Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-s...Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-step,RMS data only method for evaluating VTs in TPSSs.First,a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time,mitigating the influence of stochastic fluctuations in traction loads.Second,a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT.Third,a trend analysis method is developed to assess the evolution of the measurement performance of VTs.Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1%relative to their original accuracy levels.Additionally,the method accurately tracks deviation trends,enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.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
With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors...With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors determining consumers purchasing decisions.However,due to the high moisture content,active endogenous enzymes,and rich nutrients in aquatic products,both fresh and processed products are highly susceptible to quality deterioration during procurement,distribution,and storage,which leads to a significant decline in sensory quality and nutritional value,while also compromising safety.Today,the consumption of high-quality aquatic products has become a prevailing trend.This paper reviewed the methods for freshness evaluation and quality grading of aquatic products in terms of sensory and nutritional aspects,aiming to support the market circulation principle of"higher price for better quality"and"price based on quality",and better meeting consumer demands.Therefore,it is imperative to enhance the analysis and evaluation of aquatic product quality and to continuously refine assessment systems and methods,which is crucial for promoting industry transformation and fostering a healthy market-consumer economic cycle.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
It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typica...It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.展开更多
Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces ...Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.展开更多
Conventional ultrasound(US)evaluation of enthesitis in psoriatic arthritis(PsA)is limited by its inability to quantify metabolic alterations such as hypoxia,a key driver of disease activity.We introduce an oxygenation...Conventional ultrasound(US)evaluation of enthesitis in psoriatic arthritis(PsA)is limited by its inability to quantify metabolic alterations such as hypoxia,a key driver of disease activity.We introduce an oxygenation-integrated multimodal photoacoustic/ultrasound(PA/US)imaging framework designed to quantify entheseal oxygen saturation(SO_(2))for assessing entheseal disease activity in PsA.In this cross-sectional study,25 PsA patients underwent bilateral PA/US imaging of 12 entheses,where ultrasound lesions were scored using the Outcome Measures in Rheumatology scoring system,and PA-derived SO_(2) levels,quantified via dual-wavelength PA imaging,were classified into hyperoxia or hypoxia groups using k-means clustering.This approach provides metabolic insights complementary to conventional ultrasonic assessment.A composite score integrating hypoxia with US parameters was validated against clinical disease activity indices(Disease Activity Score 28-C-reactive protein,DAS28-CRP;Disease Activity Index for Psoriatic Arthritis,DAPSA).Among 300 entheses,103(34.3%)exhibited PA positivity,with 40(38.8%)classified as hypoxia.Hypoxia scores independently predicted DAS28-CRP(β=0.618,p=0.001)and DAPSA(β=0.612,p<0:001).The hypoxia-optimized PAUS score demonstrated superior correlation with disease activity indices compared to conventional US(DAS28-CRP:r=0.615,p=0.001 versus r=0.474,p=0.017;DAPSA:r=0.743,p<0:001 versus r=0.567,p=0.003),alongside superior diagnostic accuracy for minimal disease activity(area under the curve,AUC 0.776 versus 0.614,p=0.008)and low disease activity(AUC 0.853 versus 0.772,p=0.009).This multimodal scoring system enhances the stratification of PsA disease activity by providing unique metabolic insights,offering a potential tool for therapeutic monitoring and guiding treat-to-target strategies.展开更多
Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluat...Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.展开更多
The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention a...The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.展开更多
Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characte...Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.展开更多
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core functi...Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.展开更多
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
The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt ...The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.展开更多
The comprehensive evaluation of six properties for equipment product is an important basis for their quality control,and their correlative relationship among six properties will affect their quality level.To understan...The comprehensive evaluation of six properties for equipment product is an important basis for their quality control,and their correlative relationship among six properties will affect their quality level.To understand their correlative relationship among six properties,this paper firstly combines group evaluation with decision-making trial and evaluation laboratory(DEMATEL)model,and develops the optimization model based on group consensus to form six influent relationship matrices.Secondly,group consensus matrix is used to design super network hierarchy matrix,and the weights of six properties with relevant environment is also proposed.Thirdly,the elimination and choice translating reality(ELECTRE)model is used to make comprehensive evaluation,and an example is used to compare the results under two kinds of conditions,and illustrate the effect of the weights of six properties on the priority of equipment products.展开更多
