Search

Abstract The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

Abstract Percutaneous endoscopic lumbar discectomy (PELD) is increasingly used in patients with lumbar disc herniation due to its less invasive nature and faster recovery. In this study, we aimed to investigate the prognostic factors associated with patient satisfaction index (PSI) in PELD. A retrospective study of 337 patients who underwent PELD between November 2015 and October 2020 in our department was conducted. Preoperative data of the enrolled patients were recorded. A logistic regression model was developed to explore the preoperative factors associated with postoperative PSI. After a mean follow-up of 28.7 ± 3.6 months, 310 patients were satisfied (NASS score of 1 or 2) and 27 patients were dissatisfied (NASS score of 3 or 4), resulting in a patient satisfaction rate of 91.99%. Demographic data, clinical symptom characteristics, and neurological physical examinations (including lower limb hypesthesia, muscle strength, and tendon flex) did not show any significant differences between the four groups (NASS1,2,3,4). The satisfactory group showed a higher rate of positive Lasegue sign (P = 0.010) and higher preoperative VAS (P = 0.002). The dissatisfaction group showed a higher rate of contiguous double-level disc herniation (P = 0.003). Our findings indicated that positive Lasegue sign and high preoperative VAS were prognostic factors for patient-reported satisfaction and PELD might not be the first choice for contiguous double-level disc herniation.

Abstract Purpose To analyze the characteristics of PEEK rods retrieved in vivo, specifically their wear and deformation, biodegradability, histocompatibility, and mechanical properties. Method Six PEEK rods were retrieved from revision surgeries along with periprosthetic tissue. The retrieved PEEK rods were evaluated for surface damage and internal changes using Micro-CT, while light and electron microscopy were utilized to determine any histological changes in periprosthetic tissues. Patient history was gathered from medical records. Two intact and retrieved PEEK rods were used for fatigue testing analysis by sinusoidal load to the spinal construct. Results All implants showed evidence of plastic deformation around the screw-rod interface, while the inner structure of PEEK rods appeared unchanged with no visible voids or cracks. Examining images captured through light and electron microscopy indicated that phagocytosis of macrophages around PEEK rods was less severe in comparison to the screw-rod interface. The results of an energy spectrum analysis suggested that the distribution of tissue elements around PEEK rods did not differ significantly from normal tissue. During fatigue testing, it was found that the retrieved PEEK rods cracked after 1.36 million tests, whereas the intact PEEK rods completed 5 million fatigue tests without any failure. Conclusion PEEK rods demonstrate satisfactory biocompatibility, corrosion resistance, chemical stability, and mechanical properties. Nevertheless, it is observed that the indentation at the junction between the nut and the rod exhibits relatively weak strength, making it susceptible to breakage. As a precautionary measure, it is recommended to secure the nut with a counter wrench, applying the preset torque to prevent overtightening.

Background The comparison of coronary computed tomography angiography plaques and perivascular adipose tissue (PVAT) between patients with acute myocardial infarction (AMI) posttreatment and patients with stable coronary artery disease is poorly understood. Our objective was to evaluate the differences in coronary computed tomography angiography–quantified plaque and PVAT characteristics in patients post‐AMI and identify signs of residual inflammation. Methods and Results We analyzed 205 patients (age, 59.77±9.24 years; 92.20% men) with AMI ≤1 month and matched them with 205 patients with stable coronary artery disease (age, 60.52±10.04 years; 90.24% men) based on age, sex, and cardiovascular risk factors. Coronary computed tomography angiography scans were assessed for nonculprit plaque and vessel characteristics, plaque volumes by composition, high‐risk plaques, and PVAT mean attenuation. Both patient groups exhibited similar noncalcified plaque volumes (383.35±313.23 versus 378.63±426.25 mm3, P=0.899). However, multivariable analysis revealed that patients post‐AMI had a greater patient‐wise noncalcified plaque volume ratio (estimate, 0.089 [95% CI, 0.053–0.125], P

AimTo provide a risk prediction for bronchopulmonary dysplasia (BPD) in premature infants under the new diagnostic criteria and establish a prediction model.MethodsIn this study, we retrospectively collected case data on preterm infants admitted to the NICU from August 2015 to August 2018. A lasso analysis was performed to identify the risk factors associated with the development of BPD. A nomogram predictive model was constructed in accordance with the new diagnostic criteria for BPD.ResultA total of 276 preterm infants were included in the study.The incidence of BPD under the 2018 diagnostic criteria was 11.2%. Mortality was significantly higher in the BPD group than the non-BPD group under the 2018 diagnostic criteria (P

Rainfall is the primary trigger for geological disasters, yet accurately predicting the timing and distribution of rainfall remains challenging, contributing to the high false alarm rates of traditional short-term geological disaster early warning systems. Located in south-central China, Hunan Province experiences abundant rainfall and relatively fragile geological conditions, making it susceptible to frequent geological disasters. To address the escalating challenges of geological disaster prevention and control, a “631” progressive geological disaster meteorological risk early warning model has been constructed based on years of early warning work experience in Hunan Province. This early warning model is technically supported by the comprehensive geological disaster prevention and control information system of Hunan Province, and is based on accurate short-term rainfall forecasts and real-time precipitation. It establishes an early warning and response mechanism, issuing warning six hours in advance to forecast rainfall areas, three hours in advance for general warnings, one hour in advance for responses, and thirty minutes in advance for further response. Currently, this early warning model has been widely applied in geological disaster prevention and control efforts across the province, and greatly improving the efficiency and accuracy of early warnings. In early June 2022, Hunan Province encountered an unprecedented heavy rainfall event that exceeded historical records, and geological disasters occurred in clusters. Through the application of this early warning model, Hunan ranked first nationwide in the number of evacuees and avoidance measures implemented in 2022, demonstrating significant disaster prevention results.

ObjectiveThis study aimed to demonstrate and characterize a rare cervical spondylotic myelopathy (CSM) with sciatica-like pain as the main clinical manifestation through case presentation and systematic review.MethodsFour cases of CSM with sciatica-like pain as the main clinical manifestation were retrospectively studied. A systematic review of electronic databases such as PubMed, Embase, and Web of Science was conducted to explore the clinical characteristics of CSM with sciatica.ResultsAll four cases of CSM symptomatic of sciatica-like pain were initially diagnosed with lumbar degenerative conditions. However, due to the presence of neurological signs such as hyperactive tendon reflexes, a positive Babinski sign, or a Hoffmann sign, they underwent further cervical MRI scans. Eventually, all four cases were diagnosed with CSM and experienced relief from sciatica after cervical decompression surgery. The systematic review analyzed a total of four studies with a combined sample size of 10 cases, all of whom experienced a reduction in sciatica-like pain following cervical decompression surgery.ConclusionCSM symptomized by sciatica-like pain can often be misdiagnosed as lumbar degenerative disease. Preoperative abnormal neurological signs associated with CSM may aid in diagnosing this condition. In addition, the clinical presentation of hyperextension of the cervical spine resulting in worsening lower limb pain may serve as diagnostic indicators for this disease.

