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Investigating the nonlinear transient responses of rotating annular plate is a worthy subject in aerospace and aeronautic fields. In this paper, the nonlinear transient response induced by pulse loads for the graphene platelets reinforced metal foams (GPLRMF) annular plate with rotating motion is illustrated by time history curves and phase trajectory. Three-dimensional displacement field is captured by employing the first-order shear deformation theory (FSDT). The modeling accuracy of nonlinear transient response for the annular plate resting on viscoelastic foundation is enhanced by using von-Kármán's nonlinearity. The assumed mode method is used to solve the highly coupled nonlinear governing equations, and the fourth order Runge-Kutta iterative algorithm is applied to track the nonlinear transient response path. Detailed parametric studies are performed to investigate the effects of initial geometrical imperfection, dispersion and weight fractions of graphene platelets (GPLs), distribution types and coefficients of porosity, geometric dimensions of annular plates, viscoelastic foundation, thermal effect, types and properties of uniformly distributed pulse loads as well as rotating motion on nonlinear transient response of the annular plates. It turned out that, the blast load can significantly affect the dynamic behavior of the annular plate. Additionally, the annular plate with larger initial geometric imperfection has better deformation resistance, and the presence of the rotating motion can increase the maximum vibration amplitude of the annular plate. [ABSTRACT FROM AUTHOR]

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on multiprocessor platforms, which provides degraded services for low criticality tasks in the high criticality mode. However, the proposed solution based on the partitioning heuristic Criticality-Unaware Worst Fit Decreasing method (CU-WFD) may consume more energy. In this paper, we propose a novel partitioning algorithm named EEAA based on the genetic algorithm and explore various parameter combinations, aiming to find a task-to-processor assignment method that can further reduce energy consumption with the best parameter combinations. In addition, we conduct experiments to evaluate EEAA and the experimental result shows that EEAA achieves energy savings of 12.56% compared to CU-WFD. [ABSTRACT FROM AUTHOR]

Energy management is one of the key issues in the real-time embedded systems. The slowdown method based on the dynamic voltage scaling (DVS) and the shutdown method based on the dynamic power management (DPM) can effectively reduce energy consumption. The previous algorithm which uses the static speed to deal with the blocking situation is too conservative. We present a static task synchronization for sporadic tasks scheduling (STSST) algorithm. The dynamic speed is computed to minimize energy consumption while satisfying the time constraints in the STSST algorithm. But, the STSST algorithm assumes that each task executes with its worst case execution time. As the actual execution time of the task is often lower than its worst case execution time. A dynamic task synchronization for sporadic task (DTSST) algorithm which can reclaim the slack time generated from the early completion task is proposed. It combines the DVS technique and the DPM technique to reduce energy consumption. Furthermore, the feasibility conditions are given and proved. The experimental results show that the DTSST algorithm can reduce energy consumption up to 20.57%∼67.66% over the STSST algorithm and it consumes 43.79%∼67.66% less energy than that of the DS algorithm. [ABSTRACT FROM AUTHOR]

We address the problem of minimizing overall energy consumption for sporadic tasks with shared resources in a real time system. Previous algorithms use the static speed to deal with this problem. However, we have proposed a novel scheduling algorithm, called DSSTS, for the sporadic tasks with shared resources. The DSSTS algorithm schedules the task at the dynamic low speed and the task switches to the high speed when it is blocked by another lower priority task. It assumes that each task is executed with its worst case execution time. For energy efficiency, we have proposed a dynamic reclaiming dynamic speed sporadic tasks scheduling algorithm, called DRDSSTS. The DRDSSTS algorithm is an extension of the DSSTS algorithm. It can reclaim the dynamic slack time generated from the early completion task to adjust to the processor speed. Moreover, it can use the DPM technique to put the processor into a dormant mode to save energy when the processor is in an idle mode. The experimental results show that the DRDSSTS algorithm can reduce the energy consumption up to 2.64%–37.16% over the DSSTS algorithm and it consumes 35.16%–71.15% less energy than that of the DS algorithm. [ABSTRACT FROM AUTHOR]

