Two unusual isosativene sesquiterpene derivatives, named dendronobilol A (1) and dendronobilside A (2), and two unusual sativene sesquiterpene derivatives, named dendronobilsides B (3) and C (4), had been isolated from the stems of Dendrobium nobile. The structures of all the compounds were established using spectroscopic methods and by comparison with literature data, and their absolute configurations were confirmed via single-crystal X-ray diffraction data and electronic circular dichroism (ECD) calculations. Dendronobilol A (1) and dendronobilside A (2) possessed a unique tricyclo[4.3.0.12, 8]decan ring system, while dendronobilsides B (3) and C (4) presented a unique tricyclo[4.4.0.02, 8]decan core carbon skeleton. The above two types of sesquiterpene derivatives had been isolated and purified from plants for the first time. Compounds 1 and 2 exhibited significant effects on glucose consumption with doses of 20 and 40 μmol/L in insulin-resistant HepG2 cells, and thus improve insulin resistance.

Deceptive jamming techniques against Synthetic Aperture Radar (SAR) are crucial for defending against hostile SAR reconnaissance and safeguarding our sensitive targets and regions. The current library of deceptive jamming templates for SAR is severely insufficient. The acquisition of measured and simulated SAR target images, which are key sources for the SAR deceptive jamming template library, can be problematic due to their lengthy acquisition period and high cost. In contrast, emulational optical target images are easier to generate. To address the challenge of quickly translating rich optical target images into SAR target deceptive jamming templates of diverse poses and high fidelity, this paper proposes a SAR target deceptive jamming template generation method based on Optical-to-SAR Cycle-consistent Adversarial Networks (OTSCycGAN). Firstly, this method utilizes deformable convolutional layers to partially replace the vanilla convolutional layers in the generator, enhancing the adaptive extraction ability of geometric transformation features between samples with varying azimuth angles, elevation angles, and target types in the dataset. Furthermore, a convolution kernel attention mechanism is introduced to dynamically adjust the receptive field sizes, increasing sensitivity for various target scales. Finally, we employ a loss function that incorporates Wasserstein GAN-Gradient Penalty (WGAN-GP) loss, hybrid L1-L2 loss, and focal frequency loss to advance the image quality at the pixel level. Our experiments on the constructed Target-Optical/SAR dataset demonstrate that this method brings a significant performance boost in SAR deceptive jamming template generation. The results of the stripmap SAR deceptive jamming, using deceptive jamming templates generated by OTSCycGAN, further confirm their effectiveness.

The existence of Comb Spectrum Modulation Jamming (CSMJ) degrades the imaging quality severely, which hinders the performance of Synthetic Aperture Radar (SAR). The frequency domain-notched filtering is applicable in dealing with CSMJ but would introduce severe signal loss. In this paper, we propose a novel method for CSMJ suppression based on a dual-path residual network with the attention mechanism (DPRA-Net). We use the attention mechanism to build inter-dependencies among local and global features in the frequency domain for improving the suppression performance of DPAR-Net. Consequently, the CSMJ suppression problem is transformed into an end-to-end mapping problem, which minimizes signal loss. The validity of our algorithm has been verified on the measured data collected by Sentinel-1.

With the improvement of the cognitive ability of the jammer, the electromagnetic countermeasure environment has become more complex, posing significant challenges to the anti-jamming performance of cognitive radar. Cognitive radar’s anti-jamming capabilities often depends on the correct generation of anti-jamming strategies, which makes the strategy generation a research hotspot cognitive radar anti-jamming. This paper proposes a cognitive radar anti-jamming strategy generation algorithm based on Dueling Double DQN (D3QN). The algorithm utilizes the state-value function and advantage function to estimate the quality of each radar echo and signal, thereby reducing the update error of the policy network. Additionally, to improve the speed of strategy generation, the proposed algorithm directly inputs the original echo into the policy network. Simultaneously, a jammer model with different working modes and interference type is established, and the corresponding jamming strategy is generated by the received echo, which is closer to the real confrontation environment. The effectiveness and robustness of the proposed algorithm are verified through high-fidelity adversarial experiments with variable jamming strategies.