Spaceborne global navigation satellite system reflectometry (GNSS-R) is an emerging remote sensing technology that utilizes Earth surface reflections of GNSS signals to monitor geophysical parameters. With its unique advantages of high spatiotemporal resolution, low observational cost, wide coverage, and all-weather operation, GNSS-R has found extensive applications in ocean remote sensing. Recent successful launches of spaceborne GNSS-R platforms, such as TechDemoSat-1 in 2014, Cyclone GNSS in 2016, BuFeng-1 A/B in 2019, and FengYun-3E in 2021, have opened up new opportunities in this field. This article provides a comprehensive overview of the latest advancements in the application of spaceborne GNSS-R in ocean remote sensing. It covers satellite missions related to spaceborne GNSS-R and explores various methods and techniques for ocean remote sensing applications, including sea surface wind mapping, hurricanes, typhoons, and tropical cyclones monitoring, tsunamis and storm surges detection, sea surface altimetry and wave height measurement, sea ice sensing, and rainfall estimation, among others. Furthermore, the article discusses the challenges, prospects, and future outlook of spaceborne GNSS-R.

Doppler Scatterometer (DopScat) is a new tool for sea surface wind and current fields remote sensing with rapid global coverage and wide observation swath. Existing current inversion methods for DopScat are based on maximum likelihood estimation (MLE), and its inversion accuracy cannot meet the requirements of many offshore operations. To improve the accuracy of ocean surface current measurement for DopScat, a method using prior probability distributions extracted from historical current fields is presented. First, according to the temporal correlation of ocean surface current, the minimum root mean square differences of current speed and direction are used to select the historical ocean current data correlated to those of the observation area. Next, a fitting method based on maximum likelihood is employed to fit the selected current speed and direction data to determine their prior probability distributions. Then, the obtained distributions are used to construct the cost functions of the proposed maximum a posteriori (MAP)-based current inversion method. Finally, taking the current result provided by the MLE-based current inversion method as initial guess, the cost functions of the proposed MAP-based method are optimized to obtain the final current field. Validation experiments were conducted using simulated DopScat data based on the current generated by the ocean surface current analyses real-time model, the results show that the biases of the estimated current speed and direction are better than 0.05 m/s and 15$^{\circ }$, respectively. Compared with that of the MLE-based method, the biases are reduced by 0.16 m/s and 9$^{\circ }$, respectively.

Due to the advantage of high spectral resolution, hyperspectral imaging techniques have been extensively used in a variety of fields. Hyperspectral images (HSIs) classification is one of the fundamental tasks and attracts significant research interest. HSIs classification is pivotal as it facilitates precise identification of objects, providing invaluable insights for Earth observation tasks, such as resource management and land cover analysis. In existing studies, convolutional operations have been broadly applied for HSIs classification, especially 3-D convolution, which has shown its effectiveness in extracting spectral–spatial features from the raw HSIs. However, HSIs exhibit the characteristic of high dimensionality and pose challenges in extracting more discriminative features. In order to enhance the capability of capturing discriminative spectral–spatial features, in this article, a novel and effective 3-D sharpened cosine similarity (SCS) operation is proposed, serving as a replacement for conventional 3-D convolutional operation in HSIs classification and enhancing the classification accuracy. The 3-D SCS operation calculates and sharpens the cosine similarity between kernels and HSI input data. Based on the 3-D SCS operation, a 3-D SCS neural network is developed for HSIs classification tasks. To evaluate the effectiveness of 3-D SCS operation, experiments are conducted on three real-world HSIs datasets, including the University of Pavia, the University of Trento, and the University of Houston. Quantitative and qualitative experimental results illustrate that the SCS operation can effectively extract discriminative spectral-spatial features, achieving superior performance over the CNNs under the same model configuration.

Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture wing trajectory data from five free-flying pigeons (Columba livia). Five key motion parameters are used to quantitatively characterize wing kinematics: flapping, sweeping, twisting, folding and bending. In addition, the forelimb skeleton is mapped using an open-chain three-bar mechanism model. By systematically evaluating the relationship of joint degrees of freedom (DOFs), we configured the model as a 3-DOF shoulder, 1-DOF elbow and 2-DOF wrist. Based on the correlation analysis between wingbeat kinematics and joint movement, we found that the strongly correlated shoulder and wrist roll within the stroke plane cause wing flap and bending. There is also a strong correlation between shoulder, elbow and wrist yaw out of the stroke plane, which causes wing sweep and fold. By simplifying the wing morphing, we developed three flapping wing robots, each with different DOFs inside and outside the stroke plane. This study provides insight into the design of flapping wing robots capable of mimicking the 3D wing motion of pigeons.

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m2. Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m2. Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m2. In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively.

Abstract This work proposed KidneyRegNet, a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which comprises a feature network, and a 3D–2D CNN-based registration network. The feature network has handcrafted texture feature layers to reduce the semantic gap. The registration network is an encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with a retrospective dataset cum training data generation strategy and then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite applications. The experiment was performed on 132 U/S sequences, 39 multiple-phase CT and 210 public single-phase CT images, and 25 pairs of CT and U/S sequences. This resulted in a mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. Datasets with small transformations resulted in MCDs of 0.82 and 1.02 mm, respectively. Large transformations resulted in MCDs of 1.10 and 1.28 mm, respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategies.

