Lognormal Model Research Articles

Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

Prevalence and associated factors of teenage childbearing among Ethiopian women using semi-parametric and parametric proportional hazard and accelerated failure time models

BackgroundTeenage childbearing is a common issue for young people’s sexual and reproductive health in the world, particularly in low-income countries, and affects teenagers between the ages of 13 and 19. According to several academics, adolescent pregnancy accounts for the majority of Ethiopia’s population increase, and there has been little effort to address this threat. This study aimed to determine the prevalence and associated factors of the time to teenage childbearing in Ethiopia.MethodThis paper compares the results of the semi-parametric proportional hazard (PH), parametric PH, and accelerated failure time (AFT) models to find the model that best fits the data. The Akaike Information Criterion (AIC) was used to evaluate the performance of models examined in this investigation. Time to teenage childbearing was the study’s outcome variable, while the analysis considered various independent variables. We analyze data from the 2016 National Demographic Health Survey to assess the influence of different risk factors on teenage pregnancy among Ethiopian women.ResultsOut of the 10,274 teenagers (aged between 13 and 19) who participated in the 2016 survey, 6,430 (62.59%) were parents. The study findings revealed that these teenage parents were influenced by various time-related factors before becoming parents. The log-normal AFT model has the lowest AIC value and hence it is the best fit for this data. Results from this model indicated that significant factors influencing the time to teenage childbearing include the age of the household head, current age of the respondents, region, religion, educational attainment, wealth status, intention to use contraception, and recent sexual activity.ConclusionThis study reveals that 62.59% of surveyed teenagers aged 13 to 19 were parents. Various factors at both the individual and community levels: including the age of the household head, regional differences, religious affiliation, educational level, economic status, contraceptive intentions, and recent sexual activity, determine the time to teenage childbearing. Targeted interventions addressing these factors are essential for reducing teenage pregnancies and supporting adolescent parents effectively.

The effect of forest composition on outdoor recreation

Climate change will shift the composition of northern Minnesota forests from boreal to temperate by the end of the century. This shift in forest composition will likely affect outdoor recreation, a valuable ecosystem service and a key economic driver for the region. In this context, the objective of our paper is to empirically examine the relationship between forest composition and recreation. We analyze the effect of changes in forest composition for seven forest types on seven types of recreation using a lognormal pooled panel regression model for Minnesota's Laurentian Mixed Forest Province. Earlier research showed forest composition affected recreation at the level of broad groups of broadleaved or coniferous species. We find a statistically significant empirical association between forest composition and recreation at the forest type level (forest types within those broad groups). This relationship varies across forest types and recreation categories. For example, big game hunting is positively related to elm-ash-cottonwood and white-red-jack pine and negatively associated with aspen-birch. We find individual forest types within broad groups of broadleaved or coniferous forests, have different relationships with recreation, so that these broad groups are not sufficient in capturing the effect of forest composition on recreation. Our results are of interest in the context of current shifts in forest composition caused by climate change, which could also affect recreation. Our findings suggest adding a forest composition lens to existing policies could facilitate strategies for more effective recreation management and climate change adaptation.

Identifying the dominant influencing factors of secondary lining cracking risk in an operating mountain tunnel

PurposeA risk-based method is proposed to identify the dominant influencing factors of secondary lining cracking in an operating mountain tunnel with weak surrounding rock.Design/methodology/approachBased on the inspection data from a mountain tunnel in Southwest China, a lognormal proportional hazard model is established to describe the statistical distribution of secondary lining cracks. Then, the model parameters are obtained by using the Bayesian regression method, and the importance of influencing factors can be sorted based on the absolute values of the parameters.FindingsThe results show that the order of importance of the influencing factors of secondary lining cracks is as follows: location of the crack on the tunnel profile, rock mass grade of the surrounding rock, time to completion of the secondary lining, and void behind the secondary lining. Accordingly, the location of the crack on the tunnel profile and rock mass grade of the surrounding rock are the two most important influencing factors of secondary lining cracks in the investigated mountain tunnel, and appropriate maintenance measures should be focused on these two aspects.Originality/valueThis study provides a general and effective reference for identifying the dominant influencing factors of secondary lining cracks to guide the targeted maintenance in mountain tunnels.

