Bayesian Model Research Articles

Prediction of Cleaning Loss of Rice Wheat Combine Harvester Based on Dynamic Bayesian

The cleaning loss rate is a crucial performance metric for rice-wheat combine harvesters. Most studies on the relationship between the cleaning operation parameters of the combined harvester and the cleaning loss rate lack research on the dynamic correlation between harvest losses and operational parameters, specifically focusing on the dynamic correlation between harvest losses and multiple operational parameters such as the cleaning loss rate. In this paper, we formulate a dynamic deductive model for the scenario of harvesting loss regulation involving multiple operational parameters, delving into the dynamic associations between cleaning loss and the regulation of multiple operational parameters and constructing dynamic Bayesian network prediction model. Through experimental analysis of the dynamic Bayesian network prediction model for cleaning loss rates, the comparison of results among the other three algorithms demonstrated that it was efficient for our DBN to predict the cleaning loss rate.

Variations and trade-offs in leaf and culm functional traits among 77 woody bamboo species

BackgroundWoody bamboos are the only diverse large perennial grasses in mesic-wet forests and are widely distributed in the understory and canopy. The functional trait variations and trade-offs in this taxon remain unclear due to woody bamboo syndromes (represented by lignified culm of composed internodes and nodes). Here, we examined the effects of heritable legacy and occurrence site climates on functional trait variations in leaf and culm across 77 woody bamboo species in a common garden. We explored the trade-offs among leaf functional traits, the connection between leaf nitrogen (N), phosphorus (P) concentrations and functional niche traits, and the correlation of functional traits between leaves and culms.ResultsThe Bayesian mixed models reveal that the combined effects of heritable legacy (phylogenetic distances and other evolutionary processes) and occurrence site climates accounted for 55.10–90.89% of the total variation among species for each studied trait. The standardized major axis analysis identified trade-offs among leaf functional traits in woody bamboo consistent with the global leaf economics spectrum; however, compared to non-bamboo species, the woody bamboo exhibited lower leaf mass per area but higher N, P concentrations and assimilation, dark respiration rates. The canonical correlation analysis demonstrated a positive correlation (ρ = 0.57, P-value < 0.001) between leaf N, P concentrations and morphophysiology traits. The phylogenetic principal components and trait network analyses indicated that leaf and culm traits were clustered separately, with leaf assimilation and respiration rates associated with culm ground diameter.ConclusionOur study confirms the applicability of the leaf economics spectrum and the biogeochemical niche in woody bamboo taxa, improves the understanding of woody bamboo leaf and culm functional trait variations and trade-offs, and broadens the taxonomic units considered in plant functional trait studies, which contributes to our comprehensive understanding of terrestrial forest ecosystems.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Spatiotemporal trends and covariates of Lyme borreliosis incidence in Poland, 2010–2019

Lyme borreliosis (LB) is the most commonly diagnosed tick-borne disease in the northern hemisphere. Since an efficient vaccine is not yet available, prevention of transmission is essential. This, in turn, requires a thorough comprehension of the spatiotemporal dynamics of LB transmission as well as underlying drivers. This study aims to identify spatiotemporal trends and unravel environmental and socio-economic covariates of LB incidence in Poland, using consistent monitoring data from 2010 through 2019 obtained for 320 (aggregated) districts. Using yearly LB incidence values, we identified an overall increase in LB incidence from 2010 to 2019. Additionally, we observed a large variation of LB incidences between the Polish districts, with the highest risks of LB in the eastern districts. We applied spatiotemporal Bayesian models in an all-subsets modeling framework to evaluate potential associations between LB incidence and various potentially relevant environmental and socio-economic variables, including climatic conditions as well as characteristics of the vegetation and the density of tick host species. The best-supported spatiotemporal model identified positive relationships between LB incidence and forest cover, the share of parks and green areas, minimum monthly temperature, mean monthly precipitation, and gross primary productivity. A negative relationship was found with human population density. The findings of our study indicate that LB incidence in Poland might increase as a result of ongoing climate change, notably increases in minimum monthly temperature. Our results may aid in the development of targeted prevention strategies.

