Weibull Model Parameters Research Articles

Impact of the Affordable Care Act and Medicaid Expansion Among Patients With HIV-Associated Aggressive B-Cell Non-Hodgkin Lymphomas.

To study the influence of the Affordable Care Act (ACA) policy and its Medicaid expansion on insurance status and survival in patients with HIV with aggressive lymphoma. We used the National Cancer Database, a hospital-based national registry, to identify adults age 18-64 years with HIV-associated aggressive B-cell non-Hodgkin lymphomas (HIV-a-B-NHLs), diagnosed during 2007 to 2016. Survival analysis was performed on a subset of patients with HIV-a-B-NHL for whom location data were available who resided in Medicaid expansion-adopted and nonadopted states. Using a quasi-experimental difference-in-difference model, the difference in adjusted 2-year survival rates obtained with a flexible parametric Weibull model was compared for states that adopted the Medicaid expansion of ACA against those that did not adopt the expansion. We identified 8,231 patients with HIV-a-B-NHL and 50,650 non-HIV patients with a-B-NHL. We found that a lower proportion of individuals were uninsured at diagnosis in the expansion states compared with nonexpansion states. We also found that the ACA policy adoption led to a reduction in the proportion of uninsured individuals with HIV-a-B-NHL in expansion states of 34.9%, compared with 15.9% in non-expansion-adopted states. There was a statistically significant improvement in the 2-year survival rate among patients with HIV-a-B-NHL in the expansion compared with nonexpansion states with the adoption of ACA (7.17% v 1.58%, P = .02). Using a novel quasi-experimental model, we found that the ACA policy corresponded with a greater survival improvement in patients with HIV-a-B-NHL within Medicaid expansion-adopted states compared with nonexpansion states. We believe that this evidence should be taken into consideration in future policy making.

Investigation of aggregate gradation on air voids distribution in porous asphalt concrete using X-ray CT scanning images

Porous asphalt concrete (PAC) is typically used as a paving material for porous asphalt pavement in rainy areas due to its abundant interconnected pores structure. Not all interconnected pores in PAC are valid for permeability, mainly influenced by the composition of aggregate gradation. The main purpose of this paper is to investigate the effect of aggregate gradation on pores structure distribution in PAC by X-ray computed tomography (CT) system and digital image processing (DIP) techniques. The distributions of porosity, number and average equivalent diameter of air voids in middle part are much more evenly compared to the both ends in PAC specimens, accounting for about 70 % of the total of specimen height. The average equivalent diameter of interconnected air voids mainly ranges from 0.5 mm to 5 mm in PAC, accounting for about 80 % of the total numbers. For PAC with similar porosity, the larger the normal maximum aggregate size (NMAS), the less the number and the larger the average equivalent diameter of air voids (interconnected air voids), the smaller the difference between the interconnected porosity and porosity. For PAC with same NMAS, as the porosity increase, the number and average equivalent diameter of air voids (interconnected air voids) are respectively decreased and increased gradually, the difference between the interconnected porosity and porosity is also showed a decreasing trend. And then, the correlated equation is established among porosity, interconnected porosity and the composition of aggregate gradation. The distribution of air voids (interconnected air voids) size could be well described by the two-parameter Weibull model, and the relationships are explored and developed between Weibull model parameters and the composition of aggregate gradation.

Monitoring nonlinear large gradient subsidence in mining areas through SBAS-InSAR with PUNet and Weibull model fusion.

The subsidence of the earth's surface in mining areas is characterized by fast speed and large gradients. Conventional small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) monitoring can significantly underestimate results, making it challenging to capture the surface's temporal subsidence features. In this context, this paper proposes a method for monitoring subsidence in mining areas. It utilizes a phase unwrapping network (PUNet) and a fused Weibull model within the SBAS-InSAR framework to address nonlinear and large-gradient subsidence. The basic principle of this method is to first process the SAR images using the small baseline method to obtain the differential interferogram, utilizing the PUNet to obtain reliable large-gradient unwrapped phases. Next, the Weibull model parameters of each pixel are calculated based on the unwrapped phase, and the temporal subsidence of each point on the surface is determined using the calculated parameters. This method introduces a nonlinear model into the SBAS-InSAR solution, which is more consistent with the subsidence characteristics of mining areas. Through experimentation in a backfilled mining working face, the proposed method in this paper yields superior monitoring results compared to conventional approaches.

