Abstract This paper examines two variants of the pseudo-cleft construction which display a WHAT-NP-VP-be pattern with the VP realised with cognitive verbs and the proform do in the context of spoken British English dyadic and multi-party BBC podcasts. It is based on the premise that the construction’s referencing potentials are both cataphoric and projective, and that depending on its contexts, one of the two referencing functions is foregrounded while the other is backgrounded. The analysis focuses on those linguistic features and contextual configurations which either contribute to its cataphoric referencing function, or which go beyond the local cataphoric referencing function and indicate its projective, discourse-organising function. The research is corpus-based and uses quantitative and qualitative methodologies, filtering out the linguistic features and contextual configurations which contribute to assigning the two variants the status of a projective construction with a discourse-organising function. The features under investigation are (1) the semantics of the constitutive NPs and VPs marking for tense, aspect and modality and their uptake in the discourse, (2) degrees of continuity and discontinuity in the cohesive chains triggered by the constitutive parts of the construction. The paper shows that when semantic continuity between the what-clause and what follows is discontinued and thus deferred, the construction’s projective function is foregrounded.

Motivated by the modeling of the spatial structure of the velocity field of three-dimensional turbulent flows, and the phenomenology of cascade phenomena, a linear dynamics was recently proposed that can generate high velocity gradients from a smooth-in-space forcing term. It is based on a linear partial differential equation stirred by an additive random forcing term that is δ-correlated in time. The underlying proposed deterministic mechanism corresponds to a transport in Fourier space that aims to transfer energy injected at large scales towards small scales. The key role of the random forcing is to realize these transfers in a statistically homogeneous way. Whereas at finite times and positive viscosity the solutions are smooth, a loss of regularity is observed for the statistically stationary state in the inviscid limit. We present here simulations, based on finite volume methods in the Fourier domain and a splitting method in time, which are more accurate than the pseudospectral simulations. We show that our algorithm is able to reproduce accurately the expected local and statistical structure of the predicted solutions. We conduct numerical simulations in one, two, and three spatial dimensions, and we display the solutions both in physical and Fourier space. We additionally display key statistical quantities such as second-order structure functions and power spectral densities at various viscosities. Published by the American Physical Society 2024

AbstractWe explored the ambiphilic reactivity of SF5‐alkynes, and we proved they can act as both nucleophiles and electrophiles. We selected halogenation reactions as benchmark reactions and developed highly selective stereodivergent hydrohalogenation (I, Br, Cl, F) reactions of SF5‐alkynes. The stereochemistry is finely controlled thanks to the nature of the acids used (strong or soft) in the presence of halide source, while the high regioselectivity is governed by the strong polarization of SF5‐alkynes. Mechanistic studies supported by DFT calculations shed light on two different reaction mechanisms responsible of the excellent stereocontrol. This stereoselectivity was quantitatively rationalized with the ASM and EDA methods. A few dihalogenation reactions are reported and DFT calculations rationalize this cis‐stereoselectivity. Relative configuration of all the SF5‐haloalkenes was unambiguously determined by X‐ray diffraction. Noteworthy, several post‐functionalization reactions such as cross‐couplings, cyanation and reductions are described to strengthen the synthetic potential.

The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g. Rao entropy, functional dendrograms) or multidimensional spaces (e.g. convex hulls, kernel‐density hypervolumes), each with their own strengths and limits. Frameworks based on dissimilarity matrices either do not enable the measurement of all components of FD (i.e. richness, divergence, and regularity), or result in the distortion of the functional space. Frameworks based on multidimensional spaces do not allow for comparisons with phylogenetic diversity (PD) measures and can be sensitive to outliers.We propose the use of neighbor‐joining trees (NJ) to represent and quantify FD in a way that combines the strengths of current frameworks without many of their weaknesses. Importantly, our approach is uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles.We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel‐density hypervolumes using both simulated and empirical datasets.Using NJ, we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree‐based representations. Moreover, the method is directly comparable with taxonomic diversity (TD) and PD measures, and enables quantification of the richness, divergence and regularity of the functional space.

This review focuses on the thioredoxin domain containing 5 (TXNDC5), also known as endoplasmic reticulum protein 46 (ERp46), a member of the protein disulfide isomerase (PDI) family with a dual role in multiple diseases. TXNDC5 is highly expressed in endothelial cells, fibroblasts, pancreatic β-cells, liver cells, and hypoxic tissues, such as cancer endothelial cells and atherosclerotic plaques. TXNDC5 plays a crucial role in regulating cell proliferation, apoptosis, migration, and antioxidative stress. Its potential significance in cancer warrants further investigation, given the altered and highly adaptable metabolism of tumor cells. It has been reported that both high and low levels of TXNDC5 expression are associated with multiple diseases, such as arthritis, cancer, diabetes, brain diseases, and infections, as well as worse prognoses. TXNDC5 has been attributed to both oncogenic and tumor-suppressive features. It has been concluded that in cancer, TXNDC5 acts as a foe and responds to metabolic and cellular stress signals to promote the survival of tumor cells against apoptosis. Conversely, in normal cells, TXNDC5 acts as a friend to safeguard cells against oxidative and endoplasmic reticulum stress. Therefore, TXNDC5 could serve as a viable biomarker or even a potential pharmacological target.

