BACKGROUND Patients with gastric cancer often experience slow postoperative recovery and psychological stress,necessitating enhanced nursing care to improve their prognosis.AIM To analyze the impact of a timing-theory...BACKGROUND Patients with gastric cancer often experience slow postoperative recovery and psychological stress,necessitating enhanced nursing care to improve their prognosis.AIM To analyze the impact of a timing-theory-guided three-stage integrated nursing intervention(TSIN)on the postoperative recovery of patients undergoing gastric cancer surgery.METHODS Total 84 patients that underwent gastric cancer surgeries between June 2022 and June 2024 were selected and divided into a control group and an observation group based on perioperative nursing methods.The control group(n=42)received routine nursing care,whereas the observation group(n=42)received a timing-theory-guided TSIN.The psychological adjustment capabilities,psychological stress,cancer-related fatigue levels,postoperative recovery,and quality of life of the two groups were compared.RESULTS Compared to the control group,the observation group took lesser time to get out of bed,achieve gastrointestinal motility,have the first mealtime,along with a shorter hospital stay(P<0.05).Before nursing,there were no significant differences between groups’parameters or scores(P>0.05).After nursing,the scores for psychological stress and cancer-related fatigue decreased.In contrast,the scores for psychological adjustment capabilities and quality of life increased,with more significant improvements observed in the observation group,showing significant differences within and between the groups(P<0.05).CONCLUSION Timing theory-guided TSIN can improve the psychological adjustment capabilities of patients undergoing gastric cancer surgery,reduce psychological stress and cancer-related fatigue,accelerate postoperative recovery,and improve the quality of life.展开更多
Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood...Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood. This study, through a comprehensive review of MVT deposits across global fold-and-thrust belts, introduces a novel model elucidating the mineralization process in the context of tectonic belt evolution. It is demonstrated that during the stage Ⅰ, regional compression is introduced by early stages of plate convergence, causing the folding and thrusting and creating structural or lithological traps such as evaporite diapirs and unconformity-related carbonate dissolution-collapse structures. Thereafter, in stage Ⅱ, hydrocarbons begin to migrate and accumulate within these traps, where reduced sulfur is generated through thermochemical or bacterial sulfate reduction concurrent with or preceding Zn-Pb mineralization. In the subsequent stage Ⅲ, as plate convergence persists, the regional stress transitions from compression to transpression or extension. Under these conditions, steeply-dipping extensional faults are generated, facilitating the ascent of metalliferous brines into early-formed structural or lithological traps. Precipitation of Zn and Pb sulfides occurs through the mixing of Zn-Pb-transporting fluids with pre-existing reduced sulfur or by interaction with hydrocarbons.展开更多
Objective:This study explores the effectiveness of a three-stage blended teaching model in the Medical Microbiology course for Clinical Medicine students within the context of smart education.Methods:Clinical Medicine...Objective:This study explores the effectiveness of a three-stage blended teaching model in the Medical Microbiology course for Clinical Medicine students within the context of smart education.Methods:Clinical Medicine students from the 2021 and 2022 classes were studied,comparing the traditional and three-stage blended teaching models.Academic performance and survey feedback were analyzed.Results:The research group’s academic performance,including daily,final exam,and average scores,was significantly higher than the control group(P<0.05).Over 95%of students approved of the new teaching model,noting improvements in competencies.Conclusion:The three-stage blended teaching model effectively boosted student engagement,interaction,academic performance,and overall skills,showing potential for broader application in medical education.展开更多
Two-oriented agriculture was a complex organism coupling production,economics,society and ecology.Its development process was affected by various factors such as producers,nature,society,etc.In order to overcome measu...Two-oriented agriculture was a complex organism coupling production,economics,society and ecology.Its development process was affected by various factors such as producers,nature,society,etc.In order to overcome measurement error of traditional data envelopment analysis caused by ignoring random,three-stage DEA model was studied to remove environmental factors and random effects.On the foundation of this model was two-oriented agriculture comprehensive production efficiency of 14 cities were estimated in Hunan Province in 2008,and brown forth corresponding policy proposals to promote agricultural development.展开更多
