Parametric Methods Research Articles

The Impact of Bariatric Surgery on Nitrogen Balance at Six months Post-surgery.

Obesity, defined as abnormal or excessive fat accumulation that presents a risk to health, rose from 8.6 to 10.5% in Singapore's residents. Bariatric surgery, the primary treatment for severe obesity, induces fat and muscle loss. Adequate protein intake is vital for preventing muscle loss. This study examines nitrogen balance in individuals with obesity pre- and post-surgery. Sixteen participants with severe obesity (BMI ≥ 32.5kg/m2) undergoing bariatric surgery (14 sleeve gastrectomy, 2 Roux-en-Y gastric bypass) and 20 normal-weight controls (BMI < 25kg/m2) were recruited. Nitrogen balance, calculated from dietary protein intake and urine nitrogen excretion, was assessed. Participants with obesity were re-evaluated 6months post-surgery. Data were analyzed using parametric methods. At baseline, controls had a BMI of 20.8 ± 2.1kg/m2; those with obesity had 40.9 ± 7.3. Daily calorie and protein intake for participants with obesity were not statistically significantly different from controls (calorie intake at 1467 ± 430 vs. 1462 ± 391kcal, p = 0.9701, protein intake 74.2 ± 28.7 vs. 64.6 ± 18.3g, p = 0.2289). Post-surgery, BMI, fat-free mass, fat mass, total energy intake, carbohydrate, and protein intake decreased significantly (p < 0.01). Protein oxidation and urine nitrogen excretion did not change after bariatric surgery. However, nitrogen balance significantly reduced from 2.62 ± 5.07 to - 1.69 ± 5.07g/day (p = 0.025). Dietary protein intake is inadequate in individuals with obesity at 6months post-bariatric surgery and contributes to a state of negative nitrogen balance.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Radial Vibration Measurement of Rotating Shaft using Constant Density Fringe Pattern and Line Scan Camera

Abstract The radial vibration signals of the rotor can provide abundant information about the health condition of the machine. In this paper, a simple vision-based measurement system is proposed to simultaneously measure two-dimensional displacements in radial directions for the rotating shaft, where the system consists of a constant density fringe pattern (CDFP), a line scan camera (LSC), and a lens. The CDFP should be installed around the surface of the rotating shaft to make the density of the fringe constant along the shaft axis, while the shaft axis is vertical to the optical axis of the LSC but not parallel to the line-array sensor of the LSC. Therefore, the density of the fringe imaged on the LSC is not constant because of the modulation of the circular surface of the shaft, and the distribution of the fringe density on the LSC is a U-shaped curve. Thus, the shaft centreline orbit can be tracked by the lowest point of the density distribution curve (DDC) of the fringe. Then, an efficient and accurate parameterized instantaneous frequency estimation method (PIFEM) is employed to estimate the DDC of the fringe, because the variable density fringe can be regarded as an amplitude-modulated and frequency-modulated nonstationary signal whose instantaneous frequency function is equivalent to the DDC. Experimental results verify the feasibility and effectiveness of the proposed method by comparing it to the eddy current sensors.

Reliability evaluation in the zero‐failure Weibull case based on double‐modified hierarchical Bayes

AbstractHierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment.

The impact of maternal smoking during pregnancy and the age of smoking initiation on incident dementia: A prospective cohort study.

The impact of early-life tobacco exposure on dementia has remained unknown. Using the UK Biobank, the associations of maternal smoking during pregnancy (MSDP) and age of smoking initiation (ASI) with the onset time of all-cause dementia were estimated with accelerated failure time models. The effects of MSDP and ASI on brain structure and their genetic correlation to Alzheimer's disease (AD) were analyzed. A Mendelian randomization (MR) analysis was conducted. The time ratios for smokers starting in childhood, adolescence, and adulthood (vs never smokers) were 0.87 (0.76 to 0.99), 0.92 (0.88 to 0.96), and 0.95 (0.89 to 1.01). MSDP and smoking in adolescence altered many brain regions, including the hippocampus. In genetic analysis, MSDP was genetically and causally linked to AD, and a younger ASI was genetically correlated to a higher AD risk. Early-life smoking accelerated dementia onset and was genetically correlated to AD. MSDP demonstrated genetic and causal linkage to AD risks. Unlike the commonly used Cox proportional hazards model, this article uses a parametric survival analysis method - the accelerated failure model - to explore the relationship between exposure to onset time. It can be used as an alternative method when the proportional hazards assumption is not met. Genetic analyses including genetic correlation study and MR analysis and brain structure analyses were conducted to support our findings and explore the potential mechanisms. The study reveals the relationship between different smoking initiation periods and the onset time of dementia and shows that earlier smoking exposure has a more significant impact on dementia. It emphasizes the importance of preventing early smoking. In the future, more research focusing on the relationship between early exposure and dementia is called for to provide more detailed prevention measures for dementia that cover all age groups.