【Objective】Hybrid snakehead is an important special aquatic fish in China. In order to find out the infection status of hybrid snakehead rhabdovirus (HSHRV), the rhabdovirus disease of hybrid snakehead cultured in the Pearl River Delta was investigated from March 2021 to February 2022.【Method】The parents, fry and water samples of hybrid snakehead were randomly sampled in breeding stage, and these were randomly sampled at fixed time in culture stage and sampled when the outbreak of the virus. A total of 333 samples were collected during the whole monitoring period. Mixed samples of mung bean-sized liver, spleen and kidney were stored in liquid nitrogen for RNA extraction. And reverse-transcribed cDNA was used as template to detect rhabdovirus by real-time fluorescence quantitative PCR (qPCR).【Result】The main clinical symptoms of rhabdovirus-infected fish were that the fish swam irregularly and spin. Although there was no obvious symptom on the body surface, the liver, spleen, kidney, intestine and swim bladder became red or swollen. The qPCR results revealed that the average positive rate of HSHRV was 13.21%, which was mainly detected from April to November. Among them, the positive rate of samples detected in August was as high as 23.91%. The average positive rate of HSHRV in the culture stage (17.01%) was significantly higher than that in the breeding stage (3.26%). In terms of fish body size, 32.20% of the fry were most susceptible to HSHRV infection within 10 cm, mainly detected from March to September, while 9.52% of the fish of more than 20 cm were least susceptible to HSHRV infection.【Conclusion】The positive rates of HSHRV in hybrid snakehead in breeding stage and cultivating stage as well as in different size are determined, which provide references for further understanding the epidemic regularity of hybrid snakehead rhabdovirus disease, with a view to providing data support for the prevention and control of hybrid snakehead rhabdovirus disease.

Hyperspectral images (HSIs) provide detailed spectral information of objects to be detected and play an important role in distinguishing targets with a similar appearance. However, the characteristics of high dimensionality and complexity impose significant challenges for realizing pixelwise classification. Although existing convolutional neural networks and transformer-based models have presented promising performance for HSIs classification, they mainly extract features from spectral–spatial perspective and do not fully consider the information in the frequency domain. To address this issue, in this article, we reconsider feature extraction and HSIs classification from the frequency domain. Specifically, inspired by the observation that high-frequency information contains detailed features within a local receptive field, whereas low-frequency information provides global smooth variations, a frequency domain feature extraction (FDFE) block with dual branches is developed. In the FDFE block, a multihead neighborhood attention block and a global filter block are designed to capture high- and low-frequency features, respectively. Besides, a pixel embedding module is constructed. Based on these, a novel hierarchical dual frequency transformer network is developed. Extensive experiments are performed on three open public hyperspectral datasets to evaluate the performance of our developed method. The experimental results demonstrate that our method is efficient and robust for HSIs classification, achieving overall accuracies of 94.14%, 86.92%, and 96.72% on the University of Pavia, University of Houston, and University of Trento datasets, respectively.al

It has become urgent to address the traffic challenges faced by this group with the continuous increase in the number of visually impaired individuals. A blind guiding robot based on speed adaptation and visual recognition was designed to address this problem. The speed adaptation of the robot and the blind person is achieved through feedback control of the distance and speed in this paper. Traffic signals are identified using optimized visual recognition method based on YOLOv5 transfer learning, and man-machine interaction is realized by applying multi-module units such as real-time image, speech, and positioning. The experimental results show that the rate of change of the relative distance was controlled within 13.1%, the relative velocity deviation was controlled within 0.3 m/s, the accuracy of identifying traffic signals reached 91.88%. And when the man-machine distance gap is large, the robot can control the man-machine distance to the set distance within 0.7 s in a timely manner, which effectively ensured the travel safety of blind people and provide the groundwork for the practical application of guiding blind robots.

The performance of deep convolutional neural networks has been significantly improved in recent years as a result of additional attention mechanisms applied to the standard networks. Numerous experiments conducted have demonstrated that spectral-spatial attention enhances the network's categorization ability. The three attention modules that currently use spatial attention, spectral attention, and channel attention are isolated from each other and their interrelationships are not fully considered. To solve this problem and establish the dependencies among different channels, spectral bands, spatial height, and width simultaneously, in this article, a new cross attention module called quadlet is proposed, which can capture information using simultaneous interaction of the channel, spectral depth and spatial location to improve the classification accuracy of hyperspectral images. By incorporating the quadlet attention module, a cross-dimensional residual network (QuadNet) is proposed for HSIs classification. A series of experiments conducted on four publicly available hyperspectral datasets showed that the proposed cross-attention residual network can effectively establish the dependencies among different dimensions of input tensor and achieve 98.22%, 99.88%, 99.10%, and 96.46% overall accuracy on IN, UP, SA, and UH datasets, respectively.

This paper presents the MSSFF (multistage spectral–spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral–spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral–spatial fusion module (SSFM) in the encoder component to effectively extract and enhance these features. Additionally, an efficient Transformer (ET) is introduced in the skip connection part of deep features to capture long-term dependent features and extract global spectral–spatial information from the entire feature map. This highlights the significant potential of Transformers in modeling spectral–spatial feature maps within the context of hyperspectral remote sensing. Moreover, a spatial attention mechanism is adopted in the shallow skip connection part to extract local features. The framework demonstrates promising capabilities in hyperspectral remote sensing applications. The conducted experiments provide valuable insights for optimizing the model depth and the order of feature fusion, thereby contributing to the advancement of hyperspectral semantic segmentation research.

Abstract In order to research the variation law of the longitudinal resistance coefficient of the ore bin in the marine mining system under different length–diameter ratio, external shape, additional weight and Reynolds number, a set of experimental system for testing the resistance coefficient was designed and built independently. By analyzing the experimental results, it can be seen that under the same conditions, the resistance coefficient decreases gradually with the increase of Reynolds number and finally fluctuates around a certain value. Increasing the excitation displacement will reduce the overall resistance coefficient of the ore bin. The smaller the length-diameter ratio is, the larger the corresponding force value when the vibration acceleration of the ore bin is 0, and the larger the overall resistance coefficient is. The resistance coefficient of the cylindrical section is greater than that of the rectangular shape. In order to reduce the longitudinal vibration and the transverse towing offset, the shape of the ore bin should be cylindrical in actual design and production. At low Reynolds number, the increase of added weight will increase the resistance coefficient, while at high Reynolds number, the change of added weight will not cause the change of resistance coefficient.

Summary: The complexity of the human intervertebral disc (IVD) has hindered the elucidation of the microenvironment and mechanisms underlying IVD degeneration (IVDD). Here we determined the landscapes of nucleus pulposus (NP), annulus fibrosus (AF), and immunocytes in human IVD by scRNA-seq. Six NP subclusters and seven AF subclusters were identified, whose functional differences and distribution during different stages of degeneration (Pfirrmann I-V) were investigated. We found MCAM+ progenitor in AF, as well as CD24+ progenitor and MKI67+ progenitor in NP, forming a lineage trajectory from CD24+/MKI67+ progenitors to EffectorNP_⅓ during IVDD. There is a significant increase in monocyte/macrophage (Mφ) in degenerated IVDs (p = 0.044), with Mφ-SPP1 exclusively found in IVDD but not healthy IVDs. Further analyses of the intercellular crosstalk network revealed interactions between major subpopulations and changes in the microenvironment during IVDD. Our results elucidated the unique characteristics of IVDD, thereby shedding light on therapeutic strategies.