Error performance and capacity of subcarrier BPSK modulated THz wireless systems with pointing errors.

Terahertz (THz) technology is poised to play a pivotal role in the future sixth-generation (6 G) wireless systems, offering substantial benefits with its extremely high bandwidth and rapid transmission speeds. This paper explores subcarrier-modulated THz wireless systems, analyzing their performance under the log-normal and gamma-gamma models. It provides a detailed examination of how composite turbulence and pointing errors impact the system performance. Closed-form solutions for the bit error rate and outage probability are derived for various conditions of turbulence and pointing errors. Furthermore, the paper delves into the asymptotic error performance, presenting numerical results that confirm the accuracy of the derived solutions across different propagation distances, turbulence strengths, and pointing error scenarios. The study offers critical insights into how different conditions affect the system performance, which is essential for characterizing subcarrier THz links in forthcoming 6 G deployments.

Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-α Forest.

We make forecasts for the constraining power of the 1D wavelet scattering transform when used with a Lyman-α forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar at a level drawn from a simple log-normal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power and from second-order wavelet coefficients that probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the wavelet scattering transform are more than an order of magnitude tighter than for the power spectrum, shrinking a 4D parameter space by a factor of 10^{6}. Should these improvements be realized with the Dark Energy Spectroscopic Instrument, inflationary running would be constrained to test common inflationary models predicting α_{s}=-6×10^{-4} and neutrino mass constraints would be improved enough for a 5-σ detection of the minimal neutrino mass.

Survival Analysis of Students’ Dropout in a Nigerian University System

Formal education remains the vehicle for socio-economic development and social mobilization in any society. Academic institutions are increasingly interested in monitoring the performance of their students. This interest has given rise to the need for research, collation, analysis, and interpretation of data to formulate evidence-based academic policies that aim to improve student performance and reduce dropout rates. Data obtained from the university comprises of Gender, Age at Entry, Marital Status, Sponsorship, Mode of Entry, Program of Study and Cumulative Grade Point Average (CGPA). This study investigated how these factors influence students’ dropout at Modibbo Adama University, Yola Adamawa State Nigeria. Survival analysis was carried out using Log-logistic, Lognormal, Weibull and Cox Proportional Hazard models. The lognormal survival model was judged as the best model due to its lowest AIC value of 1199.773. The Gender, CGPA and students’ program of study have significant effect on student dropout. Also, it was observed that 41.42% of male students dropped out when compared to 38.94% female students dropped out before their graduation year. Of all the programs of study considered, Physics has the highest number of dropped out before graduation. It accounted for 68.75%. In terms of academic performance, majority (85.05%) students who had CGPA of third class at the beginning of their studies dropped out before expected years of graduation. The study recommends that government and university management provide financial support to assist the male students who are on self-sponsorship, as financial pressure may contribute to their dropout rates

Neural style transfer of weak lensing mass maps

We propose a new generative model of projected cosmic mass density maps inferred from weak gravitational lensing observations of distant galaxies (weak lensing mass maps). We construct the model based on a neural style transfer so that it can transform Gaussian weak lensing mass maps into deeply non-Gaussian counterparts as predicted in ray-tracing lensing simulations. We develop an unpaired image-to-image translation method with Cycle-Consistent Generative Adversarial Networks (Cycle GAN), which learn efficient mapping from an input domain to a target domain. Our model is designed to enjoy important advantages; it is trainable with no need for paired simulation data, flexible to make the input domain visually meaningful, and expandable to rapidly-produce a map with a larger sky coverage than training data without additional learning. Using 10,000 lensing simulations, we find that appropriate labeling of training data based on field variance allows the model to reproduce a correct scatter in summary statistics for weak lensing mass maps. Compared with a popular log-normal model, our model improves in predicting the statistical natures of three-point correlations and local properties of rare high-density regions. We also demonstrate that our model enables us to produce a continuous map with a sky coverage of ∼166deg2 but similar non-Gaussian features to training data covering ∼12deg2 in a GPU minute. Hence, our model can be beneficial to massive productions of synthetic weak lensing mass maps, which is of great importance in future precise real-world analyses.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases

BackgroundProsthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk...

