基于移动平均预测限预判新型冠状病毒肺炎疫情趋势与适时风险分级

目的·建立一种数据驱动的实用方法预测新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)疫情的演变趋势、跟踪预判疫区适时风险分级,为精准防控策略提供量化依据。方法·基于移动平均法建立移动平均预测限(moving average prediction limit,MAPL)方法。采用既往严重急性呼吸综合征(severe acute respiratory syndrome,SARS)疫情数据验证MAPL方法对疫情趋势和风险预判的实用性。跟踪COVID-19疫情从2020年1月16日起的官方公布数据,建立相应MAPL进行适时疫情预判与风险评估。结果·基于MAPL方法分析显示,2020年2月初全国COVID-19疫情达到峰值。经过积极防控,2月中旬到3月中旬全国疫情整体呈下降趋势。与湖北地区相比,非湖北地区2月中旬新增病例数下降速度快,但之后又有小幅增长。3月以来境外输入性风险的分析提示,近阶段存在中高等疫情输入性风险,建议出入境口岸采取相应防控措施,避免境外输入病例引起疫情再次蔓延。结论·MAPL方法可以辅助判断新发传染病流行发展趋势并及时预测疫区风险水平。各疫区可结合当地实际与疫情风险分级,规划落实差异化精准防控策略。3月份以来应注重境外输入性风险的防控。

基于可拓原理的掘进工作面瓦斯超限预测研究

针对掘进工作面瓦斯超限难以预测的问题,在分析瓦斯超限影响因素的基础上,采用灰色关联分析方法筛选出了掘进工作面瓦斯超限预测主要指标;应用物元可拓集合理论建立了掘进工作面瓦斯超限危险性预测的物元可拓模型,提出了掘进工作面瓦斯超限危险性预测可拓方法,划分了瓦斯超限危险性等级。通过将该方法对2个掘进工作面的瓦斯超限危险性进行预测,对掘进工作面瓦斯超限危险性预测可拓方法的可行性进行了验证。结果表明:该方法能够准确预测掘进工作面瓦斯超限危险性,具有计算简单、预测准确率高、不受主观因素影响等特点,为掘进工作面瓦斯超限预测提供了新的技术方向。

指数分布双样预测的Bayes方法

本文对指数分布的双样预测问题用Bayes方法进行了讨论 ,结果表明 ,定数截尾时 ,无信息先验分布的Bayes预测限与经典预测限相同 ,但两者的预测子不一致 ,对有替换定时截尾 ,给出了经典方法目前尚未给出的双样预测问题的Bayes预测子与预测限 ,并用数值例说明了这些方法。

指数分布单样预测的Bayes方法

对指数分布的单样预测问题用Bayes方法进行了讨论。结果表明,定数截尾时,无信息先验分布的Bayes预测限与经典预测限相同,但两种方法的预测子不一致,对有替换定时截尾,给出了经典方法目前尚未处理的单样预测问题的Bayes预测子与预测限,并用数值例说明了这些方法。

Bayes预测问题的统计分析Ⅰ

对形状参数β是已知和未知的两种情况,给出了双边截尾AM SAA模型的参数的Bayes点估计和区间估计以及某些统计量的Bayes预测限。

火电行业环境空气预测评价的研究

以2003年～2011年辽宁省境内新、扩改建火力发电厂和供热机组建设为研究对象,采用案例分析,结合国家有关大气环境影响预测评价的有关规定,就环境影响预测评价中存在的问题进行讨论,提出只有在SO2、NOx、PM10环境现状监测值为标准值的90%、30%、5%以上情况下才进行预测,否则,环境空气预测可以减免。

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Theoretical prediction of forming limit diagram of AZ31 magnesium alloy sheet at warm temperatures

A theoretical prediction on forming limit diagram（FLD） of AZ31 magnesium alloy sheet was developed at warm temperatures based on the M-K theory. Two different yield criteria of von Mises and Hill＇48 were applied in this model. Mechanical properties of AZ31 magnesium alloy used in the prediction were obtained by uniaxial tensile tests and the Fields-Backofen equation was incorporated in the analysis. In addition, experimental FLDs of AZ31 were acquired by conducting rigid die swell test at different temperatures to verify the prediction. It is demonstrated from a comparison between the predicted and the experimental FLDs at 473 K and 523 K that the predicted results are influenced by the type of yield criterion used in the calculation, especially at lower temperatures. Furthermore, a better agreement between the predicted results and experimental data for AZ31 magnesium alloy sheet at warm temperatures was obtained when Hill＇48 yield criterion was applied.

SEISMIC DAMAGE PREDICTION ON MULTI-STORYBRIOK BUILDINGS WITH TWO FRAME-SHEAR-WALL-SUPPORTED STORIES

This paper presents a method for searching the weak story by using the ultimate shear force coefficient on the multi-story brick buildings with two frame-shear-wall-supported stories. The method of seismic damage prediction is discussed according to different weak stories. When the first story is t theweak one,the damage state of the building can be determined by the displacement ratio. The prediction method is also used in a practical engineering project.

Short Term Load Forecasting Using Subset Threshold Auto Regressive Model

The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model.

高瓦斯掘进工作面瓦斯防治技术

针对高瓦斯掘进工作面生产过程中瓦斯涌出量大的问题,建立瓦斯综合治理流程,并将瓦斯治理工艺分为瓦斯超限预测、瓦斯超限防治措施和效果检验3个步骤。瓦斯治理后,掘进工作面未发生瓦斯事故,为工作面安全高效生产创造了条件。

Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill

To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm(salp swarm algorithm, SSA) and extreme learning machine(ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.