With the emergence of novel and complex jamming types, jamming recognition as the primary step in radar anti-jamming is facing tremendous challenges. However, traditional methods experience significant difficulties in identifying increasingly complicated jamming types due to excessive manual dependence and inferior generalization performance. To alleviate the above challenges, we propose a novel recognition framework called Residual Attention Network (RA-Net). Specifically, we integrate channel and spatial attention to learn refined feature representations, which benefits the final recognition accuracy. To further optimize our proposed method, we introduce a polynomial loss to learn a robust feature space. Experimental results on simulated datasets with 19 types of jamming have demonstrated improvement of our proposed RA-Net over traditional methods.

Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

Aquaglyceroporins (AQGPs), including AQP3, AQP7, AQP9, and AQP10, are transmembrane channels that allow small solutes across biological membranes, such as water, glycerol, H2O2, and so on. Increasing evidence suggests that they play critical roles in cancer. Overexpression or knockdown of AQGPs can promote or inhibit cancer cell proliferation, migration, invasion, apoptosis, epithelial-mesenchymal transition and metastasis, and the expression levels of AQGPs are closely linked to the prognosis of cancer patients. Here, we provide a comprehensive and detailed review to discuss the expression patterns of AQGPs in different cancers as well as the relationship between the expression patterns and prognosis. Then, we elaborate the relevance between AQGPs and malignant behaviors in cancer as well as the latent upstream regulators and downstream targets or signaling pathways of AQGPs. Finally, we summarize the potential clinical value in cancer treatment. This review will provide us with new ideas and thoughts for subsequent cancer therapy specifically targeting AQGPs.

Radio frequency interference (RFI) is a severe issue for synthetic aperture radar (SAR), which is unavoidable for complex electromagnetic environment and the large imaging band. The presence of RFI would reduce the signal-to-noise ratio (SNR) and influence the image interpretation. This paper proposes a RFI mitigation method joint the low rank and double sparsity (LRDS-IM) characteristics in time-frequency (TF) domain. On the basis of TF analysis, we introduce the low rank and sparse characteristics to establish a precise RFI reconstruction model. In virtue of the alternate direction iteration strategy, we can separate the SAR echo into the RFI matrix and target signal matrix. Meanwhile, the well-focused SAR image is obtained cooperating with the state-of-the-art imaging algorithm. Finally, the RFI mitigation experiments of the measured SAR data verify the effectiveness of the proposed algorithm.

The presence of wide-band interference (WBI) seriously affects the imaging quality of synthetic aperture radar (SAR) as well as the target scattering characteristics, which reduces the accuracy of subsequent SAR image interpretation. This paper proposes a WBI interference mitigation algorithm for SAR based on generative adversarial network (IM-GAN), which can extract the time-frequency features of WBI in time-frequency domain to achieve low-loss reconstruction of the target echo signal. Firstly, the short-time Fourier transform (STFT) is used to transform the radar echo signal into the time-frequency domain. Then, the GAN is utilized to reconstruct the target signal by extracting the characteristic differences between WBI and target echoes. Finally, the time-frequency spectrogram of SAR echo signal after interference mitigation is transformed to the time domain using inverse short-time Fourier transform (ISTFT). The effectiveness of IM-GAN is verified by the WBI mitigation experiments of the measured dataset in TOPSAR mode recorded by the Sentinel-1B, and its superiority is demonstrated by the comparison results of different SAR interference mitigation methods.

