Mobile Formation Coordination and Tracking Control for Multiple Non-holonomic Vehicles

This paper addresses forward motion control for trajectory tracking and mobile formation coordination for a group of non-holonomic vehicles on SE(2). Firstly, by constructing an intermediate attitude variable which involves vehicles' position information and desired attitude, the translational and rotational control inputs are designed in two stages to solve the trajectory tracking problem. Secondly, the coordination relationships of relative positions and headings are explored thoroughly for a group of non-holonomic vehicles to maintain a mobile formation with rigid body motion constraints. We prove that, except for the cases of parallel formation and translational straight line formation, a mobile formation with strict rigid-body motion can be achieved if and only if the ratios of linear speed to angular speed for each individual vehicle are constants. Motion properties for mobile formation with weak rigid-body motion are also demonstrated. Thereafter, based on the proposed trajectory tracking approach, a distributed mobile formation control law is designed under a directed tree graph. The performance of the proposed controllers is validated by both numerical simulations and experiments

Permeation Mechanism of Potassium Ions through the Large Conductance Ca2+-Activated Potassium Channel

The permeation of the potassium ion (K+) through the selectivity filter (SF) of the large conductance Ca2+-activated potassium (Slo1) channel remains an interesting question to study. Although the mode of K+ entering and leaving the SF has been revealed, the mechanism of K+ passing through the SF is still not clear. In the present study, the pattern of K+ permeation through the SF is investigated by chemical computation and data mining based on the molecular structure of Slo1 from Aplysia californica. Both bond configurations and the free energy of K+s inside the SF was studied using Discovery Studio software. The results suggested that, to accommodate increasing energy levels and to tolerate more K+s, 4-fold symmetric subunits of SF can only move at one direction that is perpendicular to the center axis. In addition, two configurations of chemical bonds between K+s and the SF are usually employed including the chelate configuration under low free energy and the complex configuration under high free energy conditions. Moreover, three patterns of bond configurations for multiple K+s within the SF are used to accommodate the energetic changes of the SF, and each pattern is composed of one or two subconformations. These findings likely resulted from the evolutionary optimization of the protein function of Slo1. The specific conductance and the voltage-gating of the Slo1 channel can be reinterpreted with the permeation mechanism of K+s found in the current study. The permeation mechanism of K+s through the SF can be used to understand the interaction between various toxins and the Slo1 channel, and can be employed to develop new drugs targeting relevant ion channels

Matrix Neural Networks

Traditional neural networks assume vectorial inputs as the network is arranged as layers of single line of computing units called neurons. This special structure requires the non-vectorial inputs such as matrices to be converted into vectors. This process can be problematic. Firstly, the spatial information among elements of the data may be lost during vectorisation. Secondly, the solution space becomes very large which demands very special treatments to the network parameters and high computational cost. To address these issues, we propose matrix neural networks (MatNet), which takes matrices directly as inputs. Each neuron senses summarised information through bilinear mapping from lower layer units in exactly the same way as the classic feed forward neural networks. Under this structure, back prorogation and gradient descent combination can be utilised to obtain network parameters e ciently. Furthermore, it can be conveniently extended for multimodal inputs. We apply MatNet to MNIST handwritten digits classi cation and image super resolution tasks to show its e ectiveness. Without too much tweaking MatNet achieves comparable performance as the state-of-the-art methods in both tasks with considerably reduced complexity

Identification of osteopontin-dependent signaling pathways in a mouse model of human breast cancer

<p>Abstract</p> <p>Background</p> <p>Osteopontin (OPN) is a secreted phosphoprotein which functions as a cell attachment protein and cytokine that signals through two cell adhesion molecules, α_vβ₃-integrin and CD44, to regulate cancer growth and metastasis. However, the signaling pathways associated with OPN have not been extensively characterized. In an in vivo xenograft model of MDA-MB-231 human breast cancer, we have previously demonstrated that ablation of circulating OPN with an RNA aptamer blocks interaction with its cell surface receptors to significantly inhibit adhesion, migration and invasion in vitro and local progression and distant metastases.</p> <p>Findings</p> <p>In this study, we performed microarray analysis to compare the transcriptomes of primary tumor in the presence and absence of aptamer ablation of OPN. The results were corroborated with RT-PCR and Western blot analysis. Our results demonstrate that ablation of OPN cell surface receptor binding is associated with significant alteration in gene and protein expression critical in apoptosis, vascular endothelial growth factor (VEGF), platelet derived growth factor (PDGF), interleukin-10 (IL-10), granulocyte-macrophage colony stimulating factor (GM-CSF) and proliferation signaling pathways. Many of these proteins have not been previously associated with OPN.</p> <p>Conclusion</p> <p>We conclude that secreted OPN regulates multiple signaling pathways critical for local tumor progression.</p

Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks

Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM). It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better

Oscillatory criteria for Third-Order difference equation with impulses

AbstractIn this paper, we investigate the oscillation of Third-order difference equation with impulses. Some sufficient conditions for the oscillatory behavior of the solutions of Third-order impulsive difference equations are obtained