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In order to improve the performance of aerodynamic and take off and landing from water surface, a new biomimetic UAV with multi-segment wing in ground effect is designed based on the hull fuselage, multi-segment wing and T-tai. The pressure nephogram, pressure coefficient and lift-drag characteristics of the UAV in different states are studied in detail with N-S equation and K-Ω-SST turbulence model. The simulation results show that the influence of ground effect decreases with the increase of the UAV height above water. When the ratio of height above water to mean geometric chord (ratio of height to chord H/c) is close to 1, the ground effect is more significant. When the UAV is 0.2 m above water, the lift coefficient and lift-drag ratio increase by 21.91% and 40.37% respectively, and the drag coefficient decreases by 15.22%. For the multi-segment wing layout, the ground effect flight mainly affects the pressure coefficient and pressure nephogram of the lower surface. And the positive pressure amplitude of pressure coefficient in lower surface gradually decreases from inside to outside along the span direction, and the pressure coefficient near the leading and trailing edges along the chord direction is small. With the pressure nephogram, the results show that the impact of ground effect on the growth rate of lift is mainly concentrated in the area below the mid and inner wing. The lift curve slope can be significantly increased by ground effect (increased by 8.89% when H/c is 1). The growth rate of lift coefficient and the decrease rate of drag coefficient gradually increase when the angle of attack increases, and the growth rate of lift-drag ratio (H/c is 1) reaches more than 26% when the angle of attack is greater than 2°. It has been proven that the design has excellent aerodynamic and flight performance with multiple flight tests of verification UAV taking off and landing from water surface and with and without ground effects. The UAV can provide flight platforms for water quality detection, water surface transportation, search and rescue reconnaissance, etc.

Online learning has become the new educational pattern during the COVID-19 pandemic and is likely to supplement conventional schooling in the post-pandemic world. Lacking prior online learning experiences, the population of K-12 students deserves our special attention. Using purposeful sampling, this study investigated K-12 online learning experiences in China based on a large-scale survey (N = 118,589). Leveraging both quantitative and qualitative evidence, this study supported online learning as a flexible alternative to conventional schooling in emergency situations with a discussion of its benefits and limitations, and revealed key findings regarding K-12 students' online learning pattern, experiences, and engagement, as well as the influencing factors. The research findings can inform the future design and implementation of online learning programs in primary and secondary schools.

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm's iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon's rank sum test shows that there are significant statistical differences between the RWOA and other algorithms [ABSTRACT FROM AUTHOR]

Steam displacement is prone to cross-flow, small swept area, large oil–water ratio, large oil–water interfacial tension, and low oil displacement efficiency. Compared with steam flooding, foam flooding can effectively reduce the residual oil in the small throat of the main flow channel and the small hole in the near flow channel and increase the overall recovery factor. Therefore, researchers carried out CO2 and chemical agent-assisted steam displacement. However, at present, there is a lack of research on the occurrence mechanism and model of residual oil. Steam flooding often encounters challenges such as cross-flow, limited sweep area, and high oil–water ratio. Foam flooding offers a promising alternative by effectively reducing residual oil in narrow throats and the near flow channel, thereby enhancing overall recovery rates compared to steam flooding alone. Therefore, chemical agent-assisted steam flooding was applied to enhance heavy oil recovery. However, the occurrence mechanism and model of residual oil after chemical agent-assisted steam is not clear. To fill this gap, the CO2 foam viscosity reducer assisted steam (CFVAS) flooding technology has been adopted and carried out in several studies. First, the foam viscosity reducer was prepared and its foam properties (viscosity reduction effect, foam volume, and half-life) were tested. Subsequently, the CFVAS displacement experiments after steam flooding were carried out, and the flow behavior of the remaining oil in multiple regions (main flow channel, near flow channel, and far flow channel) was analyzed. Finally, the shape and number of remaining oil under different displacement stages were compared, and the occurrence mode of remaining oil under CFVAS displacement was determined. The results indicate the following: (1) During steam flooding, the amount of near flow channel residual oil decreased with injected pore volumes (PV), transforming into columnar structures in small perforations and film-like formations in far flow channels. (2) CFVAS flooding, including the foam stability mechanism, flow channel adjustment mechanism, and emulsification and dispersion mechanism, can improve overall recovery rates by 55.2% by driving the remaining oil in near flow channels. (3) During CFVAS flooding stage, crude oil mobility notably improved and flooding front expanded more evenly. Residual oil primarily existed as oil-in-water (O/W) emulsions with discontinuous columns. (4) In the CFVAS flooding stage, residual oil mainly formed O/W emulsions through emulsification and dispersion, with foam-filled large and medium pores, concentrating residual oil in thick and middle throats. This work can provide important references for injecting CO2 gas into reservoirs to enhance heavy oil recovery and promote carbon sequestration. [ABSTRACT FROM AUTHOR]

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students' perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students' perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students' engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students' emotions and achievement. [ABSTRACT FROM AUTHOR]