• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3631-3640
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• 2022

Uranium mill tailing ponds (UMTPs) are risk source of debris flow and a critical source of environmental U and Rn pollution. The technology of microbial induced calcium carbonate precipitation (MICP) has been extensively studied on reinforcement of UMTs, while little attention has been paid to the effects of MICP on U & Rn release, especially when incorporation of metakaolin and bacillus subtilis (MBS). In this study, the reinforcement and U & Rn immobilization role of MBS -MICP solidification in different grouting cycle for uranium mill tailings (UMTs) was comprehensively investigated. The results showed that under the action of about 166.7 g/L metakaolin and ~50% bacillus subtilis, the solidification cycle of MICP was shortened by 50%, the solidified bodies became brittle, and the axial stress increased by up to 7.9%, and U immobilization rates and Rn exhalation rates decrease by 12.6% and 0.8%, respectively. Therefore, the incorporation of MBS can enhance the triaxial compressive strength and improve the immobilization capacity of U and Rn of the UMTs bodies solidified during MICP, due to the reduction of pore volume and surface area, the formation of more crystals general gypsum and gismondine, as well as the enhancing of coprecipitation and encapsulation capacity.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.89-95
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• 2021

As the influence of fine dust on society spreads gradually, the public's interest in indoor air is increasingly rising. Air-purifying plants are drawing keen attention due to their natural purifying function enabled by plant physiology. However, as their fine dust reduction mechanism is limited to adsorption only, vegetation bio-filters that optimize purification effects through integration with air-conditioning systems is rising as an alternative. In accordance with the relevant standard test methods, this study looked into the fine dust reduction assessment method by air-conditioning airflow volume that can be used for the industrial spread of vegetation bio-filters. In the case of PM₁₀ at 300 ㎍/m³, it was in the order of EG-B(3,500CMH, 29 min.) < EG-A (2,500CMH, 37 min.) < CG(0CMH, 64 min.) for reaching the maintenance level (100 ㎍/m³) of publicly used facilities. For reaching the WHO Guideline(50 ㎍/m³) requirement, it was in the order of EG-B (51 min.) < EG-A (160 min.) < CG (170 min.). In the case of PM_2.5, it was in the order of EG-B (26 min.) < EG-A (33 min.) < CG (57 min.) for reaching the maintenance level (50 ㎍/m³) of publicly used facilities. It was in the order of EG-B (48 min) < EG-A (140 min) < CG (158 min) for reaching the WHO Guideline (25 ㎍/m³) requirement. The findings from the analysis showed that fine dust can be reduced most efficiently when the system is operated at 3,500CMH level. The limitation of this study is that due to the absence of a way of assessing the stress of plants in vegetation bio-filters, generating optimal air-conditioning air flow of the relevant system and economics analysis against the existing facility-type air purification system have been clarified, which should be explored further though follow-up studies.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.335-346
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• 2022

Buckling failure is a typical slope instability mode that should be paid more attention to. It is difficult to provide systematic guidance for the monitoring and management of such slopes due to unclear mechanism. Here we examine buckling failure as the potential instability mode for a slope above a railway tunnel in southwest China. A comprehensive model test system was developed that can be used to conduct buckling failure experiments. The displacement, stress, and strain of the slope were monitored to document the evolution of buckling failure during the experiment. Monitoring data reveal the deformation and stress characteristics of the slope with different slipping mass thicknesses and under different top loads. The test results show that the slipping mass is the main subject of the top load and is the key object of monitoring. Displacement and stress precede buckling failure, so maybe useful predictors of impending failure. However, the response of the stress variation is earlier than displacement variation during the failure process. It is also necessary to monitor the bedrock near the slip face because its stress evolution plays an important role in the early prediction of instability. The position near the slope foot is most prone to buckling failure, so it should be closely monitored.

