Dementia death rates prediction.

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.

Gaidai reliability method for high-dimensional spatio-temporal biosystems.

This study presents novel methodology for pandemic risks assessment for a national health system of interest. The 2019 coronavirus disease (COVID-19) is a contagious disease with certain potential for worldwide spread and potentially significant effects on public health globally. Suggested methodology enables risks assessment of an epidemic, that may happen in the near future at any time, and in any national region of interest. Traditional spatio-temporal reliability methodologies do not have benefit of easily handling health system's high-dimensionality and complex cross-correlations between regional observations. Contrarily, advocated Gaidaireliability approach successfully addresses spatiotemporal clinical observations, as well as multi-regional epidemiological dynamics. This study aimed at benchmarking of a novel bio-statistical technique, enabling national health risk assessment, based on available clinical surveys with dynamically observed patient numbers, while accounting for relevant territorial mappings. The method developed in this study opens up the possibility of accurate epidemiological risk forecast for multi-regional biological and health systems. Suggested bioinformatical methodology may be used in a wide range of public health applications.

Public health system sustainability assessment by Gaidai hypersurface approach.

BACKGROUND: to determine extreme cardiovascular and cancer diseases deathrate risks at any time in any region of interest. DESIGN: Apply modern novel statistical methods to raw clinical surveillance data. METHODS: multi-centre, population-based, medical survey data-based bio statistical approach. For this study, cardiovascular and cancer diseases annual recorded deaths numbers in all 195 world countries have been selected, constituting 390D (390-dimensional) biosystem. It is challenging to model such phenomena. RESULTS: this paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient timelapse. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality. The suggested methodology coped with this challenge well. CONCLUSIONS: the suggested methodology may be used in various public health applications, based on raw clinical survey data.

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5.

Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving. Both faultless and defective products have similar color and shape at the defect location, making proper inspection challenging. To address these issues, we proposed a defect detection model called PO-YOLOv5 to achieve accurate defect detection for solenoid connectors. First, an additional prediction head was added to enable the model to acquire more semantic information to detect larger-scale defective features. Second, we introduced dynamic convolution to learn complementary connections between the four dimensions of the convolution kernel by utilizing its multidimensional attention mechanism. Replacing conventional convolution with dynamic convolution enhances the detection accuracy of the model and reduces the inference time. Finally, we validated PO-YOLOv5 versus the state-of-the-art object detection methods on the same solenoid connectors dataset. Experiments revealed that our proposed approach exhibited higher accuracy. The mAP (mean Average Precision) result of PO-YOLOv5 was found to be about 90.1%. Compared with the original YOLOv5, PO-YOLOv5 exhibited improved precision by about 3%.

COVID-19 Epidemic Forecast in Brazil.

This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.

Multi-regional COVID-19 epidemic forecast in Sweden.

The novel coronavirus disease 2019 (COVID-19) is a contagious disease with high transmissibility to spread worldwide, reported to present a certain burden on worldwide public health. This study aimed to determine epidemic occurrence probability at any reasonable time horizon in any region of interest by applying modern novel statistical methods directly to raw clinical data. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional health and stationary environmental systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of the highly pathogenic virus outbreak probability. For this study, COVID-19 daily recorded patient numbers in most affected Sweden regions were chosen. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information from dynamically observed patient numbers while considering relevant territorial mapping. The method proposed in this paper opens up the possibility of accurately predicting epidemic outbreak probability for multi-regional biological systems. Based on their clinical survey data, the suggested methodology can be used in various public health applications. Key findings are: A novel spatiotemporal health system reliability method has been developed and applied to COVID-19 epidemic data.Accurate multi-regional epidemic occurrence prediction is made.Epidemic threshold confidence bands given.

Global Cardiovascular Diseases Death Rate Prediction.

Cardiovascular diseases (CVD) are heart and blood vessels diseases with considerable morbidity and mortality and presenting worldwide public health burden, moreover CVDs are the leading cause of death globally. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of cardiovascular diseases mortality probability. Objective has been to determine extreme cardiovascular diseases death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. Design of this analysis was based on applying novel statistical methods directly to a raw clinical data, with subsequent data analysis using multicenter, population-based, medical survey data based bio-statistical approach. For this study, cardiovascular diseases annual numbers of recorded deaths in all 195 world countries were chosen. The suggested methodology can be used in various public health applications, based on their clinical survey data.

