Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping...Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for I...BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.展开更多
This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can b...With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.展开更多
The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications ...The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.展开更多
Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction mo...Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction models may fail to properly describe the embrittlement trend curves of Chinese domestic RPV steels with relatively low Cu content.Based on the screened surveillance data of Chinese domestic and similar international RPV steels,we have developed a new fluencedependent model for predicting the irradiation-embrittlement trend.The fast neutron fluence(E>1 MeV)exhibited the highest correlation coefficient with the measured TTS data;thus,it is a crucial parameter in the prediction model.The chemical composition has little relevance to the TTS residual calculated by the fluence-dependent model.The results show that the newly developed model with a simple power-law functional form of the neutron fluence is suitable for predicting the irradiation-embrittlement trend of Chinese domestic RPVs,regardless of the effect of the chemical composition.展开更多
Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of to...Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.展开更多
Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack ...Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).展开更多
Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial pro...Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.展开更多
The geologic conditions of superimposed basins in China are very complicated. This is mainly shown by multi-phase structural evolution, multiple sets of source-reservoir-cap rock combinations, multiple stages of hydro...The geologic conditions of superimposed basins in China are very complicated. This is mainly shown by multi-phase structural evolution, multiple sets of source-reservoir-cap rock combinations, multiple stages of hydrocarbon generation and expulsion from source rocks, multi-cycle hydrocarbon enrichment and accumulation, and multi-phase reservoir adjustment and reconstruction. The enrichment, accumulation and distribution of hydrocarbon is mainly controlled by the source rock kitchen, paleo- anticline, regional cap rock and intensity of tectonic movement. In this paper, the T-BCMS model has been developed to predict favorable areas of hydrocarbon accumulation in complicated superimposed basins according to time and spatial relationships among five key factors. The five factors include unconformity surface representing tectonic balancing （B）, regional cap rock representing hydrocarbon protection （C）, paleo-anticline representing hydrocarbon migration and accumulation （M）, source rock kitchen representing hydrocarbon generation and expulsion （S） and geological time （T）. There are three necessary conditions to form favorable areas of hydrocarbon accumulation. First, four key factors BCMS should be strictly in the order of BCMS from top to bottom. Second, superimposition of four key factors BCMS in the same area is the most favorable for hydrocarbon accumulation. Third, vertically ordered combination and superimposition in the same area of BCMS should occur at the same geological time. The model has been used to predict the most favorable exploration areas in Ordovician in the Tarim Basin in the main hydrocarbon accumulation periods. The result shows that 95% of the discovered Ordovician hydrocarbon reservoirs are located in the predicted areas, which indicates the feasibility and reliability of the key factor matching T-BCMS model for hydrocarbon accumulation and enrichment.展开更多
Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can ...Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers＇ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.展开更多
Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 ...Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 years and older from intensive care units of Cardiology De- partment in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system （RPSS）. The area under the curve （AUC） of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines-heart failure （GWTG-HF）. Re- sults By binary logistic regression analysis, heart rate （OR： 1.043, 95% CI： 1.030-1.057, P 〈 0.001）, left ventricular ejection fraction （OR： 0.918, 95% CI： 0.833~）.966, P 〈 0.001）, pH value （OR： 0.001, 95% CI： 0.000-0.002, P 〈 0.001）, renal dysfunction （OR： 0.120, 95% CI： 0.066M）.220, P 〈 0.001） and NT-pro BNP （OR： 3.463, 95% CI： 1.870-6.413, P 〈 0.001） were independent risk factors of in-hospital mortal- ity for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk predic- tion than GWTG-THF （AUC： 0.873 vs. 0.818, P = 0.016）. Conclusions Our risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with A/IF.展开更多
Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale roc...Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.展开更多
Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Us...Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.展开更多
Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study wa...Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
