Oklahoma State University–Stillwater (Oklahoma State University, Oklahoma State, OSU) is a public land-grant research university in Stillwater, Oklahoma. OSU was founded in 1890 under the Morrill Act. Originally known as Oklahoma Agricultural and Mechanical College (Oklahoma A&M), it is the flagship institution of the Oklahoma State University System that holds more than 35,000 students across its five campuses with an annual budget of $1.5 billion. The main campus enrollment for the fall 2019 semester was 24,071, with 20,024 undergraduates and 4,017 graduate students. OSU is classified among "R1: Doctoral Universities – Very high research activity". According to the National Science Foundation, OSU spent $198.8 million on research and development in 2021.The Oklahoma State Cowboys and Cowgirls have won 52 national championships, which ranks fourth in most NCAA team national championships after Stanford University, University of California, Los Angeles,and University of Southern California . The Oklahoma State Cowboys wrestling is the most successful NCAA Division I program of all time in any sport. As of 2021, Oklahoma State students and alumni have won 34 Olympic medals (21 gold, 5 silver, and 8 bronze). The university has produced 29 Goldwater Scholars, 18 Truman Scholars, 18 Udall Scholars, and 48 Fulbright Scholars, astronauts, and a billionaire.OSU is ranked top 10 nationally and top 100 in the world for contributions to United Nations Sustainable Development Goals.Students spend part of the fall semester preparing for OSU's Homecoming celebration, begun in 1913, which draws more than 40,000 alumni and over 70,000 participants each year to campus and is billed by the university as "America's Greatest Homecoming Celebration." The Oklahoma State University alumni network exceeds 250,000 graduates......
In Brief Value systems seem distant from real-life nursing, yet our moral frameworks are lenses through which we view the world. Do you think humanistically or Christianly in the everyday tight spots of practice?
Purpose The purpose of this paper is to investigate the mediating role of satisfaction (SAT) in relation to mobile banking service quality (MB-SQ) and continuance intention (CI) among Nepali mobile banking users. Design/methodology/approach The paper adopted a quantitative approach and cross-sectional survey research design. Data were collected with structured questionnaires from 326 mobile banking users. A partial least squares structural equation modeling (PLS-SEM) and artificial neuro network (ANN) approach were applied to examine hypotheses. Findings Results confirm a significant positive influence of MB-SQ on SAT and CI of mobile banking adoption. Moreover, MB-SQ partially mediates the relationship between SAT and CI of mobile banking adoption. Research limitations/implications Based on the findings of this research, theoretically, this paper attempted to investigate the mediating role of MB-SQ in the CI of mobile banking, and managerially, mobile banking service providers could have insights on designing mobile banking service marketing strategy. Originality/value This paper is among the earliest studies to investigate the role of MB-SQ as a higher-order reflective-reflective construct on CI. Moreover, the endogeneity issue has been tested, and ANN has been applied to investigate the predictive relevance of SAT and MB-SQ on CI of mobile banking users. Furthermore, the authors have delved into the ongoing discourse surrounding Generation Y and Generation Z, exploring their implications on CI within the realm of mobile service quality. It provides a critical juncture for understanding continuance intention in the mobile service quality context.
Identifying dynamic stability coefficients plays a pivotal role in missions that involve atmospheric entry. Although several methods exist to derive these coefficients from experimental and numerical data, there are deficiencies that need to be addressed. In this study, a modular nonlinear parameter estimation (NPE) framework is proposed, which employs a neural network and Markov chain Monte Carlo (MCMC) algorithm to infer the dynamic stability coefficients and identify the uncertainties in the predictions. Given the sensitivity of Markov chains to initial chain location, the neural network is used to generate an initial sample for the first chain that will be used in the MCMC method, where the static and dynamic stability coefficients are estimated along with the uncertainties. The time histories of the moment and angle of attack obtained from computational fluid dynamics simulations with US3D are fed into the NPE framework to estimate the static and dynamic stability coefficients. The results show that the trajectories generated from the estimated coefficients agree with those obtained from the US3D simulations.
The integration of metaverse technology into higher education institutions (HEIs) offers transformative potential for enhancing pedagogical innovation and addressing evolving educational demands. However, its effective implementation requires a systematic assessment of enablers and implementation feasibility across diverse HEI contexts. In this study, a novel T-spherical fuzzy (T-SF) hybrid model is developed to address this issue. The proposed model first collects expert evaluation information using T-spherical fuzzy sets (T-SFSs) and utilizes a similarity measure to determine expert weights. The weighted Heronian mean aggregation (WHMA) operator is then integrated to derive conservative aggregated values. Furthermore, the T-SF-WHMA-MULTIMOOSRAL hybrid model evaluates and ranks the metaverse integration across different types of HEIs. Finally, a case study is presented to illustrate the application of the T-SF-WHMA-MULTIMOOSRAL hybrid model. The results indicate that the continual feedback for teachers and students (Es7) and the sustainability and environmental impact (Es6) as the most influential enablers of metaverse integration. This study evaluates and ranks the potential for metaverse integration across four distinct types of HEIs, with the results showing that higher vocational colleges (O1) ranked highest. The findings of this study provide practical insights for guiding the adoption of metaverse technology in diverse and complex educational environments.
Dynamic stability during atmospheric entry is crucial for the safe descent of blunt-body vehicles, as oscillatory motion significantly impacts parachute deployment and mission success. Conventional identification approaches are inadequate in this regime, as they often linearize around a trim condition, assume aerodynamic coefficients vary only with Mach number, or rely on a single data source. Experimental ballistic range data provide realism but are sparse, noisy, and affected by uncertain initial conditions. Free-flight computational fluid dynamics (FF-CFD), by contrast, yields dense, time-resolved trajectories but introduces model-form bias in the dynamic-stability regime. To overcome these limitations, we propose a Data-Fusion-based Nonlinear Parameter Identification (DF-NPI) framework that integrates experimental and numerical data within a Bayesian inference formulation. The framework (i) reconstructs trajectories from sparse observations, (ii) fuses heterogeneous ballistic range and FF-CFD data, and (iii) estimates angle-of-attack-dependent dynamic stability derivatives using Markov Chain Monte Carlo sampling. By combining experimental fidelity with simulation coverage, DF-NPI delivers a robust, uncertainty-aware characterization of entry vehicle dynamic stability, enabling improved prediction tools for design and mission planning.
