Parametric Curve Fitting Research Articles

Co-infection dynamics of COVID-19 and HIV/AIDS

Although there are many results that can be used to treat and prevent Coronavirus Disease 2019 (COVID-19) and Human Immunodeficiency Virus (HIV), these diseases continue to be public health concerns and cause socioeconomic consequences. Following compromised immunity, COVID-19 is considered to be a challenge for people with HIV. People with advanced HIV are considered a vulnerable population at high risk in several case studies that discuss COVID-19 and HIV co-infection. As there is no cure for HIV and there is a chance of contracting COVID-19 again, co-infection continues to pose a problem. The purpose of this study is to investigate the impact of intervention strategies and identify the role of different parameters in risking people living with HIV to death when they get infected with COVID-19. This is achieved through the development and rigorous analysis of a mathematical model that considers a population at risk of death due to COVID-19 and HIV. The model formulation provides a detailed explanation of the transmission dynamics of COVID-19 and HIV co-infection. The solution’s invariant region, positivity, and boundedness were established. The reproduction numbers of the sub-models and the co-infection model were determined. The existence and stability of equilibria, including backward bifurcation for the COVID-19 sub-model, were examined. The epidemiological significance of backward bifurcation is that the condition {mathscr {R}}_0 less than 1 for eliminating COVID-19, though necessary, is no longer sufficient. Parametric estimation and curve fitting were performed based on data from Ethiopia. Numerical simulations were employed to support and clarify the analytical findings and to show some parameter effects on COVID-19 and HIV co-infection. Accordingly, the simulations indicated that parameters gamma _c, gamma _h, epsilon, and kappa, related to HIV patients’ exposure to other diseases and the increase in infectiousness, have a positive role in increasing the number of co-infections. On the other hand, an increase in COVID-19 vaccination (xi) shows the suppression of co-infection cases. In addition, treating co-infected individuals for COVID-19, increasing treatment rates alpha and varphi, reduces the death risk of HIV-infected individuals due to the co-infection burden. It was implied that improving vaccine delivery programs and other medical interventions have important contributions to lowering the risk of COVID-19 infection-related fatalities in HIV patients.

Health Technology Reassessment: Addressing Uncertainty in Economic Evaluations of Oncology Drugs at Time of Reimbursement Using Long-Term Clinical Trial Data.

The evidence base to support reimbursement decision making for oncology drugs is often based on short-term follow-up trial data, and attempts to address this uncertainty are not typically undertaken once a reimbursement decision is made. To address this gap, we sought to conduct a reassessment of an oncology drug (pembrolizumab) for patients with advanced melanoma which was approved based on interim data with a median 7.9 months of follow-up and for which long-term data have since been published. We developed a three-health-state partitioned survival model based on the phase 3 KEYNOTE-006 clinical trial data using patient-level data reconstruction techniques based on an interim analysis. We used a standard survival analysis and parametric curve fitting techniques to extrapolate beyond the trial follow-up time, and the model structure and inputs were derived from the literature. Five-year long-term follow-up data from the trial were then used to re-evaluate the cost-effectiveness of pembrolizumab versus ipilimumab for treatment of advanced melanoma. The best fitting parametric curves and corresponding survival extrapolations for reconstructed interim data and long-term data reconstructed from KEYNOTE-006 were different. An analysis of the 5 year long-term follow-up data generated a base case incremental cost-effectiveness ratio (ICER) that was 28% higher than the ICER based on interim trial data. Our findings suggest that there may be a trade-off between certainty and the ICER. Conducting health technology re-assessments of certain oncology products on the basis of longer-term data availability, especially for those health technology adoption decisions made based on immature clinical data, may be of value to decision makers.

Real-time Pose Tracking for a Continuum Guidewire Robot under Fluoroscopic Imaging.

Atherosclerosis is a medical condition that causes buildup of plaque in the blood vessels and narrowing of the arteries. Surgeons often treat this condition through angioplasty with catheter placements. Continuum guidewire robots offer significant advantages for catheter placements due to their dexterity. Tracking these guidewire robots and their surrounding workspace under fluoroscopy in real-time can be useful for visualization and accurate control. This paper discusses algorithms and methods to track the shape and orientation of the guidewire and the surrounding workspaces of phantom vasculatures in real-time under C-arm fluoroscopy. The shape of continuum guidewires is found through a semantic segmentation architecture based on MobileNetv2 with a Tversky loss function to deal with class imbalances. This shape is refined through medial axis filtering and parametric curve fitting to quantitatively describe the guidewire's pose. Using a constant curvature assumption for the guidewire's bending segments, the parameters that describe the joint variables are estimated in real-time for a tendon-actuated COaxially Aligned STeerable (COAST) guidewire robot and tracked through its traversal of an aortic bifurcation phantom. The accuracy of the tracking is ~90% and the execution times are within 100ms, and hence this method is deemed suitable for real-time tracking.

