Search

When conducting engineering life tests, it is common to collect necessary data from multiple production lines simultaneously. However, traditional methods are inadequate for analyzing this type of data, which is crucial for assessing reliability performance. In this study, we employ a newly proposed methodology called a joint Type-II hybrid censoring plan for gathering data from two production lines. We apply this plan to analyze three real-world engineering data sets when the parent distribution of the populations of interest is the Nadarajah–Haghighi distribution with varying shape and scale parameters. We consider two estimation approaches, maximum likelihood and Bayesian methods, to obtain point and interval estimates of the model parameters. The Bayesian estimates are obtained using the squared error loss function and the Markov Chain Monte Carlo procedure. To assess the performance of these different estimation methods, we conduct a simulation study that incorporates various testing plans. Finally, three engineering applications are considered to show the applicability of the offered methodologies.

This paper introduces the exponentiated Ailamujia distribution (EAD), extending the Ailamujia distribution (AD) to offer greater versatility in modeling positively skewed data. The core contribution lies in the rigorous derivation and examination of EAD’s reliability functions, including failure rate, survival, and mean waiting time functions, among others. Our analysis reveals novel algebraic expressions that deepen the understanding of these functions’ behavior under varying conditions. Key findings include the identification of the increasing failure rate and mean waiting time (MWT) functions, contrasted by the decreasing mean residual life (MRL) function. We provide a thorough parameter estimation framework for EAD within both classical and Bayesian contexts, introducing innovative comparisons across different estimators. Through a Monte Carlo simulation study, we establish the superior accuracy of Bayesian estimators (BEs) over Maximum Likelihood Estimators (MLEs) and Generalized Maximum Likelihood Estimators (GMLEs). These results are not only theoretically significant but also practically impactful, as evidenced by the application of EAD to real-world data, particularly active repair times for an airborne communication transceiver, where EAD outperforms other one- and two-parameter existing models.

Water quality deterioration in reservoirs threats human drinking water safety. It can be influenced by the macro-processes of meteorology, hydrology, and pollution. However, the mechanism of macro-environment affect water quality risk still is unclear. Water quality is a complex system which should be able to adapt to small fluctuation of macro-environment, but turn into deterioration under significant macro-environment oscillation. Shanxi Reservoir in Zhejiang, China, a large reservoir with a total storage capacity of 1.82 billion m3 was used as the study area to test this hypothesis. Monthly macro-environment data from 2014 to 2022 (sample size = 108), including 10 factors of meteorology, hydrology, and pollution, was used to create a Macro-Environment Index (MEI) by Principal Component Analysis. And the water quality data (sample size = 2914) was used to develop a Water Quality Index (WQI) based on Mahalanobis Distance. Bayes methods were used to analyze the relationship between monthly MEI changes (|ΔMEI|) and WQI variations (ΔWQI). Results showed that macro-environment change weakly (|ΔMEI| 0), significantly increasing (P

Mechanistically based non-animal methods for assessing skin sensitization hazard have been developed, but are not considered sufficient, individually, to conclusively define the skin sensitization potential or potency of a chemical. This resulted in the development of defined approaches (DAs), as documented in OECD TG 497, for combining information sources in a prescriptive manner to provide a determination of risk or potency. However, there are currently no DAs within OECD TG 497 that can derive a point of departure (POD) for risk assessment. The Skin Allergy Risk Assessment – Integrated Chemical Environment (SARA-ICE) DA for skin sensitization is a Bayesian statistical model that estimates a human-relevant metric of sensitizer potency, the ED01, an estimate of the human predictive patch test dermal dose at which there is 1% chance of inducing sensitization, which can be used in a risk assessment paradigm. The model accounts for variability of input data and explicitly quantifies uncertainty. SARA-ICE derives the ED01 from a variety of in vitro and in vivo test method data and is built upon historical human, murine, and in vitro test data for 434 chemicals. In addition to the ED01 POD SARA-ICE DA also provides a Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classification probability for GHS subcategories 1A, 1B and not classified (NC). Here we describe the SARA-ICE model and its evaluation, including performance versus benchmark PODs. In addition, via a case study with isothiazolinones (ITs), we demonstrate the utility of SARA-ICE for integrating different data inputs and compare the ED01 for six ITs to existing historical data.

The critical nitrogen (Nc) dilution curve is considered an effective method for crop nitrogen (N) diagnosis, fertilization decision-making, and crop modeling. The Nc dilution curve is typically described using a power function curve (Nc = A1W−A2). This study established a universal Nc dilution curve and evaluated its potential application in flue-cured tobacco (Nicotiana tabacum L.) N diagnosis, yield, and quality regulation. Field experiments were conducted involving three tobacco varieties (Honghuadajinyuan, Yunyan87, and K326) and four N application rates (0 to > 90 kg N ha−1). The study was conducted in Southwest China from 2021 to 2022. The universal Nc curve obtained across all experimental data was 3.45 W−0.39. The parameter A2 was more susceptible to genotype and environment interactions than parameter A1. The uncertainty first rapidly decreased and then remained at a lower value as biomass increased. The relationship between leaf biomass and days after transplantation and accumulated growth degree was robust. The N nutrition index (NNI) and accumulated N deficiency effectively determined leaf N status and N demand, respectively. There was an inverse correlation between the NNI, leaf yield, and between the superior and medium leaf ratios. The quantitative relationship can be used to adjust the synergistic production of tobacco yield and leaf quality.

It is important for researchers to carefully assess the feasibility of a clinical trial prior to the launch of the study. One feasibility aspect that needs to be considered includes whether investigators can expect to successfully achieve the sample size needed for their trial. In this manuscript, we present a Bayesian design in which data collected during a pilot study is used to predict the feasibility of a planned phase III trial. Specifically, we outline a model that predicts a target sample size obtained from the Gamma-Poisson distribution. In a simulation study, we showcase the utility of the proposed design by applying it to a phase III trial designed to assess the efficacy of mycophenolate mofetil in individuals with mild systemic sclerosis. We demonstrate that the predictive nature of the proposed design is particularly useful for rare disease clinical trials and has the potential to greatly increase their efficiency.