The wideband interferences (WBIs) seriously degrade the quality of the synthetic aperture radar (SAR) image. Since the WBI occupies a larger bandwidth, it is difficult to mitigate it. Some existing WBI mitigation methods based on transform-domain analysis or filter design suffer from model mismatch and signal loss. In order to solve this problem, a method based on instantaneous frequency (IF) estimation and intrinsic chirp component decomposition (ICCD) is proposed to suppress WBI in this paper. First, a WBI-contaminated SAR echo is represented in the time-frequency domain by using the short-time Fourier transform (STFT). Then, the IF estimation is carried out for the WBI components. Finally, the WBI is extracted by using the ICCD. The experimental results verify the effectiveness of the proposed method.

Radio frequency interference (RFI) is a core issue of synthetic aperture radar (SAR), which significantly reduces the signal-to-noise ratio of SAR echo and image interpretation accuracy. Therefore, RFI mitigation plays an important role in the SAR imaging. Based on the azimuth echo analysis, some methods based on low rank characteristic or sparsity are introduced to reconstruct RFI and recover target echo signal. However, the property of RFI described in these models is not accurate enough, and there is a large signal loss problem. In this paper, an interference extracted algorithm is introduced for SAR data based on low rank and sparsity property. Via the measured SAR data analysis, a separation optimization model is established joint the low rank characteristic for RFI and sparsity assumption for the target echo signal. And the optimization problem can be solved iteratively by bilateral random projection and soft threshold mapping. Meanwhile, the mask procession is used to constrain the location of RFI in 2-dimensional domain to improve the reconstruction accuracy. Finally, the RFI mitigation experiments of the measured SAR data verify the effectiveness of the proposed algorithm.

Human activities classification based on micro-Doppler effect is a hot topic in the field of radar detection. However, when there are multiple humans, the motion mechanism of the multi-human target is complex, the components are mixed, and the micro motion features of each component are highly similar, which lead to difficulties in the classification of the multi-human activities. In order to overcome these problems, a multi-human separation method based on convolution neural network Mask generation and permutation invariant training (CNN-Mask-PIT) is proposed in this paper. In this method, the multi-human target is separated on the time-frequency (TF) images. The Mask matrixes are generated by CNN, and then the generated multiple Mask matrices are multiplied with the original TF image to obtain the separated TF images, so as to realize the effective separation of multi-human separation. The permutation invariant training (PIT) is introduced to solve the label arrangement problem by minimizing the separation error. Finally, the effectiveness and robustness of the proposed method are verified on the multi-human TF images generated based on motion capture data.

Perovskites decorated with catalytically active nanoparticles show promising potential in power generation and energy conversion. Here, with the aim of developing symmetrical solid oxide fuel cells (SSOFCs) possessing high performance, an ingenious approach of in situ exsolution of metallic Ru nanoparticles under SSOFC operating condition is proposed. In this study, a novel perovskite oxide with A-site deficiency Sm0.70Sr0.20Fe0.80Ti0.15Ru0.05O3-delta (SSFTR7020) is meticulously designed and successfully synthesized. At 800 degrees C, the polarization resistance (R-p) of SSFTR7020 cathode is only 0.13 Omega cm(2), since the adidtionally formed oxygen vacancies contribute to the oxygen reduction reaction. Moreover, it is found that numerous metallic Ru nanoparticles are exsolved from SSFTR7020 perovskite support upon reduction, while no partilcle can be observed for stoichiometric SSFTR perovskite. This is because the artificially introduced deficiency can make exsolutsion become more dynamic. The maximum power output of symmetrical cell SSFTR7020 vertical bar Sm0.2Ce0.8O1.9 (SDC)vertical bar SSFTR7020 reaches 476 mW cm(-2) after operation similar to 96 h at 800 degrees C in humidified hydrogen.

