Clique immersion in graphs without fixed bipartite graph

A graph $G$ contains $H$ as an \emph{immersion} if there is an injective mapping $\phi: V(H)\rightarrow V(G)$ such that for each edge $uv\in E(H)$, there is a path $P_{uv}$ in $G$ joining vertices $\phi(u)$ and $\phi(v)$, and all the paths $P_{uv}$, $uv\in E(H)$, are pairwise edge-disjoint. An analogue of Hadwiger's conjecture for the clique immersions by Lescure and Meyniel states that every graph $G$ contains $K_{\chi(G)}$ as an immersion. We consider the average degree condition and prove that for any bipartite graph $H$, every $H$-free graph $G$ with average degree $d$ contains a clique immersion of order $(1-o(1))d$, implying that Lescure and Meyniel's conjecture holds asymptotically for graphs without fixed bipartite graph.Comment: 2 figure

An improved bidirectional gate recurrent unit combined with smoothing flter algorithm for state of energy estimation of lithium-ion batteries.

The accurate estimation of state of energy (SOE) is the key to the rational energy distribution of lithium-ion battery based energy storage equipment. This paper proposes an improved bidirectional gate recursive element combined with a time-varying bounded layer based smooth variable structure filtering algorithm. First, based on the solid temporal nature of the estimated parameters, a BiGRU neural network structure is constructed to strengthen further the influence of past and future information on the current estimates. Then, based on the traditional variable structure filtering, a time-varying bounded layer smoothing mechanism with saturation restriction (TS-VBL) is proposed to smooth the output of BiGRU to obtain a more accurate estimate. Finally, the test was conducted under 15℃ hybrid pulse power characterization (HPPC) and 35℃ Beijing bus dynamic stress test (BBDST). Compared with other algorithms, the BiGRU-TSVSF algorithm has a minor maximum estimation error of 0.00495 and 0.00722, respectively. The experimental results show that the algorithm has high precision and robustness and is of great value to the energy storage research of energy storage equipment

Incremental capacity curve health-indicator extraction based on gaussian filter and improved relevance vector machine for lithium–ion battery remaining useful life estimation.

Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on incremental capacity curve (IC) and the method of improved adaptive relevance vector machine (RVM). First, the IC curve is extracted based on the charging current and voltage data. To attenuate the noise effects on the IC curve, Gaussian filtering is used and the optimal filtering window is determined to remove the noise interference. Based on this, the peak characteristics of the IC curve are analyzed and four groups of health indicators are extracted, and the strong correlation between health indicators and capacity degradation is determined using Pearson correlation analysis. Then, to optimize the traditional fixed kernel parameter RVM model, an RVM regression model whose kernel parameters are optimized by the Bayesian algorithm is established. Finally, four sets of datasets under CS2 battery in the public dataset of the University of Maryland are carried out for experimental validation. The validation results show that the improved RVM model has better short–term prediction performance and long–term prediction stability, the RUL prediction error is less than 20 cycles, and the mean absolute error is less than 0.02. The performance of the improved RVM model is better than that of the traditional RVM model

Service capacity procurement of logistics service supply chain with demand updating and loss-averse preference

This research aims to identify the impacts of loss-averse preference on the service capacity procurement decisions with demand updating in a logistics service supply chain, which consists of one logistics service integrator and one functional logistics service provider. It starts from a basic two-stage Stackelberg game model, then, extends to three scenarios where either the integrator or the provider or neither of them has loss-averse preference. The impact of loss-averse preference on the decisions of supply chain members is discussed by comparing the four models. Our results reveal, first, the loss-averse preferences do not always affect the decisions of supply chain members. If certain conditions are satisfied, the logistics service integrator can benefit from its loss-averse preference. Second, the increased service level can affect the logistics service integrator’s procurement strategy and the functional logistics service provider’s pricing strategy. This effect is only related to the loss-averse preference of the functional logistics service provider. Last, under certain conditions, the total service capacity decreases with the increased service level, regardless of whether or not the supply chain members have loss-averse preferences

Long Noncoding RNA BCYRN1 Promotes the Proliferation of Colorectal Cancer Cells via Up-Regulating NPR3 Expression