Comparative investigations on hull line optimization based on traditional and fully parametric methods for saving energy

Comparative investigations on hull line optimization based on traditional and fully parametric methods for saving energy

A Nonlinear Quantile Regression for Accelerated Destructive Degradation Testing Data

Traditional regression approaches to accelerated destructive degradation test (ADDT) data have modeled the mean curve as representative. However, maximum likelihood estimates (MLEs) of the mean model are likely to be biased when the data are non-Gaussian or highly skewed. The median model can be an alternative for skewed degradation data. In this work, we introduce a nonlinear quantile regression (QR) approach for estimating quantile curves of ADDT data. We propose an iterative QR algorithm that uses the generalized expectation-maximization (GEM) framework to estimate the parameters of the nonlinear QR ADDT model, based on the asymmetric Laplace distribution to accommodate non-Gaussian and skewed errors. Using the asymptotic properties of the QR parameter estimates, we estimate variance-covariance matrix for the τ th QR parameters using order statistics and bootstrap methods. We propose a new prediction method of the quantile of the failure-time distribution in the normal use condition. Confidence intervals for the quantiles of the failure-time distribution are constructed using the parametric bootstrap method. The proposed model is illustrated using an industrial application and compared with the existing model. Various quantile curve estimates derived using the QR ADDT model provide a more flexible modeling framework than the traditional mean ADDT modeling approach.

Mesh adaptive-based parametric level set method for the design of heat sink based on two-layer thermal-fluid system

Mesh adaptive-based parametric level set method for the design of heat sink based on two-layer thermal-fluid system

Evaluation of moral distress and moral intelligence among medical residents

Background: Medical ethics is necessary in the medical field to protect patient rights, enhance professional conduct, manage complex situations, prevent malpractice, and advance healthcare policy. Aim: This study aimed to examine the levels of moral distress and moral intelligence among medical residents and explore the relationship between these two concepts. Methods: This descriptive-analytical cross-sectional study was performed on 221 medical residents in 2021. The participants were recruited by census sampling method. Data were collected by two standard questionnaires, including Corley’s moral distress scale (2001) and Lennick and Kiel’s moral intelligence scale (2005). Statistical analyses were conducted using both parametric and non-parametric methods. All analyses were performed by SPSS-22 software. Results: Of participants, 140 (63.4%) were non-surgical and 81 (36.6%) were surgical assistants. Moral distress was significantly higher in surgical than non-surgical disciplines, (P&lt;0.05). Further, according to the moral distress classification, 51.8% of surgical residents and 15.8% of non-surgical residents had severe moral distress. Moral intelligence was also higher in surgical than in non-surgical disciplines, but the difference was not statistically significant (P&gt;0.05), and moral intelligence was moderate to high. There was a significantly negative correlation between moral intelligence (and its four subscales) and moral distress (P &lt;0.001, r =-0.410). Conclusion: The results showed high moral distress and an inverse correlation between moral distress and moral intelligence among medical residents. The study highlights the importance of addressing issues related to moral distress and moral intelligence in medical education and training programs

Reference intervals for bone marrow cells in juvenile and young adult cats bone marrow cytology.

The current bone marrow (BM) reference intervals (RI) are based on a limited number of cats. Age-related changes in BM variables might be important, possibly affecting the interpretation of the results. Establish BM cytologic reference intervals (RIs) and association of age and sex on these findings, in healthy juvenile and young adult cats. BM aspirates of cats deemed healthy based on history and clinical, CBC, serum chemistry findings, and negative retrovirus serology were obtained and examined cytologically. The examination included a 1000-nucleated differential cell count and cell morphologic assessment. RIs were calculated using parametric, robust, and nonparametric methods. The cytologic findings were examined for associations with sex and age. The study included 40 cats (females, 22 [55%]; males, 18 [45%]) with a median age of 1.5 years (range 0.5-5). Most calculated RIs were similar to those previously reported. BM plasma cell and monocyte counts were weakly and positively correlated with age (rs, .312 and .373, respectively; P < .05). Metarubricytes were higher in females (mean, 25.1%; SD, 6.0%) than males (mean, 21.2%; SD, 6.0%; P < .05). The BM differential cell counts determined in this study can serve as RIs for cats aged 0.5-5 years.

