Online learning has emerged as a widely used learning mode and will likely supplement traditional learning in the post-pandemic era. The purpose of this study is to present student voices of online school education by investigating students’ online learning experiences during the COVID-19 pandemic in various contexts, and explain why the impacts are important to student learning and well-being. Semi-structured in-depth interviews were conducted with nine students from China, Lebanon, and the United States to gain direct insight into students’ perceptions of each country. The results showed that the online learning environment provided at the national level, such as social conflicts, and the facilities provided at the individual level, such as information access, increase the educational inequity. High-school students experienced numerous psychological changes and encountered academic cheating issues in the home online-learning environment. We recommend that online school education should make significant improvements in pedagogy, students’ mental health, and learning assessment, and consider factors beyond technology solutions.
Avocado is an economically important crop and widely cultivated in the tropical and subtropical regions of China, whereas little is known about its genetic diversification. Twenty-six EST-SSR markers were developed based on avocado EST sequences. Molecular characterization was applied to assess the genetic relationships among different accessions. In total, nine gSSRs and nine EST-SSRs were selected to illuminate the genetic relationships within the 92 samples. Diversity parameters associated to the selected markers indicated the relatively low genetic diversity and a certain degree of inbreeding in the 92 avocado accessions. Clustering method and principal coordinate analysis clearly distinguished the accessions in accordance to the botanical races and geographic regions by molecular characterization. Moreover, the botanical race or hybrids of some unknown race accessions from different geographic origins could be determined based on known genotypes in the same cluster.
Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.
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