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

We discuss the existence and non-existence of periodic in one variable and compactly supported in the other variables least energy solutions for equations with non-Lipschitz nonlinearity of the form: − Δ u = λ u p − u q in R N + 1 , where 0 < q < p < 1 and λ ∈ R. The approach is based on the Nehari manifold method supplemented by a one-sided constraint given through the functional of the suitable Pohozaev identity. The limit value of the parameter λ, where the approach is applicable, corresponds to the existence of periodic in one variable and compactly supported in the other variables least energy solutions. This value is found through the extrem values of nonlinear generalized Rayleigh quotients and the so-called curve of the critical exponents of p, q. Important properties of the solutions are derived for suitable ranges of the parameters, such as that they are not trivial with respect to the periodic variable and do not coincide with compactly supported solutions on the entire space R N + 1 .

Abstract Applying full-waveform methods to image small-scale structures of geophysical interest buried within the Earth requires the computation of the seismic wavefield over large distances compared to the target wavelengths. This represents a considerable computational cost when using state-of-the-art numerical integration of the equations of motion in three-dimensional earth models. “Box Tomography” is a hybrid method that breaks up the wavefield computation into three parts, only one of which needs to be iterated for each model update, significantly saving computational time. To deploy this method in remote regions containing a fluid-solid boundary, one needs to construct artificial sources that confine the seismic wavefield within a small region that straddles this boundary. The difficulty arises from the need to couple elastic and acoustic simulations in this region. Reconciling different displacement potential expressions used for solving the acoustic wave equation, we propose a unified framework for such hybrid simulations, a significant step towards applying “Box Tomography” in arbitrary regions inside the Earth, resulting in a thousand-fold computational cost reduction compared to standard approaches, without compromising accuracy. We also present examples of benchmarks of the hybrid simulations in the case of target regions at the ocean floor and the core-mantle boundary.

Using strontium (Sr) and barium (Ba) in otoliths to determine natal origins and understand patterns of fish movements is based on the fundamental assumption that otoliths record water chemistry signals without any major alterations. Although prior studies highlighted that fish physiology can modify the water signal in otoliths, studies for freshwater fish are scarce. We exposed different groups of Atlantic salmon parr Salmo salar to different scenarios of ambient-level variations in Sr/Ca and Ba/Ca ratios and then combined otolith chemical profiles with environmental data (water chemistry and temperature), Fulton's index, and otolith growth rates to assess what factors explain/influence the elemental ratios of Sr and Ba in otoliths. Generalized additive mixed models (GAMMs) using water-based otolith composition, temperature, Fulton's index, and “individual” as explanatory variables allow to demonstrate that water chemistry alone cannot fully explain measured ratios in otoliths, except in scenarios involving significant changes in water chemistry. Other factors (physiological effects) should be accounted for reproducing short and minimal seasonal variations in water composition, considering that inter-individual variability contributes quite significantly in most scenarios.

AbstractWe propose an approach for measuring seismic attenuation at ultrasonic frequencies on laboratory‐scale samples. We use a Gaussian filter to select a bandwidth of frequencies to identify the attenuation in a small window and, by moving the window across the frequency content of the data, we determine the frequency‐dependent attenuation function. We assess the validity of the method with three‐dimensional numerical simulations of seismic wave propagation across different sample geometries, using free surface boundary conditions. We perform the simulations using viscoelastic media under various seismic attenuation models. Our numerical results indicate that we can successfully recover the representative viscoelastic attenuation parameters of the media, regardless of the sample geometry, by processing the seismic signal recorded either within the volume or at the boundaries. Due to the equipartition phenomenon, the energy of S‐waves is consistently higher in seismic records than that of P‐waves. Therefore, we systematically recover the attenuating properties of S‐waves in the medium. We also conduct experiments of seismic wave propagation on samples of aluminum and Fontainebleau sandstone to validate our approach with real data. The quality factor of the S‐wave in the aluminum medium increases from 300 to 7000 between 60 kHz and 1.2 MHz. The Fontainebleau sandstone, which is more attenuating, exhibits a that increases from 200 at 60 kHz to 1000 at 1.2 MHz. With our approach, we are not only able to recover the attenuation property but also identify the frequency‐dependent attenuation model of the samples. Our method allows for seismic attenuation recovery at ultrasonic frequencies in low‐attenuating media.