Based on three-stage data envelopment analysis(DEA) model estimates of resource utilization efficiency of coal,we selected 29 provinces from China's 2012 input-output data and used the bootstrap DEA model to corre...Based on three-stage data envelopment analysis(DEA) model estimates of resource utilization efficiency of coal,we selected 29 provinces from China's 2012 input-output data and used the bootstrap DEA model to correct the bias.The results show that the mean overall technical efficiency,pure technical efficiency,and scale efficiency was 0.833,0.998,and 0.711 in 2012,respectively.Moreover,the comprehensive technical efficiency score indicates that the scale is invalid.Area utilization efficiencies for the eastern,central,and western regions were 0.917,0.731,and 0.629,respectively.The results prove that there are significant differences in the distribution of coal resources utilization efficiency across regions.展开更多
This paper uses a three-stage DEA model to measure the land use efficiency of the three major urban agglomerations in the Yangtze River Delta,Beijing-Tianjin-Hebei,and the Pearl River Delta from 2007 to 2018.The follo...This paper uses a three-stage DEA model to measure the land use efficiency of the three major urban agglomerations in the Yangtze River Delta,Beijing-Tianjin-Hebei,and the Pearl River Delta from 2007 to 2018.The following conclusions are drawn through research:first,the urban land use efficiency of the three major urban agglomerations in the Yangtze River Delta,Beijing-Tianjin-Hebei,and the Pearl River Delta all showed a downward trend,with the rates of decline being 6.06%,2.86%,and 24.34%respectively.In particular,the Pearl River Delta urban agglomeration had the largest decline.Second,the overall urban land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration is relatively high,and the amount of redundancy is relatively small.The rate of decline is significantly lower than the urban land use efficiency of the two major urban agglomerations in the Yangtze River Delta and the Pearl River Delta.The land use efficiency of the Yangtze River Delta and Pearl River Delta urban agglomerations is in a state of continuous decline.Third,the proportion of cities with the effectiveness of returns to scale of urban land use efficiency in the three major urban agglomerations has decreased by 10.53%,10%,and 33.34%,respectively.The Pearl River Delta has the largest decline.Fourth,the land use efficiency of the Yangtze River Delta urban agglomeration is quite different.The central-peripheral phenomenon is evident for the Beijing-Tianjin-Hebei urban agglomerations and the Pearl River Delta urban agglomerations.展开更多
The dynamic behavior of the stranded wire helical spring is described by a modified Bouc-Wen model while the model parameters must be identified using an identification method and experimental data. Existing identific...The dynamic behavior of the stranded wire helical spring is described by a modified Bouc-Wen model while the model parameters must be identified using an identification method and experimental data. Existing identification methods usually relies either solely nonlinear iterative algorithms or manually trial and error. Therefore, the identification process can be rather time consuming and effort taking. As a result, these methods are not ideal for engineering applications. To come up with a more practical method, a three-stage identification method is proposed. Periodic loading and identification simulations are carried out to verify the effectiveness of the proposed method. Noises are added to the simulated data to test the performance of the proposed method when dealing with noise contaminated data. The simulation results indicate that the proposed method is able to give satisfying results when the noise levels are set to be 0.01, 0.03, 0.05 and 0.07. In addition, the proposed method is also applied to experimental data and compared with an existing method. The experimental data is acquired through a periodic loading test. The experiment results suggest that the proposed method features better accuracy compared with the existing method. An effective approach is proposed for identifying the model parameters of the stranded wire helical spring.展开更多
From the perspective of agricultural insurance agency,this paper focuses on the study of operational efficiency of agricultural insurance agency,and analyzes the operational efficiency of agricultural insurance agency...From the perspective of agricultural insurance agency,this paper focuses on the study of operational efficiency of agricultural insurance agency,and analyzes the operational efficiency of agricultural insurance agency and its influencing factors,in order to improve the operational efficiency of agricultural insurance agency,better supply agricultural insurance and achieve the policy effect of agricultural insurance.The results of this paper are as follows:( i) Either comprehensive operational efficiency or scale efficiency of Chinese-funded agency is higher than that of foreign-funded agency,but its pure technical efficiency is lower than that of foreign-funded agency,indicating that the managerial decision ability of Chinese-funded agency is weaker than that of foreign-funded agency,and needs to be improved;( ii) The operational efficiency of professional agricultural insurance agency is higher than that of comprehensive agricultural insurance agency,and the agricultural insurance agency is greatly affected by environmental factors;( iii) The operating time of agricultural insurance agency is proportional to its operational efficiency;( iv) The quality of employees is positively correlated with the capital and cost input difference,but negatively correlated with the difference in the number of employees. Therefore,it is necessary to pay attention to the improvement of employees' working efficiency while laying emphasis on employees' quality.展开更多
