Anomaly recognition and early warning of monitoring data are of great significance in the field of modern dam safety management. Multidimensional least-squares regression model with the Pauta criterion is a well-known traditional method, but it is easy to misjudge the normal value and miss the outliers. Thereby, an online robust recognition and early warning model combining robust statistics and confidence interval is proposed to detect outliers. The threshold [Formula: see text] is set based on the derived confidence interval [Formula: see text] and the scale estimator [Formula: see text] (derived from the location M-estimator). Monitoring data obtained from a gravity dam and a rockfill dam were taken as examples to demonstrate the robust recognition and early warning model. The results show that the proposed method can effectively improve the reliability of anomaly recognition and early warnings, which is valuable in engineering applications.
For security and share of Internet IP addresses, NAT (Network Address Translation) firewalls are widely used to isolate the private networks from Internet in some enterprises/organizations. Unfortunately, NATs block the data transmitted directly between two clients under different private networks in many P2P (Peer to Peer) applications. A protocol, STUN, has been proposed by IETF to solve the problem. But if the NAT is Symmetric NAT, the STUN can't tackle it effectively. Another protocol of TURN has been proposed to complement the limitation of STUN, however, it brings the additional cost of resources. Now, almost all the research into the problem are concentrated on using middle server to transmit data among clients although this way brings the additional consumption of network bandwidth and transmission delays at the same time. To solve this problem, this paper makes many practices in Symmetric NAT traversal and proposes a new algorithm PS-STUN that can traversal Symmetric NAT and transmits data directly between the clients in the situations where the STUN isn't enabling. Through emulation tests, we prove that the algorithm is efficient and can solve the Symmetric NAT traversal trouble about STUN.
RNA editing is one of the post- or co-transcriptional processes that can lead to amino acid substitutions in protein sequences, alternative pre-mRNA splicing, and changes in gene expression levels. Although several methods have been suggested to identify RNA editing sites, there remains challenges to be addressed in distinguishing true RNA editing sites from its counterparts on genome and technical artifacts. In addition, there lacks a software framework to identify and visualize potential RNA editing sites. Here, we presented a software − ‘RED’ (RNA Editing sites Detector) − for the identification of RNA editing sites by integrating multiple rule-based and statistical filters. The potential RNA editing sites can be visualized at the genome and the site levels by graphical user interface (GUI). To improve performance, we used MySQL database management system (DBMS) for high-throughput data storage and query. We demonstrated the validity and utility of RED by identifying the presence and absence of C→U RNA-editing sites experimentally validated, in comparison with REDItools, a command line tool to perform high-throughput investigation of RNA editing. In an analysis of a sample data-set with 28 experimentally validated C→U RNA editing sites, RED had sensitivity and specificity of 0.64 and 0.5. In comparison, REDItools had a better sensitivity (0.75) but similar specificity (0.5). RED is an easy-to-use, platform-independent Java-based software, and can be applied to RNA-seq data without or with DNA sequencing data. The package is freely available under the GPLv3 license at http://github.com/REDetector/RED or https://sourceforge.net/projects/redetector.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.