Future world cancer death rate prediction.

Cancer is a worldwide illness that causes significant morbidity and death and imposes an immense cost on global public health. Modelling such a phenomenon is complex because of the non-stationarity and complexity of cancer waves. Apply modern novel statistical methods directly to raw clinical data. To estimate extreme cancer death rate likelihood at any period in any location of interest. Traditional statistical methodologies that deal with temporal observations of multi-regional processes cannot adequately deal with substantial regional dimensionality and cross-correlation of various regional variables. Setting: multicenter, population-based, medical survey data-based biostatistical approach. Due to the non-stationarity and complicated nature of cancer, it is challenging to model such a phenomenon. This paper offers a unique bio-system dependability technique suited for multi-regional environmental and health systems. When monitored over a significant period, it yields a reliable long-term projection of the chance of an exceptional cancer mortality rate. Traditional statistical approaches dealing with temporal observations of multi-regional processes cannot effectively deal with large regional dimensionality and cross-correlation between multiple regional data. The provided approach may be employed in numerous public health applications, depending on their clinical survey data.

Novel methods for coupled prediction of extreme wind speeds and wave heights.

Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories.

COVID-19 spatio-temporal forecast in England.

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.

Piezoelectric Energy Harvester Response Statistics.

Safety and reliability are essential engineering concerns for energy-harvesting installations. In the case of the piezoelectric galloping energy harvester, there is a risk that excessive wake galloping may lead to instability, overload, and thus damage. With this in mind, this paper studies bivariate statistics of the extreme, experimental galloping energy harvester dynamic response under realistic environmental conditions. The bivariate statistics were extracted from experimental wind tunnel results, specifically for the voltage-force data set. Authors advocate a novel general-purpose reliability approach that may be applied to a wide range of dynamic systems, including micro-machines. Both experimental and numerically simulated dynamic responses can be used as input for the suggested structural reliability analysis. The statistical analysis proposed in this study may be used at the design stage, supplying proper characteristic values and safeguarding the dynamic system from overload, thus extending the machine's lifetime. This work introduces a novel bivariate technique for reliability analysis instead of the more general univariate design approaches.

Improving extreme offshore wind speed prediction by using deconvolution.

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

Operational reliability study of ice loads acting on oil tanker bow.

As a result of climate change, the Arctic glaciers start to melt, and the summer season arrives, making it acceptable for trade ships. There is still shattered ice in the saltwater even though the Arctic glaciers melt in the summer. The stochastic ice loading on the ship's hull is a complex ship-ice interaction. In order to properly build a vessel, it is necessary to reliably estimate the consequent high bow stresses using statistical extrapolation techniques. The bivariate reliability approach is used in this study to compute the excessive bow forces that an oil tanker encounters while sailing in the Arctic Ocean. Two stages are taken in the analysis. First, ANSYS/LS-DYNA is used to compute the oil tanker's bow stress distribution. Second, high bow stresses are projected utilizing a unique dependability methodology to evaluate return levels associated with extended return times. This research focuses on bow loads of an oil tanker travelling in the Artic Ocean using the recorded ice thickness distribution. To take advantage of weaker ice, the vessel's itinerary across the Arctic Ocean was windy (not the shortest straight path). This results in the ship route data used being inaccurate concerning the ice thickness statistics for the area yet skewed concerning the ice thickness data that was particular to a vessel's path. Therefore, this work aims to present a quick and precise approach for estimating the high bow stresses experienced by oil tankers along a given path. Most designs incorporate univariate characteristic values, while this study advocates a bivariate reliability approach for a safer and better design.

Offloading operation bivariate extreme response statistics for FPSO vessel.

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.

Gaidai-Xing reliability method validation for 10-MW floating wind turbines.

In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

Characterisation of extreme load responses of a 10-MW floating semi-submersible type wind turbine.