Effect of temperature and relative humidity on ethylene oxide inactivation of Salmonella and Enterococcus faecium NRRL B-2354 on chia seeds and its influence on the quality of seeds

Ethylene oxide (EtO) sterilization is commonly used by the food industry to inactivate pathogens in spices, but its efficacy on edible seeds, a high-risk food commodity, remains unexplored. This study aimed to evaluate the antimicrobial efficacy of EtO gas on chia seeds and assess the impact of processing parameters on microbial reduction, byproduct formation, and seed quality. Further, Enterococcus faecium NRRL B-2354 was evaluated for its suitability as a surrogate for Salmonella. Chia seeds were inoculated with a cocktail of five strains of Salmonella enterica or E. faecium. The inoculated sample was treated with EtO (735.3 mg/L) at different combinations of relative humidity (30, 40, and 50%) and temperature (46, 53, and 60 °C) for exposure times ranging from 2 to 120 min. The results showed that EtO treatment effectively reduced the bacterial populations on chia seeds, with the inactivation trend following a non-linear pattern that was described well by the Weibull model. The treatment time required for a 5-log reduction of Salmonella was less than 10 min at 50% RH and 53–60 °C. Exposure of chia seeds to EtO for 2 min at 50% RH gave 3.15, 3.59, and 3.87 log reductions at 46, 53, and 60 °C, respectively. E. faecium was found to be a reliable surrogate for Salmonella during EtO treatment. The modified Bigelow model developed based on Weibull model parameters could be used to predict Salmonella inactivation (RMSE = 0.73 min) and E. faecium inactivation (RMSE = 0.87 min) on chia seeds during EtO treatment. Fatty acid composition, protein content, color, and germination ability of chia seeds were not impacted significantly (P > 0.05) by EtO fumigation. These findings suggest that EtO gas can effectively inactivate Salmonella on chia seeds while preserving their viability and quality.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.

Technical analysis of wind energy potentials using a modified Weibull and Raleigh distribution model parameters approach in the Gambia

Wind energy has gained prominence over the past few decades because of its environment-friendly nature and abundant availability. However, the exploration of wind energy requires adequate knowledge of the wind distribution parameters before installing the wind turbine. This study assessed the potential of harvesting wind energy at two Gambian locations by fitting the best model comparing Weibull and Raleigh Distributions. A novel approach combining the energy pattern factor and standard deviation methods in estimating the distribution parameters of the characteristics of wind in the Gambian locations of Yundum and Basse has been presented and statistically analyzed using the Weibull and Raleigh distribution functions. The results showed that the shape parameters ranged from 4.88 to 6.98 and 3.87–6.15 for Yundum and Basse locations, the Weibull scale parameter ranged from 6.60 to 10.58 m/s and 4.51–8.69 m/s for Yundum and Basse while the calculated wind power densities ranged from 139 to 718 W/m2 and 46–390 W/m2 for Yundum and Basse respectively. These results clearly show a high potential for generating electricity with wind in the study areas. The statistical analysis revealed that the Weibull models perform better at Yundum in terms of RMSD = 0.33, NSCOE=0.45,MAE=0.29 and χ2= 1.57 while the Raleigh distribution gives a better fit for Basse in terms of R2=0.88, and MAE=0.39 only making it more suitable for calculating the wind power potentials.