We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observational data from NICER and LIGO-Virgo (LV) collaborations. We find that smooth hybrid EOSs are slightly favoured in explaining NS mass-radius relations, particularly for pulsars such as PSR J0030+0451 and PSR J0740+6620. However, this preference is not definitive, as gravitational wave (GW) data does not significantly differentiate between our hybrid and nucleonic models. Our analysis also reveals tensions between older NICER data and recent measurements for PSR J0437−4715, highlighting the need for more flexible EOS models. Through two sampling approaches — one fixing the hadronic EOS set and the other without fixing the same, we demonstrate that the hybrid EOS model can incorporate stiffer EOSs, resulting in a better agreement with NICER data but leading to higher tidal deformability, which is less consistent with GW observations. In some recent publications a parameter dc, related to the trace anomaly and its derivative, is used to indicate the presence of deconfined quark matter. We find that our hadronic model, which does not include phase transition to deconfined matter, under the influence of imposed constraints, is able to predict values below 0.2 for dc at around five times saturation density. The hybrid model goes below this threshold at lower densities under the same conditions.

In this paper, we introduce a new two-parameter lifetime distribution based on the type II Topp-Leone-G family and the inverse length biased exponential distribution which is called the type II Topp-Leone inverse length biased exponential (TIITL-ILBE) distribution. Various statistical properties of the proposed distribution, including the ordinary moments, moment generating function, incomplete moments, conditional moments, vitality and mean residual life functions, moments of the residual and reversed residual lifetime functions and Rényi entropy are derived. The method of maximum likelihood for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under complete samples is investigated. Also, The methods of maximum likelihood and Bayesian for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under joint censored samples is investigated. The real life data set results utilizing that the proposed distribution exhibits greater flexibility and evaluated empirically under complete and joint censored data.

Under-five mortality is a burden on health and economic systems in developing countries. This study used under-five mortality rate (U5MR) data for Tanzania from 1960 to 2020 to predict trends of under-five mortality over the period of 2021 to 2051. Using a Bayesian state space model, it is found that the model is stable in forecasting. Results show that under-five mortality will continue to decline from 48.9 in 2020 to 32.9 in 2030, a decrease of 32.7%. But despite this decrease, Tanzania will likely not meet the Sustainable Development Goal (SDG) for under-five mortality by 2030. Additional efforts by the government through evidence-based interventions should be undertaken to improve child survival by expanding access to health care, especially in rural areas, taking into account local context.

This work examines the impact of Coronavirus disease (COVID-19) on the stock market of Group of Seven (G7) countries during the first wave of COVID-19 using daily data from March, 1st of 2020 to December, 31st of 2020. Focussing on such period, a Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of first wave of COVID-19 on the stock market performance of these G7 countries by employing a Markov Chain Monte Carlo (MCMC) method. We considered an Autoregressive (AR) model with time-varying parameters and a local linear trend model to know if the stock price of these countries during the period of the first wave of COVID-19 is changing over time. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the AR process itself was also changing over time. The stock market of the USA followed by Japan performed better than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.

The Xiaoxing'an Moutains-Sanjiang Plain is currently facing environmental deterioration, economic recession, social development obstruction, population loss, which has been seriously affecting local sustainability. Considering that their solution often requires holistic approaches, which can involve multiple aspects of social-ecological system (SES), it is significant to clarify the evolution of coupling relationship among the subsystems of SES, to identify the obstacle factors that affect the coupling coordination degree (CCD) among them, and to predict the changing trend of their CCD. Basing on the collected panel data, this study established a metrics system to evaluate the CCD among environmental, economic, social, and population subsystems of five prefecture-level cities in the Xiaoxing'an Moutains-Sanjiang Plain. The dynamic laws and characteristics of the CCD were analyzed in spatial and temporal dimensions. The key factors hindering CCD were determined by using obstacle diagnosis model, and the future CCD was predicted based on Bayesian model. It is found that the evaluation values of the environmental, social, and population subsystem as well as the CCD at the region scale showed a fluctuating upward trend, in which the evaluation value of the environmental subsystem increases the most, while the evaluation value of the economic subsystem showed a downward trend; The CCD of 5 cities in studying areas showed different trends of development, and the CCD of Jiamusi City always ranked first, indicating that the CCD of the non-resource-based city with a large population and high GDP tends to be higher than that of other cities; The CCD of 5 cities is spatially “high in the plain area, low in the mountainous area”, and the barycenter of the high CCD area gradually shifted from the northwest to the northeast during the study period; Total volume of foreign trade, total volume of actual use of foreign capital, number of college students per ten thousand people, total retail sale of consumer goods, and harmless treatment rate of domestic garbage are the main obstacle factors metrics for most cities; Environmental state, environmental governance, living standards, opening up, social stability, and population quality are the main obstacle factors at the observation dimension; According to the prediction results, except for Qitaihe, the CCD of other cities in the next 20 years showed a downward trend, and the CCD of Hegang showed the largest decline range and speed. Findings of this study may have important implications to long-term planning and policy formulation for promoting sustainability of the Xiaoxing'an Moutains-Sanjiang Plain and other similar areas.

Rabies perpetuates in Nigeria despite initiatives like the Regional Disease Surveillance System Enhancement Project, with evidence indicating suboptimal canine vaccination rates as a contributing factor. To inform effective planning of mass dog vaccination campaigns, it is crucial to understand the factors associated with variation in canine vaccination rates. We conducted a cross-sectional study in 2022 to understand factors associated with canine vaccination. We used stratified random sampling of the streets and dog-owning households to survey 4162 households from three states and the Federal Capital Territory (FCT). We then built a joint probabilistic model to understand factors associated with dog vaccination and non-vaccination. First, we modelled rabies knowledge as a latent variable indirectly measured with several targeted survey questions. This method allowed a respondent's unobserved understanding of rabies to be estimated using their responses to a collection of survey questions that targeted different aspects of rabies epidemiology and took various possible response distributions (i.e., ordinal, categorical, binary). Second, we modelled factors influencing pet owners' decisions to vaccinate their dogs against rabies and barriers to dog vaccination among dog owners whose dogs were not vaccinated against rabies. Posterior distributions revealed that the probability of dog vaccination was positively associated with the owner's latent knowledge of rabies, civil servant service employment, residence in the FCT, ownership of a single dog, providing care to dogs, and a preference for contemporary treatment following a dog bite. Conversely, non-vaccination was positively associated with private employment, residing in Anambra and Enugu states, owning multiple dogs, allowing dogs to search for leftovers, and a preference for traditional treatment after a dog bite. Cost was the primary barrier against vaccination for dog owners in Anambra and Enugu, while mistrust posed a major challenge for those in the FCT. Owners in areas with veterinary establishments cited cost as a barrier, while those without a veterinary establishment cited access as the primary barrier. Our study underscores the need to enhance rabies knowledge, tailor vaccination campaigns to specific demographics, address financial and access barriers, and combat hesitancy to improve rabies vaccination rates in Nigeria.