Probability Models of Natural Catastrophe Losses

In this article we describe parametric curve-fitting methods for modeling historical natural catastrophe losses. We summarize relevant theoretical results above Excess over Threshold Method (EOT) and provide its application to the data about total catastrophe losses in the world in period 1970-2014, published in No2/2015 Swiss Re study Sigma

Simulation and analysis on the ship energy efficiency operational indicator for bulk carriers by Monte Carlo simulation method

The ship energy efficiency management is an important topic in the field of the energy management onboard and the exhaust gases emission nowadays. The advanced model plays a vital role to improve the ship energy efficiency management when considering the variable factors. The establishment of the ship energy efficiency model through energy efficiency operational indicator (EEOI) index has been conducted through Monte Carlo simulation method along with using the operation data of a bulk carrier. A bulk carrier is chosen, namely, M/V NSU JUSTICE 250,000 DWT of VINIC Shipping Transportation Company in Vietnam. This research uses the real operational data to perform a statistical methodology which calculates the various factors used to calculate EEOI. This method is supported by Matlab program through the curve fitting tool. The normal distribution estimation and the kernel density estimation method are used for the parametric curve fitting and non-parametric curve fitting, respectively. The average weather condition (wind speed and wave height) and the fouling condition of hull have been investigated and compared with the research results. The validation of the proposed methods has been conducted through the study of the external factors influencing the research results. The research result shows the optimal operational data for the fuel consumption at each certain voyage. This paper is useful for the ship-owners and the ship-operators in the field of the ship energy efficiency management.

Identification of Saharan dust particles in Pleistocene dune sand- paleosol sequences of Fuerteventura (Canary Islands)

Automated static image analysis and newly introduced evaluation techniques were applied in this paper to identify Saharan dust material in the unique sand-paleosol sequence of Fuerteventura (Canary Islands). Measurements of ~50,000 individual mineral particles per samples provided huge amount of granulometric data on the investigated sedimentary units. In contrast to simple grain size and shape parameters of bulk samples, (1) parametric curve-fitting allowed the separation of different sedimentary populations suggesting the presence of more than one key depositional mechanisms. Additional (2) Raman-spectroscopy of manually targeted individual particles revealed a general relationship among grain size, grayscale intensity and mineralogy. This observation was used to introduce the (3) intensity based assessment technique for identification of large number of quartz particles. The (4) cluster and (5) network analyses showed that only joint analysis of size, shape and grayscale intensity properties provided suitable results, there is no specific granulometric parameter to distinguish Saharan dust due to their irregular shape characteristics. The presented methods allowed the separation of Saharan dust-related quartz grains from local sedimentary deposits, but due to the lack of robust granulometric characterization of coarsest fractions and due to the diverse geochemical properties of North African sources, exact volumetric amount of deposited dust material and sedimentation rates could not be determined from these data.

Functional Modeling of Plant Growth Dynamics

Recent advances in automated plant phenotyping have enabled the collection of time series measurements from the same plants of a wide range of traits at different developmental time scales. The availability of time series phenotypic datasets has increased interest in statistical approaches for comparing patterns of change among different plant genotypes and different treatment conditions. Two widely used methods of modeling growth with time are pointwise analysis of variance (ANOVA) and parametric sigmoidal curve fitting. Pointwise ANOVA yields discontinuous growth curves, which do not reflect the true dynamics of growth patterns in plants. In contrast, fitting a parametric model to a time series of observations does capture the trend of growth; however, these models require assumptions regarding the true pattern of plant growth. Depending on the species, treatment regime, and subset of the plant life cycle sampled, these assumptions will not always hold true. We have developed a different approach—functional ANOVA—which yields continuous growth curves without requiring assumptions regarding patterns of plant growth. We compared and validated this approach using data from an experiment measuring the growth of two maize (Zea mays L. ssp. mays) genotypes under two water availability treatments during a 21‐d period. Functional ANOVA enables a nonparametric estimation of the dynamics of changes in plant traits with time without assumptions regarding curve shape. In addition to estimating smooth curves of trait values with time, functional ANOVA also estimates the derivatives of these curves, e.g., growth rates, simultaneously. Using two different subsampling strategies, we demonstrate that this functional ANOVA method enables the comparison of growth curves among plants phenotyped on non‐overlapping days with little reduction in estimation accuracy. This means that functional ANOVA based approaches can allow larger numbers of samples and biological replicates to be scored in a single experiment given fixed amounts of phenotyping infrastructure and personnel.

