关节内侧间隙测量对髋关节脱位术后稳定性的预测

目的探讨关节内侧间隙测量对髋关节脱位术后稳定性的预测意义。方法分析2004～2007年本院10例髋关节再脱位患儿以及随机抽取的50例术后未发生再脱位患儿的影像学资料，对其术后1d、1周、1．5个月、3个月、4个月、5个月、6个月骨盆平片进行患侧髋关节内侧间隙值OA以及泪滴至髋臼外缘的距离OA的测量，并采用D值（D=OA／OA）进行标准化处理。结果无再脱位组D值基本小于0．8。其中30例采用髋关节外展支具，未出现再脱位，D值位于0．66～0．8之间；脱位组在石膏同定期间，D值基本小于0．7，此时股骨头位于髋臼内，未出现脱位；术后6周至3个月拆除石膏后，D值为0．66．0．8，未采取措施，相继出现脱位，此时D值基本大于0．8。结论测量关节内侧间隙对于评价髋关节脱位术后关节的稳定性以及预测早期再脱位有重要意义。D值小于0．66，关节稳定，不会出现再脱位；D值为0．66～0．8，关节稳定性受到影响，可能出现再脱位，需尽早采取干预措施；D值大于0．8，出现再脱位，简单保守治疗措旆失去作用．需再次手术蚕新复位。

AR基因突变雄激素不敏感综合征相关因素在性别分配中的作用分析

目的分析与青春期后AR基因突变雄激素不敏感综合征患儿的临床结局及外生殖器发育相关因素在性别分配中的作用,探讨AR基因突变雄激素不敏感综合症的最佳性别分配方案。方法以2015年11月至2018年10月浙江大学医学院附属儿童医院收治的21例出现AR基因突变的雄激素不敏感综合征(androgen insensitivity syndrome,AIS)患儿为研究对象,年龄3个月至13岁5个月,中位数为49.7个月。初诊性别女14例,男7例。外生殖器均表现为不同程度的雄性化不全,其中8例表型为完全女性化、10例为外生殖器模糊、3例为小阴茎。在分子诊断结果的基础上,将外生殖器雄性化评分(external masculinisation score,EMS)、性心理评估结果、外生殖器对雄激素刺激反应情况作为青春期后临床结局和外生殖器发育程度的预测因素,结合社会文化因素、性腺发育特点、患儿及其父母主观层面认知等因素,由本院多学科团队(multidisciplinary team,MDT)作出性别分配。结果8例(38.1%)诊断为完全性雄激素不敏感综合征(complete androgen insensitivity syndrome,CAIS)患儿的Prader评分均为0分,EMS评分为1~2分,性心理量表评估结果均表现为女性优势,雄激素治疗外生殖器无明显反应,性别分配均为女性。10例(42.9%)Prader评分为1~3分的部分性雄激素不敏感综合征(partial androgen insensitivity syndrome,PAIS)患儿EMS评分为2~9分,性心理量表评估结果除1例表现为女性优势外,其余均表现为男性优势,雄激素治疗后阴茎增长明显,性别分配均为男性。3例(14%)小阴茎患儿性心理量表评估结果均表现为男性优势,雄激素治疗后阴茎明显增长,性别分配均为男性。男性性别分配者的Prader评分和EMS评分高于女性性别分配者,且差异有统计学意义(P<0.05),雄激素治疗有效性、性心理评估结果与性别分配结果间均具有良好的关联性(P<0.05)。结论分析AR基因突变情况、EMS评分、性心理评估结果和外生殖器雄激素治疗反应结局等因素对AIS患儿未来性别认同、外生殖器发育程度等青春期后临床结局进行预测具有一定的可行性,在充分考虑社会文化因素、患儿及其父母主观层面认知情况下,由MDT进行性别分配是目前较为适宜的AIS性别分配方法。

基于支持向量机的浙江省流感样病例预警模型研究

目的：建立浙江省流感样病例预警模型，为流感疫情的早期发现提供科学依据。方法：收集整理2012年1月2日至2013年12月29日期间104周浙江省11家哨点医院门急诊中流感相关疾病病例数、各类气象因素以及流感病原阳性率，与同期流感样病例数作相关分析，寻找出流感样病例发生的相关因素。通过交叉检验选取最优参数，采用支持向量机方法建立流感样病例预警模型，并利用历史数据对模型进行验证。结果：相关性分析显示有8个因素与流感样病例相关。模型的最优参数为：C=3，s=0．009，y=0．4，验证结果显示流感样病例预警模型的同级预报正确率为50．0％，相差一级的预报正确率为96．7％。结论：支持向量机方法适用于流感样病例的预警。

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.

Comparison of the City Water Consumption Short-Term Forecasting Methods

There are a lot of methods in city water consumption short-term forecasting both inside and outside the country. But among these methods there exist many advantages and shortcomings in model establishing, solving and predicting accuracy, speed, applicability. This article draws lessons from other realm mature methods after many years′ study. It′s systematically studied and compared to predict the water consumption in accuracy, speed, effect and applicability among the time series triangle function method, artificial neural network method, gray system theories method, wavelet analytical method.

