Fabrication and frequency response of a complex ultrasonic transducer for multilayer evaluation

To characterize the thickness of a corrosive fluid system's coated facilities, such as pipes, tubes, tanks, and structural members, a complex ultrasonic transducer capable of measuring multilayered parts was fabricated and the time and frequency responses were evaluated. The target transducer was constructed with two active dual elements made from tape-cast PbNb₂O₆ sheets and an additional thin-film active element made from sol–gel spin-coated Pb(Zr_0.52Ti_0.48)O₃ films. After adjusting the properties of each active element, a complex transducer was assembled after matching the impedance of each element and considering the minimal interference between the active layers. The impulse response of the assembled complex transducer shows excellent characteristics. Moreover, the assembled transducer's capability of accurately measuring the thickness assures that it can be directly applied to related industries.     

Electrochemical characteristics of heme proteins in hydroxyethylcellulose film

A stable film made from hydroxyethylcellulose (HEC) on pyrolytic graphite (PG) electrode was employed for incorporating hemoglobin (Hb), myoglobin (Mb) and horseradish peroxidase (HRP), and the electrochemical characteristics of the proteins were studied correspondingly. Experimental results revealed that HEC film could greatly accelerate electron transfer between the proteins and electrode, and the proteins showed a thin layer electrochemical behavior in the film. Moreover, all the proteins in the film exhibited good catalytic activity towards the reduction of hydrogen peroxide (H₂O₂) in the low H₂O₂ concentration range. In the high concentration range, H₂O₂ would exhibit toxicity effect on the proteins. The electrochemical properties and electrocatalytic abilities of the three heme proteins in HEC film have been compared, and the optimal conditions for H₂O₂ biosensor fabrication have been obtained.     

Distributed patching for mobile wireless sensor networks

Mobile wireless sensor networks (MWSNs), the continuous movement of sensor nodes, may cause complete disconnection of the network or at best a part of it. The design of such networks should guarantee that all sensor nodes at all times have a path to the sink node(s). Prolonging the lifetimes of the MWSNs is a crucial design issue but should not be at the expense of other essential functions such as connectivity. In this paper, we propose an energy efficient distributed framework for connectivity maintenance of MWSNs. In the proposed framework, sensor nodes of the MWSNs schedule and control their radio frequency (RF) modules based on dynamic coordinated reconstruction mechanism, some senor nodes may lose their connectivity with the network due to mobility, depletion of energy and/or incurred faults. Two protocols are developed to patch up the disconnection of the MWSNs. The first protocol reestablishes the network connection consuming far less energy than state-of-the-art alternatives. However, it is capable of reestablishing the connection when no more than 20% of the MWSN backbone nodes lose connection concurrently. The second protocol extends this limit to operate when up to 35% of the MWSN backbone nodes lose connection simultaneously.     

The influence of user cognition on consumption decision-making from the perspective of bounded rationality

Consumer decision-making is related to the success or failure of enterprises, and products that cater to the cognitive preferences of users have become a focus of current research. Based on the theory of bounded rationality, this paper explores the cognitive process of consumer decision-making. Then, how product shape affects consumption decision-making is analyzed with eye-tracking technology. Finally, the design principle of the product form is further explored. The results demonstrate the following: (1) the perceptual cognition of users has a driving effect on consumption behavior; (2) as a key factor affecting the perceptual cognition of users, product form affects consumer decision-making by influencing the degree of approach motivation; (3) by establishing the mapping relationship between product form elements and user images, the principles of product form design can be more consistent with user image preferences. This study provides useful suggestions for how to increase the purchase behavior of users from the perspective of bounded rationality.     

A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly

Human-robot collaborative (HRC) assembly has become popular in recent years. It takes full advantage of the strength, repeatability and accuracy of robots and the high-level cognition, flexibility and adaptability of humans to achieve an ergonomic working environment with better overall productivity. However, HRC assembly is still in its infancy nowadays. How to ensure the safety and efficiency of HRC assembly while reducing assembly failures caused by human errors is challenging. To address the current challenges, this paper proposes a novel human-cyber-physical assembly system (HCPaS) framework, which combines the powerful perception and control capacity of digital twin with the virtual-reality interaction capacity of augmented reality (AR) to achieve a safe and efficient HRC environment. Based on the framework, a deep learning-enabled fusion method of HCPaS is proposed from the perspective of robot-level fusion and part-level fusion. Robot-level fusion perceives the pose of robots with the combination of PointNet and iterative closest point (ICP) algorithm, where the status of robots together with their surroundings could be registered into AR environment to improve the human's cognitive ability of complex assembly environment, thus ensuring the safe HRC assembly. Part-level fusion recognizes the type and pose of parts being assembled with a parallel network that takes an extended Pixel-wise Voting Network (PVNet) as the base architecture, on which assembly sequence/process information of the part could be registered into AR environment to provide smart guidance for manual work to avoid human errors. Eventually, experimental results demonstrate the effectiveness and efficiency of the approach.     

Complex Attributed Network Embedding for medical complication prediction

Knowledge and Information Systems - To assure the development of effective treatment plans, it is crucial for understanding the complication relationships among diseases. In practice, traditional...