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Asphalt Pavement Icing Condition Criterion and SVM-based Prediction Analysis 认领 引用
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作者 QIU Xin XU Jing-xian +1 位作者 TAO Jue-qiang YANG Qing 《Journal of Highway and Transportation Research and Development(English Edition)》 2018年第4期1-9,共9页
The relationship between precipitation types and meteorological factors was surveyed based on the effect analysis of main meteorological parameters on asphalt pavement surface icing conditions and prediction analysis ... The relationship between precipitation types and meteorological factors was surveyed based on the effect analysis of main meteorological parameters on asphalt pavement surface icing conditions and prediction analysis model of asphalt pavement temperature was established.This study aims to determine the correlation between icy pavement and meteorological factors and implement an accurate prediction of the icing condition of pavement.Considering the road slipperiness criterion presented by Norrman,a discriminative standard of icing condition of asphalt pavement surface in the central area of Zhejiang Province was proposed.Based on the above analysis results,a prediction model of pavement surface icing condition involved in asphalt pavement and ambient temperatures under the condition of different precipitation types was constructed by using support vector machine(SVM)method.Results demonstrate that(1)the distribution characteristics of daily mean air temperature,daily mean pavement temperature,daily average wind speed,and average daily rainfall have remarkable differences under the condition of different precipitation types,wherein the variation features of daily mean air temperature and daily mean pavement temperature are obvious;(2)the indirect prediction of precipitation type and pavement surface temperature could be accomplished in terms of meteorological monitoring data;(3)the influence of pavement surface icing conditions on driving safety is lower than that of rainwater freezing on a cold surface,melting snow at air temperature above O C,and melting snow at air temperature below 0 C;and(4)the SVM-based prediction model of pavement surface icing condition has an accurate analysis result with misreporting rate below 6%.The generalization ability of the proposed model is good and fully demonstrates the application prospects of SVM in the pavement weather prediction field.This study can provide theoretical and technical support for real-time warning about icy asphalt pavements in winter. 展开更多
关键词 road engineering icing prediction model support vector machine(SVM) asphalt pavement meteorological factors prediction analysis
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Prediction of Groundwater Environmental Impact : A Case Study of the Exploration Project of a Mining Area in Haiyang 认领 引用
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作者 Shengqing LI Guangming CAO 《Meteorological and Environmental Research》 2026年第2期74-77,共4页
Based on the exploration project of a mining area in Haiyang,as well as data collection and groundwater monitoring,groundwater environmental impact was predicted,and emergency protection measures were proposed.The res... Based on the exploration project of a mining area in Haiyang,as well as data collection and groundwater monitoring,groundwater environmental impact was predicted,and emergency protection measures were proposed.The results show that after preventive and control measures were adopted under abnormal conditions,the mining activities in the mining area had a relatively small impact on groundwater and were acceptable,which can provide a simple and effective method for groundwater environmental prediction of similar projects. 展开更多
关键词 Mining Groundwater environment Impact assessment Prediction analysis
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A Multi-Agent Framework for Real-Time Sentiment Monitoring and Predictive Analysis of Public Health Policies 认领 引用
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作者 Yanni Li Yuqi Ma +1 位作者 Qing Li Mingming Liang 《China CDC weekly》 SCIE CSCD 2026年第27期852-858,I0004,共7页
Introduction:Rapid policy rollouts can trigger localized dissatisfaction that is difficult to detect using text-only monitoring and single-pass large language model pipelines.This study aimed to evaluate whether a mul... Introduction:Rapid policy rollouts can trigger localized dissatisfaction that is difficult to detect using text-only monitoring and single-pass large language model pipelines.This study aimed to evaluate whether a multimodal,multi-agent framework improves the accuracy,reliability,and early warning sensitivity of public response surveillance during a long-term care policy monitoring window.Methods:This comparative evaluation study analyzed multimodal public discourse captured during a predefined monitoring window by integrating text with images and videos.The sentiment classification outputs were assessed against a human-consensus reference standard using the F1 score.Summarization reliability was quantified as the rate of unverifiable or fabricated claims in the generated policy feedback summaries.Temporal dynamics were characterized using sentiment trajectories,engagement acceleration,and topic subcluster tracking,with policy-relevant drivers estimated as shares of negative discourse volume.Results:The multi-agent framework achieved a higher sentiment classification performance,with an F1 score of 0.89 compared with 0.82 for a single-pass baseline.Robustness improved most noticeably in sarcastic and implicit complaint content,where negative intent was consistently recovered despite superficially positive phrasing.Generative reliability improved sharply,with unverifiable or fabricated claims decreasing to 1.2%versus 14.0%from the baseline.Multimodal recovery increased the captured discourse volume by 34%and added 4,200 unique data points available only in the images and videos.Conclusion:Multimodal multi-agent monitoring strengthened sentiment validity,reduced summary fabrication,and detected topic-level escalation signals in the observed monitoring window.The framework may support earlier identification of policy implementation issues,but its outputs should be interpreted as decision support signals rather than as substitutes for formal policy evaluation. 展开更多
