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Microwave photonic technology has transformed conventional radar systems, enabling the imaging of critical components on non-cooperative airborne targets like engines and wings. However, the wide bandwidth and extensive rotation angle of Microwave Photonic Inverse Synthetic Aperture Radar (MWP-ISAR) present significant challenges for precise imaging. Correcting the spatially variant characteristics of high-order range cell migration (RCM) in MWP-ISAR is particularly difficult. Additionally, variations in the scattering characteristics of different structural components introduce unknown phase errors, complicating existing imaging algorithms. To address these challenges, a novel approach is introduced. The core of this algorithm uses the energy trajectory of the echo to accurately estimate the motion parameters of non-cooperative targets. This method ensures high-precision correction of multi-order RCM through the reconstructed motion trajectory and simultaneously extracts and compensates for unknown structural phase errors. The approach begins by establishing the MWP-ISAR echo model, detailing the trajectory reconstruction and multi-order RCM correction algorithm. Despite correcting RCM, residual unknown phase errors in the echo continue to impact imaging quality. To mitigate this, a phase compensation algorithm is introduced. Based on preliminary imaging results, a separation processing algorithm isolates echoes from each structural component. Subsequently, an autofocus algorithm is employed to precisely estimate phase errors for each component. Ultimately, the method combines high-precision imaging results for all components, yielding a well-focused target image.

ABSTRACT The inevitable trend in the development of construction 3D printing is to change from printing the reduced size model to printing the full-size structure. While full-size printing is a [...]

Polycyclic (hetero)aromatic hydrocarbons (PAHs) have emerged as a focal point in current interdisciplinary research, spanning the realms of chemistry, physics, and materials science. Possessing distinctive optical, electronic, and magnetic properties, these π-functional materials exhibit significant potential across diverse applications, including molecular electronic devices, organic spintronics, and biomedical functions, among others. Despite the extensive documentation of various PAHs over the decades, the efficient and precise synthesis of π-extended PAHs remains a formidable challenge, hindering their broader application. This challenge is primarily attributed to the intricate and often elusive nature of their synthesis, compounded by issues related to low solubility and unfavored stability.

The intervention process for children with autism spectrum disorder (ASD) is inextricably associated with their parents' mental health problems, such as hopelessness, which may adversely affect resilience and indirectly impact the effectiveness of interventions for their children. Hence, the motivation to help parents of children with ASD reduce hopelessness prompted us to conduct the present study and explore the interrelationship between hopelessness symptoms and resilience. This study evaluated hopelessness and resilience using the Beck Hopelessness Scale (BHS) and the Connor–Davidson Resilience Scale (CD‐RISC). Participants met the criteria for their children's ASD diagnosis by a psychiatrist (N= 448; 54.69% mothers; Meanage= 34.59 years, SDage= 4.94 years). Moreover, we used symptom network analysis to examine the variability in network structure between fathers and mothers. The flowfunction was applied to examine which hopelessness symptoms were directly or indirectly associated with resilience. The results showed that #BHS11 (i.e., unpleasantness‐ahead) was the central symptom found in the network structure for all parents and fathers, while #BHS17 (i.e., no‐future‐satisfaction) was the central symptom in the network structure for mothers. Additionally, #BHS6 ([NOT] expect‐to‐succeed) was directly and positively associated with resilience in all three network structures (i.e., all parents, fathers, and mothers). The results of the present study provide evidence that influential symptoms should be addressed and offer guidance for further interventions to reduce hopelessness and enhance resilience among parents of children with ASD.

Background: This study aimed to identify large duplications (>5 Mb) that are harmless through long-term clinical follow-ups of fetuses and phenotype analyses of carrier family members. Methods: We retrospectively analyzed fetuses undergoing prenatal diagnosis and who had >5 Mb chromosomal duplications. Routine karyotyping and single-nucleotide polymorphism array analysis were performed to identify the source and location information of the duplicated segments. Genotype-phenotype analyses were conducted based on genetic information and phenotypes during postnatal follow-up. Results: Eight eligible cases were included. All fetuses carried maternal or paternal duplications ranging in length from 5.3 to 12.2 Mb. The locations were as follows: 2q32.3q33.1 (Chr2:192322509–199548704), 4q22.1 (Chr4: 88347368–93602855), 4q34.2q35.2 (Chr4:176956406–189189971), 4q34.3q35.2 (Chr4:180613345–189353740), 5p14.3p14.1 (Chr5:19093749–28557664), 10q22.2q23.2 (Chr10:77448435–88786593), 12q21.31q21.32 (Chr12:81983257–87322734), and 13q14.11q14.2 (Chr13: 40825382–47633710). Karyotyping revealed that these duplications occurred within their respective chromosomal regions, except in pedigrees 6 and 7. In the eight pedigrees, the coordinates and lengths of duplicated segments in family members were matched with those in fetuses. Neither the fetuses nor other carriers were clinically symptomatic. Conclusion: Our findings revealed that the eight pedigrees carrying duplications >5 Mb were asymptomatic, providing new data to inform genetic counseling for the observed segments. Impact:

We focused on unrelated fetuses among eight pedigrees who carried duplications of different chromosomal segments. These duplications had been stably transmitted through 2 or 3 generations of normal individuals. Importantly, phenotypic abnormalities were lacking, which was unexpected given that the maximum segment size was approximately 12.2 Mb.

