Objective:To explore the connotation of the integrated medical-care-rehabilitation nursing model and its application effect in elderly patients with chronic diseases.Methods:A total of 122 elderly patients with chroni...Objective:To explore the connotation of the integrated medical-care-rehabilitation nursing model and its application effect in elderly patients with chronic diseases.Methods:A total of 122 elderly patients with chronic diseases admitted to our hospital from January 2023 to June 2023 were selected and randomly divided into an observation group(62 cases)and a control group(60 cases).Both groups received routine nursing during hospitalization.After discharge,the control group received conventional continuous nursing,while the observation group was given the integrated medical-care-rehabilitation nursing model.The psychological status of the elderly patients in the two groups was compared before nursing and 6 months after nursing.Assessments were made on their clinical symptoms of mental health,self-care ability,health behaviors,and mastery of knowledge about elderly chronic diseases.Results:Six months after nursing,the scores of self-rated clinical symptoms of mental health and negative coping in both groups were lower than those before nursing(P<0.05).Meanwhile,the scores of negative coping,self-care ability,and health behaviors in both groups were higher than those before nursing(P<0.05).Conclusion:The integrated medical-care-rehabilitation nursing model can not only improve the nursing quality for elderly patients with chronic diseases but also foster their positive mentality,help them understand knowledge about diet and health care related to chronic diseases,enhance their self-care ability and health awareness,and assist them in achieving better recovery[1].展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
Anthropogenically induced land use/land cover(LULC)transformations and accelerating climatic variabilities have emerged as pivotal forces reshaping the hydrological equilibrium of fluvial systems,particularly in ecolo...Anthropogenically induced land use/land cover(LULC)transformations and accelerating climatic variabilities have emerged as pivotal forces reshaping the hydrological equilibrium of fluvial systems,particularly in ecologically sensitive basins.This study systematically interrogates the compounded ramifications of LULC dynamics and projected climate change on the hydrological response of the Upper Jemma Watershed an integral sub-catchment of the Upper Blue Nile River system.Employing the advanced QSWAT+hydrological modeling framework within a GIS interface,the analysis integrates bias‐corrected climatic projections under RCP 4.5 and RCP 8.5 scenarios alongside multi-temporal remote sensing‐derived land cover datasets.The findings unveil an unequivocal intensification of surface runoff and streamflow due to expansive agricultural encroachment,juxtaposed with a discernible decline in evapotranspiration and soil water retention.Climatic perturbations,notably temperature elevation and precipitation attenuation,further exacerbate these trends,with pronounced seasonality in hydrological fluxes.Importantly,synergistic interactions between land cover transformation and climatic anomalies manifest in nonlinear hydrological alterations,amplifying peak flows and diminishing baseflows.This underscores the riverine system's heightened vulnerability and the necessity for integrated watershed management strategies that account for multifactorial hydrological stressors.The study provides a robust empirical and modeling basis to inform adaptive water governance within transboundary river basins susceptible to environmental transitions.展开更多
The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gr...The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.展开更多
We propose a novel thermal-conscious power model for integrated circuits that can accurately predict power and temperature under voltage scaling. Experimental results show that the leakage power consumption is underes...We propose a novel thermal-conscious power model for integrated circuits that can accurately predict power and temperature under voltage scaling. Experimental results show that the leakage power consumption is underestimated by 52 % if thermal effects are omitted. Furthermore, an inconsistency arises when energy and temperature are simultaneously optimized by dynamic voltage scaling. Temperature is a limiting factor for future integrated circuits,and the thermal optimization approach can attain a temperature reduction of up to 12℃ with less than 1.8% energy penalty compared with the energy optimization one.展开更多
Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology...Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.展开更多
Friction stir additive manufacturing is a newly developed solid-state additive manufacturing technology.The material in the stirring zone can be re-stirred and reheated,and mechanical properties can be changed along t...Friction stir additive manufacturing is a newly developed solid-state additive manufacturing technology.The material in the stirring zone can be re-stirred and reheated,and mechanical properties can be changed along the building direction.An integrated model is developed to investigate the internal relations of process,microstructure and mechanical properties.Moving heat source model is used to simulate the friction stir additive manufacturing process to obtain the temperature histories,which are used in the following microstructural simulations.Monte Carlo method is used for simulation of recrystallization and grain growth.Precipitate evolution model is used for calculation of precipitate size distributions.Mechanical property is then predicted.Experiments are used for validation of the predicted grains and hardness.Results indicate that the average grain sizes on diff erent layers depend on the temperature in stirring and re-stirring processes.With the increase in building height,average grain size is decreased and hardness is increased.The increase in layer thickness can lead to temperature decrease in reheating and re-stirring processes and then lead to the decrease in average grain size and increase of hardness in stir zone.展开更多
