There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Functio...There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficien...Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.展开更多
Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the au...Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.展开更多
Microwave radiation has been widely used in various fields,such as communication,industry,medical treatment,and military applications.Microwave radiation may cause injuries to both the structures and functions of vari...Microwave radiation has been widely used in various fields,such as communication,industry,medical treatment,and military applications.Microwave radiation may cause injuries to both the structures and functions of various organs,such as the brain,heart,reproductive organs,and endocrine organs,which endanger human health.Therefore,it is both theoretically and clinically important to conduct studies on the biological effects induced by microwave radiation.The successful establishment of injury models is of great importance to the reliability and reproducibility of these studies.In this article,we review the microwave exposure conditions,subjects used to establish injury models,the methods used for the assessment of the injuries,and the indicators implemented to evaluate the success of injury model establishment in studies on biological effects induced by microwave radiation.展开更多
Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, the...Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.展开更多
Gravimetry technical guides are the scientific basis for air-sea gravimetry. However, the existing technical guides in China are behind the application requirements. This study analyzed the most important indicators o...Gravimetry technical guides are the scientific basis for air-sea gravimetry. However, the existing technical guides in China are behind the application requirements. This study analyzed the most important indicators of air-sea gravimetry, including the density of survey lines, gravimetry accuracy and space resolution, stability and reliability of the air-sea gravimeter, and proposed a gravimetry accuracy assessment system consisting of gravity RMS of error, systematic error and mean error, and an assessment system for the gravimeter stability consisting of the relative accuracy of the scale value, monthly zero-drift, RMS of the monthly nonlinear zero-drift variation and the threshold of the monthly nonlinear zero-drift variation. The mathematic models for the measurement point determination in shipborne gravimetry, E tv s correction for airborne gravimetry, platform tilt correction and evaluation air-sea gravimetry were also analyzed and modified. This work will provide technology support for the composition of the military-civil air-sea gravimetry technical guides.展开更多
Sustainable world heritage management represents an approach for managing the resources of a property by integrating environmental, economic, and social issues. It aims to provide sustainable benefits for future gener...Sustainable world heritage management represents an approach for managing the resources of a property by integrating environmental, economic, and social issues. It aims to provide sustainable benefits for future generations, while protect the property and minimize the possible adverse social, economic and environmental impacts. Indicators of sustainable development, which summarize information for decision-making, are invaluable to learn the efficiency and effectiveness of property management. Scientists in many fields devised several conceptual models of environmental statistics and indicators, of which, DPSIR (Driving forces – Pressure – State – Impact – Response) is thought to be the best available one in identifying and developing indicators of sustainable development. Based on the DPSIR conceptual model and indicator selection criteria, the present paper proposed a methodology framework for selecting indicators to assess the sustainable development of a natural heritage site. The proposed framework included a multi-level hierarchical structure for various indicators and indexes, a modified DPSIR frame to identify key issues in property management and a set of indicators for evaluating the sustainability in Sichuan Giant Panda Sanctuaries.展开更多
Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the po...Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations(HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20 th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20 th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.展开更多
This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil propertie...This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.展开更多
While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chines...While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.展开更多
BACKGROUND Cholestatic liver diseases are characterized by an accumulation of toxic bile acids(BA)in the liver,blood and other tissues which lead to progressive liver injury and poor prognosis in patients.AIM To disco...BACKGROUND Cholestatic liver diseases are characterized by an accumulation of toxic bile acids(BA)in the liver,blood and other tissues which lead to progressive liver injury and poor prognosis in patients.AIM To discover and validate prognostic biomarkers of cholestatic liver diseases based on the urinary BA profile.METHODS We analyzed urine samples by liquid chromatography-tandem mass spectrometry and investigated the use of the urinary BA profile to develop survival models that can predict the prognosis of hepatobiliary diseases.The urinary BA profile,a set of non-BA parameters,and the adverse events of liver transplant and/or death were monitored