Impact of evidence-based stroke care on patient outcomes: a multilevel analysis of an international study

Background The uptake of proven stroke treatments varies widely. We aimed to determine the association of evidence‐based processes of care for acute ischemic stroke (AIS) and clinical outcome of patients who participated in the HEADPOST (Head Positioning in Acute Stroke Trial), a multicenter cluster crossover trial of lying flat versus sitting up, head positioning in acute stroke. Methods and Results Use of 8 AIS processes of care were considered: reperfusion therapy in eligible patients; acute stroke unit care; antihypertensive, antiplatelet, statin, and anticoagulation for atrial fibrillation; dysphagia assessment; and physiotherapist review. Hierarchical, mixed, logistic regression models were performed to determine associations with good outcome (modified Rankin Scale scores 0–2) at 90 days, adjusted for patient and hospital variables. Among 9485 patients with AIS, implementation of all processes of care in eligible patients, or “defect‐free” care, was associated with improved outcome (odds ratio, 1.40; 95% CI, 1.18–1.65) and better survival (odds ratio, 2.23; 95% CI, 1.62–3.09). Defect‐free stroke care was also significantly associated with excellent outcome (modified Rankin Scale score 0–1) (odds ratio, 1.22; 95% CI, 1.04–1.43). No hospital characteristic was independently predictive of outcome. Only 1445 (15%) of eligible patients with AIS received all processes of care, with significant regional variations in overall and individual rates. Conclusions Use of evidence‐based care is associated with improved clinical outcome in AIS. Strategies are required to address regional variation in the use of proven AIS treatments

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability

Robust welding seam tracking and recognition

In the process of automatic welding based on structured light vision, the precise localization of the welding seam in an image has an important influence on the quality of the welding. However, in practice, there is much interference, such as spatter and arc, which introduces great challenges for accurate welding seam localization. In this paper, we considered welding seam localization problem as visual target tracking and based on that, we proposed a robust welding seam tracking algorithm. Prior to the start of welding, the seam is separated using a cumulative gray frequency, which is utilized to adaptively determine the initial position and size of the search window. During the welding process, large seam motion range may result in only a portion of the welding seam exists in the search window. To prevent that, a tracking-by-detection method is used to calculate the location of the search window. Usually, the intersection of seam and noise, e.g., spatter, has a severe influence on the accuracy of feature points localization. In order to solve this problem, a sequence gravity method (SGM) for extracting a smoother center line of welding seam is proposed, which is able to reduce the impact of interference. The double-threshold recursive least square method is used to fit the curve obtained by SGM with the aim of improving the real-time performance and accuracy of the system. Finally, the superiority of the proposed algorithm is well demonstrated by comparison with other solutions for seam tracking and recognition through extensive experiments

Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults

Stability analysis of second-order sliding mode control systems with input-delay using Poincaré map

This note discusses the stability of dynamical systems with input-delay under the second-order sliding mode control algorithm. Poincare´ Map is constructed to analyze the switching dynamics and to derive the stability conditions. Different parameter setting options are given for ensuring stability. Simulation examples are presented to verify the theoretical results

A robust welding seam identification method

As an automatic welding process may experience some disturbances caused by, e.g., splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights, diversified welding joints/grooves, etc., precisely identifying the welding seam has an great influence on the welding quality achieved. In this paper, a robust method for identifying this seam is proposed. Firstly, after a welding image obtained from a/the structured-light vision sensor is filtered, in a sufficiently small area, the extended Kalman filter (EKF) is used to search for the/its laser stripe in order to prevent possible disturbances. Secondly, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Thirdly, this profile is then qualitatively described and matched using a proposed character string method. Finally, the advantages of this method are compared with those of other approaches through repeated experiments

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions

Chattering analysis of time-delayed second-order sliding mode control systems using poincaré map

In this paper, chattering in a time-delayed second order sliding mode control (2-SMC) system is analyzed using the Poincaré map method. Convergence of the system under arbitrary time-delay is proved. The switching patterns are explored. The existence and uniqueness of the periodic orbit are shown. Simulations are done to support the theoretical results

A welding seam identification method based on cross-modal perception

Purpose: As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception. Design/methodology/approach: First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method. Findings: It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best. Originality/value: This paper proposes a robust method for automatically identifying and tracking a welding seam

Effect of sintering conditions on the properties of Al2O3/Ti(C,N) ceramics

This paper presents a study on the densification and mechanical properties ofAl2O3/Ti(C,N) ceramics processed using a pressureless sintering technique. The Al2O3/Ti(C,N)ceramics containing 10, 20, 30 and 40 wt% Ti(C,N) were sintered in the temperatures ranging from1500 to 1750 °C and in the atmospheres of vacuum, Ar, H2 and N2. It is found that both optimum sintering temperature and Ti C,N) content exist, where the best densification and mechanical properties are achieved. The Al2O3/Ti C,N) properties will decrease when either sintering temperature or Ti(C,N) content moves away from their optimum value. The experimental results also demonstrate that among the four atmospheres, Ar gives best results. To improve further the properties of Al2O3/Ti(C,N) composites, Al2O3 and Al2O3/Ti C,N) powders have used to cover the specimens during sintering, and experiments revealed that covering with Al2O3/Ti(C,N) powder can significantly improve the properties of Al2O3/Ti(C,N) ceramics. Furthermore, the effects of Ti(C,N) content and sintering conditions on densification and mechanical properties are explained in terms of their influences on Al2O3/Ti(C,N) microstructures