广义正交码索引调制系统的性能分析

摘 要：为了提高码索引调制（code index modulation，CIM）系统的传输效率，提出了一种具有更低复杂度的单输入单输出（single input single output，SISO）的广义正交码索引调制（generalized orthogonal code index modulation，GQCIM）系统。CIM 系统使用扩频码和星座符号传输信息，但只能激活两个扩频码索引和一个调制符号。而 GQCIM 系统以一种新颖的方式克服了只激活一个调制符号的限制，同时充分利用了调制符号的正交性，增加扩频码索引以传输更多的额外信息位，提高了系统的传输效率。此外，分析了GQCIM系统的理论性能，推导了误码率性能的上界。通过蒙特卡罗仿真验证了GQCIM系统的性能，对比发现GQCIM系统的理论和仿真性能一致。而且在相同的传输效率下，结果显示GQCIM系统的性能优于同样具有正交性的调制系统，如广义码索引调制（generalized code index modulation，GCIM）系统、CIM系统、码索引调制-正交空间调制（code index modulation aided quadrature spatial modulation，CIM-QSM）系统、码索引调制-正交空间调制（code index modulation aided spatial modulation，CIM-SM）系统、脉冲索引调制（pulse index modulation，PIM）系统。     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

Bioinformatic Prediction of an tRNASec Gene Nested inside an Elongation Factor SelB Gene in Alphaproteobacteria

In bacteria, selenocysteine (Sec) is incorporated into proteins via the recoding of a particular codon, the UGA stop codon in most cases. Sec-tRNA^Sec is delivered to the ribosome by the Sec-dedicated elongation factor SelB that also recognizes a Sec-insertion sequence element following the codon on the mRNA. Since the excess of SelB may lead to sequestration of Sec-tRNA^Sec under selenium deficiency or oxidative stress, the expression levels of SelB and tRNA^Sec should be regulated. In this bioinformatic study, I analyzed the Rhizobiales SelB species because they were annotated to have a non-canonical C-terminal extension. I found that the open reading frame (ORF) of diverse Alphaproteobacteria selB genes includes an entire tRNA^Sec sequence (selC) and overlaps with the start codon of the downstream ORF. A remnant tRNA^Sec sequence was found in the Sinorhizobium meliloti selB genes whose products have a shorter C-terminal extension. Similar overlapping traits were found in Gammaproteobacteria and Nitrospirae. I hypothesized that once the tRNA^Sec moiety is folded and processed, the expression of the full-length SelB may be repressed. This is the first report on a nested tRNA gene inside a protein ORF in bacteria.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

大数据共享背景下食品分类的探讨

食品分类方法不统一是我国食品安全相关部门间实现抽检、监测大数据信息融合共享的瓶颈。本文对我国现行的3个主要食品分类系统迚行了分析,在此基础上探讨了新的食品分类系统构建,通过制定统一的食品分类国家标准,纳入更多的食品类别,建立科学的分类原则,形成合理的分类层级和及时增补机制,构建一套统一、科学、权威的食品分类系统,解决当前食品分类不兼容导致各食品安全部门抽检数据难融合的现实问题,可实现跨部门大数据集成共享、信息互联互通,形成食品安全多部门共治的新格局,从而提高我国食品安全监管整体敁率。     

Epigenetic Targets for Oligonucleotide Therapies of Pulmonary Arterial Hypertension

Arterial wall remodeling underlies increased pulmonary vascular resistance and right heart failure in pulmonary arterial hypertension (PAH). None of the established vasodilator drug therapies for PAH prevents or reverse established arterial wall thickening, stiffening, and hypercontractility. Therefore, new approaches are needed to achieve long-acting prevention and reversal of occlusive pulmonary vascular remodeling. Several promising new drug classes are emerging from a better understanding of pulmonary vascular gene expression programs. In this review, potential epigenetic targets for small molecules and oligonucleotides will be described. Most are in preclinical studies aimed at modifying the growth of vascular wall cells in vitro or normalizing vascular remodeling in PAH animal models. Initial success with lung-directed delivery of oligonucleotides targeting microRNAs suggests other epigenetic mechanisms might also be suitable drug targets. Those targets include DNA methylation, proteins of the chromatin remodeling machinery, and long noncoding RNAs, all of which act as epigenetic regulators of vascular wall structure and function. The progress in testing small molecules and oligonucleotide-based drugs in PAH models is summarized.     

Beta-Binomial删除信道中的喷泉码文件传输协议

由于命名数据网络(NDN,Named-Data Networking)无环路、逐包、逐跳转发的特点,使得数据包回传成功率降低,而传统的TCP/IP协议中的ARQ和ACK机制对于多播会话不再适用.由于NDN中的传输信道可以等效为二进制删除信道,因此可以通过应用层编码来实现文件的可靠传输.传统的信道编码技术如卷积码、级联码和RS码等复杂度较高,而将NDN与低复杂度的喷泉码的结合可以实现分布式的存储架构,因而可通过喷泉编码在应用层协议中实现可靠的纠删机制,保证整体文件的传输可靠性.以往的研究一般是基于确定的删除概率信道模型,但是由于网络的异构性和信道噪声等因素影响,可能会造成信道丢包概率呈随机性分布.因此,本文在Beta-Binomial分布模型的前提下,根据贝叶斯统计的先验信息和中心极限定理,对随机概率下的纠删信道的文件可靠传输协议进行了数学建模和理论推导.仿真结果显示此模型更具有普适性,此传输协议可在信道状况未知的前提下从理论上求出最小发包数,减少冗余编码包,提高文件整体的投递成功率,在保证传输可靠性的同时有效提升协议传输效率.     

基于流数据聚类算法的电力大数据异常检测

针对电力大数据流的异常检测问题,该文将流数据聚类算法与电力大数据相结合,针对现有流数据聚类算法不易存储全部数据、断电数据易丢失等问题,以及流数据聚类算法对于离线阶段聚类算法实时应答的要求,从数据的完整性、安全性以及流数据聚类算法的低时间复杂度的角度出发,对CluStream流数据聚类算法进行改进,提出流式K-means聚类算法。对在线阶段,使用Redis集群进行流数据的缓冲,并设计节点时间衰减策略,增大心跳消息中有效消息所占比例;对离线阶段聚类算法进行优化,使用最佳距离法确定初始聚类中心,减少迭代次数;最后,使用所提出的流式K-means聚类算法进行用户用电异常行为检测,实验结果表明,该算法能够很好的发现用户用电异常行为。     

二维码信息系统在油脂加工企业中的应用

以二维码信息系统在国内某油脂加工企业具体应用为实例,系统介绍了二维码信息系统的平台组成及各自功能,阐述了企业利用二维码信息系统实现产品从生产源头到终端消费者全过程追溯的过程。二维码信息系统的应用使得应用企业形成了企业的大数据平台,很好地解决了信息孤岛和数据碎片的问题,帮助企业从产品的采购源头到销售终端打通数据链。