Drivers and spatial patterns of avian defaunation in tropical forests

AbstractAimWildlife overexploitation, either for food consumption or for the pet trade, is one of the main threats to bird species in tropical forests. Yet, the spatial distribution and intensity of harvesting pressure on tropical birds remain challenging to quantify. Here, we identify the drivers of hunting‐induced declines in bird abundance and quantify the magnitude and the spatial extent of avian defaunation at a pantropical scale.LocationPantropical.MethodsWe compiled 2968 abundance estimates in hunted and non‐hunted sites across the tropics spanning 518 bird species. Using a Bayesian modelling framework, we fitted species' abundance response ratios to a set of drivers of hunting pressure and species traits. Subsequently, we applied our model to quantify the spatial patterns of avian defaunation across tropical forests and to assess avian defaunation across biogeographic realms, and for species captured for the pet trade or for food consumption.ResultsBody mass and its interactions with hunter accessibility and proximity to urban markets were the most important drivers of hunting‐induced bird abundance declines. We estimated a mean abundance reduction of 12% across the tropics for all species, and that 43% of the extent of tropical forests harbour defaunated avian communities. Large‐bodied species and the Indomalayan realm displayed the greatest abundance declines. Further, moderate to high levels of defaunation extended over 24% of the pantropical forest area, with distinct spatial patterns for species captured for the pet trade (Brazil, China and Indonesia) and for food consumption (SE Asia and West Africa).Main ConclusionsOur study emphasizes the role of hunter accessibility and the proximity to urban markets as major drivers of bird abundance declines due to hunting and trapping. We further identified hotspots where overexploitation has detrimental effects on tropical birds, encompassing local extinction events, thus underscoring the urgent need for conservation efforts to address unsustainable exploitation for both subsistence and trade.

Association between hydrometeorological conditions and infectious diarrhea in mainland China: a spatiotemporal modeling study

Infectious diarrhea imposes substantial disease burden in China, with local climate identified as a key risk factor. In this study, we aimed to explore the association between hydrometeorological conditions and the incidence of infectious diarrhea, and investigate the modification effect of urbanization, which remained unclear previously. Monthly data were collected from 1 January 2021 to 31 December 2022 from each city of mainland China on cases of infectious diarrhea other than cholera, dysentery, typhoid and paratyphoid (i.e. other infectious diarrhea, OID), temperature and precipitation. We used Palmer drought severity index (PDSI) to represent local hydrometeorological conditions (dry and wet). A spatiotemporal Bayesian hierarchical model combined with distributed lag nonlinear model was specified to explore the association between PDSI and the relative risk of OID. The effect size of hydrometeorological conditions at different urbanization levels were quantified through a linear interaction term. A total of 1999 947 OID cases were included. There was a N-shaped cumulative association between PDSI and OID incidence over a maximum lag of 4 months. The effect of extreme dry conditions persisted over lag 1–3 months and peaked at the 2-month lag (RR = 2.67, 95%CI: 2.44–2.91). By contrast, the relative risk of OID increased immediately under extreme wet conditions (RR = 1.97, 95%CI: 1.68–2.32 at lag 0 month). Children and the older population were particularly susceptible to extreme dry and wet conditions, respectively. Areas with lower urbanization rate, healthcare resources and economic levels, and higher population density showed a higher risk of OID related to extreme dry conditions. While the modification effects of these urbanization characteristics were limited for extreme wet conditions. There was a nonlinear and lagged association between hydrometeorological conditions and the incidence of OID in mainland China, which may be modified in diverse patterns by urbanization indicators. Our findings will provide valuable reference for better prevention and control of OID.