Interpretation of sedimentary (sub)populations extracted from grain size distributions of Central European loess-paleosol series

Grain size proxies of aeolian dust deposits have widely been applied in environmental and sedimentary studies. However, large body of research papers are not taking into consideration that a complex grain size distribution curve cannot be an indicator of a single one environmental factor (e.g. wind speed/strength, transportation distance, aridity).The aim of the present paper is to discuss the main differences of frequently used statistical methods and to provide possible interpretations of the results by applying these various approaches on the high-resolution loess-paleosol profile of Dunaszekcső, South Hungary (Central Europe). Beside single statistical descriptors (mean, median, mode) of grain size and simple indices of size-fraction ratios (U-ratio, Grain Size Index), some more complex algorithms were also used in our paper. The applied parametric curve-fitting, end-member modelling and hierarchical cluster analysis techniques are using the whole spectrum of the measured grain size distributions and provide a more reliable and more representative results even in case of small scale variations.According to our findings, approaches which provide direct linkage among simple statistical descriptors and single atmospheric or other environmental elements are rather oversimplified as properties aeolian dust deposits are influenced by the integrated effects of several concurrent processes. Differences of more complex decomposition methods arise from the different approach and scope. End-members are determined from the unmixing based on the covariance structure of the whole grain size data-series of the section, while the parametric curve-fitting is based on the one-by-one deconvolution of the grain size distribution curves. End-members of loess-paleosol samples are regarded as representation of the average dust grain size distribution of various temporal sediment clusters of seasonal or other short-term intervals, while (sub)populations by parametric curve-fitting are proposed to illustrate process-related elements of background and dust storm depositional components for each sample. Results of cluster analysis represent similar grouping conditions as end-member modelling with a reduced sedimentary and genetically meaning.

A Software Tool for Heart AVJ Motion Tracking Using Cine Cardiovascular Magnetic Resonance Images.

One important index to assess left ventricular diastolic function is the quantitative measurement of atrioventricular junction (AVJ) motion in one cardiac cycle including systole and diastole. The best way to perform the measurement is to use a software tool that can conduct AVJ motion tracking from cine cardiovascular magnetic resonance (CMR) images. In this paper, a software tool for this purpose is presented by using the insight segmentation and registration toolkit (ITK), the visualization toolkit (VTK), and Qt. We propose a surface area-based tracking approach in the software tool. In the tracking approach to obtain the surface area swept by six points being tracked, we manually select six points from four-, three- and two-chamber views of CMR images. After that, we reconstruct the 3-D coordinates of the six points from image acquisition parameters extracted from DICOM files. We perform interpolation by using parametric cubic curve fitting techniques. From the curve fitting results, we finally obtain the surface areas for all time points in one cardiac cycle. The software tool has been successfully implemented. The functionality include single point-based tracking, surface area-based tracking by using 6 tracked points, generation of displacement, sweep surface area and velocity, and generation of tracking movies. From the software engineering practice, it is concluded that ITK, VTK, and Qt are very handy software systems to implement automatic image analysis functions for CMR images, such as quantitative measure of motion by visual tracking. The software tool provides a convenient and efficient way to measure AVJ motion and extends the scope of methods for ventricular function assessment.

