A novel bio-epoxy resin, TPEU-EP, was developed. It possesses good intrinsic flame retardancy, low smoke production, and excellent mechanical properties, showing high promise for application.
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evolution (HGWO). Because basic grey wolf optimization (GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE's strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
Metal organic framework (MOF) materials have attracted great attention due to their well-ordered and controllable pores possessing of prominent potentials for gas molecule sorption and separation performances. Organizing the MOF crystals to a continuous membrane with a certain scale will better exhibit their prominent potentials. Reports in recent years concentrate on well grown MOF membranes on specific substrates. Free standing MOF membranes could have more important applications since they are independent from the substrates. However, the method to prepare such a membrane has been a great challenge because good mechanical properties and stabilities are highly required. Here, we demonstrate a novel and facile technique for preparing the free standing membrane with a size as large as centimeter scale. The substrate we use proved itself not only a good skeleton but also an excellent precursor to fulfill the reaction. This kind of membrane owns a strong mechanical strength, based on the fact that it is much thinner than the composite membranes grown on substrates and it could exhibit good property of gas separation.
Objective.-Modern prosthetic limbs have made strident gains in recent years, incorporating terminal electromechanical devices that are capable of mimicking the human hand. However, access to these advanced control capabilities has been prevented by fundamental limitations of amplitude-based myoelectric neural interfaces, which have remained virtually unchanged for over four decades. Consequently, nearly 23% of adults and 32% of children with major traumatic or congenital upper-limb loss abandon regular use of their myoelectric prosthesis. To address this healthcare need, we have developed a noninvasive neural interface technology that maps natural motor unit increments of neural control and force into biomechanically informed signals for improved prosthetic control.Approach.-Our technology, referred to as motor unit drive (MU Drive), utilizes real-time machine learning algorithms for directly measuring motor unit firings from surface electromyographic signals recorded from residual muscles of an amputated or congenitally missing limb. The extracted firings are transformed into biomechanically informed signals based on the force generating properties of individual motor units to provide a control source that represents the intended movement.Main results.-We evaluated the characteristics of the MU Drive control signals and compared them to conventional amplitude-based myoelectric signals in healthy subjects as well as subjects with congenital or traumatic trans-radial limb-loss. Our analysis established a vital proof-ofconcept: MU Drive provides a more responsive real-time signal with improved smoothness and more faithful replication of intended limb movement that overcomes the trade-off between performance and latency inherent to amplitude-based myoelectric methods.Significance.-MU Drive is the first neural interface for prosthetic control that provides noninvasive real-time access to the natural motor control mechanisms of the human nervous system. This new neural interface holds promise for improving prosthetic function by achieving 4
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