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Considering that synthetic aperture radar (SAR) images obtained directly after signal processing are in the form of complex matrices, we propose a complex convolutional network for SAR target recognition. In this article, we give a brief introduction to complex convolutional networks and compare them with the real counterpart. A complex activation function is applied to analyze the influence of phase information in complex neural networks. Inspired by the theory of network visualization, a special kind of transfer learning based on the electromagnetic property from the attributed scattering center model is applied in our networks to modulate the first convolutional layer. The experiment shows a better performance in terms of classification accuracy compared to random weight initialization.

Automatic target recognition has been one of the hottest research in synthetic aperture radar (SAR) data processing. Noticing that popular recognition methods cannot utilize multiple features of SAR complex data, a method fused scattering center feature and deep convolutional neural network (CNN) feature is proposed in this letter. This method contains three key parts, namely, scattering center extraction and reconstruction block, CNN feature extraction block, and final feature fusion and classification block. In this process, the scattering center feature and CNN feature are fused at the level of feature maps, which retain the space information of 2-D feature maps. What is more, the proposed half end-to-end strategy realizes the automatic update of weighting parameters in feature extraction network and subnetwork, which promotes a better recognition efficiency. Experimental results on measured SAR data show that the proposed method can achieve better accuracy than other single feature-based methods and feature fusion methods.

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods.

High resolution and wide-swath imaging always suffer channel errors of the multiple azimuth channels (MACs) SAR. This paper presents an image-domain channel error estimation algorithm based on image subspace least square (ISPLS) method, and a post-imaging reconstruction algorithm for MACs SAR. The proposed method mainly consists of three parts: firstly, preprocessing and SAR imaging; secondly, the ISP-LSbased channel error estimation and calibration algorithm; thirdly, post-imaging reconstruction and ambiguity suppression. The channel phase and baseline errors are joint-estimated based on image subspace after SAR imaging, providing advantages that the higher SNR regions SAR images and the subspace method can be used to achieve a more accurate estimate with a relatively low computational load. We also propose a post-imaging reconstruction method for ambiguity suppression, which can realize imaging each channel data and then combining the multichannel SAR images. Simulated and acquired airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

As an important remote sensing means, synthetic aperture radar (SAR) has many superiorities to other sensors. How to effectively detect and locate ships in SAR images is also a popular field. In previous ship detection research, most algorithms focus on detecting the horizontal bounding box of ship targets, which ignore the rotation angle of each ships. Thus, too much background noise in the horizontal detection results makes them difficult to describe each ship accurately. Inspired by the powerful feature representation ability of convolutional neural networks (CNNs), a novel anchor-free and keypoint-based deep learning method is proposed for oriented ship detection in multiresolution SAR images. Our detector first extracts multilevel features from the input SAR image with a backbone network and feature pyramid network. Next, considering multiscale ships in multiresolution SAR images, we detect different sizes of ships on different levels of feature maps with identical head network structures. In each head network, the classification subnetwork determines each pixel in feature maps as the central pixel of this ship or not, and the regression subnetwork regresses the oriented bounding box for each ship. In the training process, the proposed oriented nonnormalized Gaussian function is used to describe the center point of ship targets, while the nonuniform weighting of the different level loss functions is used to suppress the imbalanced sample distribution. Experimental results on two authoritative SAR-oriented ship detection datasets and two Gaofen-3 images demonstrate the effectiveness and robustness of the proposed methods.

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm.

High-resolution wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system possesses a number of receiving channels along the azimuth direction, so it has the capacity of moving target indication and imaging. However, due to the radial velocity of the moving target, false targets occur in the focused image. By estimating the radial velocity and combining it with moving target imaging, false targets can be effectively suppressed. In this letter, a method of radial velocity estimation of a moving target is proposed based on the theory of subspace projection. This method does not need to estimate the real azimuth position of the moving target and can predict the processing time. Simulation and airborne measured data show the effectiveness of the proposed method.

Conventional beam-center approximation-based motion compensation (MOCO) algorithms fail to achieve an optimally focused image in the case of the high-resolution and high-frequency (HRHF) synthetic aperture radar (SAR) system. In this letter, a novel MOCO algorithm based on Doppler adjustment is developed with the ability to compensate the azimuth variant motion error. The change of the Doppler spectrum caused by the azimuth variant motion error is investigated and is eliminated by Doppler scaling. The proposed MOCO algorithm has dramatically improved precision when compared with the conventional MOCO methods in HRHF SAR imaging. Simulation experiments and extensive comparisons with other MOCO algorithms verify the effectiveness of the proposed algorithm.

Since path curvature becomes severer for higher orbit synthetic aperture radar (SAR), the stripmap mode may not provide a reliable azimuth resolution under different look angles or at different positions. Beam steering is especially valuable herein for adjusting the azimuth resolution under different observation conditions by designing the antenna steering rate. Moreover, considering that the large range migration and center range variation in the squint mode may increase the echo length and reduce the achievable scene width, we proposed a novel 2-D beam steering (TDBS) method, which promises not only a required azimuth resolution but also a wide swath (or shortened echo length) at squint when cooperated with the variable interpulse time (VIPT) technique. The simulation results obtained under different look directions are shown to validate the effectiveness of the proposed beam controlling method.