in 257 patients with cholestatic liver diseases for up to 7 years.The BA profile was characterized by calculating BA indices,which quantify the composition,metabolism,hydrophilicity,formation of secondary BA,and toxicity of the BA profile.We have developed and validated the bile-acid score(BAS)model(a survival model based on BA indices)to predict the prognosis of cholestatic liver diseases.RESULTS We have developed and validated a survival model based on BA(the BAS model)indices to predict the prognosis of cholestatic liver diseases.Our results demonstrate that the BAS model is more accurate and results in higher truepositive and true-negative prediction of death compared to both non-BAS and model for end-stage liver disease(MELD)models.Both 5-and 3-year survival probabilities markedly decreased as a function of BAS.Moreover,patients with high BAS had a 4-fold higher rate of death and lived for an average of 11 mo shorter than subjects with low BAS.The increased risk of death with high vs low BAS was also 2-4-fold higher and the shortening of lifespan was 6-7-mo lower compared to MELD or non-BAS.Similarly,we have shown the use of BAS to predict the survival of patients with and without liver transplant(LT).Therefore,BAS could be used to define the most seriously ill patients,who need earlier intervention such as LT.This will help provide guidance for timely care for liver patients.CONCLUSION The BAS model is more accurate than MELD and non-BAS models in predicting the prognosis of cholestatic liver diseases.展开更多
Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different ...Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different factors.This study provides a new suite of quantitative indicators for assessing agricultural sustainability at regional and district levels,involving environmental sustainability,social security,and economic security.Combining the PressureState-Response(PSR)model and indicator approach,this study creates a composite agricultural sustainability index for the 14 mainstream agro-climatic regions of India.The results of this study show that the Trans-Gengatic Plain Region(TGPR)ranks first in agricultural sustainability among India's 14 mainstream agro-climatic regions,while the Eastern Himalayan Region(EHR)ranks last.Higher livestock ownership,cropping intensity,per capita income,irrigation intensity,share of institutional credit,food grain productivity,crop diversification,awareness of minimum support price,knowledge sharing with fellow farmers,and young and working population,as well as better transportation facilities and membership of agricultural credit societies are influencing indicators responsible for higher agricultural sustainability in TGPR compared with EHR.Although,the scores of environmental sustainability indicators of EHR are quite good,its scores of social and economic security indicators are fairly low,putting it at the bottom of the rank of agricultural sustainability index among the 14 mainstream agroclimatic regions in India.This demonstrates the need of understanding agricultural sustainability in relation to social and economic dimensions.In a nation as diverse and complicated as India,it is the social structure that determines the health of the economy and environment.Last but not least,the sustainability assessment methodology may be used in a variety of India's agro-climatic regions.展开更多
This paper proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generators (DG) units in distribution system with different load models. It is shown that lo...This paper proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generators (DG) units in distribution system with different load models. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimized includes a short circuit level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading and the MVA intake by the grid. The optimization technique based on particle swarm optimization (PSO) is introduced. The analysis of continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using the 38-bus radial system and the IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.展开更多
The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on...The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on a horizontal surface for the Abeche site in Chad. The data used in this work were collected at the General Directorate of National Meteorology of Chad. The reliability and accuracy of different models for estimating global solar radiation were validated by statistical indicators to identify the most accurate model. The results show that among all the models, the Sabbagh model has the best performance in estimating the global solar radiation. The average is 6.354 kWh/m<sup>2</sup> with an average of -3.704%. This model is validated against NASA data which is widely used.展开更多
To provide long-term simulations of climate change at higher resolution, Regional Climate Models (RCMs) are nested in global climate models (GCMs). The objective of this work is to evaluate the Eta RCM simulations dri...To provide long-term simulations of climate change at higher resolution, Regional Climate Models (RCMs) are nested in global climate models (GCMs). The objective of this work is to evaluate the Eta RCM simulations driven by three global models, the HadGEM2-ES, BESM, and MIROC5, for the present period, 1961-1990. The RCM domain covers South America, Central America, and Caribbean. These simulations will be used for assessment of climate change projections in the region. Maximum temperatures are generally underestimated in the domain, in particular by MIROC5 driven simulations, in summer and winter seasons. Larger spread among the simulations was found in the minimum temperatures, which showed mixed signs