61 research outputs found
P-bRS: A Physarum-Based Routing Scheme for Wireless Sensor Networks
Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, we establish a novel selecting next hop model (SNH). Based on this model, we present a novel Physarum-based routing scheme (P-bRS) for WSNs to balance routing efficiency and energy equilibrium. In P-bRS, a sensor node can choose the proper next hop by using SNH which comprehensively considers the distance, energy residue, and location of the next hop. The simulation results show how P-bRS can achieve the effective trade-off between routing efficiency and energy equilibrium compared to two famous algorithms
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In situ grown palladium nanoparticles on polyester fabric as easy-separable and recyclable catalyst for Suzuki-Miyaura reaction
Palladium nanoparticles supported on low-melting polyester (Pd/LMPET) fabric were prepared through a microwave irradiation assisted method. In this way, in situ growth of Pd nanoparticles onto an easy to handle material was initiated and proceeded. The results of the characterization revealed that the palladium nanoparticles were well-dispersed on the surfaces of the polyester fibers. The Pd/LMPET fabrics were then employed in the Suzuki-Miyaura coupling. They exhibited excellent catalytic activity in ethanol/water under air atmosphere at 50 °C. Importantly, the Pd/LMPET fabrics could be separated from reaction mixture conveniently and they can still maintain good activity after 8 cycles without Pd leaching. © 2021 The Author
Cognitive internet of things: concepts and application example,”
Abstract Internet of Things (IoT) is a heterogeneous, mixed and uncertain ubiquitous network, the application prospect of which is extensive in the field of modern intelligent service. Having done a deep investigation on the discrepancies between service offering and application requirement, we believed that current IoT lacks enough intelligence and cannot achieve the expected increasing applications' performance. By integrating intelligent thought into IoT, we presented a new concept of Cognitive Internet of Things (CIoT) in this paper. CIoT can apperceive current network conditions, analyze the perceived knowledge, make intelligent decisions, and perform adaptive actions, which aim to maximize network performance. We modeled the CIoT network topology and designed cognition-process-related technologies, analyzed the payoffs of cooperative cognition based on game theory, which illustrates those novel designs can endows IoT with intelligence and fully improve system's performance. Finally, an application example was introduced based on the concept of CIoT
Reputation Revision Method for Selecting Cloud Services Based on Prior Knowledge and a Market Mechanism
The trust levels of cloud services should be evaluated to ensure their reliability. The effectiveness of these evaluations has major effects on user satisfaction, which is increasingly important. However, it is difficult to provide objective evaluations in open and dynamic environments because of the possibilities of malicious evaluations, individual preferences, and intentional praise. In this study, we propose a novel unfair rating filtering method for a reputation revision system. This method uses prior knowledge as the basis of similarity when calculating the average rating, which facilitates the recognition and filtering of unfair ratings. In addition, the overall performance is increased by a market mechanism that allows users and service providers to adjust their choice of services and service configuration in a timely manner. The experimental results showed that this method filtered unfair ratings in an effective manner, which greatly improved the precision of the reputation revision system
P-bRS: A Physarum-Based Routing Scheme for Wireless Sensor Networks
Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, we establish a novel selecting next hop model (SNH). Based on this model, we present a novel Physarum-based routing scheme (P-bRS) for WSNs to balance routing efficiency and energy equilibrium. In P-bRS, a sensor node can choose the proper next hop by using SNH which comprehensively considers the distance, energy residue, and location of the next hop. The simulation results show how P-bRS can achieve the effective trade-off between routing efficiency and energy equilibrium compared to two famous algorithms
Anatomical study of simple landmarks for guiding the quick access to humeral circumflex arteries
BACKGROUND: The posterior and anterior circumflex humeral artery (PCHA and ACHA) are crucial for the blood supply of humeral head. We aimed to identify simple landmarks for guiding the quick access to PCHA and ACHA, which might help to protect the arteries during the surgical management of proximal humeral fractures. METHODS: Twenty fresh cadavers were dissected to measure the distances from the origins of PCHA and ACHA to the landmarks (the acromion, the coracoid, the infraglenoid tubercle, the midclavicular line) using Vernier calipers. RESULTS: The mean distances from the origin of PCHA to the infraglenoid tubercle, the coracoid, the acromion and the midclavicular line were 27.7 mm, 50.2 mm, 68.4 mm and 75.8 mm. The mean distances from the origin of ACHA to the above landmarks were 26.9 mm, 49.2 mm, 67.0 mm and 74.9 mm. CONCLUSION: Our study provided a practical method for the intraoperative identification as well as quick access of PCHA and ACHA based on a series of anatomical measurements
An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days
Classification of Urban Morphology with Deep Learning: Application on Urban Vitality
10.1016/j.compenvurbsys.2021.101706Computers, Environment and Urban Systems9010170
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