The water body detection in high-resolution synthetic aperture radar (SAR) images is a challenging task due to the changing interference caused by multiple imaging conditions and complex land backgrounds. Inspired by the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is proposed to improve the performance of water body detection in high-resolution SAR images. First, for the resolution loss caused by convolutions with large stride in traditional convolutional neural network (CNN), the fully-convolutional upsampling pyramid networks (UPNs) are proposed to suppress this loss and realize pixel-wise water body detection. Then considering blurred water boundary, the fully-convolutional conditional random fields (FC-CRFs) are introduced to UPNs, which reduce computational complexity and lead to the automatic learning of Gaussian kernels in CRFs and the higher boundary accuracy. Furthermore, to eliminate the inefficient training caused by imbalanced categorical distribution in the training data set, a novel variable focal loss (VFL) function is proposed, which replaces the constant weighting factor of focal loss with the frequency-dependent factor. The proposed methods can not only improve the pixel accuracy and boundary accuracy but also perform well in detection robustness and speed. Results of GaoFen-3 SAR images are presented to validate the proposed approaches.

The rotation matching between synthetic aperture radar (SAR) and optical images is essential for their registration. In this article, we introduce a novel fast rotation matching method for SAR and optical images. In the method, the absolute directional derivative histograms (ADDHs) of SAR and optical images can be used to reflect the rotation difference between them, and then the rotation difference is removed by the cross correlation of the two images’ ADDHs. Subsequently, we generate the ADDHs by oriented Gaussian-Gamma-shaped operator. Then, the cross correlation based on fast Fourier transform is used to deal with the ADDHs of SAR and optical images in order to determine the rotation difference of the two images. The experiment results demonstrate that the proposed method can detect the rotation difference in various conditions and achieves rotation matching with high speed.

Background Nodular fasciitis (NF) is a benign disease originating from fascial tissue and most commonly occurs in the extremities, followed by the trunk, head, and neck. NF of the head and neck occurs mainly in the face and neck, and it has not been reported in the occipital region. Case summary A 30-year-old man was admitted because of a mass in the left occipital region. Imaging examination revealed a soft tissue nodule in the left occipital area. An enhanced magnetic resonance imaging scan showed characteristic inverted target and fascial tail signs. Histopathological analysis showed a large amount of spindle cell proliferation, and immunohistochemistry showed positive expression of SMA in the spindle cells in the lesion. Finally, nodular fasciitis was diagnosed. Conclusion NF of the head and neck is rare, but the possibility of NF should be considered when nodules or masses with rapid subcutaneous growth are found and tenderness in the head and neck is present. Imaging examination, in combination with clinical manifestations and histopathological examination, can improve the diagnostic accuracy for the disease. After diagnosis, local surgical resection is the first choice of treatment.

Background Endometrial stromal sarcoma (ESS) is a rare malignant mesenchymal tumor. Early in the disease, the findings on magnetic resonance imaging are similar to those of leiomyoma. When the lesion involves both vascular and cardiac tissue, it might be misdiagnosed as intravenous leiomyomatosis, which is not common in the clinic. Case summary We present the case of a 34-year-old female patient with tumor embolus, which extended from the right iliac vein and ovarian vein to the inferior vena cava (IVC), and then to the right atrium and right ventricle, and finally protruded into the pulmonary artery. The patient had undergone a hystero-myomectomy 7 years previously. Based on the findings of the imaging examinations, the diagnosis of intravenous leiomyomatosis was considered preoperatively. The patient then underwent complete resection of the endovascular and intracardiac tumor embolus. The postoperative pathology results confirmed metastatic ESS with endovascular and intracardiac involvement. The patient was discharged from hospital in good condition, and there was no sign of recurrence 5 mo after the operation. Conclusion Extending from the iliac vein and ovarian vein to the IVC, this metastatic ESS invaded both vascular and cardiac tissues. For patients with ESS involving vascular and cardiac tissues, pathological examinations are essential for the differential diagnosis, such as intravenous leiomyomatosis. In addition, due to the high recurrence rate of ESS, long-term and close follow-up evaluation is necessary.

The beam steering of high-squint terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) mounted on maneuvering platforms causes azimuth spectrum aliasing and nonlinear variation of the Doppler center with target azimuth position. A joint time-Doppler deramp (JTDD) based method is proposed and mainly contains two parts. First, for the azimuth spectrum aliasing, the unfolded 2-D spectrum is obtained by a modified linear deramp function in the azimuth time domain constructed from the 3-D motion parameters. After range cell migration correction (RCMC), the data supporting area in the azimuth time domain is expanded, and thus, aliased because of the nonlinear variation of Doppler center. Then, a nonlinear deramp operation in the Doppler domain is further proposed to obtain a nonaliasing signal. The proposed algorithm is efficient with less zero-padding due to the consideration of nonlinear components of Doppler center variation. Simulation and real SAR data processing are presented to validate the proposed algorithm.

Among recent measures for risk management, value at risk (VaR) has been criticized because it is not coherent and expected shortfall (ES) has been criticized because it is not robust to outliers. Recently, [Math. Oper. Res., 38, 393–417 (2013)] proposed a risk measure called median shortfall (MS) which is distributional robust and easy to implement. In this paper, we propose a more generalized risk measure called quantile shortfall (QS) which includes MS as a special case. QS measures the conditional quantile loss of the tail risk and inherits the merits of MS. We construct an estimator of the QS and establish the asymptotic normality behavior of the estimator. Our simulation shows that the newly proposed measures compare favorably in robustness with other widely used measures such as ES and VaR.

Traditional clutter suppression methods are mainly studied under the condition that the pulse repetition frequency (PRF) of the system is not less than the Nyquist frequency. Whereas in the high-resolution and wide-swath (HRWS) multichannel synthetic aperture radar (SAR) system, a low PRF is used to break through the minimum antenna area constraint. The low PRF case brings new challenges to the traditional clutter suppression methods. In this letter, a subspace projection clutter suppression method is proposed based on the fact that moving targets and the clutter consist in different signal subspaces. This method can be directly applied to the HRWS multichannel SAR system, and it shows better performance compared to the space-time adaptive processing (STAP) when the moving target components cannot be ignored in the clutter covariance matrix calculation. Simulated data and airborne measured data are processed to verify its effectiveness.

Background This study was conducted to evaluate and update the current prevalence of and risk factors for chronic kidney disease (CKD) and diabetic kidney disease (DKD) in a central Chinese urban population. Methods From December 2017 to June 2018, a total of 5231 subjects were randomly enrolled from 3 communities in 3 districts of Zhengzhou. CKD was defined as estimated glomerular filtration rate (eGFR) 2 or urinary albumin to creatinine ratio ≥ 30 mg/g (albuminuria). Diabetic subjects with systolic blood pressure > 140 mmHg, albuminuria or an eGFR less than 60 mL/min/1.73 m2 were classified as having DKD. Participants completed a questionnaire assessing lifestyle and relevant medical history, and blood and urine specimens were taken. Serum creatinine, uric acid, total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein and urinary albumin were assessed. The age- and sex-adjusted prevalences of CKD and DKD were calculated, and risk factors associated with the presence of reduced eGFR, albuminuria, DKD, severity of albuminuria and progression of reduced renal function were analyzed by binary and ordinal logistic regression. Results The overall adjusted prevalence of CKD was 16.8% (15.8–17.8%) and that of DKD was 3.5% (3.0–4.0%). Decreased renal function was detected in 132 participants (2.9, 95% confidence interval [CI]: 2.5–3.2%), whereas albuminuria was found in 858 participants (14.9, 95% CI: 13.9–15.9%). In all participants with diabetes, the prevalence of reduced eGFR was 6.3% (95% CI = 3.9–8.6%) and that of albuminuria was 45.3% (95% CI = 40.4–50.1%). The overall prevalence of CKD in participants with diabetes was 48.0% (95% CI = 43.1–52.9%). The results of the binary and ordinal logistic regression indicated that the factors independently associated with a higher risk of reduced eGFR and albuminuria were older age, sex, smoking, alcohol consumption, overweight, obesity, diabetes, hypertension, dyslipidemia and hyperuricemia. Conclusions Our study shows the current prevalence of CKD and DKD in residents of Central China. The high prevalence suggests an urgent need to implement interventions to relieve the high burden of CKD and DKD in China.

Elevated brain activation, or hyperexcitability, induces cognitive impairment and confers an increased risk of Alzheimer’s disease (AD). Blocking the overexcitation of the neural network may be a promising new strategy to prevent, halt, and even reverse this condition. Physical exercise has been shown to be an effective cognitive enhancer that reduces the risk of AD in elderly individuals, but the underlying mechanisms are far from being fully understood. We explored whether long-term treadmill exercise attenuates amyloid precursor protein (APP)/presenilin-1 (PS1) mutation-induced aberrant network activity and thus improves cognition by altering the numbers and/or distribution of voltage-gated sodium channels (Nav) in transgenic mice. APP/PS1 mice aged 2, 3.5, 5, 6.5, 8, and 9 months underwent treadmill exercise with different durations or at different stages of AD. The alterations in memory, electroencephalogram (EEG) recordings, and expression levels and distributions of Nav functional members (Nav1.1α, Nav1.2, Nav1.6, and Navβ2) were evaluated. The results revealed that treadmill exercise with 12- and 24-week durations 1) induced significant improvement in novel object recognition (NOR) memory and Morris water maze (MWM) spatial memory; 2) partially reduced abnormal spike activity; and 3) redressed the disturbed cellular distribution of Nav1.1α, aberrant Navβ2 cleavage augmentation, and Nav1.6 upregulation. Additionally, APP/PS1 mice in the 24-week exercise group showed better performance in the NOR task and a large decrease in Nav1.6 expression, which was close to the wild-type level. This study suggests that exercise improves cognition and neural activity by altering the numbers and distribution of hippocampal Nav in APP/PS1 mice. Long-term treadmill exercise, for about 24 weeks, starting in the preclinical stage, is a promising therapeutic strategy for preventing AD and halting its progress.

Background: A novel coronavirus (2019-nCoV) causing an outbreak of pneumonia in Wuhan, Hubei province of China was isolated in January 2020. This study aims to investigate its epidemiologic history, and analyze the clinical characteristics, treatment regimens, and prognosis of patients infected with 2019-nCoV during this outbreak. Methods: Clinical data from 137 2019-nCoV-infected patients admitted to the respiratory departments of the respiratory departments of nine tertiary hospitals in Hubei province from December 30, 2019 to January 24, 2020 were retrospectively collected, including general status, clinical manifestations, laboratory test results, imaging characteristics, and treatment regimens. Results: None of the 137 patients (61 males, 76 females, aged 20–83 years, median age 57 years) had a definite history of exposure to Huanan Seafood Wholesale Market. Major initial symptoms included fever (112/137, 81.8%), coughing (66/137, 48.2%), and muscle pain or fatigue (44/137, 32.1%), with other, less typical initial symptoms observed at low frequency, including heart palpitations, diarrhea, and headache. Nearly 80% of the patients had normal or decreased white blood cell counts, and 72.3% (99/137) had lymphocytopenia. Lung involvement was present in all cases, with most chest computed tomography scans showing lesions in multiple lung lobes, some of which were dense; ground-glass opacity co-existed with consolidation shadows or cord-like shadows. Given the lack of effective drugs, treatment focused on symptomatic and respiratory support. Immunoglobulin G was delivered to some critically ill patients according to their conditions. Systemic corticosteroid treatment did not show significant benefits. Notably, early respiratory support facilitated disease recovery and improved prognosis. The risk of death was primarily associated with age, underlying chronic diseases, and median interval from the appearance of initial symptoms to dyspnea. Conclusions: The majority of patients with 2019-nCoV pneumonia present with fever as the first symptom, and most of them still showed typical manifestations of viral pneumonia on chest imaging. Middle-aged and elderly patients with underlying comorbidities are susceptible to respiratory failure and may have a poorer prognosis.

