During the past 20 years, the expansion of bilingual education programmes in Spain has generated a situation where the voices of stakeholders frequently go unheard. Accordingly, this paper is a critical review of bilingual programmes within the Spanish context. An analysis has been carried out on stakeholder perceptions, that is, of teachers, students, management teams, and families, as reflected in the literature published between 2014 and 2023. The corpus reviewed consists of 34 papers, ranging from pre-primary to higher education, with a particular focus on stakeholders' perceptions of the implementation of bilingual education in a foreign language (English). In terms of the characteristics of the studies analysed, the predominance of teachers' perceptions over other stakeholders and the scarcity of longitudinal studies and research based on national samples should be noted. The adoption of a more robust methodological design could provide a fuller assessment of the implementation of bilingual education in Spain. Nonetheless, this review highlights the need for specific improvements at each level of education if a more learner-centred approach to teaching is to be achieved. Such improvements could include additional training opportunities, collaboration among teachers, and measures to alleviate the additional workload associated with bilingual education.

Kerala, a state in southern India, represents a success story for women in terms of both education and social justice. What lessons can we learn from Kerala? It seems the distinctive local culture may have played an important role. This chapter explores the lessons of Kerala from a philosophical perspective, drawing on philosophers Sen, Nussbaum, Chen, and others.

We offer a new explanation for the difference between cases where an auxiliary verb can and cannot contract, such as Kim is coming versus Kim is. Rather than a banning constraint, we argue that there is a positive syntactic licensing constraint. We consider, and reject, both the familiar Gap Restriction and a range of phonological explanations. Our analysis rests on the category of grammatical relations, valent, which includes all non-adjuncts (i.e. all subjects and complements); the analysis consists of a single claim, the Following Valent Constraint: that a contracted auxiliary has an overt following valent. We show how this analysis explains the full range of data that has been discussed in the literature and how a minor variant of the constraint captures the data of the Scots locative discovery expressions. We also propose a sociolinguistic explanation for the inability of auxiliaries to contract in certain environments, such as after a preposed negative. Finally, we suggest a functional explanation for the proposed constraint: It allows the hearer to predict the presence of a following valent and thereby to manage the burden of processing.

We introduce a wall model for large-eddy simulation (WMLES) applicable to rough surfaces with Gaussian and non-Gaussian distributions for both the transitionally and fully rough regimes. The model is applicable to arbitrary complex geometries where roughness elements are assumed to be underresolved, i.e. subgrid-scale roughness. The wall model is implemented using a multi-hidden-layer feedforward neural network, with the mean geometric properties of the roughness topology and near-wall flow quantities serving as input. The optimal set of non-dimensional input features is identified using information theory, selecting variables that maximize information about the output while minimizing redundancy among inputs. The model also incorporates a confidence score based on Gaussian process modelling, enabling the detection of potentially low model performance for untrained rough surfaces. The model is trained using a direct numerical simulation (DNS) roughness database comprising approximately 200 cases. The roughness geometries for the database are selected from a large repository through active learning. This approach ensures that the rough surfaces incorporated into the database are the most informative, achieving higher model performance with fewer DNS cases compared with passive learning techniques. The performance of the model is evaluated both apriori and aposteriori in WMLES of turbulent channel flows with rough walls. Over 550 channel flow cases are considered, including untrained roughness geometries, roughness Reynolds numbers and grid resolutions for both transitionally and fully rough regimes. Our rough-wall model offers higher accuracy than existing models, generally predicting wall shear stress within an accuracy range of 1%–15 %. The performance of the model is also assessed on a high-pressure turbine blade with two different rough surfaces. We show that the new wall model predicts the skin friction and the mean velocity deficit induced by the rough surface on the blade within 1%–10 % accuracy except the region with transition or shock waves. This work extends the building-block flow wall model (BFWM) introduced by Lozano-Durán & Bae (2023. J. Fluid Mech. 963, A35) for smooth walls, expanding the BFWM framework to account for rough-wall scenarios.

We investigate the effect of external oscillatory forcing on evolving two-dimensional (2-D) gravity currents, resulting from the well-known lock-exchange set-up, by superimposing a horizontally uniform oscillating pressure gradient. This pressure gradient generates a 2-D horizontally uniform laminar oscillating flow over the flat no-slip bottom that interacts with the evolving gravity current. We explore the effect of the velocity amplitude of the applied oscillating flow and its period of oscillations on the behaviour of the evolving gravity currents. A key element introduced by the external forcing is the Stokes boundary layer near the no-slip bottom wall generating differential advection near the bottom wall when the propagation direction of the gravity current and the oscillating externally imposed flow are in the same direction. This results in a phenomenon that we refer to as lifting of the gravity current, which clearly distinguishes the oscillatory forced gravity current from the freely evolving case. This phenomenon induces fine-scale density structures when the externally imposed flow is opposite to the propagation direction of the gravity current a semi-period later. We have explored the effect of lifting on the current propagation and the density structure of the gravity current front. Three separate regimes are distinguished for the evolution of the density structure in the front of the gravity current depending on the period of forcing, including a regime reminiscent of tidally forced estuarine flows. The present study shows the existence of significant effects of an oscillatory forcing on the dynamics, advection and density distribution of gravity currents.

Not all the information in a turbulent field is relevant for understanding particular regions or variables in the flow. Here, we present a method for decomposing a source field into its informative $\boldsymbol {\varPhi }_{I}(\boldsymbol {x},t)$ and residual $\boldsymbol {\varPhi }_{R}(\boldsymbol {x},t)$ components relative to another target field. The method is referred to as informative and non-informative decomposition (IND). All the necessary information for physical understanding, reduced-order modelling and control of the target variable is contained in $\boldsymbol {\varPhi }_{I}(\boldsymbol {x},t)$, whereas $\boldsymbol {\varPhi }_{R}(\boldsymbol {x},t)$ offers no substantial utility in these contexts. The decomposition is formulated as an optimisation problem that seeks to maximise the time-lagged mutual information of the informative component with the target variable while minimising the mutual information with the residual component. The method is applied to extract the informative and residual components of the velocity field in a turbulent channel flow, using the wall shear stress as the target variable. We demonstrate the utility of IND in three scenarios: (i) physical insight into the effect of the velocity fluctuations on the wall shear stress; (ii) prediction of the wall shear stress using velocities far from the wall; and (iii) development of control strategies for drag reduction in a turbulent channel flow using opposition control. In case (i), IND reveals that the informative velocity related to wall shear stress consists of wall-attached high- and low-velocity streaks, collocated with regions of vertical motions and weak spanwise velocity. This informative structure is embedded within a larger-scale streak–roll structure of residual velocity, which bears no information about the wall shear stress. In case (ii), the best-performing model for predicting wall shear stress is a convolutional neural network that uses the informative component of the velocity as input, while the residual velocity component provides no predictive capabilities. Finally, in case (iii), we demonstrate that the informative component of the wall-normal velocity is closely linked to the observability of the target variable and holds the essential information needed to develop successful control strategies.