Alterations in both local and remote connectivity were reported in amnestic mild cognitive impairment (aMCI) patients but rarely in the same group of patients. In the present study, we employed a novel resting-state functional magnetic resonance imaging (rfMRI) connectome index, regional functional homogeneity on the 2-dimensional cortical surface, to detect full-cortex vertex-wise changes of the local rfMRI connectivity in 32 aMCI patients compared with 40 healthy controls. We further used the seed-based functional connectivity to explore the remote rfMRI connectivity in aMCI. The results revealed significantly lower local connectivity in the default network and higher local connectivity in the somatomotor network in aMCI patients. Abnormal remote connectivity relevant to local connectivity was primarily detectable within the default network (decrease) and in the somatomotor and attention networks (increase). The abnormalities in the remote (not local) default network connectivity were significantly associated with episodic memory performance in patients. These distance-related connectivity profiles illustrated a dysfunctional pattern in aMCI, which extended our knowledge of this pathological aging process. [ABSTRACT FROM AUTHOR]

Dynamic voltage scaling (DVS) is a technique which is widely used to save energy in a real time system. Recent research shows that it has a negative impact on the system reliability. In this paper, we consider the problem of the system reliability and focus on a periodic task set that the task instance shares resources. Firstly, we present a static low power scheduling algorithm for periodic tasks with shared resources called SLPSR which ignores the system reliability. Secondly, we prove that the problem of the reliability-aware low power scheduling for periodic tasks with shared resources is NP-hard and present two heuristic algorithms called SPF and LPF respectively. Finally, we present a dynamic low power scheduling algorithm for periodic tasks with shared resources called DLPSR to reclaim the dynamic slack time to save energy while preserving the system reliability. Experimental results show that the presented algorithm can reduce the energy consumption while improving the system reliability. [ABSTRACT FROM AUTHOR]

Energy-aware mixed-criticality (MC) real-time scheduling has attracted the attention of many researchers. However, they only focus on the conventional MC task model, in which all low-criticality (LO) tasks are discarded in the high-criticality (HI) mode. In this work, we focus on an imprecise mixed-criticality (IMC) task model that provides degraded service for LO tasks in HI mode. Firstly, we address the energy minimization problem of scheduling IMC task sets with dynamic voltage and frequency scaling (DVFS). Secondly, we present an energy-aware IMC scheduling algorithm (EA-IMC) to solve this problem while it schedules tasks with the energy-efficient speed of the LO mode S L O , and the maximum processor speed S m a x in HI mode. Finally, the experiments are applied to evaluate the EA-IMC algorithm and the experimental results show that it can achieve on average 24.55% energy reduction compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, nonlinear resonance behavior of graphene platelets reinforced metal foams (GPLRMFs) plates under axial motion with initial geometric imperfections is investigated. Material properties, graphene platelets (GPLs) and foams of the plate are assumed to be symmetric with respect to the thickness direction. The Euler-Lagrange principle is employed to derive the equations of motion. Then, the Galerkin principle is applied to discrete nonlinear equations of motion, thus the nonlinear partial differential equations are transformed into nonlinear ordinary differential equations, the single degree of freedom of Duffing equation is obtained by reducing the dimension of the five nonlinear ordinary differential equations and determined by the perturbation method. Numerical results are presented to investigate the effects of the GPLs pattern, the foams distribution, the foams coefficient, the weight fraction of GPLs, the temperature change, axial moving velocity, and the external load on the resonance problem of the GPLRMFs plates under axial motion with geometric imperfections. It can be found that axial moving velocity and initial geometric imperfection have significant impacts on the resonance phenomena. • Consider the effects of the axial motion, initial geometric imperfection, and temperature changes. • Study the nonlinear resonance behavior of graphene platelet reinforced metal foams (GPLRMFs) plates. • Employ the Galerkin principle and Modified Lindstedt Poincare method to solve the whole problems. • The influences of different parameters on the nonlinear resonance problems are studied. • Observe the softening/hardening spring characteristics for the forced vibration response. [ABSTRACT FROM AUTHOR]