This study objective was to evaluate gender differences in hematological, hormonal and fitness variables among youth swimmers and to explore relationships between erythrocyte indices and aerobic and anaerobic capacity. 137 girls and 171 boys participated in the study and were divided into three groups based on their training experience. Blood samples were obtained to determine red blood cell counts, hemoglobin concentration, hematocrit, and plasma erythropoietin and testosterone levels. VO2max was assessed using a submaximal cycle protocol. 76 girls and 102 boys also undertook a Wingate test to determine their peak anaerobic power. Boys had higher (p < 0.05) means than girls for all hematological variables except for erythropoietin and these variables demonstrated an increase with training in boys. The average VO2max in l∙min-1 and peak anaerobic power in watts were also higher in boys (2.91 ± 0.08 and 547 ± 28, respectively) than girls (2.25 ± 0.07 and 450 ± 26, respectively). Modest but significant (p < 0.05) correlations were found between VO2max and red blood cell counts (r = 0.252), hemoglobin concentration (r = 0.345), or hematocrit (r = 0.345) and between peak anaerobic power and red blood cell counts (r = 0.304), hemoglobin concentration (r = 0.319) or hematocrit (r = 0.351). This study revealed relatively lower yet age- and gender-appropriate hematological, hormonal and fitness indices in youth swimmers. The gender-related differences in erythrocyte indices seem unrelated to erythropoietin and may be explained by the higher testosterone levels seen in boys. Given their correlation to both aerobic and anaerobic capacity, erythrocyte indices may be used as part of talent identification for sports.

Cerebral syphilitic gumma is a rarely reported disease of the central nervous system. Magnetic resonance imaging (MRI) is an important diagnostic method for syphilitic gumma. The present study aimed to describe and characterize neuroimaging results from 6 patients with pathologically diagnosed cerebral syphilitic gumma. The 6 patients (age, 32-61 years) underwent brain CT and MRI, with 1 patient also undergoing whole-body 2-deoxy-2-(fluorine-18)fluoro-D-glucose-positron emission tomography/CT (18F-FDG PET/CT). Non-enhanced CT, conventional T1 weighted imaging (T1WI) and T2WI, diffusion weighted imaging (DWI) and gadolinium-enhanced T1WI images were acquired for all patients. The CT and MRI scans were retrospectively reviewed by two experienced radiologists for consensus on the location, number, size, T1WI, T2WI and DWI signal intensity characteristics, extent of vasogenic oedema, and enhancement patterns. In total, the 6 patients exhibited 10 lesions, nine of which were located in the cerebral hemisphere, primarily in the grey matter. The remaining lesion was located in the fourth ventricle, leading to mild-to-moderate hydrocephalus. The diameters of the identified 10 lesions ranged from 0.9-6.5 cm, with a mean diameter of 3.9 cm. The main feature observed in CT was low density and in MRI the features were T1WI and DWI hypointensity and T2WI hyperintensity. A single case exhibited syphilis gumma with massive haemorrhage. Ring-like or strip-like signs (n=5), accompanied by the dural tail sign (n=2) and homogeneous enhancement (n=1), were noted on T1WI with gadolinium. The 18F-FDG PET/CT performed in one patient of a cerebral syphilis gumma revealed low uptake and metabolism. The present study indicated that gadolinium-enhanced MRI combined with 18F-FDG PET/CT and laboratory examinations are helpful in distinguishing cerebral syphilitic gumma from brain tumors and infectious diseases, therefore avoiding unnecessary surgery.

Cervical cancer, which is significantly associated with high-risk human papillomavirus (HPV) infection, currently ranks the fourth most common cancer among women worldwide. Previous literature reported that the elevated expression of G6PD was significantly correlated with the occurrence and deterioration of human cervical cancer, especially with the cervical cancer with HPV16 and HPV18 infection. In this study, we verified that G6PD expression has a strong positive correlation with HPV16 E6 levels in cervical cancer tissues and cells. In addition, regulating the expression of HPV16 E6 significantly affected the proliferation, apoptosis, migration, and invasion in the cervical cancer HeLa cells, as well as the transcript and protein levels of G6PD. The luciferase reporter assay and ChIP assay proved that HPV16 E6 stimulated the transcription of G6PD mRNA and subsequently enhanced the expression of G6PD through directly binding to the specific sites in the promoter of G6PD. Our findings reveal that HPV16 E6 is a novel regulatory factor of G6PD. Furthermore, by regulating the expression of G6PD, HPV16 E6 might promote the proliferation and migration potential, and inhibit apoptosis of cervical cancer cells, which ultimately contributed to the progression and metastasis of cervical cancer.

In a short observation time, after the range alignment and phase adjustment, the motion of a target can be approximated as a uniform rotation. The radar observing process can be simply described as multiplying an observation matrix on the ISAR image, which can be thought of as a linear system. It is known that the longer observation time is, the higher cross-range resolution is. In order to deal with the conflict between short observation time and high cross-range resolution, we introduce Kalman filtering (KF) into the ISAR imaging and propose a novel method to reconstruct a high-resolution image with short time observed data. As KF has excellent reconstruction performance, it leads to a good application in ISAR image reconstruction. At each observation aperture, the reconstructed image denotes the state vector in KF at the aperture time. It is corrected by a two-step KF process: prediction and update. As iteration continues, the state vector is gradually corrected to a well-focused high-resolution image. Thus, the proposed method can obtain a high-resolution image in a short observation time. Both simulated and real data are applied to demonstrate the performance of the proposed method.

