Global Cardiovascular Diseases Death Rate Prediction

Gaidai, Oleg; Cao, Yu; Loginov, Stas

doi:10.1016/j.cpcardiol.2023.101622

Cited by 160 publications

(48 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Identical definitions were valid for other MDOF components: Y ( t ), Z ( t ), … namely,

{Y}_1, \ldots ,{Y}_{{N}_Y};

{Z}_1, \ldots ,{Z}_{{N}_Z}

and so on. For simplicity, all system components were assumed to be nonnegative [ 30–33 ]

\begin{eqnarray} P = \mathop {\iiint}\limits_{\left( {0,\ 0,\ 0,\ \ldots } \right)}^{\left( {{\eta }_X,\ {\eta }_Y,\ {\eta }_{Z\ },\ \ldots } \right)} {p}_{X_T^{{\rm{max}}},\ Y_T^{{\rm{max}}},\ Z_T^{{\rm{max}}},\ \ldots }\left( {X_T^{{\rm{max}}},\ Y_T^{{\rm{max}}},\ Z_T^{{\rm{max}}},\ \ldots } \right)\ {\rm{d}}X_T^{{\rm{max}}}{\rm{d}}Y_{{N}_Y}^{{\rm{max}}}{\rm{d}}Z_{{N}_z}^{{\rm{max}}}\end{eqnarray}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Carbon Storage Tanker Lifetime Assessment

Gaidai

et al. 2023

Global Challenges

Self Cite

View full text Add to dashboard Cite

CO 2 capture and storage (CCS) is an important strategy to reduce global CO 2 emissions. This work presents both cutting-edge carbon storage tanker design, as well as novel reliability method making possible to extract useful information about the lifespan distribution of carbon capture systems from their recorded time history. The method outlined may be applied on more complex sustainable systems that are exposed to environmental stresses throughout the whole period of their planned service life. The latter is of paramount importance at the design stage for complex engineering systems. Novel design for CCS system is discussed and accurate numerical simulation results are used to apply suggested novel reliability methodology. Furthermore, traditional reliability approaches that deal with complex energy systems are not well suited for handling high dimensionality and cross-correlation between various system components of innovative dynamic CO 2 storage subsea shuttle tanker. This study has two distinctive key features: the state of art CCS design concept, and the novel general purpose reliability method, recently developed by authors, and particularly suitable for operational safety study of complex energy systems.

show abstract

“…Identical definitions were valid for other MDOF components: Y ( t ), Z ( t ), … namely,

{Y}_1, \ldots ,{Y}_{{N}_Y};

{Z}_1, \ldots ,{Z}_{{N}_Z}

and so on. For simplicity, all system components were assumed to be nonnegative [ 30–33 ]

\begin{eqnarray} P = \mathop {\iiint}\limits_{\left( {0,\ 0,\ 0,\ \ldots } \right)}^{\left( {{\eta }_X,\ {\eta }_Y,\ {\eta }_{Z\ },\ \ldots } \right)} {p}_{X_T^{{\rm{max}}},\ Y_T^{{\rm{max}}},\ Z_T^{{\rm{max}}},\ \ldots }\left( {X_T^{{\rm{max}}},\ Y_T^{{\rm{max}}},\ Z_T^{{\rm{max}}},\ \ldots } \right)\ {\rm{d}}X_T^{{\rm{max}}}{\rm{d}}Y_{{N}_Y}^{{\rm{max}}}{\rm{d}}Z_{{N}_z}^{{\rm{max}}}\end{eqnarray}

…”

Section: Methodsmentioning

confidence: 99%

“…and so on. For simplicity, all system components were assumed to be nonnegative [30][31][32][33] P =…”

Section: Methodsmentioning

confidence: 99%

Carbon Storage Tanker Lifetime Assessment

Gaidai

et al. 2023

Global Challenges

Self Cite

View full text Add to dashboard Cite

show abstract

“…Global wave statistics based on satellite measurements were utilized to estimate wave scatter diagrams in relevant FPSO operational Bohai Bay in situ area; Global Wave Statistics Online 18 data was utilized. Figure 2 presents the annually averaged spatial distribution of wave height and period in the Bohai Sea in situ area 7,17,[19][20][21][22][23] .…”

Section: Wave Statisticsmentioning

confidence: 99%

“…30,31 . Figure 7 gives an example numerically simulated of velocity vector field, along with pressure distribution 20,21 . The latter numerical simulation has been used for validation of above discussed experimental results.…”

Section: Experimental Validationmentioning

confidence: 99%

Offloading operation bivariate extreme response statistics for FPSO vessel

Gaidai

Cao

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

The Floating Production Storage and Offloading unit (FPSO) is an offshore unit producing and storing crude oil prior to tanker transport. An important design concern is an accurate prediction of risky dynamic hawser tensions during FPSO offloading operations. Bivariate extreme hawser tension contours are important for selecting proper design values. This paper employed the AQWA hydrodynamic software to analyze vessel hydrodynamic wave loads dynamic response, acting on FPSO vessels under realistic sea state conditions. This paper presents an efficient method for estimating FPSO bivariate response statistics based on numerical simulations validated by various experiments. The bivariate Average Conditional Exceedance Rate (ACER2D) method offers an accurate bivariate extreme value probability distribution and return period contours estimation, utilizing available data efficiently. The two-dimensional probability contours, corresponding to low probability return periods, are easily obtained by the ACER2D method. The performance of the presented method has shown that the ACER2D method provides an efficient and accurate prediction of extreme return period contours. The suggested approach may be used for FPSO vessel design, minimizing potential FPSO hawser damage. Bivariate contours yield bivariate design points, as opposed to a pair of uncoupled univariate design points with the same return period as currently adopted in the industry.

show abstract

“…Their primary aim is to analyse more than 16,000 articles in this domain based on their scientometric indicators and draw conclusions on these technologies. Gaidai et al [14] gave different evolution methods of digital health technologies in this filed. Deep learning model for classification of cardiovascular disease using empirical mode decomposition for ECG feature extraction is discussed in [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Meta-Learning Based Classification Model for Cardiovascular Disease

M¹,

Somasundaram²

2023

Preprint

View full text Add to dashboard Cite

Cardiovascular disease is a major global health concern and is the leading cause of death and disability worldwide. According to the World Health Organization, cardiovascular disease is responsible for 17.9 million deaths each year, which accounts for 31% of all global deaths. Heart disease is a major cause of mortality worldwide. Machine learning algorithms have shown promise in predicting the risk of heart attacks. Meta-learning is a type of machine-learning method which enables a system to learn how to learn. It involves a set of techniques that allow a system to improve its own learning process. In this paper, we propose a Meta-learning based classification model for Cardiovascular diseases. We consider the dataset (for heart attack classification), which contains 76 attributes with the predicted attribute being the presence of heart disease. We evaluate traditional classification models and Meta-Learning approach for heart attack classification. Additionally, we compared the results using SMOTE and without SMOTE to balance the target classes. The Meta-learning approach outperforms traditional models, providing a more accurate prediction of heart attack risk. These results suggest that the meta-learning approach can be used to improve accuracy.

show abstract

Global Cardiovascular Diseases Death Rate Prediction

Cited by 160 publications

References 22 publications

Carbon Storage Tanker Lifetime Assessment

Carbon Storage Tanker Lifetime Assessment

Offloading operation bivariate extreme response statistics for FPSO vessel

Meta-Learning Based Classification Model for Cardiovascular Disease

Contact Info

Product

Resources

About