关键词 public health policies predictive analysis multi agent framework multimodal public discourse real time sentiment monitoring public response surveillance comparative evaluation study multimodal
Prediction and Analysis of O3 Based on the ARIMA Model 认领 引用 被引量：2
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作者 FENG Dengchao LIANG Lishui LI Chunjiao 《Instrumentation》 2017年第2期44-52,共9页
Despite of the small amount in the atmosphere,ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun's high frequency ultraviolet radiation... Despite of the small amount in the atmosphere,ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun's high frequency ultraviolet radiation. In recent decades,the global ozone depletion caused by human activities is w ell know n and produces an " ozone hole",the most direct consequence of w hich is the increase in ultraviolet radiation,w hich w ill affect human survival,climatic environment,ecological environment and other important adverse impacts. Due to the implementation of the M ontreal protocol and other agreement,the total amount of ozone depleting substance in the atmosphere has been prominent reduced,w hich w ill lead to a new round of regional climate change.Therefore,predicting the changes of the total ozone in the future w ill have an important guiding significance for predicting the future climate change and making reasonable measures to deal w ith the climate change. In this paper,based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIM A model algorithm,using time series analysis,w e obtain prediction effect of ARIM A model is good by Ljung-Box Q-test and R^2,and the model can be used to predict the future ozone change. With the help of SPSS softw are,the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results,the global ozone change in the future 50 years can be forecasted,namely the atmospheric ozone layer w ill return to its 1980's standard by the middle of this century at the global scale. 展开更多
关键词 Ozone Ozone Hole ARIM A Model Prediction Analysis
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Effect of Curcumin on Aged Drosophila Melanogaster:A Pathway Prediction Analysis 认领 引用
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作者 张治国 牛旭艳 +1 位作者 吕爱平 Gary Guishan Xiao 《Chinese Journal of Integrative Medicine》 SCIE CAS CSCD 2015年第2期115-122,共8页
Objective： To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Methods： Microarray data generated from other study aiming... Objective： To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Methods： Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpdng GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection （MCODE） were used to establish the protein-protein interaction （PPI） network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery （DAVID） was used for pathway analysis. Results： A total of 87 genes expressed differentially in D. melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Conclusion： Genes and their associated pathways in D. rnelanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curnumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases. 展开更多
关键词 anti-aging curcumin Drosophila Melanogaster pathway prediction analysis protein-protein interaction network
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Prediction of alloying element effects on the mechanical behavior of high-pressure die-cast Mg-based alloys 认领 引用
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作者 Reliance Jain Sandeep Jain +5 位作者 Sheetal Kumar Dewangan Sumanta Samal Hansung Lee Eunhyo Song Younggeon Lee Byungmin Ahn 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2025年第8期3819-3828,共10页
Achieving optimal mechanical performance in high-pressure die-cast(HPDC)Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition.This study leverages ... Achieving optimal mechanical performance in high-pressure die-cast(HPDC)Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition.This study leverages machine learning(ML)techniques to accelerate the development of high-performance Mg-based alloys.Data on alloy composition and mechanical properties were collected from literature sources,focusing on HPDC Mg-based alloys.Six ML models—extra trees,CatBoost,k-nearest neighbors,random forest,gradient boosting,and decision tree—were trained to predict mechanical behavior.Cat Boost yielded the highest prediction accuracy with R2 scores of 0.95 for ultimate tensile strength(UTS)and 0.92 for yield strength(YS).Further validation using published datasets reaffirmed its reliability,demonstrating R2 values of 0.956(UTS)and 0.936(YS),MAE of 1%and 2.8%,and RMSE of 1%and 3.5%,respectively.Among these,the CatBoost model demonstrated the highest predictive accuracy,outperforming other ML techniques across multiple optimization metrics. 展开更多
关键词 Lightweight alloys High-pressure die casting Machine learning Predictive analysis Alloys development
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A Space State Forecast Model for Dam Construction Equipments Based on Analysis Prediction Theory 认领 引用
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作者 WU Qingming CHEN Yongqiang ZHANG Zhiqiang 《Wuhan University Journal of Natural Sciences》 CAS 2007年第2期307-310,共4页