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

Prediction of the Hualian Earthquakes in Taiwan and an Extended Discussion on the Method of Commensurability

The method of commensurability was used by the authors to predict the great earthquake of magnitude 7.5 that occurred on March 31,2002 in Taiwan 70km away from Hualian. Analyzing the earthquakes of magnitude≥7.0 which occurred in the Hualian area of Taiwan within the 20th century, the authors discovered that the occurrences of the earthquakes are commensurable. The earthquakes of magnitude 7.6 which occurred in Hualian of Taiwan, on September 20th, 1999 and of magnitude 7.5 which occurred 70 km away from Hualian, on March 31th, 2002 appeared at the commensurable point of K=2 and the period times the golden section, respectively. An extended discussion is carried out on the method of commensurability and its implied physical significance, especially on the contribution of the commensurable periodic extension made by Prof. Weng Wenbo.

Prediction of Injection-Production Ratio with BP Neural Network

Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.

SVM method for predicting the thickness of sandstone

The Support Vector Machine （SVM） method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters

With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well＇s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can significantly influence the rocks＇ elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.

Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory

The research on the rock burst prediction was made on the basis of seismology,rock mechanics and the data from Dongguashan Copper Mine(DCM) ,the deepest metal mine in China.The seismic responses to mining in DCM were investigated through the analyses of the spatio-temporal distribution of hypocenters,apparent stress and displacement of seismic events,and the process of the generation of hazardous seismicity in DCM was studied in the framework of the theory of asperity in the seismic source mechanism.A method of locating areas with hazardous seismicity and a conceptual model of hazardous seismic nucleation in DCM were proposed.A criterion of rockburst prediction was analyzed theoretically in the framework of unstable failure theories,and consequently,the rate of change in the ratio of the seismic stiffness of rock in a seismic nucleation area to that in surrounding area,dS/dt,is defined as an index of the rockburst prediction.The possibility of a rockburst will increase if dS/dt>0,and the possibility of rock burst will decrease if dS/dt<0.The correctness of these methods is demonstrated by analyses of rock failure cases in DCM.

Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques

Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.

Prediction Technology of Buried Water-Bearing Structures in Coal Mines Using Transient Electromagnetic Method

Buried water-conducting and water-bearing structures in front of the driving head may easily lead to water bursts in coal mines. Therefore,it is very important for the safety of production to make an accurate and timely forecast about water bursts. Based on the smoke ring effect of transient electromagnetic fields,the principle of transient electro-magnetic method used in detecting buried water-bearing structures in coal mines in advance,is discussed. Small multi-turn loop configurations used in coal mines are proposed and a field procedure of semicircular sector scanning is presented. The application of this method in one coal mine indicates that the technology has many advantages compared with others. The method is inexpensive,highly accurate and efficient. Suggestions are presented for future solutions to some remaining problems.

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

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.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

The Curl of Q Vector: A New Diagnostic Parameter Associated with Heavy Rainfall

As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this paper, a new set of analyses combining the divergence of the Q vector （DQ） with the vertical component of the curl of the Q vector （VQ） is applied to a Northeastern cold vortex rainfall case. From the derivation, it was found that the expressions of the Q vectors and their divergences in saturated moist flow （DQm） differ from those of dry and unsaturated moist atmosphere （DQ）, while the VQs of various background flows are exactly the same, which largely simplified the analyses. This case study showed that, compared with the DQ, not only can the DQm diagnose precipitation more effectively, but the VQ may also be indicative of precipitation （especially for heavy rainfall and strong convection） because of its direct, close relationship with ageostrophic motion. Thus, the VQ may be computed and analyzed with ease, and may serve as a useful tool for analyses of precipitation and strong convective svstems.

Prediction of Debris-flow Danger Area by Combining Hydrological and Inundation Simulation Methods

Debris flows have caused serious human casualties and economic losses in the regions strongly affected by the Ms8.0 Wenchuan earthquake of 2oo8. Debris flow mitigation and risk assessment is a key issue for reconstruction. The existing methods of inundation simulation are based on historical disasters and have no power of prediction. The rain- flood method can not yield detailed flow hydrograph and does not meet the need of inundation simulation. In this paper, the process of water flow was studied by using the Arc-SCS model combined with hydraulic method, and then the debris flow runoff process was calculated using the empirical formula combining the result from Arc-SCS. The peak discharge and runoff duration served as input of inundation simulation. Then, the dangerous area is predicted using kinematic wave method and Manning equation. Taking the debris flow in Huashiban gully in Beichuan County, Sichuan Province, China on 24 Sep. 2oo8 as example, the peak discharge of water flow and debris flow were calculated as 35.52 m3·s-1 and 215.66 m3·s-, with error of 4.15% compared to the measured values. The simulated area of debris-flow deposition was 161,500 m2, vs. the measured area of 144,097 m2, in error of 81.75%. The simulated maximum depth was 12.3 m, consistent with the real maximum depth between lO and 15 m according to the field survey. The minor error is mainly due to the flow impact on buildings and variations in cross-section configuration. The present methodology can be applied to predict debrisflow magnitude and evaluate its risk in other watersheds inthe earthquake area.

Seismic attributes optimization and application in reservoir prediction

Petroleum geophysicists recognize that many parameters related to oil and gas reservoirs are predicted using seismic attribute data. However, how best to optimize the seismic attributes, predict the character of thin sandstone reservoirs, and enhance the reservoir description accuracy is an important goal for geologists and geophysicists. Based on the theory of main component analysis, we present a new optimization method, called constrained main component analysis. Modeling estimates and real application in an oilfield show that it can enhance reservoir prediction accuracy and has better applicability.