Served as Social Actors or Instrumental Role? Understanding the Usage of Smart Product from the Dual Processing Perspective

Based on the Dual processing theory, this study proposes that customers form their decision of smart products usage through two paths of: emotional and functional, at the same time. These paths are related to two types of behavior modes: affect-oriented which reflects the emotional or psychological demands we need, and the task-oriented which reflects essential needs for us to make use of these products. The behavior modes, represented by anthropomorphism cues and functional cues respectively, influence different kinds of trust, affect-based and cognition-based, and then determine the usage of smart products. The results will be examine through data collection in the near future. And we hope that the results could unravel several important findings and bring some managerial implications for manufacturers to improve their products

LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation

This paper presents a lightweight, efficient calibration neural network model for denoising low-cost microelectromechanical system (MEMS) gyroscope and estimating the attitude of a robot in real-time. The key idea is extracting local and global features from the time window of inertial measurement units (IMU) measurements to regress the output compensation components for the gyroscope dynamically. Following a carefully deduced mathematical calibration model, LGC-Net leverages the depthwise separable convolution to capture the sectional features and reduce the network model parameters. The Large kernel attention is designed to learn the long-range dependencies and feature representation better. The proposed algorithm is evaluated in the EuRoC and TUM-VI datasets and achieves state-of-the-art on the (unseen) test sequences with a more lightweight model structure. The estimated orientation with our LGC-Net is comparable with the top-ranked visual-inertial odometry systems, although it does not adopt vision sensors. We make our method open-source at: https://github.com/huazai665/LGC-Ne

A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness

Cracks play a crucial role in assessing the safety and durability of manufactured buildings. However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-decoder architecture to solve this challenge. Particularly, we designed a Dilated Residual Block (DRB) and a Boundary Awareness Module (BAM). The DRB pays attention to the local detail of cracks and adjusts the feature dimension for other blocks as needed. And the BAM learns the boundary features from the dilated crack label. Furthermore, the DRB is combined with a lightweight transformer that captures global information to serve as an effective encoder. Experimental results show that the proposed network performs better than state-of-the-art algorithms on two typical datasets. Datasets, code, and trained models are available for research at https://github.com/HqiTao/CT-crackseg

Platinum single atoms anchored on ultra-thin carbon nitride nanosheets for photoreforming of glucose

Photoreforming of biomass is a fascinating process that harnesses renewable sunlight and biomass to produce hydrogen under ambient conditions, holding a significant promise for future energy sustainability. However, the main challenge lies in developing highly active and stable photocatalysts with high light harvesting efficiency. In this study, we adopted a simple yet effective approach that combines thermal exfoliation and photodeposition to anchor Pt single atoms onto ultra-thin g-C3N4 nanosheets (MCNN). The incorporation of Pt single atoms induced a distinct red-shift in the visible light region, augmenting the solar energy absorption capacity, while the enlarged surface area of g-C3N4 nanosheets improved the mass transfer. Moreover, the enhanced photoelectric properties further contributed to the superior performance of Pt-MCNN-3.0 % in the photoreforming of glucose for hydrogen evolution. Remarkably, Pt-MCNN-3.0 % demonstrated an impressive hydrogen generation rate, approximately 59 times higher than that of MCNN, after a 3 h visible-light irradiation, maintaining a satisfied photo-stability. This work addresses the critical need for design of efficient photocatalysts, bringing us one step closer to realizing the potential of biomass photoreforming as a sustainable and clean energy conversion technology