Fatigue life prediction of aviation aluminium alloy based on quantitative pre-corrosion damage analysis

A new method of quantitative pre-corrosion damage of aviation aluminium(Al-Cu-Mg)alloy was proposed,whichregarded corrosion pits as equivalent semi-elliptical surface cracks.An analytical model was formulated to describe the entire regionof fatigue crack propagation(FCP).The relationship between the model parameters and the fatigue testing data obtained in thepre-corroded experiments,crack propagation experiments and S-N fatigue experiments was discussed.The equivalent crack sizesand the FCP equation were used to calculate the fatigue life through numerical integration based on MATLAB/GUI.The resultsconfirm that the sigmoidal curve fitted by the FCP model expresses the whole change from Region I to Region III.In addition,thepredicted curves indicate the actual trend of fatigue life and the conservative result of fatigue limit.Thus,the new analytical methodcan estimate the residual life of pre-corroded Al-Cu-Mg alloy,especially smooth specimens.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Prediction of HLA-A 2.1-restricted CTL epitopes from IGFBP7 antigen of lung carcinoma

Objective： With the development of peptide-based cancer specific immunotherapy, the prediction of CTL epitopes from insulin-like growth factor-binding protein 7 （IGFBP7） is very important for some research about tumor metastasis. Because HLA-A2.1-expressing individuals cover 〉50% in the population of China, we aimed at identifying IGFBPT-encoded peptide presented by HLA-A2.1. Methods： In our study, a HLA-A2.1 restricted CTL epitope was identified by using the following two-step procedure： （a） computer-based epitope prediction from the amino acid sequence of IGFBP7 antigen; （b） Validation with epitope molecular modeling. Results： We obtained four epitopes with high immunogenicity scores by all of the three algorithms, i.e., BIMAS, SYFPEITH1 and IMTECH. Each of the four candidates satisfied the criteria of the HLA-A2.1- restricted CTL epitopes in molecular modeling analysis. Conclusion： The combination of BIMAS, SYFPEITHI and IMTECH method can improve the prediction efficiency and accuracy. Due to this research herein, this four epitopes have potential value for further studied, also have potential application in peptide-mediated immunotherapy. These epitopes may be useful in the design of therapeutic peptide vaccine for lung carcinoma and as immunotherapeutic strategies against lung carcinoma after identified by immunology experiment.

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

Rapid Prediction of Structural Responses of Double-Bottom Structures in Shoal Grounding Scenario

This study presents a simplified analytical model for predicting the structural responses of double-bottom ships in a shoal grounding scenario. This solution is based on a series of analytical models developed from elastic-plastic mechanism theories for different structural components, including bottom girders, floors, bottom plating, and attached stiffeners. We verify this simplified analytical model by numerical simulation, and establish finite element models for a typical tanker hold and a rigid indenter representing seabed obstacles. Employing the LS-DYNA finite element solver, we conduct numerical simulations for shoal-grounding cases with a wide range of slope angles and indentation depths. In comparison with numerical simulations, we verify the proposed simplified analytical model with respect to the total energy dissipation and the horizontal grounding resistance. We also investigate the interaction effect of deformation patterns between bottom structure components. Our results show that the total energy dissipation and resistances predicted by the analytical model agree well with those from numerical simulations.

Vapor-Liquid Equilibrium Prediction of Ammonia-Ionic Liquid Working Pairs of Absorption Cycle Using UNIFAC Model

On the basis of reported experimental vapor-liquid equilibrium （VLE） data of NH3-1-ethyl-3-methylimidazolium acetate （NH3-[Emim]Ac）, NH3-1-butyl-3-methylimidazolium tetrafluoroborate （NH3-[Bmim][BF4]）, NH3-1,3-dimethylimidazolium dimethyl phosphate （NH3-[Mmim]DMP） and NH3-1-ethyl-3-methylimidazolium ethylsulfate （NH3-[Emim]EtOSO3） binary systems, the interaction parameters of 14 new groups have been regressed by means of the UNIFAC model. To validate the reliability of the method, these parameters have been used to calculate the VLE data with the average relative deviation of pressures of less than 9.35%. The infinite dilution activity coefficient （ γ1∞ ） and the absorption potential （ φ1 ） are important evaluation criterions of the affinity between working pair species of the absorption cycle. The UNIFAC model is implemented to predict the values of and φ1 of t6 sets of NH3-ionic liquid （1L） systems. The work found that the φ1 gradually increases following the impact order： φ1（[Cnmim][BF4]）〈φ1（[Cnmim]EtOSO3）〈φ1（[Cnmim]DMP）〈φ1（[Cnmim]Ac） （n= 1, 2, 3, … ） at a given cation of IL species and constant temperature, and φ1（[Mmim]X）〈φ1（[Emim]X）〈φ1（[Pmim]X）〈 φ1（[Bmim]X）（X= Ac, [BF4], DMP or EtOSO3） at a given anion of IL species and constant temperature. Furthermore, the φ1 gradually increases with increasing temperature. Then, it could be concluded that the working pair NH3-[BmimlAc has the best potential research value relatively.

Statistical Prediction under an Order Restriction

In this paper,a statistical prediction problem under ordered location and scale parameters are considered.Double-shrinkage predictors are given which use all the available data and improve on single-shrinkage predictors,and hence the best equivariant predictors.

Hybrid LEAP modeling method for long-term energy demand forecasting of regions with limited statistical data

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.