This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differen...This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differential/algebraic equations(DAEs) always cause great computational burden and system non-linearity usually makes GTTO non-convex bearing multiple optima. Therefore, coupled with the three-stage decomposition model, a three-section algorithm of dynamic programming(TSDP) is proposed based on the general iteration mechanism of iterative programming(IDP) and incorporated with adaptivegrid allocation scheme and heuristic modifications. The algorithm iteratively performs dynamic programming with heuristic modifications under constant calculation loads and adaptively allocates the valued computational resources to the regions that can further improve the optimality under the guidance of local error estimates. TSDP is finally compared with IDP and interior point method(IP) to verify its efficiency of computation.展开更多
Fermentation of bioflocculant with Corynebacterium glutamicum was studied by way of kinetic modeling.Lorentzian modified Logistic model, time-corrected Luedeking–Piret and Luedeking–Piret type models were proposed a...Fermentation of bioflocculant with Corynebacterium glutamicum was studied by way of kinetic modeling.Lorentzian modified Logistic model, time-corrected Luedeking–Piret and Luedeking–Piret type models were proposed and applied to describe the cell growth, bioflocculant synthesis and consumption of substrates, with the correlation of initial biomass concentration and initial glucose concentration, respectively. The results showed that these models could well characterize the batch culture process of C. glutamicum at various initial glucose concentrations from 10.0 to 17.5 g·L-1. The initial biomass concentration could shorten the lag time of cell growth,while the maximum biomass concentration was achieved only at the optimal initial glucose concentration of16.22 g·L-1. A novel three-stage fed-batch strategy for bioflocculant production was developed based on the model prediction, in which the lag phase, quick biomass growth and bioflocculant production stages were sequentially proceeded with the adjustment of glucose concentration and dissolved oxygen. Biomass of2.23 g·L-1was obtained and bioflocculant concentration was enhanced to 176.32 mg·L-1, 18.62% and403.63% higher than those in the batch process, respectively, indicating an efficient fed-batch culture strategy for bioflocculant production.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
God depeits occurring in metamorPhaed microclastic rocks are distributed exten-sively at home and abroad. Some deposits of this type are of superlarge tonnage. The formation of gold deposits in metamorphosed microclas...God depeits occurring in metamorPhaed microclastic rocks are distributed exten-sively at home and abroad. Some deposits of this type are of superlarge tonnage. The formation of gold deposits in metamorphosed microclastic rocks involves three stages: the sedimentary stage, the regionally metamorpphic stage, and the ore-forming stage. At the first stage, microclastic sedimentary source rocks were developed in a relatively semi-enclosed reducing sea basin and were enriched in carbon, sulfur and gold. At the second stage, the gold adsorbed on organic matter and clay minerals was released and poorly concentrated during the destruction of organic matter and the depletion of clay minerals by regional metamorphism with increase temperature and pressre. At the third stage, a tectono-hydrothermal event took place. As a result, gold was leached from metamorphosed microclastic rocks, transported to ore depositional locus and/or mixed with gold of other sources in the course of migration, and finally precipitated as ores. Gold deposits of this type were eventually formed at the third stage, and they also can be classified as the orognic belt type and the activation zone type. The gold deposits occurring in metamorphosed microclastic rocks are the products of reworking processes and the influence of magmatism should be taken into consideration in some cases.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
Owing to uneven environmental monitoring site distribution,there are significant spatial data gaps for concentrations of ambientfine particles with diameters≤2.5µm(PM_(2.5))obtained using traditional monitoring ...Owing to uneven environmental monitoring site distribution,there are significant spatial data gaps for concentrations of ambientfine particles with diameters≤2.5µm(PM_(2.5))obtained using traditional monitoring methods.Satellite products are an alternative data source for locations where monitoring sites are unavailable.The Moderate Resolution Imaging Spectroradiometer(MODIS)aerosol optical depth(AOD)product has been widely used in PM_(2.5) assessment for years;however,it has obvious data gaps in winter.Here,the Visible Infrared Imaging Radiometer Suite(VIIRS)AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset.A three-stage model consisting of a corrected AOD model,mixed effects model,and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM_(2.5).Results showed overall modelfitting by cross-validation(CV)with an R^(2) of 0.92,mean absolute error of 5.72μg/m^(3),and root mean square error of 7.15μg/m^(3).The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage.The CV R^(2) value of the three-stage model(0.92)was higher than that of the two-stage model(0.9).Hence,the three-stage model could achieve a betterfit in estimating PM_(2.5) on a regional scale.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.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
