In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evalu...In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.展开更多
Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of...Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of evaluators.Based on the institutionalization of China's online car-hailing industry from 2012 to 2018,this paper analyzes the legitimacy judgment of the stakeholders from the perspective of evaluator categorization and explores the legitimation mechanism of business model innovation.It finds that evaluators judge the legitimacy of business models based on category cognition.Therefore,to achieve the bridging,spillover,and accumulation effects of legitimacy,the legitimation strategy of online car-hailing platforms should dynamically adapt to different evaluators,judgment models,and categorization standards.Ultimately,as quantitative changes lead to qualitative changes,the legitimation of innovative business models is achieved in this way.In this process,stakeholders categorize and evaluate online car-hailing based on prototypes and value goals,and establish a two-way interactive mechanism,which is from behavior guided by cognition to cognition given feedback by behavior.This paper combines the legitimacy judgment with category theory to explain how individual cognition drives the emergence of new categories and identifies a series of legitimacy strategies based on categorization,thus providing theoretical support and practical inspiration for exploring the legitimation of business model innovation.展开更多
Spinal muscular atrophy is a devastating motor neuron disease characterized by severe cases of fatal muscle weakness.It is one of the most common genetic causes of mortality among infants aged less than 2 years.Biomar...Spinal muscular atrophy is a devastating motor neuron disease characterized by severe cases of fatal muscle weakness.It is one of the most common genetic causes of mortality among infants aged less than 2 years.Biomarker research is currently receiving more attention,and new candidate biomarkers are constantly being discovered.This review initially discusses the evaluation methods commonly used in clinical practice while briefly outlining their respective pros and cons.We also describe recent advancements in research and the clinical significance of molecular biomarkers for spinal muscular atrophy,which are classified as either specific or non-specific biomarkers.This review provides new insights into the pathogenesis of spinal muscular atrophy,the mechanism of biomarkers in response to drug-modified therapies,the selection of biomarker candidates,and would promote the development of future research.Furthermore,the successful utilization of biomarkers may facilitate the implementation of gene-targeting treatments for patients with spinal muscular atrophy.展开更多
This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standar...This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standard system was established for comprehensive quality evaluation of HTD.There were obvious changes in the physicochemical properties,enzyme activities,and volatile flavor components at different storage periods,which affected the sensory evaluation of HTD to a certain extent.The results of high-throughput sequencing revealed significant microbial diversity,and showed that the bacterial community changed significantly more than did the fungal community.During the storage process,the dominant bacterial genera were Kroppenstedtia and Thermoascus.The correlation between dominant microorganisms and quality indicators highlighted their role in HTD quality.Lactococcus,Candida,Pichia,Paecilomyces,and protease activity played a crucial role in the formation of isovaleraldehyde.Acidic protease activity had the greatest impact on the microbial community.Moisture promoted isobutyric acid generation.Furthermore,the comprehensive quality evaluation standard system was established by the entropy weight method combined with multi-factor fuzzy mathematics.Consequently,this study provides innovative insights for comprehensive quality evaluation of HTD during storage and establishes a groundwork for scientific and rational storage of HTD and quality control of sauce-flavor Baijiu.展开更多
The Early Cambrian Yuertusi Formation(Є_(1)y)in the Tarim Basin of China deposits a continuously developed suite of organic-rich black mudstones,which constitute an important source of oil and gas reservoirs in the Pa...The Early Cambrian Yuertusi Formation(Є_(1)y)in the Tarim Basin of China deposits a continuously developed suite of organic-rich black mudstones,which constitute an important source of oil and gas reservoirs in the Paleozoic.However,its hydrocarbon generation and evolution characteristics and resource potential have long been constrained by deeply buried strata and previous research.In this paper,based on the newly obtained ultra-deep well drilling data,the hydrocarbon generation and expulsion model ofЄ_(1)y shale was established by using data-driven Monte Carlo simulation,upon which the hydrocarbon generation,expulsion,and retention amounts were calculated by using the diagenetic method.The research indicates that theЄ_(1)y shale reaches the hydrocarbon generation and expulsion threshold at equivalent vitrinite reflectances of 0.46%and 0.72%,respectively.The cumulative hydrocarbon generation is 68.88×10^(10)t,the cumulative hydrocarbon expulsion is 35.59×10^(10)t,and the cumulative residual hydrocarbon is 33.29×10^(10)t.This paper systematically and quantitatively calculates the hydrocarbon expulsion at various key geological periods for theЄ_(1)y source rocks in the study area for the first time,more precisely confirming that the black shale of theЄ_(1)y is the most significant source rock contributing to the marine oil and gas resources in the Tarim Basin,filling the gap in hydrocarbon expulsion calculation in the study area,and providing an important basis for the formation and distribution of Paleozoic hydrocarbon reservoirs.The prospect of deep ultra-deep oil and gas exploration in the Tarim Basin is promising.Especially,the large area of dolomite reservoirs under the Cambrian salt and source rock interiors are the key breakthrough targets for the next exploration in the Tarim Basin.展开更多
BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth o...BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.展开更多
Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state b...Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.展开更多
In 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
China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict ...China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.展开更多
Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the su...Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.展开更多
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
OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHO...OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.展开更多
With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threat...With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.展开更多
Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants h...Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.展开更多
To investigate the overall performance of reverse energy bypass scramjet,firstly a variable spe⁃cific heat method combined with a chemical balance calculation module for combustion products were used to es⁃tablish a b...To investigate the overall performance of reverse energy bypass scramjet,firstly a variable spe⁃cific heat method combined with a chemical balance calculation module for combustion products were used to es⁃tablish a benchmark scramjet performance evaluation model.Based on the test data of typical flying point of Mach 7 with the altitude of 29 km,the reliability of the model was verified.The deviations of parameters such as the to⁃tal pressure loss of combustor between the model and the test data were analyzed.Furtherly,an analytical method for post-combustion magnetohydrodynamic power generation was established;by embedding the above method into the overall performance evaluation model,performance prediction considering the power generation effect was realized.Finally,based on the above model,variety regulations of the inlet and the outlet parameters of the power generation channel and performance parameters including the engine specific impulse and the unit thrust under different enthalpy extraction ratios and load factors were analyzed.It could be concluded that the model can reliably predict the variations of key parameters.As the value of the load factor increases,the value of the conduc⁃tivity required to reach the specified enthalpy extraction ratio first decreases and then increases,which is approxi⁃mately parabolic.In order to reduce the demand for the gas conductivity for MHD power generation,the load fac⁃tor should be around 0.5.When the load factor is 0.4 and the magnetic induction intensity is 2.5 T,if the enthalpy extraction ratio reaches 0.5%,the engine specific impulse performance reduces about 3.58%.展开更多
With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation wind...With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.展开更多
To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty)...To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty).Three representative sections were examined:adobe brick masonry repaired(Area 1),well-preserved original(Area 2),and layer-by-layer ramming repaired(Area 3).THz spectral data revealed significant differences between Area 1(time delay:3.72 ps;refractive index:2.224)and Area 2(time delay:3.02 ps;refractive index:2.107),while Area 3(time delay:3.12 ps;refractive index:2.098)demonstrated nearly identical THz spectral data to Area 2.Infrared thermal imaging also showed that the Area 3 restored by layer-by-layer ramming exhibited greater uniformity with fewer instances of cracks,capillary phenomena,or biological diseases.The proposed point-surface integrated evaluation methodology synergistically combines infrared thermography mapping of heritage surfaces with THz spectral datasets acquired through in-situ micro-sampling,enabling quantitative restoration assessment and providing a novel approach for scientifically validating traditional conservation techniques.展开更多
BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from mode...BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.展开更多
BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To...BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.展开更多
Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new...Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.展开更多
文摘In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.
基金supported by the National Natural Science Foundation of China(No.72072008)the special fund for first-class discipline construction of Beijing University of Chemical Technology(No.XK1802-5).
文摘Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of evaluators.Based on the institutionalization of China's online car-hailing industry from 2012 to 2018,this paper analyzes the legitimacy judgment of the stakeholders from the perspective of evaluator categorization and explores the legitimation mechanism of business model innovation.It finds that evaluators judge the legitimacy of business models based on category cognition.Therefore,to achieve the bridging,spillover,and accumulation effects of legitimacy,the legitimation strategy of online car-hailing platforms should dynamically adapt to different evaluators,judgment models,and categorization standards.Ultimately,as quantitative changes lead to qualitative changes,the legitimation of innovative business models is achieved in this way.In this process,stakeholders categorize and evaluate online car-hailing based on prototypes and value goals,and establish a two-way interactive mechanism,which is from behavior guided by cognition to cognition given feedback by behavior.This paper combines the legitimacy judgment with category theory to explain how individual cognition drives the emergence of new categories and identifies a series of legitimacy strategies based on categorization,thus providing theoretical support and practical inspiration for exploring the legitimation of business model innovation.
