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Long-standing health inequalities in Australian society that were exposed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were described as "fault lines" in a recent call to action by a consortium of philanthropic organizations. With asthma a major contributor to childhood disease burden, studies of its spatial epidemiology can provide valuable insights into the emergence of health inequalities early in life. The aims of this study were to characterize the spatial variation of asthma prevalence among children living within Australia's 4 largest cities and quantify the relative contributions of climatic and environmental factors, outdoor air pollution, and socioeconomic status in determining this variation. A Bayesian model with spatial smoothing was developed to regress ecologic health status data from the 2021 Australian Census against groups of explanatory covariates intended to represent mechanistic pathways. The prevalence of asthma in children aged 5 to 14 years averages 7.9%, 8.2%, 8.5%, and 7.6% in Sydney, Melbourne, Brisbane, and Perth, respectively. This small inter-city variation contrasts against marked intracity variation at the small-area level, which ranges from 6% to 12% between the least and most affected locations in each. Statistical variance decomposition on a subsample of Australian-born, nonindigenous children attributes 66% of the intracity spatial variation to the assembled covariates. Of these covariates, climatic and environmental factors contribute 30%, outdoor air pollution contributes 19%, and areal socioeconomic status contributes the remaining 51%. Geographic health inequalities in the prevalence of childhood asthma within Australia's largest cities reflect a complex interplay of factors, among which socioeconomic status is a principal determinant. [ABSTRACT FROM AUTHOR]

Diagnosis of ectopic pregnancy can be complicated by nonspecific laboratory and radiographic findings. The multiple alternative diagnoses must be weighed against each other based on the entire clinical presentation. We present a case of a 20-year-old woman who arrived to the Emergency Department (ED) with abdominal pain and ended up being transferred for an Obstetrics evaluation of a possible heterotopic pregnancy. Her radiology-performed ultrasound had revealed an "intrauterine gestational sac" along with an adnexal mass near the right ovary. The patient was not undergoing assisted-reproductive fertilization, nor did she have meaningful risk factors for heterotopic pregnancy. The patient was managed expectantly over the ensuing week to see whether the intrauterine fluid was a true gestational sac. After multiple repeat ED visits, the diagnosis of ectopic pregnancy was made. Ultimately, the patient elected for surgical management of her ectopic pregnancy. This case offers a reminder of the subtleties of radiographic identification of intrauterine pregnancies and the ever-present need to "clinically correlate." [ABSTRACT FROM AUTHOR]

The feedback between evolution, population, and community dynamics is now being increasingly integrated into ecological and evolutionary research. Eco-evolutionary feedbacks occur on different biodiversity, temporal, and spatial scales, and past eco-evolutionary dynamics likely leave distinct signatures on contemporary populations and communities. Comparing observed data with alternative mechanistic hypotheses can be used to determine how eco-evolutionary processes contributed to ecological and evolutionary changes across different scales. Advances in statistical methodology can be used to determine eco-evolutionary contributions to changes in biodiversity, and these are especially useful when it is difficult to monitor genetic properties of populations and subsequent community interactions over time at high temporal and spatial resolutions. While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data. [ABSTRACT FROM AUTHOR]

When making comparisons between groups of students, a common technique is to analyze whether there are statistically significant differences between the means of each group. This convention, however, is problematic when data are negatively skewed and bounded against a performance ceiling, features that are typical of data in education settings. In such a situation, we might be particularly interested to observe group differences in the left tail, specifically among students who have room to improve, and conventional analyses of group means have limitations for detecting such differences. In this article, an alternative to these conventions is presented. Rather than comparing the means of two groups, we can instead compare how closely student data are concentrated toward the modes of each group. Bayesian methods provide an ideal framework for this kind of analysis because they enable us to make flexible comparisons between parameter estimates in custom analytical models. A Bayesian approach for examining concentration toward the mode is outlined and then demonstrated using public data from a previously reported classroom experiment. Using only the outcome data from this prior experiment, the proposed method observes a credible difference in concentration between groups, whereas conventional tests show no significant overall differences between group means. The present article underscores the limitations of conventional statistical assumptions and hypotheses, especially in school psychology and related fields, and offers a method for making more flexible comparisons in the concentration of data between groups. [ABSTRACT FROM AUTHOR]

The precautionary approach to fisheries management requires accounting of uncertainty to ensure stock sustainability. Most fisheries management is based on a single-species approach, with stocks assumed independent of one another, even though it is known that stocks interact through predation and competition for resources. The strength of these interactions depends on the relative abundance and size/age composition of stocks, but they are usually treated as fixed. Therefore, a key question is: can we simultaneously adopt the precautionary approach for multiple stocks while accounting for these interactions? Here we examine the impact of stock interactions on calculations of precautionary reference points for nine stocks in the North Sea. We combined four multispecies models using an ensemble model to rigorously quantify uncertainty and explore the rates of fishing mortality that leads to groups of stocks being fished according to the precautionary approach. We found that relaxing the assumption of stock independence meant that no fishing at all was only precautionary for six of nine stocks, and no fishing strategy was precautionary for all nine. We suggest that it is necessary to account for multispecies interactions when calculating precautionary reference points. [ABSTRACT FROM AUTHOR]

We investigate a modified cosmological model aimed at addressing the Hubble tension, considering revised dynamics in the late Universe. The model introduces a parameter c affecting the evolution equations, motivated by a modified Poisson algebra inspired by effective Loop Quantum Cosmology. Our analysis includes diverse background datasets such as Cosmic Chronometers, Pantheon+ Type Ia Supernovae (with and without the SH0ES calibration), SDSS, DESY6 and DESI Baryon Acoustic Oscillations, and background information of the Cosmic Microwave Background. We find that the model alleviates the Hubble tension in most of the dataset combinations, with cases reducing discrepancies to below 1 σ when including SH0ES. However, the model exhibits minimal improvement in the overall fit when compared to Λ CDM, and Bayesian evidence generally favors the standard model. Theoretical foundations support this approach as a subtle adjustment to low-redshift dynamics, suggesting potential for further exploration into extensions of Λ CDM. Despite challenges in data fitting, our findings underscore the promise of small-scale modifications in reconciling cosmological tensions. [ABSTRACT FROM AUTHOR]