A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease

AbstractNearly 15% (37 million) of adults in the United States (US) have chronic kidney disease (CKD). The longitudinal decline of kidney function is intricately related to the development of cardiovascular disease (CVD) and eventual “terminal” event (kidney failure and mortality) in patients with CKD. Understanding the mechanism and risk factors underlying the three key outcome processes, (1) CKD progression, (2) CVD, and (3) subsequent terminal event in the CKD patient population remains incomplete. Thus, in this work, we develop a novel trivariate joint model to study the risk factors associated with the interdependent outcomes of kidney function (as measured by longitudinal estimated glomerular filtration rate), recurrent cardiovascular events, and the terminal event. Efficient estimation and inference is proposed within a Bayesian framework using Markov Chain Monte Carlo and Bayesian P-splines for hazard functions. The proposed Bayesian framework is directly generalizable beyond trivariate outcome processes to accommodate other potential modeling of complex multi-disease processes. The method is applied to study the aforementioned trivariate processes using data from the Chronic Renal Insufficiency Cohort Study, an ongoing prospective cohort study, established by the National Institute of Diabetes and Digestive and Kidney Diseases to address the rising epidemic of CKD in the US.

Machine learning with electrocardiograms to optimize mortality risk stratification in patients with suspected acute coronary syndrome

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute (NHLBI) and National Center for Advancing Translational Sciences (NCATS). Background The importance of risk stratification in patients with suspected acute coronary syndrome (ACS) extends beyond diagnosis and immediate treatment. It influences the precision of care delivery and the allocation of resources to those at highest risk of adverse events. Purpose We sought to evaluate the prognostic value of electrocardiogram (ECG) feature-based machine learning models to risk-stratify long-term mortality in those with suspected ACS. Methods This was a multicenter prospective observational cohort study of consecutive, non-traumatic patients evaluated at the emergency department for suspected ACS. The derivation cohort included 4,015 patients from a University Medical Center (age 59±16 years, 47% women, 80% training with 10-fold cross-validation and 20% testing). Ascertainment of all-cause death was based on numerous data sources, including the Centers for Disease Control and Prevention’s National Death Index registry. We trained six machine learning methods for survival analysis using 73 morphological ECG features from presenting prehospital 10-second 12-lead ECGs. We used predicted risk scores in a variational Bayesian Gaussian mixture model to define three risk groups corresponding to low, moderate, and high-risk and compared the resulting classification performance against the HEART score. The external testing cohort included 3,095 patients from a University (age 59±15 years, 44% women). Results The mortality rate was 20.3% in the derivation cohort after an average follow-up period of 3.53 years (IQR 1.75 - 5.32). Extra Survival Trees outperformed other forecasting models during cross-validation. In the internal testing cohort, the derived risk groups were significantly predictive of survival (Kaplan-Meier log-rank test statistic = 121.14, p&amp;lt;0.001), outperforming the classification performance using the HEART score (Figure 1). Our machine learning-based risk stratification model detected &amp;gt;90% of death events missed by low-risk HEART score (Figure 2). Ruling-out the low-risk group, it achieved a negative predictive value of 93.4% with a sensitivity of 85.9% (compared to 89.0% and 75.0%, respectively, for the HEART score). This classification performance generalized well to our external testing cohort. Compared to those in the low-risk group, patients at moderate (OR = 3.62 [1.35-9.74]) and high (OR = 6.12 [2.38-15.75]) risk had significantly higher odds of 30-day cardiovascular death (n=109, 3.5%). Conclusions Using only features from the 10-second 12-lead ECG, we derived and externally validated a machine learning model that stratifies the mortality risk of patients during long-term follow up. This model outperformed current standard of care based on the HEART score. Future work should validate the potential of this decision-support tool in influencing treatment plans and resource allocation.Figure 1.Clustering performanceFigure 2.Sankey diagram

Associations between exposure to phthalates and liver function among women undergoing assisted reproductive technology