This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the Center for Monsoon System Res...This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the Center for Monsoon System Research at the Institute of Atmospheric Physics （CMSR, IAP）, Chinese Academy of Sciences. ICM integrates ECHAM5 and NEMO2.3 as its atmospheric and oceanic components, respectively, using OASIS3 as the coupler. The simulation skill of ICM is evaluated here, including the simulated climatology, interannual variation, and the influence of E1 Nifio as one of the most important factors on EA-WNP climate. ICM successfully reproduces the distribution of sea surface temperature （SST） and precipitation without climate shift, the seasonal cycle of equatorial Pacific SST, and the precipitation and circulation of East Asian summer monsoon. The most prominent biases of ICM are the excessive cold tongue and unrealistic westward phase propagation of equatorial Pacific SST. The main interannual variation of the tropical Pacific SST and EA-WNP climate E1 Nifio and the East Asia-Pacific Pattern--are also well simulated in ICM, with realistic spatial pattern and period. The simulated E1 Nifio has significant impact on EA-WNP climate, as in other models. The assessment shows ICM should be a reliable model for the seasonal prediction of EA-WNP climate.展开更多
Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence...Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.展开更多
Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become availa...Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.展开更多
Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, includ...Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, including mathematical model combined with neural network(NN), rule model based on empirical knowledge and model-choosing coordinator. Via metallurgic mechanism analysis and material balance computation, a mathematical model calculated the sulfur content in agglomerate by the material balance equation with some parameters predicted by NN method. In the other model, the relationship between sulfur content and key factors was described in the form of expert rules. The model-choosing coordinator based on fuzzy logic was introduced to decide the weight of result of each model according to process conditions. The model was tested by industrial application data and produced a relatively satisfactory prediction error. The model also preferably reflected the varying tendency of sulfur content in agglomerate as the evidence of its prediction performance.展开更多
The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire ...The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.展开更多
The paper focusses on the use of physical modelling in ground movements(induced by underground cavity collapse or mining/tunnelling)and associated soil-structure interaction issues.The paper presents first an overview...The paper focusses on the use of physical modelling in ground movements(induced by underground cavity collapse or mining/tunnelling)and associated soil-structure interaction issues.The paper presents first an overview of using 1 g physical models to solve geotechnical problems and soil-structure interactions related to vertical ground movements.Then the lg physical modelling application is illustrated to study the development of damage in masonry structure due to subsidence and cavity collapse.A largescale 1 g physical model with a 6 m^3 container and 15 electric jacks is presented with the use of a threedimensional(3D)image correlation technique.The influence of structure position on the subsidence trough is analysed in terms of crack density and damage level.The obtained results can improve the methodology and practice for evaluation of damage in masonry structures.Nevertheless,ideal physical model is difficult to achieve.Thus,future improvement of physical models(analogue materials and instrumentation)could provide new opportunities for using 1 g physical models in geotechnical and soilstructure applications and research projects.展开更多
An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduc...An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduce energy wastage and increase energy utilization, it is necessary to perform efficiency analyses and diagnoses on integrated energy systems(IESs). However, the integrated energy data necessary for energy efficiency analyses and diagnoses come from a wide variety of instruments, each of which uses different transmission protocols and data formats. This makes it challenging to handle energy-flow data in a unified manner. Thus, we have constructed a unified model for diagnosing energy usage abnormalities in IESs. Using this model, the data are divided into working days and non-working days, and benchmark values are calculated after the data have been weighted to enable unified analysis of several types of energy data. The energy-flow data may then be observed, managed, and compared in all aspects to monitor sudden changes in energy usage and energy wastage. The abnormal data identified and selected by the unified model are then subjected to big-data analysis using technical management tools, enabling the detection of user problems such as abnormalities pertaining to acquisition device, metering, and energy usage. This model facilitates accurate metering of energy data and improves energy efficiency. The study has significant implications in terms of fulfilling the energy saving.展开更多