PurposeTo determine the long-term radiological outcomes of PEEK rods in patients with lumbar degenerative diseases.MethodsRadiological outcomes of cohort cases with lumbar degenerative diseases following PEEK rods were retrospectively studied. Disc height index (DHI) and range of motion (ROM) were measured by x-rays. The CT scans and reconstruction were used to determine screw breakage, rods fracture, screw loosening and intervertebral bony fusion status. The MRI scans were used to evaluate the changes of intervertebral discs at the non-fusion segments and adjacent segments in terms of Pfirrmann Classification.ResultsA total of 40 patients completed the mean of 74.8 ± 9.6 months follow-up, with 32 patients undergoing hybrid surgery and 8 patients undergoing non-fusion surgery. The mean DHI changed from preoperative 0.34 to 0.36 at the final follow-up and the ROM declined from 8.8° preoperatively to 3.2° at the final visit, however, both had no statistical differences. Of the 40 levels underwent non-fusion procedure, 9 levels showed disc rehydration with 7 patients from Grade 4 to Grade 3 and 2 patients from Grade 3 to Grade 2. The other 30 cases did not show distinctive change. No screw loosening or rods breakage were detected during the follow-up periods.ConclusionPEEK rods have obvious protective effects on degenerated intervertebral disc of non-fusion segments and the incidence of complications related to internal fixation is low. PEEK rods pedicle screw system is safe and effective in the treatment of lumbar degenerative diseases.

High-frequency radars (HFRs) are important for remote sensing of the marine environment due to their ability to provide real-time, wide-coverage, and high-resolution measurements of the ocean surface current, wave height, and wind speed. However, due to the intricate multidimensional processing demands (e.g., time, Doppler, and space) for internal data and effective suppression of external noise, conducting quality control (QC) on radar-measured data is of great importance. In this paper, we first present a comprehensive quality evaluation model for both radial current and synthesized vector current obtained by direction-finding (DF) HFRs. In the proposed model, the quality factor (QF) is calculated for each current cell to evaluate its reliability. The QF for the radial current depends on the signal-to-noise ratio (SNR) and DF factor of the first-order Bragg peak region in the range–Doppler (RD) spectrum, and the QF for the synthesized vector current can be calculated using an error propagation model based on geometric dilution of precision (GDOP). A QC method is then proposed for processing HFR-derived surface current data via the following steps: (1) signal preprocessing is performed to minimize the effect of unwanted external signals such as radio frequency interference and ionospheric clutter; (2) radial currents with low QFs and outliers are removed; (3) the vector currents with low QFs are also removed before spatial smoothing and interpolation. The proposed QC method is validated using a one-month-long dataset collected by the Ocean State Monitoring and Analyzing Radar, model S (OSMAR-S). The improvement in the current quality is proven to be significant. Using the buoy data as ground truth, after applying QC, the correlation coefficients (CCs) of the radial current, synthesized current speed, and synthesized current direction are increased by 4.33~102.91%, 1.04~90.74%, and 1.20~62.67%, respectively, and the root mean square errors (RMSEs) are decreased by 2.51~49.65%, 7.86~27.22%, and 1.68~28.99%, respectively. The proposed QC method has now been incorporated into the operational software (RemoteSiteConsole v1.0.0.65) of OSMAR-S.

The use of remote sensing imagery has significantly enhanced the efficiency of building extraction; however, the precise estimation of building height remains a formidable challenge. In light of ongoing advancements in computer vision, numerous techniques leveraging convolutional neural networks and Transformers have been applied to remote sensing imagery, yielding promising outcomes. Nevertheless, most existing approaches directly estimate height without considering the intrinsic relationship between semantic building segmentation and building height estimation. In this study, we present a unified architectural framework that integrates the tasks of building semantic segmentation and building height estimation. We introduce a Transformer model that systematically merges multi-level features with semantic constraints and leverages shallow spatial detail feature cues in the encoder. Our approach excels in both height estimation and semantic segmentation tasks. Specifically, the coefficient of determination (R2) in the height estimation task attains a remarkable 0.9671, with a root mean square error (RMSE) of 1.1733 m. The mean intersection over union (mIoU) for building semantic segmentation reaches 0.7855. These findings underscore the efficacy of multi-task learning by integrating semantic segmentation with height estimation, thereby enhancing the precision of height estimation.

Sea ice profoundly influences ocean circulation, the polar environment, biology, climate, and commercial activities. The rapidly changing sea ice environment and increased human activities in polar regions drive the demand for sea ice monitoring. Spaceborne synthetic aperture radar (SAR) has been widely adopted for sea ice sensing due to its all-weather, high spatial resolution, and day-and-night imaging capabilities. Previous reviews have addressed sea ice sensing based on various applications and sensors. However, no meta-analysis has been performed to specifically explore spaceborne polarimetric SAR-data-based sea ice sensing. Therefore, this study aims to provide a meta-analysis of spaceborne polarimetric SAR-data-based sea ice sensing by investigating 182 articles published in the last decade. Sea ice sensing applications for retrieving four key geophysical parameters (sea ice types, concentration, thickness, and motion) as well as SAR scattering characteristics analysis for sea ice are included. The review database was created with 15 fields including quantitative and qualitative perspectives, such as SAR frequency, polarization mode, methodology, evaluation metrics, etc. This meta-analysis aims to provide comparisons among different techniques and identify current challenges to determine effective methods for sea ice sensing. Overall, a snapshot of spaceborne polarimetric SAR-data-based sea ice sensing is presented through the meta-analysis, which could benefit researchers for future studies to advance this field.

This study focuses on systematically revealing how cutting parameters influence the surface integrity of ball-end hard milled surface of AISI D2 steel and proposing optimization scheme from surface integrity, wear resistance and fatigue resistance perspective based on response surface methodology respectively. Results can be summarized into three aspects. Firstly, radial depth of cut with percent contribution ratio (PCR) 62.05% has a decisive influence on surface roughness, followed by spindle speed 13.25% and feed per tooth 6.63%. The work hardening degree was raised from 12.5% to 38.4% when spindle speed changed from 8000 rpm to 2000 rpm. Spindle speed and radial depth of cut are the most significant factor influencing residual stress. The PCR of spindle speed and radial depth of cut reached 73.47% and 18.63% for residual stress in feed direction, 47.11% and 37.51% in step-over direction, respectively. High residual compressive stress can be generated by lowering spindle speed and radial depth of cut benefiting from the aggravated squeeze between ball-end milling cutter and workpiece. Secondly, too small feed per tooth or too small radial depth of cut should be avoided from wear resistance point because though the surface microhardness can be improved, the surface quality will also be deteriorated. The combination of high spindle speed, small feed per tooth together with small radial depth of cut can meet the wear resistance and the machining efficiency requirement. Finally, a medium-sized cutting parameter combination should be adopted to realize satisfying material removal rate and fatigue resistance. This study can be used to guide the selection of cutting parameters during ball-end milling of hardened AISI D2 steel for dies/molds manufacturing industries.