Phthalates can induce hepatotoxicity in animal studies. We aimed to assess the associations of individual and mixture of urinary phthalate metabolites with serum liver function indicators among 764 women undergoing assisted reproductive technology (ART). In linear models, we observed inverse correlations between urinary mono-benzyl phthalate and serum total protein (TP) as well as globulin (β=–0.27 and –0.23, respectively, P<0.05). Additionally, negative associations were identified between mono-isobutyl phthalate and mono-butyl phthalate (MBP) and aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) (P<0.05). MBP and the sum of all phthalate metabolites (∑all.phth.m) were positively associated with bilirubin, with β ranging from 0.14 to 0.47. Most phthalate metabolites were also positively related to gamma-glutamyl transferase (GGT) (all P<0.05). In Bayesian kernel machine regression models, phthalate mixture was positively associated with bilirubin and GGT, whereas inversely associated with AST/ALT and TP. Our results suggest that phthalate exposure may impair liver function among women undergoing ART.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies.

Land-use homogenization reduces the occurrence and diversity of frugivorous birds in a tropical biodiversity hotspot.

Understanding how human-modified landscapes maintain biodiversity and provide ecosystem services is crucial for establishing conservation practices. Given that responses to land-use are species-specific, it is crucial to understand how land-use changes may shape patterns of species diversity and persistence in human-modified landscapes. Here, we used a comprehensive data set on bird distribution from the Brazilian Atlantic Forest to understand how species richness and individual occurrences of frugivorous bird species responded to land-use spatial predictors and, subsequently, assess how ecological traits and phylogeny modulated these responses. Using Bayesian hierarchical modeling, we reveal that the richness of frugivorous birds was positively associated with the amount of native forest and negatively with both agriculture and pasture amount at the landscape scale. Conversely, the effect of these predictors on species occurrence and ecological traits was highly variable and presented a weak phylogenetic signal. Furthermore, land-use homogenization (i.e., the conversion of forest to pasture or agriculture) led to pervasive consequences for forest-dependent bird species, whereas several generalist species thrived in deforested areas, replacing those sensitive to habitat disturbances.

Network meta-analysis for an ordinal outcome when outcome categorization varies across trials

BackgroundBinary outcomes are likely the most common in randomized controlled trials, but ordinal outcomes can also be of interest. For example, rather than simply collecting data on diseased versus healthy study subjects, investigators may collect information on the severity of disease, with no disease, mild, moderate, and severe disease as possible levels of the outcome. While some investigators may be interested in all levels of the ordinal variable, others may combine levels that are not of particular interest. Therefore, when research synthesizers subsequently conduct a network meta-analysis on a network of trials for which an ordinal outcome was measured, they may encounter a network in which outcome categorization varies across trials.MethodsThe standard method for network meta-analysis for an ordinal outcome based on a multinomial generalized linear model is not designed to accommodate the multiple outcome categorizations that might occur across trials. In this paper, we propose a network meta-analysis model for an ordinal outcome that allows for multiple categorizations. The proposed model incorporates the partial information provided by trials that combine levels through modification of the multinomial likelihoods of the affected arms, allowing for all available data to be considered in estimation of the comparative effect parameters. A Bayesian fixed effect model is used throughout, where the ordinality of the outcome is accounted for through the use of the adjacent-categories logit link.ResultsWe illustrate the method by analyzing a real network of trials on the use of antibiotics aimed at preventing liver abscesses in beef cattle and explore properties of the estimates of the comparative effect parameters through simulation. We find that even with the categorization of the levels varying across trials, the magnitudes of the biases are relatively small and that under a large sample size, the root mean square errors become small as well.ConclusionsOur proposed method to conduct a network meta-analysis for an ordinal outcome when the categorization of the outcome varies across trials, which utilizes the adjacent-categories logit link, performs well in estimation. Because the method considers all available data in a single estimation, it will be particularly useful to research synthesizers when the network of interest has only a limited number of trials for each categorization of the outcome.

