This paper presents a detailed experimental investigation of the near-wall transport dynamics in Taylor-Couette-Poiseuille flows using measurements of the large-scale variations of local wall-shear-rate components. A three-segment electrodiffusion probe was used to conduct multiple experiments across a wide range of Taylor numbers (300 < Ta <2520) and Reynolds numbers (2 < Re <32) to capture laminar, transitional, and turbulent flow conditions. Both azimuthal and axial wall shear rate component measurements were made in order to directly correlate the effects of flow structures on the transport of momentum in the near-wall region. The results show that the average azimuthal wall shear rate is primarily influenced by the Taylor number, while its variation with the Reynolds number remains within the experimental uncertainty. On the other hand, the large-scale shear rate dynamics are strongly influenced by the flow structures. More specifically, vortical flows have almost identical average wall shear rates as helical flows but distinctly different temporal characteristics and fluctuation magnitude. The differences in large-scale wall shear dynamics between the two flow structures arise from distinct mechanisms of momentum transfer; vortices induce high shear rates through rapid radial inflow and outflow, whereas helices generate low shear rates via a much more uniform shear distribution due to their oblique configuration. These findings suggest that the average wall shear stress alone is insufficient to determine the transport processes occurring in the wall region. Localized measurements are required in order to establish the relationship between flow structure and transport dynamics within the measurement bandwidth. These findings provide new insight into shear-induced transport in rotating flows and establish experimental benchmarks for the development and validation of transport models.
Climate change poses significant risks to transportation infrastructure, increasing maintenance demands and reducing service lifespans due to more frequent and intense extreme events. One critical impact is the alteration of scouring patterns around bridge piers, driven by higher river discharge events, which can undermine foundation stability and lead to premature structural failures. This study develops a stochastic framework to assess the stability of bridges subjected to local scour under changing climate conditions. An enhanced local scour equation is used to estimate scour depths across various climate change scenarios. The stochastic model simulates both the random occurrence and magnitude of scour events. It accounts for the non-stationarity of future climate impacts by introducing a breakpoint after which the model parameters are adjusted to reflect projected changes. Applied to a case study bridge in the UK, the framework simulates potential failure scenarios under different climate projections. The results reveal that the risk and frequency of bridge failures increase significantly under more severe climate scenarios, particularly after 2080, with the most pessimistic scenario (RCP 8.5). To address these risks, an adaptation strategy (Rip-rap) is formulated to reduce the risk of bridge failure and improve the structure's availability. This highlights the importance of proactive risk management and adaptation planning to enhance the resilience of bridge infrastructure in the face of climate change.
Hurricane-driven extreme sea levels and coastal flooding are major hazards for tropical islands, and understanding the meteorological and oceanic underlying processes is crucial as climate change might intensify these events. On 19 September 2017, Hurricane Maria passed 20 km south of Guadeloupe as a Category 4 on the Saffir-Simpson scale, generating offshore waves of up to 8 m and a maximum recorded still water level of 0.7 m above mean sea level, causing substantial flooding along the seafront. Before Maria, three pressure sensors were deployed in Sainte-Anne Bay, southeast of Guadeloupe: two along a cross-shore transect of a reef-lagoon system (forereef slope and lagoon), and one on the backshore of the opposing shoreline at Bourg Beach. To examine the drivers of extreme sea levels and coastal flooding, data analysis was complemented by numerical modelling. At the regional scale, the phase-averaged SCHISM-WWM modelling system was implemented over the whole Guadeloupe Archipelago with a resolution reaching 10 m at the study site and reproduced short waves and mean water levels. Numerical experiments revealed that wave setup and atmospheric surge contributed almost equally, but flooding was not reproduced, suggesting that additional processes may be missing. To evaluate this hypothesis, a local phase-resolving SWASH model was applied, revealing substantial infragravity (IG) waves (H-m0 = 0.3 m to 0.6 m) at the shoreline. For comparable mean water levels in the lagoon, SWASH simulated greater inundation, matching well available observations, highlighting the critical role of IG dynamics in coastal flooding assessments.
Automatic terminology or term extraction (ATE) is a Natural Language Processing (NLP) task intended to automatically identify specialized terms present in domain-specific corpora. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographical tasks, but also support and improve several complex downstream tasks, e.g., information retrieval, machine translation, topic detection, and sentiment analysis. ATE systems and datasets annotated for the task at hand have been studied and developed for decades, but more recent approaches have increasingly involved novel neural systems. Despite a large amount of new research on ATE tasks, systematic survey studies covering novel neural approaches are lacking, especially when it comes to the usage of large-scale language models (LLMs). We present a comprehensive survey of neural approaches to ATE, focusing on transformer-based neural models and the recent generative approaches based on LLMs. The study also compares these systems and previous ML-based approaches, which employed feature engineering and non-neural supervised learning algorithms.
Passiflora cincinnata is traditionally used for the management of anxiety-related disorders; however, experimental evidence supporting its neuropharmacological effects remains limited. The present study evaluated the anxiolytic-and antidepressant-like effects of the crude ethanolic extract (Pc-EtOH) obtained from the aerial parts in mice, as well as explored possible mechanisms involved. Male Swiss mice were treated orally with Pc-EtOH (100, 200, or 400 mg/kg) and submitted to a battery of behavioral tests, including the elevated plus maze, open-field, forced swimming, and tail suspension tests. Pc-EtOH at 100 mg/kg produced significant anxiolytic-and antidepressant-like effects, comparable in magnitude to reference drugs in specific parameters, without inducing motor impairment. In contrast, the highest dose (400 mg/kg) was associated with reduced locomotor activity, suggesting sedative effects. Pharmacological antagonism assays indicated a partial involvement of the GABAergic system. These findings suggest that P. cincinnata exhibits dose-dependent neurobehavioral effects, with a favorable profile at lower doses.