Bending angle is an important parameter in multi-pass roll forming. However, in actual manufacturing, there is still a lack of theoretical guidance; hence, rebound defects are commonly observed. In this paper, a bending angle distribution function with an optimized segmented boundary combination is proposed to improve the forming quality and reduce the rebound defects in multi-pass roll forming. For cap-shaped sheets, COPRA, a roll-bending simulation software, is used to investigate the bending angle distribution to optimize the process of cap-shaped roll-bending. Furthermore, the stress and strain distributions in the sheets after each forming pass is analyzed using finite element simulation. The influence law of different bending angle parameters on the quality of sheets is obtained. The finite element simulation is verified by comparing the experimental results of the change law of rebound defects of cap-shaped parts under different bending angle distribution conditions. Based on the segmented boundary combination of bending angle optimization functions, the stress and strain distributions in the sheets during roll-bending forming of the cap-shaped parts are improved, the rebound defects in the bending area are smaller, and the forming quality is better than the other bending angle distribution methods.

Abstract Background To review, analyze and characterize the pregnancy and lactation-related osteoporosis (PLO) with vertebral fractures based on the extraction data in the previous studies. Methods A comprehensive literature search of electronic databases including the PubMed, Embase and Web of Science was conducted from January 1st,1990 to December 1st, 2020. The enrolled data were pooled to analyze the baseline characteristics, clinical features, risk factors and treatment options. Results A total of 65 articles with 338 cases were enrolled for data extraction. The enrolled cases aged from 19 to 47 years, with a mean value of 35.7 years old. The average body mass index (BMI) was 22.2 kg/m2 ranged from 16.0 to 39.0 kg/m2. Of the 173 cases, 149 cases with vertebral fractures occurred in the first pregnancy, 19 cases in the second pregnancy, four cases in the third pregnancy and one case in the fourth pregnancy. Up to 91.5% of the back pain occurred within the last 3 months of pregnancy and the first 3 months after delivery. The most involved vertebral levels were L2, L1 and T12 accounting for 32.6% of all the fractures. The average fracture numbers were 4.4 levels per patient. The lumbar Z-scores were mostly recorded with a mean value of − 3.2 ranged from − 7.8 to 0. Conclusions PLO with vertebral fractures is a rare clinical entity, which is more likely to occur in older and thinner pregnant women. Back pain is the clinical complaint and mostly occurs in the late pregnancy and early lactation periods. Most vertebral fractures appear in the first pregnancy but it can occur in any time of pregnancy. Thoracolumbar region is the mostly involved region. As compared with postmenopausal osteoporotic fractures, PLO usually has multiple levels fractures. Bisphosphonates are the most widely used treatment so far, however, many factors need to be taken into account to decide which drug to choose in PLO and further studies are necessary for clear recommendation in the future.

Introduction As for coronary artery bypass grafting, although there are many direct comparative studies on different minimally invasive methods and traditional thoracotomy (off-pump/on-pump), there is still a lack of further ranking and summary of the efficacy of all surgical methods for left main coronary artery (LMCA) lesions. Combined with the current controversial views, this study aims to introduce a planned network meta-analysis (NMA) in detail, with a view to comparing the long-term efficacy and safety of multiple therapeutic methods in the treatment of patients with LMCA disease, and finally providing some reference bases for the best selection of clinical schemes.Method and analysis PubMed, Embase, Web of Science and The Cochrane Library databases will be collected from inception to June 2022 to compare the efficacy of different surgical methods in randomised controlled trials (RCTs) for LMCA disease. Main outcome endpoints: major adverse cardiovascular events, including mortality, myocardial infarction, stroke and revascularisation. Secondary outcome endpoints: (1) operation-related time, (2) the amount of blood transfusion, (3) complications including secondary thoracotomy, postoperative new atrial fibrillation, wound infection, (4) physiological score and psychological score, (5) time return to work and (6) total hospitalisation costs. The methodological quality of included RCTs will be assessed according to the Cochrane bias risk table. The Bayesian NMA will be conducted by STATA V.16.0.Ethics and dissemination The essence of this study is to summarise and analyse the original data without the approval of the ethics committee. Our research does not involve ethical issues, and the results will be published in peer-review journals.PROSPERO registration number CRD42021274712.

Abstract Cerium has been used as an implanted alloy additive for many years for it can enhance the mechanical properties of the alloy and restrain the corrosion of the implant. Moreover, cerium oxide nanoparticles are often used as an antibacterial material. However, there were few researches focusing on the antibacterial properties of Ce3+ and Ce4+ ions, instead of their corresponding oxide, as potential antibacterial coatings. Thus in this work, we loaded Ce ions into the porous structure of the ZIF‐8 nanoparticles and co‐deposited them with hydroxyapatite as a composite coating onto anodized titanium dioxide nanotubes array in order to test whether it can improve the anti‐corrosion and antibacterial properties of the materials without affecting the biocompatibility.

Abstract A new asymptotic solution for the scattered electromagnetic fields of a vertical Hertzian dipole antenna in the presence of an imperfectly conducting half‐plane for ordinary and plasmonic media is proposed. The scattered electric and magnetic field components are calculated from the intermediate Hertz potential expressed in terms of the Fourier‐Bessel transforms associated with the Sommerfeld‐type integral, which is difficult to evaluate due to the singularities of the integrand near the integration path and its oscillatory and slowly decaying integrand. Using the modified saddle point method, an approximate closed‐form solution of the far‐zone scattered electromagnetic fields including surface waves is presented. The new formulations are applied to calculate radiation patterns of different impedance half‐planes for both ordinary and plasmonic media, that is, seawater, silty clay soil, silty loam soil and lake water as ordinary, and silver and gold as plasmonic media. Furthermore, a numerical evaluation of the proposed solution at various frequencies and comparisons with two alternative state of the art solutions shows that the proposed solution has higher accuracy in terms of the normalised root‐mean‐square error and the normalised maximum absolute error for plasmonic and non‐plasmonic structures.

Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). Although they are more accurate than forecasted wind fields, reanalysis wind fields tend to have time latencies because they can only be released after the observations are obtained. In order to produce accurate estimates of wind fields in a more timely manner, we develop an adversarial learning approach to correcting forecasted wind fields to be close to reanalysis wind fields. The adversarial learning approach is conducted by an adversarial ConvLSTM network (ACLN) framework that consists of a corrector and a discriminator. The corrector aims at comprehensively capturing both spatial and temporal characteristics of a sequence of forecasted wind fields and producing a corrected forecast wind field for the final field in the sequence. The discriminator tries to distinguish corrected forecast wind field from its corresponding reanalysis wind field. The training of ACLN is alternate between the corrector and the discriminator in an adversarial fashion. The adversarial training mechanism enhances the corrector’s representational power. Additionally, the corrector exploits a residual learning architecture that effectively learns the differences between forecasted wind fields and the corresponding reanalysis wind fields. In this scenario, the well trained corrector requires neither reanalysis wind fields nor observations such that it can correct forecasted wind fields in a timely manner. Furthermore, corrected forecast wind fields are employed for oil spill drift prediction. Extensive experiments validate the effectiveness of the proposed ACLN framework in forecasted wind field correction along with oil spill drift prediction. Compared with ECMWF numerical forecasts, the ACLN achieves an average reduction of 6.2%, 6.9%, and 10.6% in RMSE, MAE, and MAPE, respectively. Compared with a basic drift prediction method, the ACLN based prediction method reduces the error by about 5000 m in the Sanchi oil spill accident. The source codes are available at https://github.com/liyongqingupc/ACLN-WindFieldCorrection, providing a baseline for correcting forecasted wind fields.

Electrocardiogram (ECG) is a critical physiological indicator that contains abundant information about human heart activities. However, it is a kind of weak low-frequency signal, which is easy to be interfered by various noises. Therefore, wearable biosensors (WBS) technique is introduced to overcome this challenge. A flexible non-contact electrode is proposed for wearable biosensors (WBS) system, which is made up of flexible printed circuits materials, and can monitor the ECG signals during exercise for a long time. It uses the principle of capacitive coupling to obtain high-quality signals, and reduces the impact of external noise through active shielding; The results showed that the proposed non-contact electrode was equivalent to a medical wet electrode. The correlation coefficient was as high as 99.70 ± 0.30% when the subject was resting, while it was as high as 97.53 ± 1.80% during exercise. High-quality ECG could still be collected at subjects walking at 7 km/h. This study suggested that the proposed flexible non-contact electrode would be a potential tool for wearable biosensors for medical application on long-term monitoring of patients’ health and provide athletes with physiological signal measurements.

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology.

The presence of rain may blur surface wave signatures and cause additional radar backscatter, which negatively affects the performance of ocean remote sensing applications (e.g., ocean surface wind and wave parameter measurement) using X-band marine radars. In this article, a novel end-to-end model is developed to detect and locate rain-contaminated pixels in X-band marine radar images based on a type of deep neural network called SegNet, which is able to segment rain-contaminated regions by classifying each pixel into three classes: rain-free, rain-contaminated, and wind-dominated rain cases. Shipborne marine radar images collected during a sea trial on the East Coast of Canada are first preprocessed and then utilized to train an ensemble of SegNet-based networks. The final classification result of each pixel will be the class chosen by most individual networks. Testing results using images obtained from both shipborne and shore-based marine radar systems manifest that the proposed model effectively segment between rain-free, rain-contaminated, and wind-dominated rain regions, with a pixel classification accuracy of 94.6% and 90.4% for Decca and Koden radar images, respectively.

Vessel speed and heading are two important kinematic parameters describing its state of motion. However, due to low spatial resolution of a compact high-frequency surface wave radar (HFSWR), vessel speed and heading cannot always be accurately estimated. Since HFSWR can measure vessel Doppler velocity with relatively high accuracy, it is possible to estimate a vessel's vector velocity based on two Doppler velocities measured along two different directions. In this article, a newly developed T/R-R composite compact HFSWR system is introduced, and a corresponding vessel velocity estimation method which employs a target's radial velocity and elliptical velocity, respectively, measured by the monostatic (T/R) and bistatic (T-R) settings is proposed. First, monostatic and bistatic tracks are independently generated using a multitarget tracking algorithm. Then, the obtained monostatic and bistatic tracks are matched using a track-to-track association method to determine the track pair belonging to each target. Subsequently, the associated track pairs are combined to produce fused tracks for improving positioning accuracy. Finally, vessel vector velocity is estimated based on the radial and elliptical velocities as well as the fused target position. Comparisons of vector velocity estimation results from radar field data with corresponding automatic identification system data demonstrate that the average root-mean-square-errors of the estimated speed and heading are 0.48 km/h and 3.9$^{\circ }$, respectively, which meets the practical requirements of a maritime surveillance system. Moreover, the velocity estimation error is analyzed via theoretical derivation and experimental verification. The proposed method shows good potential in further improving the tracking accuracy.

The presence of rain degrades the performance of sea surface parameter estimation using X-band marine radar. In this article, a novel scheme is proposed to improve wind measurement accuracy from rain-contaminated X-band marine radar data. After extracting texture features from each image pixel, the rain-contaminated regions with blurry wave signatures are first identified using a self-organizing map (SOM)-based clustering model. Then, a convolutional neural network used for image haze removal, i.e., DehazeNet is introduced and incorporated into the proposed scheme for correcting the influence of rain on radar images. In order to obtain wind direction information, curve fitting is conducted on the average azimuthal intensities of rain-corrected radar images. On the other hand, wind speed is estimated from rain-corrected images by training a support vector regression-based model. Experiments conducted using datasets from both shipborne and onshore marine radar show that compared to results obtained from images without rain correction, the proposed method achieves relatively high estimation accuracy by reducing measurement errors significantly.

In this article, the accuracy of wave direction and period estimation from X-band marine radar images under different rain rates is analyzed, and a simple subimage selection scheme is proposed to mitigate the rain effect. First, each radar image is divided into multiple subimages, and the subimages with relatively clear wave signatures are identified based on the random-forest-based classification model. Then, wave direction is estimated by performing the Radon transform on each valid subimage. As for wave period estimation, a new method is proposed. Texture features are first extracted from each pixel of the selected subimage using the gray-level co-occurrence matrix and combined as a feature vector. Those feature vectors extracted from both rain-free and rain-contaminated training samples are then used to train a random-forest-based wave period regression model. The shore-based X-band marine radar images, simultaneous rain rate data, as well as buoy-measured wave data collected on the West Coast of the United States are used to analyze the rain effect on wave parameter estimation accuracy and validate the proposed method. Experimental results show that the proposed subimage selection scheme improves the estimation accuracy of both wave direction and wave period under different rain rates, with reductions of root-mean-square errors (RMSEs) by $\text{6.9}^\circ$, $\text{6.0}^\circ$, $\text{4.9}^\circ$, and $\text{1.0}^\circ$ for wave direction under rainless, light rain, moderate rain, and heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by 0.13, 0.20, 0.30, and 0.20 s under those four rainfall intensity levels, respectively.