Impact of the Affordable Care Act and Medicaid Expansion on Insurance Coverage and Outcomes in Patients with HIV‐associated Aggressive B‐cell Non‐Hodgkin Lymphomas

Introduction: HIV-associated lymphomas affect a uniquely disadvantaged group of patients, who were often denied insurance based on preexisting conditions until the Affordable Care Act (ACA) was signed in 2010. We investigated the impact of ACA and its Medicaid expansion in 2014 which covered the low-income strata, on outcomes among patients with HIV-associated aggressive B-cell non-Hodgkin lymphomas (HIV-aB-NHL). Methods: We used the National cancer database (NCDB) to identify HIV-associated aB-NHL patients aged 18–64 years from 2004 to 2017. We defined the pre-ACA period up to 2013 and the post-ACA period after 2014. States were grouped as Medicaid expansion adopted or not adopted. To reduce bias from advancements in anti-retroviral and chemo-immuno-therapies, we compared survival outcomes between 2010 to 2013 and 2014 to 2017. Kaplan-Meier method and a parametric Weibull model were used for survival analysis. Results: We identified 10,795 eligible patients with HIV-associated aB-NHL, comprised of 1802 Hispanics (16.7%), 3915 non-Hispanic blacks [NHB (36.3%)], 4727 Non-Hispanic Whites [NHW (43.8%)] and 351 other minorities (3.2%). The predominant diagnoses were diffuse large B-cell lymphoma (63.3%) and Burkitt lymphoma (20.1%). The figure shows the distribution of insurance status at diagnosis in Medicaid expansion (n = 4611) and non-expansion (n = 3091) states before and after the implementation of ACA stratified by race. Hispanics were more likely to be from residential zip codes in the lower educational quartile. A higher proportion of patients were seen at academic medical centers in expansion states across all races, and this further improved post-ACA. There was a significant improvement in survival seen among NHB (p < 0.01) post-ACA though the trend in Hispanics was not significant (P = 0.36). A multivariable survival analysis using the Weibull model adjusting for age, sex, insurance status, Charlson-Deyo comorbidity index, income, race, and Medicaid expansion status found statistically significant improvement in the states that adopted Medicaid expansion (HR 0.88, p < 0.01) though NHB race predicted poor survival (HR 1.49, p < 0.01). Keywords: Aggressive B-cell non-Hodgkin lymphoma, Cancer Health Disparities Conflicts of interests pertinent to the abstract. L. D. Anderson Consultant or advisory role: Celgene, BMS, Amgen, GSK, Janssen, Karyopharm, Beigene, AbbVie, Cellectar, and Sanofi Y. F. Madanat Consultant or advisory role: BluePrint Medicines, GERON, OncLive, Sierra Oncology, Stemline Therapeutics and Novartis G. Kaur Honoraria: BMS and Cellectar F. T. Awan Consultant or advisory role: Genentech, Astrazeneca, Abbvie, Janssen, Pharmacyclics, Gilead sciences, Kite pharma, Celgene, Karyopharm, MEI Pharma, Verastem, Incyte, Beigene, Johnson and Johnson, Dava Oncology, BMS, Merck, Cardinal Health, ADCT therapeutics, Epizyme, Caribou Biosciences, Cellecter Bisosciences Research funding: Pharmacyclics P. Ramakrishnan Geethakumari Consultant or advisory role: Kite Pharma, Bristol Myers Squibb, Rafael Pharma, Pharmacyclics LLC, ADC Therapeutics and Cellectar Biosciences

The impact of using Elsevier ClinicalPath oncology treatment pathways on survival and cost of care.