基金supported by the Collaborative Innovation Center for Clinical and Translational Science by Chinese Ministry of Education&Shanghai,No.CCTS-2022205the“Double World-Class Project”of Shanghai Jiaotong University School of Medicine(both to JZ)。
文摘Spinal muscular atrophy is a devastating motor neuron disease characterized by severe cases of fatal muscle weakness.It is one of the most common genetic causes of mortality among infants aged less than 2 years.Biomarker research is currently receiving more attention,and new candidate biomarkers are constantly being discovered.This review initially discusses the evaluation methods commonly used in clinical practice while briefly outlining their respective pros and cons.We also describe recent advancements in research and the clinical significance of molecular biomarkers for spinal muscular atrophy,which are classified as either specific or non-specific biomarkers.This review provides new insights into the pathogenesis of spinal muscular atrophy,the mechanism of biomarkers in response to drug-modified therapies,the selection of biomarker candidates,and would promote the development of future research.Furthermore,the successful utilization of biomarkers may facilitate the implementation of gene-targeting treatments for patients with spinal muscular atrophy.
文摘This study investigated the physicochemical properties,enzyme activities,volatile flavor components,microbial communities,and sensory evaluation of high-temperature Daqu(HTD)during the maturation process,and a standard system was established for comprehensive quality evaluation of HTD.There were obvious changes in the physicochemical properties,enzyme activities,and volatile flavor components at different storage periods,which affected the sensory evaluation of HTD to a certain extent.The results of high-throughput sequencing revealed significant microbial diversity,and showed that the bacterial community changed significantly more than did the fungal community.During the storage process,the dominant bacterial genera were Kroppenstedtia and Thermoascus.The correlation between dominant microorganisms and quality indicators highlighted their role in HTD quality.Lactococcus,Candida,Pichia,Paecilomyces,and protease activity played a crucial role in the formation of isovaleraldehyde.Acidic protease activity had the greatest impact on the microbial community.Moisture promoted isobutyric acid generation.Furthermore,the comprehensive quality evaluation standard system was established by the entropy weight method combined with multi-factor fuzzy mathematics.Consequently,this study provides innovative insights for comprehensive quality evaluation of HTD during storage and establishes a groundwork for scientific and rational storage of HTD and quality control of sauce-flavor Baijiu.
基金supported by the CNPC Science and Technology Major Project of the Fourteenth Five-Year Plan(2021DJ0101)the National Natural Science Foundation of China(U19B600302,41872148)。
文摘The Early Cambrian Yuertusi Formation(Є_(1)y)in the Tarim Basin of China deposits a continuously developed suite of organic-rich black mudstones,which constitute an important source of oil and gas reservoirs in the Paleozoic.However,its hydrocarbon generation and evolution characteristics and resource potential have long been constrained by deeply buried strata and previous research.In this paper,based on the newly obtained ultra-deep well drilling data,the hydrocarbon generation and expulsion model ofЄ_(1)y shale was established by using data-driven Monte Carlo simulation,upon which the hydrocarbon generation,expulsion,and retention amounts were calculated by using the diagenetic method.The research indicates that theЄ_(1)y shale reaches the hydrocarbon generation and expulsion threshold at equivalent vitrinite reflectances of 0.46%and 0.72%,respectively.The cumulative hydrocarbon generation is 68.88×10^(10)t,the cumulative hydrocarbon expulsion is 35.59×10^(10)t,and the cumulative residual hydrocarbon is 33.29×10^(10)t.This paper systematically and quantitatively calculates the hydrocarbon expulsion at various key geological periods for theЄ_(1)y source rocks in the study area for the first time,more precisely confirming that the black shale of theЄ_(1)y is the most significant source rock contributing to the marine oil and gas resources in the Tarim Basin,filling the gap in hydrocarbon expulsion calculation in the study area,and providing an important basis for the formation and distribution of Paleozoic hydrocarbon reservoirs.The prospect of deep ultra-deep oil and gas exploration in the Tarim Basin is promising.Especially,the large area of dolomite reservoirs under the Cambrian salt and source rock interiors are the key breakthrough targets for the next exploration in the Tarim Basin.