Sandstone is widely distributed in cold regions and the freeze-thaw deterioration of them has caused many geological engineering disasters.As an important and direct index of frost resistance,the strength loss of sand...Sandstone is widely distributed in cold regions and the freeze-thaw deterioration of them has caused many geological engineering disasters.As an important and direct index of frost resistance,the strength loss of sandstones under freeze-thaw actions should be investigated to provide a guidance for the stability assessment of geological engineering.In this research,the UCS(Uniaxial compressive strength)loss of six typical sandstones with different water contents after 0,20,40 and 60 freeze-thaw cycles was measured in the laboratory.The experimental results indicated that the freeze-thaw damage was more serious in sandstones containing high water contents,and the critical saturations for causing a significant loss of UCS under freeze-thaw were 60%-80%for these sandstones.Below this critical saturation,the UCS loss of the sandstones was mainly caused by water weakening rather than freeze-thaw damage.Besides,a developed strength prediction model was proposed by combining the exponential decay function and multiple linear regression method.The initial porosity,elastic modulus and tensile strength of fresh sandstones were a good parameter combination to accurately determine the decay constant in this developed model.The main novelty of this model is that it can accurately and easily estimate the UCS loss of sandstones after any freeze-thaw cycle only using the initial parameters of fresh sandstones,but it does not need to perform freeze-thaw and mechanical strength experiments.This study not only provides an accurate prediction model of UCS under freeze-thaw,but also makes a contribution to better understanding the frost resistance mechanism of sandstones.展开更多
基金the project SILVARSTAR funded from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 101015442。
文摘Within the SILVARSTAR project,a user-friendly frequency-based hybrid prediction tool has been developed to assess the environmental impact of railway-induced vibration.This tool is integrated in existing noise mapping software.Following modern vibration standards and guidelines,the vibration velocity level in a building in each frequency band is expressed as the sum of a force density(source term),line source transfer mobility(propagation term)and building correction factor(receiver term).A hybrid approach is used that allows for a combination of experimental data and numerical predictions,providing increased flexibility and applicability.The train and track properties can be selected from a database or entered as numerical values.The user can select soil impedance and transfer functions from a database,pre-computed for a wide range of parameters with state-of-the-art models.An experimental database of force densities,transfer functions,free field vibration and input parameters is also provided.The building response is estimated by means of building correction factors.Assumptions within the modelling approach are made to reduce computation time but these can influence prediction accuracy;this is quantified for the case of a nominal intercity train running at different speeds on a ballasted track supported by homogeneous soil of varying stiffness.The paper focuses on the influence of these parameters on the compliance of the track–soil system and the free field response.We also demonstrate the use and discuss the validation of the vibration prediction tool for the case of a high-speed train running on a ballasted track in Lincent(Belgium).
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
基金supported by the National Natural Science Foundation of China (82072159)。
文摘BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
基金This work was supported by the Beijing Nova Program[Z211100002121136]Open Fund Project of State Key Laboratory of Lithospheric Evolution[SKL-K202103]+1 种基金Joint Funds of National Natural Science Foundation of China[U19B6003-02]the National Natural Science Foundation of China[42302149].We would like to thank Prof.Zhu Rixiang from the Institute of Geology and Geophysics,Chinese Academy of Sciences.
文摘With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.
基金supported by National Key R&D Program of China(No.2022YFB3104500)Natural Science Foundation of Jiangsu Province(No.BK20222013)Scientific Research Foundation of Nanjing Institute of Technology(No.3534113223036)。
文摘The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.
基金supported by the National Key R&D Program of China (No. 2019YFB1900901)the Fundamental Research Funds for the Central Universities (No. 2021MS032)
文摘Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction models may fail to properly describe the embrittlement trend curves of Chinese domestic RPV steels with relatively low Cu content.Based on the screened surveillance data of Chinese domestic and similar international RPV steels,we have developed a new fluencedependent model for predicting the irradiation-embrittlement trend.The fast neutron fluence(E>1 MeV)exhibited the highest correlation coefficient with the measured TTS data;thus,it is a crucial parameter in the prediction model.The chemical composition has little relevance to the TTS residual calculated by the fluence-dependent model.The results show that the newly developed model with a simple power-law functional form of the neutron fluence is suitable for predicting the irradiation-embrittlement trend of Chinese domestic RPVs,regardless of the effect of the chemical composition.