We found that duplications in these regions were benign in the context of prenatal genetic counseling. These results provide a foundation for addressing genotype-phenotype correlations. To our knowledge, this is the first description of normal phenotypes in individuals with duplications in these regions.

High-resolution synthetic aperture radar (HR-SAR) is extensively used in ground remote sensing applications, including disaster monitoring and resource crop assessment. This is attributed to its exceptional high-resolution imaging capabilities. Microwave photonics technology plays a crucial role in enhancing the performance of SAR systems. It enables the direct emission of ultra-wideband signals across multiple frequency bands. However, this advancement also makes microwave photonic synthetic aperture radar (MWP-SAR) susceptible to complex and diverse electromagnetic interference. Particularly, it is vulnerable to radio frequency interference (RFI), and this interference seriously affects the high-resolution imaging results. In order to solve these problems, an MWP-SAR imaging algorithm based on depth spectral sensing and spectral reconstruction that is suitable for complex electromagnetic environments is proposed. Simultaneously, a sensing-removing-recovering (SRR) anti-interference imaging framework is established. To address the high-precision detection of various unknown interferences in MWP-SAR echoes, a deep learning network is constructed. This network is based on spectrum sensing theory and achieves an interference detection probability greater than 98% in various interference scenarios. Subsequently, targeting the continuous spectrum loss in the MWP-SAR signal after interference removal, a signal reconstruction algorithm is proposed. This algorithm employs Toeplitz transformation for signal loss scenarios, facilitating the recovery of signals with extensive spectrum loss post-interference removal. The factor group sparse regularization (FGSR) algorithm is used to quickly solve the signal recovery problem. Through simulation and measured data processing, the superiority of the proposed algorithm over existing interference suppression imaging algorithms is demonstrated.

Deconvolution methods can be applied in airborne scanning radar to enhance its angular resolution for improving the collision avoidance ability in the forward-looking direction. However, as the movement speed of the airborne platform increases, the traditional convolution signal model cannot be applied because of the model errors in the amplitude profile and Doppler phase. In this article, an accurate profile-phase deconvolution method is proposed to achieve scanning radar forward-looking super-resolution imaging, particularly for high-speed platforms. On one hand, a profile-phase convolution (PPC) model is established by analyzing the influence of high-speed platform on echo amplitude profile and Doppler phase. The proposed model accurately captures the variation of beam dwell time caused by the coupling of platform motion and beam scanning, which directly affects the echo amplitude profile. On the other hand, relying on the proposed PPC model, an adaptive regularization (AR) deconvolution method is derived to avoid hyperparameter selection. Point-target and surface-target results demonstrate that the proposed PPC model and the AR deconvolution method are competent for super-resolution imaging on high-speed platforms.

Real-time detection of high-speed moving targets is a significant and wildly applicable subject in the field of target detection. It is extremely important in both civilian and military fields. However, most of the current mainstream object detection models have difficulties in fulfilling requirements of accuracy and speed simultaneously. In this article, we propose a lightweight deep feature learning network (LDFL-Network), which can accurately complete detection of high-speed objects in real time. The network utilizes Ghost convolution instead of ordinary convolution to extract image features because of its superior performance in developing a lightweight and efficient network architecture. Utilizing the principles of Ghost convolution, we propose the I-GhostConv module, which employs the involution algorithm to enable flexible feature extraction and improves the network’s expressive capacity. In addition, we propose a novel feature fusion method: adaptive cross-fusion structure. This structure adaptively allocates weights to feature maps and integrates various feature information crossly and comprehensively. Experiments are conducted on two representative datasets for high-speed motion target detection. The results of experiments demonstrate that the proposed network can accurately detect the targets in high-speed motion and achieve accuracy speed higher than mainstream target detection models.

Frequency modulation continuous wave (FMCW) radar has been paid much attention in forward-looking navigation applications because of its no-blind-range capability. However, after dechirp processing, pulse interference signals may appear in the range time domain, which seriously pollutes the whole radiation direction. In this article, a cross-domain low-rank and sparse (CD-LRS) optimization framework is proposed to enhance the angular resolution and suppress the pulse interference signals based on the scanning mode of its antenna. On the one hand, to cut off and recover the polluted signals, a low-rank spectra reconstruction approach is proposed by utilizing the low-rank characteristic of the Hankel matrix formed by the interference-rejected data in the range time domain. On the other hand, to suppress the residual interference signal and enhance the angular resolution simultaneously, an adaptive sparse reconstruction method is formed in the azimuthal time domain by adopting an alternating direction method of multipliers (ADMMs)-based solver. Compared with the traditional anti-interference methods, the proposed framework can enhance the angular resolution and suppress the interference signals based on the signal features in different domains. Simulations and experimental results are applied to verify the effectiveness of the proposed framework.