文摘BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.
基金the National Key Research Program of China under granted No.92164201National Natural Science Foundation of China for Distinguished Young Scholars No.62325403+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(62304147).
文摘Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy,high safety,and high environmental adaptability.However,the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment,rendering performance prediction arduous and delaying large-scale industrialization.Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction.This review will systematically examine how the latest progress in using machine learning(ML)algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode,anode,and electrolyte materials suitable for solid-state batteries.Furthermore,the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed,among which are state of charge,state of health,remaining useful life,and battery capacity.Finally,we will summarize the main challenges encountered in the current research,such as data quality issues and poor code portability,and propose possible solutions and development paths.These will provide clear guidance for future research and technological reiteration.
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.
基金Supported by PetroChina Prospective and Basic Technological Project(2022DJ5503).
文摘China has abundant resources of hot dry rocks.However,due to the fact that the evaluation methods for favorable areas are mainly qualitative,and the evaluation indicators and standards are inconsistent,which restrict the evaluation efficiency and exploration process of dry hot rocks.This paper is based on the understanding of the geologic features and genesis mechanisms of hot dry rocks in China and abroad.By integrating the main controlling factors of hot dry rock formation,and using index grading and quantification,the fuzzy hierarchical comprehensive method is applied to establish an evaluation system and standards for favorable areas of hot dry rocks.The evaluation system is based on four indicators:heat source,thermal channel,thermal reservoir and cap rock.It includes 11 evaluation parameters,including time of magmatic/volcanic activity,depth of molten mass or magma chamber,distribution of discordogenic faults,burial depth of thermal reservoir,cap rock type and thickness,surface thermal anomaly,heat flow,geothermal gradient,Moho depth,Curie depth,Earthquake magnitude and focal depth.Each parameter is divided into 3 levels.Applying this evaluation system to assess hot dry rock in central Inner Mongolia revealed that Class I favorable zones cover approximately 494 km^(2),while Class II favorable zones span about 5.7×10^(4) km^(2).The Jirgalangtu Sag and Honghaershute Sag in the Erlian Basin,along with Reshuitang Town in Keshiketeng Banner,Reshui Town in Ningcheng County,and Reshuitang Town in Aohan Banner of Chifeng City,are identified as Class I favorable zones for hot dry rock resources.These areas are characterized by high-temperature subsurface molten bodies or magma chambers serving as high-quality heat sources,shallow thermal reservoir depths,and overlying thick sedimentary rock layers acting as caprock.The establishment and application of the evaluation system for favorable areas of hot dry rock are expected to provide new approaches and scientific basis for guiding the practice of selecting hot dry rock areas in China.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.42225206)National Natural Science Foundation of China(42207180,42477209,42302320).
文摘Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.
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Supported by the National Key Research and Development Project,Clinical Study on the Treatment of Refractory Membranous Nephropathy with the Treatment of Strengthening Spleen and Draining Dampness in Method using Single Group Target Value Method(No.2019YFC1709403)Systematic Study on the Diagnosis and Treatment Rules of Membranous Nephropathy in Traditional Chinese Medicine(No.2023YFC35033501,No.2023YFC35033503)。
文摘OBJECTIVE:To evaluate the 10-year therapeutic efficacy of Traditional Chinese Medicine(TCM)using the Strengthening Spleen and Draining Dampness therapy in the management of idiopathic membranous nephropathy(IMN).METHODS:A single-center,retrospective analysis was conducted on patients diagnosed with IMN who met predefined inclusion and exclusion criteria.Data were collected from the Department of Nephrology at Longhua Hospital,affiliated with Shanghai University of Traditional Chinese Medicine,between January 2007 and December 2011.Clinical parameters including 24-h urinary protein,serum albumin,serum creatinine,and estimated glomerular filtration rate(e GFR,EPI)were assessed at baseline and at 1,3,5,and 10 years of follow-up.The efficacy of the Strengthening Spleen and Draining Dampness therapy was analyzed using repeated measures analysis of