High-quality deceptive jamming template generation is critical to high-fidelity deceptive jamming for synthetic aperture radar (SAR). However, SAR imaging results of a target vary greatly with different observation scenarios and performing parameters. To achieve higher-fidelity deceptive jamming performance, the jammer must have the capacity of intelligently generating a deceptive jamming template matched with a practical SAR scenario. In this paper, we propose a deceptive jamming template generative adversarial network (DJTGAN). The DJTGAN consists of a deceptive jamming template generative network and a discriminative network. The generative network combines low-frequency contents and high-frequency details of a target to boost the performance of the deceptive jamming template generation. Meanwhile, the discriminative network adopts the PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template. Several real datasets are utilized to verify the effectiveness and superiority of the proposed DJTGAN. Moreover, the strip SAR and TOPSAR deceptive jamming results utilizing the deceptive jamming templates generated by DJTGAN further validate the effectiveness of the DJTGAN. (C) 2020 Elsevier B.V. All rights reserved.

In this paper, a deceptive jamming template generative adversarial network (DJTGAN) is proposed, which can intelligently generate high-fidelity deceptive jamming template matched with the practical SAR scenario. The DJTGAN consists of a deceptive jamming template generative network and a discriminative network. The generative network combines low-frequency content and high-frequency details of the target, and the discriminative network adopts PatchGAN architecture to capture local texture statistics to improve the fidelity of the deceptive jamming template. The MSTAR dataset is utilized to verify the effectiveness of the proposed DJTGAN. Moreover, the strip SAR deceptive jamming experiment based on the deceptive jamming templates generated by DJTGAN is done to further validate the effectiveness of the DJTGAN.

The existence of wideband interference (WBI) would seriously reduce the SAR imaging quality and the following image interpretation accuracy. However, it is difficult to mitigate WBI owing to its large bandwidth and severe overlapping with useful signal. This paper proposes a WBI mitigation algorithm based on variational Bayesian inference. Firstly, a low-rank matrix factorization model for WBI is established according to the low rank characteristics of WBI in time-frequency domain. Then, we build the Bayesian posterior probability model for the low rank matrix factorization. Finally, the variational Bayesian inference is utilized to estimate the model parameters and reconstruct the WBI. The experimental results of WBI mitigation using measured WBI data acquired by the sentinel-1 satellite have verified the effectiveness of the proposed algorithm.

For accurate predictions of the combustion characteristics in constant volume combustion bombs (CVCBs), the chemical kinetics mechanism is one of the most important factors because it determines the ignition timing and oxidation rate of fuels. By introducing an n-heptane oxidation mechanism for the simulation of auto-ignition in both CVCBs and shock tubes, the correlation of ignition behaviors between in CVCBs and shock tubes was investigated in this study. It was found that the combustion of CVCBs is dramatically affected by the fuel/air mixing and the heat release processes. CVCBs and shock tubes demonstrate similar reaction pathways for the occurrence of auto-ignition, and the trigger of the low-temperature heat release is a critical factor that determines the ignition delay time in CVCBs. By analyzing sensitivity coefficients of the ignition timing on reaction pathways, a similarity factor is proposed to reveal the correlation of the ignition characteristics between CVCBs and shock tubes. It is shown that operating conditions of 800-950 K in stoichiometric fuel/air mixtures in shock tubes are most relevant for auto-ignition in CVCBs.

The investigation and classification of space targets are significant issues in spatial situational awareness, and the radar plays an important role due to its superior real-time performance, luxuriant receiving information and all-weather, all-day capabilities. Electromagnetic characteristics and classification methods of regular scatters are the foundational problems for space targets investigation and classification. Aiming at the deficiencies of complex feature extracting, local parameters sharing and difficult network expanding in the existing methods, this paper proposed a multi-mode fusion and classification method for space targets based on convolutional neural network (CNN). Firstly, a multi-mode database of regular scatterers is constructed. Secondly, the spatial domain-based fusion is performed to fuse various characteristics. Then, feature vectors are generated through CNN. Finally, the classifier and the loss function are designed to classify space targets. Experiments show that the proposed method achieves the highest classification accuracy compared to other fusion and none-fusion methods.