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Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, this paper presents a novel line feature matching approach grounded in point–line invariants through spatial invariant relationships. By leveraging a robust point feature matching algorithm, an initial set of point feature matches is acquired. Subsequently, the line feature supporting area is partitioned, and a constant ratio invariant is formulated based on the distances from point to line features within corresponding neighborhood domains. Additionally, a direction vector invariant is also introduced, jointly constructing a dual invariant for line matching. An initial matching matrix and line feature match pairs are derived using this dual invariant. Subsequent geometric constraints within line feature matches eliminate residual outliers. Comprehensive evaluations under diverse imaging conditions, along with comparisons to several state-of-the-art algorithms, demonstrate that our proposal achieved remarkable performance in terms of both accuracy and robustness. Our implementation code will be publicly released upon the acceptance of this paper.

Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.

RGB-D SLAM (Simultaneous Localization and Mapping) generally performs smoothly in a static environment. However, in dynamic scenes, dynamic features often cause wrong data associations, which degrade accuracy and robustness. To address this problem, in this paper, a new RGB-D dynamic SLAM method, PLD-SLAM, which is based on point and line features for dynamic scenes, is proposed. First, to avoid under-over segmentation caused by deep learning, PLD-SLAM combines deep learning for semantic information segmentation with the K-Means clustering algorithm considering depth information to detect the underlying dynamic features. Next, two consistency check strategies are utilized to check and filter out the dynamic features more reasonably. Then, to obtain a better practical performance, point and line features are utilized to calculate camera pose in the dynamic SLAM, which is also different from most published dynamic SLAM algorithms based merely on point features. The optimization model with point and line features is constructed and utilized to calculate the camera pose with higher accuracy. Third, enough experiments on the public TUM RGB-D dataset and the real-world scenes are conducted to verify the location accuracy and performance of PLD-SLAM. We compare our experimental results with several state-of-the-art dynamic SLAM methods in terms of average localization errors and the visual difference between the estimation trajectories and the ground-truth trajectories. Through the comprehensive comparisons with these dynamic SLAM schemes, it can be fully demonstrated that PLD-SLAM can achieve comparable or better performances in dynamic scenes. Moreover, the feasibility of camera pose estimation based on both point features and line features has been proven by the corresponding experiments from a comparison with our proposed PLD-SLAM only based on point features.

Rectal indometacin decreases the occurrence of pancreatitis after endoscopic retrograde cholangiopancreatography (ERCP). However, the population most at risk and the optimal timing of administration require further investigation. We aimed to assess whether pre-procedural administration of rectal indometacin in all patients is more effective than post-procedural use in only high-risk patients to prevent post-ERCP pancreatitis. We did a multicentre, single-blinded, randomised controlled trial at six centres in China. Eligible patients with native papilla undergoing ERCP were randomly assigned in a 1:1 ratio (with a computer-generated list) to universal pre-procedural indometacin or post-procedural indometacin in only high-risk patients, with stratification by trial centres and block size of ten. In the universal indometacin group, all patients received a single dose (100 mg) of rectal indometacin within 30 min before ERCP. In the risk-stratified, post-procedural indometacin group, only patients at predicted high risk received rectal indometacin, immediately after ERCP. Investigators, but not patients, were masked to group allocation. The primary outcome was overall ocurrence of post-ERCP pancreatitis. The analysis followed the intention-to-treat principle. This study was registered with ClinicalTrials.gov, number NCT02002650. Between Dec 15, 2013, and Sept 21, 2015, 2600 patients were randomly assigned to universal, pre-procedural indometacin (n=1297) or risk-stratified, post-procedural indometacin (n=1303). Overall, post-ERCP pancreatitis occurred in 47 (4%) of 1297 patients assigned to universal indometacin and 100 (8%) of 1303 patients assigned to risk-stratified indometacin (relative risk 0·47; 95% CI 0·34–0·66; p<0·0001). Post-ERCP pancreatitis occurred in 18 (6%) of 305 high-risk patients in the universal group and 35 (12%) of 281 high-risk patients in the risk-stratified group (p=0·0057). Post-ERCP pancreatitis was also less frequent in average-risk patients in the universal group (3% [29/992]), in which they received indometacin, than in the risk-stratified group (6% [65/1022]), in which they did not receive the drug (p=0·0003). Other than pancreatitis, adverse events occurred in 41 (3%; two severe) patients in the universal indometacin group and 48 (4%; one severe) patients in the risk-stratified group. The most common adverse events were biliary infection (22 [2%] patients vs 33 [3%] patients) and gastrointestinal bleeding (13 [1%] vs ten [1%]). Compared with a risk-stratified, post-procedural strategy, pre-procedural administration of rectal indometacin in unselected patients reduced the overall occurrence of post-ERCP pancreatitis without increasing risk of bleeding. Our results favour the routine use of rectal indometacin in patients without contraindications before ERCP. National Key Technology R&D Program, National Natural Science Foundation of China.