variance(ANOVA).Kaplan-Meier survival curves and multivariate proportional hazards model(Cox regression models)were employed to identify factors associated with treatment outcomes.RESULTS:A total of 265 patients were included,with a median follow-up duration of 96 months(36,122).TCM treatment significantly reduced 24-h urinary protein levels(P<0.001),and increased serum albumin levels(P<0.001),while serum creatinine remained stable(P=0.187).Remission rates at 1,3,5,and 10 years were 52.81%,69.71%,68.39%,and 72.36%,respectively,and the rates of avoiding composite outcome events at the same intervals were 98.27%,94.29%,94.19%,and 93.50%.In the subgroup receiving TCM only,remission rates were 56.67%,84.44%,76.32%,and 82.86%.For patients treated initially with Western Medicine followed by TCM,the rates were 52.83%,65.85%,67.47%and 67.75%.In the cohort of patients who received TCM as their first-line therapy,remission rates were 49.23%,62.50%,61.76%,and 69.23%.Multivariate Cox regression analysis revealed that the duration of TCM treatment[hazard ratio(HR)=0.826,95%confidence interval(CI)(0.779,0.876),P<0.001],presence of hypertension[HR=1.912,95%CI(1.181,3.094),P=0.008],baseline serum albumin level[HR=0.930,95%CI(0.894,0.969),P<0.001],and the rate of serum albumin increase within the first year of treatment[HR=0.930,95%CI(0.909,0.957),P<0.001]were significantly associated with clinical outcomes.CONCLUSION:The Strengthening Spleen and Draining Dampness therapy demonstrated robust short-and longterm efficacy in treating IMN,with high rates of remission and renal survival over 10 years.Key factors influencing clinical remission included the duration of TCM treatment,baseline serum albumin levels,the presence of hypertension,and the rate of increase in serum albumin within the first year.These findings suggest that this TCM approach provides a viable long-term treatment option for IMN.
基金supported by National Key Research and Development Program of China(No.2021YFD2200405(S.R.L.))Natural Science Foundation of China(Grant No.31971653).
文摘With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.
基金supported by the key project at the central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(Grant number 2060302)the National Natural Science Foundation of China(Grant number 82373982,82173929).
文摘Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.
文摘To investigate the overall performance of reverse energy bypass scramjet,firstly a variable spe⁃cific heat method combined with a chemical balance calculation module for combustion products were used to es⁃tablish a benchmark scramjet performance evaluation model.Based on the test data of typical flying point of Mach 7 with the altitude of 29 km,the reliability of the model was verified.The deviations of parameters such as the to⁃tal pressure loss of combustor between the model and the test data were analyzed.Furtherly,an analytical method for post-combustion magnetohydrodynamic power generation was established;by embedding the above method into the overall performance evaluation model,performance prediction considering the power generation effect was realized.Finally,based on the above model,variety regulations of the inlet and the outlet parameters of the power generation channel and performance parameters including the engine specific impulse and the unit thrust under different enthalpy extraction ratios and load factors were analyzed.It could be concluded that the model can reliably predict the variations of key parameters.As the value of the load factor increases,the value of the conduc⁃tivity required to reach the specified enthalpy extraction ratio first decreases and then increases,which is approxi⁃mately parabolic.In order to reduce the demand for the gas conductivity for MHD power generation,the load fac⁃tor should be around 0.5.When the load factor is 0.4 and the magnetic induction intensity is 2.5 T,if the enthalpy extraction ratio reaches 0.5%,the engine specific impulse performance reduces about 3.58%.
文摘With the increase of international trade activities and the gradual melting of the polar ice cap,the importance of the Arctic route for marine transportation has been emphasized.Prediction of the polar navigation window period is crucial for navigating in the Arctic route,which is of great significance to the selection of the route and the optimization of navigation.This paper introduces the establishment of a risk index system,determination of risk index weight,establishment of a risk evaluation model,and prediction algorithm for the window period.In addition,data sources of both environmental factors and ship factors are introducted,and their shortcomings are analyzed,followed by introduction of various methods involved in window prediction and analysis of their advantages and disadvantages.The quantitative risk evaluation and window period algorithm can provide a reference for the research of polar navigation window period prediction.