6620 Background: Cone Health is a 921 bed hospital system in North Carolina with a comprehensive cancer center. Our aim was to determine the value of using Elsevier’s ClinicalPath oncology pathways on short-term survival and cost of care from the patient and health system perspectives. Cancer treatment has become more complex as it progresses more toward precision medicine modalities. As such, clinical decision support tools, such as ClinicalPath, have gained traction. Health systems struggle to survive after the COVID pandemic, with residual financial impacts lingering into 2023. It is more important than ever before that health systems focus on value where the patient benefits and financial sustainability are improved. Methods: We used a retrospective cohort study design with matching. Exposure was the use of the ClinicalPath decision support tool in Epic (exposed) versus no use of the tool (not exposed). We matched on cancer body site, AJCC clinical stage, the goal of treatment, Elixhauser comorbidity score, and year of a cancer diagnosis. We were able to match 509 exposed to 509 unexposed. Our primary outcomes were 12-month survival from the time of the first treatment and contribution margin. We used a log-rank calculation based on our hazard ratios (Freedman method) to determine the sample size required for each group, assuming a power of 80%. We used a Weibull parametric model to assess 12-month survival with additional covariates. An OLS model was used to evaluate the contribution margin. Results: Results demonstrate that when ClinicalPath is used to guide treatment, patients are half as likely to die within 12 months from treatment compared to the unexposed (HR 0.50; 95% CI 0.37-0.68). The OLS contribution model found, on average, that margin has an associated 74.0% (0.74; 95% CI 0.58-0.90) increase when ClinicalPath is used to guide treatment compared to the unexposed. This equates to a crude increase in contribution margin for cancer treatment of $58,362, on average per case, when ClinicalPath is used. Variable direct costs are higher when using ClinicalPath. This means, on average, it costs more to treat patients on pathway using best available evidence. Still, they have better short-term survival and payers are covering the additional costs given the increase in contribution margin for our system. Conclusions: Short-term recurrence free survival is significantly improved when providers utilize ClinicalPath compared to not using the tool consistent with the primary aim of the CDS solution. Newer, more efficacious treatments may have higher initial costs, as expected. Still, the effectiveness of these treatments on survival in the real-world setting is born out in the current study. The higher initial direct costs are counterbalanced by a higher contribution margin per patient providing evidence that improved outcomes in cancer therapy can also provide a positive ROI for provider organizations.

Numerical Estimation Method for the Generalized Weibull Distribution Parameters

In this study, a new estimation method using the Runge-Kutta iteration technique is presented to improve point estimation methods. The improved method has been applied to the generalized Weibull distribution, which is a member of a family of distributions (T-X family). The estimates of the generalized Weibull model parameters were derived using the Runge-Kutta and Bayesian estimation methods based on the generalized progressive hybrid censoring scheme, via a Monte Carlo simulation. The simulation results indicated that the Runge-Kutta estimation method is highly efficient and outperforms the Bayesian estimation method based on the informative and kernel priors. Finally, two real data sets were studied to ensure the Runge-Kutta estimation method can be used more effectively than the most popular estimation methods in fitting and analyzing real lifetime data.

Influence of face sheet thickness on flexural strength characteristics of carbon/epoxy/Nomex honeycomb sandwich panels

This study presents the findings of an investigation into the effect of varying the thickness of a carbon/epoxy face sheet of a Nomex honeycomb sandwich panel on its the flexural properties. The thickness (hs) of the face sheet was varied by increasing the number of plies from one 0.25 mm thick layer to eight such layers, giving a total nominal thickness of 2.0 mm, whilst maintaining a constant thickness of the core. The flexural properties of the sandwich panels were investigated through a series of three- and four-point bending tests. A particular focus was on identifying changes in the failure mode with increasing face sheet thickness. The flexural properties of the sandwich panels were predicted using sandwich beam theory, where the deviation from the experimental values was shown to be less than 13%. A two parameter Weibull distribution model was used to predict the maximum flexural load using an analysis of variance (ANOVA) tool and an excellent level of correlation was observed with the experimental values. The difference between the predicted maximum load values and the experimental results was below 5% in all cases. A brittle mode of failure was observed in the thickest panel. The sandwich panel based on 1.5 mm thick face sheet was identified as being the most appropriate design, both in terms of strength and stiffness.

Surrogate modeling of dissolution behavior toward efficient design of tablet manufacturing processes

This paper presents surrogate models of dissolution behavior to identify the critical input parameters in tablet manufacturing processes. Dissolution behavior is a critical quality attribute, and is defined as the time profile of fraction of dissolved active pharmaceutical ingredients in a solvent from a tablet. Dissolution behavior was first calculated by mechanistic models using gPROMS® and fitted by the Weibull model. Random forest regression calculated the Weibull model parameters from the input model parameters. The developed models enabled to reduce computational cost without losing accuracy. Case studies were performed for both dry and wet granulation processes using paracetamol. The results identified key input and intermediate parameters to the dissolution behavior, e.g., granule bulk density and porosity. In addition, wet granulation had a tendency to make dissolution faster than dry granulation. The proposed approach can contribute to enhance simulation capabilities in the limited time of pharmaceutical process development.

