The difference between the domination number and the minus domination number of a cubic graph

AbstractThe closed neighborhood of a vertex subset S of a graph G = (V, E), denoted as N[S], is defined as the union of S and the set of all the vertices adjacent to some vertex of S. A dominating set of a graph G = (V, E) is defined as a set S of vertices such that N[S] = V. The domination number of a graph G, denoted as γ(G), is the minimum possible size of a dominating set of G. A minus dominating function on a graph G = (V, E) is a function g : V → {−1, 0, 1} such that g(N[v]) ≥ 1 for all vertices. The weight of a minus dominating function g is defined as g(V) =ΣvϵVg(v). The minus domination number of a graph G, denoted as γ−(G), is the minimum possible weight of a minus dominating function on G. It is well known that γ−(G) ≤ γ(G). This paper is focused on the difference between γ(G) and γ−(G) for cubic graphs. We first present a graph-theoretic description of γ−(G). Based on this, we give a necessary and sufficient condition for γ(G) −γ−(G) ≥ k. Further, we present an infinite family of cubic graphs of order 18k + 16 and with γ(G) −γ−(G) ≥

Neutralizing antibody response in the patients with hand, foot and mouth disease to enterovirus 71 and its clinical implications

Enterovirus 71 (EV71) has emerged as a significant pathogen causing large outbreaks in China for the past 3 years. Developing an EV71 vaccine is urgently needed to stop the spread of the disease; however, the adaptive immune response of humans to EV71 infection remains unclear. We examined the neutralizing antibody titers in HFMD patients and compared them to those of asymptomatic healthy children and young adults. We found that 80% of HFMD patients became positive for neutralizing antibodies against EV71 (GMT = 24.3) one day after the onset of illness. The antibody titers in the patients peaked two days (GMT = 79.5) after the illness appeared and were comparable to the level of adults (GMT = 45.2). Noticeably, the antibody response was not correlated with disease severity, suggesting that cellular immune response, besides neutralizing antibodies, could play critical role in controlling the outcome of EV71 infection in humans

Automatic Measurement and Monitoring Technology for Oil Well

Measurement technology of oil well develops and improves constantly at present. Reducing operation cost and energy consumption and improving efficiency of labor provides reliable technical guarantee for simplifying and optimizing ground process system. According to the needs of parametric measurement and monitoring for oil well, the paper combines with actual situation of the second factory in Dagang Oilfield, the second factory proposes the research on automatic measurement and monitoring technology for wireless oil well and application project, and scientific and information department in Dagang Oilfield ratifies the project. The paper studies automatic measurement and monitoring technology for wireless oil well, and evaluates the economic and social benefit

Association analysis of alpha-amylase (AMY) and cathepsin L (CTSL) SNPs with growth traits in giant tiger shrimp Penaeus monodon

Alpha-amylase (AMY) and cathepsin-L (CTSL) were selected as candidate genes for SNP discovery for growth traits of P. monodon. Six SNPs were found in AMY and three in CTSL in P. monodon. Association analyses for the candidate SNPs with important economic traits were performed in populations. That allele A at CTLS-213 SNP, AA, and GA, tended to be associated with increased body weight. Shrimps with genotype GG had significantly smaller CL, CW, and CH values than those with GT and TT genotypes (P < 0.05). While CTLS-820 SNP was found to be significantly associated with CH and FSL (P <0.05). These SNPs will be valid for marker-assisted selection breeding programs in P. monodon

Audio Contrastive based Fine-tuning

Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications. There still remains a challenge of striking the right balance between fitting the model to the training data (avoiding overfitting) and enabling it to generalise well to a new domain. Leveraging the transferability of contrastive learning, we introduce Audio Contrastive-based Fine-tuning (AudioConFit), an efficient approach characterised by robust generalisability. Empirical experiments on a variety of audio classification tasks demonstrate the effectiveness and robustness of our approach, which achieves state-of-the-art results in various settings.Comment: Under revie