The demand for fluorite resource is increasing rapidly as most fluorite deposits on Earth’s surface have been exhausted.The newly discovered fluorite deposits in Inner Mongolia are hosted by Permian metamorphosed san...The demand for fluorite resource is increasing rapidly as most fluorite deposits on Earth’s surface have been exhausted.The newly discovered fluorite deposits in Inner Mongolia are hosted by Permian metamorphosed sandy slate,intermediate-acid intrusive rocks and Cretaceous volcanic sedimentary rocks.The ore bodies are strictly controlled by faults and buried by cover rocks.The feasibility and effectiveness of multi-techniques for prospecting concealed fluorite ore bodies are evaluated,and 10 anomalies are delineated.On the basis of geological features and effectiveness of different methods,the optimum combinations of ore prospecting techniques are proposed for the exploration of zonal type and burial type concealed fluorite ore bodies.Based on comprehensive researches,an integrated exploration model is proposed:(i)select key prospecting targets based on geological backgrounds,regional geochemical anomalies of F and Ca,and remote sensing images;(ii)identify the spatial distribution of low resistivity anomaly and ore-controlling structure from geophysical survey;(iii)evaluate the mineralization potential in fault zone based on F and Ca anomalies in key sections selected from low resistivity anomaly zones;and(iv)evaluate the mineralization potential and reveal the spatial distribution of fluorite ore bodies and ore-controlling faults based on integrated geophysical and geochemical anomalies.The integrated exploration model is verified to be a powerful tool for prospecting concealed fluorite ore bodies in coverage area.展开更多
The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs...The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs),Representative Elementary Watershed(REWs) and Hydrologic Response Units(HRUs).In this paper,a new discretization approach for landforms that have similar hydrologic properties is developed and discussed here for the Integrated Hydrologic Model(IHM),a combining simulation of surface and groundwater processes,accounting for the interaction between the systems.The approach used in the IHM is to disaggregate basin parameters into discrete landforms that have similar hydrologic properties.These landforms may be impervious areas,related areas,areas with high or low clay or organic fractions,areas with significantly different depths-to-water-table,and areas with different types of land cover or different land uses.Incorporating discrete landforms within basins allows significant distributed parameter analysis,but requires an efficient computational structure.The IHM integration represents a new approach interpreting fluxes across the model interface and storages near the interface for transfer to the appropriate model component,accounting for the disparate discretization while rigidly maintaining mass conservation.The discretization approaches employed in IHM will provide some ideas and insights which are helpful to those researchers who have been working on the integrated models for surface-groundwater interaction.展开更多
Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful ...Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.展开更多
Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed perfor...Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
Streamwise Body Force Model(SBFM)could be used to simulate the force of blade on the airflow,resulting in rapid propulsion-airframe integrated simulation.However,when subjected to inlet distortion,the upstream flow fi...Streamwise Body Force Model(SBFM)could be used to simulate the force of blade on the airflow,resulting in rapid propulsion-airframe integrated simulation.However,when subjected to inlet distortion,the upstream flow field of fan stage is redistributed,which causes inaccurate prediction of fan stage performance.As inspired by the upstream influence of compressor,this paper aims to present a modification strategy for SBFM method to predict the compressor performance under circumferential inlet distortion without any knowledge of compressor geometry.Based on the linearized motion equation,the Upstream Influence Model(UIM)is introduced to predict the upstream flow field redistribution.Then the theoretical Mach number at Aerodynamic Interface Plane(AIP)position is calculated and selected to determine the corresponding body force coefficients based on the functional relationship between body force coefficients and Mach number,thus the upstream influence of compressor could be accurately quantified and the Modified Streamwise Body Force Model(MSBFM)could be established.Two studied cases are calculated with different methods and the upstream flow fields are analyzed.The prediction error of MSBFM method for compressor adiabatic efficiency is less than 3%,and the calculation efficiency is improved 20 times under the condition of ensuring computing accuracy.The MSBFM method has the potential for rapid propulsion-airframe integrated simulation.展开更多
文摘Objective:To explore the connotation of the integrated medical-care-rehabilitation nursing model and its application effect in elderly patients with chronic diseases.Methods:A total of 122 elderly patients with chronic diseases admitted to our hospital from January 2023 to June 2023 were selected and randomly divided into an observation group(62 cases)and a control group(60 cases).Both groups received routine nursing during hospitalization.After discharge,the control group received conventional continuous nursing,while the observation group was given the integrated medical-care-rehabilitation nursing model.The psychological status of the elderly patients in the two groups was compared before nursing and 6 months after nursing.Assessments were made on their clinical symptoms of mental health,self-care ability,health behaviors,and mastery of knowledge about elderly chronic diseases.Results:Six months after nursing,the scores of self-rated clinical symptoms of mental health and negative coping in both groups were lower than those before nursing(P<0.05).Meanwhile,the scores of negative coping,self-care ability,and health behaviors in both groups were higher than those before nursing(P<0.05).Conclusion:The integrated medical-care-rehabilitation nursing model can not only improve the nursing quality for elderly patients with chronic diseases but also foster their positive mentality,help them understand knowledge about diet and health care related to chronic diseases,enhance their self-care ability and health awareness,and assist them in achieving better recovery[1].
