Antifactors of regular bipartite graphs

Let $G=(X,Y;E)$ be a bipartite graph, where $X$ and $Y$ are color classes and $E$ is the set of edges of $G$. Lov\'asz and Plummer \cite{LoPl86} asked whether one can decide in polynomial time that a given bipartite graph $G=(X,Y; E)$ admits a 1-anti-factor, that is subset $F$ of $E$ such that $d_F(v)=1$ for all $v\in X$ and $d_F(v)\neq 1$ for all $v\in Y$. Cornu\'ejols \cite{CHP} answered this question in the affirmative. Yu and Liu \cite{YL09} asked whether, for a given integer $k\geq 3$, every $k$-regular bipartite graph contains a 1-anti-factor. This paper answers this question in the affirmative

The Formation and Analysis of the Concept of Tea Horse Ancient Road

Announcement of Retractio

Estimation of statistical energy analysis loss factor for fiber reinforced plastics plate of yachts

Loss factor is one of the most significant parameters of Statistical Energy Analysis (SEA) which represents the damping loss characteristics of a system and indicates the ability of its vibration energy consumption. In order to estimate it, the power input method (PIM) and the impulse response decay method (IRDM) have become widely used especially when the object of study is made of Fiber Reinforced Plastics (FRP) of which dynamic interaction is really complicated. Numerical simulation is also applied to analyze the loss factor of the spring-damping-system with single degree of freedom (SDOF) using MATLAB to introduce the identification procedure of PIM and IRDM. With the comparison of the methods, the analytical study indicates these techniques are effective for the estimation of loss factor. This paper focuses on an experimental approach to get the SEA loss factor of FRP plate and the test investigations are performed in detail. The requirements and limitations of each method applied are discussed and PIM is a better solution dealing with this kind of the composite material. The loss factor of test specimen is obtained to provide a valuable reference for the prediction and control of vibration and noise of yachts with SEA

Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach

The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation

Characterization of the fertilization independent endosperm (FIE) gene from soybean

Reproduction of angiosperm plants initiates from two fertilization events: an egg fusing with a sperm to form an embryo and a second sperm fusing with the central cell to generate an endosperm. The tryptophan-aspartate (WD) domain polycomb protein encoded by fertilization independent endosperm (FIE) gene, has been known as a repressor of hemeotic genes by interacting with other polycomb proteins, and suppresses endosperm development until fertilization. In this study, one Glycine max FIE (GmFIE) gene was cloned and its expression in different tissues, under cold and drought treatments, was analyzed using both bioinformatics and experimental methods. GmFIE showed high expression in reproductive tissues and was responsive to stress treatments, especially induced by cold. GmFIE overexpression lines of transgenic Arabidopsis were generated and analyzed. Delayed flowering was observed from most transgenic lines compared to that of wild type. Overexpression of GmFIE in Arabidopsis also leads to semi-fertile of the plants.Keywords: Polycomb proteins, fertilization independent endosperm (FIE), Glycine max, Arabidopsis thalian

Genome-scale identification of Soybean BURP domain-containing genes and their expression under stress treatments

<p>Abstract</p> <p>Background</p> <p>Multiple proteins containing BURP domain have been identified in many different plant species, but not in any other organisms. To date, the molecular function of the BURP domain is still unknown, and no systematic analysis and expression profiling of the gene family in soybean (<it>Glycine max</it>) has been reported.</p> <p>Results</p> <p>In this study, multiple bioinformatics approaches were employed to identify all the members of BURP family genes in soybean. A total of 23 BURP gene types were identified. These genes had diverse structures and were distributed on chromosome 1, 2, 4, 6, 7, 8, 11, 12, 13, 14, and 18. Phylogenetic analysis suggested that these BURP family genes could be classified into 5 subfamilies, and one of which defines a new subfamily, BURPV. Quantitative real-time PCR (qRT-PCR) analysis of transcript levels showed that 15 of the 23 genes had no expression specificity; 7 of them were specifically expressed in some of the tissues; and one of them was not expressed in any of the tissues or organs studied. The results of stress treatments showed that 17 of the 23 identified BURP family genes responded to at least one of the three stress treatments; 6 of them were not influenced by stress treatments even though a stress related <it>cis</it>-element was identified in the promoter region. No stress related <it>cis</it>-elements were found in promoter region of any BURPV member. However, qRT-PCR results indicated that all members from BURPV responded to at least one of the three stress treatments. More significantly, the members from the RD22-like subfamily showed no tissue-specific expression and they all responded to each of the three stress treatments.</p> <p>Conclusions</p> <p>We have identified and classified all the BURP domain-containing genes in soybean. Their expression patterns in different tissues and under different stress treatments were detected using qRT-PCR. 15 out of 23 BURP genes in soybean had no tissue-specific expression, while 17 out of them were stress-responsive. The data provided an insight into the evolution of the gene family and suggested that many BURP family genes may be important for plants responding to stress conditions.</p