基金Under the auspices of Knowledge Innovation Frontier Project of Institute of Soil Science,Chinese Academy of Sciences(No.ISSASIP0716 )the National Nature Science Foundation of China ( No.40701070,40571065)
文摘Hue-Saturation-Intensity （HSI） color model, a psychologically appealing color model, was employed to visualize uncertainty represented by relative prediction error based on the case of spatial prediction of pH of topsoil in the peri-urban Beijing. A two-dimensional legend was designed to accompany the visualization-vertical axis （hues） for visualizing the predicted values and horizontal axis （whiteness） for visualizing the prediction error. Moreover, different ways of visualizing uncertainty were briefly reviewed in this paper. This case study indicated that visualization of both predictions and prediction uncertainty offered a possibility to enhance visual exploration of the data uncertainty and to compare different prediction methods or predictions of totally different variables. The whitish region of the visualization map can be simply interpreted as unsatisfactory prediction results, where may need additional samples or more suitable prediction models for a better prediction results.
基金supported by the National Natural Science Foundation of China(81825009,82071505,81901358)the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2021-I2MC&T-B-099,2019-I2M-5–006)+2 种基金the Program of Chinese Institute for Brain Research Beijing(2020-NKX-XM-12)the King’s College London-Peking University Health Science Center Joint Institute for Medical Research(BMU2020KCL001,BMU2019LCKXJ012)the National Key R&D Program of China(2021YFF1201103,2016YFC1307000).
文摘Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
文摘Since chemical processes are highly non-linear and multiscale,it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators.While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables,it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes.In light of this,a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net)is proposed for mining spatiotemporal multiscale information.First,the industrial data from the Fluid Catalytic Cracking(FCC)process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)extract the multi-energy scale information of the feature subset.Then,convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data.Finally,a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output.Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP).Subsequently,the performance of Msrt Net is evaluated in predicting product yield for a 2.80×10^(6) t/a FCC unit,taking diesel and gasoline yield as examples.In conclusion,Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30%in prediction error compared to other time-series models.Furthermore,its robustness and transferability underscore its promising potential for broader applications.
基金supported by the National Basic Research Program (2006CB202308)
文摘The geologic conditions of superimposed basins in China are very complicated. This is mainly shown by multi-phase structural evolution, multiple sets of source-reservoir-cap rock combinations, multiple stages of hydrocarbon generation and expulsion from source rocks, multi-cycle hydrocarbon enrichment and accumulation, and multi-phase reservoir adjustment and reconstruction. The enrichment, accumulation and distribution of hydrocarbon is mainly controlled by the source rock kitchen, paleo- anticline, regional cap rock and intensity of tectonic movement. In this paper, the T-BCMS model has been developed to predict favorable areas of hydrocarbon accumulation in complicated superimposed basins according to time and spatial relationships among five key factors. The five factors include unconformity surface representing tectonic balancing （B）, regional cap rock representing hydrocarbon protection （C）, paleo-anticline representing hydrocarbon migration and accumulation （M）, source rock kitchen representing hydrocarbon generation and expulsion （S） and geological time （T）. There are three necessary conditions to form favorable areas of hydrocarbon accumulation. First, four key factors BCMS should be strictly in the order of BCMS from top to bottom. Second, superimposition of four key factors BCMS in the same area is the most favorable for hydrocarbon accumulation. Third, vertically ordered combination and superimposition in the same area of BCMS should occur at the same geological time. The model has been used to predict the most favorable exploration areas in Ordovician in the Tarim Basin in the main hydrocarbon accumulation periods. The result shows that 95% of the discovered Ordovician hydrocarbon reservoirs are located in the predicted areas, which indicates the feasibility and reliability of the key factor matching T-BCMS model for hydrocarbon accumulation and enrichment.