Daily and annual maximum wind speed quantiles can be estimated using extreme value theory for any metrological site of interest. These estimates are of vast importance for modelling and predicting maximum wind speed. This paper develops an efficient modelling paradigm of extreme winds by analysing daily and annual maximum wind speed data via frequentist and Bayesian methodologies. For this purpose, the generalized extreme value (GEV) model is used for yearly maxima, and the generalized Pareto distribution (GPD) is used for daily exceedance over a high threshold. In frequentist inference, the parameters of both models are estimated using the maximum likelihood and linear moments method. In contrast, the Bayesian Markov Chain Monte Carlo procedure with the Metropolis–Hasting algorithm is used. In addition, the informative priors for both models are constructed empirically using historical records of wind speed data from five other weather stations of Pakistan and one belonging to India. The results show that the Bayesian modelling provides apparent benefits in terms of improved accuracy in the estimation of the parameters as well as return levels of both distributions. Furthermore, the Bayesian analysis expresses that posterior inference might be affected by the choice of priors used to formulate the informative priors. Overall, based on assessment measures, the GPD fitted through Bayesian informative priors provides an efficient estimation strategy in terms of precision than other frameworks when uncertainty in parameters and return levels are taken into account. Our methodology can be implemented easily to other regions by considering the prior information from the border area stations of other countries (e.g., China, Afghanistan, India, and Iran). Moreover, the return level estimates of the GPD based on informative Bayesian priors are very beneficial in policymaking and wind energy generation engineering for the Thatta region of the country.

ABSTRACT: Anogenital distance (AGD) is a moderately heritable trait that can be measured at a young age that may provide an opportunity to indirectly select for improved fertility in dairy cattle. In this study, we characterized AGD and its genetic and phenotypic relationships with a range of body stature and fertility traits. We measured AGD, shoulder height, body length, and body weight in a population of 5,010 Holstein-Friesian and Holstein-Friesian × Jersey crossbred heifers at approximately 11 mo of age (AGD1). These animals were born in 2018 across 54 seasonal calving, pasture-based dairy herds. A second measure of AGD was collected in a subset of herds (n = 17; 1,956 animals) when the animals averaged 29 mo of age (AGD2). Fertility measures included age at puberty (AGEP), then time of calving, breeding, and pregnancy during the first and second lactations. We constructed binary traits reflecting the animal's ability to calve during the first 42 d of their herd's seasonal calving period (CR42), be presented for breeding during the first 21 d of the seasonal breeding period (PB21) and become pregnant during the first 42 d of the seasonal breeding period (PR42). The posterior mean of sampled heritabilities for AGD1 was 0.23, with 90% of samples falling within a credibility interval (90% CRI) of 0.20 to 0.26, whereas the heritability of AGD2 was 0.29 (90% CRI 0.24 to 0.34). The relationship between AGD1 and AGD2 was highly positive, with a genetic correlation of 0.89 (90% CRI 0.82 to 0.94). Using a GWAS analysis of 2,460 genomic windows based on 50k genotype data, we detected a region on chromosome 20 that was highly associated with variation in AGD1, and a second region on chromosome 13 that was moderately associated with variation in AGD1. We did not detect any genomic regions associated with AGD2 which was measured in fewer animals. The genetic correlation between AGD1 and AGEP was 0.10 (90% CRI 0.00 to 0.19), whereas the genetic correlation between AGD2 and AGEP was 0.30 (90% CRI 0.15 to 0.44). The timing of calving, breeding, and pregnancy (CR42, PB21, and PR42) during first or second lactations exhibited moderate genetic relationships with AGD1 (0.19 to 0.52) and AGD2 (0.46 to 0.63). Genetic correlations between AGD and body stature traits were weak (≤0.16). We conclude that AGD is a moderately heritable trait, which may have value as an early-in-life genetic predictor for reproductive success during lactation.

Background: There is limited evidence to understand what impact, if any, recovery services might have for patients across the socioeconomic spectrum after critical illness. We analysed data from a multicentre critical care recovery programme to understand the impact of this programme across the socioeconomic spectrum. Methods: The setting for this pre-planned secondary analysis was a critical care rehabilitation programme—Intensive Care Syndrome: Promoting Independence and Return to Employment. Data were collected from five hospital sites running this programme. We utilised a Bayesian approach to analysis and explore any possible effect of the InS:PIRE intervention on Health-Related Quality of Life (HRQoL) across the socioeconomic gradient. A Bayesian quantile, non-linear mixed effects regression model, using a compound symmetry covariance structure, accounting for multiple timepoints was utilised. The Scottish Index of Multiple Deprivation (SIMD) was used to measure socioeconomic status and HRQoL was measured using the EQ-5D-5L. Results: In the initial baseline cohort of 182 patients, 55% of patients were male, the median age was 58 yr (inter-quartile range: 50–66 yr) and 129 (79%) patients had two or more comorbidities at ICU admission. Using the neutral prior, there was an overall probability of intervention benefit of 100% (β=0.71, 95% credible interval: 0.34–1.09) over 12 months to those in the SIMD≤3 cohort, and an 98.6% (β=−1.38, 95% credible interval: −2.62 to −0.16) probability of greater benefit (i.e. a steeper increase in improvement) at 12 months in the SIMD≤3 vs SIMD≥4 cohort in the EQ-visual analogue scale. Conclusions: Using multicentre data, this re-analysis suggests, but does not prove, that an integrated health and social care intervention is likely to improve outcomes across the socioeconomic gradient after critical illness, with a potentially greater benefit for those from deprived communities. Future research designed to prospectively analyse how critical care recovery programmes could potentially improve outcomes across the socioeconomic gradient is warranted.

In this paper, we propose exponentiated XLindley (EXL) distribution. The novel model is adaptable due to the mixt shapes of its density and failure rate functions. The following key statistical properties of EXL distribution are derived: quantile function, moments, hazard function, mean residual life, and Rényi entropy. The parameters are estimated using the maximum likelihood, Anderson Darling, Cramer von Misses, maximum product spacing, ordinary and weighted least square estimation procedures. To examine the behavior of the estimate, Monte Carlo simulation is used. Further Bayesian technique is also utilized to estimate the EXL parameters. The traceplot and Geweke diagnostics are used to track the convergence of simulated processes. The applicability of the EXL distribution is demonstrated by three datasets from different domains such as mortality rate due to COVID-19, precipitation in inches, and failure time for repairable items. The proposed distribution provides efficient results as compared to renowned competitive distributions.

