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In this study, a peer assessment-based mobile learning approach was implemented and evaluated in a vocational design certification course. A quasi-experiment was conducted to explore the impact of the proposed approach on the learning achievements and motivations of the students with active and reflective styles. It was found that the students using the peer assessment-based mobile learning approach generally learned better than those using the conventional mobile learning approach. It was also found that there were interactive effects between learning approaches and learning styles on intrinsic motivation. The students with an active learning style outperformed those with a reflective style in intrinsic motivation when conducting the peer assessment-based mobile learning. Active learning style students also had higher extrinsic motivation than those with a reflective learning style when conducting mobile learning.

Statistical morphological variations at the population level serve as a robust foundation for species delimitation and taxonomic treatment. We employed this approach to investigate species delimitation within the Ptilagrostis dichotoma species complex (Poaceae), which is characterized by open panicles. While three species have been described, the boundaries between these species and the morphological distributions of all taxonomic traits remain unclear. In this study, we examined the geographical variability of 19 morphological traits based on at least six individuals from each of the 28 populations across the entire distributional range of the species complex. Our statistical analyses identified five distinct phenotypic clusters with specific geographical preferences. Three of these clusters corresponded to the previously described species, while the remaining two clusters represented previously undescribed species. Furthermore, our findings highlight several traits, such as basal leaf scabrosity, panicle branch pulvinae, and glume length, which exhibit distinct gaps between phenotypic units and prove highly valuable for species delimitation. Finally, two new species are described and a key is provided to differentiate the five species. This morphological revision establishes a basis for future studies on species boundaries and divergences of the complex based on other evidence. [ABSTRACT FROM AUTHOR]

In this work, we determined an approximate eigen solutions of Modified multi-parameter exponential potential using supersymmetric quantum mechanics approach (SUSY) with improved Greene-Aldrich approximation to the centrifugal term. The energy equation and its corresponding normalised radial wave function were fully obtained. The proposed potential reduces to other useful potentials like Rosen-Morse, Hellmann, Yukawa and Coulomb potential as special cases. The thermodynamic properties like the vibrational mean energy (Uβ,V), Vibrational heat capacity (Cβ,V), vibrational entropy (Sβ,V) and vibrational free energy (Fβ,V) of the interacting potential were studied via partition function (Zβ,V) obtained from the resulting energy equation. This study was applied to three diatomic molecules: Chromium hydride (CrH), Titanium Hydride (TiH) and Thiocynate (ScN). To ascertain the high degree of our analytical mathematical accuracy, we compared the results of special cases with an existing results. These were found to be in excellent agreement with the existing results.

This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. The DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade reconstruction framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via 'zero-shot transfer'), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to direct numerical simulation (DNS) results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pretrained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore the potential of EC-SRGAN for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges. [ABSTRACT FROM AUTHOR]

The presence and circumferential oscillation of rivulets can excite large-amplitude vibrations for cables. Inspired by this phenomenon, a minute attachment was employed to simulate the upper-rivulet motion and successfully excited a large-amplitude vibration for a circular cylinder in a wind tunnel. By minute attachment, we mean a stainless-steel strip exhibiting morphological and dimensional characteristics analogous to the rivulet. The role of the circumferentially oscillating minute attachment on the flow-induced vibration of a circular cylinder was explored by detailed flow and structural measurements. The experimental results demonstrated that as wind velocity increased, the vibration amplitude of the model progressively increased, and the impact of the attachment on aerodynamics increased as well. It was also found that the wake behavior shifted from alternating vortex shedding to periodic "expansion–contraction" flapping in the boundary layers with increasing wind velocity. In particular, the oscillating minute attachment could control the boundary layer separation and form a "low velocity zone" on the upper surface of the model that varied with the attachment. Then, the aerodynamic lift force was synchronized with the attachment oscillation frequency, which was equal to the natural frequency of the vibrating circular cylinder. As a consequence, the vibration amplitude was rapidly developed. [ABSTRACT FROM AUTHOR]

Experimental investigations were conducted on a single vortex ring impinging on a concave hemi-cylindrical shell with Dm/De = 2 at different Reynolds numbers. Vortex rings with five different Reynolds numbers were generated for experimental studies, i.e., Re = 750, 1500, 3000, 5000, and 7000. The planar laser-induced fluorescence visualizations and two-dimensional particle image velocimetry measurements were used in the experiment. The vorticity field based on the Eulerian framework and the finite-time Lyapunov exponent (FTLE) field based on the Lagrangian framework were used to identify the dynamic processes of vortex rings, respectively. The results show that as the vortex rings impinge on concave surfaces from Re = 750 to Re = 7000, the extension of the main vortex ring in the straight-edged direction is larger than that in the concave direction, and the instability of the vortex ring is promoted. While the Reynolds number is increasing, the vortex ring deformation becomes larger, and the overall vortex ring cross section becomes smaller, leading to a larger attenuation of the vortex ring rotation. Calculations performed by the FTLE field were used to derive the Lagrangian coherent structure to analyze the boundaries of the vortex ring motion process, clearly observe the shape of the secondary vortex connecting segments, and verify the speculation by the vortex ring trajectory identification results. Finally, a dynamic model of vortex rings impinging a concave surface was proposed, and the inference of the experimental process was explained by the model. [ABSTRACT FROM AUTHOR]

This study develops a flexible deep learning framework aimed at reconstructing the global turbulent wakes from the randomly distributed sparse data. The framework is based on a Generative Adversarial Networks where the generator utilizes U-Net architecture and a constraint module is integrated into the training process. It is designed to overcome challenges posed by the chaotic behavior of turbulent fields, randomness in sensor layouts, and sparse sensor numbers. The efficacy of the model is validated across three high-fidelity datasets, including laminar wake behind a circular cylinder, turbulent wake behind a circular cylinder, and turbulent wake behind a square cylinder. The proposed model demonstrates the ability to accurately reconstruct flow patterns of both turbulent and laminar wakes, even utilizing merely 0.043% of the data from the target flow field. The proposed model exhibits significant generalization capability, which means that the model has a nearly independence from the distributions of sensors and a robust adaptation across the inputs with unseen sensor numbers. Ablation studies elucidate the distinct and complementary roles of each module within the model. Additionally, the behavior of the bottleneck tensor is analyzed through visualization, including comparisons with the lift coefficient, quantitative analyses and dimensionality reduction. These visualizations confirm the ability of the model to extract distinctive phase information reliably from sparse data, thereby guiding the reconstruction of global flow patterns. These findings highlight the potential of the model for applications in fluid dynamics where data is collected in a variable manner. [ABSTRACT FROM AUTHOR]