基金Project supported by the Special Scientific Research Project for Public Interest(Grant No.GYHY201206009)the Fundamental Research Funds for the Central Universities,China(Grant Nos.lzujbky-2012-13 and lzujbky-2013-11)the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)
文摘Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers＇ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.
文摘Objectives The aim of this study was to develop a clinical risk model that is predictive of in-hospital mortality in elderly patients hos- pitalized with acute heart failure （AHF）. Methods 2486 patients who were 60 years and older from intensive care units of Cardiology De- partment in the hospital were analyzed. Independent risk factors for in-hospital mortality were obtained by binary logistic regression and then used to establish the risk prediction score system （RPSS）. The area under the curve （AUC） of receiver operator characteristic and C-statistic test were adopted to assess the performance of RPSS and to compare with previous get with the guidelines-heart failure （GWTG-HF）. Re- sults By binary logistic regression analysis, heart rate （OR： 1.043, 95% CI： 1.030-1.057, P 〈 0.001）, left ventricular ejection fraction （OR： 0.918, 95% CI： 0.833~）.966, P 〈 0.001）, pH value （OR： 0.001, 95% CI： 0.000-0.002, P 〈 0.001）, renal dysfunction （OR： 0.120, 95% CI： 0.066M）.220, P 〈 0.001） and NT-pro BNP （OR： 3.463, 95% CI： 1.870-6.413, P 〈 0.001） were independent risk factors of in-hospital mortal- ity for elderly AHF patients. Additionally, RPSS, which was composed of all the above-mentioned parameters, provided a better risk predic- tion than GWTG-THF （AUC： 0.873 vs. 0.818, P = 0.016）. Conclusions Our risk prediction model, RPSS, provided a good prediction for in-hospital mortality in elderly patients with A/IF.
文摘Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.
基金This work has been supported by the National 863 Key Project Grant No. 2008AA042901, National Natural Science Foundation of China Grant No.70631003 and No.90718037, Foundation of Hefei University of Technology Grant No. 2010HGXJ0083.
文摘Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.
基金the National Key Research and Development Program of ChinaKey Projects for Strategic International Innovative Cooperation in Science and Technology(2018YFE0207800)+1 种基金Fundamental Research Funds for the Central Universities(2572019BA03)partly by the China Scholarship Council(CSC No.2016DFH417)。
文摘Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金supported by National Natural Science Foundation of China(Nos.42072300 and 41702291).
文摘Sandstone is widely distributed in cold regions and the freeze-thaw deterioration of them has caused many geological engineering disasters.As an important and direct index of frost resistance,the strength loss of sandstones under freeze-thaw actions should be investigated to provide a guidance for the stability assessment of geological engineering.In this research,the UCS(Uniaxial compressive strength)loss of six typical sandstones with different water contents after 0,20,40 and 60 freeze-thaw cycles was measured in the laboratory.The experimental results indicated that the freeze-thaw damage was more serious in sandstones containing high water contents,and the critical saturations for causing a significant loss of UCS under freeze-thaw were 60%-80%for these sandstones.Below this critical saturation,the UCS loss of the sandstones was mainly caused by water weakening rather than freeze-thaw damage.Besides,a developed strength prediction model was proposed by combining the exponential decay function and multiple linear regression method.The initial porosity,elastic modulus and tensile strength of fresh sandstones were a good parameter combination to accurately determine the decay constant in this developed model.The main novelty of this model is that it can accurately and easily estimate the UCS loss of sandstones after any freeze-thaw cycle only using the initial parameters of fresh sandstones,but it does not need to perform freeze-thaw and mechanical strength experiments.This study not only provides an accurate prediction model of UCS under freeze-thaw,but also makes a contribution to better understanding the frost resistance mechanism of sandstones.