文摘BACKGROUND Patients with gastric cancer often experience slow postoperative recovery and psychological stress,necessitating enhanced nursing care to improve their prognosis.AIM To analyze the impact of a timing-theory-guided three-stage integrated nursing intervention(TSIN)on the postoperative recovery of patients undergoing gastric cancer surgery.METHODS Total 84 patients that underwent gastric cancer surgeries between June 2022 and June 2024 were selected and divided into a control group and an observation group based on perioperative nursing methods.The control group(n=42)received routine nursing care,whereas the observation group(n=42)received a timing-theory-guided TSIN.The psychological adjustment capabilities,psychological stress,cancer-related fatigue levels,postoperative recovery,and quality of life of the two groups were compared.RESULTS Compared to the control group,the observation group took lesser time to get out of bed,achieve gastrointestinal motility,have the first mealtime,along with a shorter hospital stay(P<0.05).Before nursing,there were no significant differences between groups’parameters or scores(P>0.05).After nursing,the scores for psychological stress and cancer-related fatigue decreased.In contrast,the scores for psychological adjustment capabilities and quality of life increased,with more significant improvements observed in the observation group,showing significant differences within and between the groups(P<0.05).CONCLUSION Timing theory-guided TSIN can improve the psychological adjustment capabilities of patients undergoing gastric cancer surgery,reduce psychological stress and cancer-related fatigue,accelerate postoperative recovery,and improve the quality of life.
基金funded by National Natural Science Foundation of China (Grant Nos. 42125204, 92155305, 42103068, 42372114, 42372115)。
文摘Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood. This study, through a comprehensive review of MVT deposits across global fold-and-thrust belts, introduces a novel model elucidating the mineralization process in the context of tectonic belt evolution. It is demonstrated that during the stage Ⅰ, regional compression is introduced by early stages of plate convergence, causing the folding and thrusting and creating structural or lithological traps such as evaporite diapirs and unconformity-related carbonate dissolution-collapse structures. Thereafter, in stage Ⅱ, hydrocarbons begin to migrate and accumulate within these traps, where reduced sulfur is generated through thermochemical or bacterial sulfate reduction concurrent with or preceding Zn-Pb mineralization. In the subsequent stage Ⅲ, as plate convergence persists, the regional stress transitions from compression to transpression or extension. Under these conditions, steeply-dipping extensional faults are generated, facilitating the ascent of metalliferous brines into early-formed structural or lithological traps. Precipitation of Zn and Pb sulfides occurs through the mixing of Zn-Pb-transporting fluids with pre-existing reduced sulfur or by interaction with hydrocarbons.
基金Henan Province Medical Education Research Project(Exploration and Reform of the“Three-Fusion”Teaching Model for Medical Microbiology Courses under the New Medical Education Context,WJLX2024199)Teaching Reform Research Project of Pingdingshan University(Exploration of a Three-Stage Hybrid Teaching Model for Basic Medical Courses in Clinical Medicine under the Context of Smart Education,2023-JY42)“Ideological and Political Education”Project for the“Medical Microbiology and Parasitology”Course at Pingdingshan University.
文摘Objective:This study explores the effectiveness of a three-stage blended teaching model in the Medical Microbiology course for Clinical Medicine students within the context of smart education.Methods:Clinical Medicine students from the 2021 and 2022 classes were studied,comparing the traditional and three-stage blended teaching models.Academic performance and survey feedback were analyzed.Results:The research group’s academic performance,including daily,final exam,and average scores,was significantly higher than the control group(P<0.05).Over 95%of students approved of the new teaching model,noting improvements in competencies.Conclusion:The three-stage blended teaching model effectively boosted student engagement,interaction,academic performance,and overall skills,showing potential for broader application in medical education.
文摘Two-oriented agriculture was a complex organism coupling production,economics,society and ecology.Its development process was affected by various factors such as producers,nature,society,etc.In order to overcome measurement error of traditional data envelopment analysis caused by ignoring random,three-stage DEA model was studied to remove environmental factors and random effects.On the foundation of this model was two-oriented agriculture comprehensive production efficiency of 14 cities were estimated in Hunan Province in 2008,and brown forth corresponding policy proposals to promote agricultural development.
