POSSUM评分预测骨科高龄患者髋关节置换术后并发症发生率和死亡率的价值

目的探讨POSSUM评分预测骨科高龄患者术后并发症发生率和死亡率的价值。方法对200例骨科高龄患者进行POSSUM评分,预测术后并发症和死亡例数,并与实际值进行比较。结果根据POSSUM系统预测,55例发生并发症,而实际发生并发症49例,差异无统计学意义(P=0.871);预测术后死亡人数为16例,实际死亡人数为2例,差异有统计学意义(P=0.031)。结论 POSSUM评分能较好地预测骨科高龄患者术后并发症,但对术后死亡存在过度预测。

Overexpression of lysine specific demethylase 1 predicts worse prognosis in primary hepatocellular carcinoma patients

AIM:To investigate the clinicopathological features and prognostic value of lysine specific demethylase 1(LSD1) in hepatocellular carcinoma(HCC).METHODS:We examined LSD1 expression in 60 paired liver cancer tissues and adjacent noncancerous tissues by quantitative real time polymerase chain reaction(qRT-PCR) and Western blotting.In addition,we analyzed LSD1 expression in 198 HCC samples by immunohistochemistry.The relationship between LSD1 expression,clinicopathological features and patient survival was investigated.RESULTS:Immunohistochemistry,Western blotting,and qRT-PCR consistently confirmed LSD1 overexpression in HCC tissues compared to adjacent non-neoplastic tissues(P < 0.01).Additionally,immunostaining showed more LSD1-positive cells in the higher tumor stage(T3-4) and tumor grade(G3) than in the lower tumor stage(T1-2,P < 0.001) and tumor grade(G1-2,P < 0.001),respectively.Moreover,HCC patients with high LSD1 expression had significantly lower 5-year overall survival rates(P < 0.001) and lower 5-year disease-free survival rates(P < 0.001),respectively.A Cox proportional hazards model further demonstrated that LSD1 over-expression was an independent predictor of poor prognosis for both 5-year disease-free survival [hazards ratio(HR) = 1.426,95%CI:0.672-2.146,P < 0.001] and 5-year overall survival(HR = 2.456,95%CI:1.234-3.932,P < 0.001) in HCC.CONCLUSION:Our data suggest for the first time that the overexpression of LSD1 protein in HCC tissues indicates tumor progression and predicts poor prognosis.

Temperature gradients in concrete box girder bridge under effect of cold wave

The temperature distributions of a prestressed concrete box girder bridge under the effect of cold wave processes were analyzed. The distributions were found different from those under the effect of solar radiation or nighttime radiation cooling and should not be simplified as one dimensional. A temperature predicting model that can accurately predict temperatures over the cross section of the concrete box girder was developed. On the basis of the analytical model, a two-dimensional temperature gradient model was proposed and a parametric study that considered meteorological factors was performed. The results of sensitivity analysis show that the cold wave with shorter duration and more severe temperature drop may cause more unfavorable influences on the concrete box girder bridge. Finally, the unrestrained linear curvatures, self-equilibrating stresses and bending stresses when considering the frame action of the cross section, were derived from the proposed temperature gradient model and current code provisions, respectively. Then, a comparison was made between the value calculated against proposed model and several current specifications. The results show that the cold wave may cause more unfavorable effect on the concrete box girder bridge, especially on the large concrete box girder bridge. Therefore, it is necessary to consider the thermal effect caused by cold wave during the design stage.

A genetic Gaussian process regression model based on memetic algorithm

Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.

A SON solution for cell outage detection using a cooperative prediction approach

In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.

Soft measurement for component content based on adaptive model of Pr/Nd color features

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

Calculation of terminal velocity in transitional flow for spherical particle

The terminal velocity has been widely used in extensive fields, but the complexity of drag coefficient expression leads to the calculation of terminal velocity in transitional flow （1 〈 Re ≤ 1000） with much more difficulty than those in laminar flow （Re ≤ 1） and turbulent flow （Re ≥ 1000）. This paper summarized and compared 24 drag coefficient correlations, and developed an expression for calculating the terminal velocity in transitional flow, and also analyzed the effects of particle density and size, fluid density and viscosity on terminal velocity. The results show that 19 of 24 previously published correlations for drag coefficient have good prediction performance and can be used for calculating the terminal velocity in the entire transitional flow with higher accuracy. Adapting two dimensionless parameters （w＊, d＊）, a proposed explicit correlation, w＊=-25.68654 × exp （-d＊/77.02069）＋ 24.89826, is attained in transitional flow with good performance, which is helpful in calculating the terminal velocity.

New Approach to the Flexible Planning of Learning Process in High Education System

Expediency of this work is conditioned by the inconsistency between the market requirement of the specialists and the planning process of high educational system. For solving this problem it is important to make consulting or expect system for flexible planning of teaching modules of every specialty. We make an attempt to consider this problem in two aspects： the prediction of market demand for planning taking into consideration of studies duration and scheduling of educational process. The prediction task consists in data acquisition of market requirement for each profession in discrete time interval to predict dynamic evolution of every specialty. The solution of the prediction task will be using to determination of prognostic quantity of students for each specialty. As regards the second aspect, it consists in finding a schedule of the teaching modules, i.e. the distribution of subjects in the semesters, keeping the total limits of credits, to update and adapt syllabus. In this paper, we present a genetic algorithm as a solution method for the modular scheduling problem. Genetic algorithms （GAs） allow a more general approach to the scheduling problem, which is rated using a fitness function. GA can be successfully applied to find optimized sequential schedules.

Modeling on Heat and Mass Transfer in Stored Wheat during Forced Cooling Ventilation

A mathematical model based on the theory of heat and mass transfer in porous media was developed to simulate the evolution of grain temperature and moisture content in a wheat storage bin during ventilation with cooling air at the constant temperature and humidity.Unlike the previous works on this aspect,the present work was not focused on cooling the stored grain by ventilation with ambient air,but with the refrigerated air.Validation was performed by comparing between predicted and measured grain temperature and grain moisture content for two cases.Predicted data were in reasonable good agreement with measured ones.The model and the parameter values used in the model are applicable for predicting temperature and moisture of stored grains under ventilation conditions.

Transient emission wavelength estimation for DFB laser with temperature tuning

The prediction method of dynamic wavelength is proposed for temperature tuning process. The temperature of the thermistor integrated in laser diode(LD) module is recorded to predict the LD chip temperature. Then according to the injection current and priori tuning characteristics of the LDs, the emission wavelength is estimated in real time. The method is validated by using a 1.58 μm distributed feedback(DFB) LD. The absorption spectra of mixture gas of CO_2 and CO are measured by means of the thermal tuning gas sensing system. The center wavelength of each absorption line is compared with the data in HITRAN2012 database. The results show that the deviations are less than 5 pm. This method fully meets the needs of spectroscopic measurement, and can be applied to spectroscopy, optical communications and other fields.