Consumer Preferences for Native Chicken Meat in India: Implications for Sustainable Production and Household Dynamics

Indigenous and native chickens play a pivotal role in rural economies, providing income and food security. India is one of the world's top producers of poultry, with a total chicken population of 851.8 million in 2019 and 37.2% representing native or indigenous breeds. In recent years, the rising demand for native chicken meat and government-backed projects in India have led to a shift from traditional backyard chicken raising to semi-intensive and intensive farms, empowering marginalized communities and women. Consumer preferences for native chicken meat in India was examined in this study, employing a two-part model with a logit model and log-normal OLS model to analyse responses from 503 consumers. Additionally, factor analysis revealed nine key factors influencing attitudes. Gender, age, income, education, marital status, cohabitation, household size, the presence of seniors or children, and consumption frequency significantly shaped preferences. Factors such as psychological well-being, affordability, knowledge, nutrition, food quality, fitness, purity, and sustainability had negative effects on preference and quantity purchased. The study reveals a strong consumer preference for native chicken meat, attributed to its organic nature, including traditional farming and sustainable production. To enhance sustainability of semi-intensive and intensive native chicken farming, it is imperative to address pricing disparities and ensure the consistent availability of native chicken meat. Achieving long-term sustainability in native chicken farming requires aligning consumer demand with ethical and environmentally sustainable farming practices.

Improved general unit hydrograph model for dam-break flood hydrograph

Dam breaks pose significant risks, necessitating simple and effective predictive models for disaster management. This study aims to enhance the understanding of dam-break flood hydrographs, an aspect less emphasized in traditional research focusing mainly on peak discharge and flow depth values. Employing a series of experiments, an improved general unit hydrograph model was used to analyze both discharge and flow depth hydrographs. This model is then critically compared with the standard gamma and log-normal asymmetric distribution models. The results reveal that the improved unit hydrograph model significantly outperforms these traditional models, particularly in predicting the rising stage of discharge hydrographs, evidenced by a 65 % reduction in root mean square error. Comparison of this model with real dam failures in natural river valleys shows its potential to be applied to changes in river slope and different dam-break rates. These advancements present a more precise methodology for predicting dam-break flood hydrographs, offering benefits for disaster management strategies.

Covariance matrices for the Lyman-α forest using the lognormal approximation

We investigate the nature of correlations in the small-scale flux statistics of the Lyman-α (Lyα) forest across redshift bins. Understanding and characterising these correlations is important for unbiased cosmological and astrophysical parameter inference using the Lyα forest. We focus on the 1-dimensional flux power spectrum (FPS) and mean flux (F̅) simulated using the semi-numerical lognormal model we developed in earlier work. The lognormal model can capture the effects of long wavelength modes with relative ease as compared to full smoothed particle hydrodynamical (SPH) simulations that are limited by box volume. For a single redshift bin of size Δz ≃ 0.1, we show that the lognormal model predicts positive cross-correlations between k-bins in the FPS, and a negative correlation for F̅ × FPS, in qualitative agreement with SPH simulations and theoretical expectations. For measurements across two neighbouring redshift bins of width Δ z each (using long flux skewers of length 2Δ z that are 'split' in half), the lognormal model predicts an anti-correlation for FPS × FPS and a positive correlation for F̅ × FPS, caused by modes with the longest wavelengths. This is in contrast to SPH simulations which predict a negligible magnitude for cross-redshift correlations derived from such `split' skewers, and we discuss possible reasons for this difference. Finally, we perform a preliminary test of the impact of neglecting long wavelength modes on parameter inference, finding that whereas the correlation structure of neighbouring redshift bins has relatively little impact, the absence of long wavelength modes in the model can lead to ≳ 2 - σ biases in the inference of astrophysical parameters. Our results motivate a more careful treatment of long wavelength modes in analyses that rely on the small-scale Lyα forest for parameter inference.

