On safe offshore energy exploration in the Gulf of Eilat

Sun, Jiayao; Gaidai, Oleg; Xing, Yihan; Wang, Fang; Liu, Zirui

doi:10.1002/qre.3402

Cited by 35 publications

(2 citation statements)

References 73 publications

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“…Figure 5a) presents prediction by Gumbel method, it is seen that advocated methodology in Fig. 5b) a) performs significantly more accurate than Gumbel extrapolation method, in terms of CI, however predicted risk levels are very close, namely near 0.03% [35,[52][53][54][55][56][57][58].…”

Section: Resultsmentioning

confidence: 93%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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Background Prevalence of dementia illness, causing certain morbidity and mortality globally, places burden on global public health. This study primary goal was to assess future risks of dying from severe dementia, given specific return period, within selected group of regions or nations. Methods Traditional statistical approaches do not have benefits of effectively handling large regional dimensionality, along with nonlinear cross-correlations between various regional observations. In order to produce reliable long-term projections of excessive dementia death rate risks, this study advocates novel bio-system reliability technique, that being particularly suited for multi-regional environmental, biological, and health systems. Data Raw clinical data has been used as an input to the suggested population-based, bio-statistical technique using data from medical surveys and several centers. Results Novel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. Suggested methodology shown to be capable of dealing efficiently with spatiotemporal clinical observations of multi-regional nature. Accurate disease risks multi-regional spatiotemporal prediction being done, relevant confidence intervals have been presented as well. Conclusions Based on available clinical survey dataset, the proposed approach may be applied in a variety of clinical public health applications. Confidence bands, given for predicted dementia-associated death rate levels with return periods of interest, have been reasonably narrow, indicating practical values of advocated prognostics.

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Section: Resultsmentioning

confidence: 93%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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“…Novel method was validated versus a well‐established bivariate Weibull method, using both analytical synthetic data and measured GPEH system dynamics. Finally, advocated Using advocated method for systems with more than two dimensions obviously cannot be validated versus bivariate statistical methods; hence, it can be validated versus direct Monte Carlo simulations; for details, see authors’ latest works (Gaidai, Liu, et al., 2023; Gaidai, Wang, Yakimov, Sun, et al., 2023; Gaidai, Xing, Xu, & Balakrishna, 2023; Gaidai, Xu, Yakimov, et al., 2023; Gaidai, Yakimov, and Balakrishna, 2023; Gaidai, Yakimov, Wang, Hu, et al., 2023; Gaidai, Yakimov, Wang, Zhang, et al., 2023; Gaidai, Yan, Xing, Xu, et al., 2023; Liu et al., 2023; Sun et al., 2023; Yakimov, Gaidai, Wang, & Wang, 2023; Yakimov, Gaidai, Wang, Xu, et al., 2023). Suggested methodology may be well used in a wide range of engineering areas of applications.…”

Section: Discussionmentioning

confidence: 99%

Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

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Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method

Gaidai,

Cao,

Zhu

et al. 2024

Analytical Science Advances

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Accurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID‐19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population‐based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,—not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio‐statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross‐correlation between various regional observations. These methods deal with temporal observations of multi‐regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio‐system hazard assessment technique that is particularly suited for multi‐regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID‐19/severe acute respiratory syndrome coronavirus 2 (SARS‐COV2) epidemic risks have been examined in the current study; however, COVID‐19/SARS‐COV2 infection transmission mechanisms have not been discussed.

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On safe offshore energy exploration in the Gulf of Eilat

Cited by 35 publications

References 73 publications

Dementia death rates prediction

Dementia death rates prediction

Energy harvester reliability study by Gaidai reliability method

Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method

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