Bounce and cyclic cosmology in extended nonlinear massive gravity

We investigate non-singular bounce and cyclic cosmological evolutions in a universe governed by the extended nonlinear massive gravity, in which the graviton mass is promoted to a scalar-field potential. The extra freedom of the theory can lead to certain energy conditions violations and drive cyclicity with two different mechanisms: either with a suitably chosen scalar-field potential under a given Stuckelberg-scalar function, or with a suitably chosen Stuckelberg-scalar function under a given scalar-field potential. Our analysis shows that extended nonlinear massive gravity can alter significantly the evolution of the universe at both early and late times.Comment: 20 pages, 5 figures, version published at JCA

Modeling and analysis of the vibration characteristics of a new type of in-arm hydropneumatic suspension of a tracked vehicle

We investigated the nonlinear elastic characteristic of an in-arm hydropneumatic suspension unit (ISU) as well as the damping characteristic of the controllable vane absorber. Due to the strong nonlinear characteristic of the ISU, simplify is needed to accurately model the vehicle. Based on the theory of multibody dynamics, a virtual prototype of the tracked vehicle was built, which includes the hydraulic system, the multi-dynamics of running gears and a road surface model. The virtual prototype is validated using both a static balancing test and a stiffness characteristics test. By simulating the road impact loading of the tracked vehicle when travelling on a trapezoidal US military road, the changes of the pitch angle and the acceleration of seats and centroid of the tracked vehicle was analyzed for two different damping ratios. We also determined key ride indicators for different speeds. The stiffness and damping characteristics of the ISU were tested in a bench experiment

Supervised Learning in SNN via Reward-Modulated Spike-Timing-Dependent Plasticity for a Target Reaching Vehicle

Spiking neural networks (SNNs) offer many advantages over traditional artificial neural networks (ANNs) such as biological plausibility, fast information processing, and energy efficiency. Although SNNs have been used to solve a variety of control tasks using the Spike-Timing-Dependent Plasticity (STDP) learning rule, existing solutions usually involve hard-coded network architectures solving specific tasks rather than solving different kinds of tasks generally. This results in neglecting one of the biggest advantages of ANNs, i.e., being general-purpose and easy-to-use due to their simple network architecture, which usually consists of an input layer, one or multiple hidden layers and an output layer. This paper addresses the problem by introducing an end-to-end learning approach of spiking neural networks constructed with one hidden layer and reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) synapses in an all-to-all fashion. We use the supervised reward-modulated Spike-Timing-Dependent-Plasticity learning rule to train two different SNN-based sub-controllers to replicate a desired obstacle avoiding and goal approaching behavior, provided by pre-generated datasets. Together they make up a target-reaching controller, which is used to control a simulated mobile robot to reach a target area while avoiding obstacles in its path. We demonstrate the performance and effectiveness of our trained SNNs to achieve target reaching tasks in different unknown scenarios

Transposable-Element Associated Small RNAs in Bombyx mori Genome

Small RNAs are a group of regulatory RNA molecules that control gene expression at transcriptional or post-transcriptional levels among eukaryotes. The silkworm, Bombyx mori L., genome harbors abundant repetitive sequences derived from families of retrotransposons and transposons, which together constitute almost half of the genome space and provide ample resource for biogenesis of the three major small RNA families. We systematically discovered transposable-element (TE)-associated small RNAs in B. mori genome based on a deep RNA-sequencing strategy and the effort yielded 182, 788 and 4,990 TE-associated small RNAs in the miRNA, siRNA and piRNA species, respectively. Our analysis suggested that the three small RNA species preferentially associate with different TEs to create sequence and functional diversity, and we also show evidence that a Bombyx non-LTR retrotransposon, bm1645, alone contributes to the generation of TE-associated small RNAs in a very significant way. The fact that bm1645-associated small RNAs partially overlap with each other implies a possibility that this element may be modulated by different mechanisms to generate different products with diverse functions. Taken together, these discoveries expand the small RNA pool in B. mori genome and lead to new knowledge on the diversity and functional significance of TE-associated small RNAs

Uncalibrated 3D stereo image-based dynamic visual servoing for robot manipulators

Abstract — This paper introduces a new comprehensive so-lution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are measured in pixels). This model is used to compute a full-rank Image Jacobian Matrix (Jimg), which solves several common problems presented on the classical image Jacobians, e.g., image space singularities and local minima. This Jacobian is a fundamental key for the image-based control design, where uncalibrated stereo camera systems can be used to drive a robot manipulator. Furthermore, an adaptive second order sliding mode visual servo control is designed to track 3D visual motions using the 3D trajectory errors defined in the Visual Cartesian Space. The stability of the control in closed loop with a dynamic robot system is formally analyzed and proved, where exponential convergence of errors in the Visual Cartesian Space and task space without local minima are demonstrated. The complete control system is evaluated both in simulation and on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the stereo camera system change on-line and the kinematic/dynamic robot parameters are considered as unknown. This approach offers a proper solution for the common problem of visual occlusion, since the stereo system can be moved to obtain a clear view of the task at any time. I

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein–small molecule docking

Abstract Background In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein–small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. Results The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies. Conclusions Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein–small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand