Model Assumptions Research Articles

Gene-Environment Interaction Between CYP1B1 and Oral Contraception on Frontal Fibrosing Alopecia.

Frontal fibrosing alopecia (FFA) is an increasingly prevalent form of follicular lichen planus, causing irreversible hair loss predominantly in postmenopausal individuals. An earlier genome-wide meta-analysis of female FFA identified risk loci in genes implicated in self-antigen presentation and T-cell homeostasis, including HLA-B*07:02, ST3GAL1, and SEMA4B. However, CYP1B1, which is important for hormone metabolism, was also implicated with the substitution of serine for asparagine at position 453 (c.1358A>G, p.Asn453Ser) exhibiting a protective effect against FFA. Increasing understanding of genetic and environmental variables and their interactions will improve understanding of disease pathogenesis and has the potential to inform risk mitigation strategies. To investigate whether oral contraceptive pill (OCP) use modulates the protective effect of the common missense variant in CYP1B1 (c.1358A>G, p.Asn453Ser) on FFA risk. This gene-environment interaction study using a case-control design enrolled female patients with FFA from UK-based dermatology clinics. The patients were matched with unrelated age- and ancestry-matched female control individuals derived from UK Biobank in a 1:66 ratio, determined by the first 4 principal components from genome-wide genotypes. Data were collected from July 2015 to September 2017, and analyzed from October 2022 to December 2023. The main outcomes were the modulatory effect of OCP use on the contribution of the CYP1B1 missense variant to female FFA risk and a formal gene-environment interaction test evaluated by a logistic regression model with a multiplicative interaction term, under the assumptions of an additive genetic model interaction term, under the assumptions of an additive genetic model. Of the 489 female patients with FFA, the mean (SD) age was 65.8 (9.7) years, and 370 (75.7%) had a history of OCP use. Of the 34 254 age- and ancestry-matched control individuals, the mean (SD) age was 65.0 (8.4) years, and previous OCP use was reported in 31 177 (91.0%). An association between female FFA and the CYP1B1 risk allele was observed in individuals who reported OCP use (odds ratio, 1.90 [95% CI, 1.50-2.40]; P = 8.41 × 10-8) but not in those with no documented exposure to OCPs (odds ratio, 1.16 [95% CI, 0.82-1.64]; P = .39). A full gene-environment interaction model demonstrated a significant additive statistical interaction between c.1358A, p.453Asn, and history of OCP use on FFA risk (OR for interaction, 1.63 [95% CI, 1.07-2.46]; P = .02). This gene-environment interaction analysis suggests that the protective effect of the CYP1B1 missense variant on FFA risk might be mediated by exposure to OCPs. The allele that encodes an asparagine at position 453 of CYP1B1 was associated with increased odds of FFA only in participants with OCP history.

On duration effects in non-life insurance pricing

AbstractThe paper discusses duration effects on the consistency of mean parameter and dispersion parameter estimators in exponential dispersion families (EDFs) that are the standard models used for non-life insurance pricing. Focus is on the standard generalised linear model assumptions where both the mean and variance, conditional on duration, are linear functions in terms of duration. We derive simple convergence results that highlight consequences when the linear conditional moment assumptions are not satisfied. These results illustrate that: (i) the resulting mean estimators always have a relevant asymptotic interpretation in terms of the duration adjusted actuarially fair premium—a premium that only agrees with the standard actuarial premium using a duration equal to one, given that the expected value is linear in the duration; (ii) deviance based estimators of the dispersion parameter in an EDF should be avoided in favour of Pearson estimators; (iii) unless the linear moment assumptions are satisfied, consistency of dispersion and plug-in variance estimators can not be guaranteed and may result in spurious over-dispersion. The results provide explicit conditions on the underlying data generating process that will lead to spurious over-dispersion that can be used for model checking. This is illustrated based on real insurance data, where it is concluded that the linear moment assumptions are violated, which results in non-negligible spurious over-dispersion.

Triangle area model (TAM) for predicting germination: An approach to enhance hydrothermal time model applications

A thorough examination of assumptions in hydrothermal time models revealed areas for enhancing model performance. We introduce the Triangle Area Model (TAM), which uses the area of right-angled triangles to calculate hydrothermal time for predicting population germination fractions (g). TAM is characterized by its depiction of triangles, considering insightful parameters such as the distance of germination temperature (T) to the base (Tb), optimal (To), and ceiling (Tc) temperatures, the range of Tc – To, To – Tb, and the germination water potential (Ψ), i.e. mean base water potential (Ψb(g)), along with potential g that may occur with T and Ψ combinations within Tc – Tb when Ψ > Ψb(g). Applied to germination data from Ambrosia psilostachya L., Cynanchum acutum L., and Bidens pilosa L., TAM achieves an RMSE of 0.03 for A. psilostachya and C. acutum, and 0.05 for B. pilosa. Moreover, TAM demonstrates an R2 of 0.96, 0.97, and 0.98 for the respective species. TAM significantly outperforms earlier models through a comparison with varying T and Ψ. TAM determined Tb for A. psilostachya, C. acutum, and B. pilosa as 0.19, 14.57, and 5.76 °C; To as 25.1, 39.9, and 29.8 °C; and Tc as 46.7, 53, and 41°C, for the respective species. It also estimates Ψb(g) as -1.48 for A. psilostachya, -0.98 for C. acutum, and -0.97 for B. pilosa. The TAM approach deepens our understanding of temperature-moisture processes influencing plant survival, colonization, and habitat expansion for these three invasive alien species. Furthermore, it can be more widely applied for estimating TT and HTT across different growth stages, enhancing the prediction accuracy of plant phenological development.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Sp1149. II. Spectroscopy of H ii Regions near the Critical Curve of MACS J1149 and Cluster Lens Models

Galaxy-cluster gravitational lenses enable the study of faint galaxies even at large lookback times, and, recently, time-delay constraints on the Hubble constant. There have been few tests, however, of lens model predictions adjacent to the critical curve (≲8″) where the magnification is greatest. In a companion paper, we use the GLAFIC lens model to constrain the Balmer L–σ relation for H ii regions in a galaxy at redshift z = 1.49 strongly lensed by the MACS J1149 galaxy cluster. Here we perform a detailed comparison between the predictions of 10 cluster lens models that employ multiple modeling assumptions with our measurements of 11 magnified, giant H ii regions. We find that that the models predict magnifications an average factor of 6.2 smaller, a ∼2σ tension, than that inferred from the H ii regions under the assumption that they follow the low-redshift L–σ relation. To evaluate the possibility that the lens model magnifications are strongly biased, we next consider the flux ratios among knots in three images of Sp1149, and find that these are consistent with model predictions. Moreover, while the mass-sheet degeneracy could in principle account for a factor of ∼6 discrepancy in magnification, the value of H 0 inferred from SN Refsdal’s time delay would become implausibly small. We conclude that the lens models are not likely to be highly biased, and that instead the H ii regions in Sp1149 are substantially more luminous than the low-redshift Balmer L–σ relation predicts.

A Rasch-based validation of the University of Tehran English Proficiency Test (UTEPT)

Recently, psychometricians and researchers have voiced their concern over the exploration of language test items in light of Messick’s validation framework. Validity has been central to test development and use; however, it has not received due attention in language tests having grave consequences for test takers. The present study sought to examine the validity of the University of Tehran English language proficiency test (UTEPT) using 6 aspects of Messick’s validation framework (i.e., content, structural, external, generalizability, substantive, and consequential). To examine each validity dimension, the Rasch model was considered. To this end, the data of 394 test takers who had taken the test in 2023 were cleaned and analyzed for the assumptions of the Rasch model and then for the 6 aforementioned validity aspects. The results indicated that the structural (i.e., unidimensionality), generalizability (i.e., measurement invariance), and substantive aspects held. However, the evidence for the content, external, and consequential aspects of validity was less definitive. The implications of the findings for the validity of the test, for the realm of language testing, and for item developers and item writers will be discussed.