A modified lognormal approximation of the Lyman-α forest: comparison with full hydrodynamic simulations at 2 ≤ z ≤ 2.7

Observations of the Lyman-α forest in distant quasar spectra with upcoming surveys are expected to provide significantly larger and higher-quality datasets. To interpret these datasets, it is imperative to develop efficient simulations. One such approach is based on the assumption that baryonic densities in the intergalactic medium (IGM) follow a lognormal distribution. We extend our earlier work to assess the robustness of the lognormal model of the Lyman-α forest in recovering the parameters characterizing IGM state, namely, the mean-density IGM temperature (T 0), the slope of the temperature-density relation (γ), and the hydrogen photoionization rate (Γ12), by comparing with high-resolution Sherwood SPH simulations across the redshift range 2 ≤ z ≤ 2.7. These parameters are estimated through a Markov Chain Monte Carlo (MCMC) technique, using the mean and power spectrum of the transmitted flux. We find that the usual lognormal distribution of IGM densities cannot recover the parameters of the SPH simulations. This limitation arises from the fact that the SPH baryonic density distribution cannot be described by a simple lognormal form. To address this, we extend the model by scaling the linear density contrast by a parameter ν. While the resulting baryonic density is still lognormal, the additional parameter gives us extra freedom in setting the variance of density fluctuations. With this extension, values of T 0 and γ implied in the SPH simulations are recovered at ∼ 1 - σ (≲ 10%) of the median (best-fit) values for most redshifts bins. However, this extended lognormal model cannot recover Γ12 reliably, with the best-fit value discrepant by ≳ 3 - σ for z > 2.2. Despite this limitation in the recovery of Γ12, whose origins we explain, we argue that the model remains useful for constraining cosmological parameters.

Reference prior for Bayesian estimation of seismic fragility curves

One of the key elements of probabilistic seismic risk assessment studies is the fragility curve, which represents the conditional probability of failure of a mechanical structure for a given scalar measure derived from seismic ground motion. For many structures of interest, estimating these curves is a daunting task because of the limited amount of data available; data which is only binary in our framework, i.e., only describing the structure as being in a failure or non-failure state. A large number of methods described in the literature tackle this challenging framework through parametric log-normal models. Bayesian approaches, on the other hand, allow model parameters to be learned more efficiently. However, the impact of the choice of the prior distribution on the posterior distribution cannot be readily neglected and, consequently, neither can its impact on any resulting estimation. This paper proposes a comprehensive study of this parametric Bayesian estimation problem for limited and binary data. Using the reference prior theory as a cornerstone, this study develops an objective approach to choosing the prior. This approach leads to the Jeffreys prior, which is derived for this problem for the first time. The posterior distribution is proven to be proper (i.e., it integrates to unity) with the Jeffreys prior but improper with some traditional priors found in the literature. With the Jeffreys prior, the posterior distribution is also shown to vanish at the boundaries of the parameters’ domain, which means that sampling the posterior distribution of the parameters does not result in anomalously small or large values. Therefore, the use of the Jeffreys prior does not result in degenerate fragility curves such as unit-step functions, and leads to more robust credibility intervals. The numerical results obtained from two different case studies—including an industrial example—illustrate the theoretical predictions.

Study on stress–strain relationship and constitutive model of recycled aggregate concrete under axial cyclic compression

AbstractThe effects of dry or saturated state, water–cement ratio, coarse aggregate type and steel fiber on the stress–strain relationship of RAC under cyclic loading were studied. What is more, the characteristics of stress–strain curve, plastic strain, stress degradation and stiffness degradation of RAC under cyclic loading were analyzed. The microstructure of RAC was analyzed. Based on the principle of surface energy, the reason for the decrease in compressive strength of wet concrete was analyzed. In addition, the envelope equation of stress–strain curve under cyclic loading, unloading and reloading curve was established. The test results showed that the peak stress of the envelope line of RAC under cyclic loading was affected by dry or saturated state, water–cement ratio and coarse aggregate type, but the addition of steel fiber could reduce the degradation rate of stress and stiffness of RAC after peak strain. The plastic strain accumulation of RAC before and after the peak stress was quite different, but dry or saturated state, water–cement ratio, coarse aggregate type and steel fiber have no obvious influence on the plastic strain accumulation of RAC. The pores and cracks of RAC provide space and channels for the moisture absorption and water absorption. The Weibull–Lognormal statistical distribution envelope model, unloading curve model and reloading curve model established were in good agreement with the experimental curves. The research results provided a reference for the study of the full curve constitutive model of RAC under cyclic loading.

