The development of a difference-in-differences estimator is a new move in patent policy evaluation research. However, such an estimator neglects the possibility that academic patent activities follow a spatial autoregressive process with respect to the dependent variable. The objective of this study was to propose a spatial difference-in-differences estimator accounting for possible spatial spillover effects. In this study, an empirical analysis of a sample of 31 Chinese provinces from 2010 to 2019 indicates that an incentive patent policy has a positive impact on the output and commercialization of academic patents, with positive effects also spilling over into neighboring provinces. This study further found that incentive patent policies play a placebo role in academic patent activities. Provincial patent policies are merely a proxy for other variables that characterize the systemic differences between provinces that implement patent policies and those that do not. Therefore, the promotion of academic patent activities cannot be attributed to policy incentives.
<p id="p00005">Comprehensively understanding the mechanisms underlying the formation of ecosystem services is a prerequisite for maintaining the sustainable supply of ecosystem services. Plant functional traits directly participate in a variety of ecosystem processes, which in turn affect the supply of ecosystem services. Revealing the relationship between plant functional traits and ecosystem services is an important way to understand the formation mechanism of ecosystem services. Based on a systematic literature review, 86 papers on plant functional properties and ecosystem services were retrieved in the Web of Science database, and data for 466 pairs of plant functional traits and ecosystem services and 83 plant functional traits were collected. The current status of research on the relationship between plant functional traits and ecosystem services was revealed. Moreover, the main plant functional traits that affect different ecosystem services and their mechanisms underlying their impacts were also demonstrated. The results show that the research on the relationship between plant functional traits and ecosystem services mostly focuses on natural ecosystems such as grasslands and forests. Most of these studies focus on ecosystem products providing and supporting services, including biomass, net primary productivity, and soil fertility. Based on the impacts of plant functional traits on different ecosystem services, the plant functional traits can be clustered into five categories: soil-conservation-related traits, water-cycle-related traits, ecosystem- multifunction- related traits, product-providing-related traits, and pollination-biocontrol-related traits. The impacts of climate change, human activities, and variations in spatial and temporal scales on the relationship between plant functional traits and ecosystem services need to be further explored.</p>
The potential effect of short-term exposure to air pollution on mitochondrial DNA (mtDNA) methylation remains to be explored. This study adopted an experimental exposure protocol nested with an intervention study on L-arginine (L-Arg) supplementation. Participants walked along a traffic road for 2h in the last day of a 14-day intervention to investigate the effects of short-term personal exposure to air pollution on platelet mtDNA methylation and the possible modifying effects of L-Arg supplementation. Results showed that short-term personal exposure to air pollutants was associated with hypomethylation in platelet mtDNA. Specifically, 2-h fine particulate matter (PM2.5) exposure during the outdoor walk was significantly associated with hypomethylation in mt12sRNA and ATP6; 24-h black carbon (BC) exposure from the start of the walk till next morning and 2-h NO2 exposure was significantly associated with hypomethylation in ATP8; and 24-h PM2.5 exposure, 2-h and 24-h BC exposure were significantly associated with hypomethylation in D-loop. Supplementation with L-Arg could mitigate the air pollution effects on methylation in ATP8 and D-loop. These findings suggest that platelet mtDNA methylation may be sensitive effect biomarker for short-term exposure to air pollution and may help deepen the understanding of the epigenetic mechanisms of adverse cardiovascular effects of air pollution.
