A method for predicting the water-flowing fractured zone height based on an improved key stratum theory

In the process of using the original key stratum theory to predict the height of a water-flowing fractured zone(WFZ),the influence of rock strata outside the calculation range on the rock strata within the calculation range as well as the fact that the shape of the overburden deformation area will change with the excavation length are ignored.In this paper,an improved key stratum theory(IKS theory)was proposed by fixing these two shortcomings.Then,a WFZ height prediction method based on IKS theory was established and applied.First,the range of overburden involved in the analysis was determined according to the tensile stress distribution range above the goaf.Second,the key stratum in the overburden involved in the analysis was identified through IKS theory.Finally,the tendency of the WFZ to develop upward was determined by judging whether or not the identified key stratum will break.The proposed method was applied and verified in a mining case study,and the reasons for the differences in the development patterns between the WFZs in coalfields in Northwest and East China were also fully explained by this method.

Phase equilibrium data prediction and process optimizationin butadiene extraction process

In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene production by acetonitrile.The accuracy of five prediction methods,UNIFAC(UNIQUAC Functional-group Activity Coefficients),UNIFAC-LL,UNIFAC-LBY,UNIFAC-DMD and COSMO-RS,applied to the butadiene extraction process was verified using partial phase equilibrium data.The results showed that the UNIFAC-DMD method had the highest accuracy in predicting phase equilibrium data for the missing system.COSMO-RS-predicted multiple systems showed good accuracy,and a large number of missing phase equilibrium data were estimated using the UNIFAC-DMD method and COSMO-RS method.The predicted phase equilibrium data were checked for consistency.The NRTL-RK(non-Random Two Liquid-Redlich-Kwong Equation of State)and UNIQUAC thermodynamic models were used to correlate the phase equilibrium data.Industrial device simulations were used to verify the accuracy of the thermodynamic model applied to the butadiene extraction process.The simulation results showed that the average deviations of the simulated results using the correlated thermodynamic model from the actual values were less than 2%compared to that using the commercial simulation software,Aspen Plus and its database.The average deviation was much smaller than that of the simulations using the Aspen Plus database(>10%),indicating that the obtained phase equilibrium data are highly accurate and reliable.The best phase equilibrium data and thermodynamic model parameters for butadiene extraction are provided.This improves the accuracy and reliability of the design,optimization and control of the process,and provides a basis and guarantee for developing a more environmentally friendly and economical butadiene extraction process.

Genomic selection of eight fruit traits in pear

Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study,we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cost. We evaluated GS under two scenarios:(1) five-fold cross-validation in an interspecific pear family;(2) independent validation. Based on the cross-validation scheme, the prediction accuracy (PA) of eight fruit traits varied between 0.33 (fruit core vertical diameter)and 0.65 (stone cell content). Except for single fruit weight, a slightly better prediction accuracy (PA) was observed for the five parametrical methods compared with the two non-parametrical methods. In our TP of 310 individuals, 2 000 single nucleotide polymorphism (SNP) markers were sufficient to make reasonably accurate predictions. PAs for different traits increased by 18.21%-46.98%when the TP size increased from 50to 100, but the increment was smaller (-4.13%-33.91%) when the TP size increased from 200 to 250. For independent validation, the PAs ranged from 0.11 to 0.45 using rrBLUP method. In summary, our results showed that the TP size and SNP numbers had a greater impact on the PA than prediction methods. Furthermore, relatedness among the training and validation sets, and the complexity of traits should be considered when designing a TP to predict the test panel.

Calculation and Analysis of TVMS Considering Profile Shifts and Surface Wear Evolution Process of Spur Gear

Profile shift is a highly effective technique for optimizing the performance of spur gear transmission systems.However,tooth surface wear is inevitable during gear meshing due to inadequate lubrication and long-term operation.Both profile shift and tooth surface wear(TSW)can impact the meshing characteristics by altering the involute tooth profile.In this study,a tooth stiffness model of spur gears that incorporates profile shift,TSW,tooth deformation,tooth contact deformation,fillet-foundation deformation,and gear body structure coupling is established.This model efficiently and accurately determines the time-varying mesh stiffness(TVMS).Additionally,an improved wear depth prediction method for spur gears is developed,which takes into consideration the mutually prime teeth numbers and more accurately reflects actual gear meshing conditions.Results show that consideration of the mutual prime of teeth numbers will have a certain impact on the TSW process.Furthermore,the finite element method(FEM)is employed to accurately verify the values of TVMS and load sharing ratio(LSR)of profile-shifted gears and worn gears.This study quantitatively analyzes the effect of profile shift on the surface wear process,which suggests that gear profile shift can partially alleviate the negative effects of TSW.The contribution of this study provides valuable insights into the design and maintenance of spur gear systems.