In recent years,the amount of waste generated during milling has increased dramatically,and improper disposal poses a significant environmental challenge.To mitigate environmental pollution and enhance the road perfor...In recent years,the amount of waste generated during milling has increased dramatically,and improper disposal poses a significant environmental challenge.To mitigate environmental pollution and enhance the road performance of emulsified asphalt cold recycled mixtures(ECRM),this study employed recycled asphalt pavement(RAP)and reclaimed inorganic binder stabilized aggregate(RAI)as dual recycled materials for ECRM preparation.The blending ratios of reclaimed base and surface layer mixtures significantly influence ECRM's performance,with adjusted proportions substantially improving compressive strength and dynamic modulus.Firstly,three distinct proportioning options were developed for the recycled materials.Mix designs incorporating varying RAP/RAI ratios were used to determine the optimal mix parameters:moisture content,cement dosage,and emulsified asphalt content.Subsequently,comprehensive performance evaluations were conducted through high-temperature wheel tracking tests,freeze-thaw splitting tests,uniaxial compression tests,and dynamic modulus measurements to analyze the pavement characteristics of the three ECRM formulations.Experimental results demonstrate:Compared with ECRM with a blending ratio of RAP:RAI:new aggregate=30:50:20(Option 1),the dynamic stability,freeze-thaw splitting strength ratio,compressive strength,and compressive resilient modulus of ECRM under Option 3(RAP:RAI:new aggregate=50:30:20)decreased by 31.8%,5.2%,16.4%,and 13.1%,respectively.This indicates that increasing RAP content while reducing RAI proportion enhances the tensile strength of ECRM,yet adversely affects its high-temperature stability,moisture resistance,and compressive performance.This work not only addresses the challenge of jointly utilizing asphalt pavement waste and base waste,but also provides a cost-effective and sustainable method for the stable application of milling material resources in road engineering.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
Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous g...Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.展开更多
Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to...Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.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
基金supported by the National Natural Science Foundation of China(No.52107125)Applied Basic Research Project of Sichuan Province(No.2022NSFSC0250)Chengdu Guojia Electrical Engineering Co.,Ltd.(No.KYL202312-0043).
文摘Critical for metering and protection in electric railway traction power supply systems(TPSSs),the measurement performance of voltage transformers(VTs)must be timely and reliably monitored.This paper outlines a three-step,RMS data only method for evaluating VTs in TPSSs.First,a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time,mitigating the influence of stochastic fluctuations in traction loads.Second,a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT.Third,a trend analysis method is developed to assess the evolution of the measurement performance of VTs.Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1%relative to their original accuracy levels.Additionally,the method accurately tracks deviation trends,enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.
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.
基金Supported by Tangshan Talent Funding Project in 2025(B202304018).
文摘With the improvement of living standards and the shift in societal consumption attitudes,consumers demand for the quality of aquatic products is increasingly stringent.Freshness and quality have become primary factors determining consumers purchasing decisions.However,due to the high moisture content,active endogenous enzymes,and rich nutrients in aquatic products,both fresh and processed products are highly susceptible to quality deterioration during procurement,distribution,and storage,which leads to a significant decline in sensory quality and nutritional value,while also compromising safety.Today,the consumption of high-quality aquatic products has become a prevailing trend.This paper reviewed the methods for freshness evaluation and quality grading of aquatic products in terms of sensory and nutritional aspects,aiming to support the market circulation principle of"higher price for better quality"and"price based on quality",and better meeting consumer demands.Therefore,it is imperative to enhance the analysis and evaluation of aquatic product quality and to continuously refine assessment systems and methods,which is crucial for promoting industry transformation and fostering a healthy market-consumer economic cycle.
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.
基金the support from National Natural Science Foundation of China(No.52275153)the Frontier Technologies R&D Program of Jiangsu,China(No.BF2024068)+1 种基金The Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,ChinaResearch Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics),China(Nos.MCAS-I-0425K01,MCAS-I-0423G01)。
文摘It is well recognized that Structural Health Monitoring(SHM)reliability evaluation is a key aspect that needs to be urgently addressed to promote the wide application of SHM methods.However,the existing studies typically transfer the Non-Destructive Testing/Evaluation(NDT/E)reliability metrics to SHM without a systematic analysis of where these metrics originated.Seldom attentions are paid to the evaluation conditions which are very important to apply these metrics.Aimed at this issue,a new condition control-based Dual-Reliability Evaluation(Dual-RE)method for SHM is proposed.This new method is proposed based on a systematic analysis of the whole framework of reliability evaluation from instrument to NDT,and emphasis is paid to the evaluation condition control.Based on these analyses,considering the special online application scenario of SHM,the proposed Dual-RE method contains two key components:Integrated Sensor-based SHM-RE(IS-SHM-RE)and Critical Service Condition-based SHM-RE(CSC-SHM-RE).ISSHM-RE evaluates the reliability of integrated SHM sensor and system themselves under approximate repeatability conditions,while CSC-SHM-RE assesses SHM reliability under the dominant uncertainties during service,namely intermediate conditions.To demonstrate the Dual-RE,crack monitoring by using the Guided Wave-based-SHM(GW-SHM)on aircraft lug structures is taken as a case study.Both the crack detection and sizing performance are evaluated from accuracy and uncertainty.
