Defect-modulated thermal transport behavior of BAs under high pressure

Boron arsenide (BAs) is a covalent semiconductor with a theoretical intrinsic thermal conductivity approaching 1300 W/m K. The existence of defects not only limits the thermal conductivity of BAs significantly but also changes its pressure-dependent thermal transport behavior. Using both picosecond transient thermoreflectance and femtosecond time-domain thermoreflectance techniques, we observed a non-monotonic dependence of thermal conductivity on pressure. This trend is not caused by the pressure-modulated phonon–phonon scattering, which was predicted to only change the thermal conductivity by 10%–20%, but a result of several competing effects, including defect–phonon scattering and modification of structural defects under high pressure. Our findings reveal the complexity of the defect-modulated thermal behavior under pressure.The authors are grateful for the support from the National Science Foundation (NASCENT, Grant No. EEC-1160494; Center for Dynamics and Control of Materials DMR-1720595; CBET- 2211660); F.T., Z.R., and L.S. were supported by the Office of Naval Research under Multidisciplinary University Research Initiative (Grant No. N00014-16-1-2436).Center for Dynamics and Control of Material

Finite element methods for structures with large stochastic variations

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Finite element methods for structures with large stochastic variations

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Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead

A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning

Chinese herbal medicine classification is an important research task in intelligent medicine, which has been applied widely in the fields of smart medicinal material sorting and medicinal material recommendation. However, most current mainstream methods are semi-automatic, with low efficiency and poor performance. To tackle this problem, a novel Chinese herbal medicine classification method based on mutual learning has been proposed. Specifically, two small student networks are designed for collaborative learning, and each of them collects knowledge learned from the other one respectively. Consequently, student networks obtain rich and reliable features, which will further improve the performance of Chinese herbal medicinal classification. In order to validate the performance of the proposed model, a dataset with 100 Chinese herbal classes (about 10,000 samples) was utilized and extensive experiments were performed. Experimental results verify that the proposed method is superior to those of the latest models with equivalent or even fewer parameters, specifically, obtaining 3∼5.4% higher accuracy rate and 13∼37% lower loss. Moreover, the mutual learning model achieves 80.8% Chinese herbal medicine classification accuracy

A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning

Chinese herbal medicine classification is an important research task in intelligent medicine, which has been applied widely in the fields of smart medicinal material sorting and medicinal material recommendation. However, most current mainstream methods are semi-automatic, with low efficiency and poor performance. To tackle this problem, a novel Chinese herbal medicine classification method based on mutual learning has been proposed. Specifically, two small student networks are designed for collaborative learning, and each of them collects knowledge learned from the other one respectively. Consequently, student networks obtain rich and reliable features, which will further improve the performance of Chinese herbal medicinal classification. In order to validate the performance of the proposed model, a dataset with 100 Chinese herbal classes (about 10,000 samples) was utilized and extensive experiments were performed. Experimental results verify that the proposed method is superior to those of the latest models with equivalent or even fewer parameters, specifically, obtaining 3∼5.4% higher accuracy rate and 13∼37% lower loss. Moreover, the mutual learning model achieves 80.8% Chinese herbal medicine classification accuracy