to perform physical exercise risk evaluation （PERE）, in which personalized factors are deterrrdned basing on grey correlation analysis, analytic hierarchy process （AHP） method is used to structure the large numbers of risk factors, and fuzzy comprehensive evaluation （FCE） is applied to fuzzify the factors and compute the exercise risk level. Finally, an actual calculation example is used to verify the feasibility of the method.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
文摘In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.
文摘BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
基金supported by the National Natural Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Outstanding Youth Project of Hunan Provincial Department of Education,China(Grant No.23B0008).
文摘In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.
基金Innovation Program of Shanghai Municipal Education Commission,China(No.12YZ191)
文摘Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.42225206)National Natural Science Foundation of China(42207180,42477209,42302320).
文摘Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.
文摘Purpose–The study aims to build a high-precision longitudinal dynamics model for heavy-haul trains and validate it with line test data,present an optimization method for multi-stage cyclic brakes based on the model and conduct a multi-objective detailed evaluation of the driver’s manipulation during cyclic braking.Design/methodology/approach–The high-precision longitudinal train dynamics model was established and verified by the cyclic braking test data of the 20,000 t heavy-haul combination train on the long and steep downgrade.Then the genetic algorithm is employed for optimization subsequent to decoupling multiple cyclic braking procedures,with due consideration of driver operation rules.For evaluation,key manipulation assessments in the scenario are prioritized,supplemented by multi-objective evaluation requirements,and the computational model is employed for detailed evaluation analysis.Findings–Based on the model,experimental data reveal that the probability of longitudinal force error being less than 64.6 kN is approximately 68%,95%for less than 129.2 kN and 99.7%for less than 193.8 kN.Upon optimizing manipulations during the cyclic braking,the maximum reduction in coupler force spans from 21%∼23.9%.Andtheevaluation scoresimply that a proper elevationof the releasingspeed favorssafety.A high electric braking force,although beneficial to some extent for energy-saving,is detrimental to reducing coupler force.Originality/value–The results will provide a theoretical basis and practical guidance for further ensuring the safety and energy-efficient operation of heavy haul trains on long downhill sections and improving the operational quality of heavy-haul trains.
基金Supported by PetroChina Prospective and Basic Technological Project(2022DJ5503).
文摘China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.
基金Supported by the National Key Research and Development Project,Clinical Study on the Treatment of Refractory Membranous Nephropathy with the Treatment of Strengthening Spleen and Draining Dampness in Method using Single Group Target Value Method(No.2019YFC1709403)Systematic Study on the Diagnosis and Treatment Rules of Membranous Nephropathy in Traditional Chinese Medicine(No.2023YFC35033501,No.2023YFC35033503)。
文摘OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
