The University of Texas Rio Grande Valley (UTRGV) is a public research university with multiple campuses throughout the Rio Grande Valley region of Texas and is the southernmost member of the University of Texas System. The University of Texas Rio Grande Valley (UTRGV) was created by the Texas Legislature in 2013 after the consolidation of the University of Texas at Brownsville/Texas Southmost College and the University of Texas–Pan American.In 2019 The University of Texas Rio Grande Valley enrolled in the fall 29,619 students, making the public university the ninth-largest university in the state of Texas and the fourth largest (student enrollment) academic institution in The University of Texas system. In 2018, UTRGV is also one of the largest universities in the U.S. to have a majority Hispanic student population; 89.2% of its students are Hispanic, virtually all of them Mexican Americans. It was classified in 2020 among "R2: Doctoral Universities – High research activity".S.S.
This study presents a comprehensive theoretical framework for mixed-phase ice accretion, focusing on the transition from rime to glaze icing. A fully non-linear multilayer model that is applicable locally at every point on the icing surface is formulated to capture the coupled evolution of the rime, glaze, and surface water film layers, incorporating transient conduction and sublimation/evaporation. The analysis identifies the key nondimensional parameters governing the icing transition and quantifies their influence on layer growth and interfacial temperature evolution. After the rime-to-glaze transition, the freezing fraction is found to be highly sensitive to the coupled interaction among these parameters, which in turn governs the dynamics of glaze and water-layer development. To enable reduced-order modeling, a simplified local depth-averaged formulation is derived which reproduces the overall growth trends with good accuracy. A comparison against the Myers model supports the need for the present more accurate local formulation of rime-to-glaze transition under conditions of slow ice accretion, with implications for atmospheric icing prediction and flight safety.
Effective communication is critical in crowdfunding, where information asymmetry poses a significant challenge. While prior research has emphasized the persuasive power of rhetorical content, less is known about how the structure of rhetoric-beyond its content-shapes backers' cognitive and behavioral responses. This study examines the role of hierarchical rhetoric in crowdfunding and its impact on actual funding outcomes, backers' elaboration, perceived information sharing quality, and their willingness to fund. Drawing on computational linguistics and dual-process theories of information processing, we conducted a multiphase investigation that combines observational data from the Kickstarter platform with controlled experiments. Our findings show that the use of hierarchically structured rhetoric is positively associated with improved funding outcomes, longer information processing duration, higher perceived information sharing quality, and greater willingness to fund. We also found that these effects differ across project categories, suggesting that the effectiveness of rhetorical structure is context dependent. This study contributes to the literature on platform-mediated communication and crowdfunding by highlighting the cognitive and perceptual significance of rhetorical structure in shaping backer funding behavior and offering practical guidance for entrepreneurs seeking to optimize communication strategies.
PurposeThis study explores how initial exposure to immersive spatial computing experiences using AR headsets generates lasting inspiration and shapes consumers expected long-term life consequences (i.e., enhancement of reality, perceived substitutability and social impact).Design/methodology/approachThe study uses a time-lagged research design based on 148 first-time users of spatial computing devices (AR headsets). Respondents were interviewed once shortly after being exposed to AR and a few days later. Data is analyzed using partial least squares structural equation modeling (PLS-SEM).FindingsUsers' immediate "inspired-by" experiences predict increased "inspired-to" intentions days later. Such inspiration translates into anticipated consequences such as virtually customizing their physical environments, substituting physical products with AR content, and influencing social relationships with other users.Research limitations/implicationsThe current research focuses on positive life outcomes for consumers. However, the ubiquitous and pervasive use of AR may also lead to negative, undesired effects.Practical implicationsThe study demonstrates that AR experiences can produce detectable effects long after initial exposure and underscores that AR adoption results from the synergy of hardware and content, providing insights for future research in immersive spatial computing technologies.Originality/valueThe current research is one of the first to study AR users over time. Drawing on inspiration theory, findings show that an initial exposure to spatial computing through AR can have lasting effects when consumers think about how these technologies could impact their lives.
Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, and prion diseases, are characterized by the dynamical spread of toxic proteins through the brain. In prion diseases, cellular prion protein ( PrP^C ), produced by neurons, misfolds into a toxic form, known as scrapie prion protein ( PrP^Sc ). PrP^Sc induces neuronal stress which ultimately leads to cell death. In this paper, we develop mathematical models for the progression of prion diseases, incorporating a cellular defense mechanism that introduces a delay term affecting protein translation and a volatility term accounting for unaccounted biological factors influencing the system. We also extend the model to capture the spatial spread of toxic proteins over the brain connectome. Our first objective is to establish the existence and uniqueness of a global positive solution to the prion disease models. Afterwards, we analyze the asymptotic behavior of the models by identifying regimes of persistence and extinction of toxic proteins. For the deterministic delayed systems, we perform a stability analysis for the persistence and demonstrate that the system undergoes a Hopf bifurcation. We also study the intensity of fluctuations of the equilibrium state of the stochastic model. Additionally, we present numerical simulations to illustrate the model dynamics using biologically relevant parameters.
Background and Objective While vaccination remains central to controlling the COVID-19 pandemic, the emergence of SARS-CoV-2 variants with partial resistance to immune responses has highlighted the need for complementary therapeutic strategies. Among these, antiviral agents that inhibit viral entry mechanisms are of particular interest. Animal venoms, especially scorpion venoms, are a rich source of bioactive peptides with potential antiviral properties. This study aimed to evaluate peptides derived from the Moroccan scorpion Androctonus mauretanicus as inhibitors of SARS-CoV-2 spike glycoprotein, which mediates virus entry into host cells via ACE2 receptor binding.Material and Methodology Six peptides from the venom of the scorpion A. mauretanicus were first selected according to rigorous bioinformatic and experimental criteria, and their 3D structures were obtained or modeled. Their antiviral potential was then screened using the Stack-AVP stacked learning framework. The interactions of promising peptides with the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein were modeled by molecular docking using HADDOCK 2.4 and ClusPro 2.0. The most stable complexes were subjected to molecular dynamics simulations (200 ns) with GROMACS to assess their conformational stability (RMSD, Rg, RMSF) and interactions. Trajectories were analyzed by principal component analysis (PCA) and free energy landscape (FEL) construction, while binding affinity was predicted with PRODIGY.Results Four peptides (AM1, AM3, AM4 and AM5) showed strong predicted antiviral activity (>85%). Docking identified AM5 as the most affinity ligand (Delta G = -14.0 kcal/mol), targeting the S2 fusion domain, followed by AM3 (allosteric mechanism), AM4 (targeting the furin cleavage site), and AM1 (specific RBD inhibitor). MD simulations revealed that AM1, AM3, and AM5 form structurally stable complexes (low and constant RMSD). In contrast, AM4 induces significant conformational instability (high and non-convergent RMSD) and overall decompaction. Thermodynamic analyses (FEL) confirm the superior stability of the AM3 and AM5 complexes. These results position AM5 as the most promising blocking candidate.