Inferring the pattern and rate of past sea-level changes from uncertainty-prone proxy records requires formal statistical analyses, preferably in a hierarchical framework. The commonly used error-in-variables method treats the relative sea level as a collection of random variables drawn from the multivariate Gaussian distribution. However, this method does not make any use of prior information about the sea-level index points as constraints in the inferential process, thereby leading to anomalously large uncertainties for the time periods when observational data are absent. Here, a hierarchical Bayesian model of past sea-level changes is presented. Specifically, the stochastically varying relative sea level is modeled as a piecewise linear process with an additive independent Brownian increment arriving in a Gaussian fashion. The treatment of temporal uncertainties associated with the sea-level index points in the partially observed proxy records also differs from the existing methods. Instead of calibrating the radiocarbon ages individually, the corresponding calendar ages are treated as random variables and inferred recursively according to their temporal order. Illustrative studies using synthetic and real-world data demonstrate the promise of this model. • A Bayesian framework of relative sea-level change is provided. • Temporal ordering constraint is imposed on age modeling. • Sea-level changes are modeled as a Markov-Wiener process. • Utility of the model is demonstrated with synthetic and real-world data. [ABSTRACT FROM AUTHOR]

West Nile Virus (WNV) is a mosquito-borne pathogen that primarily infects birds. Infections can spillover to humans and cause a spectrum of clinical symptoms, including WNV neuroinvasive disease. The extrinsic incubation period (EIP) is the time taken for a mosquito to become infectious following the ingestion of an infected blood meal. Characterising how the EIP varies with temperature is an essential part of predicting the impact and transmission dynamics of WNV. We re-analyse existing experimental data using Bayesian time delay models, allowing us to account for variation in how quickly individual mosquitoes developed disseminated WNV infections. In these experiments, cohorts of Culex pipiens mosquitoes were infected with WNV and kept under different temperature conditions, being checked for disseminated infection at defined timepoints. We find that EIPs are best described with a Weibull distribution and become shorter log-linearly with temperature. Under 18°C, less than 1% of infected Cx. pipiens had a disseminated infection after 5 days, compared to 9.73% (95% CrI: 7.97 to 11.54) at 25°C and 42.20% (95% CrI: 38.32 to 46.60) at 30°C. In the hottest experimental temperature treatment (32°C), the EIP 50 was estimated at 3.78 days (CrI: 3.42 to 4.15) compared to over 100 days in the coolest treatment (15°C). The variance of EIPs was found to be much larger at lower temperatures than higher temperatures, highlighting the importance of characterising the time delay distribution associated with the EIP. We additionally demonstrate a competitive advantage of WNV strain WN02 over NY99, where the former infects mosquitoes more quickly at colder temperatures than the latter. This research contributes crucial parameters to the WNV literature, providing essential insights for modellers and those planning interventions. [ABSTRACT FROM AUTHOR]

• A framework is outlined to apply Bayesian statistical methods to petrochronologic data. • Approach widely applicable to rocks or minerals that preserve a relative age progression. • Bayesian approach produces more objective and reproducible interpretations. • Refined dates and uncertainties enhance interpretations of petrologic and tectonic processes. • Methods applied to samples from the Appalachian and Trans-Hudson orogens. Constraining the absolute time and duration of geologic processes is one of the great challenges and goals in Earth sciences. Increasingly, the integration of geochronologic constraints with petrologic information is being qualitatively applied to understanding the timescales of metamorphic, igneous, tectonic, and fluid-related processes. Many rocks and geochronometers preserve relative age constraints such as compositional zoning or cross cutting relationships. This prior information can be formalized in a Bayesian statistical framework to generate a probabilistic posterior chronology. As we show here, these "age-sequence" models can enhance precision on geochronologic dates and rates and insight into tectonic models. Bayesian modeling of complex, concentrically, zoned monazite from the northern Appalachian orogen was used to develop a detailed temperature-time history through the Acadian (∼405–395 Ma) and Neoacadian (∼380–350 Ma) orogenies with significantly reduced uncertainties (40–70 %). Modeling of zoned monazite from a southern Trans-Hudson orogen granulite yielded durations of 0.5+9/ -0.4 Ma and 20+5/ -8 Ma for biotite-dehydration melting and suprasolidus conditions, respectively. The relatively short intervals of heating and peak conditions are consistent with a back-arc tectonic setting. A complementary approach, Bayesian change point detection, provides a framework to constrain the timing of compositional changes that can be linked with metamorphic reactions. Applying this approach in the northern Appalachian orogen demonstrates contrasting durations of low-Y monazite crystallization (∼10 vs ∼30 myr) in regions with different pressure-temperature histories. Compositionally distinct monazite domains can be linked with garnet stability, which provides a key constraint on tectonic models. Bayesian statistical analysis represent a powerful tool that can be widely applied to refine the absolute time and duration of geologic processes. A more objective and reproducible set of interpretations are produced by this more formal, although not necessarily complex, statistical analysis. [ABSTRACT FROM AUTHOR]

Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location. [Display omitted] Here is a list of possible highlights of this methodology proposed in the research paper. • Challenging Problem: Addresses the complexity of source identification in groundwater contamination modeling. • Novel Methodology: Introduces a new approach to narrow down potential contamination source locations. • Reduction of variables: Reduces complexity by reducing the distribution of location variables to a set of probable locations. • Use of Observation Data: Utilizes aquifer parameters and observed data to improvise source location identification. • High Accuracy: Achieves high accuracy of 88% with a 0.745 probability of locating the nearest source at a distance of 11.6 m. [ABSTRACT FROM AUTHOR]

The aim of this study is to validate the FIDBv2 online procedure for adult age-at-death estimation using root dentine translucency (RDT) and periodontal retraction (PR) of single-rooted teeth in a worldwide sample. The sample includes 4810 teeth of 2559 individuals from 16 countries of America, Europe and Asia. Bias and inaccuracy between documented (DA) and estimated ages (EA) were calculated. Pearson and Intraclass Correlation Coefficients were computed to assess the strength of agreement between pairs of data, while Kolmogorov-Smirnov Z was used to evaluate the statistical significance of the differences. The percentages of correctly estimated cases within different age ranges were obtained to find trends in the reliability of the results. Most of the biases (-4.61–1.31 years) and inaccuracies (4.81–9.72 years) are low. The dispersion of EA increases with age and almost all the DA-EA correlations are above 0.75. DA-RDT and DA-PR correlations are positive, most of the former being high (0.74–0.91), and the latter being low (0.11–0.54). The highest percentages of correct estimations are identified for the ±7.5 and ±10 years ranges, and most comparisons of bias and inaccuracy between countries are non-significant. The high correlations between DA and EA suggest that the method is robust and reliable for a global application. Mean errors are low, with the best results found in the 30–69-year-old cohort. This research supports that the method is effective and accurate for age estimation in forensic contexts worldwide, thus reaffirming it is a generalizable procedure locally and internationally. • The FIDBv2 method is effective for age estimation in forensic contexts worldwide. • A strong association of RDT with age-at-death was identified in all the samples. • The method is robust and reliable, with minimal impact of interpopulational variation. • Its relevance in the analysis of commingled and damaged samples is underscored. • The results support incorporating the FIDBv2 method within Daubert standards. [ABSTRACT FROM AUTHOR]

This work researches the drivers of e-commerce conversion of one of the largest e-commerce companies in the Netherlands. We focus on product page conversion, i.e., the probability that a customer who visits a specific product page also buys the product. This probability differs between products, which we explain by a variety of factors like pricing, delivery times, reviews, seller type, and the quality of the content. Understanding the drivers of conversion is the first step in increasing it, and therefore an important step in increasing overall sales. We describe the process of transforming Big Data into valuable insights using Bayesian statistics and apply a Bayesian Binomial model to a dataset of 15 million records using a distributed MCMC algorithm. In addition, we apply a simple Binomial GLM on a small sample of these 15 million records for comparison. We find that using the full 15 million records results in a significant reduction in the variance of the estimated parameters. In terms of the actual drivers of e-commerce conversion, we observe that products with competitive pricing, short delivery times, and good content all achieve a higher conversion on average. Furthermore, reviews, average review score, and the number of reviews for a product have a large positive effect on conversion compared to other characteristics. This provides a unique insight into what makes people decide whether or not to purchase a product. • We explain the drivers behind e-commerce conversion. • We transform Big Data into valuable insights using Bayesian statistics. • We use the consensus MCMC algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposal of Columbia Supercontinent reaches 20 years of age. • Statistical methods developed to test reconstructions. • SAMBA model appears valid from 1.78 to 1.50 Ga only. The proposal that a Paleo-Mesoproterozoic supercontinent, Columbia, preceded the existence of Rodinia was forwarded in 2002. A similar proposal that same year provided a detailed discussion of 2.1–1.8 Ga orogenic belts distributed throughout the globe thought to represent the assembly of Columbia. In the 20 years that followed, evidence in favor of Columbia has grown but its exact configuration and geometrical relationships between various cratons remains elusive. One of several challenges in reconstructing Columbia is the paucity of high-quality paleomagnetic data. Progress is slow, but steady, in this realm as it requires painstaking field and laboratory work to generate a new pole. Despite large gaps in the database, we have generated testable models of Columbia. Unfortunately, there are seldom coeval poles from many continents and reconstructions represent small time-slices in Columbia's lifespan. In this contribution, we review the history of the Columbia hypothesis, evidence mounted in support of the idea and status of the model. We present a statistical method to evaluate goodness of fit in paleomagnetically based reconstructions where data are separated in both time and space. Baltica and Amazonia are often linked together with eastern Laurentia to form a long-lived accretionary orogenic margin of Columbia. This so-called SAMBA model was originally posited as a one billion year long (1.8–0.8 Ga) fixture in both Columbia and Rodinia. Paleomagnetic data are supportive of this connection from ∼ 1.75–1.5 Ga, but not during younger times. We also test a recent model of Columbia based on the highest quality paleomagnetic poles available at that time. Our analysis reveals numerous discrepancies between data and models that will need to be addressed as work towards reconstructing Columbia continues unabated. [ABSTRACT FROM AUTHOR]

• Capacity factor estimation of an offshore wind project in Rio de Janeiro, Brazil. • The methodology provides point and interval estimates for the capacity factor. • Based on a Bayesian inference approach, it is useful during the planning phase. • The capacity factor estimate for the offshore wind farm project reaches 60 %. • Compatibility between results from wind speed measurements and reanalysis. This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The calculation of the capacity factor depends on the power curve model and the probabilistic wind speed model, which in this work follows a Weibull distribution, whose scale and shape parameters are estimated using a Bayesian approach where the priors are independent gamma distributions, the likelihood is the Weibull distribution, and the posteriors are obtained by Hamiltonian Monte Carlo. The application of the described methodology is illustrated through a case study of an offshore wind power project located on the Northern coast of the Rio de Janeiro State, Brazil. [ABSTRACT FROM AUTHOR]

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries. [ABSTRACT FROM AUTHOR]

• Cognitive reflection measures one's ability to activate effortful deliberation. • Successfully overriding intuitive responses is posited to rely on, in part, trait-like attributes known as thinking dispositions. • Bayesian modeling reveals that a thinking disposition intervention (TDI) can increase cognitive reflection. • This relationship is not mediated by need for cognition and intellectual humility. • Implications for dual process theories and the role of TDI's in enhancing reasoning are discussed. Cognitive reflection is a construct measuring one's ability to disengage automatic or type I processing and instead activate effortful deliberation, described as type II processing in dual process theories. Successfully overriding intuitive responses is posited to rely on, in part, trait-like attributes known as thinking dispositions, though a causal link has not been established. The present research is the first randomized controlled trial investigating the effect of a novel thinking disposition intervention (TDI)—a 7-week educational curriculum—on cognitive reflection performance. In the present study, participants (N = 338) were randomized to either the TDI or a control condition. A Bayesian modeling approach suggests positive and moderate effects. Moreover, those in the treatment were over twice as likely to make CRT gains than those in the control group. Additional mediation and moderation analyses were conducted to test whether the TDI impacts CRT via thinking dispositions such as need for cognition and intellectual humility, and/or whether such attributes meaningfully interact with the intervention. Thinking dispositions do not appear to mediate this relationship, neither do they moderate the effect of the intervention. Implications for dual process theory are discussed. [ABSTRACT FROM AUTHOR]

• Agri-environment schemes (AES) aim to halt biodiversity loss on agricultural land. • We assessed habitat selection by farmland birds for AES using distance sampling models. • We hypothesized functional responses in habitat selection by four bird species. • We found negative functional responses to the availability of AES and non-AES fields. • Functional responses in habitat selection should be considered in future AES research. Agri-environment schemes (AES), as part of the European Union's Common Agricultural Policy, are intended to help prevent the decline of farmland biodiversity. Nevertheless, the ecological effectiveness of AES in supporting farmland bird populations remains inconclusive across studies. This inconsistency highlights a research gap: What behavioral mechanisms contribute to the variation in farmland bird populations? This variability may arise because farmland birds alter their habitat selection in response to available habitat—a phenomenon known as functional responses in habitat selection. Here, we examined the effects of AES and non-AES land-use variables on habitat selection of farmland birds, taking into account the species-specific functional response to availability. We built two types of hierarchical distance sampling models to analyze observational data of four farmland bird species from line-transect surveys during the breeding season in Saxony, Germany. First, we built mixed-effects models to estimate the marginal effects of AES and non-AES land-use variables on the occurrence of farmland birds. Second, we integrated linear models into the distance sampling model to relate habitat selection estimates to habitat availability. Results from the first mixed-effects model showed positive and negative effects of AES on habitat selection. In the second model, we observed inverse relationships between habitat selection and availability for both AES and non-AES variables. These results support the hypothesis of negative functional responses, as we found a decrease in the tendency of farmland birds to select both AES and non-AES land uses as their availability increased. Our findings suggest that the varying effects of AES on bird occurrences reported in the literature may depend on cross-study differences in AES availability. We propose that functional responses in habitat selection should be considered as a phenomenon in future AES research. Our study also highlights the importance of optimal AES provision and their spatial allocation in the agricultural landscape. [Display omitted] [ABSTRACT FROM AUTHOR]

Turbulent flows, characterized by their chaotic and stochastic nature, have historically presented formidable challenges to predictive computational modeling. Traditional eddy-resolved numerical simulations often require vast computational resources, making them impractical or infeasible for numerous engineering applications. As an alternative, deep learning-based surrogate models have emerged, offering data-drive solutions. However, these are typically constructed within deterministic settings, leading to shortfall in capturing the innate chaotic and stochastic behaviors of turbulent dynamics. In this study, we introduce a novel generative framework grounded in probabilistic diffusion models for versatile generation of spatiotemporal turbulence under various conditions. Our method unifies both unconditional and conditional sampling strategies within a Bayesian framework, which can accommodate diverse conditioning scenarios, including those with a direct differentiable link between specified conditions and generated unsteady flow outcomes, as well as scenarios lacking such explicit correlations. A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes. We evaluate and showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments, including: (1) the synthesis of Large Eddy Simulations (LES) simulated instantaneous flow sequences from unsteady Reynolds-Averaged Navier–Stokes (URANS) inputs; (2) holistic generation of inhomogeneous, anisotropic wall-bounded turbulence, whether from given initial conditions, prescribed turbulence statistics, or entirely from scratch; (3) super-resolved generation of high-speed turbulent boundary layer flows from low-resolution data across a range of input resolutions. Collectively, our numerical experiments highlight the merit and transformative potential of the proposed methods, making a significant advance in the field of turbulence generation. [ABSTRACT FROM AUTHOR]

Semi-empirical formulae like the Yamamura model provide a quick reference of sputter yields for applications such as sputter depth profiling and secondary ion mass spectrometry. Fit parameters in such models are prone to errors which can propagate into the prediction of sputter yields. We compare experimental sputter yields to predictions of the Yamamura model using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm. The model parameters Q (linear scaling) and s (power-law scaling) are explored. The results from MCMC are then compared to propositions of Seah (Surface and Interface Analysis, 2005) and extended to a collection of target materials by fitting simulated yields for argon and neon using TRIDYN. Q was found to be proportional to the threshold energy and a simple relation is proposed. The simplicity notwithstanding, Q is speculated to be dependent on a multitude of parameters such as density, energy transfer and orbital filling. [Display omitted] • Sputter yields updated for Mo, Ru, Pd, and W under Ar and Ne ion bombardment. • Fit parameters for Yamamura model determined using Bayesian algorithms among nearly all elements. • Linear scaling (Q) of sputter yields found to be correlated with efficiency of energy transfer and sputter threshold energy E t h rather than density. • Q directly proportional to E t h over the entirety of elements in the periodic table. [ABSTRACT FROM AUTHOR]

This paper introduces a Bayesian Inference model for dynamically changing the neighbor discovery parameters in order to minimize the time to recover from long radio link disconnections in tactical networks. We start with the hypothesis that given a multi-layer tactical system, we can use the in/out chains to learn the distribution of changes in the radio data link with an increasing confidence as time goes by. Moreover, we propose that a static neighbor discovery configuration (e.g., hello time, aggregation time, and so on) in dynamic networks can never be optimal and that it is possible to calculate the close to optimal neighbor discovery configuration dynamically. Thus, we introduce a Bayesian Inference model composed of a Markov-Chain-Monte-Carlo (MCMC) and a Long Short-Term Memory (LSTM) to compute a dynamic configuration by learning the probability distribution of the link conditions (computed using the in/out model) and adapting the configuration parameters dynamically according to this distribution. Our hypothesis was verified with numerical calculations using different patterns of link data rate change. The quantitative analysis suggests that at the tactical edge dynamic control signaling can improve connectivity in ever-changing network scenarios. [ABSTRACT FROM AUTHOR]

Background: Human gait is a complex task resulting from the interaction of sensory perception, muscle force output, and sensory-motor integration, which declines with the aging process and impacts gait speed in older women.Research Question: What are the separate and combined impacts of sensory-motor factors on gait speed of older women?Methods: Sixty healthy older women (69.3 ± 5.9 years) volunteered for this study. A previous screening using Pearson's correlation selected variables significantly correlated with gait speed: age, plantar tactile perception, lower limb explosive force, and mean velocity (MV) of the center of pressure (CoP). Simple and multivariate regression models were performed with selected variables. The magnitude of evidence was obtained using Bayesian inference, determining posterior probabilities based on our data.Results: Gait speed was negatively correlated with age and positively correlated with plantar tactile perception, MV (Romberg index), and lower limb explosive force. The coefficient of determination (R2) varied between 0.06 for plantar tactile perception and 0.22 for explosive force (p < 0.05). The multivariate model, including age, MV (Romberg index), and lower limb explosive force, explained 44% (R2 = 0.44) of the variance in gait speed, with a small standard error of estimate (0.14 m/s). Bayesian inference confirmed the good posterior probability of the model.Significance: Age, plantar tactile perception, MV (Romberg index), and lower limb explosive force impact gait speed, whereas the combination of the first three factors has an excellent posterior probability of predicting or affecting gait speed. [ABSTRACT FROM AUTHOR]

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions associated with UQ. • We provided an extensive review of uncertainty quantification methods in deep learning. • We covered popular and efficient Bayesian approaches for uncertainty quantification. • We listed notable ensemble techniques for quantifying uncertainty. • We discussed various applications of uncertainty quantification methods. • We summarized major open challenges and research gaps in uncertainty quantification. [ABSTRACT FROM AUTHOR]

Despite widespread use of branched glycerol dialkyl glycerol tetraethers (brGDGTs) for paleo-temperature reconstruction, no global calibration for their application in lakes has been generated since improved analytical methods have allowed for the separation of the structural isomers. This is a substantial obstacle for the application of this tool as soil calibrations underestimate temperature values when applied to lake sediments. Here, we present a comprehensive dataset (N = 272) of lacustrine brGDGT distributions, consisting of both new and previously reported samples, spanning a wide range of geographical locations, air temperatures, and lakewater pH values. Empirical Orthogonal Function (EOF) analysis on the fractional abundance of brGDGTs indicates that temperature exerts a strong control (explaining 58 % of the variance) on brGDGT distribution. The influence of water chemistry is weaker, with pH and conductivity explaining 24 % of the variance. We use our dataset to generate a new Bayesian calibration to the mean temperature of Months Above Freezing (MAF), which has an R 2 = 0.82 and RMSE = 2.9 ° C. Application of the new temperature calibration to a previously published lake core record spanning the last 900 years demonstrates that it generates values comparable to instrumental observations of MAF. Our new calibration facilitates the use of lacustrine brGDGTs to reconstruct continental temperatures, a vital piece of information for understanding past climates. [ABSTRACT FROM AUTHOR]

• A collection of 180 textiles was compiled. • Six scenarios are posed as well as hypotheses and relevant populations. • Items suitable for the relevant population are filtered using SQL queries. • Examples for both textiles and glass are provided. Evaluation of forensic evidence using Bayesian statistics requires the formulation of hypotheses. Many hypotheses, especially those presenting the defence viewpoint imply that traces can be attributed to an arbitrary member of a relevant population. The exact items or persons that comprise the relevant population may vary from case to case. Therefore, the statistical evaluation of evidential value based on databases cannot make use of a fixed set of items or persons. In the current paper, methodology is presented to filter the contents of a database such that only items that are considered relevant are selected. Six scenarios, including those related to fibre, textile, and glass evidence are described, together with the hypotheses and relevant populations that may be evaluated by an expert. In addition, we show how items representing the defined relevant population can be extracted from a database using SQL code. Images of the items in the (filtered) relevant population provide an overview of the selected items and hence direct feedback to the examiner. In this way, erroneous codes or unwanted side effects can be identified and corrected. It is concluded that the filtering procedure is effective in cases where the relevant population is demarcated accurately. [ABSTRACT FROM AUTHOR]

British Columbia (BC) Canada has a large take-home naloxone (THN) program, implemented as part of the provincial response to the ongoing toxic unregulated drug supply emergency. Ascertaining the rate of use of THN kits is vital to understanding the full impact of the program. However, this is a challenging problem due to under-reporting of kit distribution. This study aims to estimate the total number of THN kits used based on the number of THN kits shipped, the number of THN kits reported as distributed, and the number of THN kits reported as used. We used BC THN shipment and distribution records (February 2015 to August 2023) to inform a simple Bayesian model of naloxone kit distribution and use. A logistic regression term by health region and distribution site type was incorporated to account for variable under-reporting, and a convolution term was incorporated to account for kit distribution. We find the number of THN kits reported as used, and the number of total THN kits distributed, are largely under-reported. An estimated 1,500 (95 % CrI: 1,430 - 1,590) THN kits per 10,000 BC population were used, of which 288 per 10,000 had been reported as used. Of all the THN kits shipped, the model estimated that 43 % (95 % CrI: 41–45 %) of kits were used. We also found variation in both distribution and use by distribution site type, with kits distributed from overdose prevention sites having the highest rate of use (56 %; 95 % CrI: 53–59 %). Across all sites, kit use is approximately five times higher than has been reported. Our framework can also be applied to other localities where THN programs operate, in order to better estimate the true reach and impact of take home naloxone distribution. [ABSTRACT FROM AUTHOR]

We present a method for obtaining unbiased signal estimates in the presence of a significant unknown background, eliminating the need for a parametric model for the background itself. Our approach is based on a minimal set of conditions for observation and background estimators, which are typically satisfied in practical scenarios. To showcase the effectiveness of our method, we apply it to simulated data from the planned dielectric axion haloscope MADMAX. [ABSTRACT FROM AUTHOR]

Continuous cover forestry (CCF) is forest management based on ecological principles and this management type is currently re-visited in many countries. CCF woodlands are known for their structural diversity in terms of tree size and species and forest planning in CCF needs to make room for multiple forest development pathways as opposed to only one management target. As forest management diversifies and management types such as CCF become more common, models used for projecting forest development need to have a generic and flexible bottom-up design. They also need to be able to handle uncertainty to a larger extent and more comprehensively than is necessary with single, traditional forest management types. In this study, a spatial tree model was designed for analyzing a data set involving 18 plots from CCF stands in Southern Finland. The tree model has specific ingrowth, growth and mortality model components, each including a spatially explicit competition effect involving neighboring trees. Approximations were presented that allow inference of the model components operating in annual steps based on time-series measurements from several years. We employed Bayesian methodology and posterior predictive distributions to simulate forest development for short- and long-term projections. The Bayesian approach allowed us to incorporate uncertainties related to model parameters in the projections, and we analyzed these uncertainties based on three scenarios: (1) known plot and tree level random effects, (2) known plot level random effects but unknown tree level random effects, and (3) unknown random effects. Our simulations revealed that uncertainties related to plot effects can be rather high, particularly when accumulated across many years whilst the length of the simulation step only had a minor effect. As the plot and tree effects are not known when tree models are applied in practice, in such cases, it may be possible to significantly improve model projections for a single plot by taking one-off individual-tree growth measurements from the plot and using them for calibrating the model. Random plot effects as used in our tree model are also a way of describing environmental conditions in CCF stands where other traditional descriptors based on stand height and stand age fail to be suitable any more. [Display omitted] • Spatially-explicit tree models can be used to project continuous cover forestry (CCF). • Bayesian methodology was employed to model uncertainties associated with projections. • Uncertainties were high if plot or stand effects were unknown. • Random plot effects can describe environmental conditions in CCF stands. • The length of simulation steps had a minor effect on the simulation results. [ABSTRACT FROM AUTHOR]

Digital control in manufacturing processes produces significant amounts of metadata. The production process metadata, such as thermal and optical measurements, enables a higher degree of property grading than uninstrumented manufacturing and feedback for fault detection. This study explores how metadata can design fatigue-resistant structures using physically grounded models such as density functional theory, cyclic plasticity, and fracture mechanics that train machine learning algorithms. Machine learning models work very efficiently in their trained physical space. In comparison, mechanistic models are computationally costly for complex phenomena such as fatigue. We show how fatigue can be administered consistently at all scales by energy-based criteria and how a mechanistic function can be built based on this concept. The energy mechanistic function allows exact quantification of the effect of the existing flaws from manufacturing on fatigue lifetime under certain load boundary conditions. Since the mechanistic function is local and subscale to the prediction scale of the machine learning model, it can be used to build density functions for probabilistic regression of the fatigue property on the scale above. The analysis is applied to the selective laser melting process due to the availability of digital control and metadata generation during deposition. • Application of mechanistic machine learning for fatigue lifetime prediction. • Meta-material fatigue design of components during manufacturing. • Highthrouput fatigue lifetime prediction with minimal experimental lead time. • Using atomistic cohesion energy as a fatigue failure criterion. [ABSTRACT FROM AUTHOR]

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts. [ABSTRACT FROM AUTHOR]

The range of survey methodologies for measuring daily activity, catch and harvest (i.e. creel surveys) of recreational anglers, is increasing with the advent of new technologies and improvements in remote sensing. Individual creel survey types frequently give different insights into a fishery due to their unique sources of methodological bias and coverage, which creates a problem for resource managers since markedly different estimates of important fishery metrics can result. We demonstrate a joint estimation approach using a Bayesian statistical framework that can bring together multiple survey types to derive a single estimate for important metrics. This framework is applied to data collected from a relatively large winter fishery and integrates three traditional creel methodologies (i.e. roving, access and aerial counts), each with very different sources of bias, to derive a common estimate of angler effort. Models integrating two survey types are found to be have lower uncertainty in their estimates. Further, reductions in effort for any one survey type is found to be buffered by the joint estimation approach, such that resource managers will likely find benefits in using more than one survey methodology in an integrated fashion to monitor a fishery. • We describe a novel Bayesian model of angler activity that allows for more accurate expansion of survey counts to estimate daily activity. • Joint estimation models provided consistent estimates of effort with low uncertainty even with data reduction. • Results confirm the importance of better representing commercially-operated recreational huts in survey methods. • Models that combined roving creels with access creels or aerial surveys can provide more reliable and consistent estimates. [ABSTRACT FROM AUTHOR]

1) To show the validation criteria of the product of probabilities (PPs). Bayes' theorem (BT) is widely used in the ionizing radiation field; and 2) To demonstrate that terms P(B|A) and P (A|B) in the BT are not new probabilistic metrics, but the own respective probabilities of B and An events. Mathematical derivations based on strong probabilistic foundations, and with their respective illustrations. a) Determination of the validationcriteria of the PPs; and b) There is demonstration of incoherencies of the BT. 1) The tumor control probability (TCP) of independent radiation oncology treatments to patients with more than one target, calculated as the product of individual TCPs is an incoherent determination; 2) Given previously explained about BT terms, the use of this theorem should be re-considered; and 3). P (A∩B) P(A)*P(B) only when N × P(A) and N × P(B) are a subset of a population N, and if A and B are independent and simultaneous events. • It provides validation criteria of the product of probabilities (PPs). • The use of the Bayes' theorem should be re-considered. • UTCP = TCP × NTCP0 is an excellent example using PPs. • It provides a new proposal for the composite TCP expression. [ABSTRACT FROM AUTHOR]

Objectives: In trial-based economic evaluation, some individuals are typically associated with missing data at some time point, so that their corresponding aggregated outcomes (eg, quality-adjusted life-years) cannot be evaluated. Restricting the analysis to the complete cases is inefficient and can result in biased estimates, while imputation methods are often implemented under a missing at random (MAR) assumption. We propose the use of joint longitudinal models to extend standard approaches by taking into account the longitudinal structure to improve the estimation of the targeted quantities under MAR.Methods: We compare the results from methods that handle missingness at an aggregated (case deletion, baseline imputation, and joint aggregated models) and disaggregated (joint longitudinal models) level under MAR. The methods are compared using a simulation study and applied to data from 2 real case studies.Results: Simulations show that, according to which data affect the missingness process, aggregated methods may lead to biased results, while joint longitudinal models lead to valid inferences under MAR. The analysis of the 2 case studies support these results as both parameter estimates and cost-effectiveness results vary based on the amount of data incorporated into the model.Conclusions: Our analyses suggest that methods implemented at the aggregated level are potentially biased under MAR as they ignore the information from the partially observed follow-up data. This limitation can be overcome by extending the analysis to a longitudinal framework using joint models, which can incorporate all the available evidence. [ABSTRACT FROM AUTHOR]

The reactivity of carbonaceous surfaces bears a fundamental role in various fields, from atmospheric chemistry and catalysis to graphene and nanoparticles. This reactivity is mainly driven by the surface chemical composition and by the strength of the interaction between the adsorbates and the surface (physi-/chemisorption). While the surface composition of complex natural samples can be well characterized, adsorption energies (ergo, adsorption processes) of the corresponding adsorbate/adsorbent systems are often overlooked. We propose a novel laser-based method for measuring the adsorption energy of chemical species on various carbonaceous surfaces. The proof of concept of this original method has first been demonstrated by deriving adsorption energies of various systems consisting of polycyclic aromatic hydrocarbons and activated carbon. The great potential of this fast, spatially resolved, and surface-sensitive method, which can also act as a defect density probe at the mesoscale, has been further demonstrated through the study of systems of increasing complexity. Image 1 [ABSTRACT FROM AUTHOR]

Background: Bayesian methods, with the predictive probability (PredP), allow multiple interim analyses with interim posterior probability (PostP) computation, without the need to correct for multiple looks at the data. The objective of this paper was to illustrate the use of PredP by simulating a sequential analysis of a clinical trial.Methods: We used data from the Laryngobloc trial that planned to include 480 patients to demonstrate the equivalence of success between a laryngoscopy performed with the Laryngobloc® device and a control device. A crossover Bayesian design was used. The success rates of the two laryngoscopy devices were compared. Interim analyses, computed from random numbers of subjects, were simulated.Results: The PostP of equivalence rapidly reached the predefined bound of 0.95. The PredP computed with an equivalence margin of 10% reached the efficacy bound between 352 and 409 of the 480 included patients. If a frequentist analysis had been made on the basis of 217 out of 480 subjects, the study would have been prematurely stopped for equivalence. The PredP indicated that this result was nonetheless unstable and that the equivalence was, thus far, not guaranteed.Conclusions: Based on these interim analyses, we can conclude with a sufficiently high probability that the equivalence would have been met on the primary outcome before the predetermined end of this particular trial. If a Bayesian approach using PredP had been used, it would have allowed an early termination of the trial by reducing the calculated sample size by 15-20%. [ABSTRACT FROM AUTHOR]

DNA metabarcoding of faeces or gut contents has greatly increased our ability to construct networks of predators and prey (food webs) by reducing the need to observe predation events directly. The possibility of both false positives and false negatives in DNA sequences, however, means that constructing food networks using DNA requires researchers to make many choices as to which DNA sequences indicate true prey for a particular predator. To date, DNA-based food networks are usually constructed by including any DNA sequence with more than a threshold number of reads. The logic used to select this threshold is often not explained, leading to somewhat arbitrary-seeming networks. As an alternative strategy, we demonstrate how to construct food networks using a simple Bayesian model to suggest which sequences correspond to true prey. The networks obtained using a well-chosen fixed cutoff and our Bayesian approach are very similar, especially when links are resolved to prey families rather than species. We therefore recommend that researchers reconstruct diet data using a Bayesian approach with well-specified assumptions rather than continuing with arbitrary fixed cutoffs. Explicitly stating assumptions within a Bayesian framework will lead to better-informed comparisons between networks constructed by different groups and facilitate drawing together individual case studies into more coherent ecological theory. Note that our approach can easily be extended to other types of ecological networks constructed by DNA metabarcoding of pollen loads, identification of parasite DNA in faeces, etc. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the test time is too long and the test efficiency is affected, an adaptive test patterns reordering method based on Gamma distribution was proposed. And a probability model based on Gamma distribution for the probability of each test pattern hitting fault was established. During the test, the circuit to be tested was added to the sample space, and the parameters of probability model were updated dynamically, and the test patterns were reordered synchronously. The experimental results showed that the reordered test patterns have higher test quality, which can reduce the test time and test cost of the faulty circuit. The algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process. • A probability model based on Gamma distribution is established. • Calculate the probability of fault detected for each test pattern. • Test patterns are reordered; The performance of the test set has been improved. [ABSTRACT FROM AUTHOR]

Kinetics is an important part of food science and statistics is a necessary key element in modeling. Ordinary least-squares (OLS) regression is mostly used to obtain parameter estimates and their uncertainties; this is done within the frequentist framework. This article introduces Bayesian statistics as an alternative to OLS. The background of Bayesian statistics is briefly explained, emphasizing the difference with the frequentist approach. Basically, frequentists go for the probability of data given a hypothesis, resulting in point estimates, while Bayesians go for the probability of a hypothesis given the data, resulting in probability distributions for parameters. This study shows how to apply the Bayesian approach to kinetic problems using freely available R packages. To focus on the Bayesian approach, the kinetic problem presented is a trivial zero-order reaction concerning the formation of furan in a soy sauce. The main result is numerical and graphical output showing probability distributions of parameters. Interpretation of regression results is shown leading to the conclusion that the Bayesian approach yields a more intuitive result with richer information than the conventional OLS approach. The pros and cons of the Bayesian approach are highlighted, the major pro being the intuitive and informative result and the major con that one has to learn and apply a programming language like R or Python. The Bayesian approach is very general and the outline shown here can be applied easily to much more complicated kinetic models. • It is shown how to deal rigorously with uncertainty in regression problems applied to kinetics. • Ordinary least squares regression is compared with Bayesian regression. • The pros and cons of Bayesian regression are discussed. • The advantages of Bayesian regression are illustrated. [ABSTRACT FROM AUTHOR]

