A note on Stokes' problem in dense granular media using the μ(I)\mu(I)--rheology

The classical Stokes' problem describing the fluid motion due to a steadily moving infinite wall is revisited in the context of dense granular flows of mono-dispersed beads using the recently proposed $\mu(I)$--rheology. In Newtonian fluids, molecular diffusion brings about a self-similar velocity profile and the boundary layer in which the fluid motion takes place increases indefinitely with time $t$ as $\sqrt{\nu t}$, where $\nu$ is the kinematic viscosity. For a dense granular visco-plastic liquid, it is shown that the local shear stress, when properly rescaled, exhibits self-similar behaviour at short-time scales and it then rapidly evolves towards a steady-state solution. The resulting shear layer increases in thickness as $\sqrt{\nu_g t}$ analogous to a Newtonian fluid where $\nu_g$ is an equivalent granular kinematic viscosity depending not only on the intrinsic properties of the granular media such as grain diameter $d$, density $\rho$ and friction coefficients but also on the applied pressure $p_w$ at the moving wall and the solid fraction $\phi$ (constant). In addition, the $\mu(I)$--rheology indicates that this growth continues until reaching the steady-state boundary layer thickness $\delta_s = \beta_w (p_w/\phi \rho g )$, independent of the grain size, at about a finite time proportional to $\beta_w^2 (p_w/\rho g d)^{3/2} \sqrt{d/g}$, where $g$ is the acceleration due to gravity and $\beta_w = (\tau_w - \tau_s)/\tau_s$ is the relative surplus of the steady-state wall shear-stress $\tau_w$ over the critical wall shear stress $\tau_s$ (yield stress) that is needed to bring the granular media into motion... (see article for a complete abstract).Comment: in press (Journal of Fluid Mechanics

Suicide as a Compensable Claim under Workers\u27 Compensation Statutes: A Guide for the Lawyer and the Psychiatrist

We live in a highly complex, industrialized environment. Specific work-related events occurring within this context frequently impact negatively on those who are essential to the operation of our industrial system. Often the impact of events produces human misery, suffering and death. Men and women are injured, maimed and killed. Workers\u27 compensation statutes exist to ameliorate the plight of workers and their families through the utilization of compensation in the form of cash-wage benefits and medical care. The economic burden of compensation is ultimately borne by consumers, because the cost of insurance taken out by employers is passed on in the price of the goods and services produced. Both workers\u27 compensation statutes and the systems produced by those statutes are appropriate responses to the perceived needs of all who have an interest in a productive economy and a just social order. As in any decision-making system designed to deal with complex cases, however, there exist opportunities for disagreement concerning the legal disposition of certain cases. This article deals with one of those matters which is subject to such serious debate: When should suicide, following a trauma experienced within the job context, give rise to a compensable claim under workers\u27 compensation statutes? Although there has been prior general commentary on the problem, this article will offer some additional and perhaps useful information to both the attorney representing the claimant and the psychiatrist called to testify as an expert witness. A working comprehension of the psychiatric aspects of suicide is necessary in order to prepare and prosecute a successful claim or to organize a defense in this area of the law. Additionally, the expert, even if carefully selected, must understand the legal framework in which his or her testimony will be interpreted. Unquestionably, a working relationship between the attorney and the psychiatrist is a necessity in terms of preparation. Many practitioners, however, may initially fail to realize that the early decisions recognizing suicide as a compensable workers\u27 compensation claim erroneously focus upon knowledge, cognition and uncontrollable impulses. Since the articulated models utilized by these courts in the earlier cases have become entrenched in some jurisdictions and in the minds of many judicial decision-makers, the psychiatric expert will often be forced to apply modern theories of psychiatry to illegitimately unscientific modes of legal analysis. These factors become increasingly important since many courts, when confronted with the generally liberal application of workers\u27 compensation statutes and the advent of modern scientific insights into the causes of suicide, have been forced to consider the issue of whether an employee\u27s suicide which follows a compensable on-the-job injury should constitute a separate ground for workers\u27 compensation benefits. The theory behind successful claims of this nature has been that an employee who has suffered a compensable injury, which in turn triggers a psychiatric disorder resulting in suicide, is entitled to both an inter vivos compensation award and a death benefit. It is clear, however, that since suicide is generally defined as an intentional act (without regard to whether or not suicide is psychiatrically considered volitional or nonvolitional), many jurisdictions would deny workers\u27 compensation death benefits to employees who take their own lives. Classic concepts indicate that recovery should be granted, if at all, only when there has been a work related harmful change in the human organism, arising out of and in the course of employment ... Nevertheless, when a direct causal relationship can be established among a work-related injury, a psychiatric disorder, and subsequent suicide, traditional notions should be updated and expanded to permit death benefit awards. Courts, when confronted with this dilemma, have utilized four different types of analyses. These modes of analysis can properly be identified as the Sponatski test, the New York rule, the English rule and the chain of causation test. This article examines the evolution of judicial understanding of suicide as related to the workers\u27 compensation system, endorses the liberal chain of causation test, and demonstrates that the chain of causation approach most closely corresponds with modern psychiatric theory

