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
We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework...We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework incorporates(1)grid configuration,(2)transport dynamics,(3)chemical mechanisms,(4)aerosol processes,(5)wet/dry deposition parameterizations,and(6)heterogeneous chemistry treatments associated with sulfate,nitrous acid(HONO)chemistry,and aerosol/cloud–photolysis interactions(APIs/CPIs).Openly shared with the atmospheric research community,the model facilitates integration of advanced physicochemical schemes to enhance simulation accuracy.Globally,the model demonstrates realistic representations of ozone(O_(3))and aerosol optical depth.The EPICC model generally demonstrates robust performance in simulating regional concentrations of O_(3) and PM_(2.5)(and its components)in China.It successfully captures vertical profiles of both global and regional O_(3).Notably,the model mitigates frequently reported sulfate underestimations in highly industrialized regions of China.The model accurately captures two regional severe pollution episodes observed in eastern China(January/June 2021).Sensitivity experiments highlight the critical roles of heterogeneous chemical mechanisms associated with sulfate,HONO chemistry,APIs,and CPIs in capturing PM_(2.5) and O_(3) concentrations in China.Improved sulfate mechanisms result in an increase of approximately 32.4%(2.8μg m^(−3))in simulated winter sulfate concentrations when observations exceed 10μg m^(−3).Enhanced HONO elevates winter O_(3) and PM_(2.5) by≤20 and≤10μg m^(−3),respectively.Overall,CPIs dominate over APIs in improving O_(3) and PM_(2.5) simulations across China.Locally,APIs mitigate PM_(2.5) and O_(3) discrepancies in the Sichuan Basin.Seasonal cloud–chemistry coupling explains the weaker impact of PM_(2.5) in summer.展开更多
With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends...With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.展开更多
Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competiti...Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.展开更多
It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problemat...It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.展开更多
Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medic...Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Re-fracturing horizontal wells is a critical strategy for enhancing recovery from tight oil reservoirs,but its success depends on the evaluation of candidate wells and locations.This process is complicated by producti...Re-fracturing horizontal wells is a critical strategy for enhancing recovery from tight oil reservoirs,but its success depends on the evaluation of candidate wells and locations.This process is complicated by production-induced alterations in reservoir pressure and geomechanical responses.This study introduces a workflow to evaluate re-fracturing potential by integrating coupled fluid flow and geomechanical modeling for the production of initial hydraulic fractures.We developed a numerical model that simulates the poroelastic response of a tight oil reservoir to depletion from an initial set of hydraulic fractures.To quantify the re-fracturing potential along the horizontal wellbore,a novel composite re-fracturing potential index is proposed where fracture shape,stress,and pressure are considered.This index considers four key physical factors:current reservoir pressure,fracture initiation ease,fracture geometry favorability,and fracture propagation efficiency considering tortuosity.Numerical simulations were conducted for scenarios with both uniform and non-uniform initial hydraulic fractures.The results consistently demonstrate that the optimal locations for re-fracturing are the midpoints between existing fractures,where a favorable balance of high reservoir pressure and altered stress conditions exists.The analysis reveals that the overall re-fracturing potential tends to increase with production time,suggesting that a period of depletion can enhance the geomechanical conditions for subsequent stimulation.Furthermore,a sensitivity analysis on the index weighting factors shows that the optimum re-fracturing strategy is highly dependent on the primary field objective,whether it is maximizing resource contact,ensuring geomechanical feasibility,or avoiding operational complexity.The study concludes that heterogeneity in the initial fracture network creates complex and asymmetric potential profiles,which implies the necessity of case-specific and integrated analysis over simplified assumptions.The proposed methodology provides a framework for optimizing re-fracturing designs in tight oil reservoirs.展开更多