of errors. The spatial correlations of temperature simulations against the CRU observations show better agreement for the MIROC5 driven simulations. The nested simulations underestimate precipitation in large areas over the continent in austral summer, whereas in winter overestimate occurs in southern Amazonia, and underestimate in southern Brazil and eastern coast of Northeast Brazil. The annual cycle of the near-surface temperature is underestimated in all model simulations, in all regions in Brazil, and in most of the year. The temperature and precipitation frequency distributions reveal that the RCM and GCM simulations contain more extreme values than the CRU observations. Evaluations of the climatic extreme indicators show that in general hot days, warm nights, and heat waves are increasing in the period, in agreement with observations. The Eta simulations driven by HadGEM2-ES show wet trends in the period, whereas the Eta driven by BESM and by MIROC5 show trends for drier conditions.展开更多
Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (...Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (DEM) models to enhance our understanding of microfiltration membrane clogging. The models were validated by comparing them to experimental data, demonstrating reasonable consistency. Subsequently, a parametric study was conducted on a cross-flow model, exploring the influence of key parameters on clogging. Findings show that clogging is a complex phenomenon affected by various factors. The mean inlet velocity and transmembrane flux were found to directly impact clogging, while the confinement ratio and cosine of the membrane pore entrance angle had an inverse relationship with it. Two clog types were identified: internal (inside the pore) and external (arching at the pore entrance), with the confinement ratio determining the type. This study introduced a dimensionless number as a quantitative clogging indicator based on transmembrane flux, Reynolds number, filtration time, entrance angle cosine, and confinement ratio. While this hypothesis held true in simulations, future studies should explore variations in clogging indicators, and improved modeling of clogging characteristics. Calibration between numerical and physical times and consideration of particle volume fraction will enhance understanding.展开更多
文摘There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
基金Analysis and Research on Online Learning in Higher Vocational Colleges Based on Kirkpatrick Model-Taking the Course of Physiology as an Example(Project No.:D/2021/03/91)The excellent teaching team of Physiology of Suzhou Vocational College of Health Science and Technology in 2019(Project number:JXTD201912).
文摘Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.
基金supported by the Natural Science Foundation of CQ CSTC under Grant No.cstc.2018jcyj A2073Chongqing Social Science Plan Project under Grant No.2019WT59+3 种基金Science and Technology Research Program of Chongqing Education Commission under Grant No.KJZD-M202100801Mathematic and Statistics Team from Chongqing Technology and Business University under Grant No.ZDPTTD201906Open Project from Chongqing Key Laboratory of Social Economy and Applied Statistics under Grant No.KFJJ2022056Chongqing Graduate Research Innovation Project under Grant No.CYS23568。
文摘Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.
基金supported by the National Natural Science Foundation of China(61801506)。
文摘Microwave radiation has been widely used in various fields,such as communication,industry,medical treatment,and military applications.Microwave radiation may cause injuries to both the structures and functions of various organs,such as the brain,heart,reproductive organs,and endocrine organs,which endanger human health.Therefore,it is both theoretically and clinically important to conduct studies on the biological effects induced by microwave radiation.The successful establishment of injury models is of great importance to the reliability and reproducibility of these studies.In this article,we review the microwave exposure conditions,subjects used to establish injury models,the methods used for the assessment of the injuries,and the indicators implemented to evaluate the success of injury model establishment in studies on biological effects induced by microwave radiation.
文摘Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.
基金The National Natural Science Foundation of China (41804011, 41706111)The National Major Development Program of China(2016YFC0303007,2016YFB0501704)+1 种基金The Great Scientific Instrument Development Project of China(2011YQ12004503)The National Basic Research Program of China(973 Program)(613219).
文摘Gravimetry technical guides are the scientific basis for air-sea gravimetry. However, the existing technical guides in China are behind the application requirements. This study analyzed the most important indicators of air-sea gravimetry, including the density of survey lines, gravimetry accuracy and space resolution, stability and reliability of the air-sea gravimeter, and proposed a gravimetry accuracy assessment system consisting of gravity RMS of error, systematic error and mean error, and an assessment system for the gravimeter stability consisting of the relative accuracy of the scale value, monthly zero-drift, RMS of the monthly nonlinear zero-drift variation and the threshold of the monthly nonlinear zero-drift variation. The mathematic models for the measurement point determination in shipborne gravimetry, E tv s correction for airborne gravimetry, platform tilt correction and evaluation air-sea gravimetry were also analyzed and modified. This work will provide technology support for the composition of the military-civil air-sea gravimetry technical guides.