Productivity duration among farmers in semi arid regions in Zimbabwe

This paper applied the concept of survival analysis to estimate productivity duration among A1 farmers in Zimbabwe and to identify the main factors influencing the probability of productivity. The Cox hazard model and the Weibull parametric model were applied in this study to analyse farmer productivity and a sample of 176 randomly selected farmers and a questionnaire was administered to solicit information on the wages, education level and the level of government support in terms of input subsidies. The results showed that educated farmers are more likely to be productive and take lesser time to increase their productivity levels compared to less educated farmers. This then pointed to the fact that the government of Zimbabwe should have targeted the education of farmers first before embarking on resettling the A1 smallholder farmers and this can also be considered in future land reforms. The study recommended that there be farmer education initiatives to speed up their graduation from survivor to hazard farmers.

Mimicking Clinical Trials Using Real-World Data: A Novel Method and Applications.

Simulating individual-level trial data when only summary data are available is often useful for meta-analysis, forming external control arms and calibrating trial results to real-world data (RWD). The joint distribution of baseline characteristics in a trial is usually simulated by combining its summary data with RWD's correlations. However, RWD correlations may not be a perfect proxy for the trial. A misspecified correlation structure could bias any analysis in which the outcomes generating models are nonlinear or include effect modifiers. We developed an iterative algorithm using copula and resampling, which was based on the estimated propensity score for the likelihood of enrollment in a trial given participants' characteristics. Validation was performed using Monte Carlo simulations under different scenarios in which the marginal and joint distributions of covariates differ between trial samples and RWD. Two applications were illustrated using an actual trial and the Surveillance, Epidemiology, and End Results-Medicare data. We calculated the standardized mean difference (SMD) to assess the generalizability of the trial and explored the feasibility of generating an external control by applying a parametric Weibull model trained in RWD to predict survival in the simulated trial cohort. Across all scenarios, approximated correlations derived from the algorithm were closer to the true correlations than the RWD's correlations. The algorithm also successfully reproduced the joint distribution of characteristics for the actual trial. A similar SMD was observed using simulated data and individual-level trial data. The 95% confidence intervals were overlapped between adjusted survival estimates from the simulated trial and actual trial Kaplan-Meier estimates. The algorithm could be a feasible way to simulate individual-level data when only summary data are available. Further research is needed to validate our approach with larger sample sizes. The correlation structure is crucial to building the joint distribution of patient characteristics, and a misspecified correlation structure could potentially influence predicted outcomes.An iterative algorithm was developed to approximate a trial's correlation structure using published summary trial data and real-world data.The algorithm could be a feasible way to simulate individual-level trial data when only trial summary data are available.

Cesarean versus vaginal delivery for breech presentation is an independent risk factor for long-term pediatric respiratory hospitalization of the offspring.

To compare the long-term respiratory morbidity of offspring born by cesarean delivery for breech presentation with that of those delivered vaginally. A population-based cohort analysis including all singleton breech deliveries between the years 1991 and 2014, comparing long-term respiratory morbidity of offspring born in breech presentation, according to mode of delivery. Offspring with congenital malformations, perinatal deaths, and instrumental deliveries were excluded. Respiratory morbidity included hospitalizations (up to age 18 years), as recorded in hospital records. A Kaplan-Meier survival curve compared cumulative respiratory morbidity. A Weibull parametric survival model controlled for confounders and repeat deliveries. A total of 7337 breech deliveries were included; 6376 (86.9%) cesarean deliveries and 961 (13.1%) vaginal breech deliveries. The Kaplan-Meier survival curve demonstrated higher cumulative incidence of respiratory morbidity in the cesarean delivery group compared with vaginal delivery (log rank test P= 0.006). Using a Weibull parametric survival model to control for confounders, cesarean delivery was found to be an independent risk factor for long-term respiratory morbidity of the offspring (adjusted hazard ratio 1.87, 95% confidence interval 1.32-2.65, P< 0.001). Cesarean versus vaginal delivery for breech presentation is an independent risk factor for long-term pediatric respiratory morbidity of the offspring.