Anti-collision equipments system is developed to solve the collision problems of dam construction equipments, and in the system the determination of equipments＇ space state is important. A uniform moving equation of ... Anti-collision equipments system is developed to solve the collision problems of dam construction equipments, and in the system the determination of equipments＇ space state is important. A uniform moving equation of equipments is established based on the analysis prediction theory and the movements states of equipments. Method of least square was employed to deal with discrete data of equipments＇ space position. Fitting equation matched with the movement equation was presented to do data fitting, and a relevant algorithm was given. Applying the fitting equation, current and future space state of equipments can be accurately predicted. Finally, a case is given and results show that numerical values of data were steady and their precision was high. In LongTan dam construction of the equipments antiollision system, applying this method to forecast the equipments＇ space states and practical running of the system indicate that this method can improve the precision of position, obtain the better forecasting effect and increase the robustness of the system. 展开更多
关键词 construction equipments analysis prediction anticollision system method of least square
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A j,υ model for the analysis and prediction of tides 认领 引用 被引量：1
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作者 Chen Zongyong, Huang Zuke, Zhou Tianhua, Tang Enxiang and Wang Yuzhou Ocean University of Qingdao,Qingdao,China No, 57653 Unit of Chinese People’s Liberation Army,China 《Acta Oceanologica Sinica》 SCIE CAS 1990年第4期475-486,共12页
In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwri... In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable. 展开更多
关键词 model for the analysis and prediction of tides A j
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Analysis and Prediction for China's Economy in '99 认领 引用
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《China's Foreign Trade》 EI 1999年第1期46-46,共1页
关键词 Analysis and Prediction for China’s Economy in
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Prediction for asphalt pavement water film thickness based on artificial neural network 认领 引用 被引量：12
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作者 Ma Yaolu Geng Yanfen +1 位作者 Chen Xianhua Lu Yankun 《Journal of Southeast University(English Edition)》 EI CAS 2017年第4期490-495,共6页
In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural netw... In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film. 展开更多
关键词 pavement engineering water film thickness artificial neural network hydrodynamic method prediction analysis
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Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities 认领 引用 被引量：3
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作者 David A.Wood 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第6期444-457,共14页
The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical... The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R2~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R2~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R2~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially. 展开更多
关键词 Well-log brittleness index estimates Data record sample densities Zoomed-in data interpolation Correlation-free prediction analysis Mineralogical and elastic influences
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An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM 认领 引用 被引量：6
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作者 Bingjie Yan Jun Wang +8 位作者 Zhen Zhang Xiangyan Tang Yize Zhou Guopeng Zheng Qi Zou Yao Lu Boyi Liu Wenxuan Tu Neal Xiong 《Computers, Materials & Continua》 SCIE EI 2020年第9期1473-1490,共18页
New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government ca... New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage. 展开更多
关键词 COVID-19 LSTM model predictive analysis
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Simulation and Analysis of Back Siltation in a Navigation Channel Using MIKE 21 认领 引用 被引量：9
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作者 ZHANG Kuncheng LI Qingjie +4 位作者 ZHANG Jing SHI Hongyuan YU Jing GUO Xinchang DU Yonggang 《Journal of Ocean University of China》 SCIE CAS CSCD 2022年第4期893-902,共10页
The channel back-siltation problem has been restricting the development of channels,and its monitoring is limited by funds and natural conditions.Moreover,predicting the channel back-siltation situation in a timely an... The channel back-siltation problem has been restricting the development of channels,and its monitoring is limited by funds and natural conditions.Moreover,predicting the channel back-siltation situation in a timely and accurate manner is difficult.Hence,a numerical simulation of the back-siltation problem in the sea area near the channel is of great significance to the maintenance of a channel.In this study,the back siltation of a deep-water channel in the Lanshan Port area of the Port of Rizhao after dredging is predicted.This paper relies on the MIKE 21 software to establish the wave,tidal current,and sediment numerical models and uses measured data from two observation stations in the study area for verification.On this basis,taking one month as an example,the entire project channel was divided into five sections,and three observation points were set on each section.The results show that the area with offshore siltation is located in the northerly direction of the artificial anti-wave building.Siltation occurred on the northern seabed in the sea a little farther from the shore.Siltation occurred on the seabed surface far away from the shoreline,and with the increase in the distance from the shoreline,the amount of siltation in the south,center,and north became gradually closed,and the results can be used to guide actual engineering practices.This study will play a positive role in promoting the dredging project of Rizhao Lanshan Port. 展开更多