Parameter Estimation and Hypothesis Testing on Bivariate Log-Normal Regression Models

This study aims to introduce a bivariate Log-Normal regression model and to develop a technique for parameter estimation and hypothesis testing. We term the model Bivariate Log-Normal Regression (BLNR). The estimation procedure is conducted by the standard Maximum Likelihood Estimation (MLE) employing the Newton-Raphson method. To perform hypothesis testing, we adapt the Maximum Likelihood Ratio Test (MLRT) for simultaneous testing with test statistics which, for large n, follows Chi-Square distribution with degrees of freedom p. In addition, the partial testing is derived from a central limit theorem which results in a Z-test statistic.
 Keywords: parameter estimation, hypothesis testing, bivariate log, normal regression

Abstract 2387: Drivers of precision in oncology trials: A landscape analysis of biomarkers and treatments

Abstract Background: Molecular profiling is powerful to match patients’ tumor profile with targeted therapies in oncology. While our understanding of treatments and biomarkers that enhance overall response rates is growing, the level of consistency at which treatment response is assured in biomarker-enriched (BE) cohorts (e.g. immune-checkpoint inhibitors in PD-L1-positive tumors) remains poorly understood. In essence, an optimal biomarker would not only result in high mean survival but also precise, closely clustered response of all patients around this mean. Objectives: The primary objective of this study is to develop a method to measure the variability of survival outcomes from published Kaplan-Meier (KM) curves in trials. The secondary objective is to assess whether novel treatments, when given in the presence of predictive biomarkers, improve not only mean survival, but also its precision. This is clinically relevant as higher precision guarantees patients a treatment outcome closer to the mean, thereby providing safeguards against non-response. Methods: We conducted a criteria-based search in PubMed and EMBASE to identify all phase-II and -III drug trials on breast cancer and non-small cell lung cancer (NSCLC) published between 2018-2022. We developed an algorithm to derive pseudo-individual patient data (p-IPD) from KM curves. We measured variability of survival outcomes in treatment and control groups using ratios of coefficients of variation (CVRs) derived from log-normal models and restricted mean survival time. This novel computational approach enables comparing the consistency of treatment responses in BE subgroups versus total, i.e. intention-to-treat (ITT), populations in each trial. Results: From screening 780 publication records (NSCLC: n = 405; breast cancer: n = 375), we identified 67 biomarker-stratified drug trials (NSCLC: n = 41; breast cancer: n = 26). We successfully constructed p-IPD using KM curves from ITT and BE cohorts for the first 18 trials (NSCLC: n = 10; breast cancer: n = 8) from a total of 11,373 patients (NSCLC: n = 7,547; breast cancer: n = 3,826). The preliminary meta-analysis for overall survival on these first 18 trials shows that BE treatment groups respond more precisely, with a variability reduction from 0.93 [95%-CI: 0.91, 0.96] in the ITT group to 0.86 [0.83, 0.91] in the BE subgroup. This variability reduction varies across treatment, biomarker, and tumor types. Finally, we observe different variability estimates in trials (n = 6) that are stratified by subgroups of different PD-L1 expression. Conclusion: This is the first study that presents a viable approach to analyze the variability of biomarker-treatment pairs in oncology at large scale. We find a reduction of treatment variability in breast and lung cancer trials in BE populations. Our work offers an orthogonal approach to measure biomarker utility with the potential to inform biomarker discovery and trial design. Citation Format: Maximilian Schuessler, Elizaveta Skarga, Pascal Geldsetzer, Ying Lu, Maike Hohberg. Drivers of precision in oncology trials: A landscape analysis of biomarkers and treatments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2387.

Abstract 7429: A web-based application to co-register elemental imaging with histopathology to enhance the study of metal bioaccumulation within tumors