A hybrid room acoustic modeling approach combining image source, acoustic diffusion equation, and time-domain discontinuous Galerkin methods

In this paper a hybrid model is introduced that constructs a broadband room impulse response using a geometrical (image source method) and a statistical method (acoustic diffusion equation) for the high-frequency range, supported by a wave-based method (time-domain discontinuous Galerkin method) for the low-frequency range. A crucial element concerns the construction of the high-frequency impulse response where a transition from a predominantly specular (image source) to a predominantly diffuse sound-field (diffusion equation) is required. To achieve this transition an analytical envelope is introduced. A key factor is the room-averaged scattering coefficient which accounts for all scattering behavior of the room and determines the speed of transition from a specular to a non-specular sound-field. To evaluate its performance, the model is compared to a broadband wave-based solver for two reference scenarios. The hybrid model shows promising results in terms of reverberation time (T20), center time (Ts) and bass-ratio (BR). Aspects such as the used geometrical complexity, the ‘room-averaged’ scattering coefficients, and other model simplifications and assumptions are discussed.

Analytical design of non-grounded tuned mass-damper-inerter for base-excited structures

This research contributes new analytical formulae for H∞ optimal tuning of non-grounded tuned mass-damper-inerter (TMDI) for minimizing free-end displacement of base-excited cantilevered structures. The derivation relies on the fixed-point theory, making use of single-mode modelling of the cantilevered structure while accommodating arbitrary TMDI placement along the structural height. Optimal TMDI tuning parameters are derived for given dominant mode shape of structure and TMDI inertial properties (i.e., secondary mass and inertance) under two different types of harmonic excitations with frequency-independent displacement and acceleration amplitudes. The applicability of the derived TMDI tuning formulae for response mitigation of lightly damped cantilevered structures under stationary broadband support excitation is established through comparisons with numerically optimal TMDI properties for a wide range of TMDI inertial properties. Moreover, the analytical TMDI tuning formulae derived in this study achieve enhanced structural performance for base-excited structures compared to those from the literature, derived under various modelling assumptions and optimality criteria. Lastly, the potential of the proposed TMDI tuning for structural response mitigation is numerically evaluated by examining displacement, acceleration, and energy dissipation response history data of an experimentally identified reinforced concrete bridge pier model subjected to 100 earthquake ground motion (GM) records. Overall, reported results demonstrate that the derived analytical formulae can significantly extend the practical application of TMDI as a bona fide dynamic vibration absorber for base-excited structures by circumventing the need for computationally demanding numerical TMDI tuning optimization.

Prediction of Rock's Brittleness and Dynamic Properties Utilizing Effective Artificial Intelligence Approaches

This research aims to determine the brittleness index (BI) and engineering properties of limestone specimens. In addition, this study evaluates the effect of moisture on the developed models to predict the BI and shear wave velocity (Vs), based on the point load index (Is50), dry and saturated tensile strength (Ts-d and Ts-s), and porosity. Gaussian process regression (GPR), multilayer feed-forward neural network (MFFNN), and multiple linear regression (MLR) predictive models were utilized. Microscopic examination of the limestone specimens revealed that calcite is the predominant mineral. It was observed that samples with higher calcite content exhibited greater brittleness and strength properties while having lower porosity. The results obtained from the MLR analysis demonstrated that it is possible to accurately forecast the brittleness index (BI), as well as the dry and saturated shear wave velocities (Vs-d and Vs-s) at the specific sites under investigation. The moisture effect on developed models showed that Vs prediction in dry conditions (Vs-d) was less accurate compared to the saturated conditions (Vs-s). Conversely, the relationships developed for estimating the BI in dry conditions exhibited higher accuracy. The analysis of all model assumptions using MLR indicated that the models could be reliably utilized. However, the MFFNN and GPR methods were found to be more conservative in estimating these properties. Moreover, the study identified the best transfer function and training algorithm for predicting Vs and BI. The evaluation metrics, such as R2 and RMSE revealed that GPR demonstrated higher precision compared to MFFNN and MLR.

Non-proportional hazards model with a PVF frailty term: application with a melanoma dataset

Survival data analysis often uses the Cox proportional hazards (PH) model. This model is widely applied due to its straightforward interpretation of the hazard ratio under the assumption that the hazard rates for two subjects remain constant over time. However, in several randomized clinical trials with long-term survival data comparing two new treatments, it is frequently observed that Kaplan-Meier plots exhibit crossing survival curves. This violation of the PH assumption of the Cox PH model can not be applied to evaluate the treatment's effect on survival. This paper introduces a novel long-term survival model with non-PH that incorporates a frailty term into the hazard function. This model allows us to examine the effect of prognostic factors on survival and quantify the degree of unobservable heterogeneity. The model parameters are estimated using the maximum likelihood estimation procedure, and we evaluate the performance of the proposed models through simulation studies. Additionally, we demonstrate the applicability of our approach by fitting the models to a real skin cancer dataset.

Comparative effectiveness of first-line systemic treatments for metastatic castration-resistant prostate cancer: a systematic review and network meta-analysis.

No head-to-head trials had been performed to estimate the relative effectiveness of poly ADP-ribose polymerase inhibitor (PARPi) and androgen receptor signaling inhibitor (ARSi) in the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC). We aimed to perform a systematic review and network meta-analysis to evaluate the comparative effectiveness of various systemic treatment agents for patients with mCRPC. A comprehensive literature search was conducted for abstracts and full-text articles from the database's inception through April 27, 2023. The study concentrated on assessing radiographic progression-free survival (rPFS) for both overall and homologous recombination repair mutation (HRRm) population, with overall survival (OS) as the secondary measure. Under the Bayesian framework, the overall effect was pooled using the fixed-effects model in base case analysis. Scenario analysis using restricted mean survival time (RMST) methods was performed to test the robustness of the results. Nine studies with 6,830 patients and 8 unique treatment options were included. Network meta-analysis demonstrated that talazoparib in combination with enzalutamide (TALA + ENZA; overall population, hazard ratio [HR], 0.20; 95% credible interval [CrI]: 0.16-0.26; RMST, 3.51; 95% confidence interval [CI] 2.46-4.60; HRRm population, HR, 0.15; 95% CrI: 0.09-0.23; RMST, 4.14; 95% CI 2.84-5.39) was superior to other treatments in the first-line setting in terms of rPFS. The results of Bayesian framework and RMST models showed consistent efficacy ranks. When extrapolated to overall survival benefit, within the Bayesian framework, olaparib plus abiraterone acetate and prednisone (OLAP + AAP) achieved the highest OS benefit for the overall population, which was not statistically significant when compared to TALA + ENZA. However, TALA + ENZA achieved the highest OS benefit at 3years by applying RMST. We suggest that talazoparib in combination with enzalutamide is probably a preferred treatment agent for the overall population and HRRm patients with mCRPC. Given the limitations of network framework and the modeling assumptions undertaken to finalize the analyses, results should be cautiously interpreted.

The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.

Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.

Using lineups to evaluate goodness of fit of animal movement models

Abstract Movement models are frequently fit to animal location data to understand how individuals respond to and interact with local environmental features. Several open‐source software packages are available for analysing animal movements and can facilitate parameter estimation, yet there are relatively few methods available for evaluating model goodness of fit. We describe how a simple graphical technique, the lineup protocol, can be used to evaluate goodness of fit of integrated step‐selection analyses and hidden Markov models, but the method can be applied much more broadly. We leverage the ability to simulate data from fitted models and demonstrate the approach using both an integrated step‐selection analysis and a hidden Markov model applied to fisher (Pekania pennanti) data. A variety of responses and movement metrics can be used to evaluate models, and the lineup protocol can be tailored to focus on specific model assumptions or movement features that are of primary interest. Although it is possible to evaluate statistical significance using a formal hypothesis test, the method can also be used in a more exploratory fashion (e.g. to explore variability in model behaviour across stochastic simulations or to identify areas where the model could be improved). We provide coded examples and vignettes to demonstrate the flexibility of the approach. We encourage movement ecologists to consider how their models will be applied when choosing appropriate graphical responses for evaluating goodness of fit.