The failure and collapse of coal pillar ribs represent a significant hazard in the mining industry,with the associated risk of fatalities and injuries anticipated to rise as mining operations advance to greater depths...The failure and collapse of coal pillar ribs represent a significant hazard in the mining industry,with the associated risk of fatalities and injuries anticipated to rise as mining operations advance to greater depths.The development of support guidelines through an enhanced understanding of pillar damage and rock–support interaction mechanisms is crucial to resolving this issue.Bonded block models(BBMs)represent a convenient tool for this purpose,as they can reasonably reproduce the rock fracturing process;however,it is not known to what extent this modeling technique can be applied to simulate pillar failure mechanisms and support interaction in anisotropic rock masses,such as coal.To bridge this gap in research,hypothetical coal pillar BBMs of different width-to-height ratios were developed and calibrated to match Mark–Bieniawski's pillar strength equation,along with a few other attributes from the literature(stress levels at the edge of pillars and the transition from brittle to strain-hardening behavior with increasing width-to-height ratio).Elongated blocks were employed to capture the anisotropic behavior of coal mass.With the reliability of the model established,a few different support patterns were evaluated to ensure that the outputs are broadly consistent with expectations.Finally,simulations of roadway development and additional mining activities were completed considering geo-mining conditions representative of underground coal mines in the USA.The good consistency between model response and expected behaviors per field observation demonstrates the potential of BBMs to be used as a support design tool.展开更多
Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual pat...Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.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
Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a ...Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.展开更多
With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based i...With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based instruction to competency-oriented teaching.A postgraduate student competency evaluation model can serve as a framework to organize and guide both teaching and research activities at the postgraduate level.A number of relevant research efforts have already been conducted in this area.Graduate education plays a vital role not only as a continuation and enhancement of undergraduate education but also as essential preparation for future research endeavors.An analysis of the acceptance of competency evaluation models refers to the assessment of how various stakeholders perceive the importance of different components within the model.Investigating the degree of acceptance among diverse groups-such as current undergraduate students,current postgraduate students,graduates with less than three years of work experience,and those with more than three years of work experience-can offer valuable insights for improving and optimizing postgraduate education and training practices.展开更多
This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3...This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3SMv2-MPAS)at 60 km to 10 km resolution.Multi-source observational data are utilized to validate sea surface temperature/salinity,sea ice,three-dimensional thermal-saline structures,mixed layer depth,ocean heat content,and sea surface height.Key results show the following:(1)E3SMv2-MPAS captures seasonal-to-decadal variability in surface fields and sea ice,but shows systematic biases in sea surface temperature of western boundary currents(inadequate eddy parameterization)and Arctic sea surface salinity(misrepresented freshwater fluxes and mixing processes).(2)The model robustly represents three-dimensional climate variability,yet underestimates mixed layer depth in key regions(Antarctic Circumpolar Current and North Atlantic),revealing deficiencies in extreme mixing.(3)Ocean heat content distributions are well-simulated.(4)Sea surface height spatial patterns and interannual variability are accurately reproduced.This work identifies critical refinements for unstructured-mesh models:mesoscale eddy parameterization,polar ocean-sea ice coupling,and multi-scale energy processes,advancing high-resolution climate model development and laying the groundwork for improved ocean forecasting systems.展开更多
With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,...With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.展开更多
This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from ...This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.展开更多
Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in t...Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in the otolaryngology department of our hospital from June 2022 to October 2024 were included in the study and equally divided into two groups using a convenient sampling method.30 nurses who chose traditional Chinese medicine skill teaching management were included in the control group,and 30 nurses who chose the new empowerment teaching method based on Kirkpatrick’s evaluation model were included in the observation group.Relevant indicators such as clinical teaching environment perception,theoretical knowledge scores of Chinese medicine nursing,and excellent rate of practical operation assessment were compared.Results:The nurses in the observation group had higher scores for clinical teaching environment perception than the control group(P<0.05).However,the midterm and final exam scores for theoretical knowledge of Chinese medicine nursing were higher in the observation group than in the control group(P<0.05).Compared with the control group,the observation group had a higher excellent rate of practical operation assessment(93.33%>73.33%)and a higher Chinese medicine nursing ability score[(215.69±19.73)points>(184.87±15.66)points](P<0.05).Conclusion:Applying the new empowerment teaching method based on Kirkpatrick’s evaluation model to Chinese medicine nursing teaching in otolaryngology can help nurses understand the theoretical knowledge of Chinese medicine nursing and optimize the clinical teaching environment,thereby promoting their practical skills and Chinese medicine nursing abilities.展开更多
Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in...Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in all directions.These symp-toms and poor prognosis affect people's physical health and quality of life.Currently,the specific mechanisms of FS remain unclear,and there is variability in treatment methods and their efficacy.Additionally,the early symptoms of FS are difficult to distinguish from those of other shoulder diseases,complicating early diagnosis and treatment.Therefore,it is necessary to develop and utilize animal models to under-stand the pathogenesis of FS and to explore treatment strategies,providing insights into the prevention and treatment of human FS.This paper reviews the rat models available for FS research,including external immobilization models,surgical internal immobilization models,injection modeling models,and endocrine modeling models.It introduces the basic procedures for these models and compares and analyzes the advantages,disadvantages,and applicability of each modeling method.Finally,our paper summarizes the common methods for evaluating FS rat models.展开更多
Natural gas hydrates(hereinafter referred to as hydrates)are a promising clean energy source.However,their current development is far from reaching commercial exploitation.Reservoir stimulation tech-nology provides ne...Natural gas hydrates(hereinafter referred to as hydrates)are a promising clean energy source.However,their current development is far from reaching commercial exploitation.Reservoir stimulation tech-nology provides new approaches to enhance hydrate development effectiveness.Addressing the current lack of quantitative and objective methods for evaluating the fracability of hydrate reservoirs,this study clarifies the relationship between geological and engineering fracability and proposes a comprehensive evaluation model for hydrate reservoir fracability based on grey relational analysis and the criteria importance through intercriteria correlation method.By integrating results from hydraulic fracturing experiments on hydrate sediments,the fracability of hydrate reservoirs is assessed.The concept of critical construction parameter curves for hydrate reservoirs is introduced for the first time.Additionally,two-dimensional fracability index evaluation charts and three-dimensional fracability construction condition discrimination charts are established.The results indicate that as the comprehensive fracability index increases,the feasibility of forming fractures in hydrate reservoirs improves,and the required normalized fracturing construction parameters gradually decrease.The accuracy rate of the charts in judging experimental results reached 89.74%,enabling quick evaluations of whether hydrate reservoirs are worth fracturing,easy to fracture,and capable of being fractured.This has significant engineering implications forthehydraulicfracturingof hydratereservoirs.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
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National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab)supported by the National Natural Science Foundation of China (Grant No. 92044302)the National Key Research Development Program of China (Grant No. 2022YFC3700703)
文摘We present a comprehensive description and benchmark evaluation of the global–regional chemical transport model called the Emission and Atmospheric Processes Integrated and Coupled Community(EPICC)model.The framework incorporates(1)grid configuration,(2)transport dynamics,(3)chemical mechanisms,(4)aerosol processes,(5)wet/dry deposition parameterizations,and(6)heterogeneous chemistry treatments associated with sulfate,nitrous acid(HONO)chemistry,and aerosol/cloud–photolysis interactions(APIs/CPIs).Openly shared with the atmospheric research community,the model facilitates integration of advanced physicochemical schemes to enhance simulation accuracy.Globally,the model demonstrates realistic representations of ozone(O_(3))and aerosol optical depth.The EPICC model generally demonstrates robust performance in simulating regional concentrations of O_(3) and PM_(2.5)(and its components)in China.It successfully captures vertical profiles of both global and regional O_(3).Notably,the model mitigates frequently reported sulfate underestimations in highly industrialized regions of China.The model accurately captures two regional severe pollution episodes observed in eastern China(January/June 2021).Sensitivity experiments highlight the critical roles of heterogeneous chemical mechanisms associated with sulfate,HONO chemistry,APIs,and CPIs in capturing PM_(2.5) and O_(3) concentrations in China.Improved sulfate mechanisms result in an increase of approximately 32.4%(2.8μg m^(−3))in simulated winter sulfate concentrations when observations exceed 10μg m^(−3).Enhanced HONO elevates winter O_(3) and PM_(2.5) by≤20 and≤10μg m^(−3),respectively.Overall,CPIs dominate over APIs in improving O_(3) and PM_(2.5) simulations across China.Locally,APIs mitigate PM_(2.5) and O_(3) discrepancies in the Sichuan Basin.Seasonal cloud–chemistry coupling explains the weaker impact of PM_(2.5) in summer.