This paper presents a machine learning (ML) approach to wind turbine (WT) lightning incidence analysis in complex terrain, based on the information obtained from a lightning location system (LLS). A particular ML model of the WTs lightning incidence is developed, using Bayesian statistical learning and Gaussian process regression, and trained on the actual LLS data. The model is developed around a known proposition that the lightning strike frequency data are emanating from a Poisson stochastic process. It further makes use of an attractive radius concept of lightning attachment, employs a sophisticated analysis of the WT effective height—which leverages terrain elevation data—and introduces spatial autocorrelation of lightning strikes. It provides a probabilistic risk assessment of WT lightning damage, along with a statistical measures of the associated monetized financial losses. Proposed ML model benefits from the Bayesian ability to quantify uncertainty of model parameters, and employ hierarchical model structure that informs model parameters through the mutual higher-level hyperpriors. Proposed model enables both investors and insurance companies to asses risks associated with lightning incidence to WTs, considering historical LLS data and future wind farm installation locations. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the parameter estimation problem, using the Bayesian methodology, for the drift coefficient of some linear (parabolic) SPDEs driven by a multiplicative noise of special structure. We take the spectral approach by assuming that one path of the first N Fourier modes of the solution is continuously observed over a finite time interval. First, we show that the model is regular and fits into classical local asymptotic normality framework, and thus the MLE and the Bayesian estimators are weakly consistent, asymptotically normal, efficient, and asymptotically equivalent in the class of loss functions with polynomial growth. Secondly, and mainly, we prove a Bernstein–Von Mises type result, that strengthens the existing results in the literature, and that also allows to investigate the Bayesian type estimators with respect to a larger class of priors and loss functions than that covered by classical asymptotic theory. In particular, we prove strong consistency and asymptotic normality of Bayesian estimators in the class of loss functions of at most exponential growth. Finally, we present some numerical examples that illustrate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Accurate temperature records for the deep geological past are a vital component of paleoclimate research. Distributional changes of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids in geological archives including paleosoils are a promising indicators to infer past continental air temperatures. However, the 'orphan' status of the brGDGTs, the potential effect of temperature-independent parameters on their relative distribution, and the uneven geographical distribution of the soils used for calibration contribute to the high uncertainty of brGDGT-based transfer functions (root mean squared error, RMSE: ±5 °C). Here, we expand the soil dataset from the previous calibration(s) with new and published soil data. We use Bayesian statistics to calibrate the relationship of the 5-methyl brGDGTs (MBT′ 5Me) and mean annual air temperature (MAAT). The addition of soils from warm (>28 °C) environments from India substantially increases the upper limit of the Bayesian calibration (BayMBT) from 25 to 29 °C, aiding in the generation of temperature records for past greenhouse climates, such as the Eocene. The BayMBT model also effectively minimizes the structured MAAT residuals prevalent in previous calibrations, therefore giving the opportunity to explore confounding factors on the calibration. We formulate a set of alternative calibration models to test the effect of specific environmental parameters and show that soils at mid-latitudes with temperature seasonalities >20 °C are not well described by the BayMBT model. We find that the MBT′ 5Me index is best correlated to the average temperature of all months >0 °C, called the BayMBT 0 model. This finding supports the hypothesis that brGDGT production ceases or slows down in the winter months. However, a persistent feature of the BayMBT model and previous calibrations is the significant scatter at mid-latitudes, which is speculatively linked with a possible increase in diversity of microbial brGDGT-producing communities in these locations. [ABSTRACT FROM AUTHOR]

• Blood biomarkers from different molecular pathways are useful for early and specific AD diagnosis. • A logistic regression model with a Bayesian variable selection procedure included lipids and lipid peroxidation biomarkers. • The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes. • Compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets. Alzheimer Disease (AD) is a complex pathology, in which several biochemical pathways could be involved. Therefore, the development of clinical studies combining different nature biomarkers in an AD diagnosis approach is required. Specifically, the present study evaluated blood biomarkers from different molecular pathways (epigenomics, lipid metabolism, lipid peroxidation), to obtain an early and specific AD diagnosis approach. The participants were classified into early AD (n = 53), and non-AD (healthy controls, other dementias) (n = 83). Blood samples were collected and biochemical determinations (microRNAs, lipids, lipid peroxidation compounds) were carried out by quantitative PCR and liquid chromatography coupled to mass spectrometry, respectively. Then, a logistic regression model with a Bayesian variable selection procedure was developed. The Bayesian variable selection procedure for microRNAs did not show any relevant variable. Therefore, microRNA biomarkers were excluded. So, the developed model considered only lipids and lipid peroxidation compounds. The corresponding selected variables were age, 18:0 LPC, PGE 2 , isoprostanes and, isofurans. The validated model (by leave-one-out cross-validation) provided satisfactory diagnosis indexes (AUC 0.83, Sensitivity 87 %, Specificity 79 %). The developed model included biomarkers from different pathways (lipid metabolism, oxidative stress), achieving a promising approach to early, specific and, minimally invasive AD diagnosis. Nevertheless, further work to validate clinically these preliminary results with an external cohort is required. Also, the integration of different compounds coming from several biochemical pathways could constitute a relevant research field for the development of AD therapeutic targets. [ABSTRACT FROM AUTHOR]

The problem of estimating brake pad wear of cars has been considered, using friction work approximated from the product of longitudinal braking force and distance travelled during braking. The braking force is estimated from a longitudinal dynamics model with inputs from an inertial measurement sensing unit with Kalman filter based sensor fusion, and standard vehicle on-board diagnostics data. Wear coefficients are fitted with Bayesian linear regression on measurements from two series: (I) 16 measurements of previously worn pads over approx. 4,800 km with average wear of approx. -0.5 mm and (II) seven measurements of almost new pads over approx. 1,850 km and approx. -0.15 mm of wear. The approximated friction work appears to correlate well with the wear. The wear coefficients converge quickly after just 4-5 measurements. Coefficients ranging from − 4. 95 ⋅ 1 0 − 5 mm 3/J to − 2. 08 ⋅ 1 0 − 5 mm 3/J are obtained across the cases, when assuming a coefficient of friction μ = 0. 6 obtained through pin-on-disc (POD) tests. The wear coefficients are validated using POD tests, showing good agreements with the indirectly estimated wear coefficients from vehicle longitudinal dynamics. Data from series (II) is used for prediction of remaining useful life (RUL), where one additional measurement is carried out after 16,552 km. The RUL prediction requires knowledge of the relationship between usage feature and distance travelled. Different approaches are investigated for the predictions: wear based on only distance travelled, linear relationship between usage feature and distance, and three levels of braking severity based on the usage data. Four of the approaches underestimate the distance significantly at around 4,000 to 8,000 km against the actual distance of 16,552 km, while the low severity estimate is closest at 20,460 km, agreeing with expectations due to the driving being primarily on highways. • Wear estimation of brake pads of passenger cars using on-road driving data. • Estimation of friction work from vehicle longitudinal dynamics • Validation using pin-on-disc setup. [ABSTRACT FROM AUTHOR]