The global average size of offshore wind turbines has increased steadily from 1.5 MW to 6 MW from 2000 to 2020. With this backdrop, the research community has recently looked at huge 10-15 MW class floating offshore wind turbines (FOWTs). The larger rotor, nacelle structure and tower have more significant structural flexibility. The larger structural flexibility, controller dynamics, aerodynamics, hydrodynamics, and various environmental conditions result in complex structural responses. The structural load effects of a very large FOWT could be more severe than that of the lower MW classes. Accurate quantification of the extreme dynamic responses of FOWT systems is essential in the design of the Ultimate Limit State (ULS) due to the fully-coupled interaction between the FOWT system and environmental conditions. Motivated by this, extreme responses of the 10 MW semi-submersible type FOWT are investigated using the average conditional exceedance rate (ACER) and Gumbel methods. Three operating conditions representing below-rated (U = 8 m/s), rated (U = 12 m/s) and above-rated (U = 16 m/s) regions were considered. The aim is to guide future research on large FOWTs by indicating the expected ULS loads.

Novel methods for reliability study of multi-dimensional non-linear dynamic systems.

This research presents two unique techniques for engineering system reliability analysis of multi-dimensional non-linear dynamic structures. First, the structural reliability technique works best for multi-dimensional structural responses that have been either numerically simulated or measured over a long enough length to produce an ergodic time series. Second, a novel extreme value prediction method that can be used in various engineering applications is proposed. In contrast to those currently used in engineering reliability methodologies, the novel method is easy to use, and even a limited amount of data can still be used to obtain robust system failure estimates. As demonstrated in this work, proposed methods also provide accurate confidence bands for system failure levels in the case of real-life measured structural response. Additionally, traditional reliability approaches that deal with time series do not have the benefit of being able to handle a system's high dimensionality and cross-correlation across several dimensions readily. Container ship that experiences significant deck panel pressures and high roll angles when travelling in bad weather was selected as the example for this study. The main concern for ship transportation is the potential loss of cargo owing to violent movements. Simulating such a situation is difficult since waves and ship motions are non-stationary and complicatedly non-linear. Extreme movements greatly enhance the role of nonlinearities, activating effects of second and higher order. Furthermore, laboratory testing may also be called into doubt due to the scale and the choice of the sea state. Therefore, data collected from actual ships during difficult weather journeys offer a unique perspective on the statistics of ship movements. This work aims to benchmark state-of-the-art methods, making it possible to extract necessary information about the extreme response from available on-board measured time histories. Both suggested methods can be used in combination, making them attractive and ready to use for engineers. Methods proposed in this paper open up possibilities to predict simply yet efficiently system failure probability for non-linear multi-dimensional dynamic structure.

Carbon Storage Tanker Lifetime Assessment.

CO2 capture and storage (CCS) is an important strategy to reduce global CO2 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 CO2 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.

Prediction of death rates for cardiovascular diseases and cancers.

Background: To estimate cardiovascular and cancer death rates by regions and time periods. Design: Novel statistical methods were used to analyze clinical surveillance data. Methods: A multicenter, population-based medical survey was performed. Annual recorded deaths from cardiovascular diseases were analyzed for all 195 countries of the world. It is challenging to model such data; few mathematical models can be applied because cardiovascular disease and cancer data are generally not normally distributed. Results: A novel approach to assessing the biosystem reliability is introduced and has been found to be particularly suitable for analyzing multiregion environmental and healthcare systems. While traditional methods for analyzing temporal observations of multiregion processes do not deal with dimensionality efficiently, our methodology has been shown to be able to cope with this challenge. Conclusions: Our novel methodology can be applied to public health and clinical survey data.

Oil tanker under ice loadings.

As a result of global warming, the area of the polar pack ice is diminishing, making merchant travel more practical. Even if Arctic ice thickness reduced in the summer, fractured ice is still presenting operational risks to the future navigation. The intricate process of ship-ice interaction includes stochastic ice loading on the vessel hull. In order to properly construct a vessel, the severe bow forces that arise must be accurately anticipated using statistical extrapolation techniques. This study examines the severe bow forces that an oil tanker encounters when sailing in the Arctic Ocean. Two stages are taken in the analysis. Then, using the FEM program ANSYS/LS-DYNA, the oil tanker bow force distribution is estimated. Second, in order to estimate the bow force levels connected with extended return periods, the average conditional exceedance rate approach is used to anticipate severe bow forces. The vessel's itinerary was planned to take advantage of the weaker ice. As a result, the Arctic Ocean passage took a meandering route rather than a linear one. As a result, the ship route data that was investigated was inaccurate with regard to the ice thickness data encountered by a vessel yet skewed with regard to the ice thickness distribution in the region. This research intends to demonstrate the effective application of an exact reliability approach to an oil tanker with severe bow forces on a particular route.