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It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality.

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

The Gulf Stream is a vital limb of the North Atlantic circulation that influences regional climate, sea level, and hurricane activity. Given the Gulf Stream's relevance to weather and climate, many studies have attempted to estimate trends in its volumetric transport from various data sets, but results have been inconclusive, and no consensus has emerged whether it is weakening with climate change. Here we use Bayesian analysis to jointly assimilate multiple observational data sets from the Florida Straits to quantify uncertainty and change in Gulf Stream volume transport since 1982. We find with virtual certainty (probability P > 99%) that Gulf Stream volume transport through the Florida Straits declined by 1.2 ± 1.0 Sv in the past 40 years (95% credible interval). This significant trend has emerged from the data set only over the past ten years, the first unequivocal evidence for a recent multidecadal decline in this climate‐relevant component of ocean circulation. Plain Language Summary The Gulf Stream is a major ocean current located off the East Coast of the United States. It carries a tremendous amount of seawater and along with it heat, carbon, and other ocean constituents. Because of this, the Gulf Stream plays an important role in weather and climate, influencing phenomena as seemingly unrelated as sea level along coastal Florida and temperature and precipitation over continental Europe. Given how important this ocean current is to science and society, scientists have tried to determine whether the Gulf Stream has undergone significant changes under global warming, but so far, they have not reached a firm conclusion. Here we report our effort to synthesize available Gulf Stream observations from the Florida Straits near Miami, and to assess whether and how the Gulf Stream transport there has changed since 1982. We conclude with a high degree of confidence that Gulf Stream transport has indeed slowed by about 4% in the past 40 years, the first conclusive, unambiguous observational evidence that this ocean current has undergone significant change in the recent past. Future studies should try to identify the cause of this change. Key Points Gulf Stream volume transport through Florida Straits declined by 1.2 ± 1.0 Sv during the past 40 years (95% credible interval) We find a weakening trend in the Gulf Stream by applying Bayesian methods to synthesize cable, in situ, and satellite data sets congruently

Clinical intelligence about a patient’s risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models—one for each event—and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

Robust scientific inference is crucial to ensure evidence-based decision making. Accordingly, the selection of appropriate statistical tools and experimental designs is integral to achieve accuracy from data analytical processes. Environmental monitoring of water quality has become increasingly common and widespread as a result of technological advances, leading to an abundance of datasets. We conducted a scoping review of the water quality literature and found that correlation and linear regression are by far the most used statistical tools. However, the accuracy of inferences drawn from ordinary least squares (OLS) techniques depends on a set of assumptions, most prominently: (a) independence among observations, (b) normally distributed errors, (c) equal variances of errors, and (d) balanced designs. Environmental data, however, are often faced with temporal and spatial dependencies, and unbalanced designs, thus making OLS techniques not suitable to provide valid statistical inferences. Generalized least squares (GLS), linear mixed-effect models (LMMs), and generalized linear mixed-effect models (GLMMs), as well as Bayesian data analyses, have been developed to better tackle these problems. Recent progress in the development of statistical software has made these approaches more accessible and user-friendly. We provide a high-level summary and practical guidance for those statistical techniques.

Background Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() for censored data misspecifies the default computation of deviance function, which limits likelihood-based Bayesian model comparison. Results To establish an automatic approach to specifying the correct deviance function in JAGS, we propose a simple and generic alternative modeling strategy for the analysis of censored outcomes. The two illustrative examples demonstrate that the alternative strategy not only properly draws posterior samples in JAGS, but also automatically delivers the correct deviance for model assessment. In the survival data application, our proposed method provides the correct value of mean deviance based on the exact likelihood function. In the drug safety data application, the deviance information criterion and penalized expected deviance for seven Bayesian models of censored data are simultaneously computed by our proposed approach and compared to examine the model performance. Conclusions We propose an effective strategy to model censored data in the Bayesian modeling framework in JAGS with the correct deviance specification, which can simplify the calculation of popular Kullback–Leibler based measures for model selection. The proposed approach applies to a broad spectrum of censored data types, such as survival data, and facilitates different censored Bayesian model structures.

We present the new R package instrument to perform Bayesian estimation of person explanatory multidimensional item response theory. The package implements an exploratory multidimensional item response theory model and a higher-order multidimensional item response theory model, a type of confirmatory multidimensional item response theory. Explanation of person parameters is accomplished by fixed and random effect linear regression models. Estimation is carried out using Hamiltonian Monte Carlo in Stan. In this article, we provide a detailed description of the models; we use the instrument package to demonstrate fitting explanatory item response models with fixed and random effects (i.e., mixed modeling) of person parameters in R; and, we perform a simulation study to evaluate the performance of our implementation of the models.

