Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

Design and Simulation Analysis of Bolt Group Connection of BS-Type Flange Cast Steel Right-Angle Sea Valve

BS type flanged cast steel right-angle sea valve is an important valve used to stop the backflow of medium in the ship pipeline system. The valve and the pipeline are connected by a bolt connection. To ensure the reliability of the bolt connection, the theoretical calculation and finite element method are used to verify the reliability of the design of bolt connection. The theoretical result and the result of finite element analysis (using ANSYS) show that the largest stress on the bolt is located in the middle of the bolt. This paper provides solutions for the verifying the design of bolt connection in valves based on comparing the results of theoretical calculation and finite element analysis

Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis

Product reviews often contain a large number of implicit aspects and object-attribute co-existence cases. Unfortunately, many existing studies in Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can make it difficult to extract opinions comprehensively and fairly. In this paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple Extraction (EASQE), which aims to hierarchically decompose aspect terms into entities and aspects to avoid information loss, non-exclusive annotations, and opinion misunderstandings in ABSA tasks. To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have also proposed a novel two-stage sequence-tagging based Trigger-Opinion framework as the baseline for the EASQE task. Empirical evaluations show that our Trigger-Opinion framework can generate satisfactory EASQE results and can also be applied to other ABSA tasks, significantly outperforming state-of-the-art methods. We have made the four datasets and source code of Trigger-Opinion publicly available to facilitate further research in this area

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption

The role of upfront primary tumor resection in asymptomatic patients with unresectable stage IV colorectal cancer: A systematic review and meta-analysis

BackgroundControversy exists over the role of upfront primary tumor resection (PTR) in asymptomatic patients with unresectable stage IV colorectal cancer (CRC). The purpose of this study was to evaluate the effect of upfront PTR on survival outcomes and adverse outcomes.MethodsSearches were conducted on PubMed, EMBASE, Web of Science, and Cochrane Library from inception to August 2021. Studies comparing survival outcomes with or without adverse outcomes between PTR and non-PTR treatments were included. Review Manager 5.3 was applied for meta-analyses with a random-effects model whenever possible.ResultsOverall, 20 studies with 3,088 patients were finally included in this systematic review. Compared with non-PTR, upfront PTR was associated with better 3-year (HR: 0.69, 95% CI, 0.57–0.83, P = 0.0001) and 5-year overall survival (OS) (HR: 0.77, 95% CI, 0.62–0.95, P = 0.01), while subgroup analysis indicated that there was no significant difference between upfront PTR and upfront chemotherapy (CT) group. In addition, grade 3 or higher adverse effects due to CT were more frequent in the PTR group with marginal significance (OR: 1.74, 95% CI, 0.99–3.06, P = 0.05), and other adverse outcomes were comparable.ConclusionsPTR might be related to improved OS for asymptomatic patients with unresectable stage IV CRC, whereas receiving upfront CT is a rational alternative without detrimental influence on survival or adverse outcomes compared with upfront PTR.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=27267

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

ObjectivesThe purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features.MethodsIn this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features.ResultsCystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927–0.973) in the training set and 0.91 (95% CI: 0.860–0.929) in the external validation set.ConclusionA reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method