基金support supplied by the National Natural Science Foundation of China(Nos.72571136,72271120)the Ministry of Education of the People’s Republic of China Humanities and Social Science project(No.24YJA630087)。
文摘With the rapid development of the aviation industry,air travel has become one of the most important modes.Improving the service quality of civil aviation airports is crucial to their competitiveness.This study intends to develop a scientific and rational evaluation methodology and framework for assessing service quality in civil aviation airports,thereby providing a theoretical foundation and practical guidance for enhancing service standards in the aviation industry.First,the study constructs a CRITIC-bidirectional grey possibility clustering model,which uses the CRITIC method to determine the weights of indicators and integrates the forward grey possibility clustering model and the inverse grey possibility clustering model to determine possibility functions from two perspectives.Second,a service quality evaluation index system for civil airports is constructed from four dimensions,and the weights of each index within the system are subsequently calculated.Finally,the constructed model is applied to evaluate the service quality of nine domestic civil airports.Based on the clustering results,targeted countermeasures and suggestions are proposed.Empirical results demonstrate that,compared to the traditional grey possibility clustering model,the proposed model balances the objectivity of indicator weighting,the objectivity of possibility function construction,and the simplicity of the computational process,thereby possessing significant theoretical and practical implications.
基金National Social Science Fund of China,No.24BTJ037Significant Project of the National Social Science Foundation of China,No.23&ZD102+1 种基金The Key Research Base for Philosophy and Social Sciences in Hangzhou:ESG and Sustainable Development Research Center,No.25JD053Zhejiang Provincial Statistical Scientific Research Project,No.25TJZZ12。
文摘Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.
基金supported by University Grant Agency of Matej Bel University in Banská Bystrica project number UGA-14-PDS-2025.
文摘It is known that correlation does not imply causality.Some relationships identified in the analysis of data are coincidental or unknown,and some are produced by real-world causality of the situation,which is problematic,since there is a need to differentiate between these two scenarios.Until recently,the proper−semantic−causality of the relationship could have been determined only by human experts from the area of expertise of the studied data.This has changed with the advance of large language models,which are often utilized as surrogates for such human experts,making the process automated and readily available to all data analysts.This motivates the main objective of this work,which is to introduce the design and implementation of a large language model-based semantic causality evaluator based on correlation analysis,together with its visual analysis model called Causal heatmap.After the implementation itself,the model is evaluated from the point of view of the quality of the visual model,from the point of view of the quality of causal evaluation based on large language models,and from the point of view of comparative analysis,while the results reached in the study highlight the usability of large language models in the task and the potential of the proposed approach in the analysis of unknown datasets.The results of the experimental evaluation demonstrate the usefulness of the Causal heatmap method,supported by the evident highlighting of interesting relationships,while suppressing irrelevant ones.
基金Guangzhou Science and Technology Program,Grant/Award Numbers:2025B03J0110,2024A03J1074,2024A03J0927。
文摘Large language models(LLMs)show considerable potential to revolutionize healthcare through their performance across diverse clinical applications.Given the inherent constraints of LLMs and the critical nature of medical practice,a rigorous and systematic evaluation of their medical competence is imperative.This study presents a comprehensive review of the established methodologies and benchmarks for evaluating the medical competence of LLMs,encompassing a thorough analysis of current assessment practices across medical knowledge,clinical practice competence,and ethical-safety considerations.By integrating clinician competency assessment frameworks into LLMs evaluation,we propose a structured tri-dimensional framework that systematically organizes existing evaluation approaches according to medical theoretical knowledge,clinical practice ability,and ethical-safety considerations.Furthermore,this research provides critical insights into future developmental trajectories while establishing foundational frameworks and standardization protocols for the integration of LLMs into medical practice.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金funding from the National Natural Science Foundation of China(No.U24B6001)the CNPC Innovation Fund(No.2021DQ02-0502).
