Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.

Application of the Queuing Theory in Characterizing and Optimizing the Passenger Flow at the Airport Security

This paper presents mathematics models that describe and optimize the passenger flow at the airport security checkpoints by applying the queuing theory. Firstly, a Poisson process is used to estimate the flow of passengers waiting for going through the security. Then, the Poisson distribution is combined with a multiple M/M/s model. Following that, an arrival model (passengers’ arriving at the checkpoints preparing for security examination and departure) with Gumbel extreme value estimation is described that predicts the busiest time in the busiest airport. Real case data collected from several major airports worldwide is used for creating a hybrid Poisson model to generate the simulation of passenger volume. At last, Markov Chain theory is applied to the analysis to randomly simulate the flow of enplaned passengers again, and the results of these two simulations are compared and discussed, revealing that the hybrid Poisson model is the more accurate one. After successfully characterizing the passenger flow mathematically, two methods for optimizing the passenger flow are then provided in two different respects: one is bypassing passengers and creating an express pass;while the other one promotes Pre-Check service application.

Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

大规模交易数据库的一种有效聚类算法(英文)

研究大规模交易数据库的聚类问题 ,提出了一种二次聚类算法—— CATD.该算法首先将数据库划分成若干分区 ,在每个分区内利用层次聚类算法进行局部聚类 ,把交易初步划分成若干亚聚类 ,亚聚类的个数由聚类间的距离参数控制 .然后对所有的亚聚类进行全局聚类 ,同时识别出噪声 .由于采用了分区方法和聚类的支持向量表示法 ,该算法只需扫描一次数据库 ,聚类过程在内存中进行 ,因此能处理大规模的数据库 .

Periodic Solutions for N-Body-Type Problems

The authors consider non-autonomous N-body-type problems with strong force type potentials at the origin and sub-quadratic growth at infinity.Using Ljusternik-Schnirelmann theory,the authors prove the existence of unbounded sequences of critical values for the Lagrangian action corresponding to non-collision periodic solutions.

Estimating Nonlinear DSGE Models with Moments Based Methods

这篇文章为非线性的 DSGE 模型建议一条新途径到接近的时刻。这条途径快、足够地精确估计非线性的 DSGE 模型。一个小金融 DSGE 模型被建议途径的几修正反复估计。片刻的近似接近大样品蒙特卡罗评价的结果。用我们的建议途径的参数评价的质量接近中央差别 Kalman 过滤器(CDKF ) ；并且我们的建议途径快得多。