Trends and projections of age-appropriate vaccination coverage in 41 low- and middle- income countries in Asia and Sub-Saharan Africa, 2000–2030

IntroductionRoutine immunization programs have focused on increasing vaccination coverage, which is equally important for decreasing vaccine-preventable diseases (VPDs), particularly in low- and lower-middle-income countries (LMICs). We estimated the trends and projections of age-appropriate vaccination coverage at the regional and national levels, as well as place of residence and wealth index in LMICs.MethodsIn total, 174 nationally representative household surveys from 2000 to 2020 from 41 LMICs were included in this study. Bayesian hierarchical regression models were used to estimate trends and projections of age-appropriate vaccination.ResultsThe trend in coverage of age-appropriate Bacillus Calmette-Guérin (BCG), third dose of diphtheria, tetanus, and pertussis (DTP3), third dose of polio (polio3), and measles-containing vaccine (MCV) increased rapidly from 2000 to 2020 in LMICs. Findings indicate substantial increases at the regional and national levels, and by area of residence and socioeconomic status between 2000 and 2030. The largest rise was observed in East Africa, followed by South and Southeast Asia. However, out of the 41 countries, only 10 countries are estimated to achieve 90% coverage of the BCG vaccine by 2030, five of DTP3, three of polio3, and none of MCV. Additionally, by 2030, wider pro-urban and -rich inequalities are expected in several African countries.ConclusionSignificant progress in age-appropriate vaccination coverage has been made in LMICs from 2000 to 2020. Despite this, projections show many countries will not meet the 2030 coverage goals, with persistent urban–rural and socioeconomic disparities. Therefore, LMICs must prioritize underperforming areas and reduce inequalities through stronger health systems and increased community engagement to ensure high coverage and equitable vaccine access.

Enhanced climate projections over Sindh, Pakistan: a bayesian model averaging ensemble methodology

Enhanced climate projections over Sindh, Pakistan: a bayesian model averaging ensemble methodology

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

Most quantifiers have many meanings.

In this paper, we investigate, by means of a computational model, how individuals map quantifiers onto numbers and how they order quantifiers on a mental line. We selected five English quantifiers (few, fewer than half, many, more than half, and most) which differ in truth conditions and vagueness. We collected binary truth value judgment data in an online quantifier verification experiment. Using a Bayesian three-parameter logistic regression model, we separated three sources of individual differences: truth condition, vagueness, and response error. Clustering on one of the model's parameter that corresponds to truth conditions revealed four subgroups of participants with different quantifier-to-number mappings and different ranges of the mental line of quantifiers. Our findings suggest multiple sources of individual differences in semantic representations of quantifiers and support a conceptual distinction between different types of imprecision in quantifier meanings. We discuss the consequence of our findings for the main theoretical approaches to quantifiers: the bivalent truth-conditional approach and the fuzzy logic approach. We argue that the former approach neither can explain inter-individual differences nor intra-individual differences in truth conditions of vague quantifiers. The latter approach requires further specification to fully account for individual differences demonstrated in this study.

History of traumatic brain injury is associated with increased grey-matter loss in patients with mild cognitive impairment.

To investigate whether a history of traumatic brain injury (TBI) is associated with greater long-term grey-matter loss in patients with mild cognitive impairment(MCI). 85 patients with MCI were identified, including 26 with a previous history of traumatic brain injury (MCI[TBI-]) and 59 without (MCI[TBI+]). Cortical thickness was evaluated by segmenting T1-weighted MRI scans acquired longitudinally over a 2-year period. Bayesian multilevel modelling was used to evaluate group differences inbaseline cortical thickness and longitudinalchange, as well as group differences in neuropsychological measures of executive function. At baseline, theMCI[TBI+] group had less grey matter within right entorhinal, left medial orbitofrontal and inferior temporal cortexareas bilaterally. Longitudinally, theMCI[TBI+] group also exhibited greater longitudinal declines in left rostral middle frontal, the left caudal middle frontal and left lateral orbitofrontalareas sover the span of 2years (median = 1-2%, 90%HDI [-0.01%: -0.001%], probability of direction(PD) = 90-99%). The MCI[TBI+] group also displayedgreater longitudinal declines in Trail-Making-Test (TMT)-derived ratio (median: 0.737%, 90%HDI: [0.229%: 1.31%], PD = 98.8%) and differences scores (median: 20.6%, 90%HDI: [-5.17%: 43.2%], PD = 91.7%). Our findings support the notion that patients with MCI and a history of TBI are at risk of accelerated neurodegeneration, displayinggreatest evidence for cortical atrophy within the left middle frontal and lateral orbitofrontal frontal cortex. Importantly, these results suggest that long-term TBI-mediated atrophy is more pronounced in areas vulnerable to TBI-related mechanical injury, highlighting their potential relevance for diagnostic forms of intervention in TBI.