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
文摘Anthropogenically induced land use/land cover(LULC)transformations and accelerating climatic variabilities have emerged as pivotal forces reshaping the hydrological equilibrium of fluvial systems,particularly in ecologically sensitive basins.This study systematically interrogates the compounded ramifications of LULC dynamics and projected climate change on the hydrological response of the Upper Jemma Watershed an integral sub-catchment of the Upper Blue Nile River system.Employing the advanced QSWAT+hydrological modeling framework within a GIS interface,the analysis integrates bias‐corrected climatic projections under RCP 4.5 and RCP 8.5 scenarios alongside multi-temporal remote sensing‐derived land cover datasets.The findings unveil an unequivocal intensification of surface runoff and streamflow due to expansive agricultural encroachment,juxtaposed with a discernible decline in evapotranspiration and soil water retention.Climatic perturbations,notably temperature elevation and precipitation attenuation,further exacerbate these trends,with pronounced seasonality in hydrological fluxes.Importantly,synergistic interactions between land cover transformation and climatic anomalies manifest in nonlinear hydrological alterations,amplifying peak flows and diminishing baseflows.This underscores the riverine system's heightened vulnerability and the necessity for integrated watershed management strategies that account for multifactorial hydrological stressors.The study provides a robust empirical and modeling basis to inform adaptive water governance within transboundary river basins susceptible to environmental transitions.
基金Supported by the Aeronautical Science Foundation of China(2010ZB52011)the Funding of Jiangsu Innovation Program for Graduate Education(CXLX11-0213)the Nanjing University of Aeronautics and Astronautics Research Funding(NS2010055)~~
文摘The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.
文摘We propose a novel thermal-conscious power model for integrated circuits that can accurately predict power and temperature under voltage scaling. Experimental results show that the leakage power consumption is underestimated by 52 % if thermal effects are omitted. Furthermore, an inconsistency arises when energy and temperature are simultaneously optimized by dynamic voltage scaling. Temperature is a limiting factor for future integrated circuits,and the thermal optimization approach can attain a temperature reduction of up to 12℃ with less than 1.8% energy penalty compared with the energy optimization one.
文摘Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.
基金financially supported by the National Natural Science Foundation of China(No.11572074).
文摘Friction stir additive manufacturing is a newly developed solid-state additive manufacturing technology.The material in the stirring zone can be re-stirred and reheated,and mechanical properties can be changed along the building direction.An integrated model is developed to investigate the internal relations of process,microstructure and mechanical properties.Moving heat source model is used to simulate the friction stir additive manufacturing process to obtain the temperature histories,which are used in the following microstructural simulations.Monte Carlo method is used for simulation of recrystallization and grain growth.Precipitate evolution model is used for calculation of precipitate size distributions.Mechanical property is then predicted.Experiments are used for validation of the predicted grains and hardness.Results indicate that the average grain sizes on diff erent layers depend on the temperature in stirring and re-stirring processes.With the increase in building height,average grain size is decreased and hardness is increased.The increase in layer thickness can lead to temperature decrease in reheating and re-stirring processes and then lead to the decrease in average grain size and increase of hardness in stir zone.