• Advances in nano research
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• v.12 no.5
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• pp.529-547
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• 2022

Graphene is one of the strongest, stiffest, and lightest nanoscale materials known to date, making it a potentially viable and attractive candidate for developing lightweight structural composites to prevent high-velocity ballistic impact, as commonly encountered in defense and space sectors. In-plane twist in bilayer graphene has recently revealed unprecedented electronic properties like superconductivity, which has now started attracting the attention for other multi-physical properties of such twisted structures. For example, the latest studies show that twisting can enhance the strength and stiffness of graphene by many folds, which in turn creates a strong rationale for their prospective exploitation in high-velocity impact. The present article investigates the ballistic performance of twisted bilayer graphene (tBLG) nanostructures. We have employed molecular dynamics (MD) simulations, augmented further by coupling gaussian process-based machine learning, for the nanoscale characterization of various tBLG structures with varying relative rotation angle (RRA). Spherical diamond impactors (with a diameter of 25Å) are enforced with high initial velocity (V_i) in the range of 1 km/s to 6.5 km/s to observe the ballistic performance of tBLG nanostructures. The specific penetration energy (E_p^*) of the impacted nanostructures and residual velocity (V_r) of the impactor are considered as the quantities of interest, wherein the effect of stochastic system parameters is computationally captured based on an efficient Gaussian process regression (GPR) based Monte Carlo simulation approach. A data-driven sensitivity analysis is carried out to quantify the relative importance of different critical system parameters. As an integral part of this study, we have deterministically investigated the resonant behaviour of graphene nanostructures, wherein the high-velocity impact is used as the initial actuation mechanism. The comprehensive dynamic investigation of bilayer graphene under the ballistic impact, as presented in this paper including the effect of twisting and random disorder for their prospective exploitation, would lead to the development of improved impact-resistant lightweight materials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.75-93
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• 2023

The game industry has grown not only by combining them with various technologies also introducing new types of business models such as P2P, F2P, and P2W. Furthermore, games which implemented X2E model with blockchain technology are recently in the spotlight of the public attention. As domestic game companies have also prospect the blockchain games feasible, they are seeking ways to expand their global market share by strengthening the X2E model. Hence, by carrying this new business model out, it is expected to diversify their global revenue stream, which was previously confined to Asia region. This study analyzed the case of companies that have implemented the P2E and C2E models in order to suggest the direction of development for the game platform in Web 3.0 era. The cases of P2E game platform, which constitute of Axie Infinity and Mir 4, encompass the compensation structure, the stabilization mechanism of the in-game token economy, and future strategies regarding blockchain gaming. Likewise, the platform structure, business model, and future growth potential was discussed in terms of C2E scheme, focusing on the ZEPETO and Roblox cases. Based on the above case analysis, this study attempted to provide information on the current limitations and development directions of the P2E and C2E platforms. The current limitations in legal and industrial aspects should be addressed to facilitate the blooming of blockchain and P2E game industry. In addition, the necessity of not only social support also improvement on the technology and social stigma of full-time creators is ought to be emphasized in an effort to encourage the development of C2E platforms.

• Nutrition Research and Practice
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• v.16 no.3
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• pp.298-313
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• 2022

BACKGROUND/OBJECTIVES: The effectiveness of natural compounds in improving athletic ability has attracted attention in both sports and research. Gynostemma pentaphyllum (Thunb.) leaves are used to make traditional herbal medicines in Asia. The active components of G. pentaphyllum, dammarane saponins, or gypenosides, possess a range of biological activities. On the other hand, the anti-fatigue effects from G. pentaphyllum extract (GPE) and its effective compound, gypenoside L (GL), remain to be determined. MATERIALS/METHODS: This study examined the effects of GPE on fatigue and exercise performance in ICR mice. GPE was administered orally to mice for 6 weeks, with or without treadmill training. The biochemical analysis in serum, glycogen content, mRNA, and protein expressions of the liver and muscle were analyzed. RESULTS: The ExGPE (exercise with 300 mg/kg body weight/day of GPE) mice decreased the fat mass percentage significantly compared to the ExC mice, while the ExGPE showed the greatest lean mass percentage compared to the ExC group. The administration of GPE improved the exercise endurance and capacity in treadmill-trained mice, increased glucose and triglycerides, and decreased the serum creatine kinase and lactate levels after intensive exercise. The muscle glycogen levels were higher in the ExGPE group than the ExC group. GPE increased the level of mitochondrial biogenesis by enhancing the phosphorylation of peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) protein and the mRNA expression of nuclear respiratory factor 1, mitochondrial DNA, peroxisome proliferator-activated receptor-δ, superoxide dismutase 2, and by decreasing the lactate dehydrogenase B level in the soleus muscle (SOL). GPE also improved PGC-1α activation in the SOL significantly through AMPK/p38 phosphorylation. CONCLUSIONS: These results showed that GPE supplementation enhances exercise performance and has anti-fatigue activity. In addition, the underlying molecular mechanism was elucidated. Therefore, GPE is a promising candidate for developing functional foods and enhancing the exercise capacity and anti-fatigue activity.