A patient post-Fontan palliation with a venous collateral unusually arising from the renal vein. Since renal vein oxygen saturations are relatively high, there was not systemic desaturation despite a right-to-left shunt.

In 1968 the inhabitants of the Chagos Islands were forcibly displaced by the British to set up a US military base on Diego Garcia, in an act which Chagossians have contested for over 50 years. At the time, and to the present, the UK Foreign and Commonwealth Office (FCO) attempted to legitimise the displacement by disingenuously claiming that the Chagossians were a mobile population of contract workers. Through archival analysis, this paper addresses the FCO representation of the islanders as a mobile ‘floating population’ of ‘contract workers’, linked to the figure of the ‘migrant’. At the same time, it problematises the legal contestation of the islanders’ displacement through a politicisation of stasis, linked to claims to ‘indigenous’ status based on long-held ties with the islands, as well as a discrete ‘Ilois’ or ‘Chagossian’ identity category. It argues that these debates reproduce distinctions between ‘migrants’ and ‘natives’ which obscure mobile political relations, including the imperial mobilities that constitute ‘national’ polities, as well as the histories of enforced mobility of enslaved and indentured labourers. Drawing on Glissant’s concept of errantry, the paper highlights the need to multiply conceptual and legal frameworks and create additional frameworks that can recognise mobile forms of rootedness.

The study is the first to examine the effects of children’s and therapists’ in-session behaviors on later therapeutic alliance (TA; i.e., relational bond, task collaboration) as rated by children and therapists in an intervention for children with aggressive behavior. One hundred eighty children (ages 9.3–11.8; 69% male; 78% Black), screened as having aggressive behavior by teacher and parent ratings, received a 32-session group-based cognitive-behavioral intervention (Coping Power) at their schools. TA ratings were collected from children and therapists at mid- and end-of-intervention using the Therapeutic Alliance Scale for Children. Children’s and therapists’ behaviors during the first 16 sessions were coded by independent observers. Children’s negative in-session behaviors predicted lower child- and therapist-rated TA (averaged across mid- and end-of-intervention). Children’s in-session positive behaviors, at both the individual and group-wide level, predicted higher later TA. Therapists’ efforts to manage deviant behavior predicted stronger child-reported ratings of the relational bond and of child- and therapist-rated task collaboration. Exploratory analyses indicate that the effect of children’s in-session behaviors on later TA is moderated by therapists’ skills in managing the group and in managing deviant talk and behavior in sessions. Clinical and research implications of the findings are discussed.

Over the past two decades, there has been growing interest in analyzing the effects of educational programs on outcomes using process data from computer-based testing and learning environments. However, most analyses focus on final outcomes at the end of a test or session, overlooking their functional nature over time and neglecting causal mechanisms. To address this gap, this article proposes a novel causal mediation framework for identifying and estimating functional natural direct effects, functional natural indirect effects, and functional total effects, along with their subgroup effects. We define these effects using potential outcomes and provide nonparametric identification strategies depending on whether post-treatment covariates are present or not. We then develop estimation methods using generalized additive models, a flexible and robust tool for analyzing functional data. Through a simulation study, we assess the finite-sample performance of the proposed approach by comparing it to parametric regression methods. We also demonstrate our approach by examining the effects of extended time accommodations on two functional outcomes using process data from the National Assessment of Educational Progress. Our mediation approach with functional outcomes effectively captures dynamic causal mechanisms underlying the program’s effects and pinpoints when and for whom each effect manifests throughout the testing period.

The current study presents an HPSG analysis for deliminative verbal reduplication in Mandarin Chinese. We provide a detailed description of the phenomenon. After discussing reduplication’s interaction with verb classes and aspect markers, we argue that it is better analyzed as a morphological rather than a syntactic process. We put forward a lexical rule for verbal reduplication in Mandarin Chinese, and the different forms of reduplication are captured in an inheritance hierarchy. The interaction between verbal reduplication and aspect marking is handled by multiple inheritance. This analysis covers all forms of deliminative verbal reduplication in Mandarin Chinese and has none of the shortcomings of previous analyses.

The Latent Position Model (LPM) is a popular approach for the statistical analysis of network data. A central aspect of this model is that it assigns nodes to random positions in a latent space, such that the probability of an interaction between each pair of individuals or nodes is determined by their distance in this latent space. A key feature of this model is that it allows one to visualize nuanced structures via the latent space representation. The LPM can be further extended to the Latent Position Cluster Model (LPCM), to accommodate the clustering of nodes by assuming that the latent positions are distributed following a finite mixture distribution. In this paper, we extend the LPCM to accommodate missing network data and apply this to non-negative discrete weighted social networks. By treating missing data as “unusual” zero interactions, we propose a combination of the LPCM with the zero-inflated Poisson distribution. Statistical inference is based on a novel partially collapsed Markov chain Monte Carlo algorithm, where a Mixture-of-Finite-Mixtures (MFM) model is adopted to automatically determine the number of clusters and optimal group partitioning. Our algorithm features a truncated absorb-eject move, which is a novel adaptation of an idea commonly used in collapsed samplers, within the context of MFMs. Another aspect of our work is that we illustrate our results on 3-dimensional latent spaces, maintaining clear visualizations while achieving more flexibility than 2-dimensional models. The performance of this approach is illustrated via three carefully designed simulation studies, as well as four different publicly available real networks, where some interesting new perspectives are uncovered.

We analyse the monetary-fiscal policy mix in post-war Europe, focusing on France and Italy, to trace the historical dynamics of debt and inflation. Using a Markov-switching DSGE model, we identify distinct policy regimes: a Passive Monetary-Active Fiscal (PM/AF) regime before the late 1980s/early 1990s, an Active Monetary-Passive Fiscal (AM/PF) regime associated with central bank independence and EMU convergence, and a third regime marked by the ELB and active fiscal measures aimed at recovery. Simulations reveal that the PM/AF regime in France led to price volatility but stabilised debt, while AM/PF curbed inflation at the cost of rising debt. In contrast, Italy’s procyclical fiscal policy in downturns exacerbated imbalances, aggregate volatility, and low growth. We further assess the implications of policy credibility and uncertainty.

The generalized Gompertz distribution—an extension of the standard Gompertz distribution as well as the exponential distribution and the generalized exponential distribution—offers more flexibility in modeling survival or failure times as it introduces an additional parameter, which can account for different shapes of hazard functions. This enhances its applicability in various fields such as actuarial science, reliability engineering and survival analysis, where more complex survival models are needed to accurately capture the underlying processes. The effect of heterogeneity has generated increased interest in recent times. In this article, multivariate chain majorization methods are exploited to develop stochastic ordering results for extreme-order statistics arising from independent heterogeneous generalized Gompertz random variables with increased degree of heterogeneity.