The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.
This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.
Top-k spatial preference query ranks objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for wide range of location based services such as hotel browsing and apartment searching; several algorithms have been proposed to process them in Euclidean space. Although, few algorithms study top-k preference queries in a road network, however, they all focus on undirected road network. To the best of our knowledge, this is the first attempt to investigate the problem of processing the top-k spatial preference queries in a directed road networks. Computation of data object score requires examining the scores of feature objects in its spatial neighborhood. This may raise the processing cost resulting in high query processing time. Therefore, in this paper we propose a new preference query search algorithm called PSA that can efficiently answer the top-k spatial preference queries in directed road network. Experimental study shows that our algorithm significantly reduces the query processing time compared to baseline solution for a wide range of problem settings.
Deep learning techniques have been widely applied to classify tree species and segment tree structures. However, most recent studies have focused on the canopy and trunk segmentation, neglecting the branch segmentation. In this study, we proposed a new approach involving the use of the PointNet++ model for segmenting the canopy, trunk, and branches of trees. We introduced a preprocessing method for training LiDAR point cloud data specific to trees and identified an optimal learning environment for the PointNet++ model. We created two learning environments with varying numbers of representative points (between 2048 and 8192) for the PointNet++ model. To validate the performance of our approach, we empirically evaluated the model using LiDAR point cloud data obtained from 435 tree samples scanned by terrestrial LiDAR. These tree samples comprised Korean red pine, Korean pine, and Japanese larch species. When segmenting the canopy, trunk, and branches using the PointNet++ model, we found that resampling 25,000–30,000 points was suitable. The best performance was achieved when the number of representative points was set to 4096.
Introduction: A close link is proposed between smell impairment and Alzheimer's disease (AD) pathogenesis, pointing to the possibility that damaged olfaction may lead to cognitive deterioration. As a step to evaluate this possibility in rodent models, we used behavioral test battery to investigate whether cognitive and anxiety-like behaviors are altered in mice deprived of olfaction.Affective behavior was tested because cognitive deterioration, the canonical symptom of AD, is often accompanied by emotional symptoms called the behavioral and psychological symptoms of dementia (BPSD).Method: For olfactory deprivation, bulbectomy or methimazole-induced epithelial degeneration was used. Then, mice were subjected to behavioral test battery.Results: After olfactory deprivation, anxiety-like behavior emerged, with severer manifestation in bulbectomized than methimazole-treated mice. Avoidance behavior from 2MT, a synthesized derivative of a fox urine component, was suppressed by bulbectomy but exaggerated by methimazole treatment, which suggests that these two procedures differentially impact on olfactory information processing as is implied from the markedly different modes of anatomical damage after these two treatments. Novel object recognition test revealed that cognitive performance was not impaired by either treatment. However, Morris water maze (MWM) test, in which cognition was assessed in a wet, stressful environment, showed that both deprivation procedures worsened cognitive performance. Conclusion:We conclude that the suppressing influence on MWM performance by olfactory deprivation may be related to an elevated vulnerability to stressful environment, drawing the inference that emotional effects of smell impairment might play an important part in the proposed, smell-linked cognitive impairment in AD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.