文摘Re-fracturing horizontal wells is a critical strategy for enhancing recovery from tight oil reservoirs,but its success depends on the evaluation of candidate wells and locations.This process is complicated by production-induced alterations in reservoir pressure and geomechanical responses.This study introduces a workflow to evaluate re-fracturing potential by integrating coupled fluid flow and geomechanical modeling for the production of initial hydraulic fractures.We developed a numerical model that simulates the poroelastic response of a tight oil reservoir to depletion from an initial set of hydraulic fractures.To quantify the re-fracturing potential along the horizontal wellbore,a novel composite re-fracturing potential index is proposed where fracture shape,stress,and pressure are considered.This index considers four key physical factors:current reservoir pressure,fracture initiation ease,fracture geometry favorability,and fracture propagation efficiency considering tortuosity.Numerical simulations were conducted for scenarios with both uniform and non-uniform initial hydraulic fractures.The results consistently demonstrate that the optimal locations for re-fracturing are the midpoints between existing fractures,where a favorable balance of high reservoir pressure and altered stress conditions exists.The analysis reveals that the overall re-fracturing potential tends to increase with production time,suggesting that a period of depletion can enhance the geomechanical conditions for subsequent stimulation.Furthermore,a sensitivity analysis on the index weighting factors shows that the optimum re-fracturing strategy is highly dependent on the primary field objective,whether it is maximizing resource contact,ensuring geomechanical feasibility,or avoiding operational complexity.The study concludes that heterogeneity in the initial fracture network creates complex and asymmetric potential profiles,which implies the necessity of case-specific and integrated analysis over simplified assumptions.The proposed methodology provides a framework for optimizing re-fracturing designs in tight oil reservoirs.
基金sponsored by the Alpha Foundation for the Improvement of Mine Safety and Health,Inc.(ALPHA FOUNDATION).
文摘The failure and collapse of coal pillar ribs represent a significant hazard in the mining industry,with the associated risk of fatalities and injuries anticipated to rise as mining operations advance to greater depths.The development of support guidelines through an enhanced understanding of pillar damage and rock–support interaction mechanisms is crucial to resolving this issue.Bonded block models(BBMs)represent a convenient tool for this purpose,as they can reasonably reproduce the rock fracturing process;however,it is not known to what extent this modeling technique can be applied to simulate pillar failure mechanisms and support interaction in anisotropic rock masses,such as coal.To bridge this gap in research,hypothetical coal pillar BBMs of different width-to-height ratios were developed and calibrated to match Mark–Bieniawski's pillar strength equation,along with a few other attributes from the literature(stress levels at the edge of pillars and the transition from brittle to strain-hardening behavior with increasing width-to-height ratio).Elongated blocks were employed to capture the anisotropic behavior of coal mass.With the reliability of the model established,a few different support patterns were evaluated to ensure that the outputs are broadly consistent with expectations.Finally,simulations of roadway development and additional mining activities were completed considering geo-mining conditions representative of underground coal mines in the USA.The good consistency between model response and expected behaviors per field observation demonstrates the potential of BBMs to be used as a support design tool.
基金supported by the Natural Science Foundation of Guangdong Province(No.2021B1515120053)Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515140166).
文摘Background:Therapeutic responses of breast cancer vary among patients and lead to drug resistance and recurrence due to the heterogeneity.Current preclinical models,however,are inadequate for predicting individual patient responses towards different drugs.This study aimed to investigate the patient-derived breast cancer culture models for drug sensitivity evaluations.Methods:Tumor and adjacent tissues from female breast cancer patients were collected during surgery.Patient-derived breast cancer cells were cultured using the conditional reprogramming technique to establish 2D models.The obtained patient-derived conditional reprogramming breast cancer(CRBC)cells were subsequently embedded in alginate-gelatin methacryloyl hydrogel microspheres to form 3D culture models.Comparisons between 2D and 3D models were made using immunohistochemistry(tumor markers),MTS assays(cell viability),flow cytometry(apoptosis),transwell assays(migration),and Western blotting(protein expression).Drug sensitivity tests were conducted to evaluate patient-specific responses to anti-cancer agents.Results:2D and 3D culture models were successfully established using samples from eight patients.The 3D models retained histological and marker characteristics of the original tumors.Compared to 2D cultures,3D models exhibited increased apoptosis,enhanced drug resistance,elevated stem cell marker expression,and greater migration ability—features more reflective of in vivo tumor behavior.Conclusion:Patient-derived 3D CRBC models effectively mimic the in vivo tumor microenvironment and demonstrate stronger resistance to anti-cancer drugs than 2D models.These hydrogel-based models offer a cost-effective and clinically relevant platform for drug screening and personalized breast cancer treatment.