Operational Analysis of a Freeway Variable Speed Limit System in St. Louis, Missouri

Variable speed limit (VSL) systems have been more widely implemented in Europe compared to the United States. Studies have demonstrated positive safety impacts of such systems; however, there are very few studies that used actual deployment data to investigate the operational benefits of VSL systems. This article presents the operational impacts of a VSL deployment on Interstate 270 in the state of Missouri in the United States. Techniques such as parametric curve fitting, nonparametric methods, and other statistical tests were used to identify the changes between before and after traffic conditions. The effect of VSL on traffic performance was investigated at eight heavily congested locations. The two-dimensional Kolmogorov–Smirnov test results indicated that flow–occupancy diagram changes were statistically significant at seven out of eight locations. The slopes of flow–occupancy plots for over critical occupancies were found to be steeper after VSL. Slight changes in critical occupancy were observed after VSL implementation. However, the changes were inconsistent across locations, with some witnessing an increase and others witnessing a decrease. The maximum flow prior to breakdown (the prebreakdown flow) decreased at four locations and increased at four locations after VSL. The maximum flow after breakdown (the postbreakdown flow) decreased at three locations and increased at five locations after VSL. The average daily duration of congestion decreased at five locations and increased at three locations after VSL. Findings of this study help to develop VSL control algorithms that are more efficient in improving VSL traffic operations benefits.

Sensitivity analysis in optimized parametric curve fitting

Purpose – Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications. In the literature, several approaches have been proposed to solve this problem. However, previous works lack formal characterization of the curve fitting problem and assessment on the effect of several parameters (i.e. scalars that remain constant in the optimization problem), such as control points number (m), curve degree (b), knot vector composition (U), norm degree (k), and point sample size (r) on the optimized curve reconstruction measured by a penalty function (f). The paper aims to discuss these issues. Design/methodology/approach – A numerical sensitivity analysis of the effect of m, b, k and r on f and a characterization of the fitting procedure from the mathematical viewpoint are performed. Also, the spectral (frequency) analysis of the derivative of the angle of the fitted curve with respect to u as a means to detect spurious curls and peaks is explored. Findings – It is more effective to find optimum values for m than k or b in order to obtain good results because the topological faithfulness of the resulting curve strongly depends on m. Furthermore, when an exaggerate number of control points is used the resulting curve presents spurious curls and peaks. The authors were able to detect the presence of such spurious features with spectral analysis. Also, the authors found that the method for curve fitting is robust to significant decimation of the point sample. Research limitations/implications – The authors have addressed important voids of previous works in this field. The authors determined, among the curve fitting parameters m, b and k, which of them influenced the most the results and how. Also, the authors performed a characterization of the curve fitting problem from the optimization perspective. And finally, the authors devised a method to detect spurious features in the fitting curve. Practical implications – This paper provides a methodology to select the important tuning parameters in a formal manner. Originality/value – Up to the best of the knowledge, no previous work has been conducted in the formal mathematical evaluation of the sensitivity of the goodness of the curve fit with respect to different possible tuning parameters (curve degree, number of control points, norm degree, etc.).

Development of empirical and analytical fragility functions using kernel smoothing methods

SummaryFragility functions that define the probabilistic relationship between structural damage and ground motion intensity are an integral part of performance‐based earthquake engineering or seismic risk analysis. This paper introduces three approaches based on kernel smoothing methods for developing analytical and empirical fragility functions. A kernel assigns a weight to each data that is inversely related to the distance between the data value and the input of the fragility function of interest. The kernel smoothing methods are, therefore, non‐parametric forms of data interpolation. These methods enable the implicit treatment of uncertainty in either or both of ground motion intensity and structural damage without making any assumption about the shape of the resulting fragility functions. They are particularly beneficial for sparse, noisy, or non‐homogeneous data sets. For illustration purposes, two types of data are considered. The first is a set of numerically simulated responses for a four‐story steel moment‐resisting frame, and the second is a set of field observations collected after the 2010 Haiti earthquake. The results demonstrate that these methods can develop continuous representations of fragility functions without specifying their functional forms and treat sparse data sets more efficiently than conventional data binning and parametric curve fitting methods. Moreover, various uncertainty analyses are conducted to address the issues of over‐fitting, bias, and confidence intervals. Copyright © 2014 John Wiley & Sons, Ltd.