文摘Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.
基金supported by the National Natural Science Foundation of China(62325112)the National Key Research and Development Program of China(2023YFC2411700,2023YFC2411705)+2 种基金the National Natural Science Foundation of China(U22A2023)the National High-Level Hospital Clinical Research Funding(2022-PUMCH-C-009,2022-PUMCH-B-064,2022-PUMCH-D-002)the National Basic Research Program of China(973 Program,2014CB541801).
文摘Conventional ultrasound(US)evaluation of enthesitis in psoriatic arthritis(PsA)is limited by its inability to quantify metabolic alterations such as hypoxia,a key driver of disease activity.We introduce an oxygenation-integrated multimodal photoacoustic/ultrasound(PA/US)imaging framework designed to quantify entheseal oxygen saturation(SO_(2))for assessing entheseal disease activity in PsA.In this cross-sectional study,25 PsA patients underwent bilateral PA/US imaging of 12 entheses,where ultrasound lesions were scored using the Outcome Measures in Rheumatology scoring system,and PA-derived SO_(2) levels,quantified via dual-wavelength PA imaging,were classified into hyperoxia or hypoxia groups using k-means clustering.This approach provides metabolic insights complementary to conventional ultrasonic assessment.A composite score integrating hypoxia with US parameters was validated against clinical disease activity indices(Disease Activity Score 28-C-reactive protein,DAS28-CRP;Disease Activity Index for Psoriatic Arthritis,DAPSA).Among 300 entheses,103(34.3%)exhibited PA positivity,with 40(38.8%)classified as hypoxia.Hypoxia scores independently predicted DAS28-CRP(β=0.618,p=0.001)and DAPSA(β=0.612,p<0:001).The hypoxia-optimized PAUS score demonstrated superior correlation with disease activity indices compared to conventional US(DAS28-CRP:r=0.615,p=0.001 versus r=0.474,p=0.017;DAPSA:r=0.743,p<0:001 versus r=0.567,p=0.003),alongside superior diagnostic accuracy for minimal disease activity(area under the curve,AUC 0.776 versus 0.614,p=0.008)and low disease activity(AUC 0.853 versus 0.772,p=0.009).This multimodal scoring system enhances the stratification of PsA disease activity by providing unique metabolic insights,offering a potential tool for therapeutic monitoring and guiding treat-to-target strategies.
基金supported by the National Natural Science Foundation of China(Grant No.42277147)Ningbo Public Welfare Research Program(Grant No.2024S081)Ningbo Natural Science Foundation(Grant No.2024J186).
文摘Rock brittleness is a critical property in geotechnical and energy engineering,as it directly influences the prediction of rock failure and stability assessment.Although numerous methods have been developed to evaluate brittleness,many fail to comprehensively account for the impacts of microstructural changes,mineralogical characteristics,and stress conditions on energy evolution during failure.This study proposes a novel approach for brittleness evaluation based on the energy evolution throughout the post-peak failure process,integrating two micromechanical mechanisms:crack propagation and frictional sliding.A new brittleness index is defined as the ratio of generated surface energy to released elastic energy,providing a unified framework for assessing both Class I and Class II mechanical behaviors.The brittleness of cyan,white,and gray sandstones was investigated under various confining pressures and moisture conditions using X-ray diffraction(XRD),scanning electron microscopy(SEM),and conventional triaxial compression(CTC)tests.The results demonstrate that brittleness decreases with increasing confining pressure,due to suppressed crack propagation,and increases under saturated conditions,as moisture enhances crack propagation.By establishing connections between mineral composition,microstructural features,and stress-induced responses,the proposed method overcame limitations of previous approaches and offered a more precise tool for evaluating rock brittleness under diverse environmental scenarios.
基金National Natural Science Foundation of China,Grant/Award Number:82000102 and 82270112。
文摘The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.
基金Supported by the Central Public-interest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022024016)the Central Public-interest Scientific Institution Basal Research Fund,CAFS(Nos.2023TD52,2023TD76)the earmarked fund for CARS(No.CARS-49)。
文摘Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.
基金Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
基金supported by the Natural Science Foundation of China(Grant No.52574047 and Grant No.52374045)Key Project of Sichuan Provincial Joint Fund for Science Technology and Education,China(Grant No.2025NSFSC2008).