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Project(52274096)supported by the National Natural Science Foundation of ChinaProject(WS2023A03)supported by the State Key Laboratory Cultivation Base for Gas Geology and Gas Control,China。
文摘Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.
文摘With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based instruction to competency-oriented teaching.A postgraduate student competency evaluation model can serve as a framework to organize and guide both teaching and research activities at the postgraduate level.A number of relevant research efforts have already been conducted in this area.Graduate education plays a vital role not only as a continuation and enhancement of undergraduate education but also as essential preparation for future research endeavors.An analysis of the acceptance of competency evaluation models refers to the assessment of how various stakeholders perceive the importance of different components within the model.Investigating the degree of acceptance among diverse groups-such as current undergraduate students,current postgraduate students,graduates with less than three years of work experience,and those with more than three years of work experience-can offer valuable insights for improving and optimizing postgraduate education and training practices.
基金The National Key R&D Program of China under contract No.2021YFC3101503the Science and Technology Innovation Program of Hunan Province under contract No.2022RC3070+1 种基金the National Natural Science Foundation of China under contract Nos 42305176 and 42276205the Hunan Provincial Natural Science Foundation of China under contract No.2023JJ10053.
文摘This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3SMv2-MPAS)at 60 km to 10 km resolution.Multi-source observational data are utilized to validate sea surface temperature/salinity,sea ice,three-dimensional thermal-saline structures,mixed layer depth,ocean heat content,and sea surface height.Key results show the following:(1)E3SMv2-MPAS captures seasonal-to-decadal variability in surface fields and sea ice,but shows systematic biases in sea surface temperature of western boundary currents(inadequate eddy parameterization)and Arctic sea surface salinity(misrepresented freshwater fluxes and mixing processes).(2)The model robustly represents three-dimensional climate variability,yet underestimates mixed layer depth in key regions(Antarctic Circumpolar Current and North Atlantic),revealing deficiencies in extreme mixing.(3)Ocean heat content distributions are well-simulated.(4)Sea surface height spatial patterns and interannual variability are accurately reproduced.This work identifies critical refinements for unstructured-mesh models:mesoscale eddy parameterization,polar ocean-sea ice coupling,and multi-scale energy processes,advancing high-resolution climate model development and laying the groundwork for improved ocean forecasting systems.
文摘With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.
基金supported in part by the Education Reform Key Projects of Heilongjiang Province(Grant No.SJGZ20220011,SJGZ20220012)the Excellent Project of Ministry of Education and China Higher Education Association on Digital Ideological and Political Education in Universities(Grant No.GXSZSZJPXM001)。
文摘This paper proposes a multivariate data fusion based quality evaluation model for software talent cultivation.The model constructs a comprehensive ability and quality evaluation index system for college students from a perspective of engineering course,especially of software engineering.As for evaluation method,relying on the behavioral data of students during their school years,we aim to construct the evaluation model as objective as possible,effectively weakening the negative impact of personal subjective assumptions on the evaluation results.