Multi-Dimensional Evaluation of an Augmented Reality Head-Mounted Display User Interface for Controlling Legged Manipulators

Controlling assistive robots can be challenging for some users, especially those lacking relevant experience. Augmented Reality (AR) User Interfaces (UIs) have the potential to facilitate this task. Although extensive research regarding legged manipulators exists, comparatively little is on their UIs. Most existing UIs leverage traditional control interfaces such as joysticks, Hand-held (HH) controllers, and 2D UIs. These interfaces not only risk being unintuitive, thus discouraging interaction with the robot partner, but also draw the operator’s focus away from the task and towards the UI. This shift in attention raises additional safety concerns, particularly in potentially hazardous environments where legged manipulators are frequently deployed. Moreover, traditional interfaces limit the operators’ availability to use their hands for other tasks. Towards overcoming these limitations, in this article, we provide a user study comparing an AR Head Mounted Display (HMD) UI we developed for controlling a legged manipulator against off-the-shelf control methods for such robots. This user study involved 27 participants and 135 trials, from which we gathered over 405 completed questionnaires. These trials involved multiple navigation and manipulation tasks with varying difficulty levels using a Boston Dynamics (BD) Spot ® , a 7 DoF Kinova ® robot arm, and a Robotiq ® 2F-85 gripper that we integrated into a legged manipulator. We made the comparison between UIs across multiple dimensions relevant to a successful human-robot interaction. These dimensions include cognitive workload, technology acceptance, fluency, system usability, immersion and trust. Our study employed a factorial experimental design with participants undergoing five different conditions, generating longitudinal data. Due to potential unknown distributions and outliers in such data, using parametric methods for its analysis is questionable, and while non-parametric alternatives exist, they may lead to reduced statistical power. Therefore, to analyse the data that resulted from our experiment, we chose Bayesian data analysis as an effective alternative to address these limitations. Our results show that AR UIs can outpace HH-based control methods and reduce the cognitive requirements when designers include hands-free interactions and cognitive offloading principles into the UI. Furthermore, the use of the AR UI together with our cognitive offloading feature resulted in higher usability scores and significantly higher fluency and Technology Acceptance Model (TAM) scores. Regarding immersion, our results revealed that the response values for the Augmented Reality Immersion (ARI) questionnaire associated with the AR UI are significantly higher than those associated with the HH UI, regardless of the main interaction method with the former, i.e., hand gestures or cognitive offloading. Derived from the participants’ qualitative answers, we believe this is due to a combination of factors, of which the most important is the free use of the hands when using the HMD, as well as the ability to see the real environment without the need to divert their attention to the UI. Regarding trust, our findings did not display discernible differences in reported trust scores across UI options. However, during the manipulation phase of our user study, where participants were given the choice to select their preferred UI, they consistently reported higher levels of trust compared to the navigation category. Moreover, there was a drastic change in the percentage of participants that selected the AR UI for completing this manipulation stage after incorporating the cognitive offloading feature. Thus, trust seems to have mediated the use and non-use of the UIs in a dimension different from the ones considered in our study, i.e., delegation and reliance. Therefore, our AR HMD UI for the control of legged manipulators was found to improve human-robot interaction across several relevant dimensions, underscoring the critical role of UI design in the effective and trustworthy utilisation of robotic systems.

Implementing and evaluating a veterans crisis line quality improvement initiative: The safety planning pilot program.

The Veterans Crisis Line (VCL) is part of the U.S. Department of Veterans Affairs' suicide prevention mission. In 2021, VCL assessed the impact of a pilot implementation project of conducting six-part safety plans (SPs) instead of VCL's usual risk mitigation plan. VCL responders offered to complete six-part SPs with eligible callers. Parametric and nonparametric methods compared call characteristics and Veteran Health Administration (VHA) utilization of eligible callers, by SP completion. We forecasted the operational impact of VCL-wide implementation. 27.37% (N = 448/1,637) of calls to designated responders were eligible for SPs. Of those, 27.23% (N = 122/448) completed SPs. Common barriers were call interruptions and the veteran declining. Among veteran callers who use VHA, SP completers were more likely to accept clinical referrals and had more outpatient mental health appointments before and after their VCL call. Calls involving SPs had a call plus documentation time 175% longer than eligible calls without SPs (87.78 vs. 49.66 min). If SPs were implemented VCL-wide, this would require 3-5(4.12%) more responders per hour to maintain current VCL call answer speed. SPs are adaptable to VCL; however, implementation presents logistical barriers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian predictive modeling for gas purification using breakthrough curves