关键词 channel back siltation numerical simulation back silting analysis and prediction
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Energy production and consumption prediction and their response to environment based on coupling model in China 认领 引用 被引量：4
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作者 LI Qiang REN Zhiyuan 《Journal of Geographical Sciences》 SCIE CSCD 2012年第1期93-109,共17页
The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption an... The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption and total discharge of industrial waste water, gas and residue of China via the energy and environmental quality data from 1978 to 2009 in China by use of GM（1,1） model and gravity center model, based on which the paper also analyzes the dynamic variation in regional difference in energy production, consumption and environmental quality and their relationship. The results are shown as follows. 1） The gravity center of energy production is gradually moving southwestward and the entire movement track approxi-mates to linear variation, indicating that the difference of energy production between the east and west, south and north is narrowing to a certain extent, with the difference between the east and the west narrowing faster than that between the south and the north. 2） The gravity center of energy consumption is moving southwestward with perceptible fluctuation, of which the gravity center position from 2000 to 2005 was relatively stable, with slight annual position variation, indicating that the growth rates of all provinces and autonomous regions are basically the same. 3） The gravity center of the total discharge of industrial waste water, gas and residue is characterized by fluctuation in longitude and latitude to a certain degree. But, it shows a southwestward trend on the whole. 4） There are common ground and discrepancy in the variation track of the gravity center of the energy production consumption of China, and the comparative analysis of the gravity center of them and that of total discharge of industrial waste water, gas and residue shows that the environmental quality level is closely associated with the energy production and consumption （especially the energy consumption）, indicating that the environment cost in economy of energy is higher in China. 展开更多
关键词 energy production energy consumption industrial waste water gas and residue prediction and analysis space response China
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Prediction of effluent concentration in a wastewater treatment plant using machine learning models 认领 引用 被引量：10
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作者 Hong Guo Kwanho Jeong +5 位作者 Jiyeon Lim Jeongwon Jo Young Mo Kim Jong-pyo Park Joon Ha Kim Kyung Hwa Cho 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2015年第6期90-101,共12页
Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process mi... Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process. 展开更多
关键词 Artificial neural network Support vector machine Effluent concentration Prediction accuracy Sensitivity analysis
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Identification and predictive analysis for participants at ultra-high risk of psychosis:A comparison of three psychometric diagnostic interviews 认领 引用 被引量：1
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作者 Peng Wang Chuan-Dong Yan +4 位作者 Xiao-Jie Dong Lei Geng Chao Xu Yun Nie Sheng Zhang 《World Journal of Clinical Cases》 SCIE 2022年第8期2420-2428,共9页
BACKGROUND An accurate identification of individuals at ultra-high risk(UHR)based on psychometric tools to prospectively identify psychosis as early as possible is required for indicated preventive intervention.The di... BACKGROUND An accurate identification of individuals at ultra-high risk(UHR)based on psychometric tools to prospectively identify psychosis as early as possible is required for indicated preventive intervention.The diagnostic comparability of several psychometric tools,including the comprehensive assessment of at risk mental state(CAARMS),the structured interview for psychosis-risk syndrome(SIPS)and the bonn scale for the assessment of basic symptoms(BSABS),is unknown.AIM To address the psychometric comparability of CAARMS,SIPS and BSABS for subjects who are close relatives of patients with schizophrenia.METHODS In total,189 participants aged 18-58 years who were lineal relative by blood and collateral relatives by blood up to the third degree of kinship of patients with schizophrenia were interviewed in the period of May 2017 to January 2019.Relatives of the participants diagnosed schizophrenia were excluded.All the participants were assessed for a UHR state by three psychometric tools(CAARMS,SIPS and BSABS).The psychometric diagnosis results included at risk of psychosis(UHR+),not at risk of psychosis(UHR-)and psychosis.Demographic and clinical characteristics were also measured.The inter-rater agreement was assessed for evaluation of the coherence of the three scales.Transition rates for UHR+subjects to psychosis within 2 years were also recorded.RESULTS The overall agreement percentages were 93.12%,92.06%and 93.65%of CAARMS and SIPS,SIPS and BSABS and CAARMS and BSABS,respectively.The overall agreement percentage of the relative functional impairment of the three groups(UHR+,not at risk of psychosis and psychosis)were 89.24%,86.36%and 88.12%,respectively.The inter-rater reliability of the CAARMS,SIPS and BSABS total score was 0.90,0.89 and 0.85.The inter-rater reliability was very good to excellent for all the subscales of these three instruments.For CAARMS,SIPS and BSABS,the kappa coefficient about UHR criteria agreement was 0.87,0.84 and 0.82,respectively(P<0.001).The transition rates of UHR+to psychosis within 2 years were 16.7%(CAARMS),10.0%(SIPS)and 17.7%(BSABS).CONCLUSION There is good diagnostic agreement between the CAARMS,SIPS and BSABS towards identification of UHR participants who are close relatives of patients with schizophrenia. 展开更多