文摘Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in the otolaryngology department of our hospital from June 2022 to October 2024 were included in the study and equally divided into two groups using a convenient sampling method.30 nurses who chose traditional Chinese medicine skill teaching management were included in the control group,and 30 nurses who chose the new empowerment teaching method based on Kirkpatrick’s evaluation model were included in the observation group.Relevant indicators such as clinical teaching environment perception,theoretical knowledge scores of Chinese medicine nursing,and excellent rate of practical operation assessment were compared.Results:The nurses in the observation group had higher scores for clinical teaching environment perception than the control group(P<0.05).However,the midterm and final exam scores for theoretical knowledge of Chinese medicine nursing were higher in the observation group than in the control group(P<0.05).Compared with the control group,the observation group had a higher excellent rate of practical operation assessment(93.33%>73.33%)and a higher Chinese medicine nursing ability score[(215.69±19.73)points>(184.87±15.66)points](P<0.05).Conclusion:Applying the new empowerment teaching method based on Kirkpatrick’s evaluation model to Chinese medicine nursing teaching in otolaryngology can help nurses understand the theoretical knowledge of Chinese medicine nursing and optimize the clinical teaching environment,thereby promoting their practical skills and Chinese medicine nursing abilities.
基金National Key R&D Program of China,Grant/Award Number:2021YFC2502100,2023YFC3603404 and 2019YFA0111900The National Natural Science Foundation of China,Grant/Award Number:82072506,82272611 and 92268115+7 种基金The Hunan Provincial Science Fund for Distinguished Young Scholars,Grant/Award Number:2024JJ2089The Hunan Young Talents of Science and Technology,Grant/Award Number:2021RC3025The Provincial Clinical Medical Technology Innovation Project of Hunan,Grant/Award Number:2023SK2024 and 2020SK53709The Provincial Natural Science Foundation of Hunan,Grant/Award Number:2020JJ3060The National Natural Science Foundation of Hunan Province,Grant/Award Number:2023JJ30949The National Clinical Research Center for Geriatric Disorders,Xiangya Hospital,Grant/Award Number:2021KFJJ02 and 2021LNJJ05The Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:CX20230308 and CX20230312The Independent Exploration and Innovation Project for Postgraduate Students of Central South University,Grant/Award Number:2024ZZTS0163。
文摘Frozen shoulder(FS),also known as adhesive capsulitis,is a condition that causes contraction and stiffness of the shoulder joint capsule.The main symptoms are per-sistent shoulder pain and a limited range of motion in all directions.These symp-toms and poor prognosis affect people's physical health and quality of life.Currently,the specific mechanisms of FS remain unclear,and there is variability in treatment methods and their efficacy.Additionally,the early symptoms of FS are difficult to distinguish from those of other shoulder diseases,complicating early diagnosis and treatment.Therefore,it is necessary to develop and utilize animal models to under-stand the pathogenesis of FS and to explore treatment strategies,providing insights into the prevention and treatment of human FS.This paper reviews the rat models available for FS research,including external immobilization models,surgical internal immobilization models,injection modeling models,and endocrine modeling models.It introduces the basic procedures for these models and compares and analyzes the advantages,disadvantages,and applicability of each modeling method.Finally,our paper summarizes the common methods for evaluating FS rat models.
基金support of the National Natural Science Foundation of China(Grant No.52074332).
文摘Natural gas hydrates(hereinafter referred to as hydrates)are a promising clean energy source.However,their current development is far from reaching commercial exploitation.Reservoir stimulation tech-nology provides new approaches to enhance hydrate development effectiveness.Addressing the current lack of quantitative and objective methods for evaluating the fracability of hydrate reservoirs,this study clarifies the relationship between geological and engineering fracability and proposes a comprehensive evaluation model for hydrate reservoir fracability based on grey relational analysis and the criteria importance through intercriteria correlation method.By integrating results from hydraulic fracturing experiments on hydrate sediments,the fracability of hydrate reservoirs is assessed.The concept of critical construction parameter curves for hydrate reservoirs is introduced for the first time.Additionally,two-dimensional fracability index evaluation charts and three-dimensional fracability construction condition discrimination charts are established.The results indicate that as the comprehensive fracability index increases,the feasibility of forming fractures in hydrate reservoirs improves,and the required normalized fracturing construction parameters gradually decrease.The accuracy rate of the charts in judging experimental results reached 89.74%,enabling quick evaluations of whether hydrate reservoirs are worth fracturing,easy to fracture,and capable of being fractured.This has significant engineering implications forthehydraulicfracturingof hydratereservoirs.
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.