In the context of reinforced concrete components in chloride-rich environments, establishing a priori models describing the degradation process of component durability by integrating existing research findings, and updating these prior models based on field inspection data has emerged as a hotspot in predicting the service life of reinforced concrete components. At present, such methods can only update some parameters in the prior models based on the introduction of field inspection results. They cannot achieve the information update of the function expression of the prior models by fusing the comprehensive durability evaluation results of the components. Given this, a non-homogeneous Markov chain model has constructed to replace the multivariate display functions nested recursion in describing the long-term performance degradation process of RC members. By utilizing the time series and state-space discretization properties of the Markov model, the multiple time-varying patterns coupled with numerous control parameters in the recursive formulation are transformed into time-dependent equations for the elements of the Markov model's state transition matrix. At the same time, a Bayesian updating method for the time-dependent equations of the non-homogeneous Markov chain state transition matrix elements is proposed, enabling the dynamic updating of the non-homogeneous Markov model using the overall current state assessment of the RC component, rather than relying on the detection results of a single indicator. From the perspective of simplifying the mathematical model, the paper addresses the issue of information updating in the degradation process. It provides a new technical approach for dynamically updating the long-term performance degradation time series model of components using state evaluation results. Manuscript uses a simulated case study and a real case study to illustrate the application process of the proposed method and to examine the predictive performance of the model. The results indicate that the updated model predictions envelop both the predictions from the prior model and the actual state assessment results, effectively integrating common understanding with individualized information.

Hypertension is characterized by the persistent elevation of blood pressure (BP) above the normal range or the use of antihypertensive medication. It represents a major global health issue and serves as a significant risk factor for conditions such as stroke, myocardial infarction, vascular disease, and chronic kidney disease. This study aimed to model the longitudinal measurements of diastolic blood pressure and systolic blood pressure in hypertensive patients using a Bayesian approach. The data were obtained from the records of Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital. It encompassed the basic demographic and clinical characteristics of 200 hypertensive patients who commenced antihypertensive treatment between September 2015 and March 2020. Descriptive analysis and a Linear mixed-effects model were employed to analyze the data, utilizing the Bayesian approach with the JMbayes package in R software. The results of this study revealed significant associations between age, time, family history, related diseases, and the mean change of systolic blood pressure (SBP) measurements within the bivariate linear mixed model. Likewise, time, residence, related diseases, and the interaction of time with sex and diabetes demonstrated a significant effect on the mean change of diastolic blood pressure (DBP) measurements in antihypertensive patients. By employing a bivariate linear mixed model, an association of 0.8565 was observed between the evolution (AE) of SBP and DBP. Based on the study findings, we can summarize that age, time, family history, and related diseases have a significant impact on the mean change of SBP, while time, residence, related diseases, and the interaction of time with sex and diabetes affect the mean change of DBP. Furthermore, a strong association exists between the evolution of SBP and DBP.

The Kumaraswamy distribution (KD) is widely applied for modeling data in practical domains, such as medicine, engineering, economics, and physics. The present work proposes the Bayesian estimators of KD parameters through the use of type-II censoring data. Both E-Bayesian and Bayesian estimation approaches are briefly described, along with several loss functions, namely, linex loss function (LLF), weighted linex loss function (WLLF), and composite linex loss function (CLLF). In the Bayesian framework, gamma distribution has been utilized as a conjugate distribution in view of finding theoretical results. The E-Bayesian estimators for the hyperparameter using different distributions are developed. Moreover, a novel loss function referred to as the weighted composite loss function (WCLLF) in the estimation perspective is established. Finally, the Monte Carlo simulation approach is carried out to reveal that the new suggested loss function outperforms several counterparts in determining the shape parameter of the KD.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

The coronavirus (COVID-19) pandemic resulted in major disruptions to the food service industry and regulatory food inspections. The objective of this study was to conduct an interrupted time series analysis to investigate the impact of the COVID-19 pandemic on food safety inspection trends in Toronto, Canada. Inspection data for restaurants and take-out establishments were obtained from 2017 to 2022 and summarized as weekly counts of inspections, pass ratings, and total infractions. Bayesian segmented regression was conducted to evaluate the impact of the pandemic on weekly infraction and inspection pass rates. On average, a 0.31-point lower weekly infraction rate (95% credible interval [CI]: 0.23, 0.40) and a 2.0% higher probability of passing inspections (95% CI: 1.1%, 3.0%) were predicted in the pandemic period compared to prepandemic. Models predicted lower infraction rates and higher pass rates immediately following the pandemic, with additional variability compared to the prepandemic period, that were regressing back toward pre-pandemic levels in 2022. Seasonal effects were also identified, with infraction rates highest in April and pass rates lowest in August. The COVID-19 pandemic resulted in an initial positive effect on food safety outcomes in restaurants and take-out food establishments in Toronto, but this effect appears to be temporary. This finding could be due to the beneficial impact of COVID-19 protection measures in these establishments or other factors such as less volume of customers. Additional research is needed to investigate causes of the identified differences as well as seasonal and long-term inspection trends postpandemic. Results can inform future food safety inspection planning, outreach, and pandemic preparedness.

This paper studies the statistical inferences of the unknown parameters of some reliability models using progressively hybrid censoring of type-I and type-II. We apply the maximum likelihood and Bayesian approaches to perform the statistical inferences of the parameters when the data follow Weibull, power Lindley, and Sarhan–Tadj–Hamilton distributions. Bayes method is implemented under the squared error loss function when the parameters are independent and follow gamma prior distributions with known hyperparameters. The maximum likelihood estimates cannot be obtained in an analytic form. Therefore we use the R function “optim” to find those estimates. Also, the joint posterior distribution of the unknown parameters cannot be obtained in closed form. Therefore, we will use the Markov Chain Monte Carlo method to approximate the Bayesian analysis. We constructed the highest posterior density intervals of the model parameters. We analyzed three real data sets for illustration and comparison purposes. We found out that Sarhan–Tadj–Hamilton distribution fits those three real data sets better than Weibull and Power Lindley distribution using both complete and progressively hybrid censored samples. We used expected experimentation time to discuss the optimal test plans. Simulation studies are performed to investigate the proposed methods. Based on the simulation results, we could conclude that Bayesian method does not provide better estimations than the maximum likelihood method.