Probabilistic Graphical Model Representation in Phylogenetics

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (1) reproducibility of an analysis, (2) model development and (3) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and non-specialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis-Hastings or Gibbs sampling of the posterior distribution

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]

The effectiveness of psychological interventions for post-traumatic stress disorder in children, adolescents and young adults: A systematic review and meta-analysis

Background: Children and adolescents display different symptoms of post-traumatic stress disorder (PTSD) than adults. Whilst evidence for the effectiveness of psychological interventions has been synthesised for adults, this is not directly applicable to younger people. Therefore, this systematic review and meta-analysis synthesised studies investigating the effectiveness of psychological interventions for PTSD in children, adolescents and young adults. It provides an update to previous reviews investigating interventions in children and adolescents, whilst investigating young adults for the first time. / Methods: We searched published and grey literature to obtain randomised control trials assessing psychological interventions for PTSD in young people published between 2011 and 2019. Quality of studies was assessed using the Cochrane Risk of Bias tool. Data were analysed using univariate random-effects meta-analysis. / Results: From 15 373 records, 27 met criteria for inclusion, and 16 were eligible for meta-analysis. There was a medium pooled effect size for all psychological interventions (d = −0.44, 95% CI −0.68 to −0.20), as well as for Trauma-Focused Cognitive Behavioural Therapy (TF-CBT) and Eye Movement Desensitisation and Reprocessing (EMDR) (d = −0.30, 95% CI −0.58 to −0.02); d = −0.46, 95% CI −0.81 to −0.12). / Conclusions: Some, but not all, psychological interventions commonly used to treat PTSD in adults were effective in children, adolescents and young adults. Interventions specifically adapted for younger people were also effective. Our results support the National Institute for Health and Care Excellence guidelines which suggest children and adolescents be offered TF-CBT as a first-line treatment because of a larger evidence base, despite EMDR being more effective

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

Retired A Stars and Their Companions VIII: 15 New Planetary Signals Around Subgiants and Transit Parameters for California Planet Search Planets with Subgiant Hosts

We present the discovery of seven new planets and eight planet candidates around subgiant stars, as additions to the known sample of planets around "retired A stars." Among these are the possible first three-planet systems around subgiant stars, HD 163607 and HD 4917. Additionally, we present calculations of possible transit times, durations, depths, and probabilities for all known planets around subgiant (3 9%

Towards a conversational agent for threat detection in the internet of things.

A conversational agent to detect anomalous traffic in consumer IoT networks is presented. The agent accepts two inputs in the form of user speech received by Amazon Alexa enabled devices, and classified IDS logs stored in a DynamoDB Table. Aural analysis is used to query the database of network traffic, and respond accordingly. In doing so, this paper presents a solution to the problem of making consumers situationally aware when their IoT devices are infected, and anomalous traffic has been detected. The proposed conversational agent addresses the issue of how to present network information to non-technical users, for better comprehension, and improves awareness of threats derived from the mirai botnet malware