Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

Effectiveness of collateral arteries embolization before endovascular aneurysm repair to prevent type II endoleaks: A systematic review and meta-analysis.

OBJECTIVES: This study aimed to evaluate the effect of preventive collateral arteries embolization before endovascular aneurysm repair (EVAR) to reduce type II endoleaks (T2EL), aneurysm enlargement, and re-interventions. METHODS: A comprehensive search of PubMed, MEDLINE, Web of Science, and Embase was conducted to identify articles in English, related to preventive collateral arteries embolization before EVAR, published until October 2020. RESULTS: A total of 12 relevant studies, including 11 retrospective studies and one randomized controlled trial, were identified and fulfilled the specified inclusion criteria. A total of 1706 patients in 11 studies were involved in the meta-analysis. The overall incidence of T2EL was 17.3% in the embolization group vs. 34.5% in the control group (OR 0.36, p < 0.01). The incidence of persistent T2EL was 15.3% vs. 30.0% (OR 0.37, p < 0.01). Five studies reported the incidence of sac enlargement, with the rate 10.2% vs. 24.9% (OR 0.25, p < 0.01). Nine studies reported T2EL related re-interventions, and it was 1.3% in the embolization group and 10.4% in control (OR 0.14, p < 0.01). The technical success of collateral arteries embolization was 92.1% (455/494) in the 12 studies. 1.2% (10/829) patients suffered a mild complication of collateral arteries embolization, and 2/829 patients died because of the embolization. CONCLUSION: Collateral arteries embolization is a promising measure to prevent the occurrence of T2EL, sac enlargement, and re-intervention. High-quality studies need to be conducted to provide stronger evidence-based medical suggestions about the embolize operation.

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side.

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor-critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection.

Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system's operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.

Defect Severity Identification for a Catenary System Based on Deep Semantic Learning.

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F1-score value.

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach.

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

Study on Mechanical Characteristics of Segmental Joints of a Large-Diameter Shield Tunnel under Ultrahigh Water Pressure.

At present, there is no clear design standard for segmental joints of large-diameter shield tunnels under high water pressure. In this paper, a theoretical calculation model for the bending stiffness of segmental joints under high water pressure is proposed. The numerical simulation method is used to investigate the failure and crack formation processes of single-layer and double-layer lining segments under large axial forces. The effects of axial force, bolt strength, and concrete strength on the bending stiffness of joints are then studied using a theoretical calculation model of segmental joints. The results show that under extremely high water pressure, the influence of double lining on joint stiffness is limited. It is more rational and safe to compute the bending stiffness of segmental joints using this theoretical model rather than the numerical simulation method. The parameter analysis reveals that increasing the bolt strength has a minor impact on bending stiffness and deformation, whereas increasing the concrete strength has the opposite effect. The influence of ultimate bearing capacity and deformation decreases non-linearly as the axial force increases.

Howling Detection and Suppression Based on Segmented Notch Filtering.

The existing adaptive echo cancellation based howling (typically in hearing aids) removal methods have several drawbacks such as insufficient attenuation of the howling component, slow response and nonlinear distortion. To solve these problems, we propose a segmented notch filtering based scheme. Specifically, firstly, it is proved that the attenuation value can reach -330 dB at any detected howling frequency; secondly, the filter coefficients can be readily calculated by a closed-form formula, yielding a fast response to the sudden howling accident; thirdly, the closed-form formula of this filter is theoretically an even function, indicating that this filter possesses a linear transfer characteristic. In combination with proper segmentation and precisely removing these transient samples arising from FIR (Finite Impulsive Response) filtering, nonlinear distortion can be entirely avoided. Experimental results show that our proposed scheme can not only accurately estimate the howling frequency, but can also completely remove it, which yields a high-quality output waveform with a recovery SNR of about 22 dB. Therefore, the proposed segmented notching based scheme possesses vast potential for hearing aid development and other relevant applications.

Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network.

As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot meet the requirements. To solve this problem, a real-time closed-loop detection method based on a dynamic Siamese networks is proposed in this paper. First, a dynamic Siamese network-based fast conversion learning model is constructed to handle the impact of external changes on key frame judgments, and an elementwise convergence strategy is adopted to ensure the accurate positioning of key frames in the closed-loop judgment process. Second, a joint training strategy is designed to ensure the model parameters can be learned offline in parallel from tagged video sequences, which can effectively improve the speed of closed-loop detection. Finally, the proposed method is applied experimentally to three typical closed-loop detection scenario datasets and the experimental results demonstrate the effectiveness and robustness of the proposed method under the interference of complex scenes.

Efficacy of digital single-operator cholangioscopy and factors affecting its accuracy in the evaluation of indeterminate biliary stricture.

