Intrinsic Reduced Attitude Formation with Ring Inter-Agent Graph

This paper investigates the reduced attitude formation control problem for a group of rigid-body agents using feedback based on relative attitude information. Under both undirected and directed cycle graph topologies, it is shown that reversing the sign of a classic consensus protocol yields asymptotical convergence to formations whose shape depends on the parity of the group size. Specifically, in the case of even parity the reduced attitudes converge asymptotically to a pair of antipodal points and distribute equidistantly on a great circle in the case of odd parity. Moreover, when the inter-agent graph is an undirected ring, the desired formation is shown to be achieved from almost all initial states

Global nonexistence of solutions for the viscoelastic wave equation of Kirchhoff type with high energy

In this paper we consider the viscoelastic wave equation of Kirchhoff type: $$u_{tt}-M(\|\nabla u\|_{2}^{2})\Delta u+\int_{0}^{t}g(t-s)\Delta u(s){\rm d}s+u_{t}=|u|^{p-1}u$$ with Dirichlet boundary conditions. Under some suitable assumptions on $g$ and the initial data, we established a global nonexistence result for certain solutions with arbitrarily high energy.Comment: 12 page

Video Event Recognition by Dempster-Shafer Theory

Abstract. This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method em-ploying evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with de-grees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78 % and 100 % for four composite events are encouraging.

Cynaropicrin inhibits lung cancer proliferation by targeting EGFR/AKT signaling pathway

Purpose: To investigate the anti-proliferative effect of cynaropicrin on lung cancer cell lines, and the underlying molecular mechanism. Methods: The effect of cynaropicrin treatment on the viabilities of H1975 and H460 cells was measured using Cell Counting Kit-8. Apoptosis was analysed by annexin-V/FITC staining, while protein expressions were assayed by western blotting. Results: Treatment of H1975 and H460 cells with cynaropicrin at doses of 0.25 – 2.0 μM led to a marked reduction in their viability (p < 0.05). In cynaropicrin-treated H1975 and H460 cells, there was significant increase in apoptosis, when compared to control cells. Caspase-3 and caspase-9 levels were also significantly increased in H1975 and H460 cells on treatment with cynaropicrin at doses of 0.25 and 2.0 μM while treatment with cynaropicrin at doses of 0.25 - 2.0 μM significantly down-regulated the mRNA expression of CCND1 in the two cell lines (p < 0.05). Cynaropicrin markedly inhibited mRNA and protein expressions of EGFR, and also downregulated AKT in H1975 and H460 cells (p < 0.05). However, cynaropicrin significantly increased the expressions of miR-202 and miR-370. Conclusion: Cynaropicrin exerts anti-proliferative and proapoptotic effects on H1975 and H460 lung cancer cells via deactivation of EGFR/AKT signaling pathway. Moreover, it upregulated the expressions of miR-202 and miR-370 in these cells. Thus, cynaropicrin has potentials for the treatment of lung cancer

High -density lipoprotein cholesterol as a predictor for diabetes mellitus

Background: Diabetes is a prevalent chronic disease around the world. To evaluate the risk of diabetes comprehensively, we developed a score model for risk prediction with HDL-C as a protective factor. Methods: We extracted physical examination data of 2728 individuals. The data contain 18 demographic and clinical variables. To identify the statistical significant feature variables, the backward stepwise logistic regression was used based on the data of the “exploratory population”. To ascertain the cutoff value of the selected variables, we used the Youden index. Then we assigned each variable level a score according to the estimated regression model coefficients and then calculated the individual’s total score. We gained the cutoff value for the total score through the Youden Index and stratified the total score into four levels. We employed the data of “validation population” to test the performance of the score model based on the area under the ROC curve. Results: Age, LDL-C, HDL-C, BMI, family history of diabetes, diastolic blood pressure and TCHO were selected as statistically significant variables. The diabetes risk score range varied from 0 to 17. The risk level categorized by the total score was low, middle, high and extremely high, with a score range of 0-2, 3-7, 8-12 and 13-17, respectively. Conclusions: The score model based on physical examination data is an efficient and valuable tool to evaluate and monitor the potential diabetes risk for both healthy and unhealthy people at an individual level