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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. Key points: ▪ Novel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. ▪ Accurate disease risks multi-regional spatiotemporal prediction being done. ▪ Confidence bands given for predicted dementia-associated death rate levels with return periods of interest. [ABSTRACT FROM AUTHOR]

Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimensional structural systems that are either measured or numerically simulated during representative observational time span. As this study demonstrates, it is possible to predict risks of dynamic system failure or damage given the in situ environmental load pattern. As an engineering example for this reliability, the authors have chosen 10-MW floating wind turbines and their dynamic responses, under environmental loadings, caused by wind and waves. The aim of this study was to benchmark a state-of-the-art approach suitable for the reliable study of offshore wind turbines. Existing reliability methods do not easily cope with dynamic system high dimensionality. The advocated reliability technique enables accurate and efficient assessment of dynamic system failure probability, accounting for system nonlinearities and high dimensionality as well as cross-correlations between different system components. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health. This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy. Epidemiology and public health have obvious environmental relevance and significance, therefore accurate prediction and prognostic tools are of paramount importance. [ABSTRACT FROM AUTHOR]

In contrast to the well-known bivariate statistical approach, which is known to properly anticipate extreme response levels for two-dimensional systems, the research validates a unique structural reliability method that is particularly appropriate for multi-dimensional structural responses. Traditional dependability techniques that deal with time series don't have an edge when it comes to handling high-dimensional systems and cross-correlation between several dimensions. A Floating Production Storage and Offloading Unit (FPSO) is a tanker that can produce, store and transport hydrocarbon products such as oil. The hawser during the offloading process is an important part used during operations, and a thorough prediction of its extreme tension is essential for operational safety. Failure of the offloading process may lead to great environmental harm and economic loss. This study assessed hydrodynamic forces acting on FPSO vessels in real-world environmental conditions based on the potential flow theory. A brief discussion of the experimental verification of numerical results is also included. The outlined method may be effectively employed during the vessel design phase for choosing the best vessel specifications to reduce potential FPSO hawser tension. The ability to foresee high hawser strains during the FPSO offloading operation is essential for reliability and safety. • The paper validates novel structural reliability Gaidai-Fu-Xing (GFX) method, particularly suitable for multi-dimensional structural responses, versus well established bivariate statistical method, that is known to accurately predict two-dimensional system extreme response levels. • Classic reliability methods, dealing with time series do not have an advantage of dealing easily with system high dimensionality and cross-correlation between different dimensions. • Robust prediction of extreme hawser tensions during the FPSO offloading operation is an important safety concern. [ABSTRACT FROM AUTHOR]