Global geometric torsion estimation in adolescent idiopathic scoliosis

Several attempts have been made to measure geometrical torsion in adolescent idiopathic scoliosis (AIS) and quantify the three-dimensional (3D) deformation of the spine. However, these approaches are sensitive to imprecisions in the 3D modeling of the anatomy and can only capture the effect locally at the vertebrae, ignoring the global effect at the regional level and thus have never been widely used to follow the progression of a deformity. The goal of this work was to evaluate the relevance of a novel geometric torsion descriptor based on a parametric modeling of the spinal curve as a 3D index of scoliosis. First, an image-based approach anchored on prior statistical distributions is used to reconstruct the spine in 3D from biplanar X-rays. Geometric torsion measuring the twisting effect of the spine is then estimated using a technique that approximates local arc-lengths with parametric curve fitting centered at the neutral vertebra in different spinal regions. We first evaluated the method with simulated experiments, demonstrating the method's robustness toward added noise and reconstruction inaccuracies. A pilot study involving 65 scoliotic patients exhibiting different types of deformities was also conducted. Results show the method is able to discriminate between different types of deformation based on this novel 3D index evaluated in the main thoracic and thoracolumbar/lumbar regions. This demonstrates that geometric torsion modeled by parametric spinal curve fitting is a robust tool that can be used to quantify the 3D deformation of AIS and possibly exploited as an index to classify the 3D shape.

Interannual variability in the timing of New England shellfish toxicity and relationships to environmental forcing

Routine monitoring along the coast of the Gulf of Maine (GoM) reveals shellfish toxicity nearly every summer, but at varying times, locations, and magnitudes. The responsible toxin is known to be produced by the dinoflagellate Alexandrium fundyense, yet there is little apparent association between Alexandrium abundance and shellfish toxicity. One possibility is that toxic cells are persistent in offshore areas and variability in shellfish toxicity is caused not by changes in overall abundance, but rather by variability in transport processes. Measurements of offshore Alexandrium biomass are scarce, so we bypass cell abundance as an explanatory variable and focus instead on the relations between shellfish toxicity and concurrent metrics of GoM meteorology, hydrology, and oceanography. While this yields over two decades (1985–2005) of data representing a variety of interannual conditions, the toxicity data are gappy in spatial and temporal coverage. We address this through a combination of parametric curve fitting and hierarchical cluster analysis to reveal eight archetypical modes of seasonal toxicity timing. Groups of locations are then formed that have similar interannual patterns in these archetypes. Finally, the interannual patterns within each group are related to available environmental metrics using classification trees. Results indicate that a weak cross-shore sea surface temperature (SST) gradient in the summer is the strongest correlate of shellfish toxicity, likely by signifying a hydrological connection between offshore Alexandrium populations and near-shore shellfish beds. High cumulative downwelling wind strength early in the season is revealed as a precursor consistent with this mechanism. Although previous studies suggest that alongshore transport is important in moving Alexandrium from the eastern to western GoM, alongshore SST gradient is not an important correlate of toxicity in our study. We conclude by discussing the implications of our results for designing efficient and effective shellfish monitoring programs along the GoM coast.

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

Analyses of the microbial diversity across the human microbiome.

Analysis of human body microbial diversity is fundamental to understanding community structure, biology and ecology. The National Institutes of Health Human Microbiome Project (HMP) has provided an unprecedented opportunity to examine microbial diversity within and across body habitats and individuals through pyrosequencing-based profiling of 16 S rRNA gene sequences (16 S) from habits of the oral, skin, distal gut, and vaginal body regions from over 200 healthy individuals enabling the application of statistical techniques. In this study, two approaches were applied to elucidate the nature and extent of human microbiome diversity. First, bootstrap and parametric curve fitting techniques were evaluated to estimate the maximum number of unique taxa, Smax, and taxa discovery rate for habitats across individuals. Next, our results demonstrated that the variation of diversity within low abundant taxa across habitats and individuals was not sufficiently quantified with standard ecological diversity indices. This impact from low abundant taxa motivated us to introduce a novel rank-based diversity measure, the Tail statistic, (“τ”), based on the standard deviation of the rank abundance curve if made symmetric by reflection around the most abundant taxon. Due to τ’s greater sensitivity to low abundant taxa, its application to diversity estimation of taxonomic units using taxonomic dependent and independent methods revealed a greater range of values recovered between individuals versus body habitats, and different patterns of diversity within habitats. The greatest range of τ values within and across individuals was found in stool, which also exhibited the most undiscovered taxa. Oral and skin habitats revealed variable diversity patterns, while vaginal habitats were consistently the least diverse. Collectively, these results demonstrate the importance, and motivate the introduction, of several visualization and analysis methods tuned specifically for next-generation sequence data, further revealing that low abundant taxa serve as an important reservoir of genetic diversity in the human microbiome.

