scholarly journals Research on the Evaluation of Service Status of Tunnel Structure Near the Station

2021 ◽  
Author(s):  
Z J Lin ◽  
S Gao ◽  
J Li ◽  
J C Wang
Keyword(s):  
Disease Detection ◽  
Tunnel Structure ◽  
Distribution Law ◽  
Water Leakage ◽  
Operation Period ◽  
Status Level

To study the service status of the tunnel structure near the station, a disease detection of Metro Line 1 near the station during the operation period was carried out. The four major tunnel structural diseases including water leakage, segment stagger, open joints, and lining cracks were counted. The distribution law of the structural diseases along the longitudinal and circumferential directions of the interval tunnel was analyzed. The service status of segment components and connecting bolts were evaluated based on existing specification. The overall service status of the tunnel was determined. The service status level of the upstream line was determined as “iii”, and the service status was “degradation”, the service status level of the downstream line was determined as “iv” and the service status was “deteriorated”.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

Potassium Pre-Inserted K1.04Mn8O16 Cathode Materials for Aqueous Li-Ion and Na-Ion Hybrid Capacitors

2019 ◽  
Author(s):  
Yamin Zhang ◽  
Lina Chen ◽  
Chongyang Hao ◽  
Xiaowen Zheng ◽  
Yixuan Guo ◽  
...  
Keyword(s):  
Cycling Performance ◽  
High Energy ◽  
High Energy Density ◽  
Potassium Ions ◽  
Tunnel Structure ◽  
Hybrid Supercapacitor ◽  
Hybrid Supercapacitors ◽  
Li Ion ◽  
Power Densities ◽  
Hybrid Capacitors

For the applications of aqueous Li-ion hybrid capacitors and Na-ion hybrid capacitors, potassium ions are pre-inserted into MnO<sub>2</sub> tunnel structure, the as-prepared K<sub>1.04</sub>Mn<sub>8</sub>O<sub>16</sub> materials consist of <a>nanoparticles</a> and nanorods were prepared by facile high-temperature solid-state reaction. <a></a>The as-prepared materials were well studied andthey show outstanding electrochemical behavior. We assembled hybrid supercapacitors with commercial activated carbon (YEC-8A) as anode and K<sub>1.04</sub>Mn<sub>8</sub>O<sub>16 </sub>as cathode. It has high energy densities and power densities. Li-ion capacitors reach a high energy density of 127.61 Wh kg<sup>-1 </sup>at the power density of 99.86 W kg<sup>-1</sup> and Na-ion capacitor obtains 170.96 Wh kg<sup>-1 </sup>at 133.79 W kg<sup>-1</sup>. In addition, the <a>hybrid supercapacitor</a>s demonstrate excellent cycling performance which maintain 97 % capacitance retention for Li-ion capacitor and 85 % for Na-ion capacitor after 10,000 cycles.

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