BAYESIAN NETWORKS AND STRUCTURAL EQUATION MODELLING TO INVESTIGATE THE PASSENGERS’ PERCEPTIONS IN HIGH-SPEED RAIL SYSTEMS

Ensuring sustainability in the global world today depends on perception management as well as financial management. In order to manage the perceptions, which are inherently latent variables as they are measured indirectly through their indicators, they must be accurately handled and modelled comprehensively. In the present study, a hybrid technique combining Bayesian Networks (BN) and Structural Equation Modelling (SEM), which are regarded as causal models, was used to investigate the perceptions of High-Speed Rail System (HSRS) passengers. In order to provide insight into the customer retention strategy for HSRS, the analyses were performed on the survey data gathered from the frequent users of HSRS operating between 2 cities of Turkey. After the measurement model of the perception variables through SEM was established, the relationships between the variables were learned using BN knowledge extraction algorithms. As a result, relationships from image to trust and loyalty, from trust to perceived value, from perceived value to satisfaction, and from satisfaction to loyalty were determined. Final interpretations were made in terms of risk management with the help of the probabilistic predictive ability of the BN by setting evidence on the satisfaction levels of the perceptions.

Impact of Sharing Imprecise Information on Fixed-Bin Selective Assembly

Fixed-Bin Selective Assembly (FBSA) is widely used to improve the assembly quality of single units of different component types. FBSA has parts with similar dimensions, but with slight deviations due to production variances, sorted into pre-determined bins. A selection is made from paired bins for assembly. To ensure enough components, assemblers adopt a proactive or reactive strategy. For the proactive, predictions of component usage quantities are used to adjust processes for the internally manufactured matched component prior to production. With the reactive, component inventory levels are adjusted after assembly. The performance of each can be improved with information about incoming part dimensions. However, the communicated information may be imprecise. We developed a Bayesian Measurement Error Model to evaluate the impact of imprecision, in the supplier’s communicated information, on the efficacy of FBSA with either strategy. The model was tested with parameters obtained from a US assembler of combustion engines. We find that in most scenarios, the proactive approach results in better FBSA efficacy on average. A key exception was when imprecise information about the mean was communicated, resulting in no significant difference. The reactive approach is typically easier to implement, therefore it may be preferred when communicated information about the mean of incoming part dimensions is often inaccurate.

New statistical framework for interlaboratory evaluation of anti-doping testing results by WADA

AbstractThe World Anti-doping Agency (WADA) International Standard for Laboratories (ISL), developed as part of the World Anti-Doping Program, requires satisfactory laboratory performance in the WADA External Quality Assessment Scheme (EQAS) in order to obtain and maintain WADA accreditation. Under this mandate, WADA regularly distributes urine and blood test samples to anti-doping laboratories to continuously monitor their proficiency. Over the years, WADA has employed classical, generic statistical methods, in accordance to ISO 13528, to evaluate quantitative EQAS results. Here, we set out the rationale for a modern statistical approach that recognizes and addresses the particular features of the measurement results typically obtained in such tests and present an approach involving Bayesian measurement models and statistical data analysis that is tailored specifically to anti-doping testing.