The unprecedented growth of mobile technology has generated an increase in malware and raised concerns over malware threats. Different approaches have been adopted to overcome the malware attacks yet this spread is still increasing. To combat this issue, this study proposes an Android malware detection system based on permission features using Bayesian classification. The permission features were extracted via the static analysis technique. The 10,000 samples for the judgement were obtained from AndroZoo and Drebin databases. The experiment was then conducted using two algorithms for feature selection: information gain and chi-square. The best accuracy rate of detection of permission features achieved was 91.1%.

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (μ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of −1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice, and thus, it is necessary to establish a useful method to reverse the rockhead profile using site investigation results. As a general method to reflect the spatial distribution of geo-material properties based on field measurements, the conditional random field (CRF) was improved in this paper to simulate rockhead profiles. Besides, in geotechnical engineering practice, measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent. As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty, CRF was implemented with the aid of the Bayesian framework in this study. More importantly, this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work. The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result, the subjectivity in determining prior mean can be minimized. Finally, both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles, while the influence of the latter is less significant than that of the former.

Background: Access to green spaces can provide opportunities for physical activities and social interactions in urban areas during times with strict social distancing. In particular COVID-19 transmission is reduced in ventilated areas. During several waves of the pandemic, this study explores the association between access to urban green spaces and COVID-19 transmission at the district level in Norway's capital, Oslo., Methods: We used daily numbers of confirmed laboratory PCR tests on district levels reported from the second to the fifth wave of the COVID-19 pandemic, from October 15, 2020 to April 15, 2022 in Oslo. We included the population's access to urban green spaces using two objective measurements: percentage of green area (%Ga) and vegetation cover (NDVI) using 300 and 1000 m buffers. The socio-demographic variables percentage of low-income population, average life expectancy and population density were also included. A Bayesian Susceptible-Infected-Removed (SIR) model was used to take advantage of the daily updated data on COVID-19 incidence and account for spatial and temporal dependencies in the statistical analysis., Results: We found that low income as well as population density were significantly associated with incidence of COVID-19, but for the second and third waves only. For the second wave, a one percent increase in the proportion with low income at district level increased the risk of COVID-19 by 7 % (95 % CI: 3 % - 11 %) We did not find associations between access to green space and incidence rate for any of the buffer sizes. The second and third waves were more governed by socio-demographic factors than the fourth and fifth wave., Conclusions: Incidence rate of COVID-19 was not associated with access to green space, but to the socio-demographic variables; income, population density, and life expectancy. Access to green space is equally distributed among districts in Oslo which may explain our findings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Geir Aamodt reports financial support was provided by the Research Council of Norway. Suleman Atique reports financial support was provided the Research Council of Norway. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Harmful algal blooms pose a significant threat to marine ecosystems, aquaculture industries, and human health. To mitigate these risks, agencies around the globe perform regular monitoring and operate early warning systems based on expected risk levels. However, bloom dynamics can be influenced by a large range of physical and biological factors, leading to high uncertainty in predictions of future blooms. Here, we explore the effectiveness of ensemble models for forecasting risk of algal blooms and associated toxins in Scotland, employing a diverse set of candidate models, including tree-based approaches, neural networks, and hierarchical Bayesian regression. These models predicted the probability that algal densities or biotoxin concentrations would exceed a threshold (either 'amber' status in the traffic light guidance system, or 'detection') in the next week using publicly available environmental products combined with regulatory monitoring data from dozens of locations in Scotland (2015-2022; Alexandrium spp., Dinophysis spp., Karenia mikimotoi, Pseudo-nitzschia spp. [+ delicatissima, serriata groups], domoic acid (DA), okadaic acid / dinophysistoxins / pectenotoxins (DSTs), paralytic shellfish toxins (PSTs)). The forecasted probabilities from the candidate models were used as inputs for a stacking ensemble model. Compared to individual candidate and null models, the ensemble models consistently improved forecasting performance across two years of withheld out-of-sample validation data, as assessed by five distinct performance metrics (ensemble skill scores among metrics and targets: mean = 0.499, middle 95 % = 0.214-0.900; skill score give improvement over the null model, with 1 indicating perfect performance). Performance varied by monitoring target, with best forecasts for DSTs (mean ensemble skill: 0.747) and poorest for K. mikimotoi (mean ensemble skill: 0.334). Autoregressive terms and regional spatiotemporal patterns emerged as the most informative predictors, with effects of environmental conditions contingent on the algal density or toxin concentration. Our results demonstrate the clear advantage of the ensemble approach. The operational implementation of these models provides probabilistic forecasts to enhance Scotland's monitoring program and early warning system. Ensemble modelling leverages the combined strengths of the wide array of modern techniques available, offering a promising path toward improved forecasts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models. Combined cross-validation (CV) and bootstrapping (B) methods were used to build six predictive models. The entropy-based coastal groundwater quality index (ECWQI) was converted into a normalized index (ECWQIn), which was divided into five classes from very poor to excellent. The self-organizing map (SOM), spatial autocorrelation and fuzzy logic model were used to identify spatial groundwater quality patterns based on 12 physicochemical variables collected from 67 groundwater wells. The SOM analysis identified four distinct spatial patterns, including EC-TDS-Cl - , MgpH, Ca 2+ K + NO₃ - , and HCO₃ - SO₄ 2- Na + F - . The results showed that both the ANN (CV) and ANN (B) models performed better than other optuna-based models during the test phase (RMSE = 0.041, MAE = 0.026, R2 = 0.971, RAE = 0.15 = 21 and CC = 0.986) and (RMSE = 0.041, MAE = 0.025, R2 = 0.969, RAE = 0.119 and CC = 0.975), respectively. SO 4 2- , Cl - and F - played an important role in the prediction accuracy. F- and SO 4 2- showed higher spatial autocorrelation, which affected groundwater quality degradation. In addition, the ANN (CV) and ANN (B) models showed a Gaussian distribution of model errors (small standard error, <1 %), indicating the stability of the model. These results indicate the efficiency of the ANN model in predicting groundwater quality in coastal areas, which would help regional water managers in real-time monitoring and management of sustainable groundwater resources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Breast cancer is the most commonly diagnosed cancer among women worldwide. While previous studies have reported urban and rural differences in breast cancer outcomes, the level of heterogeneity within these broad regions is currently unknown., Methods: Population-level data from Queensland Cancer Register including 58,679 women aged at least 20 years who were diagnosed with breast cancer in Queensland, Australia, 2000-2019 were linked to BreastScreen Queensland and Queensland Hospital Admitted Patients Data Collection to estimate five breast cancer outcomes: incidence, proportion of localised disease and screen-detected cases (via public-funded program), surgical rates, and 5-year survival. Bayesian spatial models were used to smooth outcomes across 512-517 small areas in Queensland., Results: The incidence of breast cancer was not proportionally distributed, with urban regions having higher rates. Less than half (47 %) of women were diagnosed with localised disease, 91 % had surgery, with five-year relative survival of 92 %. There was no evidence of geographic variation in the proportion of localised disease, surgical rates, or survival over Queensland. Publicly-funded screening detected 38 % of cases, with lower proportion of screen-detected cases observed in Queensland's urbanised south-east corner., Conclusion: Although the disparities in health outcomes faced by Australians living in rural areas have received increased attention, this study found limited evidence for spatial variation in breast cancer outcomes along the continuum of care across Queensland. These results suggest the detection and management practices for breast cancer may provide an achievable benchmark for other cancer types in reducing the geographical disparity in cancer outcomes., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