BACKGROUND AND AIMS: Indeterminate biliary stricture remains a significant diagnostic challenge. The current method of ERCP with bile duct brush cytology has substantial room for improvement. We aimed to determine the efficacy of a digital single-operator cholangioscopy (DSOC) in evaluation of indeterminate biliary stricture. METHODS: An observational cohort study was conducted among the patients who underwent DSOC for the indication of indeterminate biliary stricture at a tertiary academic medical center. The outcomes of interests were the accuracy of DSOC in visual interpretation and bile duct sample and identification of any factor(s) that could influence its effectiveness. RESULTS: One hundred five patients were included. The overall accuracy of DSOC in visual interpretation was 89.5%, whereas the accuracy of bile duct sample was 83.2%. The sensitivities of visual impression and bile duct sample were 89.1% and 69.8% and their specificities were 90% and 97.9%, respectively. The degree of endoscopists' experience with fewer than 25 cases and the severity of hyperbilirubinemia negatively impacted the accuracy of DSOC. Among 55 patients with definitive diagnosis of malignant stricture, the sensitivity of combined intraductal forceps biopsy sampling and brush cytology was 80.6%, whereas the sensitivity of brush cytology alone was 47.1%. CONCLUSIONS: DSOC augments ERCP in evaluating indeterminate biliary stricture. The acquisition of intraductal forceps biopsy samples should be a requisite in evaluation of indeterminate biliary stricture with DSOC. Discovery of modifiable factors such as the degree of endoscopists' expertise and the severity of hyperbilirubinemia, which can influence the accuracy of DSOC, warrants further studies on patient preprocedure optimization and an endoscopic training program that will cultivate procedural competency.

Pulmonary cryptococcosis characteristics in immunocompetent patients-A 20-year clinical retrospective analysis in China.

BACKGROUND: Pulmonary cryptococcosis (PC) is not considered an rare, opportunistic infection anymore. The immunocompetent population accounts for an increasing proportion of the morbidity. OBJECTIVE: This study investigated the clinical characteristics of PC patients spanning 20 years, in a referral centre of China. PATIENTS/METHODS: We retrospectively investigated the clinical data of 99 patients with PC who were diagnosed at Peking Union Medical College Hospital (PUMCH) from January 1998 to December 2017. RESULTS: Pulmonary cryptococcosis incidence in PUMCH has seen sharp increase in two decades. There were 40.4% (40/99), 17.2% (17/99) and 42.4% (42/99) immunocompetent, mildly immunocompromised and severe immunocompromised patients, respectively. Significantly higher (P = .035) male predominance in immunocompetent and mildly immunocompromised groups (68.4%, 39/57) compared with severe immunocompromised group (45.2%, 19/42) was found. Overall, 27.5% (11/40) immunocompetent patients reported a significant difference (P = .02) in history of more than weekly drinking, higher than mildly or severe immunocompromised. No significant difference occurred in symptoms and radiographic characteristics among the groups. In pulmonary computerised tomography findings, the non-air pathway feature was the dominant distribution characteristics in all patients with PC (P = .002). The gap in body dissemination frequency between immunocompetent combined with mildly immunocompromised (5.26%, 3/57) and severe immunocompromised (19.0%, 8/42) was marginally significant (P = .05). CONCLUSIONS: Gender and alcohol drinking could be PC risk factors of concern in patients without severe immunodeficiency. No significant difference occurred in symptoms or radiographic characteristics between patients with different levels of immune status. The unique radiographic non-air pathway distribution in the lung may be the feature of Cryptococcus invasion that may enhance accurate diagnosis.

Patterns of Use and Correlates of Patient Satisfaction with a Large Nationwide Direct to Consumer Telemedicine Service.

BACKGROUND: Despite its rapid expansion, little is known about use of direct to consumer (DTC) telemedicine. OBJECTIVE: To characterize telemedicine patients and physicians and correlates of patient satisfaction DESIGN: Cross-sectional study PARTICIPANTS: Patients and physicians of a large nationwide DTC telemedicine service MAIN MEASURES: Patient characteristics included demographics and whether or not they reported insurance information. Physician characteristics included specialty, board certification, and domestic versus international medical training. Encounter characteristics included time of day, wait time, length, coupon use for free or reduced-cost care, diagnostic outcome, prescription receipt, and patient/physician geographic concordance. Patients rated satisfaction with physicians on scales of 0 to 5 stars and reported where they would have sought care had they not used telemedicine. Logistic regression was used to assess factors associated with 5-star physician ratings. KEY RESULTS: The analysis included 28,222 encounters between 24,040 patients and 277 physicians completed between January 2013 and August 2016. Sixty-five percent of patients were under 40 years and 32% did not report insurance information. Family medicine was the most common physician specialty (47%) and 16% trained at a non-US medical school. Coupons were used in 24% of encounters. Respiratory infections were diagnosed in 35% of encounters and 69% resulted in a prescription. Had they not used telemedicine, 43% of patients reported they would have used urgent care/retail clinic, 29% would have gone to the doctor's office, 15% would have done nothing, and 6% would have gone to the emergency department. Eighty-five percent of patients rated their physician 5 stars. High satisfaction was positively correlated with prescription receipt (OR 2.98; 95%CI 2.74-3.23) and coupon use (OR 1.47; 95%CI 1.33-1.62). CONCLUSIONS: Patients were largely satisfied with DTC telemedicine, yet satisfaction varied by coupon use and prescription receipt. The impact of telemedicine on primary care and emergency department use is likely to be small under present usage patterns.