关键词 Psychosis Ultra-high risk Psychosis-Risk syndrome Psychometric diagno-stic Predictive analysis
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Cause Analysis on the Missing Report of First Thunderstorm Weather in Shenyang City in 2010 认领 引用 被引量：1
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作者 隋东 刘凯 +2 位作者 韦涛 祖歌 曹志贤 《Meteorological and Environmental Research》 2010年第10期71-74,共4页
The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm we... The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm weather accurately.In our paper,the reasons for missing report of this thunderstorm weather were analyzed,and analysis on thunderstorm potential was carried out by means of mesoscale analysis technique,providing technical index and vantage point for the prediction of thunderstorm potential.The results showed that the reasons for missing report of this weather process were as follows:surface temperature at prophase was constantly lower going against the development of convective weather;the interpreting and analyzing ability of numerical forecast product should be improved;the forecast result of T639 model was better than that of Japanese numerical forecast;the study and application of mesoscale analysis technique should be strengthened,and this service was formally developed after thunderstorm weather on June 1,2010. 展开更多
关键词 Thunderstorm Missing report Cause analysis:Predicting vantage point China
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Predictive analysis of stress regime and possible squeezing deformation for super-long water conveyance tunnels in Pakistan 认领 引用
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作者 Wang Chenghu Bao Linhai 《International Journal of Mining Science and Technology》 EI CAS 2014年第6期825-831,共7页
The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers.In this study,the authors put forward a systematic solution for this problem.Databases from the World Str... The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers.In this study,the authors put forward a systematic solution for this problem.Databases from the World Stress Map and the Crustal Stress of China,and previous research findings can offer prediction of stress orientations in an engineering area.At the same time,the Andersonian theory can be used to analyze the possible stress orientation of a region.With limited in-situ stress measurements,the Hock-Brown Criterion can be used to estimate the strength of rock mass in an area of interest by utilizing the geotechnical investigation data,and the modified Sheorey's model can subsequently be employed to predict the areas'stress profile,without stress data,by taking the existing in-situ stress measurements as input parameters.In this paper,a case study was used to demonstrate the application of this systematic solution.The planned Kohala hydropower plant is located on the western edge of Qinghai-Tibet Plateau.Three hydro-fracturing stress measurement campaigns indicated that the stress state of the area is SH-Sh〉Sv or SH〉Sv〉Sh.The measured orientation of Sn is NEE(N70.3°-89°E),and the regional orientation of SH from WSM is NE,which implies that the stress orientation of shallow crust may be affected by landforms.The modified Sheorey model was utilized to predict the stress profile along the water sewage tunnel for the plant.Prediction results show that the maximum and minimum horizontal principal stres-ses of the points with the greatest burial depth were up to 56.70 and 40.14 MPa,respectively,and the stresses of areas with a burial depth of greater than 500 m were higher.Based on the predicted stress data,large deformations of the rock mass surrounding water conveyance tunnels were analyzed.Results showed that the large deformations will occur when the burial depth exceeds 300 m.When the burial depth is beyond 800 m,serious squeezing deformations will occur in the surrounding rock masses,thus requiring more attention in the design and construction.Based on the application efficiency in this case study,this prediction method proposed in this paper functions accurately. 展开更多
关键词 Super-long water conveyance tunnel In-situ stress state Squeezing deformation Prediction analysis Kohala hydropower plant
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Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis 认领 引用
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作者 Zheng Rong Shun'an Zhong Nathan Michael 《Journal of Beijing Institute of Technology》 EI CAS 2018年第1期92-102,共11页
An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and... An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion. 展开更多
关键词 observability analysis observability prediction motion primitive motion suggestion monocular visual-inertial state estimation active perception
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Analysis of China’s Crude Oil Supply and Demand in the Year 2010 and the Year 2020 认领 引用 被引量：1
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作者 Wang Baoyi Zhang Baosheng 《Petroleum Science》 CAS 2005年第3期89-92,共4页
Along with the speedy development of the economic growth in China, the shortage of oil and gas becomes more and more serious. Based on summarizing some related research results, the prediction of China＇s oil demand a... Along with the speedy development of the economic growth in China, the shortage of oil and gas becomes more and more serious. Based on summarizing some related research results, the prediction of China＇s oil demand and supply in the year 2010 and the year 2020 has been given in the paper. The oil supply and demand situation is discussed on three different levels. Accordingly, suggestions about the oil supply safety and the national economy safety strategies have been given. 展开更多
关键词 Oil supply and demand prediction and analysis oil supply safety strategy
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