Nucleic acid extraction is the first step in nucleic acid analysis. Currently, designing an efficient, robust, and automated nucleic acid extraction system has been a challenging topic. Moreover, the nucleic acid extraction system is commonly designed based on the magnetic bead method, which is the most widely used in clinical practice, yet there is a lack of methods to characterize and measure the beads and fluid motion processes. This leads researchers to rely on biological experimental results for evaluating the performance of the system. To address these issues, we proposed a highly efficient and automated nucleic acid extraction system, including an ultrasound-assisted extraction instrument and a microfluidic chip. This instrument integrated ultrasound technology throughout the nucleic acid extraction process, significantly enhanced efficiency while eliminating the need for complex functional modules or additional reagent materials. It can complete extraction within 12 min, whereas manual methods or other instruments typically take 30 min and more. Furthermore, we presented a noncontact flow pattern measurement approach that combines acoustic knowledge and machine learning, which can be used to capture fluid motion within a miniature confined chamber under ultrasonic stimulation. Multidimensional experiments were conducted on the system. First, we annotated 1946 flow field images and achieved an average recognition precision of 96.4%. Based on measurements and analysis of the actual flow fields, enhancements were made to the ultrasound stimulation strategy, which significantly improved the system performance. As a final point, the biological experimental verification results indicated that the system performed comparably to the “gold standard” manual nucleic acid extraction method. The coefficient of variation (CV) remained under 5%, signaling excellent stability. In particular, it demonstrated a 100% detection rate for low-concentration samples ( $\mathrm {1.7 \times }{10}^{3}$ copies/mL).

The ability of wide-area and fast-revisit observation with beam scanning in both azimuth and elevation dimensions has allowed the airborne real aperture radar to be a hotspot in applications of aircraft formation identification and airborne warning and control system. However, the azimuth-elevation 2-D angular resolution is limited by antenna aperture size. When dealing with azimuth-elevation 2-D angular super-resolution of airborne real aperture radar for aerial targets, the existing super-resolution methods suffer from three problems: model mismatch, range and azimuth-elevation coupling, and high computational complexity, which lead to poor resolution and efficiency for the aerial target resolving. To circumvent this problem, this article proposes an azimuth-elevation 2-D joint super-resolution strategy for airborne real aperture radar. First, we establish an airborne azimuth-elevation 2-D scanning signal model, which accurately represents the migration of velocity in different directions. Then, an azimuth-elevation 2-D range migration correction method is proposed, which eliminates the range and azimuth-elevation coupling. To improve the efficiency of target resolution, a fast conjugate gradient iterative adaptive method is finally derived to rapidly reconstruct the azimuth-elevation 2-D target scatterings. Simulated and real datasets demonstrate that the proposed method is not only suitable for airborne 2-D angular super-resolution, but also achieves a competitive resolution capability with quite lower computational complexity.

Ambient temperatures trigger hospitalisation, mortality, and emergency department visits for myocardial infarction (MI). However, nonoptimum temperature-related risks of fatal and nonfatal MI have not yet been compared.

Radar countermeasure (RCM) is increasingly important in modern warfare. Fast and accurate jamming decision made by RCM systems can provide timely electronic protection for important or high-value targets. To quickly find a jamming policy against the multifunctional radar, a jamming policy generation scheme via heuristic programming reinforcement learning is proposed in this article, where the jamming strategy can be dynamically adjusted using interactive self-learning. First, the relationships between radar operation modes and jamming modes are investigated under self-defense jamming in an air-to-air RCM scenario, which is modeled as a Markov decision process. Especially, the potential energy is employed to construct a heuristic reward function that avoids the jammer getting stuck in the local jamming action space. In addition, a model relating to radar state transition is derived using the real experience from the jammer–radar interaction, which narrows the state-action space. The simulated experience programmed by this model can accelerate to update the value function. The proposed method decreases sample complexity while increasing sample utilization, reducing the time for jamming decision-making. Numerical experimental results demonstrate that the proposed method has superior jamming decision performance compared with prevailing methods, effectively improving the real-time performance and interference effectiveness of RCM systems.