SPARC facilitates the generation of plausible hypotheses regarding underlying biochemical mechanisms by structurally characterizing protein sequence constraints. Such constraints appear as residues co-conserved in functionally related subgroups, as subtle pairwise correlations (i.e., direct couplings), and as correlations among these sequence features or with structural features. SPARC performs three types of analyses. First, based on pairwise sequence correlations, it estimates the biological relevance of alternative conformations and of homomeric contacts, as illustrated here for death domains. Second, it estimates the statistical significance of the correspondence between directly coupled residue pairs and interactions at heterodimeric interfaces. Third, given molecular dynamics simulated structures, it characterizes interactions among constrained residues or between such residues and ligands that: (a) are stably maintained during the simulation; (b) undergo correlated formation and/or disruption of interactions with other constrained residues; or (c) switch between alternative interactions. We illustrate this for two homohexameric complexes: the bacterial enhancer binding protein (bEBP) NtrC1, which activates transcription by remodeling RNA polymerase (RNAP) containing σ54, and for DnaB helicase, which opens DNA at the bacterial replication fork. Based on the NtrC1 analysis, we hypothesize possible mechanisms for inhibiting ATP hydrolysis until ADP is released from an adjacent subunit and for coupling ATP hydrolysis to restructuring of σ54 binding loops. Based on the DnaB analysis, we hypothesize that DnaB ‘grabs’ ssDNA by flipping every fourth base and inserting it into cavities between subunits and that flipping of a DnaB-specific glutamine residue triggers ATP hydrolysis.

Dengue Hemorrhagic Fever (DHF) is still a threat to humanity that cause death and disability due to changes in environmental and socioeconomic conditions, especially in tropical areas. A critical assessment of the models and methods is necessary. The vital role of stochastic processes of infectious disease research in Bayesian statistical models helps provide an explicit framework for understanding disease transmission dynamics between hosts (humans) and vectors (mosquitoes). This research presents a Bayesian stochastic process for the cross-infection SIR-SI model as a differential equation (susceptibility–infection–recovery for the human population; susceptible–infectious for the vector population) for the dynamic transmission of DHF. Given the difficulties of solving the differential equations precisely, we propose a computational model to approach the solution based on the Runge-Kutta family approach, namely the Euler and the four-order Runge-Kutta methods. The methods used for the discretization process of the SIR-SI model for computational process needs. For comparison purposes, we use a monthly DHF dataset of 10 Kendari, Indonesia, districts from 2019 to 2021. Parameter estimation of the Bayesian SIR-SI model based on the Euler and four-order Runge-Kutta method was updated using Markov Chain Monte Carlo (MCMC) Bayesian. The Euler and four-order Runge-Kutta methods have converged at 10,000 iterations with burn-in 80,000. The numerical simulation results show that the four-order Runge-Kutta approach has the slightest deviance, 106.5. Therefore, this approach is the best one. The relative risk analysis shows that the dynamics of DHF cases fluctuate from January to July every year. However, from January to May, there was a high consistency of DHF cases. Two districts with high case consistency were found, namely Kadia and Wua-Wua. Furthermore, because the spread of DHF cases has a spatial effect, the Kadia and Wua-Wua districts need serious attention to suppress the rate of reach of DHF in Kendari City. Intensive observation and intervention in the Kadia and Wua-Wua districts should be carried out in early January to stop the breeding of mosquito larvae. In other neighbourhoods such as Puwatu, Kambu and Kendari Barat, the DHF cases occur temporally as the impacts of DHF cases in the Kadia and Wua-Wua districts. It may facilitate the statisticians to develop further models to adopt a better understanding to control the DHF dynamically. In brief,

Studying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimate uncertainty. This paper investigates a new approach, fully Bayesian ComBat, where Monte Carlo sampling is used for statistical inference. When comparing fully Bayesian and empirical Bayesian ComBat, we found Empirical Bayesian ComBat more effectively removed scanner strength information and was much more computationally efficient. Conversely, fully Bayesian ComBat better preserved biological disease and age-related information while performing more accurate harmonization on traveling subjects. The fully Bayesian approach generates a rich posterior distribution, which is useful for generating simulated imaging features for improving classifier performance in a limited data setting. We show the generative capacity of our model for augmenting and improving the detection of patients with Alzheimer’s disease. Posterior distributions for harmonized imaging measures can also be used for brain-wide uncertainty comparison and more principled downstream statistical analysis. Code for our new fully Bayesian ComBat extension is available at https://github.com/batmanlab/BayesComBat.

The selection of significant biomarkers is essential in researching cancer diagnosis and treatment. The independence screening method works substantively to select crucial features based on the conditional marginal selection method. But it may pretermit the concoction effect of some marginally less essential covariates. We aim to obtain a significant biomarker-specific prediction on overall survival to know their survival and death risk. In this work, an iterative sure independence screening (ISIS) scheme has been applied to extract features from the high-dimensional dataset of adenocarcinoma lung cancer. Conventional and Bayesian approaches of the Cox proportional hazard (CPH) model have been used for analyzing the data to provide interpretation and conclusions about survival estimates. The accelerated failure time model is also used as an alternative to the CPH model. A forest plot is employed to show the graphical representation of the meta-analysis of the study design. Utilizing ISIS, we selected up to 20 relevant features From the entire dataset of adenocarcinoma lung cancer; some of them are liable to produce a positive hazard ratio greater than 1, and some are less than 1. The P values associated with the selected biomarkers imply their statistical significance. Fourteen biomarkers have been identified with a hazard ratio of less than 1; the remaining 20 biomarkers are greater than 1. These 14 biomarkers produce less risk of death for patients with adenocarcinoma lung cancer, and the remaining six biomarkers result in a high risk of death from adenocarcinoma lung cancer.