When evaluating findings of trace-trace or trace-reference fibre comparisons that involve materials recovered from frequented environments, the probabilities of the secondary or higher transfer of these fibres need to be considered. Such a complex process of transfer can be roughly divided into three main parts, the acquisition of these fibres from the environment, followed by their persistence, and finally their re-transfer during the event of interest. The first step was explored in a previous article, while existing literature is already rich in data on fibre persistence. This study focuses on the third step of the process, particularly in assault scenarios. Two participants with known rates of fibre acquisition from their domicile environment were recruited to wear six shirts of two levels of surface roughness for eight hours in their home. These shirts (intermediates) were then collected and worn by combat athletes during an assault simulation by the volunteer playing the role of assailant. Meanwhile, the recipient wore a standardized blanked white t-shirt (recipients). Both sets of shirts were collected and analysed for target fibres identified from the home environment of the two participants. The quantity of these target fibres on each of these shirts were recorded and compared to the previously recorded acquisition rate of the two participants. Surface roughness was found to play a role in transfer, where a greater share of fibres was retained on the rougher intermediates. The class of fibre did not appear to influence the quantity transferred. Finally, the results were used to establish posterior models in four different scenarios that describe varying levels of information available to the forensic expert. These models could be roughly classified into count-based, which involved a direct modelling of the number of fibres transferred with a negative binomial-beta conjugate, and proportion-based, involving first the modelling of the acquisition using a negative binomial distribution, and the subsequent transfer with a binomial distribution, using the results of the first model as the size and a beta prior distribution to model the proportion transferred. Proportion-based models outperformed the count-based ones, and increasing levels of information permitted increased specificity towards each scenario. [Display omitted] •T-shirts were collected after 8 h of wear in the home environment.•Collected shirts were worn during a 90 s assault simulation by combat athletes.•Target fibres from the home environment were counted on recipient and intermediate.•Surface roughness of the intermediate was found to have a strong effect.•A two-step model of acquisition followed by transfer was found to be effective.

Quality control of geometric shape deviation in additive manufacturing relies on statistical deviation models. However, resource constraints limit the manufacture of test shapes, and consequently impede the specification of deviation models for new shape varieties. We present an adaptive Bayesian methodology that effectively combines in-plane deviation data and models for a small sample of previously manufactured, disparate shapes to aid in the model specification of in-plane deviation for a broad class of new shapes. The power and simplicity of this general methodology is demonstrated with illustrative case studies on in-plane deviation modeling for polygons and straight edges in free-form shapes using only data and models for cylinders and a single regular pentagon. Our Bayesian approach facilitates deviation modeling in general, and thereby can help advance additive manufacturing as a high-quality technology. Supplementary materials for this article are available online.

Investigating the relationship between task‐related hemodynamic responses and cortical excitability is challenging because it requires simultaneous measurement of hemodynamic responses while applying noninvasive brain stimulation. Moreover, cortical excitability and task‐related hemodynamic responses are both associated with inter‐/intra‐subject variability. To reliably assess such a relationship, we applied hierarchical Bayesian modeling. This study involved 16 healthy subjects who underwent simultaneous Paired Associative Stimulation (PAS10, PAS25, Sham) while monitoring brain activity using functional Near‐Infrared Spectroscopy (fNIRS), targeting the primary motor cortex (M1). Cortical excitability was measured by Motor Evoked Potentials (MEPs), and the motor task‐related hemodynamic responses were measured using fNIRS 3D reconstructions. We constructed three models to investigate: (1) PAS effects on the M1 excitability, (2) PAS effects on fNIRS hemodynamic responses to a finger tapping task, and (3) the correlation between PAS effects on M1 excitability and PAS effects on task‐related hemodynamic responses. Significant increase in cortical excitability was found following PAS25, whereas a small reduction of the cortical excitability was shown after PAS10 and a subtle increase occurred after sham. Both HbO and HbR absolute amplitudes increased after PAS25 and decreased after PAS10. The probability of the positive correlation between modulation of cortical excitability and hemodynamic activity was 0.77 for HbO and 0.79 for HbR. We demonstrated that PAS stimulation modulates task‐related cortical hemodynamic responses in addition to M1 excitability. Moreover, the positive correlation between PAS modulations of excitability and hemodynamics brought insight into understanding the fundamental properties of cortical function and cortical excitability. We showed a high probability of positive correlations between cortical excitability and task‐related hemodynamic responses. This study also demonstrated the power of the Bayesian data analysis dealing with relatively high variability and small sample size data while providing informative inferences.