文摘Sustainable world heritage management represents an approach for managing the resources of a property by integrating environmental, economic, and social issues. It aims to provide sustainable benefits for future generations, while protect the property and minimize the possible adverse social, economic and environmental impacts. Indicators of sustainable development, which summarize information for decision-making, are invaluable to learn the efficiency and effectiveness of property management. Scientists in many fields devised several conceptual models of environmental statistics and indicators, of which, DPSIR (Driving forces – Pressure – State – Impact – Response) is thought to be the best available one in identifying and developing indicators of sustainable development. Based on the DPSIR conceptual model and indicator selection criteria, the present paper proposed a methodology framework for selecting indicators to assess the sustainable development of a natural heritage site. The proposed framework included a multi-level hierarchical structure for various indicators and indexes, a modified DPSIR frame to identify key issues in property management and a set of indicators for evaluating the sustainability in Sichuan Giant Panda Sanctuaries.
文摘Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations(HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20 th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20 th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.
文摘This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.
文摘While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.
基金Supported by the University of Nebraska Medical Center-Clinical Research Center and Great Plains Health Research Consortium,No.NR98-134.
文摘BACKGROUND Cholestatic liver diseases are characterized by an accumulation of toxic bile acids(BA)in the liver,blood and other tissues which lead to progressive liver injury and poor prognosis in patients.AIM To discover and validate prognostic biomarkers of cholestatic liver diseases based on the urinary BA profile.METHODS We analyzed urine samples by liquid chromatography-tandem mass spectrometry and investigated the use of the urinary BA profile to develop survival models that can predict the prognosis of hepatobiliary diseases.The urinary BA profile,a set of non-BA parameters,and the adverse events of liver transplant and/or death were monitored in 257 patients with cholestatic liver diseases for up to 7 years.The BA profile was characterized by calculating BA indices,which quantify the composition,metabolism,hydrophilicity,formation of secondary BA,and toxicity of the BA profile.We have developed and validated the bile-acid score(BAS)model(a survival model based on BA indices)to predict the prognosis of cholestatic liver diseases.RESULTS We have developed and validated a survival model based on BA(the BAS model)indices to predict the prognosis of cholestatic liver diseases.Our results demonstrate that the BAS model is more accurate and results in higher truepositive and true-negative prediction of death compared to both non-BAS and model for end-stage liver disease(MELD)models.Both 5-and 3-year survival probabilities markedly decreased as a function of BAS.Moreover,patients with high BAS had a 4-fold higher rate of death and lived for an average of 11 mo shorter than subjects with low BAS.The increased risk of death with high vs low BAS was also 2-4-fold higher and the shortening of lifespan was 6-7-mo lower compared to MELD or non-BAS.Similarly,we have shown the use of BAS to predict the survival of patients with and without liver transplant(LT).Therefore,BAS could be used to define the most seriously ill patients,who need earlier intervention such as LT.This will help provide guidance for timely care for liver patients.CONCLUSION The BAS model is more accurate than MELD and non-BAS models in predicting the prognosis of cholestatic liver diseases.
文摘There is demonstrated how it is possible to refine graphitic nanotubes’ chiral indices based on the appropriate geometrical model for their structure.
文摘Analyzing agricultural sustainability is essential for designing and assessing rural development initiatives.However,accurately measuring agricultural sustainability is complicated since it involves so many different factors.This study provides a new suite of quantitative indicators for assessing agricultural sustainability at regional and district levels,involving environmental sustainability,social security,and economic security.Combining the PressureState-Response(PSR)model and indicator approach,this study creates a composite agricultural sustainability index for the 14 mainstream agro-climatic regions of India.The results of this study show that the Trans-Gengatic Plain Region(TGPR)ranks first in agricultural sustainability among India's 14 mainstream agro-climatic regions,while the Eastern Himalayan Region(EHR)ranks last.Higher livestock ownership,cropping intensity,per capita income,irrigation intensity,share of institutional credit,food grain productivity,crop diversification,awareness of minimum support price,knowledge sharing with fellow farmers,and young and working population,as well as better transportation facilities and membership of agricultural credit societies are influencing indicators responsible for higher agricultural sustainability in TGPR compared with EHR.Although,the scores of environmental sustainability indicators of EHR are quite good,its scores of social and economic security indicators are fairly low,putting it at the bottom of the rank of agricultural sustainability index among the 14 mainstream agroclimatic regions in India.This demonstrates the need of understanding agricultural sustainability in relation to social and economic dimensions.In a nation as diverse and complicated as India,it is the social structure that determines the health of the economy and environment.Last but not least,the sustainability assessment methodology may be used in a variety of India's agro-climatic regions.