In this article, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height ($H_s$) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal-to-noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level cooccurrence matrix methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce $H_s$ estimation. Radar data are collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the $H_s$ estimation accuracy, with reductions of root-mean-square errors by 0.33 and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression and the gated recurrent unit network.

Oil spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS detection (OSD) is necessary for ocean management. In this regard, remote sensing (RS) plays a key role due to multiple advantages over large and remote ocean environments. In this study, a new OSD framework based on a deep learning algorithm was developed for optical RS imagery. The proposed method was based on a multiscale multidimensional residual kernel convolution neural network. The proposed method investigated the deep features by the two-dimensional multiscale residual blocks and, then, utilized them at one-dimensional multiscale residual blocks. In this study, Landsat-5 satellite imagery acquired over the Gulf of Mexico was applied to evaluate the performance of the proposed method. The overall accuracy of the proposed method was more than 95%, and the miss detection and false alarm rates were less than 5%, indicating its high potential for OSD. Moreover, it was observed that the proposed method had better performance compared to other OSD algorithms that were investigated in this study.

BackgroundAminophylline is widely used in the treatment of preterm infants, but it can cause feeding intolerance events, in which gut microbial dysbiosis may have a role. This study aims to investigate the relationship between the gut microbiome of preterm infants treated with aminophylline and the occurrence of feeding intolerance and weight gain rate.MethodsThis study included a cohort of 118 preterm infants. Survival analysis and multivariate Cox regression were used to evaluate the relationship between aminophylline treatment and the occurrence of feeding intolerance. 16S rRNA V4 region gene sequencing was used to characterize the microbiome of fecal samples from the cohort. Linear discriminant analysis effect size was used to analyze the differential abundance of bacteria related to aminophylline treatment. Wilcoxon test, Kruskal–Wallis test, Spearman correlation coefficients and generalized linear mixed models were used to analyze the correlation between the differential bacteria and feeding intolerance events as well as the weight gain.ResultsThe results showed that the use of aminophylline could significantly increase the occurrence of feeding intolerance. The relative abundances of Streptococcus and Rothia in the gut microbiome of preterm infants were positively correlated with both the occurrence of feeding intolerance and the use of aminophylline, while the relative abundance of Staphylococcus was negatively correlated. In particular, preterm infants with a lower relative abundance of Rothia were more likely to develop feeding intolerance associated with aminophylline, and this difference existed before the onset of feeding intolerance. Moreover, it took longer for individuals with a lower relative abundance of Streptococcus to reach 2 kg weight. The contribution of Streptococcus to weight gain was greater than that of Bifidobacterium or Lactobacillus.ConclusionThe gut microbiome in preterm infants treated with aminophylline was characterized by a decrease in Streptococcus and Rothia and an increase in Staphylococcus. These microbes, especially Rothia, were positively correlated with the occurrence of feeding intolerance. Streptococcus but not Bifidobacter likely participated in the weight gain of preterm infants in early life.

Objectives: Initial microbial colonization of the oral after birth provides a vital stimulus for neonatal immune and development. The establishment of the gut microbiota has been shown to differ between very low birth weight (VLBW) infants delivered by caesarian section (C-section) and those delivered vaginally. The objective of this study was to investigate the community structure of the oral microbiota in VLBW infants delivered by the two modes. Methods: In total, 23 VLBW infants who were hospitalized in the neonatal intensive care unit of Shenzhen BaoAn Maternity & Child Health care Hospital (Shenzhen, China) were recruited for this study: 12 infants delivered vaginally, and the other 11 infants delivered by C-section. The assessment of oral microbiota community was performed using 16S rRNA gene sequence analysis. Results: The results demonstrated that the oral bacterial communities were dominated by the phylum Proteobacteria in both groups. Higher relative abundance of genera Ureaplasma and Pantoea were observed in the vaginal delivery infants, but genera Corynebacterium, Methylobacterium and Variovorax were more prevalent in cesarean-born infants. Furthermore, many metabolic pathways with significant differences between the two groups were detected, mostly related to vitamin, amino acid metabolism and diseases. Additionally, ɑ-diversity and clinical data showed no significant differences between the two groups. Conclusions: This study indicated that the mode of delivery influences the oral bacterial structure of VLBW infants after birth, but the consequences for neonatal development should be researched in a further study. Key Words: oral microbiota, very low birth weight infants, the mode of delivery, 16s rRNA gene sequencing

In this paper, the application of coherent ultra-high frequency (UHF) Doppler radar for ship target detection over river is investigated. Due to the wide beam and high resolution of UHF radar, ship target echoes are usually significantly extended in both the range and Doppler dimensions of the radar Range-Doppler (R-D) spectrum. The range and radial velocity of the extended target are difficult to be determined using a constant false alarm rate (CFAR) detector, especially for the low-radial-velocity case in which the detection performance of CFAR detector is deteriorated due to strong river clutter. To solve this problem, an applied clustering method is proposed to detect and classify multiple targets and obtain corresponding target centers from the CFAR outputs. The target extension characteristics, which are used for clustering, are modeled and employed in segments for different range. The effectiveness of the proposed method is validated using both simulated and field data and the clustering method can classify extended targets without the need of knowing the number of targets beforehand.

Flash floods can cause massive damages because of their rapid evolution. To reduce or prevent harm caused by a flash flood, it is vital to have information about its formation and spread. Hence, providing real-time surveillance flood is essential. Considering Hurricane Harvey and Hurricane Irma as two case studies, six different data preparation approaches (DPAs) for flood detection based on the Cyclone Global Navigation Satellite System (CYGNSS) data and the Random Under-Sampling Boosted (RUSBoost) classification algorithm are investigated in this article. Taking flood and land as two classes, flash flood detection is tackled as a binary classification problem. Eleven observables are extracted from the delay-Doppler maps (DDMs) for feature selection. These observables, alongside two features from an ancillary data, are considered in feature selection. All the combinations of these observables with and without ancillary data are fed into the classifier with 5-fold cross-validation one by one. Based on the test results, five observables with the ancillary data are selected as a suitable feature vector for flood detection here. Using the selected feature vector, six different DPAs are investigated and compared to find the best one for flash flood detection. Then, the performance of the proposed method is compared with that of a support vector machine (SVM) based classifier. For Hurricane Harvey and Hurricane Irma, the selected method is able to detect 89.00% and 85.00% of flooded points, respectively, with a resolution of 500m × 500m, and the detection accuracy for non-flooded land points is 97.20% and 71.00%, respectively.