Predictors of time to recovery of preterm neonates with respiratory distress syndrome admitted in University of Gondar comprehensive specialized hospital neonatal intensive care unit North West Ethiopia.

BackgroundRespiratory distress syndrome (RDS) is caused by a deficiency of a molecule called surfactant. It occurs in newborns born before 37 weeks of gestation. It is a main cause of morbidity and mortality in the early neonatal period.Therefore, this study aims to assess median time to recovery and predictors of preterm neonates with respiratory distress syndrome admitted in University of Gondar comprehensive specialized hospital Northwest Ethiopia 2020.MethodsInstitution based retrospective follow up study was conducted on 386 preterm neonates with hyaline membrane disease who were admitted in the neonatal intensive care unit from January, 2016 to December 2018. The data were entered in to EPI info version 7.0 and transferred to Stata version 14.0 for analysis. Both bi-variable and multi variable Weibull parametric model were fitted to identify predictors with 95% confidence interval of hazard ratio (HR) and p-value. P-value less than 0.05 in the multivariable model showed the presence of significant association between covariates and the dependent variable.ResultsThe overall median length of recovery were 11 day with an interquartile range of (7, 16) neonate-days. Being a product of multiple pregnancy (AHR 1.67; 95%CI (1.25, 2.23)), vaginal mode of delivery (AHR 1.6; 95%CI (1.13, 2.26)), and neonatal hypothermia at admission (AHR 1.6; 95%CI (1.13, 2.26)) were found to be significant predictors of time to recovery.ConclusionIn this study the median time to recovery of preterm neonates with respiratory distress syndrome was slower than the clinical recommendations. Receiving bag and mask ventilation and hypothermia decreased the recovery whereas, vaginal delivery gestational age at birth, being multiple pregnancy, birth weight ≥2000grams were enhance the recovery of preterm neonates with RDS.

Crowding-out Effect of SOEs on Market-based Organizational Forms in China: Implication for Korea

Purpose - We examine how an old organizational form interacts with a new one to maintain the former’s status and legitimacy during the change process in institutional logics. We focus on how state-owned enterprises (SOEs) crowd out three market-based organizational forms to maintain legitimacy and status during institutional change from state logic to market logic in China. In addition, we examine the moderating effects of export intensity and firm age of market-based enterprises on the aforementioned relationship.&#x0D; Design/Methodology/Approach - To test our hypotheses, we use a panel data set on manufacturing firms in China from 2011 to 2015. We utilize event history analysis because our dependent variable is the exit of market-based firms. We select the parametric Weibull model because it outperforms other survival models.&#x0D; Findings - Our results generally support our hypotheses. SOEs crowd out market-based organizational forms mainly owing to identity conflict. SOEs are likely to be hostile toward market-based firms with strong market identity, such as those with high export intensity and old market-based firms.&#x0D; Research - Implications Findings provide implications for policy makers and market-based enterprise managers in Korea. We highlight that Chinese and Korean SOEs are not precisely comparable but they are similar in several ways. We propose that policy makers in Korea develop and implement policy measures to control SOEs’ negative actions toward market-based firms and to dilute SOEs’ state identity. Lastly, we suggest that Korean market-based enterprise managers consider the effect of market identity on their survival and build connections with the government to strengthen market-based firms’ competitiveness and legitimacy.

