In the text-linguistic tradition of register analysis, registers have often been seen as varieties of language defined by situations of use (see e.g., Biber & Conrad 2019). Recently, however, registers have been reconceptualized so as to permit more robust analyses of situations, intra-register variation in situational characteristics, and functional correspondences between situational and linguistic variables (e.g., Biber & Egbert 2023). As part of this program, Biber and Egbert have recently called for psycholinguistic research into the nature of registers and register variation (Biber & Egbert 2023: 19). However, few researchers have attempted to theorize registers as explicitly psycholinguistic constructs (cf., Keller 2021). Toward this end, this paper proposes the Discourse Category Model (DCM) of cognitive processes underlying register acquisition, representation, and processing. In this model, registers are mental categories of discourse events. This view contrasts with a purely functional view of register variation that sees registers as corpus-linguistic epiphenomena rather than cognitive phenomena. The model is intended to provide a framework for investigating the nature of registers and register variation from an explicitly cognitive orientation. Testable predictions generated by the model are presented along with research paradigms that may be useful for testing them.
Mangrove habitats in Myanmar’s Irrawaddy Delta are increasingly exposed to climatic variability and intensifying land-use pressure, yet long-term vegetation greenness dynamics at the delta scale remain insufficiently characterized. In this study, we have integrated Moderate Resolution Imaging Spectroradiometer–derived Normalized Difference Vegetation Index (NDVI) with Land-Use and Land-Cover (LULC) datasets to quantify three decades (1994–2024) of vegetation change and its environmental drivers. LULC maps were generated using a Random Forest classifier (overall accuracy: 88
. Magnetizable piezoelectric beams exhibit strong couplings between mechanical, electric, and magnetic fields, significantly affecting their highfrequency vibrational behavior. Ensuring exponential stability under boundary feedback controllers is challenging due to the uneven distribution of highfrequency eigenvalues in standard finite difference models. While numerical filtering can mitigate instability as the discretization parameter tends to zero, its reliance on explicit spectral computations is computationally demanding. This work introduces two novel model reduction techniques for stabilizing magnetizable piezoelectric beams. First, a finite element discretization using linear splines is developed, improving numerical stability over standard finite differences. However, this method still requires numerical filtering to eliminate merical investigations further reveal a direct dependence of the optimal filtering threshold on feedback amplifiers. To overcome these limitations, an alternative order-reduction finite difference scheme is proposed, eliminating the need for numerical filtering. Using a Lyapunov-based framework, we establish exponential stability with decay rates independent of the discretization parameter. The reduced model also exhibits exponential error decay and uniform energy convergence to the original system. Numerical simulations validate the effectiveness of the proposed methods, and we construct an algorithm for separating eigenpairs for the proper application of the numerical filtering. Comparative spectral analyses and energy decay results confirm the superior stability and efficiency of the proposed approach, providing a robust framework for model reduction in coupled partial differential equation systems.
Generative artificial intelligence (GenAI) is reshaping scientific publishing, increasing efficiency while challenging research integrity. Exercise science journals are adapting to this shift through evolving editorial policies and authorship guidelines. While many journals and publishers have artificial intelligence (AI) policies regarding authorship and disclosure of use, there are inconsistencies in their scope and application. In this paper, challenges related to GenAI use including overreliance on outputs, data privacy concerns, and misinformation are addressed in addition to practical recommendations for responsible integration. Editorial leaders must ensure that GenAI enhances rather than replaces human judgment through clear disclosure, rigorous peer review, and continuous policy refinement. Maintaining transparency, accountability, and critical oversight will be essential for safeguarding the credibility and trustworthiness of scholarly communication in the age of AI-assisted science.
