农村饮用水水源地硝酸盐污染特性研究

农业面源污染是农村饮用水水源地的主要污染因素,其中以硝酸盐污染问题最为突出。根据山东省沂水县域内279处地下水型农村饮用水水源地水质检测结果,本文从取水深度、海拔高程、地形坡度等3个方面对农村饮用水水源地的硝酸盐污染特性进行了系统研究。结果表明,水源地硝酸盐超标率和超标程度均会随着水源地取水深度、海拔高程以及地形坡度的升高而降低。研究结论可为主管部门采取有效的预防和治理措施提供借鉴,并可指导新建水源地的选址。     

Learning bipartite Bayesian networks under monotonicity restrictions

ABSTRACT

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.     

煤系地层致密砂岩气甜点区地震逐级预测——以鄂尔多斯盆地东南缘下二叠统山西组2~3亚段为例

鄂尔多斯盆地东南部下二叠统山西组2~3亚段(以下简称山2~3亚段)为该盆地重要的天然气勘探目的层,但该亚段储层薄、厚度变化快、非均质性强,储层预测和勘探目标优选难度大。为了准确预测该亚段煤系地层致密砂岩气甜点区、提高天然气勘探成功率,针对该套储层的特征和预测难点,提出了90°相移技术识别河道外形、模型约束波阻抗反演刻画砂体厚度和子波衰减梯度属性识别含气砂体的地震逐级预测技术。研究结果表明:①山2~3亚段上覆5号煤地震强反射层,下伏储层地震反射能量弱,加之为稀疏二维地震测网、井控程度低,致使致密砂岩气甜点区预测难度大;②所提出的技术方法通过地震逐级预测约束,可以有效地刻画河道砂体分布并识别有效含气储层,提高了对勘探开发目标预测的精度;③基于该技术方法指导部署的勘探开发目标实钻效果好,地震预测结果横向分辨率高,真实地反映了河道及河道砂体的变化特征。结论认为,采用该方法可以有效地解决二维地震勘探区煤系地层强非均质性、薄储层致密砂岩气甜点区预测的地质难题。     

High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

基于JJG(闽)1086-2018放射治疗CT模拟定位机X射线辐射源的质控分析

整个精确放疗流程最关键、最基础的一个环节就是定位CT扫描精确程度,做好定位CT的质控关乎整个放疗流程的成败。介绍了JJG(闽)1086-2018放射治疗CT模拟定位机X射线辐射源在检定和质控中的流程和方法,从剂量性能要求、图像成像质量和机器机械精度方面对CT模拟定位机的质量控制做了定量的要求。连续8个月的测试结果表明:CT测量值误差在30HU以内,图像均匀性≤5.0HU,空间分辨力7Lp/cm,低对比度分辨力在对比度为1.0%,0.5%,0.3%时分别≤3.0mm,≤5.0mm,≤6.0mm,达到了规程的要求。CT模拟定位机的图像质量和机械精确程度得到了很好的保障,定位CT的剂量准确,影像参数和位置精确度有规可循。     

Second order velocity slip and thermal jump of Cu–water nanofluid over a cone in the presence of nonlinear radiation and nonuniform heat source/sink using homotopy analysis method

The purpose of the present paper is to explore the second order slip effects on nanofluid flow over a vertical cone. The effects of nonlinear thermal radiation and nonuniform heat source/sink are also taken into account. Water with copper nanoparticles is used as nanofluid in this investigation. The governing partial differential equations for the flow are converted into ordinary differential equations by using transformations and then are solved using homotopy analysis method. The influence of various important parameters on velocity, temperature, skin‐friction, and Nusselt number are presented through graphs. Results indicate that the velocity and magnitude of skin friction decrease with a rise in first and second order velocity slips. A raise in either first or second order temperature jump causes a fall in temperature. Nonlinear radiation increases the more rapidly when compared to the linear radiation case.     

Influence of hexagonal to orthorhombic phase transformation on diffusion-controlled dendrite evolution in directionally solidified Sn–Ni peritectic alloy

The hexagonal to orthorhombic (HO) transformation from β-Ni₃Sn₂ (hexagonal) phase to α’-Ni₃Sn₂ (orthorhombic) phase was confirmed in directionally solidified Sn–Ni peritectic alloys. It is shown that the remelting/resolidification process which is caused by both the temperature gradient zone melting (TGZM) and Gibbs?Thomson (G?T) effects can take place on secondary dendrites. Besides, the intersection angle between the primary dendrite stem and secondary branch (θ) is found to increase from π/3 to π/2 as the solidification proceeds. This is the morphological feature of the HO transformation, which can change the diffusion distance of the remelting/ resolidification process. Thus, a diffusion-based analytical model is established to describe this process through the specific surface area (S_V) of dendrites. The theoretical prediction demonstrates that the remelting/resolidification process is restricted when the HO transformation occurs during peritectic solidification. In addition, the slope of the prediction curves is changed, indicating the variation of the local remelting/resolidification rates.     

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.