Pulse diagnosis equipment used in Traditional Chinese Medicine (TCM) has long been developed for collecting pulse information and in TCM research. However, it is still difficult to implement pulse taking automatically or efficiently in clinical practice. Here, we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment, which ensures high efficiency, reliability and data integrity of pulse diagnosis information. A four-degree-of-freedom pulse taking platform together with a wrist bracket can satisfy the spatial positioning and angle requirements for individually adaptive pulse acquisition. Three-dimensional reconstruction of a wrist surface and an image localization model are combined to provide coordinates of the acquisition position and detection direction automatically. Three series elastic joints can not only simulate the TCM pulse taking method that “Three fingers in a straight line, the middle finger determining the ‘Guan’ location and finger pulp pressing on the radial artery,” but also simultaneously carry out the force-controlled multi-gradient pressing process. In terms of pulse information integrity, this proposed protocol can generate rich pulse information, including basic individual information, pulse localization distribution, multi-gradient dynamic pulse force time series, and objective pulse parameters, which can help establish the fundamental data sets that are required as the pulse phenotype for subsequent comprehensive analysis of pulse diagnosis. The implementation of this scheme is beneficial to promote the standardization of the digitalized collection of pulse information, the effectiveness of detecting abnormal health status, and the promotion of the fundamental and clinical research of TCM, such as TCM pulse phenomics.

Compared with conventional microwave regime radars, microwave photonic (MWP) radar is capable of transmitting extremely large bandwidth signals, wherein the frequencies of such signals distribute across multiple bands. In practical applications, the large bandwidth of MWP radar may be split into multiple discrete subbands due to various considerations such as antijamming, resource-saving, and communication band avoidance. Nonetheless, it leads to the fact that MWP radar suffers from the challenging problems of sidelobes’ elevation and main lobes’ broadening. These problems will affect the image quality seriously. To address this issue, a spectrum recovery algorithm based on an improved truncated Schatten- $p$ norm and sparse regularizer-alternating direction method of multipliers (TSPN-ADMM) network is proposed in this article. This algorithm can efficiently recover the lost spectrum in MWP radar applications and further improve the imaging quality of the MWP radar. In the lost spectrum recovery problem, the parameters of the recovery algorithm directly determine the recovery performance. The different forms of lost spectrum possessed by MWP radar make the selection of parameters for the spectrum recovery algorithm extremely difficult. As a consequence, in this article, the spectrum recovery problem for MWP radar can be reformulated into a matrix completion problem by exploiting its joint sparsity and low-rankness. Based on the traditional TSPN-ADMM algorithm, an improved TSPN-ADMM-Net approach is proposed using the algorithm unrolling technique, wherein the hyperparameters in the TSPN-ADMM algorithm are optimized in an end-to-end training manner. Consequently, the algorithm proposed in this article can achieve excellent recovery results when dealing with the multiple spectrum missing situations existing in MWP radar. The effectiveness of the algorithm is verified by a combination of numerical simulations and actual MWP radar data.

Multistatic airborne synthetic aperture radar (MuA-SAR) systems can achieve high-resolution imaging in a short time by fusing observation data from multiple radar platforms. However, its imaging quality relies on a rigorous design of the spatial configuration (SC) of each platform, mainly including the relative spatial separation and velocity. The rigorously designed SCs make it difficult to obtain in actual flight and weaken the flexibility advantage brought by the airborne platforms. Therefore, it is meaningful and necessary to explore a new SC design method to obtain relaxed SCs under the condition of ensuring imaging quality. In this article, to relax the limitations of SC, an optimal design method for MuA-SAR SC is proposed. First, the relationship between the SC, wavenumber spectrum (WS) distribution, and imaging performance is established, and it visually reveals the configuration limitations. Second, an optimized search space of SC is defined by the peak-to-sidelobe ratio (PSLR) to relax the space to compromised configurations. Finally, the SC design problem is transformed into a constrained multiple objective optimization problem (CMOP) which is solved by the multiple objective particle swarm optimization (MOPSO) algorithm. The simulation results show that the proposed method can still obtain the optimized SC beyond the strictly restricted configuration space, which expands the SC limitations of the MuA-SAR system.

Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.

The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input–multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this article presents a subaperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method but also allows for computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on an XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (TX) and 16 receive (RX) antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.

Scale information is a significant index for target measurement by real aperture radar (RAR). However, the measured target scale information by RAR is inaccurate because of the limited angular resolution. In this article, to enhance the scale measurement ability of RAR, a generalized hybrid regularization (GHR) approach is proposed by combining the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm. On the one hand, the GHR approach is proposed to simultaneously enhance the angular resolution and the scale information of targets by combing the generalized regularization norms. The GS regularization norm can improve the reconstructed angular resolution due to its sparsity over the L1 norm. The GTV regularization norm can preserve the steep target contour because of its edge enhancement ability over the total variation (TV) norm. On the other hand, based on the GHR optimization function, an adaptive iterative reweighted (AIR) solver is proposed to reduce the number of manually selected regularization parameters, allowing for accurate scale information reconstruction. Simulations and experiments verify the performance of the proposed method. Based on the proposed approach and solver, the target scale information can be accurately observed.

Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target recognition (ATR) algorithms perform poorly with limited training data availability, resulting in a critical need to increase SAR ATR performance. In this study, a new method to improve SAR ATR when training data are limited is proposed. First, an embedded feature augmenter is designed to enhance the extracted virtual features located far away from the class center. Based on the relative distribution of the features, the algorithm pulls the corresponding virtual features with different strengths toward the corresponding class center. The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features. Second, a dynamic hierarchical-feature refiner is proposed to capture the discriminative local features of the samples. Through dynamically generated kernels, the proposed refiner integrates the discriminative local features of different dimensions into the global features, further enhancing the inner-class compactness and inter-class separability of the extracted features. The proposed method not only increases the amount of information available for supervised training but also extracts the discriminative features from the samples, resulting in superior ATR performance in problems with limited SAR training data. Experimental results on the moving and stationary target acquisition and recognition (MSTAR), OpenSARShip, and FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR performance of the proposed method in response to limited SAR training data.

Ship recognition in synthetic aperture radar (SAR) images is essential for many applications in maritime surveillance tasks. Recently, convolutional neural network (CNN)-based methods tend to be the mainstream in SAR recognition. Though considerable developments have been achieved, there are still several challenging issues toward superior ship recognition performance: 1) ships have a large variance in size, making it difficult to recognize ships by using single-scale features of CNN. 2) The SAR ship’s large aspect ratio presents an obvious geometric characteristic. However, standard convolution is limited by the fixed convolution kernel, which is less effective in processing elongated SAR ships. 3) Existing CNN classifiers with softmax loss are less powerful to deal with intraclass diversity and interclass similarity in SAR ships. In this article, we propose a task-specific hierarchically designed network with a spherical space classifier (HDSS-Net) to alleviate the above-mentioned issues. First, to realize SAR ship recognition with large size variation, a feature aggregation module (FAM) is designed to obtain a feature pyramid that has strong representational power at all scales. Second, a FeatureBoost module (FBM) is devised to provide rectangular receptive fields to refine the features generated by FAM. Finally, a novel spherical space classifier (SSC) is proposed to expand the interclass margin and compress the intraclass feature distribution by fully taking advantage of the property of spherical space. The experimental results on two benchmark datasets (OpenSARShip and FUSAR-Ship) jointly show that the proposed HDSS-Net performs better than classic CNN methods and novel SAR ship recognition CNN methods.

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical applications, the enormous data in high-frame-rate SAR system to obtain the multiframe images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this article proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., tensor alternating direction method of multiplier network (TADMM-Net), which is more computationally efficient in the imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor alternating direction method of multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging applications. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms, including higher performance in the case with low sampling amount, lower storage complexity, and no requirement for handcraft hyperparameters adjustment. The methodology was tested on high-frame-rate SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods in high-frame-rate SAR imaging applications.

ABSTRACTNon-cooperative aerial target classification is one of the most attractive but challenging tasks in radar remote sensing applications. Multiview high-range resolution profiles (HRRPs) of the aerial target contain abundant information and will benefit to classification. In this paper, a new aerial target classification method based on an end-to-end lightweight feature learning network (LFL-Net) with multiview HRRPs is proposed. The aerial target classification scenario using multiview HRRPs is first studied and modelled. Then a LFL-Net with multi-inputs and some distinct modules is designed to effectively learn the target classification information from the multiview HRRPs. Therefore, the proposed method can achieve accurate and reliable classification results under different signal-to-noise ratios (SNRs). Experimental results have shown the superiorities of the proposed non-cooperative aerial target classification method.

Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance. [ABSTRACT FROM AUTHOR]

ABSTRACTWith the aim of designating a destination’s stage of development by examining local residents’ responses to tourism, this study links local response theories and the Tourism Area Life Cycle (TALC) model, providing an easy-to-operate approach to evaluate a destination’s stage of development. By examining the local responses from two destinations in China with similar attractions and connecting their response features with the TALC model, the authors designate the two destinations studied as at consolidation and development stages, respectively. The study contributes to a deeper understanding of the features of destination development at various stages and highlights the importance of local responses as a key indicator that should be addressed in destination planning and management.

A highly chemoselective reaction between 5-aminoisoxazoles and α-diazocarbonyl compounds has been described. Both Wolff rearrangement and N–H insertion products can be obtained selectively by the judicious choice of reaction conditions. In the case of the Wolff rearrangement reactions, the N-isoxazole amides are accessed as the sole products under thermal conditions. On the other hand, α-amino acid derivatives of N-isoxazolescan be obtained through N–H insertion reactions in the presence of catalytic Rh2(Oct)4. Both reactions proceed under mild reaction conditions and feature a broad substrate scope.