Abstract Essential trace elements are crucial in various biological processes, including tumor growth, migration, and metastasis. For instance, copper ions are involved in mitochondrial metabolism, cell proliferation, tumor migration, metastasis and proangiongenic pathways. Localizing where metal ion homeostasis is disrupted is vital for understanding these processes beyond what bulk analysis can reveal. Elemental imaging offers high-resolution, quantifiable multi-elemental distribution maps, providing a more detailed analysis compared to traditional bulk measurements. However, aligning findings with tissue structures identified in histopathological data can be challenging. To address this, we developed a web-based application for the co-registration and analysis of Whole Slide Images (WSI) and elemental imaging data at the cellular level. The application integrates WSI with elemental maps generated by technologies such as ultrafast laser ablation and inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS), as well as a range of other elemental imaging techniques. Hosted by the Biomedical National Elemental Imaging Resource, the current application allows for a streamlined data integration, co-registration, and analysis, including: 1) uploading WSI, elemental maps, and pathologist annotations, 2) automated preprocessing to identify tissue regions, 3) co-registration to merge WSI and elemental maps, 4) custom import, real-time overlay/editing of pathologist annotations transferred from WSI, 5) plotting, statistical tools and 6) data export. This application enables users to compare elemental abundance within/between tissue structures annotated on H&E or immunohistochemical slides. Derived data can pinpoint elements and mixtures whose abundance differs significantly across various pathological conditions. As an initial test, we applied the software on breast HER2/ER/PR tumors, varying in molecular subtype/histology. Preliminary results, using Bayesian lognormal regression models, adjusting for tumor subtypes, indicated a subtype-dependent variation in copper abundance. The relative difference in copper levels between tumor and stromal regions was notably less in HER2 tumors (p=0.026) compared to HR tumors (p<0.0001). Conversely, iron showed a consistently higher abundance in stromal areas across both subtypes (p<0.0001). Future enhancements include improved co-registration capabilities, hotspot analysis, machine learning, mixture effects, and cloud-based architecture. This method uniquely enables integration of elemental data with spatial transcriptomics, mapping gene transcripts on tissue slides. With current ST assays aligned with H&E slides, this tool facilitates co-registration, offering insights into biological pathways affected by metal homeostasis disruptions. Citation Format: Yunrui Lu, Ramsey Steiner, Serin Han, Matthew Chan, Tracy Punshon, Brian Jackson, Linda Vahdat, Jonathan Marotti, Joshua Levy. A web-based application to co-register elemental imaging with histopathology to enhance the study of metal bioaccumulation within tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7429.

Exploring the effect of mountainous terrain on weather-related crashes

Mountainous areas pose unique challenges to transportation safety, with their complex terrain and harsh weather conditions. The presence of steep terrain can lead to winding roads with a high degree of azimuth change per mile, thereby increasing the potential for hazardous driving conditions. Additionally, steep terrain is often associated with fog formation, which can obscure roads by clouds. Thus, there is a need to study weather-related crashes and identify associated risk factors in mountainous areas. To address this issue, the Tobit, latent class Tobit (LCT), and two-part truncated log normal (TPTLN) models were employed to investigate the potential interactions between topography and weather-related crash rates, while accounting for the censored nature of crash data. Data from the western region of North Carolina, which is home to numerous mountains experiencing a wide range of weather conditions was used for this purpose. This region comprises a 3570-mile road network across seven cities, with elevations ranging from 1000 ft. to >6158 ft. above mean sea level. Crash data from 2015 to 2017 was obtained from the Highway Safety Information System (HSIS). The results revealed that topography significantly affect weather-related crashes in mountainous areas. Higher elevations and steeper slopes are associated with lower crash rates. Also, the study highlights the importance of considering topography when assessing transportation safety in mountainous areas. The findings help develop transportation safety policies and interventions aimed at improving safety in these areas.

Intermittency as a Consequence of a Stationarity Constraint on the Energy Flux.

In his seminal work on turbulence, Kolmogorov made use of the stationary hypothesis to determine the power density spectrum of the velocity field in turbulent flows. However, to our knowledge, the constraints that stationary processes impose on the fluctuations of the energy flux have never been used in the context of turbulence. Here, we recall that the power density spectra of the fluctuations of the injected power, the dissipated power, and the energy flux have to converge to a common value at vanishing frequency. Hence, we show that the intermittent Gledzer-Ohkitani-Yamada (GOY) shell model fulfills these constraints. We argue that they can be related to intermittency. Indeed, we find that the constraint on the fluctuations of the energy flux implies a relation between the scaling exponents that characterize intermittency, which is verified by the GOY shell model and in agreement with the She-Leveque formula. It also fixes the intermittency parameter of the log-normal model at a realistic value. The relevance of these results for real turbulence is drawn in the concluding remarks.