Implementation of a Multi-functional Ambulatory Urodynamics Monitoring System Based on Newly Devised Abdominal Pressure Measurement

The measurement of the rectal pressure is considered to be the 'gold standard' for the assessment of the abdominal pressure. However, conventional rectal catheters can cause erroneous results and are not comfortable for the patients. To reduce these problems, we devised a non-invasive technique for the measurement of the abdominal pressure using the parametric curve fitting method, based on linear, polynomial, exponential or sine equation modeling, between the rectal pressure and electromyographic (sEMG) signals recorded simultaneously from the abdomen. The sEMG signals and rectal pressure were obtained simultaneously from 12 patients with neurogenic bladders due to spinal cord injury (age = 53.2 ± 11.9 years, BMI = 24.4 ± 2.7, ASIA classification: D). Using our algorithm, the correlation coefficient and root mean square error (RMSE) between the measured and estimated abdominal pressure obtained by the quartic polynomial modeling were 0.86 ± 0.05 and 4.70 ± 1.56, respectively. The results obtained herein suggest that the sEMG signals can be used reliably for the indirect measurement of the abdominal pressure in ambulatory urodynamics monitoring systems.

Extending the working calibration ranges of four hexachlorocyclohexane isomers in gas chromatography–electron capture detector by radial basis function neural network

A radial basis function neural network (RBFNN) method was developed for the first time to model the nonlinear calibration curves of four hexachlorocyclohexane (HCH) isomers, aiming to extend their working calibration ranges in gas chromatography–electron capture detector (GC–ECD). Other 14 methods, including seven parametric curve fitting methods, two nonparametric curve fitting methods, and five other artificial neural network (ANN) methods, were also developed and compared. Only the RBFNN method, with logarithm-transform and normalization operation on the calibration data, was able to model the nonlinear calibration curves of the four HCH isomers adequately. The RBFNN method accurately predicted the concentrations of HCH isomers within and out of the linear ranges in certified test samples. Furthermore, no significant difference (p>0.05) was found between the results of HCH isomers concentrations in water samples calculated with RBFNN method and ordinary least squares (OLS) method (R2>0.9990). Conclusively, the working calibration ranges of the four HCH isomers were extended from 0.08–60ng/ml to 0.08–1000ng/ml without sacrificing accuracy and precision by means of RBFNN. The outstanding nonlinear modeling capability of RBFNN, along with its universal applicability to various problems as a “soft” modeling method, should make the method an appealing alternative to traditional modeling methods in the calibration analyses of various systems besides the GC–ECD.

Parametric peak stress functions of 90° pipe bends with ovality under steady-state creep conditions

Stress-based life prediction techniques are commonly used to estimate the failure life of pressurised pipe-related components, such as welds and bends, under creep conditions. Previous research has shown that reasonable life predictions can be obtained, based on the steady-state peak stresses, compared with the life predictions obtained from creep damage modelling. In this work, a series of parametric steady-state peak rupture stress functions of right-angled pipe bends with ovality are presented, which are based on the results obtained from finite element (FE) analyses, covering a number of material property and geometry parameters in practical ranges. Methods used to determine the stress functions are described. The FE analyses have been performed using axisymmetric models, subjected to internal pressure only, with a Norton creep law. Typical examples of parametric peak stress curve fitting are shown. In particular, the accuracy of the interpolation and extrapolation abilities of the stress functions is assessed. The results show that in most cases the interpolated and extrapolated peak stresses are accurate to within ±3% and ±5%, respectively.

Evaluation of the optimum induction gap for the GEM-MIGAS

A gas electron multiplier with a micro-induction gap amplifying structure (GEM-MIGAS) is formed when a conventional GEM is operated with a short induction gap, typically set at 50 μm. The main aims of this study were to examine the charge amplification in the induction gap of the GEM-MIGAS and then to evaluate the optimum induction gap thickness for achieving the maximum charge amplification in the region. For the present study, the GEM-MIGAS was operated in the gas flow mode using He/iso-C 4H 10 (85/15%). It was also possible to determine the optimum gap thickness, where the dependence of the charge amplification was least sensitive to the small variations of the gap thickness and to the ambient variables, such as pressure and temperature. This was accomplished by setting the voltage across the GEM-holes to a small value as to allow the transfer of electrons from the drift region into the induction region, without incurring multiplication within the holes. Through parametric curve fitting of the charge gain-induction gap voltage characteristics, the optimum induction gap for sustaining highest charge gains was evaluated and compared with trends observed using a conventional Micromegas.