基金supported by the National Basic Research Program of China (Grant Nos.2012CB955604 and 2014CB953903)the National Natural Sciences Foundation of China (Grant No.41375112)
文摘This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the Center for Monsoon System Research at the Institute of Atmospheric Physics （CMSR, IAP）, Chinese Academy of Sciences. ICM integrates ECHAM5 and NEMO2.3 as its atmospheric and oceanic components, respectively, using OASIS3 as the coupler. The simulation skill of ICM is evaluated here, including the simulated climatology, interannual variation, and the influence of E1 Nifio as one of the most important factors on EA-WNP climate. ICM successfully reproduces the distribution of sea surface temperature （SST） and precipitation without climate shift, the seasonal cycle of equatorial Pacific SST, and the precipitation and circulation of East Asian summer monsoon. The most prominent biases of ICM are the excessive cold tongue and unrealistic westward phase propagation of equatorial Pacific SST. The main interannual variation of the tropical Pacific SST and EA-WNP climate E1 Nifio and the East Asia-Pacific Pattern--are also well simulated in ICM, with realistic spatial pattern and period. The simulated E1 Nifio has significant impact on EA-WNP climate, as in other models. The assessment shows ICM should be a reliable model for the seasonal prediction of EA-WNP climate.
基金supported by the Project of the 12th Five-year National Sci-Tech Support Plan of China(2011BAK12B09)China Special Project of Basic Work of Science and Technology(2011FY110100-2)
文摘Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.
基金Supported by Grant-in-Aid for Young Scientists(A)(Grant No.26700021)Japan Society for the Promotion of Science and Strategic Information and Communications R&D Promotion Programme(Grant No.142103011)Ministry of Internal Affairs and Communications
文摘Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.
文摘Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, including mathematical model combined with neural network(NN), rule model based on empirical knowledge and model-choosing coordinator. Via metallurgic mechanism analysis and material balance computation, a mathematical model calculated the sulfur content in agglomerate by the material balance equation with some parameters predicted by NN method. In the other model, the relationship between sulfur content and key factors was described in the form of expert rules. The model-choosing coordinator based on fuzzy logic was introduced to decide the weight of result of each model according to process conditions. The model was tested by industrial application data and produced a relatively satisfactory prediction error. The model also preferably reflected the varying tendency of sulfur content in agglomerate as the evidence of its prediction performance.
基金supported by the National Key Research and Development Program of China(No.2018YFC0214102)。
文摘The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.
文摘The paper focusses on the use of physical modelling in ground movements(induced by underground cavity collapse or mining/tunnelling)and associated soil-structure interaction issues.The paper presents first an overview of using 1 g physical models to solve geotechnical problems and soil-structure interactions related to vertical ground movements.Then the lg physical modelling application is illustrated to study the development of damage in masonry structure due to subsidence and cavity collapse.A largescale 1 g physical model with a 6 m^3 container and 15 electric jacks is presented with the use of a threedimensional(3D)image correlation technique.The influence of structure position on the subsidence trough is analysed in terms of crack density and damage level.The obtained results can improve the methodology and practice for evaluation of damage in masonry structures.Nevertheless,ideal physical model is difficult to achieve.Thus,future improvement of physical models(analogue materials and instrumentation)could provide new opportunities for using 1 g physical models in geotechnical and soilstructure applications and research projects.
基金supported by National Key Research and Development Program of China (No.2017YFB903304)the State Grid Science and Technology Program (Hybrid Simnlation Key Technology for Integrated Energy System and Platform Construction)
文摘An integrated energy service company in an industrial park or commercial building is responsible for managing all energy sources in their local region, including electricity, water, gas, heating, and cooling. To reduce energy wastage and increase energy utilization, it is necessary to perform efficiency analyses and diagnoses on integrated energy systems(IESs). However, the integrated energy data necessary for energy efficiency analyses and diagnoses come from a wide variety of instruments, each of which uses different transmission protocols and data formats. This makes it challenging to handle energy-flow data in a unified manner. Thus, we have constructed a unified model for diagnosing energy usage abnormalities in IESs. Using this model, the data are divided into working days and non-working days, and benchmark values are calculated after the data have been weighted to enable unified analysis of several types of energy data. The energy-flow data may then be observed, managed, and compared in all aspects to monitor sudden changes in energy usage and energy wastage. The abnormal data identified and selected by the unified model are then subjected to big-data analysis using technical management tools, enabling the detection of user problems such as abnormalities pertaining to acquisition device, metering, and energy usage. This model facilitates accurate metering of energy data and improves energy efficiency. The study has significant implications in terms of fulfilling the energy saving.
