• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Journal of Animal Science and Technology
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• v.64 no.1
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• pp.183-186
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• 2022

Lactiplantibacillus plantarum (L. plantarum) ST was isolated from De'ang pickled tea in Yunnan Province, China. The genomes of strain ST were fully sequenced and analyzed using the PacBio RS II sequencing system. Our previous study has shown that L. plantarum ST is a potential probiotic strain. It had strong tolerance in the simulated artificial gastrointestinal tract, and in the antagonism tests, this strain showed strong antibacterial activity. Therefore, as a probiotic, it may be used in animal breeding. L. plantarum ST genome was composed of 1 circular chromosome and 7 plasmids. The length of the whole genome was 3320817 bp, and the annular chromosome size was 3058984 bp, guanine + cytosine (G ± C) content (%) was 44.76%, which contained 2945 protein-coding sequences (CDS). This study will contribute to a further comprehensive understanding of L. Plantarum ST at the genomic level and provide a theoretical basis for its future application in animal breeding.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4049-4061
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• 2022

TRISO-coated particle fuel is widely used in high temperature gas cooled reactors and other advanced reactors. The performance of coated fuel particle is one of the fundamental bases of reactor safety. The failure probability of coated fuel particle should be evaluated and determined through suitable fuel performance models and methods during normal and accident condition. In order to better facilitate the design of coated particle fuel, a new TRISO fuel performance code named FRAT (Fission product Release Analysis Tool) was developed. FRAT is designed to calculate internal gas pressure, mechanical stress and failure probability of a coated fuel particle. In this paper, FRAT was introduced and benchmarked against IAEA CRP-6 benchmark cases for coated particle failure analysis. FRAT's results agree well with benchmark values, showing the correctness and satisfactory applicability. This work helps to provide a foundation for the credible application of FRAT.

• Advances in nano research
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• v.14 no.6
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• pp.541-555
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• 2023

The extensive use of polymeric matrix composites in the athletic sector may be attributed to its high strength-to-weight ratio, production economy, and a longer lifespan than conventional materials. This study explored the impact of carbon nanotubes on the properties of different composite field sports equipment components. The test specimens were fabricated using the compression molding technique. The insertion of carbon nanotubes increases mechanical properties related to the process parameters to account for an improvement in the stick sections' overall performance. The dynamic response of functionally graded reinforced nanocomposite wire structure is examined in this paper on the bases of high-order hyperbolic beam theory lined to the size-dependent nonclassical nonlocal theory under the external mechanical load due to the physical activities. Finally, the impact of different parameters on the stability of nanocomposite structures is discussed in detail.

• Geomechanics and Engineering
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• v.35 no.4
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• pp.411-424
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• 2023

Reasonable estimates of tunnel lining dislocations in the operation stage, especially under longitudinal differential settlement, are important for the design of waterproof gaskets. In this paper, a modified shell-joint model is proposed to calculate shield tunnel dislocations under longitudinal differential settlement, with the ability to consider the nonlinear shear stiffness of the joint. In the case of shell elements in the model, an elastoplastic damage constitutive model was adopted to describe the nonlinear stress-strain relationship of concrete. After verifying its applicability and correctness against a full-scale tunnel test and a joint shear test, the proposed model was used to analyze the dislocation behaviors of a shield tunnel in Shanghai Metro Line 2 under longitudinal differential settlement. Based on the results, when the tunnel structure is solely subjected to water-earth load, circumferential and longitudinal joint dislocations are all less than 0.1 mm. When the tunnel suffers longitudinal differential settlement and the curvature radius of the differential settlement is less than 300 m, although maximum longitudinal joint dislocation is still less than 0.1 mm, the maximum circumferential joint dislocation is approximately 10.3 mm, which leads to leakage and damage of the tunnel structure. However, with concavo-convex tenons applied to circumferential joints, the maximum dislocation value reduces to 4.5 mm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.485-497
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• 2024

Ring beam is the main anchorage zone of the tendons in the nuclear power prestressed concrete containment vessel (PCCV). Its safety is crucial and has a great influence on the overall performance of PCCV. In this paper, two half-scale ring beams were tested to investigate the mechanical performance of the anchorage zone in the PCCV under multidirectional pressure. The effect of working condition with different tension sequences was investigated. Additionally, a half axisymmetric plane model of the containment was established by the finite element simulation to further predict the experimental responses and propose the local reinforcement design in the anchorage zone of the ring beam. The results showed that the ultimate load of the specimens under both working conditions was greater than the nominal ultimate tensile force. The original reinforcement design could meet the bearing capacity requirements, but there was still room for optimization. The ring beam was generally under pressure in the anchorage area, while the splitting force appeared in the under-anchor area, and the spalling force appeared in the corner area of the tooth block, which could be targeted for local strengthening design.

• Bulletin of the Korean Chemical Society
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• v.35 no.9
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• pp.2726-2732
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• 2014

The promoting effect of rhodium on the structure and activity of the supported Cu-Co based catalysts for CO hydrogenation was investigated in detail. The samples were characterized by DRIFTS, $N_2$-adsorption, XRD, $H_2$-TPR, $H_2$-TPD and XPS. The results indicated that the introduction of rhodium to Cu-Co catalysts resulted in modification of metal dispersion, reducibility and crystal structure. DRIFTS results of CO hydrogenation at reaction condition (P=2 MPa, $T=260^{\circ}C$) indicated the addition of 1 wt % rhodium improved hydrogenation ability of Cu-Co catalysts. The ethanol selectivity and CO conversion were both improved by 1 wt % Rh promoted Cu-Co based catalysts. The alcohol distribution over un-promoted and rhodium promoted Cu-Co based catalysts obeys A-S-F rule and higher chain growth probability was got on rhodium promoted catalyst.

• Bulletin of the Korean Chemical Society
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• v.34 no.9
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• pp.2711-2719
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• 2013

Hydrogen bonding interaction between alcohols and water molecules is an important characteristic in the aqueous solutions of alcohols. In this paper, a series of molecular dynamics simulations have been performed to investigate the aqueous solutions of low molecular weight alcohols (methanol, ethylene glycol and glycerol) at the concentrations covering a broad range from 1 to 90 mol %. The work focuses on studying the effect of the alcohols molecules on the hydrogen bonding of water molecules in binary mixtures. By analyzing the hydrogen bonding ability of the hydroxyl (-OH) groups for the three alcohols, it is found that the hydroxyl group of methanol prefers to form more hydrogen bonds than that of ethylene glycol and glycerol due to the intra-and intermolecular effects. It is also shown that concentration has significant effect on the ability of alcohol molecule to hydrogen bond water molecules. Understanding the hydrogen bonding characteristics of the aqueous solutions is helpful to reveal the cryoprotective mechanisms of methanol, ethylene glycol and glycerol in aqueous solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.