The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more scattering information than regular SAR images. Polarimetric decomposition is often used to extract scattering information from polarimetric SAR. However, distinguishing all land cover types using only one polarimetric decomposition in complex ecological environments such as coastal wetlands is not easy, and thus integration of multiple decomposition algorithms is an effective means of land cover classification. More than 20 decompositions were used in this research to extract polarimetric scattering features. Furthermore, a new algorithm combining random forest (RF) with sequential forward selection (SFS) was applied, in which the importance values of all polarimetric features can be evaluated quantitatively, and the polarimetric feature set can be optimized. The experiments were conducted in the Jiangsu coastal wetlands, which are located in eastern China. This research demonstrated that the classification accuracies were improved relative to regular decision tree methods, and the process of polarimetric scattering feature set optimization was intuitive. Furthermore, the scattering matrix elements and scattering features derived from H / α , Yamaguchi3, VanZyl3, and Krogager decompositions were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands.

Although magic angle spinning (MAS) solid-state NMR is a powerful technique to obtain atomic-resolution insights into the structure and dynamics of a variety of chemical and biological solids, poor sensitivity has severely limited its applications. In this study, we demonstrate an approach that suitably combines proton-detection, ultrafast-MAS and multiple frequency dimensions to overcome this limitation. With the utilization of proton-proton dipolar recoupling and double quantum (DQ) coherence excitation/reconversion radio-frequency pulses, very high-resolution proton-based 3D NMR spectra that correlate single-quantum (SQ), DQ and SQ coherences of biological solids have been obtained successfully for the first time. The proposed technique requires a very small amount of sample and does not need multiple radio-frequency (RF) channels. It also reveals information about the proximity between a spin and a certain other dipolar-coupled pair of spins in addition to regular SQ/DQ and SQ/SQ correlations. Although 1 H spectral resolution is still limited for densely proton-coupled systems, the 3D technique is valuable to study dilute proton systems, such as zeolites, small molecules, or deuterated samples. We also believe that this new methodology will aid in the design of a plethora of multidimensional NMR techniques and enable high-throughput investigation of an exciting class of solids at atomic-level resolution.

Micro gas turbines are widely used in distributed power generation systems. However, the combustion of gas turbine combustors produces a large amount of nitrogen oxides (NOx), which pollute the environment and endanger human life. To reduce environmental pollution, low-emission combustors have been developed. In recent years, there has been an increasing focus on the use of low-heat-value gas fuels, and it is necessary to study the NOx emissions from low heat value gas fuel combustors. Data-driven deep learning methods have been used in many fields in recent years. In this study, a variational autoencoder was introduced for the prediction of NOx production inside the combustor. The combustor used was a micro rich–quench–lean combustor designed by the research group using coal bed gas as a fuel. The internal NO distribution contour was obtained as the dataset using simulation methods, with a size of 60 images. The model architecture parameters were obtained through hyperparameter exploration using the grid search method. The model accurately predicted the distribution of NO inside the combustor. The method can be applied in the prediction of a wider range of parameters and offers a new way of designing combustors for the power industry.