文摘Fracability is a critical indicator for evaluating the exploration and development potential of coalbed methane reservoirs and assessing the effectiveness of hydraulic fracturing stimulation operations.Its core function is to characterize the complexity of the induced fracture network and the resulting effective stimulated volume.In this study,we quantified fracture area and geometric complexity using true triaxial fracturing experiments and computed tomography three-dimensional(3D)reconstruction technology,combined with the box-counting method to calculate the 3D fractal dimension of the fracture surfaces.The results revealed that the total fracture surface area per unit volume of the stimulated reservoir effectively characterized reservoir fracability;specifically,both a larger total fracture surface area and a higher fractal dimension corresponded to better reservoir fracability.Fracture complexity was enhanced by a decrease in the horizontal principal stress difference or an increase in the injection rate.Under optimal conditions of a 3 MPa stress difference and an injection rate of 60 mL/min,fracability improved by 27.6%.Furthermore,liquid carbon dioxide(CO_(2))improved fracability by 50.7%compared to using water as the fracturing fluid,a result attributed to its low viscosity and strong diffusion capacity,which activated a greater number of natural fractures.A fracability evaluation model integrating brittleness,fracture toughness,and dimensionless net pressure was developed using regression analysis,which demonstrated high reliability with a strong determination coefficient(R^(2))of 0.9019.This study clarifies the logical relationships among fracture area,complexity,and fractal dimension,providing a novel method for evaluating the fracability of coal reservoirs.
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 NARI Relays Electric Co.,Ltd.under the Project“Research on Evaluation of Clearing Results and Switching Criteria for Primary-Backup Systems in Electricity SpotMarkets”(Project No.CGSQ240800443).
文摘The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.
文摘The comprehensive evaluation of six properties for equipment product is an important basis for their quality control,and their correlative relationship among six properties will affect their quality level.To understand their correlative relationship among six properties,this paper firstly combines group evaluation with decision-making trial and evaluation laboratory(DEMATEL)model,and develops the optimization model based on group consensus to form six influent relationship matrices.Secondly,group consensus matrix is used to design super network hierarchy matrix,and the weights of six properties with relevant environment is also proposed.Thirdly,the elimination and choice translating reality(ELECTRE)model is used to make comprehensive evaluation,and an example is used to compare the results under two kinds of conditions,and illustrate the effect of the weights of six properties on the priority of equipment products.
基金sponsored by National Natural Science Foundation of China(No.52308466)SASAC Science and Technology Innovation Project(JF-23-01-0063)Shaanxi Provincial Transportation Research Project(25-84 K,25-85 K).
文摘In recent years,the amount of waste generated during milling has increased dramatically,and improper disposal poses a significant environmental challenge.To mitigate environmental pollution and enhance the road performance of emulsified asphalt cold recycled mixtures(ECRM),this study employed recycled asphalt pavement(RAP)and reclaimed inorganic binder stabilized aggregate(RAI)as dual recycled materials for ECRM preparation.The blending ratios of reclaimed base and surface layer mixtures significantly influence ECRM's performance,with adjusted proportions substantially improving compressive strength and dynamic modulus.Firstly,three distinct proportioning options were developed for the recycled materials.Mix designs incorporating varying RAP/RAI ratios were used to determine the optimal mix parameters:moisture content,cement dosage,and emulsified asphalt content.Subsequently,comprehensive performance evaluations were conducted through high-temperature wheel tracking tests,freeze-thaw splitting tests,uniaxial compression tests,and dynamic modulus measurements to analyze the pavement characteristics of the three ECRM formulations.Experimental results demonstrate:Compared with ECRM with a blending ratio of RAP:RAI:new aggregate=30:50:20(Option 1),the dynamic stability,freeze-thaw splitting strength ratio,compressive strength,and compressive resilient modulus of ECRM under Option 3(RAP:RAI:new aggregate=50:30:20)decreased by 31.8%,5.2%,16.4%,and 13.1%,respectively.This indicates that increasing RAP content while reducing RAI proportion enhances the tensile strength of ECRM,yet adversely affects its high-temperature stability,moisture resistance,and compressive performance.This work not only addresses the challenge of jointly utilizing asphalt pavement waste and base waste,but also provides a cost-effective and sustainable method for the stable application of milling material resources in road engineering.
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supported by the National Natural Science Foundation of China(No.52104018,52274030)China National Petroleum Corporation(CNPC)Innovation Foundation(No.2024DQ02-0303)China National Petroleum Corporation(CNPC)14th Five-Year Plan Major Strategic Scientific and Technological Project for Prospective and Fundamental Research(2024DJ86).
文摘Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.
基金funded by National Natural Science Foundation of China(52004238)China Postdoctoral Science Foundation(2019M663561).
文摘Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy system.Sharp decline of gas production has been widely observed in shale gas reservoirs.How to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and uncertainty.This work develops an integrated framework for evaluating shale gas well production based on data-driven models.Firstly,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production factors.Data processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production levels.Finally,the random forest algorithm is used to forecast shale gas production.The prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in China.Forty-one wells are randomly selected to predict cumulative gas production using the optimal regression model.The proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.
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.
