The University Computer Foundation (UCF) course, which is a compulsory theoretical course for freshmen, especially science and engineering department students, lays the foundation for more advanced courses. However, the UCF course covers a wide variety of concepts, including the computer hardware, which are difficult for freshmen to comprehend. In recent years, virtual reality (VR) has become prevalent in many educational settings. To improve the teaching of the UCF course, we developed a VR program about introductory computer hardware and invited freshmen from two universities in China to take part in the experiment as participants. Four aspects of computer contents, namely the evolutionary history of computers, the computer components, the computer assembly, and the workflow of computer hardware, are displayed in the program. During the experiment, students' behavioral data were recorded. Then, the data were analyzed and the reasons were speculated for the differences among groups of various categories. From the experiment, the relationships between the students' behavior and their groups were found, and we demonstrated that our VR program is effective at attracting the students' curiosity and increasing their understanding of computer hardware in the UCF course. The study concludes with suggestions for practitioners and researchers in the field of VR for university education.
Objective: This study was to investigate the therapeutic effect of high-frequency repetitive magnetic stimulation (HF-rMS) at the sacrum for chronic constipation in Parkinson’s patients (PD). Materials and Methods: Eventually 48 PD patients were enrolled from July 2019 to October 2020, and randomly divided into the HF-rMS group (the intervention group, n = 24) and the sham HF-rMS group (the control group, n = 24). The intervention group received HF-rMS at the sacrum, whereas the control group received ineffective magnetic stimulation. We performed clinical evaluation before and after HF-rMS treatment, including constipation score scale (KESS questionnaire), Unified Parkinson’s Disease Rating Scale (UPDRS-III exercise examination), Hoehn-Yahr (H-Y) stage of motor function; simple mental status scale (MMSE), anxiety/depression table (HAD-A/HAD-D), the activity of daily living (ADL), and quality of life scale for patients with constipation (PAC-QOL) to evaluate symptoms and satisfaction of PD patients with chronic constipation. Results: There was no significant difference in the clinical characteristics between the two groups. As compared to the control group, the HF-rMS group displayed a larger change (pre and posttreatment) in the KESS scores of PD patients with chronic constipation, suggesting a significant improvement. Moreover, HF-rMS significantly promoted the mood, activity of daily living, and quality of life of PD patients when comparing the alteration of HAD-A/HAD-D scores, ADL scores, and PAC-QOL scores between the two groups. Finally, there was no significant difference in the change of the UPDRS III score and the MMSE score between the two groups. Conclusion: HF-rMS at the sacrum can improve chronic constipation in PD patients.
It is shown that great progress was recently made in the treatment of repetitive transcranial magnetic stimulation (rTMS) for neurological and psychiatric diseases. This study aimed to address how rTMS exerted it therapeutic effects by regulating competitive endogenous RNAs (ceRNAs) of lncRNA-miRNA-mRNA. The distinction of lncRNA, miRNA and mRNA expression in male status epilepticus (SE) mice treated by two different ways, low-frequency rTMS (LF-rTMS) vs. sham rTMS, was analyzed by high-throughput sequencing. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Gene–Gene Cross Linkage Network was established; pivotal genes were screened out. qRT-PCR was used to verify gene–gene interactions. Our results showed that there were 1615 lncRNAs, 510 mRNAs, and 17 miRNAs differentially which were expressed between the LF-rTMS group and the sham rTMS group. The expression difference of these lncRNAs, mRNAs, and miRNAs by microarray detection were consistent with the results by qPCR. GO functional enrichment showed that immune-associated molecular mechanisms, biological processes, and GABA-A receptor activity played a role in SE mice treated with LF-rTMS. KEGG pathway enrichment analysis revealed that differentially expressed genes were correlated to T cell receptor signaling pathway, primary immune deficiency and Th17 cell differentiation signaling pathway. Gene–gene cross linkage network was established on the basis of Pearson’s correlation coefficient and miRNA. In conclusion, LF-rTMS alleviates SE through regulating the GABA-A receptor activity transmission, improving immune functions, and biological processes, suggesting the underlying ceRNA molecular mechanisms of LF-rTMS treatment for epilepsy.
With the rapid development of computer technology, building pose estimation combined with Augmented Reality (AR) can play a crucial role in the field of urban planning and architectural design. For example, a virtual building model can be placed into a realistic scenario acquired by a Unmanned Aerial Vehicle (UAV)Â to visually observe whether the building can integrate well with its surroundings, thus optimizing the design of the building. In the work, we contribute a building dataset for pose estimation named BD3D. To obtain accurate building pose, we use a physical camera which can simulate realistic cameras in Unity3D to simulate UAVs perspective and use virtual building models as objects. We propose a novel neural network that combines MultiBin module with PoseNet architecture to estimate the building pose. Sometimes, the building is symmetry and ambiguity causes its different surfaces to have similar features, making it difficult for CNNs to learn the differential features between the different surfaces. We propose a generalized world coordinate system repositioning strategy to deal with it. We evaluate our network with the strategy on BD3D, and the angle error is reduced to [Formula: see text] from [Formula: see text]. Code and dataset have been made available at: https://github.com/JellyFive/Building-pose-estimation-from-the-perspective-of-UAVs-based-on-CNNs .
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