When using Laguerre and Hermite spectral methods to numerically solve PDEs in unbounded domains, the number of collocation points assigned inside the region of interest is often insufficient, particularly when the region is expanded or translated in order to safely capture the unknown solution. Simply increasing the number of collocation points cannot ensure a fast convergence to spectral accuracy. In this paper, we propose a scaling technique and a moving technique to adaptively cluster enough collocation points in a region of interest in order to achieve fast spectral convergence. Our scaling algorithm employs an indicator in the frequency domain that both is used to determine when scaling is needed and informs the tuning of a scaling factor to redistribute collocation points in order to adapt to the diffusive behavior of the solution. Our moving technique adopts an exterior-error indicator and moves the collocation points to capture the translation. Both frequency and exterior-error indicators are defined using only the numerical solutions. We apply our methods to a number of different models, including diffusive and moving Fermi--Dirac distributions and nonlinear Dirac solitary waves, and demonstrate recovery of spectral convergence for time-dependent simulations. A performance comparison in solving a linear parabolic problem shows that our frequency scaling algorithm outperforms the existing scaling approaches. We also show our frequency scaling technique is able to track the blowup of average cell sizes in a model for cell proliferation. In addition to the Laguerre and Hermite basis functions with exponential decay at infinity, we also successfully apply the frequency-dependent scaling technique into rational basis functions with algebraic decay at infinity.
Particulate composites are one of the widely used materials in producing numerous state-of-the-art components in biomedical, automobile, aerospace including defence technology. Variety of modelling techniques have been adopted in the past to model mechanical behaviour of particulate composites. Due to their favourable properties, particle-based methods provide a convenient platform to model failure or fracture of these composites. Smooth particle hydrodynamics (SPH) is one of such methods which demonstrate excellent potential for modelling failure or fracture of particulate composites in a Lagrangian setting. One of the major challenges in using SPH method for modelling composite materials depends on accurate and efficient way to treat interface and boundary conditions. In this paper, a master-slave method based multi-freedom constraints is proposed to impose essential boundary conditions and interfacial displacement constraints in modelling mechanical behaviour of composite materials using SPH method. The proposed methodology enforces the above constraints more accurately and requires only smaller condition number for system stiffness matrix than the procedures based on typical penalty function approach. A minimum cut-off value-based error criteria is employed to improve the computational efficiency of the proposed methodology. In addition, the proposed method is further enhanced by adopting a modified numerical interpolation scheme along the boundary to increase the accuracy and computational efficiency. The numerical examples demonstrate that the proposed master-slave approach yields better accuracy in enforcing displacement constraints and requires approximately the same computational time as that of penalty method. (C) 2020 China Ordnance Society. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co.
Electrical synapses provide rapid, bidirectional communication in nervous systems, accomplishing tasks distinct from and complementary to chemical synapses. Here, we demonstrate an artificial electrical synapse based on second-order conductance transition (SOCT) in an Ag-based memristor for the first time. High-resolution transmission electron microscopy indicates that SOCT is mediated by the virtual silver electrode. Besides the conventional chemical synaptic behaviors, the biphasic plasticity of electrical synapses is well emulated by integrating the device with a photosensitive element to form an optical pre-processing unit (OPU), which contributes to the retinal neural circuitry and is adaptive to ambient illumination. By synergizing the OPU and spiking neural network (SNN), adaptive pattern recognition tasks are accomplished under different light and noise settings. This work not only contributes to the further completion of synaptic behaviour for hardware-level neuromorphic computing, but also potentially enables image pre-processing with light adaptation and noise suppression for adaptive visual recognition.
Abstract Recent studies indicate that synaptic scaling is a vital mechanism to solve instability risks brought by the positive feedback of synaptic weight change related with standalone Hebbian plasticity. There are two kinds of synaptic scaling in the neural network, including local scaling and global scaling, both important for stabilizing the neural function. In this paper, for the first time, local synaptic scaling is emulated based on the MoS2 neuristor. The first-principle calculation reveals that synaptic scaling achieved by the neuristor is associated with an internal residual Li+-related weak dynamical process. Experimental results show the potential of achieving global synaptic scaling by the same device. Moreover, inspired by the synaptic scaling in the human brain, a new method of weight mapping called weight scaling mapping (WSM) is proposed to improve the stability of an artificial neural network (ANN). The simulation results indicate that WSM can improve the accuracy and anti-noise ability of the network compared with the traditional mapping method. These findings provide new insight into bionic research and help advance the construction of stable neuromorphic systems.