Due to the constraint of minimum antenna area, azimuth ambiguity resolving is a challenging task in the ship focusing for single-channel synthetic aperture radar (SAR) mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing algorithm based on azimuth ambiguity resolving is proposed in this paper. For ship SAR imaging with small-aperture data, the energies of different targets are separated in Doppler domain with different Doppler ambiguity numbers. Thus, the Doppler ambiguity number of a single target can be estimated by residual envelope inclination. Then, the target can be accurately focused and located at the correct position by the known Doppler ambiguity number. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results are presented to validate the proposed algorithm.

The traditional low voltage ride through (LVRT) control method is mainly adjusted from the inside of the new energy field station. Generally, by improving the Crowbar circuit or optimizing the excitation control of the converter, the renewable energy can be operated without offline at a low voltage and reduce its reactive power absorption to the system. Based on the current LVRT status, this paper proposes a DFIG control strategy during LVRT, and introduces the sensitivity coefficient to calculate the influence factor of the wind farm on the faulty node in the regional network to optimize the reactive power distribution of the wind farm near the faulty node. This stable working state of DFIG during LVRT speeds up the recovery speed of the system after it breaks away from the fault. The simulation analysis on the Yunnan regional power grid verifies the feasibility and superiority of the theory, and provides a new idea for the control strategy of renewable energy during LVRT.

Introduction: Physical fitness is an adaptive state that varies with an individual's growth and maturity status. Considering that the difference in skeletal maturity already existed among preschool children, this study was designed to determine the influence of skeletal age and chronological age on preschoolers' physical fitness performance.Methods: This cross-sectional study was conducted in 945 healthy preschoolers (509 males, 436 females) aged between 3.0 and 6.0 years in Shanghai, China. We used the method of TW3-C RUS to determine skeletal age. Chronological age was measured by subtracting the date of birth from the test date. Sit and reach, 2 × 10 m shuttle run test, standing long jump, tennis ball throw, 5 m jump on both feet, and balance beam walk were considered for physical fitness performance. Correlation coefficients and partial correlations adjusting height and weight were used to determine the relationships among the variables of skeletal age/ relative skeletal age, chronological age/relative chronological age, and physical fitness items.Results: Skill-related physical fitness was weakly to moderately associated with skeletal age (the absolute value of r: 0.225–0.508, p < 0.01) and was moderately to strongly associated with chronological age (the absolute value of r: 0.405–0.659, p < 0.01). Health-related physical fitness items (BMI and sit and reach) showed a fairly weak to no correlation with skeletal age and chronological age. After adjusting the height and weight, an extremely weak to no correlation was observed between skeletal age and both health- and skill-related physical fitness, and weak-moderate correlations were noted between chronological age and skill-related physical fitness (the absolute value of r: 0.220–0.419, p < 0.01). In children in Grade 1, skill-related physical fitness (except for balance beam walk) showed a weak to moderate correlation with relative chronological age (the absolute value of r: 0.227–0.464, p < 0.05).Conclusion: (1) both skeletal age and chronological age are associated with skill-related rather than health-related physical fitness performance, and after adjusting height and weight, chronological age, rather than skeletal age, is associated with skill-related physical fitness performance; (2) for preschool children, skill-related physical fitness performance is influenced by relative chronological age rather than individual differences in skeletal maturation, especially in the lower grades.

This paper investigates the imaging problems in squint beam-steering synthetic aperture radar (SBS-SAR) mounted on high-speed platforms with constant acceleration. The cross-range-dependent range cell migration (RCM) is compensated by keystone transform (KT) and time domain RCM correction (RCMC). By derotation and phase compensation, the KT of Doppler folded signal is achieved without zero-padding. For azimuth processing, the signal is reconstructed by the nonlinear phase and range-dependent derotation. Then, the space-variant (SV) Doppler chirp rate is corrected by time domain azimuth nonlinear chirp scaling (ANCS). After frequency domain matched filtering, the full aperture signal is focused in the 2-D time domain. The algorithm is validated by simulated SAR data, including the evaluation of RCMC with KT, geometric correction, and the focusing performance.

Purpose Only a few studies have investigated the brain morphology abnormalities in structural MRI in patients with drug-naive idiopathic generalized epilepsy (IGE) and mainly focused on brain volume changes. In the present study, we aimed to investigate the changes in three morphologic measurement differences including cortical thickness, cortical volume, and surface area using FreeSurfer in a pediatric cohort of recent-onset, drug-naive IGE. Methods Forty-five recent-onset, drug-naive patients diagnosed with IGE and 32 demographically matched healthy controls were recruited. All participants underwent structural MRI scans with a 3.0 T MR system. FreeSurfer, an automated cortical surface reconstruction toolbox, was applied to compare the cortical morphology between patients and controls. The brain regions with significant group differences after multiple comparison correction were extracted in common space for each patient, and then correlated with their clinical characteristics (including onset age, duration of epilepsy, and mini-mental state examination (MMSE)) using partial correlation analysis with age, sex and intracranial volume as covariates. Results Compared with controls, IGE patients showed decreased cortical thickness in the left rostral middle frontal gyrus, decreased cortical volume in the right cuneus and left superior frontal gyrus that extended to the precentral gyrus, and decreased surface area in the right cuneus and right inferior parietal gyrus. None of these regions showed significant relationships with clinical measurements in the patient group. Conclusion Our findings suggest that cortical thickness, cortical volume, and surface area changes occurred in the early stage of IGE. These findings provide structural neuroimaging evidence underlying the pathology of IGE.