Long Noncoding RNA Expression Signatures of Abdominal Aortic Aneurysm Revealed by Microarray.

OBJECTIVE: This study is aimed at observing the role of long noncoding RNAs (lncRNAs) in the pathogenesis of abdominal aortic aneurysm (AAA). METHODS: LncRNA and mRNA expression signatures of AAA tissues and normal abdominal aortic tissues (NT) were analyzed by microarray and further verified by Real-time quantitative reverse-transcription PCR (qRT-PCR). The lncRNAs-mRNAs targeting relationships were identified using computational analysis. The effect of lnc-ARG on 5-lipoxygenase (ALOX5) expression was tested in HeLa cells. RESULTS: Differential expressions of 3,688 lncRNAs and 3,007 mRNAs were identified between AAA and NT tissues. Moreover, 1,284 differentially expressed long intergenic noncoding RNAs and 206 differentially expressed enhancer-like lncRNAs adjacent to protein-coding genes were discerned by bioinformatics analysis. Some differentially expressed lncRNAs and mRNAs between AAA and normal tissue samples were further verified using qRT-PCR. A co-expression network of coding and noncoding genes was constructed based on the correlation analysis between the differentially expressed lncRNAs and mRNAs. In addition, the lnc-ARG located within the upstream of ALOX5 was sorted as a noncoding transcript by analyzing the protein-coding potential using computational analysis. Furthermore, we found that lnc-ARG can decrease the mRNA level of ALOX5 and reactive oxygen species production in HeLa cells. CONCLUSION: This study revealed new lncRNA candidates are related to the pathogenesis of AAA.

Alkenyl carbonyl derivatives in enantioselective redox relay Heck reactions: accessing α,ß-unsaturated systems.

A highly enantioselective and site-selective Pd-catalyzed arylation of alkenes linked to carbonyl derivatives to yield α,ß-unsaturated systems is reported. The high site selectivity is attributed to both a solvent effect and the polarized nature of the carbonyl group, both of which have been analyzed through multidimensional analysis tools. The reaction can be performed in an iterative fashion allowing for a diastereoselective installation of two aryl groups along an alkyl chain.

Incorporation of postoperative CT data into clinical models to predict 5-year overall and recurrence free survival after primary cytoreductive surgery for advanced ovarian cancer.

PURPOSE: The use of multivariable clinical models to assess postoperative prognosis in ovarian cancer increased. All published models incorporate surgical debulking. However, postoperative CT can detect residual disease (CT-RD) in 40% of optimally resected patients. The aim of our study was to investigate the added value of incorporating CT-RD evaluation into clinical models for assessment of overall survival (OS) and progression free survival (PFS) in patients after primary cytoreductive surgery (PCS). METHODS: 212 women with PCS for advanced ovarian cancer between 01/1997 and 12/2011, and a contrast enhanced abdominal CT 1-7 weeks after surgery were included in this IRB approved retrospective study. Two radiologists blinded to clinical data, evaluated all CT for the presence of CT-RD, and Cohen's kappa assessed agreement. Cox proportional hazards regression with stepwise selection was used to develop OS and PFS models, with CT-RD incorporated afterwards. Model fit was assessed with bootstrapped Concordance Probability Estimates (CPE), accounting for over-fitting bias by correcting the initial estimate after repeated subsampling. RESULTS: Readers agreed on the majority of cases (179/212, k=0.68). For OS and PFS, CT-RD was significant after adjusting for clinical factors with a CPE 0.663 (p=0.0264) and 0.649 (p=0.0008). CT-RD was detected in 37% of patients assessed as optimally debulked (RD<1cm) and increased the risk of death (HR: 1.58, 95% CI: 1.06-2.37%). CONCLUSION: CT-RD is a significant predictor after adjusting for clinical factors for both OS and PFS. Incorporating CT-RD into the clinical model improved the prediction of OS and PFS in patients after PCS for advanced ovarian cancer.

Bipedal Robot Gait Generation Using Bessel Interpolation.