Predicting intrinsic disorder in proteins： an overview

The discovery of intrinsically disordered proteins （IDP） （i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures） came as an unexpected surprise, as the existence of such proteins is in contradiction to the traditional ＂sequence →structure →function＂ paradigm. Accurate prediction of a protein＇s predisposition to be intrinsically disordered is a necessary prerequisite for the further understanding of principles and mechanisms of protein folding and function, and is a key for the elaboration of a new structural and functional hierarchy of proteins. Therefore, prediction of IDPs has attracted the attention of many researchers, and a number of prediction tools have been developed. Predictions of disorder, in turn, are playing major roles in directing laboratory experiments that are leading to the discovery of ever more disordered proteins, and thereby leading to a positive feedback loop in the investigation of these proteins. In this review of algorithms for intrinsic disorder prediction, the basic concepts of various prediction methods for IDPs are summarized, the strengths and shortcomings of many of the methods are analyzed, and the difficulties and directions of future development of IDP prediction techniques are discussed.

A novel method for predicting breakthrough time of horizontal wells in bottom water reservoirs

Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionless time were derived from 10 influencing factors of the problem by using dimensional analysis. Simulations of horizontal well in reservoir with bottom water were run to find the prediction correlation. A general and concise functional relationship for predicting breakthrough time was established based on simulation results and theoretical analysis. The breakthrough time of one conceptual model predicted by the correlation is very close to the result by Eclipse with less than 2% error. The practical breakthrough time of one well in Helder oilfield is 10 d, and the predicted results by the method is 11.2 d, which is more accurate than the analytical result. Case study indicates that the method could predict breakthrough time of horizontal well under different reservoir conditions accurately. For its university and ease of use, the method is suitable for quick prediction of breakthrough time.

Application of Geophysical and Remote Sensing Methods to Predict for Potash Resource

1 Introduction Potassium is listed as one of the shortage of mineral resources in china.Geophysical and remote sensing technology plays an important role in prospecting for potash ressources.

AN EXTENSION OF THE METHOD FOR PREDICTING PERMEABILITY THROUGH POLYMER MEMBRANES FROM SIMPLE GASES TO WATER VAPOR

It is found that there is a linear relationship between log P-w, and the parameter term V-f/0.5 E(coh) [1+(delta(w) - delta(p))(2)/delta(p)(2), from the water permeability (P-w) data of 21 polymers covering 4 orders of magnitude. This correlation may be useful in choosing membrane materials for dehumidification of gases.

Method of Predicting Water Content in Crude Oil Based on Measuring Range Automatic Switching

Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.

The tempo-spatial evolution characteristics of the load/unload response ratio before strong earthquakes in California of America and its predicting implications

In this paper, the tempo-spatial evolution characteristics of the load/unload response ratio (namely LURR or Y value) before strong earthquakes with magnitude over 6 during 1976~1994 in California of America are studied in detail. The results show that there appear some high-Y regions cohering with the regional tectonic trend in a great area 3~4 years before strong earthquakes and these high-Y regions migrate from the periphery to the epicenter region at a speed of tens of kilometers per year. The load/unload response ratio (LURR) anomalies near the epicenter region characterizes a type of (ascend ? descend( and appear and increase steeply until one year or less before most earthquakes. (Positive( earthquakes form usually a concentration area; in and near which the main shock occurs. We have analyzed the different and same characters of earthquakes between California of American and the Chinese mainland. Basing on these results, we discuss the approach and method how to predict and estimate the three parameters (place, time and magnitude) of a strong earthquake in California of American by applying the characteristics of the LURR.

Comparison of TEC prediction methods in mid-latitudes with GIM maps

There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-latitude and equatorial regions. This paper compares the TEC prediction methods in the midlatitude zone according to the data of the Juliusruh, Rostov, Manzhouli stations in 2008 and 2015. For a long-term prediction, the IRI-Plas and Ne Quick models are compared with the Global Ionospheric Maps(GIM) presented by the Jet Propulsion Laboratory(JPL) and the Technical University of Catalonia(UPC).For a short-term prediction, the Standard Persistence Model(SPM) method, a 27 day median model, and the proposed short-term prediction method are compared for one day ahead. It is shown that for all stations the IRI-Plas model provides better compliance with GIM maps than the Ne Quick model irrespective of a solar activity level. An average absolute error lays in the range of 3 e3.5 TECU, relative root square mean(RMS) error in the range of 22 e27% in 2015 and 1.7 e2 TECU, 20 e25% in 2008. For the Ne Quick model, these estimates were 6.7 e8.2 TECU and 42 e45% in 2015 and 2.2 e3.6 TECU, 30 e37% in2008. For the short-term forecast, the best results were obtained by the SPM method with an average absolute error in the range of 1.95 e2.15 TECU in 2015 and 0.59 e0.98 TECU in 2008, a relative RMS error in the range of 17 e21% in 2015, 11.5 e15% in 2008. For the proposed short-term prediction method, these errors were 2.04 e2.2 TECU and 12 e14% in 2015 and 0.7 e1.0 TECU, 7 e11% in 2008. Using medians, the errors were 3.1 e3.4 TECU and 17 e21% in 2015 and 1.0 e1.3 TECU, 10 e15% in 2008. The dependence of results on the Dst-index was obtained.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