One of the goals of coastal ecological research is to describe, quantify and predict human effects on coastal ecosystems. Broad cross-systems assessments to classify ecosystem status or condition have been developed, but are not updated frequently, likely because a lot of information and effort is needed to implement them. Such assessments could be more useful if the probability of being in a class indicating status or condition could be predicted using widely available data and information, providing a useful way to interpret changes in underlying predictors by considering their expected impact on ecosystem condition. To illustrate a possible approach, we used chlorophyll-a as an indicator of condition, in place of the intended comprehensive condition assessment. We demonstrated a predictive approach starting with a random forest model to inform variable selection, then used a Bayesian multilevel ordered categorical regression to quantify a coastal trophic state index and predict system status. We initially fit the model using non-informative priors to water quality data (total nitrogen and phosphorus, dissolved inorganic nitrogen and phosphorus, secchi depth) from 2010 and a regional factor. We then updated the model using prior distributions based on posterior parameter distributions from the initial fit and data from 2015. The Bayesian model demonstrates an intuitive way to update a model or analysis with new data while retaining the benefit of prior knowledge and maintaining flexibility to consider new kinds of information. To illustrate how the model could be used, we applied our developed trophic state index and classification to a time series of water quality data from Boston Harbor, a coastal ecosystem that has undergone significant changes in nutrient inputs. The analysis shows how water quality status and trends in Boston Harbor can be understood in the comparative ecological context provided by data from estuaries around the continental US and illustrates how the analytical approach could be used as an interpretive tool by non-practitioners of Bayesian statistics as well as a framework for further model development and analysis.

Purpose: To investigate spatiotemporal correlations among ganglion cell complex (GCC) superpixel thickness measurements and explore underlying patterns of longitudinal change across the macular region. Design: Longitudinal cohort study. Subjects: One hundred eleven eyes from 111 subjects from the Advanced Glaucoma Progression Study with ≥ 4 visits and ≥ 2 years of follow-up. Methods: We further developed our proposed Bayesian hierarchical model for studying longitudinal GCC thickness changes across macular superpixels in a cohort of glaucoma patients. Global priors were introduced for macular superpixel parameters to combine data across superpixels and better estimate population slopes and intercepts. Main Outcome Measures: Bayesian residual analysis to inspect cross-superpixel correlations for subject random effects and residuals. Principal component analysis (PCA) to explore underlying patterns of longitudinal macular change. Results: Average (standard deviation [SD]) follow-up and baseline 10-2 visual field mean deviation were 3.6 (0.4) years and −8.9 (5.9) dB, respectively. Superpixel-level random effects and residuals had the greatest correlations with nearest neighbors; correlations were higher in the superior than in the inferior region and strongest among random intercepts, followed by random slopes, residuals, and residual SDs. PCA of random intercepts showed a first large principal component (PC) across superpixels that approximated a global intercept, a second PC that contrasted the superior and inferior macula, and a third PC, contrasting inner and nasal superpixels with temporal and peripheral superpixels. PCs for slopes, residual SDs, and residuals were remarkably similar to those of random intercepts. Conclusions: Introduction of cross-superpixel random intercepts and slopes is expected to improve estimation of population and subject parameters. Further model enhancement may be possible by including cross-superpixel random effects and correlations to address spatiotemporal relationships in longitudinal data sets.

Background: Statistical models developed in frequentist and Bayesian context along with machine learning algorithms can encompass the multifactorial effect of the prognostic factors in predicting the outcome. This paper is aimed to compare the effect estimates and predictive performance of Bayesian, frequentist and machine learning algorithm in predicting the two-year mortality of patients diagnosed with squamous cell carcinoma (SCC) of oral cavity. Materials and methods: Logistic regression (LR), Binary Discriminant analysis (BDA), Naïve Bayes (NB), Bayesian regression (BLR), K nearest neighbor (KNN), Artificial neural network (ANN) and Random Forest (RF) models were built. The effect estimate of each prognostic factor was estimated and compared by LR and BLR model. 10-fold cross-validation was performed for internal validation of the models. The predictive performance of the models was assessed and compared. Results: BLR model had lower and narrower effect estimates in comparison to LR model. Age and smoking are the biggest prognostic risk factors for SCC whereas surgery had the best response amongst the mode of treatment. Random forest had an AUROC of 0.86 (0.82, 0.90) whereas it was estimated to be 0.77 (0.71, 0.82) for both BLR and LR models. Conclusions: BLR model had better precision in estimating the effect size of prognostic factors and can be an alternative for predicting mortality in patients with SCC of oral cavity. Machine learning classifiers had the best predictive ability as compared to statistical models.

Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans). Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individual anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory of motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package

Modern social science suggests that fatalistic beliefs are generally detrimental to mental well-being because these beliefs reflect a lack of perceived efficacy and control. However, many religions downplay the role of personal agency and emphasize the importance of external factors that determine people's lives (e.g., God’s will and fate). Thus, individual and cross-cultural differences in the links between fatalism and well-being are to be expected. Using a sample of 38,426 individuals from 34 countries and Bayesian multilevel modeling, this study shows that personal and national religiosity moderate the relationship between fatalism and life satisfaction. Whereas fatalism is negatively associated with life satisfaction in more secular countries and among less religious individuals, it is unrelated to life satisfaction in more religious countries and among highly religious individuals.

Objectives: This study aimed at assessing shared spatial risk of childhood undernutrition indicators in Malawi. Study design: Cross-sectional design. Methods: The shared spatial component model was fitted to childhood undernutrition indicators, namely: stunting, wasting and underweight, using 5066 child records of the 2015/16 Malawi demographic health survey data. The spatial components were districts, and were modeled by the convolution prior, with the structured components being assigned the conditional autoregressive distribution. Results: There is significant clustering of shared spatial risk of stunting and wasting (Moran I = 0.464, p-value = 0.009), and wasting and underweight (Moran I = 0.392, p-value = 0.026), and the risk maps show southern districts, followed by central districts being at greater risk of jointly having stunting and wasting, wasting and underweight, compared to the northern region districts. The shared spatial risk of stunting and underweight is randomly dispersed across the country (Moran I = - 0.044, p-value = 0.539). Conclusion: Interventions to reduce the shared risk of child undernutrition should focus on the southern region districts and those in the central region, and a suggestion is made to address the issue of overpopulation and effects of climate change.