基金supported by the Fundamental Research Funds for the Central Universities(No.2652019191)China Geological Survey(No.1212011120187)the National Natural Science Foundation of China(No.41502347)。
文摘The demand for fluorite resource is increasing rapidly as most fluorite deposits on Earth’s surface have been exhausted.The newly discovered fluorite deposits in Inner Mongolia are hosted by Permian metamorphosed sandy slate,intermediate-acid intrusive rocks and Cretaceous volcanic sedimentary rocks.The ore bodies are strictly controlled by faults and buried by cover rocks.The feasibility and effectiveness of multi-techniques for prospecting concealed fluorite ore bodies are evaluated,and 10 anomalies are delineated.On the basis of geological features and effectiveness of different methods,the optimum combinations of ore prospecting techniques are proposed for the exploration of zonal type and burial type concealed fluorite ore bodies.Based on comprehensive researches,an integrated exploration model is proposed:(i)select key prospecting targets based on geological backgrounds,regional geochemical anomalies of F and Ca,and remote sensing images;(ii)identify the spatial distribution of low resistivity anomaly and ore-controlling structure from geophysical survey;(iii)evaluate the mineralization potential in fault zone based on F and Ca anomalies in key sections selected from low resistivity anomaly zones;and(iv)evaluate the mineralization potential and reveal the spatial distribution of fluorite ore bodies and ore-controlling faults based on integrated geophysical and geochemical anomalies.The integrated exploration model is verified to be a powerful tool for prospecting concealed fluorite ore bodies in coverage area.
基金Under the auspices of National Natural Science Foundation of China(No.40901026)Beijing Municipal Science & Technology New Star Project Funds(No.2010B046)+1 种基金Beijing Municipal Natural Science Foundation(No.8123041)Southwest Florida Water Management District(SFWMD) Project
文摘The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs),Representative Elementary Watershed(REWs) and Hydrologic Response Units(HRUs).In this paper,a new discretization approach for landforms that have similar hydrologic properties is developed and discussed here for the Integrated Hydrologic Model(IHM),a combining simulation of surface and groundwater processes,accounting for the interaction between the systems.The approach used in the IHM is to disaggregate basin parameters into discrete landforms that have similar hydrologic properties.These landforms may be impervious areas,related areas,areas with high or low clay or organic fractions,areas with significantly different depths-to-water-table,and areas with different types of land cover or different land uses.Incorporating discrete landforms within basins allows significant distributed parameter analysis,but requires an efficient computational structure.The IHM integration represents a new approach interpreting fluxes across the model interface and storages near the interface for transfer to the appropriate model component,accounting for the disparate discretization while rigidly maintaining mass conservation.The discretization approaches employed in IHM will provide some ideas and insights which are helpful to those researchers who have been working on the integrated models for surface-groundwater interaction.
基金financially supported by the Health and Family Planning Commission of Hubei Province(No.WJ2017F047)the Health and Family Planning Commission of Wuhan(No.WG17D05)
文摘Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.
基金co-supported by the National Natural Science Foundation of China(Nos.61890921,61890924)the National Science and Technology Major Project,China(No.J2019-1-0019-0018).
文摘Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金supported by the National Natural Science Foundation of China(Nos.52176032,51976005 and 52006002)the Advanced Jet Propulsion Creativity Center,China(No.HKCX2020-02-013)+2 种基金the National Science and Technology Major Project,China(Nos.2017-II-0004-0016 and 2017-II-0005-0018)the Fundamental Research Funds for the Central Universities,Chinathe Beijing Nova Program,China。
文摘Streamwise Body Force Model(SBFM)could be used to simulate the force of blade on the airflow,resulting in rapid propulsion-airframe integrated simulation.However,when subjected to inlet distortion,the upstream flow field of fan stage is redistributed,which causes inaccurate prediction of fan stage performance.As inspired by the upstream influence of compressor,this paper aims to present a modification strategy for SBFM method to predict the compressor performance under circumferential inlet distortion without any knowledge of compressor geometry.Based on the linearized motion equation,the Upstream Influence Model(UIM)is introduced to predict the upstream flow field redistribution.Then the theoretical Mach number at Aerodynamic Interface Plane(AIP)position is calculated and selected to determine the corresponding body force coefficients based on the functional relationship between body force coefficients and Mach number,thus the upstream influence of compressor could be accurately quantified and the Modified Streamwise Body Force Model(MSBFM)could be established.Two studied cases are calculated with different methods and the upstream flow fields are analyzed.The prediction error of MSBFM method for compressor adiabatic efficiency is less than 3%,and the calculation efficiency is improved 20 times under the condition of ensuring computing accuracy.The MSBFM method has the potential for rapid propulsion-airframe integrated simulation.
