The aim of this article and the ensuing Special Issue is to assess, après fifteen years, the effects on the EU legal and political system of the overhaul of executive delegated powers inaugurated by the Lisbon Treaty. It identifies core parameters – i.e. (institutional) balance of powers, (democratic) legitimacy, control and accountability, effectiveness of EU policy implementation – considered by the contributions to this Special Issue to map and examine, both constitutionally and normatively, the EU system of delegated powers in law and practice. It also puts forward seven overarching reflections revealing some of the core issues and challenges posed by the current stage of development of the post-Lisbon EU system of delegated powers.

The amplitude modulation coefficient, $R$, that is widely used to characterize nonlinear interactions between large- and small-scale motions in wall-bounded turbulence is not compatible with detecting the convective nonlinearity of the Navier–Stokes equations. Through a spectral decomposition of $R$ and a simplified model of triadic convective interactions, we show that $R$ suppresses the signature of convective scale interactions, but is strongly influenced by linear interactions between large-scale motions and the background mean flow. We propose an additional coefficient that is specifically designed for the detection of convective nonlinearities, and we show how this new coefficient, $R_T$, quantifies the turbulent kinetic energy transport involved in turbulent scale interactions and reveals a classical energy cascade across widely separated scales.

Some effects are considered to be higher level than others. High-level effects provide expressive and succinct abstraction of programming concepts, while low-level effects allow more fine-grained control over program execution and resources. Yet, often it is desirable to write programs using the convenient abstraction offered by high-level effects, and meanwhile still benefit from the optimizations enabled by low-level effects. One solution is to translate high-level effects to low-level ones.

This paper studies how algebraic effects and handlers allow us to simulate high-level effects in terms of low-level effects. In particular, we focus on the interaction between state and nondeterminism known as the local state, as provided by Prolog. We map this high-level semantics in successive steps onto a low-level composite state effect, similar to that managed by Prolog’s Warren Abstract Machine. We first give a translation from the high-level local-state semantics to the low-level global-state semantics, by explicitly restoring state updates on backtracking. Next, we eliminate nondeterminism altogether in favour of a lower-level state containing a choicepoint stack. Then we avoid copying the state by restricting ourselves to incremental, reversible state updates. We show how these updates can be stored on a trail stack with another state effect. We prove the correctness of all our steps using program calculation where the fusion laws of effect handlers play a central role.

A common way of acquiring multiword expressions is through language input, such as during reading and listening. However, this type of learning is slow. Identifying approaches that optimize learning from input, therefore, is an important language-learning endeavor. In the present study, 85 learners of English as a foreign language read short texts with 42 figurative English phrasal verbs, repeated three times. In a counterbalanced design, we manipulated access to definitions (before text, after text, no definition) and typographic enhancement (with bolding, without bolding). The learning was measured by immediate and delayed gap-fill and meaning generation posttests. All posttests showed that learning with definitions was better than without, and that access to definitions after reading was more beneficial than before reading. Typographic enhancement effectively promoted contextual learning of phrasal verbs and increased the learning advantage associated with presenting definitions after reading.

The evolutionary process of mixing induced by Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities typically progresses through three stages: initial instability growth, subsequent mixing transition and ultimate turbulent mixing. Accurate prediction of this entire process is crucial for both scientific research and engineering applications. For engineering applications, Reynolds-averaged Navier–Stokes (RANS) simulation stands as the most viable method currently. However, it is noteworthy that existing RANS mixing models are primarily tailored for the fully developed turbulent mixing stage, rendering them ineffective in predicting the crucial mixing transition. To address that, the present study proposes a RANS mixing transition model. Specifically, we extend the idea of the intermittent factor, which has been widely employed to integrate with turbulence models for predicting boundary layer transition, to mixing problems. Based on a high-fidelity simulation of a RT case, the intermittent factor defined based on enstrophy is extracted and then applied to RANS calculations, showing that it is possible to accurately predict mixing transition by introducing the intermittent factor to the turbulence production from the baseline K-L turbulence mixing model. Furthermore, to facilitate practical predictions, a transport equation has been established to model the spatio-temporal evolution of the intermittent factor. Coupled with the K-L model, the intermittent factor provided by the transport equation is applied to modify the Reynolds stress in RANS calculations. Thereafter, the present transition model has been validated in a series of tests, demonstrating its accuracy and robustness in the capturing mixing process in different types and stages of interfacial mixing flows.

Biped wall-climbing robots (BWCRs) serve as viable alternatives to human workers for inspection and maintenance tasks within three-dimensional (3D) curtain wall environments. However, autonomous climbing in such environments presents significant challenges, particularly related to localization and navigation. This paper presents a pioneering navigation framework tailored for BWCRs to navigate through 3D curtain wall environments. The framework comprises three essential stages: Building Information Model (BIM)-based map extraction, 3D climbing path planning (based on our previous work), and path tracking. An algorithm is developed to extract a detailed 3D map from the BIM, including structural elements such as walls, frames, and ArUco markers. This generated map is input into a proposed path planner to compute a viable climbing motion. For path tracking during actual climbing, an ArUco marker-based global localization method is introduced to estimate the pose of the robot, enabling adjustments to the target foothold by comparing desired and actual poses. The conducted experiments validate the feasibility and efficacy of the proposed navigation framework and associated algorithms, aiming to enhance the autonomous climbing capability of BWCRs.

Language is the primary technology clinical ethicists use as they offer guidance about norms. Like any other piece of technology, to use the technology well requires attention, intention, skill, and knowledge. Word choice becomes a matter of professional practice. The Brief Report offers clinical ethicists several reasons for rejecting the phrase “aggressive care.” Instead, ethicists should consider replacing “aggressive care” with the adjacent concept of a “recovery-focused path.” The virtues of this neologism include: the opportunity to set aside the emotion of “aggression,” the phrase’s accuracy when capturing the intention of the patient or their representative, and an unappreciated rhetorical force—and transparent logic—that arises when the patient’s recovery is unlikely.

This study aimed to investigate the effects of physical multimorbidity on the trajectory of cognitive decline over 17 years and whether vary across wealth status. The study was conducted in 9035 respondents aged 50+ at baseline from nine waves (2002–2019) of the English Longitudinal Study of Aging. A latent class analysis was used to identify patterns of physical multimorbidity, and mixed multilevel models were performed to determine the association between physical multimorbidity and trajectories of cognitive decline. Joint analyses were conducted to further verify the influence of wealth status. Four patterns of physical multimorbidity were identified. Mixed multilevel models with quadratic terms of time and status/patterns indicated significant non-linear trajectories of multimorbidity on cognitive function. The magnitude of the association between complex multisystem patterns and cognitive decline increased the most as follow-up progressed. Individuals with high wealth and hypertension/diabetes patterns have significantly lower composite global cognitive z scores over time as compared with respiratory/osteoporosis patterns. Physical multimorbidity at baseline is associated with the trajectory of cognitive decline, and the magnitude of the association increased over time. The trend of cognitive decline differed in specific combinations of wealth status and physical multimorbidity.