文摘To scientifically evaluate the restoration performance of ancient city walls,Terahertz time-domain spectroscopy(THz-TDS)and infrared thermal imaging technology were applied to assess the Desheng Fortress(Ming Dynasty).Three representative sections were examined:adobe brick masonry repaired(Area 1),well-preserved original(Area 2),and layer-by-layer ramming repaired(Area 3).THz spectral data revealed significant differences between Area 1(time delay:3.72 ps;refractive index:2.224)and Area 2(time delay:3.02 ps;refractive index:2.107),while Area 3(time delay:3.12 ps;refractive index:2.098)demonstrated nearly identical THz spectral data to Area 2.Infrared thermal imaging also showed that the Area 3 restored by layer-by-layer ramming exhibited greater uniformity with fewer instances of cracks,capillary phenomena,or biological diseases.The proposed point-surface integrated evaluation methodology synergistically combines infrared thermography mapping of heritage surfaces with THz spectral datasets acquired through in-situ micro-sampling,enabling quantitative restoration assessment and providing a novel approach for scientifically validating traditional conservation techniques.
文摘BACKGROUND Despite the developments in the field of kidney transplantation,the already existing diagnostic techniques for patient monitoring are considered insufficient.Protein biomarkers that can be derived from modern approaches of proteomic analysis of liquid biopsies(serum,urine)represent a promising innovation in the monitoring of kidney transplant recipients.AIM To investigate the diagnostic utility of protein biomarkers derived from proteomics approaches in renal allograft assessment.METHODS A systematic review was conducted in accordance with PRISMA guidelines,based on research results from the PubMed and Scopus databases.The primary focus was on evaluating the role of biomarkers in the non-invasive diagnosis of transplant-related com-plications.Eligibility criteria included protein biomarkers and urine and blood samples,while exclusion criteria were language other than English and the use of low resolution and sensitivity methods.The selected research articles,were categorized based on the biological sample,condition and methodology and the significantly and reproducibly differentiated proteins were manually selected and extracted.Functional and network analysis of the selected proteins was performed.RESULTS In 17 included studies,58 proteins were studied,with the cytokine CXCL10 being the most investigated.Biological pathways related to immune response and fibrosis have shown to be enriched.Applications of biomarkers for the assessment of renal damage as well as the prediction of short-term and long-term function of the graft were reported.Overall,all studies have shown satisfactory diagnostic accuracy of proteins alone or in combination with conventional methods,as far as renal graft assessment is concerned.CONCLUSION Our review suggests that protein biomarkers,evaluated in specific biological fluids,can make a significant contribution to the timely,valid and non-invasive assessment of kidney graft.
文摘BACKGROUND Liver transplantation aims to increase the survival of patients with end-stage liver diseases and improve their quality of life.The number of organs available for transplantation is lower than the demand.To provide fair organ distribution,predictive mortality scores have been developed.AIM To compare the Acute Physiology and Chronic Health Evaluation IV(APACHE IV),balance of risk(BAR),and model for end-stage liver disease(MELD)scores as predictors of mortality.METHODS Retrospective cohort study,which included 283 adult patients in the postoperative period of deceased donor liver transplantation from 2014 to 2018.RESULTS The transplant recipients were mainly male,with a mean age of 58.1 years.Donors were mostly male,with a mean age of 41.6 years.The median cold ischemia time was 3.1 hours,and the median intensive care unit stay was 5 days.For APACHE IV,a mean of 59.6 was found,BAR 10.7,and MELD 24.2.The 28-day mortality rate was 9.5%,and at 90 days,it was 3.5%.The 28-day mortality prediction for APACHE IV was very good[area under the curve(AUC):0.85,P<0.001,95%CI:0.76-0.94],P<0.001,BAR(AUC:0.70,P<0.001,95%CI:0.58–0.81),and MELD(AUC:0.66,P<0.006,95%CI:0.55-0.78),P<0.008.At 90 days,the data for APACHE IV were very good(AUC:0.80,P<0.001,95%CI:0.71–0.90)and moderate for BAR and MELD,respectively,(AUC:0.66,P<0.004,95%CI:0.55–0.77),(AUC:0.62,P<0.026,95%CI:0.51–0.72).All showed good discrimination between deaths and survivors.As for the best value for liver transplantation,it was significant only for APACHE IV(P<0.001).CONCLUSION The APACHE IV assessment score was more accurate than BAR and MELD in predicting mortality in deceased donor liver transplant recipients.
文摘Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.