文摘This paper proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generators (DG) units in distribution system with different load models. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimized includes a short circuit level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading and the MVA intake by the grid. The optimization technique based on particle swarm optimization (PSO) is introduced. The analysis of continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using the 38-bus radial system and the IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.
文摘The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on a horizontal surface for the Abeche site in Chad. The data used in this work were collected at the General Directorate of National Meteorology of Chad. The reliability and accuracy of different models for estimating global solar radiation were validated by statistical indicators to identify the most accurate model. The results show that among all the models, the Sabbagh model has the best performance in estimating the global solar radiation. The average is 6.354 kWh/m<sup>2</sup> with an average of -3.704%. This model is validated against NASA data which is widely used.
基金The authors thank:the Brazilian Ministry of Science,Technology,and Innovation for supporting the work through Global Environmental Facility funding(UNDP BRA/05/G31)the Secretariat for Strategic Affairs of the presidency of Brazil for additional funding,Martin Juckes from the British Atmospheric Data Centre for making available HadGEM2-ES dataset+1 种基金and Seita Emori and Tokuta Yokohata from the National Institute for Environmental Studies for making available the MIROC5 dataset.S.C.Cthanks the Brazilian National Council for Scientific and Technological Development for the grant PQ 308035/2013-5.
文摘To provide long-term simulations of climate change at higher resolution, Regional Climate Models (RCMs) are nested in global climate models (GCMs). The objective of this work is to evaluate the Eta RCM simulations driven by three global models, the HadGEM2-ES, BESM, and MIROC5, for the present period, 1961-1990. The RCM domain covers South America, Central America, and Caribbean. These simulations will be used for assessment of climate change projections in the region. Maximum temperatures are generally underestimated in the domain, in particular by MIROC5 driven simulations, in summer and winter seasons. Larger spread among the simulations was found in the minimum temperatures, which showed mixed signs of errors. The spatial correlations of temperature simulations against the CRU observations show better agreement for the MIROC5 driven simulations. The nested simulations underestimate precipitation in large areas over the continent in austral summer, whereas in winter overestimate occurs in southern Amazonia, and underestimate in southern Brazil and eastern coast of Northeast Brazil. The annual cycle of the near-surface temperature is underestimated in all model simulations, in all regions in Brazil, and in most of the year. The temperature and precipitation frequency distributions reveal that the RCM and GCM simulations contain more extreme values than the CRU observations. Evaluations of the climatic extreme indicators show that in general hot days, warm nights, and heat waves are increasing in the period, in agreement with observations. The Eta simulations driven by HadGEM2-ES show wet trends in the period, whereas the Eta driven by BESM and by MIROC5 show trends for drier conditions.
文摘Microfiltration membrane technology has been widely used in various industries for solid-liquid separation. However, pore clogging remains a persistent challenge. This study employs (CFD) and discrete element method (DEM) models to enhance our understanding of microfiltration membrane clogging. The models were validated by comparing them to experimental data, demonstrating reasonable consistency. Subsequently, a parametric study was conducted on a cross-flow model, exploring the influence of key parameters on clogging. Findings show that clogging is a complex phenomenon affected by various factors. The mean inlet velocity and transmembrane flux were found to directly impact clogging, while the confinement ratio and cosine of the membrane pore entrance angle had an inverse relationship with it. Two clog types were identified: internal (inside the pore) and external (arching at the pore entrance), with the confinement ratio determining the type. This study introduced a dimensionless number as a quantitative clogging indicator based on transmembrane flux, Reynolds number, filtration time, entrance angle cosine, and confinement ratio. While this hypothesis held true in simulations, future studies should explore variations in clogging indicators, and improved modeling of clogging characteristics. Calibration between numerical and physical times and consideration of particle volume fraction will enhance understanding.