• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4142-4149
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• 2021

In the radiation protection application, the metal-polymer composites have been developed for their radiation shielding properties. In this research, the elastomer composites doped by 10 ㎛ and 100nm size of lead, bismuth and tungsten particles as filler with 30 and 60 wt percentages were prepared. To survey the shielding properties of the polymer composites using gamma-ray emitted from 152Eu and 137Cs sources, the gamma flux was measured by using NaI(Tl) detector, then the linear attenuation coefficient was calculated. Also, the Monte Carlo simulation (MCs) method was used. The results showed a direct relationship between the linear attenuation coefficients of the absorbent and filler ratio. Also, the decrease in the particle size of the shielding material in each weight percentage improved the radiation shielding features. When the dimension of the particles was in the order of nano-size, more attenuation was achieved. At low energies used for medical diagnostic X-ray applications due to the predominance of the photoelectric effect, bismuth and lead were suitable selection as filler.

• Geomechanics and Engineering
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• v.16 no.5
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• pp.527-538
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• 2018

The study on the performance of the nonlinear friction tuned mass dampers (FTMD) for the mitigation of the seismic responses of the structures is a topic that still inspires the efforts of researchers. The present paper aims to carry out a numerical study on the optimum tuning of TMD and FTMD parameters using a multi-objective particle swarm optimization (MOPSO) algorithm including soil-structure interaction (SSI) effects for seismic applications. Considering a 3-story structure, the performances of the optimized TMD and FTMD are compared with the uncontrolled structure for three types of soils and the fixed base state. The simulation results indicate that, unlike TMDs, optimum tuning of FTMD parameters for a large preselected mass ratio may not provide a best and optimum design. For low mass ratios, optimal selection of friction coefficient has an important key to enhance the performance of FTMDs. Consequently, a free parameter search of all FTMD parameters provides a better performance in comparison with considering a preselected mass ratio for FTMD in the optimum design stage of the FTMD. Furthermore, the SSI significant effects on the optimum design of the TMD and FTMD. The simulation results also show that the FTMD provides a better performance in reducing the maximum top floor displacement and acceleration of the building in different soil types. Moreover, the performance of the TMD and FTMD decrease with increasing soil softness, so that ignoring the SSI effects in the design process may give an incorrect and unrealistic estimation of their performance.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Physical Therapy Rehabilitation Science
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• v.11 no.2
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• pp.154-164
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• 2022

Objective: The purpose of this study is to select a cognitive training game that can evaluate five cognitive domains and to study their validity with existing cognitive evaluation tools. Design: Methods: Delphi survey was conducted through the 2nd questionnaire for 30 experts to select games suitable for training 5 cognitive domains. Five cognitive training games and Mini Mental State Examination - Korea(MMSE-K), and cognitive impairment screening test(CIST) were conducted for 82 elderly in the community. Pearson correlation analysis was performed to find out the correlation of the three tests. The ROC curve was used to calculate the selection criteria for the game results for the screening evaluation of the presence or absence of mild cognitive impairment. Results: The coefficient of variation to evaluate the stability of the Delphi survey was less than 0.50 in most game items. The 'correct answers' and 'level' of the five final selected game items showed a statistically significant positive correlation with the CIST and MMSE-K scores. CIST score and 'time' of all game items except 'number making_time' showed a statistically significant negative correlation. Conclusions: The validity of the cognitive training program using smart devices was evaluated, and the criteria for classifying the cognitive domain and distinguishing the presence or absence of cognitive impairment were confirmed.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2445-2453
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• 2021

Since the nuclear data forms a vital component in reactor physics computations, the nuclear community needs processing codes as tools for translating the Evaluated Nuclear Data Files (ENDF) to simulate nuclear-related problems such as an ACE format that is used for MCNP. Errors, inaccuracies or discrepancies in library processing may lead to a calculation that disagrees with the experimentally measured benchmark. This paper provides an overview of the processing and preparation of ENDF/B-VIII.0 incident neutron data with NECP-Atlas and NJOY codes for implementation in the MCNP code. The resulting libraries are statistically inter-compared and tested by conducting benchmark calculations, as the mutualcomparison is a source of strong feedback for further improvements in processing procedures. The database of the benchmark experiments is based on a selection taken from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP handbook) and those proposed by Russell D. Mosteller. In general, there is quite good agreement between the NECP-Atlas1.2 and NJOY21^(1.0.0.json) results with no substantial differences, if the correct input parameters are used.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.17-24
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• 2021

There is a problem that unknown paintings are sophisticated in the level of forgery, making it difficult for even experts to determine whether they are genuine or counterfeit. These problems can be suspected of forgery even if the genuine product is submitted, which can lead to a decline in the value of the work and the artist. To address these issues, in this paper, we propose a system to classify chromaticity data among extracted data through objective analysis into quadrants, extracting comparisons and intersections, and estimating authors of unknown paintings using XRF and hyperspectral spectrum data from corresponding points.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Steel and Composite Structures
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• v.42 no.1
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• pp.59-74
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• 2022

This study introduces a general reliability-based, performance-based design framework to design frames regarding their uncertainties and user-defined design goals. The Iterative-R method extracted from the main framework can designate a proper R (i.e., response modification factor) satisfying the design goal regarding target reliability index and pre-defined probability of collapse. The proposed methodology is based on FEMA P-695 and can be used for all systems that FEMA P-695 applies. To exemplify the method, multiple three-dimensional, four-story steel special moment-resisting frames are considered. Closed-form relationships are fitted between frames' responses and the modeling parameters. Those fits are used to construct limit state functions to apply reliability analysis methods for design safety assessment and the selection of proper R. The frameworks' unique feature is to consider arbitrarily defined probability density functions of frames' modeling parameters with an insignificant analysis burden. This characteristic enables the alteration in those parameters' distributions to meet the design goal. Furthermore, with sensitivity analysis, the most impactful parameters are identifiable for possible improvements to meet the design goal. In the studied examples, it is revealed that a proper R for frames with different levels of uncertainties could be significantly different from suggested values in design codes, alarming the importance of considering the stochastic behavior of elements' nonlinear behavior.

• Journal of Forest and Environmental Science
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• v.40 no.2
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• pp.111-117
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• 2024

An evaluation was carried out on variability in morphology of fruits and seeds (number and weight) of Tetrapleura tetraptera (Schumach. and Thonn.) Taub. from different populations across its distribution range in Nigeria. Bulk fruit samples were collected and examined for variations in morphological characters. Differences in morphological character of fruits and seeds among the populations were determined using analysis of variance at 5% level of probability. The relationships among morphological characters were determined using Pearson correlation coefficient (r). Significant variations (p<0.05) existed among T. tetraptera populations for all the evaluated characters: fruit length, fruit width, number of seeds per fruit and seed weight. A positive significant strong correlation (r=0.96) was found between seed weight and number of seeds per fruit, while no correlation existed between fruit length, width and number of seeds. Seed weight was positively correlated with minimum altitude (r=0.97) and maximum altitude (r=0.99) of seed populations. Number of seeds was also significantly correlated with maximum altitude (r=0.965). There was no significant correlation between geo-climatic variables and fruit dimensions (length and width). Observed variations in morphological traits within and across populations of T. tetraptera may be used as proxy to estimate genetic diversity and selection of superior trees for improved productivity.