• Mycobiology
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• v.47 no.2
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• pp.230-241
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• 2019

The Great Sebkha of Oran is a closed depression located in northwestern of Algeria. Despite the ranking of this sebkha among the wetlands of global importance by Ramsar Convention in 2002, no studies on the fungal community in this area have been carried out. In our study, samples were collected from two different regions. The first region is characterized by halophilic vegetation and cereal crops and the second by a total absence of vegetation. The isolated strains were identified morphologically then by molecular analysis. The biotechnological interest of the strains was evaluated by testing their ability to grow at different concentration of NaCl and to produce extracellular enzymes (i.e., lipase, amylase, protease, and cellulase) on solid medium. The results showed that the soil of sebkha is alkaline, with the exception of the soil of cereal crops that is neutral, and extremely saline. In this work, the species Gymnoascus halophilus, Trichoderma gamsii, the two phytopathogenic fungi, Fusarium brachygibbosum and Penicillium allii, and the teleomorphic form of P. longicatenatum observed for the first time in this species, were isolated for the first time in Algeria. The halotolerance test revealed that the majority of the isolated are halotolerant. Wallemia sp. and two strains of G. halophilus are the only obligate halophilic strains. All strains are capable to secrete at least one of the four tested enzymes. The most interesting species presenting the highest enzymatic index were Aspergillus sp. strain A4, Chaetomium sp. strain H1, P. vinaceum, G. halophilus, Wallemia sp. and Ustilago cynodontis.

• Journal of the Korea Institute of Building Construction
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• v.19 no.3
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• pp.247-254
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• 2019

The number of construction accident deaths in Korea is increasing and most causes of construction accidents are human factors. Voluntary participation of construction workers in safety activities is likely to improve these human factors and increase the prevention effect of construction accidents. Therefore, there is a need to study the motivation of workers to meet the voluntary participation of construction workers. In this respect, the purpose of this study is to compare the importance of construction field engineers and construction managers about the safety motivation factors of construction workers. This study analyzed the results of the questionnaire survey about safety motivation factors and conducted a T-test for these factors. The results of this study can be used as managing method to effective on-site safety management by minimizing the difference between the two groups according to motivation factors of construction workers.

• Animal Bioscience
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• v.34 no.2
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.27-37
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• 2020

The Fukushima Daiichi accident in 2011 revealed some vulnerabilities of existing Nuclear Power Plants (NPPs) under extended Station Blackout (SBO) accident conditions. One of the key Severe Accident Management (SAM) strategies developed post Fukushima accident is the In-Vessel Retention (IVR) Strategy which aims to retain the structural integrity of the Reactor Pressure Vessel (RPV). RELAP/SCDAPSIM/MOD3.4 is selected to predict the thermal-hydraulic response of APR1400 undergoing an extended SBO. To assess the effectiveness of the IVR strategy, it is essential to quantify the underlying uncertainties. In this work, both the epistemic and aleatory uncertainties are considered to identify the success window of the IVR strategy. A set of in-vessel relevant phenomena were identified based on Phenomena Identification and Ranking Tables (PIRT) developed for severe accidents and propagated through the thermal-hydraulic model using Wilk's sampling method. For this work, a Systems Engineering (SE) approach is applied to facilitate the development process of assessing the reliability and robustness of the APR1400 IVR strategy. Specifically, the Kossiakoff SE method is used to identify the requirements, functions and physical architecture, and to develop a design verification and validation plan. Using the SE approach provides a systematic tool to successfully achieve the research goal by linking each requirement to a verification or validation test with predefined success criteria at each stage of the model development. The developed model identified the conditions necessary for successful implementation of the IVR strategy which maintains the vessel integrity and prevents a melt-through.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.57-65
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• 2022

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.252-258
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• 2022

In this study, we propose a method for analyzing students' understanding of probability and statistics in small lectures at universities. A computer-based test for probability and statistics was performed on 95 science and engineering college students. After dividing the students' responses into 7 clusters using the Robust PCA and the Gaussian mixture model, the achievement of each subject was analyzed for each cluster. High-ranking clusters generally showed high achievement on most topics except for statistical estimation, and low-achieving clusters showed strengths and weaknesses on different topics. Compared to the widely used PCA-based dimension reduction followed by clustering analysis, the proposed method showed each group's characteristics more clearly. The characteristics of each cluster can be used to develop an individualized learning strategy.

• Journal of Animal Science and Technology
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• v.65 no.5
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• pp.1094-1104
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• 2023

Aggression in horses may cause serious accidents during riding and non-riding activities. Hence, predicting the temperament of horses is essential for selecting suitable horses and ensuring safety during the activity. In certain animals, such as hamsters, plasma melatonin concentrations have been correlated with aggressive behavior. However, whether this relationship applies to horses remains unclear. To address this research gap, this study aimed to evaluate differences in the plasma melatonin concentrations among horses of different breeds, ages, and sexes and examine the correlation between plasma melatonin concentrations and the temperament of the horses, including docility, affinity, dominance, and trainability. Blood samples from 32 horses were collected from the Horse Industry Complex Center of Jeonju Kijeon College. The docility, affinity, dominance, and trainability of the horses were assessed by three professional trainers who were well-acquainted with the horses. Plasma melatonin concentrations were measured using an enzyme-linked immunosorbent assay. The consequent values were compared between the horses of different breeds, ages, and sexes using a three-way analysis of variance and least significant difference post hoc test. Linear regression analysis was employed to identify the relationship between plasma melatonin concentrations and docility, affinity, dominance, and trainability. The results showed that the plasma melatonin concentrations significantly differed with breeds in Thoroughbred and cold-blooded horses. However, there were no differences in the plasma melatonin concentrations between the horse ages and sexes. Furthermore, plasma melatonin concentrations did not exhibit a significant correlation with the ranking of docility, affinity, dominance, and trainability.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.221-226
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• 2024

In information retrieval, queries come in various types, ranging from abstract queries to those containing specific keywords, making it a challenging task to accurately produce results according to user demands. Additionally, search systems must handle queries encompassing various elements such as typos, multilingualism, and codes. Reranking is performed through training suitable documents for queries using DeBERTa, a deep learning model that has shown high performance in recent research. To evaluate the effectiveness of the proposed method, experiments were conducted using the test collection of the Product Search Track at the TREC 2023 international information retrieval evaluation competition. In the comparison of NDCG performance measurements regarding the experimental results, the proposed method showed a 10.48% improvement over BM25, a basic information retrieval model, in terms of search through query error handling, provisional relevance feedback-based product title-based query expansion, and reranking according to query types, achieving a score of 0.7810.