In practical applications of array system, the incident signals may come from either circular or noncircular sources, or both. The problem of direction-of-arrival (DOA) estimation is herein addressed for both circular and noncircular wideband source signals. Based on the concept of effective rank, the aligned augmented effective noise subspace, spanned by the subordinate eigenvectors of the aligned array output augmented covariance matrix, is constructed for each scanning angle. It is shown that the aligned augmented effective noise subspace corresponding to a true DOA of signal is: 1) approximately orthogonal to a frequency-invariant one-dimensional subspace spanned by the aligned augmented transfer vector of the signal in the presence of multiple strictly noncircular sources; and 2) approximately orthogonal to a frequency-invariant two-dimensional subspace spanned by the aligned augmented transfer matrix of the signal in the presence of multiple circular or partially noncircular sources. This enables the noncircularity exploited wideband DOA estimation based on rank-reduction and norm-nulling. The performance of the proposed method has been evaluated and compared with some existing popular wideband DOA estimators by extensive simulations.

Magnesium silicate hydrate (M-S-H) samples were prepared utilizing reactive magnesia and quartz with six various Mg/Si molar ratios by the autoclaved curing system. The characteristics and microstructure of M-S-H samples were examined using XRD, TGA, FTIR, SEM and 29Si MAS NMR. Additionally, silica fume was utilized as the siliceous raw material to prepare the M-S-H samples that served as controls. The results showed quartz can be utilized as siliceous raw material for preparing M-S-H. Compared with the samples employing silica fume, the M-S-H samples utilizing quartz as the siliceous material had a layered structure and a significant amount of silicate polymerization. Moreover, the SEM images of M-S-H utilizing quartz as the raw material exhibited a denser structure when compared to the microstructure of granular morphology of M-S-H with silica fume. Remarkably, the existence of quartz as a siliceous raw material led to the production of M-S-H with an antigorite structure. At the same time, the 29Si MAS NMR observations revealed that quartz as a siliceous raw material is favorable for the conversion of Q2to Q3, resulting in improvement of polymerization degree of M-S-H silicone tetrahedron. The study demonstrated that it is the feasible to prepare M-S-H by replacing silica fume with quartz, which can provide a reference and choice for the preparation of green cementitious materials.

Maternal-effect genes are especially critical for early embryonic development after fertilization and until massive activation of the embryonic genome occurs. By applying a tandem mass tag (TMT)-labeled quantitative proteomics combined with RNA sequencing approach, the proteome of the buffalo was quantitatively analyzed during parthenogenesis of mature oocytes and the two-cell stage embryo. Of 1908 quantified proteins, 123 differed significantly. The transcriptome was analyzed eight stages (GV, MII, 2-cell, 4-cell, 8-cell, 16-cell, morula, blastocyst) of Buffalo using the RNA sequencing approach, and a total of 3567 unique genes were identified to be differently expressed between all consecutive stages of pre-implantation development. Validation of proteomics results (TUBB3, CTNNA1, CDH3, MAP2K1), which are involved in tight junction and gap junction, revealing that the maternal expression of the proteins possibly plays a role in the formation of cellular junctions firstly after parthenogenetic activation. Correlation and hierarchical analyses of transcriptional profiles and the expression of NPM2 and NLRP5 mRNA of buffalo in vitrodeveloped oocytes and parthenogenetic embryos indicated that the “maternal-to-zygotic transition” (MZT) process might exist in the model of parthenogenesis, which is similar to a normally fertilized embryo, and may occur between the 8-cell to 16-cell stage. These data provide a rich resource for further studies on maternal proteins and genes and are conducive to improving nuclear transfer technology.

Inflammation is the fundamental protective response; however disordered immuno-response can cause chronic human disease, including cancer. Inflammatory cells and mediators are essential to the tumor microenvironment and dissection of this complex molecular and cellular milieu may elucidate a connection between cancer and inflammation and help to identify potential novel therapeutic targets. Thus, focusing on transcription factor NF-ΚB and E2F1 in inflammation-associated cancer is urgent. NF-ΚB activation is prevalent in carcinomas, mainly driven by inflammatory cytokines in the tumor microenvironment. E2F1 is also involved in regulating immune responses. Understanding the crosstalk between the two pathways may contribute to the development of novel anti-cancer drugs.

Livestock and poultry skeletal muscle development was regulated by a variety of genetic factors playing an essential role in this process. In our de novo sequencing, a missense mutation in ISPDexon 2 showed a selected signature for well-developed muscles. However, the relationship between this mutation and muscle phenotypes remains unclear.

Abstract: Synthetic aperture radar (SAR) automatic target recognition is an important application in SAR. How to extract features has restricted the application of SAR technology seriously. In this paper, a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed, which integrates class and neighborhood information. This method can reinforce discriminative power using maximum interclass distance, so it can improve recognition rate effectively.

