探究PCR技术在食品微生物检测中的应用

食品安全问题是世界上最难解决的公共卫生问题。随着时代的发展,对食品安全的要求越来越高。研究如何优化技术检验质量,提高食品检验效果已成为必然趋势,特别是在一些特定的环境中,更应该重视食品中的微生物检测,防止微生物污染。目前,沙门氏菌、副溶血性弧菌等致病菌较为常见,做好此类致病菌的检测,可以有效提高食品安全工作效率。要做好以上工作,需要一套成熟的检测技术。目前,在实际的应用中,单荧光定量PCR检测技术比较流行。基于此,本文重点介绍了PCR技术在实际食品微生物检测过程中的应用,并分析了改进该技术有效性的个人见解。     

基于新技术在食品微生物检验检测中的应用分析

近些年来,国内食品制造行业的发展速度越来越快,针对食品安全问题的监管成为了重中之重。然而,由于食品行业门槛低,在食品制造与销售环节的监管不到位,食品安全问题频繁出现,给民众的生命安全带来危害,妨碍了食品行业的可持续发展。面对此种严峻形势,国家有关部门加大了对食品微生物的检验检测力度,如何合理运用新型技术完成食品安全的检测是一项重大挑战。本文通过阐释食品微生物的相关概念,说明了食品微生物检验检测成本、培养结果,并且分析了新技术在食品微生物检验检测中的具体应用,从而有效提升新技术在食品微生物检验检测中的总体应用水平。     

Insurance fraud detection: A statistically validated network approach

Fraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out-of-sample fraud scams under investigation by the judicial authorities.     

LAT模型中基于次用户权值优化的频谱感知

全双工认知无线电LAT（Listen-and-Talk）模型因自干扰消除不完全而使得频谱感知能力下降。为提升频谱感知性能，提出了LAT模型下基于次用户加权的协作式频谱感知方法，分别研究了决策融合和数据融合下次用户最优权值的算法。求解最优权值时利用了凸优化方法，在处理非凸问题时利用了序列规划的方法，得到了较好的优化结果。仿真实验表明，在加权融合机制下的LAT模型得到了趋近于传统LBT（Listen-before-Talk）模型的频谱感知能力。     

微弱信号多站时差协同检测技术

针对现有时差相关检测方法难以有效检测微弱信号的现状,为提升现有检测系统对微弱信号的检测性能，提出了一种针对微弱信号的多站时差协同检测技术。通过在空间域内将多组时差站的相关结果积累，然后搜索二维网格中的最大值得到检验统计量。分析了检验统计量的概率分布，明确检验统计量在纯噪声条件下服从极值I型分布，依据奈威-皮尔逊（Neyman-Pearson）准则得到了检测门限。蒙特卡洛仿真结果表明，相同条件下，该方法比传统的相关检测的处理增益高约10lgK dB（K为时差线数目）     

进度检测在大型煤化工项目中的应用

在煤化工的项目建设中,进度控制是必不可少的一环,项目进度检测是精准测量一个项目进展程度的指标。文章应用赢得值原理、工作分解结构(work breakdown structure)、权重划分建立了一套有效的进度检测系统,保证项目计划控制实时反馈,及时制定纠偏措施,进而确保项目平稳有序进行。     

Is a co-jump in prices a sparse jump?

Systematic co-jumps in asset prices are generally thought to account for only a small proportion of overall jumps. In actual observations, however, jumps in asset prices are often persistent, and the time of persistence varies. In this context, we develop a new rule to identify co-jumps and improve traditional tests by considering different sampling frequencies and different sampling starting points to re-evaluate the occurrence rate of systematic co-jumps in financial assets. We conduct a simulation experiment to show that the current test procedures generally underestimate the number of co-jumps when considering persistence, but that the proposed procedure can identify co-jumps more accurately. We also perform an empirical analysis using price data from the Shanghai 50 Index and its 25 constituent stocks in China’s stock market. The average proportion of systematic co-jumps detected by the improved s-BNS is approximately 30%, which shows that the co-jump and even the systematic co-jump are not sparse jumps. The results also reveal the shortcomings of traditional jump tests in estimating persistent jumps and demonstrate that the proposed method can better detect the possible nondiversifiable risks between market indices and their constituent stocks, thereby contributing to financial risk management.     

基于FFT的突发扩频信号兼容快捕算法

针对传统突发扩频信号捕获算法的缺陷,即无法自适应兼容处理不同同步头时长信号,提出了一种改进的基于FFT的突发扩频信号兼容快速捕获算法。根据不同时长信号的实际应用特性,利用同步头信号尾部对齐的格式特点,采用子矩阵形式存储不同时长信号的码相关结果,通过多支路并行捕获处理,实现突发信号的自适应兼容捕获。同时,通过矩阵的FFT运算同步实现全相干累积来提高信号载噪比,通过时频并行处理提高处理效率,最大限度地优化了弱信号条件的捕获性能。仿真验证表明,算法可以实现不同扩频信号的兼容快速捕获,研究为同步头时长不等的低载噪比信号提供了一种有效的兼容快速捕获方法。     

契比雪夫加权应用被动合成孔径处理算法研究

提出了将契比雪夫加权应用于被动合成孔径处理算法。被动合成孔径技术对小孔径 基阵沿直线运动接收到的信号进行合成处理，从而达到虚拟大孔径基阵方位分辨力效果。将 契比雪夫加权应用于线阵合成孔径前后波束图的指向性研究，对主瓣宽度变化予以合理解释 。理论分析结合仿真验证表明，被动合成孔径处理算法应用契比雪夫加权可以在给定的旁瓣 高 度要求下获得等旁瓣级，等旁瓣级下可提高对弱目标信号的检测分辨能力。     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.