Influencing factors of resident satisfaction in smart community services： An empirical study in Chengdu

Smart communities have shown great advantages in China\u27s pandemic control, but also exposed the shortcomings that some smart community services (SCS) are out of touch with residents\u27 needs in the post-pandemic era. Therefore, This study aims to explore those SCSs were needed to promote the sustainable development of smart communities. Based on the expectation disconfirmation theory and the modified ASCI model, this study establishes a smart community service resident satisfaction model and analyzes it with Amos structural equation model. The study results are as follows: (1) SCS outcome, ICT infrastructure, and SCS delivery all have a positive influence on resident satisfaction and their performances decrease in turn. (2) some of the factors that drive resident satisfaction most, such as Smart Property Service and Public Facility, have a lower rating. (3) residents are more concerned about the cost (including financial and emotional costs) than the quality of the SCSs. (4) Most residents\u27 expectations of SCS are irrational and that’s why it does not have a significant impact on satisfaction. (5) Resident Satisfaction is an important factor in enhancing Resident Confidence in SCS and promoting Resident Participation in improving SCS. This enlightens us that improving resident satisfaction is one of the effective ways to promote the sustainable development of Smart Community and continuously enhance the emergency response capabilities of grassroots communities in the post-pandemic era

Learning to Occlusion-Robustly Estimate 3-D States of Deformable Linear Objects from Single-Frame Point Clouds

Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remains a challenging open issue due to the high dimensionality of the state space, frequent occlusion, and noises. This paper focuses on learning to robustly estimate the states of DLOs from single-frame point clouds in the presence of occlusions using a data-driven method. We propose a novel two-branch network architecture to exploit global and local information of input point cloud respectively and design a fusion module to effectively leverage both the advantages. Simulation and real-world experimental results demonstrate that our method can generate globally smooth and locally precise DLO state estimation results even with heavily occluded point clouds, which can be directly applied to real-world robotic manipulation of DLOs in 3-D space.Comment: ICRA2023 submissio

Efficient and Scalable Graph Similarity Joins in MapReduce

Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results

Nebulization using ZnO/Si surface acoustic wave devices with focused interdigitated transducers

Propagation of surface acoustic waves (SAWs) on bulk piezoelectric substrates such as LiNbO3 and quartz, exhibits an in-plane anisotropic effect due to their crystal cut orientations. Thin film SAW devices, such as those based on ZnO or AlN, offer potential advantages, including isotropic wave velocities in all in-plane directions, higher power handling capability, and potentially lower failure rates. This paper reports experimental and simulation results of nebulization behaviour for water droplets using ZnO/Si surface acoustic wave devices with focused interdigital transducers (IDTs). Post-deposition annealing of the films at various temperatures was applied to improve the quality of the sputtering-deposited ZnO films, and 500 °C was found to be the optimal annealing temperature. Thin film ZnO/Si focused SAW devices were fabricated using the IDT designs with arc angles ranging from 30° to 90°. Nebulization was significantly enhanced with increasing the arc angles of the IDTs, e.g., increased nebulization rate, reduced critical powers required to initialise nebulization, and concentration of the nebulised plume into a narrower size of spray. Effects of applied RF power and droplet size have been systematically studied, and increased RF power and reduced droplet size significantly enhanced the nebulization phenomena

Underdetermined blind separation by combining sparsity and independence of sources

In this paper, we address underdetermined blind separation of N sources from their M instantaneous mixtures, where N>M , by combining the sparsity and independence of sources. First, we propose an effective scheme to search some sample segments with the local sparsity, which means that in these sample segments, only Q(Q < M) sources are active. By grouping these sample segments into different sets such that each set has the same Q active sources, the original underdetermined BSS problem can be transformed into a series of locally overdetermined BSS problems. Thus, the blind channel identification task can be achieved by solving these overdetermined problems in each set by exploiting the independence of sources. In the second stage, we will achieve source recovery by exploiting a mild sparsity constraint, which is proven to be a sufficient and necessary condition to guarantee recovery of source signals. Compared with some sparsity-based UBSS approaches, this paper relaxes the sparsity restriction about sources to some extent by assuming that different source signals are mutually independent. At the same time, the proposed UBSS approach does not impose any richness constraint on sources. Theoretical analysis and simulation results illustrate the effectiveness of our approach