Serving as an effective tool for describing both randomness and fuzziness of qualitative concepts, the cloud model has become a common topic of research. By realizing the uncertain transformation between qualitative concepts and quantitative data, the cloud model provides a new way to deal with group decision making problems in the linguistic environment. However, classical methods based on the cloud model mainly focus on addressing problems with a small number of decision makers. In this paper, we apply the cloud model to the decision making problems that involve a large group of decision makers. Specifically, we first define a new measure of fuzzy distance for clouds based on the $\boldsymbol { \alpha -cuts}$ . On the basis of the proposed fuzzy distance measure, we then present a new similarity measure between clouds. Next, we construct an improved clustering approach based on the traditional hierarchical clustering algorithm. Furthermore, we develop a hybrid weight scheme to obtain the cluster weight vector, which takes both the subgroup size and the variance into consideration. Moreover, we present a consensus-based method based on the cloud model for large group decision making with linguistic information. Finally, in order to confirm the validity and effectiveness of the proposed method, we give an application in the context of the Belt and Road Initiative of China, and perform some detailed comparisons to show the advantages of the proposed method.

Digital acousto-optic holography uses sound wave field interacted with interested object to modulate laser beam to generate an optical digital hologram. A numerical reconstruction of the digital hologram is carried out to reproduce the object information. This technology is able to image the interior structure of non-transparent object with high precision.

We have investigated the internal structure of materials by use of a digital acousto-optic holographic technique. The innovative technique is effective in detecting the internal structure information of functional gradient materials. The present visible-light-source-based digital holographic techniques are not able to detect the internal structure information of non-transparent objects and that motivates us to seek the ultrasound-based digital optical holography alternative. The characteristic model of functional gradient materials is modeled through finite element modeling software. The effects of material composition, shape and density on the ultrasound field distribution are simulated, which reveal the propagation pattern of the ultrasound in functional gradient materials as the shape, density and elastic modulus of the medium affect the acoustic properties. The dynamic changes of quantitative mapping relationship between the internal structural information and the sound field is also analyzed. Results indicate that the change is correlated with the internal structural information of the object. The simulation results provide useful quantifiable information for the subsequent research of digital acousto-optic holography to detect the internal structure of materials. Our research is applicable to various industrial problems where traditional digital holographic techniques alone fail to deliver solutions.

The small satellite SAR has received increasing attention due to its flexibility and low cost. But limited by the data transmission technology, real-time transmission of a large amount of raw data generated by the spaceborne spotlight SAR can hardly be achieved. Meanwhile, the azimuth bandwidth of the spotlight mode is larger than the PRF, resulting in aliasing of the azimuth spectrum. Based on these, this paper proposes a real-time scheme for small satellite SAR with spotlight mode. The method can solve the problem of data transmission and eliminate spectrum overlap in Doppler domain by means of sub-aperture processing. The modified range migration algorithm (RMA) is used to perform range compression and range cell migration compensation (RCMC) on sub-aperture data. Then dechirp in the azimuth time domain is applied to obtain the low-resolution complex image focused in the range time-azimuth frequency domain. Finally, all theected onto a grid image with azimuth interval matching the azimuth full-resolution to complete image fusion.

Ghost targets occur when moving targets are processed as stationary scene in the high-resolution and wide-swath azimuth multichannel SAR system, so moving targets require special treatment. Combining the subspace theory with the system, this paper proposes a clutter suppression method and a moving target radial velocity estimation method. The proposed clutter suppression method does not need pre-processing and it preforms better than the spacetime adaptive processing when the moving target component occupies a large proportion in the received data. And the proposed velocity estimation method has lower time complexity than the method based on the minimum entropy, so it is appropriate for the time sensitive applications. The processing of the airborne measured data verifies the effectiveness of the methods.

Background Body mass index (BMI) and skeletal age (SA) are important indicators of individual growth and maturation. Although the results have not been unified, most studies indicated that accelerated skeletal maturation is associated with overweight/obesity. However, there have so far been insufficient studies about the association between accelerated skeletal maturation and overweight/obesity in preschoolers, particularly Asian children. A cross-sectional study was conducted on Chinese children to verify the association between accelerated skeletal maturation and overweight/obesity at preschool age. Methods The study involved 1330 participants aged 3.1–6.6 years old (730 males and 600 females) in Shanghai, China. The skeletal age was determined according to the method of TW3-C RUS. Accelerated skeletal maturation was defined as relative SA (SA minus chronological age [CA]) ≥1.0 years. BMI was classified as thinness, normal weight, overweight, and obesity according to the International Obesity Task Force (IOTF) BMI cut-offs. The Chi-square was performed to determine the statistically significant difference in the frequency of accelerated skeletal maturation in BMI and age categories. The logistic regression model analyzed the association between accelerated skeletal maturation and overweight/obesity. Results The percentage of accelerated skeletal maturation increased with BMI (7.8% of children in thinness group had accelerated skeletal maturation; the percentage increased to 30.8% in obese group. x2 = 89.442, df = 3, P ) and age group (at age 3.5, 3.5% of participants had accelerated skeletal maturation; at age 6.0 years, this increased to 27.8%. x2 = 43.417, df = 5, P ). Logistic regression analysis showed that children with overweight and obesity are more likely to have accelerated skeletal maturation than children with normal weight after adjusting for gender and age (Overweight, odds ratio [OR] = 3.27, 95% confidence interval [CI]: 2.20–4.87; Obese, OR = 4.73, 95% CI: 2.99–7.48). Conclusions There is an association between accelerated skeletal maturation and overweight/obesity among preschool children. This study suggests that accelerated skeletal maturation might coexist with overweight/obesity in preschool children, and interventions, such as dietary modifications and increasing levels of physical activity, should be employed to prevent both accelerated skeletal maturation and overweight/obesity as early as preschool age.