Crude secondary attack rate (SAR) of COVID-19 in Taiwan was 0.84% using nationwide contact-tracing data till April 8, 2020. The random-effect Bayesian metaanalysis yielded 95% credible intervals of 0.42%–1.69% and 0.08%–8.32%, respectively, for estimated SAR pooling from 15 case series and for predicted SAR in the future if pandemic continues.

The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n=45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n=10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.

Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. We recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.

Background: Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the “best” model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision. Objective: By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose–response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress. Methods: The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties. Results: This system was tested and validated versus 24 previously published in-vivo microarray dose–response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78–0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established. Discussion: Overall, this study demonstrates that dose–response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose–response data analysis with outstanding functionalities to quantify uncertainty from various sources.

Water scarcity is a significant constraint in agricultural ecosystems of arid regions, necessitating sustainable development of agricultural water resources. This study innovatively combines Bayesian theory and Water Footprint (WF) to construct a Bayesian Network (BN). Water quantity and quality data were evaluated comprehensively by WF in agricultural production. This evaluation integrates WF and local water resources to establish a sustainability assessment framework. Selected nodes are incorporated into a BN and continuously updated through structural and parameter learning, resulting in a robust model. Results reveal a nearly threefold increase of WF in the arid regions of Northwest China from 1989 to 2019, averaging 189.95 × 10 8  m 3 annually. The region's agricultural scale is expanding, and economic development is rapid, but the unsustainability of agricultural water use is increasing. Blue WF predominates in this region, with cotton having the highest WF among crops. The BN indicates a 70.1 % probability of unsustainable water use. Sensitivity analysis identifies anthropogenic factors as primary drivers influencing water resource sustainability. Scenario analysis underscores the need to reduce WF production and increase agricultural water supply for sustainable development in arid regions. Proposed strategies include improving irrigation methods, implementing integrated water-fertilizer management, and selecting drought-resistant, economically viable crops to optimize crop planting structures and enhance water use efficiency in current agricultural practices in arid regions. This study not only offers insights into water management in arid regions but also provides practical guidance for similar agricultural contexts. The BN model serves as a flexible tool for informed decision-making in dynamic environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Cardiovascular disease (CVD) risk scores provide point estimates of individual risk without uncertainty quantification. The objective of the current study was to demonstrate the feasibility and clinical utility of calculating uncertainty surrounding individual CVD-risk predictions using Bayesian methods., Study Design and Setting: Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2-12.5), a Bayesian Weibull model was derived to predict the 10-year risk of recurrent CVD events., Results: Model coefficients and individual predictions from the Bayesian model were very similar to that of a traditional ('frequentist') model but the Bayesian model also predicted 95% credible intervals (CIs) surrounding individual risk estimates. The median width of the individual 95%CrI was 5.3% (IQR 3.6-6.5) and 17% of the population had a 95%CrI width of 10% or greater. The uncertainty decreased with increasing sample size used for derivation of the model. Combining the Bayesian Weibull model with sampled hazard ratios based on trial reports may be used to estimate individual estimates of absolute risk reduction with uncertainty measures and the probability that a treatment option will result in a clinically relevant risk reduction., Conclusion: Estimating uncertainty surrounding individual CVD risk predictions using Bayesian methods is feasible. The uncertainty regarding individual risk predictions could have several applications in clinical practice, like the comparison of different treatment options or by calculating the probability of the individual risk being below a certain treatment threshold. However, as the individual uncertainty measures only reflect sampling error and no biases in risk prediction, physicians should be familiar with the interpretation before widespread clinical adaption., Competing Interests: Declaration of competing interest The authors report no conflicts of interest relevant to the current study., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Spatiotemporal analysis of out-of-hospital cardiac arrest (OHCA) risk is essential to design targeted public health strategies. Such information is lacking in the state of Queensland and Australia more broadly. Methods: We developed a spatiotemporal Bayesian model accounting for spatial and temporal dimensions, space-time interactions, and demographic factors. The model was fit to data of all OHCA cases attended by paramedics in Queensland between January 2007 and December 2019. Parameter inference was performed using the integrated nested Laplace approximation method. We estimated and thematically mapped area-year risk of OHCA occurrence for all 78 local government areas (LGAs) in Queensland. Results: We observed spatial variability in OHCA risk among the LGAs. Areas in the north half of the state and two areas in the south exhibited the highest risk; whereas OHCA risk was lowest in the west and south west parts of the state. Demographic factors did not have significant impact on the heterogeneity of risk between the LGAs. An overall trend of modestly decreasing risk of OHCA was found. Conclusions: This study identified areas of high OHCA risk in Queensland, providing valuable information to guide public health policy and optimise resource allocation. Further research is needed to investigate the specifics of the areas that may explain their risk profile.

Knowing the uncertainty in a prediction is critical when making expensive investment decisions and when patient safety is paramount, but machine learning (ML) models in drug discovery typically only provide a single best estimate and ignore all sources of uncertainty. Predictions from these models may therefore be over-confident, which can put patients at risk and waste resources when compounds that are destined to fail are further developed. Probabilistic predictive models (PPMs) can incorporate all sources of uncertainty and they return a distribution of predicted values that represents the uncertainty in the prediction. We describe seven sources of uncertainty in PPMs: data, distribution function, mean function, variance function, link function(s), parameters, and hyperparameters. We use toxicity prediction as a running example, but the same principles apply for all prediction models. The consequences of ignoring uncertainty and how PPMs account for uncertainty are also described. We aim to make the discussion accessible to a broad non-mathematical audience. Equations are provided to make ideas concrete for mathematical readers (but can be skipped without loss of understanding) and code is available for computational researchers (https://github.com/stanlazic/ML_uncertainty_quantification).

COVID-19 (SARS COV2 n-corona virus) is the newfangled virus of the coronavirus family. COVID-19 can cause serious illness with symptoms of fever, cold, cough, and respiratory blockage. COVID-19 is a contagious virus, which originated in Wuhan, China. After one month, WHO declared it as a Pandemic due to its rapid spreading. Presently, Indonesia is also facing a hard time controlling the spread. Hence, it is essential to understand the spread rate in Indonesia and to analyze the strategies to minimize the virus spread. The proposed study can be used to assess variations in virus spread both nationally, and sub-nationally. This allows public health officials and policy-makers to track the progress of the outbreak in near real-time using an epidemiologically valid measure.