ABSTRACT
Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.
ABSTRACT
BACKGROUND: Sleep quality is related to physical and mental health. Though bedtime procrastination has been identified as a potentially key source of poor sleep quality, related research is scarce. The goal of our study was to determine bedtime procrastination among nursing students and identify its influencing factors. METHODS: This cross-sectional study comprised 1827 junior college nursing students. The data were collected from November to December 2021 using a mobile app-based survey. We evaluated demographic factors, Big Five personality traits, self-regulatory fatigue, future time perspective, and problematic mobile phone use. Multiple linear regression analysis was used to identify independent characteristics that influence bedtime procrastination among junior college nursing students. RESULTS: The mean bedtime procrastination score in junior college nursing students was 25.11 ± 6.88. Family monthly income of 3000-6000 RMB (ß = 0.740; p = 0.015), as well as that of > 6000 RMB (ß = 1.708; p = 0.001), and an extroverted personality (ß = 0.225; p = 0.001), self-regulatory fatigue (ß = 0.135; p < 0.001), and problematic mobile phone use (ß = 0.078; p < 0.001) had significant positive effects on bedtime procrastination. Conscientious personality (ß = - 0.284; p = 0.003), neurotic personality (ß = - 0.203; p = 0.031), and future time perspective (ß = - 0.141; p < 0.001) had significant negative effects on bedtime procrastination. CONCLUSION: The nursing students who participated in this study had moderate levels of bedtime procrastination. Bedtime procrastination was predicted by higher monthly household income; personality traits of extroversion, conscientiousness, and neuroticism; self-regulatory fatigue; future time perspective; and problematic mobile phone use. PRACTICAL IMPLICATIONS: We recommend that effective measures are needed to help alleviate bedtime procrastination and improve the health and well-being of nursing students.
ABSTRACT
Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.
ABSTRACT
Branching morphogenesis is a general mechanism that increases the surface area of an organ. In chicken feathers, the flat epithelial sheath at the base of the follicle is transformed into periodic branches. How exactly the keratinocytes are organized into this pattern remains unclear. Here we show that in the feather follicle, the pre-branch basal keratinocytes have extensive filopodia, which contract and smooth out after branching. Manipulating the filopodia via small GTPases RhoA/Cdc42 also regulates branch formation. These basal filopodia help interpret the proximal-distal FGF gradient in the follicle. Furthermore, the topological arrangement of cell adhesion via E-Cadherin re-distribution controls the branching process. Periodic activation of Notch signaling drives the differential cell adhesion and contraction of basal filopodia, which occurs only below an FGF signaling threshold. Our results suggest a coordinated adjustment of cell shape and adhesion orchestrates feather branching, which is regulated by Notch and FGF signaling.
