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Dispersive acoustic metamaterials (AMMs) with graded refractive index is promising in acoustic sensing enhancement by providing acoustic rainbow trapping (ART) effect. However, previous solutions to this type of AMMs are based on 2D unit cells that require infinite or very large size in the third dimension; they cannot integrate with conventional acoustic sensors without deteriorating the ability of acoustic wave manipulation. In order to advance their practical applications, this paper proposes an axisymmetric, microphone embedded AMM device with gradient profile as well as gradient plate thickness and gradient gap width along the axis. Both numerical simulations and experiments are performed to examine the ART effect of the proposed AMM device and demonstrate its excellent capacity of acoustic pressure amplification and multi-band acoustic filtering. Deploying a microphone in the gap does not disturb the acoustic field, so the proposed AMM sensor experimentally achieves remarkable acoustic gains over one order of magnitude. Sound waves of different frequencies are picked up by microphones at different gaps, realizing the real-time spatially splitting of the sound spectrum. The proposed AMM provides excellent immunity to broadband noise and a little weak suppression of the single tone noise while significantly amplifying acoustic signals of a given frequency.

The RAS-associated domain family 9 (RASSF9), a RAS-associated domain family gene, is expressed in a variety of tissues. However, its roles in tumorigenesis, particularly in non-small cell lung cancer (NSCLC), are still not understood well. In the present study, we aimed to examine the potential roles of RASSF9 in NSCLC and the underlying mechanisms. Our data showed that RASSF9 expression was upregulated in NSCLC tissues and cell lines. Increased expression of RASSF9 promotes NSCLC cell proliferation. On the contrary, knockdown of RASSF9 represses cell proliferation. Moreover, the effects of RASSF9 on NSCLC cell proliferation were further confirmed in vivo by using a subcutaneous tumor model. Mechanistically, pharmacological intervention studies revealed that the MEK/ERK axis is targeted by RASSF9 for transducing its regulatory roles on NSCLC cell proliferation. Collectively, our data indicate that RASSF9 plays a key role in tumorigenesis of NSCLC by stimulating tumor cell proliferation, which relies on activation of the MEK/ERK axis. Thus, RASSF9 might be a druggable target for developing novel agents for treating NSCLC.

Exploitation in improving the performance of underwater acoustic detection is endless. Acoustic metamaterials (AMMs), which have been rapidly emerging in recent years, can improve underwater acoustic detection performance through extraordinary acoustic wave manipulation to adapt to the ever-increasing new demands and challenges of water acoustic detection, such as the growing sophisticated, long-range, and high efficiency. In this paper, a gradient subwavelength structure is proposed inspired by similar structure AMMs in air; Simulations and experiments are carried out to study its feasibility and basic mechanism in underwater application. This type of AMM can trap a specific frequency of acoustic waves at a particular location, while achieving the amplification of acoustic signals. This work opens up new prospects for the development of acoustic sensors based on metamaterials underwater.

This paper discussed the properties of Sm2O3 doped Ba0·85Ca0·15Ti0·90Zr0·10O3 (BCTZ–xSm) ceramics, which were prepared under the high temperature solid state reaction method. Piezoelectric and dielectric properties were observed from these BCZT–xSm ceramics later. Results revealed that the addition of Sm2O3 improved the piezoelectric property of BCZT ceramics significantly while affecting the Curie temperature (Tc) negatively. The BCZT ceramic with x = 0·2 mol.-% displayed an excellent electrical behaviour with excellent piezoelectric coefficient (d33 = 554 pC N− 1), planar coupling factor (KP = 53%), dielectric constant () and dielectric loss (tan δ = 1·8%) when sintered at 1450°C.

The multiscale phenomena is widely encountered in many nonlinear systems. Uncovering the dynamic characteristics of wind signals is a challenging problem with significant engineering applications. In order to solve this problem, we infer multiscale complex networks from wind speed signals in different seasons and heights. Specifically, we obtain a set of wind speed time series with different time scales using the coarse graining technique and then map those time series into multiscale complex networks using phase space reconstruction technique. Furthermore, we introduce three measures, i.e., clustering coefficient entropy (CCE), mean values of CCE with large scales, variances of CCE, to quantitatively analyze the dynamic and seasonal characteristics of wind signals. The results suggest that the CCE and its derivative index can accurately reflect the dynamic and seasonal changes of wind speed signals. The combination of complex network analysis and multiscale analysis provides an effective method for the detection of wind field behavior.

Time series denoising is an inevitable problem in many processes of time series analysis and forecasting. In this paper, a novel denoising method based on empirical mode decomposition (EMD) and dictionary learning is proposed. With the method of EMD, the original signals are adaptively decomposed into a finite number of intrinsic mode functions (IMF). Then we apply the wavelet thresholding method to distinguish the noise components and effective components of the original signals for subsequent dictionary learning. Next, the K-SVD dictionary training algorithm is used to learn inner structures and properties of effective compositions and noise respectively. Finally, the noise of time series can be removed using this combined method of EMD and dictionary learning. To demonstrate the effect of combination strategy, we compare the performance of our proposed method to two state-of-the-art algorithms, i.e., wavelet thresholding algorithm (WTA) and EMD thresholding algorithm (EMD-TA) with two commonly used evaluating metrics: signal-to-noise ratio (SNR) and correlation coefficient (CC). The results show that the proposed denoising method provides a competitive performance to the traditional denoising techniques.