This paper was to probe into whether IL-6 improved insulin resistance by AMPK and p38MAPK signal pathway. The C2C12 cell lines were purchased from ATCC. The cells of control group were cultured in high-glucose DMEM culture medium. CaCl2 with different concentrations was respectively used to culture C2C12 cells; then, glucose concentrations in the nutrient solution following 24 h were measured in the preliminary experiment. Firstly, cells of A group (control group) were normally cultured C2C12 cells, cells of B, C, and D group were respectively cultured with palmitic acid 48 h, palmitic acid 24 h, and IL-6 24 h, and IL-6-shRNA 48 h, glucose concentrations, IL-6 mRNA and GLUT4 mRNA levels, p-p38MAPK, p-AMPK, p-IRS-1, and p-PI-3K protein contents of each group in the nutrient solution following 48 h were measured. Secondly, cells of A group (control group) were palmitic acid-cultured C2C12 cells, cells of B and C group were respectively cultured with palmitic acid 24 h and CaCl2 24 h, and palmitic acid 24 h CaCl2 24 h, and IL-6-shRNA 24 h, and the same indexes were measured with the first experiment. The preliminary experimental result showed that CaCl2 feed can induce C2C12 cell contraction, and CaCl2 with different concentrations was able to decrease significantly glucose concentrations in the nutrient solution following 24 h (p

A imaging passive localization method for wideband signal is proposed in this paper. By introducing synthetic aperture radar (SAR) imaging method, the location of the signal emitter is directly given in SAR image. The key of this method is to focus unknown wideband data in range and azimuth domain. To focus the data in range domain without signal parameter, a new pulse compress method is proposed by constructing reference signal from raw data. To focus the data in azimuth domain without knowing range, a range-searching azimuth focus method is proposed by constructing azimuth focus functions with different range. Simulation result validate the effectiveness of the proposed method.

Because of computing and storage limitation of one satellite, it is difficult to process large amounts of data of spaceborne synthetic aperture radars. A new method for networked satellites data processing based on intersatellite cloud computing system (ICCS) using a “stage-by-stage” CS algorithm is proposed in this paper, which is built with FPGA chips as the computing kernel of the system. Different from the traditional CS algorithm processing, the system divides data processing into three stages. The computing tasks are reasonably allocated to different data processing units (i.e. satellites) in each stage. The method effectively saves computing and storage resources of satellites, improves the utilization rate of a single satellite, and shortens the data processing time. GF-3 satellite SAR raw data is processed by the system, with the performance of the method verified.

Recurrent miscarriage (RM) affects 5% of women, it has an adverse emotional impact on women. Because of the complexities of early development, the mechanism of recurrent miscarriage is still unclear. We hypothesized that abnormal placenta leads to early recurrent miscarriage (ERM). The aim of this study was to identify ERM associated factors in human placenta villous tissue using proteomics. Investigation of these differences in protein expression in parallel profiling is essential to understand the comprehensive pathophysiological mechanism underlying recurrent miscarriage (RM).To gain more insight into mechanisms of recurrent miscarriage (RM), a comparative proteome profile of the human placenta villous tissue in normal and RM pregnancies was analyzed using iTRAQ technology and bioinformatics analysis used by Ingenuity Pathway Analysis (IPA) software.In this study, we employed an iTRAQ based proteomics analysis of four placental villous tissues from patients with early recurrent miscarriage (ERM) and four from normal pregnant women. Finally, we identified 2805 proteins and 79,998 peptides between patients with RM and normal matched group. Further analysis identified 314 differentially expressed proteins in placental villous tissue (≥1.3-fold, Student's t-test, p 0.05); 209 proteins showed the increased expression while 105 proteins showed decreased expression. These 314 proteins were analyzed by Ingenuity Pathway Analysis (IPA) and were found to play important roles in the growth of embryo. Furthermore, network analysis show that Angiotensinogen (AGT), MAPK14 and Prothrombin (F2) are core factors in early embryonic development. We used another 8 independent samples (4 cases and 4 controls) to cross validation of the proteomic data.This study has identified several proteins that are associated with early development, these results may supply new insight into mechanisms behind recurrent miscarriage.

The focusing of squint beam-steering synthetic aperture radar (SAR) data is a challenging task for the technical difficulty of space-variant range cell migration correction (RCMC). It is more complicated when considering the manoeuver of high-speed platforms. The conventional Doppler domain RCMC method is with low performance and requires a large amount of zero-padding. This Letter proposes a modified RCMC method which removes the space-variant range cell migration (RCM) components by deramp-keystone transform and then compensates the unified RCM terms in the time domain. Compared with the conventional method, the proposed one is with high performance and can avoid zero-padding. This method is validated by the simulated SAR data.

For the ultrahigh-resolution airborne stepped-frequency synthetic aperture radar, very large synthetic bandwidth and very long integration time may lead to a 2-D space-variant (SV) motion error when the aircraft flies off the ideally straight trajectory due to the atmospheric turbulence. This new type of error complicates the motion estimation and motion compensation (MOCO). For the motion estimation, we present a jointly 2-D SV motion error estimation method to simultaneously consider the range-variant motion error and the azimuth-variant motion error. For the MOCO, we propose a 2-D SV-MOCO method. The method is implemented through three processing steps: 1) two-step MOCO for the space-invariant motion error and the range-variant phase error; 2) range block-based chirp-z transform (CZT) for the range-variant envelope error; and 3) range block division for the range-dependent azimuth-variant phase error based on the azimuth subaperture method. Finally, processing of simulated data and real data validates the proposed methods.