In this article, an effective schematic is developed for estimating sea ice thickness (SIT) from the reflectivity (Γ) produced with TechDemoSat-1 (TDS-1) Global Navigation Satellite System-Reflectometry data. Here, Γ is formulated as the product of the propagation loss due to SIT and the reflection coefficient of underlying seawater. The effect of surface roughness on Γ is neglected when only considering signals of coherent reflection. In practice, Γ at the specular point is first generated using TDS-1 data. Afterwards, SIT is calculated from TDS-1 Γ based on the proposed reflectivity model, and verified with two sets of reference SIT data; one is obtained by the Soil Moisture Ocean Salinity (SMOS) satellite, and the other is the combined SMOS/Soil Moisture Active Passive (SMAP) measurements. This analysis is performed on the data with SIT less than 1m. Through comparison, good consistency between the derived TDS-1 SIT and the reference SIT is obtained, with a correlation coefficient (r) of 0.84 and a root-mean-square difference (RMSD) of 9.39 cm with SMOS, and an r of 0.67 and an RMSD of 9.49 cm with SMOS/SMAP, which demonstrates the applicability of the developed model and the utility of TDS-1 data for SIT estimation. In addition, this method is proved to be useful for improving existing sea ice detection accuracy.

Ionospheric clutter is a major factor affecting the performance of high-frequency surface wave radar (HFSWR). Previous studies have been mainly focused on the development of ionospheric clutter suppression methods involving delicate signal processing techniques or additional antennas. However, ionospheric clutter originates from the interaction between HF waves and the ionosphere, thus it contains the characteristics of the latter. Therefore, ionospheric clutter can be analyzed to obtain the parameters of the reflecting ionosphere, expanding the value of HFSWR. This article presents the preliminary coordinated observation and analysis results of the characteristics of the ionosphere at mid-latitudes of China using both HFSWR and ionosonde. The results demonstrate the existence of an oblique skywave propagation path (0.5 jump, 1 jump, etc.) in addition to the vertical reflection path and ionosphere-ocean mixed path. HFSWR beams were also found split into an O-trace and an X-trace after entering the ionosphere. Furthermore, range-folded ionospheric echoes are related to the short-term thickening of the F2-layer, showing strong fluctuation in the range-Doppler spectrum of HFSWR. These observed characteristics of ionospheric echoes are useful for the development of an efficient ionospheric clutter suppression algorithm for HFSWR and further investigation of the ionospheric mechanisms at mid-latitudes.

Nickel-titanium shape memory alloys have been widely used in medical fields due to their unique shape memory effect, superelasticity, corrosion resistance and biocompatibility. However, there are still many processing difficulties to achieve required machined surface quality due to their high toughness and low elasticity. This paper proposes a sequential processing operations including milling, burnishing and electrochemical polishing. The effects of electrochemical parameters on surface quality, such as surface roughness, work hardening and surface grain size, are investigated in detail using orthogonal experiment. It is found that the electrochemical polishing parameters have the most significant effect on improving the surface roughness. With adjusting electrochemical polishing parameters, the surface roughness can be reduced to one tenth of the original value obtained by milling operations. Moreover, the current density in electrochemical polishing has more important influence on the surface quality than the distance and polishing time does, respectively. The conditions of 1.5 A/cm2 current density, 7 cm electrode distance and 20 s polishing time can obtain the lower surface roughness, hardening degree and the larger grain size. The effects of electrochemical parameters on surface roughness, work hardening and grain size are related to each other. The degree of work hardening can be qualitatively estimated by surface roughness and grain size to avoid damage to the machined surface. Keywords: Nickel-titanium, Shape memory alloy, Processing optimization, Electrochemical polishing, Milling, Surface roughness, Work hardening, Grain size

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. In recent years, regenerative medicine, tissue engineering and the development of new materials have become the focus of attention this field, and electrospinning technology to prepare nanofibrous materials for the treatment of cardiovascular diseases has attracted people's attention. Unlike previous reviews, this research enumerates the experimental methods and applications of electrospinning technology combined with nanofibrous materials in the directions of myocardial infarction repair, artificial heart valves, artificial blood vessels and cardiovascular patches from the perspective of cardiovascular surgery. In the end, this review also summarizes the limitations, unresolved technical challenges, and possible future directions of this technology for cardiovascular disease applications.

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

Objectives: Prior studies have proposed that maternal intrapartum antibiotic exposure shapes the gut microbiota and, subsequently the child's health. However, the effect of maternal intrapartum antibiotic exposure and its influence on the development of the neonatal oral microbiota in early infancy has not yet been reported. The aim of this study was to compare the initial oral microbiota immediately after birth of healthy infants with and without intrapartum antibiotic exposure. Methods: Twenty-two newborns of the BaoAn Maternal and Child Care Hospital (Shenzhen, China) were recruited for this study, 11 born to mothers without intrapartum antibiotic exposure (NT group) and 11 to mothers with intrapartum antibiotic prophylaxis with cefamezin (AT group). Oral microbiome profiles were determined by 16S rRNA sequencing based on the V3V4 hyper-variable regions. Results: Phylum Firmicutes was most frequently detected in subjects both groups and a higher frequency was observed in the NT group than the AT group. Phyla Actinobacteria, Bacteroidetes and Proteobacteria were more abundant after intrapartum antibiotics exposure. Genus Lactobacillus belonging to Firmicutes was predominant in the neonates not exposed to antibiotics, while significantly higher percentages of genera Klebsiella, Roseburia, Propionibacterium, Faecalibacterium, Escherichia/Shigella, Corynebacterium, Bifidobacterium, and Bacteroides were noted in AT infants than NT infants. Further function analysis demonstrated that lipopolysaccharide biosynthesis and amino acid–related metabolic function was enriched in the AT group, and carbohydrate metabolism pathways were more abundant in the NT group. Conclusions: These findings revealed distinctions in both taxa and metabolic function of oral microbiota between antibiotics-treated and unexposed groups, which indicated that maternal intrapartum antibiotic treatment is a key regulator of the initial neonatal oral microbiome. Key Words: Neonatal oral microbiome, Maternal intrapartum antibiotics, 16S rRNA sequencing