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot's joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

An AAM-Based Identification Method for Ear Acupoint Area.

Ear image segmentation and identification is for the "observation" of TCM (traditional Chinese medicine), because disease diagnoses and treatment are achieved through the massaging of or pressing on some corresponding ear acupoints. With the image processing of ear image positioning and regional segmentation, the diagnosis and treatment of intelligent traditional Chinese medicine ear acupoints is improved. In order to popularize ear acupoint therapy, image processing technology has been adopted to detect the ear acupoint areas and help to gradually replace well-trained, experienced doctors. Due to the small area of the ear and the numerous ear acupoints, it is difficult to locate these acupoints based on traditional image recognition methods. An AAM (active appearance model)-based method for ear acupoint segmentation was proposed. The segmentation was illustrated as 91 feature points of a human ear image. In this process, the recognition effects of the ear acupoints, including the helix, antihelix, cymba conchae, cavum conchae, fossae helicis, fossae triangularis auriculae, tragus, antitragus, and earlobe, were divided precisely. Besides these, specially appointed acupoints or acupoint areas could be prominent in ear images. This method made it possible to partition and recognize the ear's acupoints through computer image processing, and maybe own the same abilities as experienced doctors for observation. The method was proved to be effective and accurate in experiments and can be used for the intelligent diagnosis of diseases.

Calcification circumference, area, and location are associated with carotid stent expansion rate: high-resolution magnetic resonance vessel wall imaging study.

Background: The plaque imaging findings associated with the stent expansion rate (SER) of the carotid artery are not well known. The purpose of this study was to investigate the imaging findings associated with SER. Methods: It was a retrospective investigation. Based on the kind of carotid stents used, retrospective data from 89 patients who had carotid artery stenting (CAS) for atherosclerotic carotid stenosis were gathered and divided into two groups: open-cell stents and closed-cell stents. Patients underwent preoperative carotid high-resolution magnetic resonance vessel wall imaging (HR-VWI). Use HR-VWI to quantitatively evaluate carotid wall thickness and plaque components. Calculate SER using digital subtraction angiography (DSA). All patients' baseline and HR-VWI imaging features were retrospectively analyzed. Simple and multivariable linear regression analysis was used to determine the imaging findings associated with SER of open-cell and closed-cell stents. Results: A total of 89 patients (mean age, 70±8 years; 69 men) were included in the final analysis. Among 89 patients, 35 patients were treated with open-cell stents. Fifty-four patients were treated with closed-cell stents. In the open-cell stents group, the Maximum single-slice calcification circumference score, maximum wall thickness (WTmax), and total calcification location score with P<0.10 in the simple linear regression analysis were included in the multivariable linear regression analysis. The results of the multivariable linear regression revealed that only the Maximum single-slice calcification circumference score (ß=-9.35; 95% CI: -18.15 to -0.56; P=0.03) was associated with SER of open-cell stents. In the closed-cell stents group, the Maximum single-slice calcification circumference score, WTmax, maximum area percentage of calcification, calcification volume, and total calcification location score with P<0.10 in the simple linear regression analysis were included in the multivariable linear regression analysis. The results of the multivariable linear regression revealed that the Maximum area percentage of calcification (ß=-0.67; 95% CI: -1.29 to -0.05; P=0.03), Maximum single-slice calcification circumference score (ß=-8.43; 95% CI: -13.36 to -3.49; P=0.001) and total calcification location score (ß=-0.37; 95% CI: -1.08 to 0.09; P=0.02) were associated with SER of closed-cell stents. Conclusions: Calcified plaques are associated with SER of the carotid artery. Calcification circumference correlates with SER of open-cell stents. Calcification circumference, calcification area, and calcification location are related to SER of closed-cell stents, which may provide a new consideration for clinicians when choosing carotid artery stents.

A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network.

Continuous exploration of the ocean has made underwater image processing an important research field, and plenty of CNN (convolutional neural network)-based underwater image enhancement methods have emerged over time. However, the feature-learning ability of existing CNN-based underwater image enhancement is limited. The networks were designed to be complicated or embed other algorithms for better results, which cannot simultaneously meet the requirements of suitable underwater image enhancement effects and real-time performance. Although the composite backbone network (CBNet) was introduced in underwater image enhancement, we proposed OECBNet (optimal underwater image-enhancing composite backbone network) to obtain a better enhancement effect and shorten the running time. Herein, a comprehensive study of different composite architectures in an underwater image enhancement network was carried out by comparing the number of backbones, connection strategies, pruning strategies for composite backbones, and auxiliary losses. Then, a CBNet with optimal performance was obtained. Finally, cross-sectional research of the obtained network with the state-of-the-art underwater enhancement network was performed. The experiments showed that our optimized composite backbone network achieved better-enhanced images than those of existing CNN-based methods.