Urban forests maintain the ecological balance of cities and are significant in promoting the sustainable development of cities. Therefore, using advanced remote sensing technology to accurately extract forest green space in the city and monitor its change in real-time is very important. Taking Nanjing as the study area, this research extracted 55 vegetation phenological features from Sentinel-2A time series images and formed a feature set containing 81 parameters together with 26 features, including polarimetric- and texture-related information extracted from dual-polarization Sentinel-1A data. On the basis of the improved ABC (ABC-LIBSVM) feature selection method, the optimal feature subset was selected, and the forest coverage areas in the study area were accurately described. To verify the feasibility of the improved feature selection method and explore the potential for the development of multi-source time series remote sensing for urban forest feature extraction, this paper also used the random forest classification model to classify four different feature sets. The results revealed that the classification accuracy based on the feature set obtained by the ABC-LIBSVM algorithm was the highest, with an overall accuracy of 86.80% and a kappa coefficient of 0.8145. The producer accuracy and user accuracy of the urban forest were 93.21% and 82.45%, respectively. Furthermore, by combining the multi-source time series Sentinel-2A optical images with Sentinel-1A dual-polarization SAR images, urban forests can be distinguished from the perspective of phenology, and polarimetric- and texture-related features can contribute to the accurate identification of forests.

Flame monitoring of industrial combustors with high-reliability sensors is essential to operation security and performance. An ion current flame sensor with a simple structure has great potential to be widely used, but a weak ion current is the critical defect to its reliability. In this study, parameters of the ion current sensor used for monitoring flames on a Bunsen burner are suggested, and a method of further improving the ion current is proposed. Effects of the parameters, including the excitation voltage, electrode area, and electrode radial and vertical positions on the ion current, were investigated. The ion current grew linearly with the excitation voltage. Given that the electrodes were in contact with the flame fronts, the ion current increased with the contact area of the cathode but independent of the contact area of the anode. The smaller electrode radial position resulted in a higher ion current. The ion current was insensitive to the anode vertical position but largely sensitive to the cathode vertical position. Based on the above ion current regularities, the sensor parameters were suggested as follows: The burner served as a cathode and the platinum wire acted as an anode. The excitation voltage, anode radial and vertical positions were 120 V, 0 mm, and 6 mm, respectively. The method of further improving the ion current by adding multiple sheet cathodes near the burner exit was proposed and verified. The results show that the ion current sensor with the suggested parameters could correctly identify the flame state, including the ignition, combustion, and extinction, and the proposed method could significantly improve the magnitude of the ion current.

INTRODUCTION:Fully covered self-expandable metal stents (FCSEMSs) are commonly placed in patients with biliary stricture during endoscopic retrograde cholangiopancreatography (ERCP). However, up to 40% of migration has been reported, resulting in treatment failure or the requirement for further intervention. Here, we aimed to investigate the effects of metal clip anchoring on preventing the migration of FCSEMS.METHODS:Consecutive patients requiring placement of FCSEMS were included in this multicenter randomized trial. The enrolled patients were randomly assigned in a 1:1 ratio to receive clip anchoring (clip group) or not (control group). The primary outcome was the migration rate at 6 months after stent insertion. The secondary outcomes were the rates of proximal and distal migration and stent-related adverse events. The analysis followed the intention-to-treat principle.RESULTS:From February 2020 to November 2022, 180 patients with biliary stricture were enrolled, with 90 in each group. The baseline characteristics were comparable between the 2 groups. The overall rate of stent migration at 6 months was significantly lower in the clip group compared with the control group (16.7% vs 30.0%, P = 0.030). The proximal and distal migration rates were similar in the 2 groups (2.2% vs 5.6%, P = 0.205; 14.4% vs 22.2%, P = 0.070). Notably, none of the patients (0/8) who received 2 or more clips experienced stent migration. There were no significant differences in stent-related adverse events between the 2 groups.DISCUSSION:Our data suggest that clip-assisted anchoring is an effective and safe method for preventing migration of FCSEMS without increasing the adverse events.