This study proposes a predictive model for assessing adsorber performance in gas purification processes, specifically targeting the removal of chemical warfare agents (CWAs) using breakthrough curve analysis. Conventional parameter estimation methods, such as Brunauer-Emmett-Teller analysis, encounter challenges due to the limited availability of kinetic and equilibrium data for CWAs. To overcome these challenges, we implement a Bayesian parametric inference method, facilitating direct parameter estimation from breakthrough curves. The model’s efficacy is confirmed by applying it to H2S purification in a fixed-bed setup, where predicted breakthrough curves aligned closely with previous experimental and numerical studies. Furthermore, the model is applied to sarin with ASZM-TEDA carbon, estimating key parameters that could not be assessed through conventional experimental techniques. The reconstructed breakthrough curves closely match actual measurements, highlighting the model’s accuracy and robustness. This study not only enhances filter performance prediction for CWAs but also offers a streamlined approach for evaluating gas purification technologies under limited experimental data conditions.

Identifying frequency de-correlated dust residuals in B-mode maps by exploiting the spectral capability of bolometric interferometry

Astrophysical polarized foregrounds represent the most critical challenge in cosmic microwave background (CMB) $B$-mode experiments, requiring multifrequency observations to constrain astrophysical foregrounds and isolate the CMB signal. However, recent observations indicate that foreground emission may be more complex than anticipated. Not properly accounting for these complexities during component separation can lead to a bias in the recovered tensor-to-scalar ratio. In this paper we investigate how the increased spectral resolution provided by band-splitting in bolometric interferometry (BI) through a technique called spectral imaging can help control the foreground contamination in the case of an unaccounted-for Galactic dust frequency de-correlation along the line of sight (LOS). We focused on the next-generation ground-based CMB experiment CMB-S4 and compared its anticipated sensitivity, frequency, and sky coverage with a hypothetical version of the same experiment based on BI (CMB-S4/BI). We performed a Monte Carlo analysis based on parametric component separation methods (FGBuster and Commander) and computed the likelihood of the recovered tensor-to-scalar ratio, $r$. The main result is that spectral imaging allows us to detect systematic uncertainties on $r$ from frequency de-correlation when this effect is not accounted for in the component separation. Conversely, an imager such as CMB-S4 would detect a biased value of $r$ and would be unable to spot the presence of a systematic effect. We find a similar result in the reconstruction of the dust spectral index, and we show that with BI we can more precisely measure the dust spectral index when frequency de-correlation is present and not accounted for in the component separation. The in-band frequency resolution provided by BI allows us to identify dust LOS frequency de-correlation residuals where an imager with a similar level of performance would fail. This creates the possibility of exploiting this potential in the context of future CMB polarization experiments that will be challenged by complex foregrounds in their quest for $B$-mode detection.

Optimizing Built Environment in Urban Negative Spaces Using Parametric Methods—Research on a High-Density City in China

In the early stage of architectural design, addressing the challenges posed by negative spaces in high-density urban environments is crucial for enhancing spatial efficiency and building sustainability. Multiple studies employed digital methods and tools to address these issues, such as parametric design, simulation, and genetic algorithms, to investigate architectural generation approaches for urban negative spaces. This article proposes an integrated design process that involves finding the location and form of negative spaces, generating solutions using slime mold and wasp algorithms, and optimizing and analyzing solutions using the Wallacei plugin in Grasshopper. This comprehensive approach underscores the potential of parametric design to yield a multitude of solutions while also acknowledging the convergence challenges encountered during simulations, particularly in optimizing for optimal sunlight exposure during the winter solstice and minimal solar radiation in the summer. Analyzing the optimization goals and parameter values of the 15th Pareto optimal solution in the 100th generation reveals: (1) a higher number of units leads to positive correlation growth in both objectives; (2) within a certain number of units, parametrically generated solutions facilitate the convergence of optimization goals, yielding optimal outcomes. Therefore, factors such as the range of unit quantities and proportions need consideration during early-stage parametric design and simulation. This study explores a design methodology for negative spaces in high-density urban cities, validating the feasibility of various mainstream generation methods and offering insights for future research.

Structural entropy minimization combining graph representation for money laundering dentification

AbstractMoney laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates.