Are you a silican or a silican′t?By using a cocondensation method, well defined rhodium nanoparticles and chiral reagent (−)‐cinchonidine were immobilized and stabilized on mesoporous silica nanoparticle surface simultaneously. For the enantioselective hydrogenation of ethyl pyruvate, the enantioselectivity was very well preserved during the catalyst recycling without any additional chiral reagent supplement.

Friedliche Koexistenz: Brønsted‐Säuren und ‐Basen wurden an verschiedenen Oberflächen eines mesoporösen Siliciumoxid‐Nanopartikels fixiert. Die innere Oberfläche wurde durch Cokondensation funktionalisiert und die äußere durch der Synthese nachgeschaltetes Aufpfropfen. Mit dem difunktionalisierten Nanopartikel gelang eine zweistufige Reaktionssequenz, die mit Säure und Base im selben Reaktionsgefäß nicht möglich ist (siehe Schema).

Immobilization for enantioselectivity:Covalent tethering of a rhodium(I) phosphine complex on the surface of highly ordered mesoporous silica nanoparticles (MSN) gives rise to the RhPMSN/(−)‐ cinchonidine catalytic system, which gives 50 % eein the hydrogenation of ethyl pyruvate. In contrast, no enantioselectivity is detected in the hydrogenation of ethyl pyruvate catalyzed by the very similar homogeneous system [RhCl(PPh3)3]/(−)‐cinchonidine.

This paper is concerned with the infinite horizon linear quadratic optimal control for discrete‐time stochastic systems with both state and control‐dependent noise. Under assumptions of stabilization and exact observability, it is shown that the optimal control law and optimal value exist, and the properties of the associated discrete generalized algebraic Riccati equation (GARE) are also discussed. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

The enantioselective hydrogenation of ethyl pyruvate catalyzed by polyvinylpyrrolidone-stabilized rhodium nanocluster (Rh/PVP) modified by cinchonidine and quinine was studied. The results show that cinchonidine and quinine not only can induce the enantioselectivity in the hydrogenation of ethyl pyruvate, but also can greatly accelerate the reaction. Under the optimum conditions, 298K, 5MPa of hydrogen pressure and 4.3×10−3mol/l of cinchonidine in tetrahydrofuran, the enantiomeric excess of R-(+)-ethyl lactate and turnover frequency (TOF) of ethyl pyruvate reach up to 42.2% e.e. and 941h−1, respectively. The rate of hydrogenation is faster by a factor of about 50 in the presence of cinchonidine than that without it. Quinine exhibits the similar effect.

Towards the goal of finding new ketone electrophores suitable as molecular labels for electrophoric release tags, pentafluorophenoxyacetone (1) and pentafluorbenzyloxyacetone (2) were prepared. Both ketones were evaluated by electron capture (EC) and electron impact (EI) modes of mass spectrometry (MS). By EC-MS, 1 nearly gave a single ion (as desired), whereas 2 gave many ions. This behavior was completely reversed in EI-MS. To account for certain ion fragments in the EC mass spectrum of 2, an anion radical McLafferty-type rearrangement and loss of a carbene neutral were postulated. Electron impact of 1 gave an abundant ion at m/z 117 (C⁵F³⁺), which was suggested to be a diyne cation. © 1998 John Wiley & Sons, Ltd.

Quasimolecular peptide [M + H]+ions are generated by matrix‐assisted (2,5‐dihydroxybenzoic acid/D‐fructose) laser desorption/ionization (frequency‐tripled Nd: YAG at 355 nm) from leucine enkephalin, bradykinin, the non‐apeptide Arg–Leu–Cys–Ile–Phe–Ser–Cys–Phe–Arg, angiotensin I, bovine insulin chain B and some of their deuterated derivatives. The ions are cooled and axialized by azimuthal quadrupolar irradiation in the presence of argon collision gas in the ‘source’ compartment of a dual cubic Penning trap. Following ion transfer to the low‐pressure ‘analyzer’ trap, ultrahigh FT‐ICR mass resolving power is obtained for protonated oligopeptide quasimolecular [M + H]+ions: e.g. m/Δm50%≈ 1 500 000 for bradykinin (m/z≈ 1060) after frequency drift correction and 100 000 for insulin B‐chain, in which Δm50%is the magnitude‐mode peak full width at half‐maximum peak height. These results constitute the highest mass resolving power yet demonstrated for internally‐generated MALDI ions at 3 T, and compare favorably with results obtained at much higher‐magnetic field with externally‐generated ions. High mass resolution is important for resolving adducts and chemical modifications of a peptide or protein and (as demonstrated here) for facile determination of the degree of deuteration from an H/D exchange experiment. Limitations to FT‐ICR mass resolving power by Coulombic ion–ion interactions at high ion density in the trap are demonstrated experimentally and discussed.

