Coefficient of Subgrade Reaction for the Permeable Block and Base System at Korea GI and LID Center

2018 ◽  
pp. 69-75
Author(s):  
Yong-Jin Choi ◽  
Seungjun Lee ◽  
Jeongho Oh ◽  
Jaehun Ahn ◽  
Jongwon Jung
Keyword(s):  
Base System ◽  
Subgrade Reaction

Beurteilung der Adriamycin-Kardiotoxizität mittels der Äquilibrium-Radionuklid-Ventrikulographie

1987 ◽  
Vol 26 (05) ◽  
pp. 206-211 ◽  
Author(s):  
P. Knesewitsch ◽  
N. H. Göldel ◽  
S. Fritsch ◽  
E. Moser
Keyword(s):  
Left Ventricular ◽  
Peak Filling Rate ◽  
Base System ◽  
Filling Rate ◽  
Time To Peak ◽  
Ejection Rate ◽  
Follow Up Studies ◽  
Pathologic Findings ◽  
Peak Ejection Rate

Results of 606 equilibrium radionuclide ventriculographies (ERNV) performed in 348 non-selected patients receiving Adriamycin (ADM) therapy were stored in a data base system. The aim of the study was to assess the influence of a potential cardiotoxic therapy on left ventricular pump function. Increasing ADM doses yielded a significant (p <0.05) decrease of the resting ejection fraction (R-gEF), the peak ejection rate and the peak filling rate. Enddiastolic and endsystolic volumes increased significantly. Stroke volume, heart rate and time to peak filling rate did not change significantly. 368 follow-up studies were performed in 128 patients: 65/128 patients presented a decrease of R-gEF, but only in 45 of these patients R-gEF values fell into the pathologic range. In 44 of these follow-ups, R-gEF remained unchanged. In 19 patients, a R-gEF increase was observed. At the beginning of ADM therapy 14% of the patients had subnormal R-gEF values. With increasing ADM doses pathologic findings increased to 86% in patients with ADM doses higher than 500 mg/m2.

DATABASE DESIGN FOR E-GOVERNANCE APPLICATION: A FRAMEWORK FOR THE MANAGEMENT INFORMATION SYSTEMS OF THE VIETNAM COMMITTEE FOR ETHNIC MINORITY AFFAIRS

2017 ◽  
Author(s):  
Nguyen Viet Hung ◽  
Phan Van Hung ◽  
Be Trung Anh
Keyword(s):  
Decision Making ◽  
Information System ◽  
Ethnic Minority ◽  
Ethnic Minorities ◽  
Good Governance ◽  
Local Governance ◽  
Geographical Information ◽  
Base System ◽  
Challenges And Opportunities ◽  
Competency Based

Data mode “good governance” developed in the last century for process of sustainable base system, providing basic information and on-line services, supports the development, challenges and opportunities in the context of globalization and integration. In this paper I discuss a framework for the design of e-Local Governance (eLG) that integrates Information System (IS), Geographical Information System (GIS) and Atlas with focus on ethnic minorities in Vietnam. The design framework is based on various classifications such categories as sex, age, ethnic group, education background and income. The database system is built to enhance the Committee for Ethnic Minority Affairs (CEMA) capabilities in the planning and decision making process by providing the authorities with data, internet GIS, internet communication and some ecological economic models to disseminate results to the ethnic minorities. The unique feature of the CEMADATA using GIS is that it helps users not only to improve the public services and to provide information and encourage ethnic minorities to participate in decision making processes, but also to support the competency-based training for IT staff

Design and implementation of ontology-based knowledge base system for marine hydrocarbon geology

2010 ◽  
Vol 30 (2) ◽  
pp. 532-536
Author(s):  
Guo-he LI ◽  
Xin-ying YANG ◽  
Ting YE ◽  
Hong-jun SUN ◽  
Xian-ming TANG ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Base System ◽  
Design And Implementation ◽  
Knowledge Base System

Design calculation model of oil fluid property parameter knowledge database for production logging

2020 ◽  
Vol 20 (3) ◽  
pp. 951-958
Author(s):  
Wenguang Song ◽  
Qiongqin Jiang
Keyword(s):  
Crude Oil ◽  
Expert Knowledge ◽  
Calculation Model ◽  
Physical Parameters ◽  
Base System ◽  
Design Calculation ◽  
Calculation Methods ◽  
Fluid Property ◽  
Knowledge Base System ◽  
Parameter Values

The fluid property parameter calculation affects the accuracy of the interpretation the accuracy, in the interpretation of the liquid production profile. Therefore, it is particularly important to accurately calculate the physical property parameter values, in the establishment of the fluid property parameter expert knowledge base system. The main physical parameters include the following calculation methods of the oil. The oil property parameter conversion formula mainly studies the formulas such as bubble point pressure, dissolved gas-oil ratio, crude oil volume coefficient, crude oil density, crude oil viscosity, and crude oil compression coefficient. Design expert knowledge base system, it is based on the calculation methods of these physical parameters. A computational fluid property parameter model is constructed by training production log sample data. Finally, the interactive and friendly product interpretation software model was developed in 9 wells’ data. The design calculation model can increase the accuracy to achieve 95% of oil fluid property parameter. Accurately calculate fluid property parameter values.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

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