Efficacy of targeted screening for familial hypercholesterolemia in children and adults aged &lt;44 years: retrospective, prospective observational study

BACKGROUND: Considering the high cardiovascular risk in patients with familial hypercholesterolemia (FH), early diagnosis and treatment are the basis for the prevention of vascular accidents. In the absence of universal screening, the results of more targeted diagnostics are of interest — the search for lipid metabolism disorders in young relatives of patients with atherosclerotic vascular diseases.&#x0D; OBJECTIVE: To determine the effectiveness of targeted screening for identifying patients with FH, which will justify its widespread use in clinical practice.&#x0D; MATERIALS AND METHODS: At the start of the observational study, 584 patients were identified from the vascular centers of the Krasnoyarsk Regional Hospital, Krasnoyarsk Interdistrict Clinical Hospital No. 20 named after. I.S. Berzon, who had lipid profile data and early cardiovascular events (all forms of coronary heart diseases), including reconstructive vascular interventions up to 55 years in men and up to 60 years in women in the case of first-degree relatives and up to 50 years in second-degree relatives. The study examined patients’ relatives aged 44 years. To diagnose FH in participants aged 16 years, the scale developed by a network of Dutch lipid clinics was used; in participants aged 16 years, the S. Broome criteria were used. Targeted screening and lipid parameters in individuals with probable, possible, and definite FH were analyzed. Data processing was performed using Microsoft Excel and Statistica v. 12.0 (USA). Parametric (calculation of the median and 25th and 75th quartiles) and nonparametric (statistical significance of differences was determined using the Mann–Whitney and χ2 tests) methods were used.&#x0D; RESULTS: The study examined 70 children and 104 adults aged 44 years whose first- and/or second-degree relatives suffered premature vascular events. In the general group of participants, 42 (24.14%) patients had FH (of which 18 were 17 years old) and 37 (21.2%) had hyperlipoproteinemia (a). Definite FH was documented in 16 (9.1%) patients, probable in another 16, and possible in 9. Hyperlipoproteinemia (a) was found in 37 (21.2% of general group) patients, including 12 children (17.1% of pediatric group).&#x0D; CONCLUSION: Not more than 30% of the population are aware of the hereditary nature of lipid metabolism disorders and atherosclerotic diseases. Targeted screening is the least expensive and most effective tool for identifying patients with FH and hyperdipoproteinemia (a).

MR-Scout : Automated Synthesis of Metamorphic Relations from Existing Test Cases

Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on metamorphic relations (MRs), that govern multiple related inputs and their outputs. However, designing MRs is challenging, as it requires domain-specific knowledge. This hinders the widespread adoption of MT. We observe that developer-written test cases can embed domain knowledge that encodes MRs. Such encoded MRs could be synthesized for testing not only their original programs but also other programs that share similar functionalities. In this paper, we propose MR-Scout to automatically synthesize MRs from test cases in open-source software (OSS) projects. MR-Scout first discovers MR-encoded test cases (MTCs), and then synthesizes the encoded MRs into parameterized methods (called codified MRs ), and filters out MRs that demonstrate poor quality for new test case generation. MR-Scout discovered over 11,000 MTCs from 701 OSS projects. Experimental results show that over 97% of codified MRs are of high quality for automated test case generation, demonstrating the practical applicability of MR-Scout . Furthermore, codified-MRs-based tests effectively enhance the test adequacy of programs with developer-written tests, leading to 13.52% and 9.42% increases in line coverage and mutation score, respectively. Our qualitative study shows that 55.76% to 76.92% of codified MRs are easily comprehensible for developers.

Methods for non-proportional hazards in clinical trials: A systematic review.

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Parametric Solid Models of the At-Term Uterus From Magnetic Resonance Images.

Birthing mechanics are poorly understood, though many injuries during childbirth are mechanical, like fetal and maternal tissue damage. Several biomechanical simulation models of parturition have been proposed to investigate birth, but many do not include the uterus. Additionally, most solid models rely on segmenting anatomical structures from clinical images to generate patient geometry, which can be time-consuming. This work presents two new parametric solid modeling methods for generating patient-specific, at-term uterine three-dimensional geometry. Building from an established method of modeling the sagittal uterine shape, this work improves the uterine coronal shape, especially where the fetal head joins the lower uterine wall. Solid models of the uterus and cervix were built from five at-term patients' magnetic resonance imaging (MRI) sets. Using anatomy measurements from MRI-segmented models, two parametric models were created-one that employs an averaged coronal uterine shape and one with multiple axial measurements of the coronal uterus. Through finite element analysis, the two new parametric methods were compared to the MRI-segmented high-fidelity method and a previously published elliptical low-fidelity method. A clear improvement in the at-term uterine shape was found using the two new parametric methods, and agreement in principal Lagrange strain directions was observed across all modeling methods. These methods provide an effective and efficient way to generate three-dimensional solid models of patient-specific maternal uterine anatomy, advancing possibilities for future research in computational birthing biomechanics.