基金the National Social Science Foundation of China(No.11BGL028)Higher Education Research Fund for the Doctoral Program of China(No.20110095110003)
文摘Based on three-stage data envelopment analysis(DEA) model estimates of resource utilization efficiency of coal,we selected 29 provinces from China's 2012 input-output data and used the bootstrap DEA model to correct the bias.The results show that the mean overall technical efficiency,pure technical efficiency,and scale efficiency was 0.833,0.998,and 0.711 in 2012,respectively.Moreover,the comprehensive technical efficiency score indicates that the scale is invalid.Area utilization efficiencies for the eastern,central,and western regions were 0.917,0.731,and 0.629,respectively.The results prove that there are significant differences in the distribution of coal resources utilization efficiency across regions.
文摘This paper uses a three-stage DEA model to measure the land use efficiency of the three major urban agglomerations in the Yangtze River Delta,Beijing-Tianjin-Hebei,and the Pearl River Delta from 2007 to 2018.The following conclusions are drawn through research:first,the urban land use efficiency of the three major urban agglomerations in the Yangtze River Delta,Beijing-Tianjin-Hebei,and the Pearl River Delta all showed a downward trend,with the rates of decline being 6.06%,2.86%,and 24.34%respectively.In particular,the Pearl River Delta urban agglomeration had the largest decline.Second,the overall urban land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration is relatively high,and the amount of redundancy is relatively small.The rate of decline is significantly lower than the urban land use efficiency of the two major urban agglomerations in the Yangtze River Delta and the Pearl River Delta.The land use efficiency of the Yangtze River Delta and Pearl River Delta urban agglomerations is in a state of continuous decline.Third,the proportion of cities with the effectiveness of returns to scale of urban land use efficiency in the three major urban agglomerations has decreased by 10.53%,10%,and 33.34%,respectively.The Pearl River Delta has the largest decline.Fourth,the land use efficiency of the Yangtze River Delta urban agglomeration is quite different.The central-peripheral phenomenon is evident for the Beijing-Tianjin-Hebei urban agglomerations and the Pearl River Delta urban agglomerations.
基金Supported by National Natural Science Foundation of China(Grant Nos.51375508,51375517)the Key Technologies R&D Program of China(Grant No.2012BAF12B09)the Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China(Grant No.IRT1196)
文摘The dynamic behavior of the stranded wire helical spring is described by a modified Bouc-Wen model while the model parameters must be identified using an identification method and experimental data. Existing identification methods usually relies either solely nonlinear iterative algorithms or manually trial and error. Therefore, the identification process can be rather time consuming and effort taking. As a result, these methods are not ideal for engineering applications. To come up with a more practical method, a three-stage identification method is proposed. Periodic loading and identification simulations are carried out to verify the effectiveness of the proposed method. Noises are added to the simulated data to test the performance of the proposed method when dealing with noise contaminated data. The simulation results indicate that the proposed method is able to give satisfying results when the noise levels are set to be 0.01, 0.03, 0.05 and 0.07. In addition, the proposed method is also applied to experimental data and compared with an existing method. The experimental data is acquired through a periodic loading test. The experiment results suggest that the proposed method features better accuracy compared with the existing method. An effective approach is proposed for identifying the model parameters of the stranded wire helical spring.
文摘From the perspective of agricultural insurance agency,this paper focuses on the study of operational efficiency of agricultural insurance agency,and analyzes the operational efficiency of agricultural insurance agency and its influencing factors,in order to improve the operational efficiency of agricultural insurance agency,better supply agricultural insurance and achieve the policy effect of agricultural insurance.The results of this paper are as follows:( i) Either comprehensive operational efficiency or scale efficiency of Chinese-funded agency is higher than that of foreign-funded agency,but its pure technical efficiency is lower than that of foreign-funded agency,indicating that the managerial decision ability of Chinese-funded agency is weaker than that of foreign-funded agency,and needs to be improved;( ii) The operational efficiency of professional agricultural insurance agency is higher than that of comprehensive agricultural insurance agency,and the agricultural insurance agency is greatly affected by environmental factors;( iii) The operating time of agricultural insurance agency is proportional to its operational efficiency;( iv) The quality of employees is positively correlated with the capital and cost input difference,but negatively correlated with the difference in the number of employees. Therefore,it is necessary to pay attention to the improvement of employees' working efficiency while laying emphasis on employees' quality.
