Non parametric estimation of the diffusion coefficients of a diffusion with jumps

In this article, we consider a jump diffusion process ${\left(X_t\right)}_{t\ge 0}$, with drift function $b$, diffusion coefficient $\sigma$ and jump coefficient ${\xi }^2$. This process is observed at discrete times $t=0,\Delta ,\dots ,n\Delta$. The sampling interval $\Delta$ tends to 0 and the time interval $n\Delta$ tends to infinity. We assume that ${\left(X_t\right)}_{t\ge 0}$ is ergodic, strictly stationary and exponentially $\beta$-mixing. We use a penalized least-square approach to compute adaptive estimators of the functions ${\sigma }^2+{\xi }^2$ and ${\sigma }^2$. We provide bounds for the risks of the two estimators.     

A robust decentralized reputation management system for service selection

Reputation mechanism is a novel approach to automate QoS-aware service selection in service oriented computing. The reputation system collects ratings on QoS that consumers feedback and aggregates them to derive a reputation value, which can in turn assist other consumers in service selection in future. However, current approaches fail to combat the malicious ratings and hence the calculated reputation values can be biased severely or even manipulated. Moreover, the centralized management of rating data restricts its application to large open environment. In this paper, we present a robust decentralized reputation system which can resist various unfair ratings and manipulation behaviours. It can evolve and become more mature against malicious ratings with the system running continuously. At last, we experimentally verify the robustness of the proposed approach through a simulation study.     

三维荧光光谱的特征区域选择方法

将数学中的二元凸函数判定和数据挖掘中的聚类分析方法结合,提出了针对三维荧光的光谱区域选择方法,并利用此种方法从光谱图中提取出含有丰富光谱信息的凸集区域。对水体中总有机碳的检测和白酒中黄曲霉素的检测进行了实验研究,实验结果表明,采用本文提出的三维荧光光谱区域选择方法提高了模型的精度,与利用全光谱所建立的回归模型相比,模型精度分别提高了6.17%和4.97%。     

Performance monitoring of a mammalian cell based bioprocess using Raman spectroscopy

Being able to predict the final product yield at all stages in long-running, industrial, mammalian cell culture processes is vital for both operational efficiency, process consistency, and the implementation of quality by design (QbD) practices. Here we used Raman spectroscopy to monitor (in terms of glycoprotein yield prediction) a fed-batch fermentation from start to finish. Raman data were collected from 12 different time points in a Chinese hamster ovary (CHO) based manufacturing process and across 37 separate production runs. The samples comprised of clarified bioprocess broths extracted from the CHO cell based process with varying amounts of fresh and spent cell culture media. Competitive adaptive reweighted sampling (CoAdReS) and ant colony optimization (ACO) variable selection methods were used to enhance the predictive ability of the chemometric models by removing unnecessary spectral information. Using CoAdReS accurate prediction models (relative error of predictions between 2.1% and 3.3%) were built for the final glycoprotein yield at every stage of the bioprocess from small scale up to the final 5000 L bioreactor. This result reinforces our previous studies which indicate that media quality is one of the most significant factors determining the efficiency of industrial CHO-cell processes. This Raman based approach could thus be used to manage production in terms of selecting which small scale batches are progressed to large-scale manufacture, thus improving process efficiency significantly.     

Comprehensive two‐dimensional gas chromatography coupled with static headspace sampling to analyze volatile compounds: Application to almonds

The hyphenation of static headspace sampling with comprehensive 2D GC equipped with a modulator based on capillary flow technology and a flame ionization detector was used to separate and identify 43 representative target volatile compounds (light hydrocarbons, carbonyls, pyrazines, alcohols, furans, and benzenes) frequently detected in the roasting process of nuts. Five column combinations with differing degrees of orthogonality (one conventional and four inverted phase sets) were tested in order to obtain the best conditions for analyzing these volatile compounds. Optimization of the working conditions for each of the different column combinations was performed by means of a central composite design. The best results in terms of separation and differentiation among the different chemical groups were achieved with a combination of inverted phase columns (first dimension: highly polar, INNOWax; second dimension: mid‐polar, ZB‐35). Additionally, a reference template was developed to provide an effective and rapid analysis of the target compounds. Finally, the proposed method was successfully employed to identify volatile compounds in raw and roasted almond samples from the Spanish cultivar Largueta.     

基于改进免疫遗传算法的近红外光谱变量选择方法

该文在免疫遗传算法（IGA）的基础上,提出一种改进免疫遗传算法（iIGA）用于近红外光谱波长变量的选择。该算法舍去了原算法中固定抗体相似度阈值的思想,取而代之的是抗体相似度阈值自适应,同时引入精英保留策略和贪心算法思想,使得算法朝着正确的方向进行局部性探优。将该算法在玉米的淀粉和蛋白质含量数据集上进行实验测试,建立偏最小二乘（PLS）分析模型,并与IGA、遗传算法（GA）以及全谱方法进行了对比。结果表明,在玉米淀粉含量的预测上,iIGA相较于原IGA算法,预测集均方根误差（RMSEP）从0.312 0降至0.298 0,预测集预测精度提升4.5%;在玉米蛋白质含量的预测上,RMSEP从0.124 4降至0.110 3,预测集预测精度提升11.3%。分别对预测淀粉和蛋白质模型的RMSEP值进行显著性检验,F值分别为165.22和182.05,P值分别为9.5 × 10－23和4.5 × 10－24,P值均小于0.05,因此,iIGA能显著提升模型预测精度。