Exponential and Weibull models for spherical and spherical-shell diffusion-controlled release systems with semi-absorbing boundaries

We consider the classical problem of particle diffusion in d-dimensional radially-symmetric systems with absorbing boundaries. A key quantity to characterise such diffusive transport is the evolution of the proportion of particles remaining in the system over time, which we denote by P(t). Rather than work with analytical expressions for P(t) obtained from solution of the corresponding continuum model, which when available take the form of an infinite series of exponential terms, single-term low-parameter models are commonly proposed to approximate P(t) to ease the process of fitting, characterising and interpreting experimental release data. Previous models of this form have mainly been developed for circular and spherical systems with an absorbing boundary. In this work, we consider circular, spherical, annular and spherical-shell systems with absorbing, reflecting and/or semi-absorbing boundaries. By proposing a moment matching approach, we develop several simple one and two parameter exponential and Weibull models for P(t), each involving parameters that depend explicitly on the system dimension, diffusivity, geometry and boundary conditions. The developed models, despite their simplicity, agree very well with values of P(t) obtained from stochastic model simulations and continuum model solutions.

Using two and three-parameter Weibull statistical model for predicting the loading rate effect on low-temperature fracture toughness of asphalt concrete with the ENDB specimen

Loading rate can affect significantly the load bearing capacity and cracking resistance of asphalt mixtures that are basically visco-elastic materials. On the other hand, the scatter of mechanical test results obtained from the asphalt mixtures (as multi ingredients and randomly distributed heterogeneous materials) would be high. Therefore, in this research the effect of loading rate on mode I fracture toughness of a fixed hot mix asphalt (HMA) mixture is investigated experimentally using several test replicates and the obtained data are analyzed using two and three parameter Weibull statistical models. The Edge Notch Disc Bend (ENDB) specimen, (that is a circular disc containing an edge crack along the diameter and loaded by three point bending) is utilized for conducting the KIc experiments under three loading rates of 0.5, 5 and 10 mm/min. For each loading rate, twenty five ENDB specimens are tested at a fixed low temperature of −12 °C and the probability of failure and Weibull distribution curves are obtained. It is shown that both two and three parameter Weibull models can be utilized successfully for predicting the mode I fracture behavior of tested asphalt material in the considered range for the loading rate. By increasing the loading rate from 0.5 to 10 mm/min, the mean KIc values and other Weibull parameters are increased. The Weibull distribution parameters obtained experimentally from the loading rates of 5 and 10 mm/min, are also predicted in terms of the Weibull parameters of the ENDB specimen tested at loading rate of 0.5 mm/min considered as a reference case.

A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty

BackgroundThere is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA).MethodsThe study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared.ResultsOverall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use).ConclusionThe Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores

Thermal inactivation of pathogenic and spoilage organisms in low and intermediate moisture foods is of critical importance for guaranteeing microbiological safety and stability of these products. Producers tendentially reduce salt in low and intermediate moisture foods because of nutritional health considerations, but it is unclear how this affects microbial inactivation rates during pasteurization. In this study we predict the time to achieve a pre-defined 6-log reduction for Salmonella enterica subsp. enterica serovar Napoli (hereafter: S. Napoli) and Eurotium herbariorum mould spores (hereafter: E. herbariorum spores) and the relationship with product characteristics. We tested 31 design products for heat inactivation of S. Napoli and 29 design products for heat inactivation of E. herbariorum spores. We used a global Bayesian regression combining primary Weibull models with a secondary regression model to relate pasteurization temperature and product characteristics (water activity (aw), pH, and fractions of sodium chloride, sucrose and oil on product) to microbial counts. With this model, we predict the time to 6-log reduction. Thermal inactivation rates were much higher for vegetative S. Napoli than for E. herbariorum spores. Also, inactivation curves were non-linear for many experiments. There were significant associations between the Weibull model parameters and temperature, and pH and aw for S. Napoli and E. herbariorum spores, respectively. We parameterized an inactivation model for S. Napoli and E. herbariorum spores using design products with a broad range of characteristics and showed how the simplified approach of using D-values does not accurately describe the non-linearity of thermal inactivation for both types of organism. Results of our model can be used to produce accurate heat inactivation predictions as input for the pasteurization process in factories where intermediate moisture foods are manufactured.