Abstract 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. 

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Future world cancer death rate prediction

This paper demonstrates the validity of the Naess-Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess-Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess-Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess-Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response. 

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Offshore tethered platform springing 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.

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Piezoelectric Energy Harvester Response Statistics

Novel coronavirus disease is spread worldwide with specific mortality and burdens worldwide public health. Due to the non-stationarity and complicated nature of the novel coronavirus, it is challenging to model such a phenomenon, delivering reliable long-term forecasts of the extreme death rate. The present study describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental health systems, observed over a sufficient period, resulting in a reliable long-term forecast of the novel coronavirus registration rate. This study analysed COVID-19 patient numbers from different US states, constituting an example of a multi-state model observed during the years 2020-2022. 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. The present study presents a novel statistical method to analyse raw clinical data using a multicenter, population-based and medical survey data-based bio-statistical approach. Non-dimensional parameter 𝜆 is introduced to measure multi-state risk level. The suggested method predicts a 100-year return period risk level along with its 95% confidence interval band. This methodology can be used in various public health applications based on clinical survey data.

A novel bio-system reliability approach for multi-state COVID-19 epidemic forecast

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. 

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

Floating offshore wind turbines (FOWT) generate green renewable energy and are a vital part of the modern offshore wind energy industry. Robust predicting extreme offshore loads during FOWT operations is an important safety concern. Excessive structural bending moments may occur during certain sea conditions, posing an operational risk of structural damage. This paper uses the FAST code to analyze offshore wind turbine structural loads due to environmental loads acting on a specific FOWT under actual local environmental conditions. The work proposes a unique Gaidai-Fu-Xing structural reliability approach that is probably best suited for multi-dimensional structural responses that have been simulated or measured over a long period to produce relatively large ergodic time series. In the context of numerical simulation, unlike existing reliability approaches, the novel methodology does not need to re-start simulation again each time the system fails. As shown in this work, an accurate forecast of the probability of system failure can be made using measured structural response. Furthermore, traditional reliability techniques cannot effectively deal with large dimensionality systems and cross-correction across multiple dimensions. The paper aims to establish a state-of-the-art method for extracting essential information concerning extreme responses of the FOWT through simulated time-history data. Three key components of structural loads are analyzed, including the blade-root out-of-plane bending moment, tower fore-aft bending moment, and mooring line tension. The approach suggested in this study allows predicting failure probability efficiently for a non-linear multi-dimensional dynamic system as a whole.

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A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

As there is no need of permanent magnet (PM) material and only silicon steel sheet required on the rotor, synchronous reluctance machine (SynRM) can be used for many applications and draws a great research interest. For the SynRM, the torque ripple is a big issue and a great of work could been done on reducing it. In this paper, asymmetrical magnetic flux barriers in the SynRM rotor were studied comprehensively, including angle and width of each layer and each side of the magnetic barrier. The SynRMb with asymmetrical and parallel magnetic flux barrier was found as the best way to design SynRM based on the multi-objective design optimization method. Moreover, each parameter was studied to show the design rule of the asymmetrical magnetic flux barrier. As the average torque will be reduced with the asymmetrical barrier is used, the grain-oriented silicon steel is used on stator teeth of the SynRMb (SynRMbG) was proposed and studied. The analysis results show that the proposed new method can make the SynRM have better performance.

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Torque ripple reduction of synchronous reluctance machine by using asymmetrical barriers and hybrid magnetic core

Cargo ship aft panel stresses prediction by deconvolution

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. 

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

In this paper, a low-cost high-performance axial flux Vernier machine (AFVM) with soft magnetic composite (SMC) cores and ferrite magnet is proposed, analyzed and optimized. By cooperating with spoke magnet rotor configuration and 3D magnetic flux stator structure, the proposed AFVM can output higher torque compared with the traditional permanent magnet machine though low-cost low-energy-density ferrite magnet and low-permeability SMC material are adopted. The main electromagnetic topology and operation principle of the proposed AFVM are introduced; then, its main dimensions are investigated and optimized by using sequential optimization method. Moreover, a new multilevel Taguchi design optimization which is an improvement of multilevel optimization is proposed for the torque and power factor optimization. Lastly, its main performance of AFVM optimized by the sequential optimization method and multilevel Taguchi design optimization method has been compared with a normal axial flux machine in terms of torque ability, power factor and efficiency based on 3D finite element method.

Kelin Wang

Papers

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

Torque ripple reduction of synchronous reluctance machine by using asymmetrical barriers and hybrid magnetic core

Cargo ship aft panel stresses prediction by deconvolution

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

Analysis and design optimization of a low-cost axial flux Vernier machine with SMC cores and ferrite magnets