Background/Aims: Long noncoding RNAs (lncRNAs) constitute a large proportion of noncoding transcripts that have recently emerged as a new class of important regulators in cancers. LncRNA BCYRN1, also known as BC200, has a potential function in tumorigenesis. However, the clinical significance of BCYRN1 and its effect on colorectal cancer (CRC) progression remains unclear. Methods: Quantitative reverse transcriptase PCR (qRT-PCR) was performed to investigate the expression of BCYRN1 in CRC tissues and cell lines. The biological function of BCYRN1 was also investigated through knockdown and overexpression of BCYRN1 in vitro. Microarray bioinformatics analysis was performed to analyze the putative targets of BCYRN1. Results: The results showed that BCYRN1 expression was significantly upregulated in 96 CRC tumor tissues compared with para-carcinoma control tissues. Additionally, BCYRN1 overexpression was associated with larger tumor size and advanced pathological stages in CRC patients. In vitro BCYRN1 knockdown significantly inhibited the proliferation and apoptosis of CRC cells. Furthermore, NPR3 was identified to be a target of BCYRN1 and was downregulated by BCYRN1 knockdown. Conclusion: Together, we provide the first evidence that BCYRN1 plays an oncogenic role in CRC cells. BCYRN1 may be a promising prognostic biomarker and a potential therapeutic target for CRC

Analysis on epidemiological characteristics of nontyphoid Salmonella infection in Shanghai, 2018

Objective To identify the epidemiological characteristics of nontyphoid Salmonella infection in Shanghai, and provide scientific basis for the prevention and control. Methods The descriptive epidemiological method was used to analyze the epidemiological information of the collected diarrhea cases and the nontyphoid Salmonella test result from 26 foodborne disease surveillance sentinel hospitals in Shanghai. Results A total of 5 580 infectious diarrhea cases and specimens were enrolled. The positive detection rate of nontyphoid Salmonella was 4.19% (234/5 580). The serotypes were mainly Salmonella Enteritidis and Salmonella Typhimurium. There was no difference in the positive detection rate of nontyphoid Salmonella among different genders, ages and occupations. Spring and summer were the seasons when nontyphoid Salmonella positive detection rate was high. The positive rate of nontyphoid Salmonella in patients with fever or watery stool was significantly higher than in patients without fever or waterless stool. The positive rate of nontyphoid Salmonella was relatively high among the patients from small and medium-sized catering service and canteen. Conclusion Salmonella Enteritidis and Salmonella Typhimurium were the main serotypes of nontyphoid Salmonella in Shanghai, and the peak of nontyphoid Salmonella infection occured in spring and summer. The authorities should strengthen the supervision and management of catering service and canteen

A Study of Viscoelastic Model of Polymers in Shear Flow Based on Molecular Dynamic Simulations

In this study, the rheological properties and physical significations of an incompressible viscoelastic (inCVE) the inCVE model was investigated by employing molecular dynamics calculations. Polypropylene (PP) and polystyrene (PS) polymers were selected as candidate materials, the corresponding cell models consisting of five chains of 80 (PP) and 30 (PS) units were built successively. The energy minimization and anneal treatment were launched to optimize the unfavorable structures. The periodic boundary condition, COMPASS force field and the Velocity-Verlet algorithm were employed to calculate the shear flow behavior of chains. The sample data were collected and fitted based on the Matlab platform, and the analysis of the variance (ANOVA) method was performed to determine the validity of the model. Experimental results reveal that the inCVE model matches well with the pseudo-plastic fluids. Compared with the Ostwald-de Waele power law model and Cross model, it is effective and robust, and exhibits a three-stage rheological characteristic. Moreover, it characterizes the stress yield, activation energy, temperature dependence and viscoelastic response of polymers

Conservation and implications of eukaryote transcriptional regulatory regions across multiple species

<p>Abstract</p> <p>Background</p> <p>Increasing evidence shows that whole genomes of eukaryotes are almost entirely transcribed into both protein coding genes and an enormous number of non-protein-coding RNAs (ncRNAs). Therefore, revealing the underlying regulatory mechanisms of transcripts becomes imperative. However, for a complete understanding of transcriptional regulatory mechanisms, we need to identify the regions in which they are found. We will call these transcriptional regulation regions, or TRRs, which can be considered functional regions containing a cluster of regulatory elements that cooperatively recruit transcriptional factors for binding and then regulating the expression of transcripts.</p> <p>Results</p> <p>We constructed a hierarchical stochastic language (HSL) model for the identification of core TRRs in yeast based on regulatory cooperation among TRR elements. The HSL model trained based on yeast achieved comparable accuracy in predicting TRRs in other species, e.g., fruit fly, human, and rice, thus demonstrating the conservation of TRRs across species. The HSL model was also used to identify the TRRs of genes, such as p53 or <it>OsALYL1</it>, as well as microRNAs. In addition, the ENCODE regions were examined by HSL, and TRRs were found to pervasively locate in the genomes.</p> <p>Conclusion</p> <p>Our findings indicate that 1) the HSL model can be used to accurately predict core TRRs of transcripts across species and 2) identified core TRRs by HSL are proper candidates for the further scrutiny of specific regulatory elements and mechanisms. Meanwhile, the regulatory activity taking place in the abundant numbers of ncRNAs might account for the ubiquitous presence of TRRs across the genome. In addition, we also found that the TRRs of protein coding genes and ncRNAs are similar in structure, with the latter being more conserved than the former.</p