COMPARISON OF WATERPROOF RELIABILITY OF 10KV XLPE POWER CABLE JOINT IN SHANGHAI URBAN AREA

ABSTRAC

Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems.~Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as $10^{-6}$, and the absolute errors for algebraic variables is maintained at $10^{-5}$, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems

Nestin and CD133: valuable stem cell-specific markers for determining clinical outcome of glioma patients

<p>Abstract</p> <p>Aim</p> <p>Gliomas represent the most frequent neoplasm of the central nervous system. Unfortunately, surgical cure of it is practically impossible and their clinical course is primarily determined by the biological behaviors of the tumor cells. The aim of this study was to investigate the correlation of the stem cell markers Nestin and CD133 expression with the grading of gliomas, and to evaluate their prognostic value.</p> <p>Methods</p> <p>The tissue samples consisted of 56 low- (WHO grade II), 69 high- (WHO grade III, IV) grade gliomas, and 10 normal brain tissues. The expression levels of Nestin and CD133 proteins were detected using SABC immunohistochemical analysis. Then, the correlation of the two markers' expression with gliomas' grading of patients and their prognostic value were determined.</p> <p>Results</p> <p>Immunohistochemical analysis with anti-Nestin and anti-CD133 antibodies revealed dense and spotty staining in the tumor cells and their expression levels became significantly higher as the glioma grade advanced (<it>p </it>< 0.05). There was a positive correlation between the two markers' expression in different gliomas tissues (rs = 0.89). The low expression of the two markers significantly correlated with long survival of the glioma patients (<it>p </it>< 0.05). The survival rate of the patients with Nestin+/CD133+ expression was the lowest (<it>p </it>< 0.01), and the multivariate analysis confirmed that conjoined expression of Nestin+/CD133+ and Nestin-/CD133- were independent prognostic indicators of gliomas (both <it>p </it>< 0.01, Cox proportional hazard regression model).</p> <p>Conclusion</p> <p>These results collectively suggest that Nestin and CD133 expression may be an important feature of human gliomas. A combined detection of Nestin/CD133 co-expression may benefit us in the prediction of aggressive nature of this tumor.</p

Efficacy of early prone or lateral positioning in patients with severe COVID-19: a single-center prospective cohort

Abstract Background Position intervention has been shown to improve oxygenation, but its role in non-invasively ventilated patients with severe COVID-19 has not been assessed. The objective of this study was to investigate the efficacy of early position intervention on non-invasively ventilated patients with severe COVID-19. Methods This was a single-center, prospective observational study in consecutive patients with severe COVID-19 managed in a provisional ICU at Renmin Hospital of Wuhan University from 31 January to 15 February 2020. Patients with chest CT showing exudation or consolidation in bilateral peripheral and posterior parts of the lungs were included. Early position intervention (prone or lateral) was commenced for &amp;gt; 4 hours daily for 10 days in these patients, while others received standard care. Results The baseline parameters were comparable between the position intervention group (n = 17) and the standard care group (n = 35). Position intervention was well-tolerated and increased cumulative adjusted mean difference of SpO2/FiO2 (409, 95% CI 86 to 733) and ROX index (26, 95% CI 9 to 43) with decreased Borg scale (−9, 95% CI −15 to −3) during the first 7 days. It also facilitated absorption of lung lesions and reduced the proportion of patients with high National Early Warning Score 2 (≥ 7) on days 7 and 14, with a trend toward faster clinical improvement. Virus shedding and length of hospital stay were comparable between the two groups. Conclusions This study provides the first evidence for improved oxygenation and lung lesion absorption using early position intervention in non-invasively ventilated patients with severe COVID-19, and warrants further randomized trials. </jats:sec

Research on Guide Line Identification and Lateral Motion Control of AGV in Complex Environments

During actual operations, Automatic Guided Vehicles (AGV) will inevitably encounter the phenomena of overexposure or shadowy areas, and unclear or even damaged guide wires, which interfere with the identification of guide wires. Therefore, this paper aims to solve the shortcomings of existing technology at the software level. Firstly, a Fast Guide Filter (FGF) is adopted with the two-dimensional gamma function with variable parameters, and an image preprocessing algorithm in a complex illumination environment is designed to get rid of the interference of illumination. Secondly, an ant colony edge detection algorithm is proposed, and the guide wire is accurately extracted by secondary screening combined with the guide wire characteristics; A variable universe Fuzzy Sliding Mode Control (FSMC) algorithm is designed as a lateral motion control method to realize the accurate tracking of AGV. Finally, the experimental platform is used to comprehensively verify the series of algorithms designed in this paper. The experimental results show that the maximum deviation can be limited to 1.2 mm, and the variance of the deviation is less than 0.2688 mm2

Research on Guide Line Identification and Lateral Motion Control of AGV in Complex Environments

During actual operations, Automatic Guided Vehicles (AGV) will inevitably encounter the phenomena of overexposure or shadowy areas, and unclear or even damaged guide wires, which interfere with the identification of guide wires. Therefore, this paper aims to solve the shortcomings of existing technology at the software level. Firstly, a Fast Guide Filter (FGF) is adopted with the two-dimensional gamma function with variable parameters, and an image preprocessing algorithm in a complex illumination environment is designed to get rid of the interference of illumination. Secondly, an ant colony edge detection algorithm is proposed, and the guide wire is accurately extracted by secondary screening combined with the guide wire characteristics; A variable universe Fuzzy Sliding Mode Control (FSMC) algorithm is designed as a lateral motion control method to realize the accurate tracking of AGV. Finally, the experimental platform is used to comprehensively verify the series of algorithms designed in this paper. The experimental results show that the maximum deviation can be limited to 1.2 mm, and the variance of the deviation is less than 0.2688 mm2

GPS Path Tracking Control of Military Unmanned Vehicle Based on Preview Variable Universe Fuzzy Sliding Mode Control

In the process of the continuous development and improvement of modern military systems, military unmanned vehicles play an important role in field reconnaissance and strategic deployment. In this paper, the precise tracking algorithm of a military unmanned vehicle, based on GPS navigation, is studied. Firstly, the optimal preview point is obtained according to the data points of a differential GPS signal. Secondly, the pure tracking algorithm is used to calculate the demand steering angle, and a variable universe fuzzy sliding mode controller is designed to control the lateral motion of the vehicle, which is verified by the joint simulation platform of Simulink and CarSim, under multiple working conditions. Finally, the actual vehicle is verified by using the Autobox platform. The results show that the lateral motion control of path tracking designed in this paper can achieve an accurate and effective control effect, and has real-time performance for engineering applications