基金Supported by the National Basic Research Program of China(2012CB720500)the National High Technology Research and Development Program of China(2013AA040702)
文摘This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differential/algebraic equations(DAEs) always cause great computational burden and system non-linearity usually makes GTTO non-convex bearing multiple optima. Therefore, coupled with the three-stage decomposition model, a three-section algorithm of dynamic programming(TSDP) is proposed based on the general iteration mechanism of iterative programming(IDP) and incorporated with adaptivegrid allocation scheme and heuristic modifications. The algorithm iteratively performs dynamic programming with heuristic modifications under constant calculation loads and adaptively allocates the valued computational resources to the regions that can further improve the optimality under the guidance of local error estimates. TSDP is finally compared with IDP and interior point method(IP) to verify its efficiency of computation.
基金Supported by the National Natural Science Foundation of China(21206143,51378444)the program for New Century Excellent Talents of Education Ministry of China(ncet-13-0501)
文摘Fermentation of bioflocculant with Corynebacterium glutamicum was studied by way of kinetic modeling.Lorentzian modified Logistic model, time-corrected Luedeking–Piret and Luedeking–Piret type models were proposed and applied to describe the cell growth, bioflocculant synthesis and consumption of substrates, with the correlation of initial biomass concentration and initial glucose concentration, respectively. The results showed that these models could well characterize the batch culture process of C. glutamicum at various initial glucose concentrations from 10.0 to 17.5 g·L-1. The initial biomass concentration could shorten the lag time of cell growth,while the maximum biomass concentration was achieved only at the optimal initial glucose concentration of16.22 g·L-1. A novel three-stage fed-batch strategy for bioflocculant production was developed based on the model prediction, in which the lag phase, quick biomass growth and bioflocculant production stages were sequentially proceeded with the adjustment of glucose concentration and dissolved oxygen. Biomass of2.23 g·L-1was obtained and bioflocculant concentration was enhanced to 176.32 mg·L-1, 18.62% and403.63% higher than those in the batch process, respectively, indicating an efficient fed-batch culture strategy for bioflocculant production.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
文摘God depeits occurring in metamorPhaed microclastic rocks are distributed exten-sively at home and abroad. Some deposits of this type are of superlarge tonnage. The formation of gold deposits in metamorphosed microclastic rocks involves three stages: the sedimentary stage, the regionally metamorpphic stage, and the ore-forming stage. At the first stage, microclastic sedimentary source rocks were developed in a relatively semi-enclosed reducing sea basin and were enriched in carbon, sulfur and gold. At the second stage, the gold adsorbed on organic matter and clay minerals was released and poorly concentrated during the destruction of organic matter and the depletion of clay minerals by regional metamorphism with increase temperature and pressre. At the third stage, a tectono-hydrothermal event took place. As a result, gold was leached from metamorphosed microclastic rocks, transported to ore depositional locus and/or mixed with gold of other sources in the course of migration, and finally precipitated as ores. Gold deposits of this type were eventually formed at the third stage, and they also can be classified as the orognic belt type and the activation zone type. The gold deposits occurring in metamorphosed microclastic rocks are the products of reworking processes and the influence of magmatism should be taken into consideration in some cases.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by National Key Research and Development Program[grants number 2016YFC0201700].
文摘Owing to uneven environmental monitoring site distribution,there are significant spatial data gaps for concentrations of ambientfine particles with diameters≤2.5µm(PM_(2.5))obtained using traditional monitoring methods.Satellite products are an alternative data source for locations where monitoring sites are unavailable.The Moderate Resolution Imaging Spectroradiometer(MODIS)aerosol optical depth(AOD)product has been widely used in PM_(2.5) assessment for years;however,it has obvious data gaps in winter.Here,the Visible Infrared Imaging Radiometer Suite(VIIRS)AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset.A three-stage model consisting of a corrected AOD model,mixed effects model,and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM_(2.5).Results showed overall modelfitting by cross-validation(CV)with an R^(2) of 0.92,mean absolute error of 5.72μg/m^(3),and root mean square error of 7.15μg/m^(3).The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage.The CV R^(2) value of the three-stage model(0.92)was higher than that of the two-stage model(0.9).Hence,the three-stage model could achieve a betterfit in estimating PM_(2.5) on a regional scale.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
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.