We propose a novel ion cyclotron resonance ion trap capable of confining ions even at high pressure. The trap consists of three capacitively coupled axial sections, each composed of four circular cross-section rods parallel to the magnetic field axis. Ion confinement along the magnetic field direction is provided by applying the same static voltage to each set of “endcap” rods. As for a two-dimensional quadrupole mass filter, a sufficiently high rf frequency (several MHz) leads to an “effective” electrostatic “pseudopotential” well with a minimum on the trap central axis. Ions are confined radially by the combination of an applied axial static magnetic field and a radially inward-directed electric field resulting from differential rf voltages applied to each set of four rods. Ion confinement properties are revealed from a Paul traplike “stability diagram,” whereas ion trajectories are analyzed in terms of Penning-type ion cyclotron rotation, magnetron rotation, and axial oscillation motional modes. Ion cyclotron frequency increases with the strength of the rf trapping field. Ion magnetron motion becomes stable if the rf voltage is high enough. Therefore, ion trajectories can be stable even in the presence of ion-neutral collisions. Adding an ac potential to a Penning trap should dramatically increase the upper mass detection limit.

Accumulation of very long chain fatty acids (VLCFAs) due to defects in ATP binding cassette protein D1 (ABCD1) is thought to underlie the pathologies observed in adrenoleukodystrophy (ALD). Pursuing a substrate reduction approach based on the inhibition of elongation of very long chain fatty acid 1 enzyme (ELOVL1), we explored a series of thiazole amides that evolved into compound 27─a highly potent, central nervous system (CNS)-penetrant compound with favorable in vivopharmacokinetics. Compound 27selectively inhibits ELOVL1, reducing C26:0 VLCFA synthesis in ALD patient fibroblasts, lymphocytes, and microglia. In mouse models of ALD, compound 27treatment reduced C26:0 VLCFA concentrations to near-wild-type levels in blood and up to 65% in the brain, a disease-relevant tissue. Preclinical safety findings in the skin, eye, and CNS precluded progression; the origin and relevance of these findings require further study. ELOVL1 inhibition is an effective approach for normalizing VLCFAs in models of ALD.

A reactor, into which all reagents can be fed continuously and from which the reaction mixture can be continuously removed to a gravity separator, where product can be continuously removed and the catalyst phase recycled to the reactor, has been constructed and used for the hydroformylation of alkenes in fluorocarbon solvents.

In this paper, we propose the use of the hierarchical cascading technique (HCT) for the finite element method (FEM) analysis of bulk acoustic wave (BAW) devices. First, the implementation of this technique is presented for the FEM analysis of BAW devices. It is shown that the traveling-wave excitation sources proposed by the authors are fully compatible with the HCT. Furthermore, a HCT-based absorbing mechanism is also proposed to replace the perfectly matched layer (PML). Finally, it is demonstrated how the technique is much more efficient in terms of memory consumption and execution time than the full FEM analysis.

This paper describes extraction of parameters of an extended coupling-of-modes (COM) model including coupling between Rayleigh and shear-horizontal (SH) surface acoustic waves (SAW) on the SiO2-overlay/Cu-grating/LiNbO3-substrate structure. First, dispersion characteristics of two SAWs are calculated by the finite element method (FEM), and are fitted with those given by the extended COM. Then variation of COM parameters is expressed in polynomials in terms of the SiO2 and Cu thicknesses and the rotation angle Th of LiNbO3. Then it is shown how the optimal Th giving the SH SAW suppression changes with the thicknesses. The result agrees well with that obtained directly by FEM. It is also shown the optimal Th changes abruptly at certain Cu thickness, and is due to decoupling between two SAW modes.

This paper investigates how lateral propagation of Rayleigh and shear horizontal (SH) surface acoustic waves (SAWs) changes with rotation angle th and SiO2 and electrode thicknesses, h SiO2 and h Cu, respectively. The extended thin plate model is used for purpose. First, the extraction method is presented for determining parameters appearing in the extended thin plate model. Then, the model parameters are expressed in polynomials in terms of h SiO2, h Cu, and th. Finally, a piston mode structure without phase shifters is designed using the extracted parameters. The possible piston mode structures can be searched automatically by use of the polynomial expression. The resonance characteristics are analyzed by both the extended thin plate model and three-dimensional (3D) finite element method (FEM). Agreement between the results of both methods confirms validity and effectiveness of the parameter extraction process and the design technique.

This paper discusses the possibility of realizing multimode filters composed of multiple single-mode resonators by using radio frequency surface and bulk acoustic wave (SAW/BAW) technologies. First, the filter operation and design principle are given. It is shown that excellent filter characteristics are achievable by combining multiple single-mode resonators with identical capacitance ratios provided that their resonance frequencies and clamped capacitances are set properly. Next, the effect of balun performance is investigated. It is shown that the total filter performance is significantly degraded by balun imperfections such as the common-mode rejection. Then, two circuits are proposed to improve the common-mode rejection, and their effectiveness is demonstrated.

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