Cross-person activity recognition using reduced kernel extreme learning machine

Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance.     

Calibration plots for risk prediction models in the presence of competing risks

A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo‐values that are combined with a nearest neighborhood smoother and a cross‐validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.     

基于笔形束散射核的非均匀模体透射平面散射线分析

目的:利用蒙特卡罗方法分析透射平面上散射光子的物理性质以及非均匀模体厚度对散射核的影响,为基于电子射野影像设备（EPID）的在体剂量验证研究提供基础。方法:利用EGSnrc建立笔形束散射核模型,并模拟获得X射线穿过非均匀模体（水肺水/水骨水模体）以及相应等效厚度水模后30 cm处透射平面上的多种散射线能量注量分布,并分析水肺水/水骨水模体与其等效厚度水模体在散射线能量注量分布上的差异。结果:散射核中一阶康普顿散射线最大能量注量在1×10-4 MeV·cm-2数量级,当离轴距离为8~12 cm时下降至最大值的一半,而散射核中其它散射线能量注量最大值在1×10-5 MeV·cm-2数量级附近或以下。对于水肺水/水骨水模体,散射核能量注量相对偏差变化为±1.2%~±11.5%,且随模体非均匀层厚度增大而增大。结论:散射核中一阶康普顿散射线占比最大,同时也贡献了大部分能量注量相对偏差,在通过散射核来重建非均匀模体后EPID平面上的射线分布时,应着重考虑一阶康普顿散射线对重建结果的影响,并对其进行有效的修正。     

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R²) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R² = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.     

和法在糖尿病中的运用

糖尿病寒热错杂、虚实兼夹,可分为湿热蕴脾证、肝郁气滞证、肝胃郁热证、胃热阴虚证、肠道湿热证、血脉瘀滞证等证型,治疗当运用和法辛开苦降、疏肝理气、清泻热结、滋阴清热、清热利湿、活血化瘀,选用半夏泻心汤、四逆散、大黄黄连泻心汤、大柴胡汤、白虎加入参汤、葛根芩连汤、桃核承气汤以调整气机、平调寒热、补虚泻实,并在药物治疗的基础...     

基于微粒群优化算法和支持向量机的软测量建模

在分析基本微粒群优化算法(PSO)和支持向量机(SVM)原理的基础上,采用带有末位淘汰机制的微粒群优化算法优化支持向量机的参数,建立了延迟焦化装置粗汽油干点软测量的微粒群支持向量机模型.该方法利用支持向量机结构风险最小化原则和PSO算法快速全局优化的特点,用于软测量建模.仿真实验表明:所建模型的泛化性能较好,模型具有较高的精度.     

离子法测定中蒙药材苦杏仁中六种阴离子的含量

目的: 建立一种同时测定中蒙药苦杏仁药材中F^－、Cl^－、Br^－、NO^－₃ 、SO2^－₄ 、PO^3－₄ 阴离子的含量测定方
法。方法:采用离子色谱法、阴离子色谱柱、氢氧化钠梯度淋洗、电导检测苦杏仁6 种无机阴离子。结果: F^－、
Cl^－、Br^－、NO^－₃ 、SO₂^－4 、PO^3－₄ 的加样回收率介于97%～ 102%,苦杏仁中F－、Cl－、Br－、NO－3 、SO2－4 、PO3－
4 重复性良好,含量分别为0．057%0．084%,0．063%,0．070%,0．30%,0．12%。结论: 离子色谱法可以同时检测苦杏仁中6 种阴离
子,灵敏度高,结果准确可行,为苦杏仁的质量控制提供了一种简便快速的方法．     

γ-Zeins are essential for endosperm modification in quality protein maize

Essential amino acids like lysine and tryptophan are deficient in corn meal because of the abundance of zein storage proteins that lack these amino acids. A natural mutant, opaque 2 (o2) causes reduction of zeins, an increase of nonzein proteins, and as a consequence, a doubling of lysine levels. However, o2’s soft inferior kernels precluded its commercial use. Breeders subsequently overcame kernel softness, selecting several quantitative loci (QTLs), called o2 modifiers, without losing the high-lysine trait. These maize lines are known as “quality protein maize” (QPM). One of the QTLs is linked to the 27-kDa γ-zein locus on chromosome 7S. Moreover, QPM lines have 2- to 3-fold higher levels of the 27-kDa γ-zein, but the physiological significance of this increase is not known. Because the 27- and 16-kDa γ-zein genes are highly conserved in DNA sequence, we introduced a dominant RNAi transgene into a QPM line (CM105Mo2) to eliminate expression of them both. Elimination of γ-zeins disrupts endosperm modification by o2 modifiers, indicating their hypostatic action to γ-zeins. Abnormalities in protein body structure and their interaction with starch granules in the F1 with Mo2/+; o2/o2; γRNAi/+ genotype suggests that γ-zeins are essential for restoring protein body density and starch grain interaction in QPM. To eliminate pleiotropic effects caused by o2, the 22-kDa α-zein, γ-zein, and β-zein RNAis were stacked, resulting in protein bodies forming as honeycomb-like structures. We are unique in presenting clear demonstration that γ-zeins play a mechanistic role in QPM, providing a previously unexplored rationale for molecular breeding.     

"苦杏仁"更名为"杏仁"的商榷

通过考释“苦杏仁”与“杏仁”的源流,发现现代有关中药著作及《药典》中“苦杏仁”既未遵照古代主要本草命名传统,也未参考临床用药习惯,其命名欠妥,建议应以“杏仁”作为正名。     

不同产地薏苡仁资源营养成分分析与评价

目的对我国12个薏苡主产区薏苡仁资源的营养品质进行分析与评价,为薏苡资源的合理开发利用提供基础资料。方法采用常规生化分析方法。结果13种薏苡仁营养成分的平均值为:蛋白质15.80%,脂肪5.49%,均高于米、面;碳水化合物63.05%,低于米、面;粗纤维和灰分分别为1.75%与1.68%。薏苡仁的Ca含量丰富,高于米、面;VB1、VB2、VB6、Fe、Zn、Se平均含量与米、面相当。薏苡仁的谷氨酸、亮氨酸含量丰富,均高于米、面;赖氨酸含量较低;但含硫氨基酸平均含量丰富,为6.05mg/g,均高于米、面。以AAS为标准,13个样品中,赖氨酸为第一限制性氨基酸,第二限制性氨基酸随产地不同而异,云南产等8个样品为苏氨酸,四川产等5个样品为异亮氨酸,贵州产为缬氨酸。平均EAA/TAA、EAA/NEA值分别为35.86%、55.91%,平均EAAI值58.91%。结论我国薏苡资源营养较齐全,蛋白质具有较好的氨基酸组成,属于较优质蛋白质。