Analysis of two interactive burning droplets with different temperatures

This paper presents a comprehensive theoretical analysis of the interaction between two quasi-steady burning droplets with differing temperatures, sizes and distances, building upon the mass-flux-potential model and flame-sheet assumption. In contrast to existing research, this study introduces a fresh perspective on droplet interactions by considering the different temperatures of the droplets. Utilizing the bispherical coordinate approach, theoretical solutions for the Stefan flow, scalar fields, droplet evaporation/burning rates, interaction coefficients and flame positions have been derived successfully. A comparison with extensive numerical simulations indicates a good agreement between the analytical and numerical results under a variety of conditions. It is revealed that proximity between the droplets causes non-uniform evaporation rates on their surfaces, and in some cases, leads to condensation on the cooler droplet. Notably, when the temperatures of the two droplets differ, this results in an uneven temperature distribution across the flame surface, and increasing the temperature of one droplet substantially elevates the temperature of the nearby flame. This study also establishes a criterion for the transition between different combustion modes, specifically between group and separated combustion. The findings of this study are crucial in deepening our understanding of evaporation and combustion processes, as well as the dynamics of flame spreading, local ignition, and extinction in systems involving multiple droplets.

Statistics of Gaussian Polymer Chains in Harmonic Applied Fields.

The model of an ideal polymer chain in a harmonic applied field has broad applicability in situations involving polymer confinement and deformation due to applied stress. In this work we (1) formulate a general analytical model for a continuous Gaussian chain under a harmonic applied potential and (2) evaluate the statistical mechanics of this model given the potential, obtaining partition functions and moment generating functions that describe the chain configurations. Closed-form expressions for the squared radius of gyration, potential energy, partition function, and moment generating function for the center of mass are obtained for a general and multidimensional harmonic field. The expressions are compared with results of Monte Carlo simulations of a discrete Gaussian chain as well as results for related systems obtained from the literature. The theory derived here is used to test the applicability of the current model assumptions to relations from the literature describing polymer confinement and deformation in experiment, theory, and simulations.

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.

Enhancing Mental Health Predictions: A Gradient Boosted Model for Sri Lankan Camp Refugees

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

Euclid preparation. XLII. A unified catalogue-level reanalysis of weak lensing by galaxy clusters in five imaging surveys

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the combined cluster and weak-lensing pipeline ( COMB-CL ). Notably COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV will be able to measure the weak lensing masses of around $ $ (considering shot noise only) or $ $ (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Investigating the Effect of the Separation of Scales in Reduced Order Battery Modelling: Implications on the Validity of the Newman Model

Abstract Modelling lithium-ion battery behaviour is essential for performance prediction and design improvement. However, this task is challenging due to processes spanning many length scales, leading to computationally expensive models. Reduced order models have been developed to address this, assuming a "separation of scales" between micro- and macroscales. This study compares two approaches: direct microstructure-resolved 3D domain electrochemical modelling and a simplified 1D homogenized model, similar to the Doyle-Fuller-Newman model. &amp;#xD;&amp;#xD;The research investigates the validity of the scale separation assumption in continuum electrode-level models by varying scale separation factors, boundary conditions, and geometries. The findings reveal that in 3D models, more tortuous, less porous microstructures deviate more from 1D predictions, especially under higher discharge rates. However, under realistic conditions, with an electrode featuring eight particles across its thickness and typical transport properties, the 3D model predicts only a slight (2%) increase in current compared to the 1D model at a high rate of 7C (approximately 350 A/m²).&amp;#xD;&amp;#xD;These results suggest that the separation of scales assumption in the DFN model is generally suitable for a wide range of operating conditions. However, 1D models may overlook local variations in electrolyte concentration and potential, crucial for understanding degradation mechanisms.&amp;#xD;

Data-Adaptive Multivariate Test for Genomic Studies Using Fused Lasso

In genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter. This strategy, however, may miss signals that are jointly detectable with other variants. Multivariate analysis is another popular approach, which tests grouped variants with a predefined group, e.g., based on a gene, pathway, or physical location. However, the power will be diminished if the modeling assumption is not suited to the data. Therefore, data-adaptive testing that relies on fewer modeling assumptions is preferable. Possible approaches include a data-adaptive test proposed by Ueki (2021), which applies to various data-adaptive regression models using a generalization of Yanai’s generalized coefficient of determination. While several regression models are possible choices for the data-adaptive test, this paper focuses on the fused lasso that can count for the effect of adjacent variants and investigates its performance through comparison with other existing tests. Simulation studies demonstrate that the test using fused lasso has a high power compared to the existing tests including the univariate regression test, saturated regression test, SKAT (sequence kernel association test), burden test, SKAT-O (optimized sequence kernel association test), and the tests using lasso, ridge, and elastic net when assuming a similar effect of adjacent variants.