高压直流换流站中滤波电容器的振动与可听噪声(英文)

The filter capacitor stack is one of the main acoustic noise sources in high-voltage DC(HVDC) converter stations.As HVDC systems are built more and more recently,it is significant to research the audible noise of filter capacitors.In this paper,the current situation of research on vibration and audible noise of filter capacitors in HVDC converter stations,which is departed into three parts—generation mechanism,prediction methods,and reduction measures,is presented and the research achievements are discussed.Scholars have built the model that the alternating electric force caused by the voltage conduces to the vibration,which propagates to the enclosure and radiates audible noise.As a result,the parts contributing most to the generation of audible noise are the top and the bottom of capacitors. In the noise level prediction respect,several methods have been prospected including impact hammer,sweep frequency, impact current,monopole and Kirchhoff formula method,which are suitable for single capacitors or capacitors stacks individually.However,the sweep frequency method is restricted by experiment condition,and the impact current method needs further research and verified.On the other hand,CIGRE WG14.26 provides three sound reduction measures,but all of them are not so practicable,while MPP absorber and compressible space absorber prospected by Dr.Wu Peng are proved to be effective.The sound barriers are also considered by scholars,and the acoustic directivity performance of capacitors is also researched.Besides,the developing direction of each research field is prospected in corresponding part.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

PREDICTION OF FATIGUE LIVES OF RC BEAMS STRENGTHENED WITH CFL UNDER RANDOM LOADING

The investigation on fatigue lives of reinforced concrete （RC） structures strength- ened with fiber laminate under random loading is important for the repairing or the strengthening of bridges and the safety of the traffic. In this paper, two methods are developed for predicting the fatigue lives of RC structures strengthened with carbon fiber [aminate （CFL） under random loading based on a residual life and a residual strength model. To discuss the efficiency of the model, 12 RC beams strengthened with CFL are tested under random loading by the MTS810 testing system. The predicted residual strength approximately agrees with test results.

A novel method to predict static transmission error for spur gear pair based on accuracy grade

This paper proposes a novel method to predict the spur gear pair’s static transmission error based on the accuracy grade,in which manufacturing errors(MEs),assembly errors(AEs),tooth deflections(TDs)and profile modifications(PMs)are considered.For the prediction,a discrete gear model for generating the error tooth profile based on the ISO accuracy grade is presented.Then,the gear model and a tooth deflection model for calculating the tooth compliance on gear meshing are coupled with the transmission error model to make the prediction by checking the interference status between gear and pinion.The prediction method is validated by comparison with the experimental results from the literature,and a set of cases are simulated to study the effects of MEs,AEs,TDs and PMs on the static transmission error.In addition,the time-varying backlash caused by both MEs and AEs,and the contact ratio under load conditions are also investigated.The results show that the novel method can effectively predict the range of the static transmission error under different accuracy grades.The prediction results can provide references for the selection of gear design parameters and the optimization of transmission performance in the design stage of gear systems.

Study on the automatic process of line heating for pillow shape plate

This paper focuses on the process for pillow shape plate by line heating technique, which is widely applied in the production of ship hull. Based on the analysis of primary parameters and experimental data in line heating process, the amount of local contraction generated by line heating has been illustrated. Then, combining with the computational result of local deformation determined by shell plate development, an optimization method for line heating parameters has been studied. This prediction system may provide rational arrangements of heating lines and technical parameters of process. By integrating the prediction system into the line heating robot for pillow shape plate, the automatic process of line heating for pillow shape plate can be achieved.

Statistical analysis on energy field of seismicity in Ningxia and its neighborhood region

The random function theory is used in the paper. Taking the regional seismicity energy as the random function of space and time, the features of small seismicity field in Ningxia and its neighborhood region are studied by the analytical method of natural orthogonal function expansion. The chief part of the field, i.e., the temporal changes of time weight coefficients of first several typical fields is analyzed. We have found that their values had upward and downward changes of a large amplitude before moderate-strong earthquakes and showed variation features correlating to moderate-strong earthquakes occurred in the region and its surrounding areas. From the earthquake examples in Ningxia region, we can make the conclusion that the method of natural orthogonal function expansion of earthquake energy stochastic field is an earthquake analysis and prediction method that is worth further exploration.

Bitcoin price change and trend prediction through twitter sentiment and data volume

Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease.Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease.In this paper,we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease.We utilise not only sentiment extracted from tweets,but also the volume of tweets.We present results from experiments exploring the relation between sentiment and future price at different temporal granularities,with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change.Two different neural network models are explored and evaluated,one based on recurrent nets and one based on convolutional networks.An additional model is presented to predict the magnitude of change,which is framed as a multi-class classification problem.It is shown that this model yields more reliable predictions when used alongside a price trend prediction model.The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy(63%).