The yaw steering may be ineffective to compensate for the large Doppler centroid in geosynchronous SAR (Geo-SAR) in many cases. Therefore, the available studies are concentrated on the 2-D beam-steering, rather than the yaw steering. However, the range and azimuth may be severely coupled without any beam-steering or with the 2-D beam-steering. Fortunately, we found that the yaw steering could overcome this serious problem, and can bring several benefits in terms of the performance improvement of the system. In this paper, we first illustrate the benefits of the yaw steering, and then provide two aspects of the system design with the incorporation of demonstrating the benefits of the yaw steering. The demonstrated three main benefits of the yaw steering are: 1) makes the 2-D side-lobes nearly orthogonal, and improves the imagery quality; 2) largely reduces the synthetic aperture time, and decreases the computation burden of imaging processing; and 3) substantially mitigates the azimuth-variation of raw data, and simplifies the imaging processing.

For high-resolution wide-swath synthetic aperture radar imaging algorithms, signal reconstruction is a key step. The steering vector plays an important role in signal reconstruction, which can be constructed by the ambiguity components. The information of the ambiguity components, e.g., number and index, is usually regarded to be constant and known. However, we find that the information of ambiguity components is always a piecewise function of the baseband frequency. This means that the steering vector cannot be preconstructed accurately and it will negatively affect the signal reconstruction. This paper presents an improved signal reconstruction method based on the Doppler spectrum estimation. The proposed method can estimate the variant ambiguity components to form the steering vector exactly by the Capon estimation. As a result, the method is able to restore the Doppler spectrum entirely and performs well on the noise reduction. Moreover, the baseband Doppler centroid and antenna pattern can be obtained in the proposed method. Simulated data and airborne raw data are processed to validate the algorithm.

Using elastic mechanics and fracture mechanics, analyzing the coal seam hydraulic fracturing breakdown pressure, given its theoretical formula. According to hydraulic fracturing stress status, given the form of two typical hydraulic fracture morphology. Analyzing hydraulic fracturing highly elliptical shape. The displacement field in plane stress state is given, and the theoretical formula of fracturing radius of hydraulic fracturing is deduced. The fracturing technology of underground fracturing is presented, and the fracturing location and fracturing parameters are determined. In Sihe Coal Mine conducted fracturing test, the test results showed that: the average of drainage volume of fracturing hole improved 4.4 times compared with non-pressed-hole. The extraction compliance time is reduced by 38%. Roadway tunneling speed was improved by 15%. It can solve the problem of gas overrun in roadway excavation well, and has a good application and popularization value.

Due to Earth’s rotation and an elliptical satellite orbit, large Doppler centroids along satellite orbit inevitably occur in geosynchronous Earth orbit synthetic aperture radar (GEOSAR). Nonzero Doppler centroid causes a large range migration, which complicates the data acquisition and design of imaging algorithms. Thus, beam steering is used to decrease the centroid. At the same time, the ground observation of interest is prerequisite for applications. Thus, a unique two-dimensional (2-D) beam-steering method to simultaneously consider the reduction of Doppler centroid and ground observation for the GEOSAR is studied. The minimum-Doppler plane is proposed to minimize the centroid and to guarantee the beams that illuminate the area of interest. Subsequently, to achieve required ground coverage, beam directions determined by the minimum-Doppler plane are slightly adjusted. The method has been validated through the simulation of two types of orbits.

The ultralong synthetic aperture time and a very large scene cause severe 2-D spatial variation in geosynchronous synthetic aperture radar. The range variation was corrected using the range cell migration equalization and the modified chirp scaling function. The azimuth variation correction with the singular value decomposition and the azimuth nonlinear scaling was studied. The validity of the proposed imaging algorithm has been assessed. Satisfactory results were obtained in the removal of the azimuth variation, and the focusing of point targets from a synthetic aperture up to 1000 s and a scene of 150 km (azimuth) $\times$ 130 km (range).

To meet the demand for large area environment monitoring during short synthetic aperture time, this paper proposes an imaging algorithm for high squint beam steering (HS-BS) SAR platforms with constant acceleration. To achieve a good performance and high efficiency, the baseband azimuth scaling (BAS) method cannot be directly applied for the azimuth focusing. The signal is calculated based on high squint concentric circle (HS-CC) range model. The conventional derotation operation is modified by nonlinear phase and range-dependent derotation. Then the space-variant (SV) Doppler chirp rate is corrected by BAS method without sub-aperture processing.

Due to the limited information of two-dimensional (2D) radar images, the study of three-dimensional (3D) radar image reconstruction has received significant attention. However, the target attitude obtained by the existing 3D reconstruction methods is unknown. In addition, using a single perspective, one can only get 3D reconstruction result of a simple target. For a complex target, due to occlusion and scattering characteristics, 3D reconstruction information obtained from a single perspective is limited. To tackle the above two problems, this paper proposes a new method for multi-perspective 3D reconstruction and single perspective instantaneous target attitude estimation. This method consists of three steps. First, the result of 3D reconstruction with unknown attitude is obtained by the traditional matrix factorization method. Then, in order to obtain the attitude of a target 3D reconstruction, additional constraints are added to the projection vectors which are computed from the matrix factorization method. Finally, the information from different perspectives are merged into a single layer information according to certain rules. After the information fusion, a multi-perspective 3D reconstruction structure with better visibility and more information is obtained. Simulation results have proved the effectiveness and robustness of the proposed method.