• Genomics & Informatics
• /
• 제20권2호
• /
• pp.19.1-19.15
• /
• 2022

Alfalfa (Medicago sativa) is an important food and feed crop which rich in mineral sources. The WUSCHEL-related homeobox (WOX) gene family plays important roles in plant development and identification of putative gene families, their structure, and potential functions is a primary step for not only understanding the genetic mechanisms behind various biological process but also for genetic improvement. A variety of computational tools, including MAFFT, HMMER, hidden Markov models, Pfam, SMART, MEGA, ProtTest, BLASTn, and BRAD, among others, were used. We identified 34 MsWOX genes based on a systematic analysis of the alfalfa plant genome spread in eight chromosomes. This is an expansion of the gene family which we attribute to observed chromosomal duplications. Sequence alignment analysis revealed 61 conserved proteins containing a homeodomain. Phylogenetic study sung reveal five evolutionary clades with 15 motif distributions. Gene structure analysis reveals various exon, intron, and untranslated structures which are consistent in genes from similar clades. Functional analysis prediction of promoter regions reveals various transcription binding sites containing key growth, development, and stress-responsive transcription factor families such as MYB, ERF, AP2, and NAC which are spread across the genes. Most of the genes are predicted to be in the nucleus. Also, there are duplication events in some genes which explain the expansion of the family. The present research provides a clue on the potential roles of MsWOX family genes that will be useful for further understanding their functional roles in alfalfa plants.

• 한국지열·수열에너지학회논문집
• /
• 제18권3호
• /
• pp.1-6
• /
• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.

• Geomechanics and Engineering
• /
• 제28권4호
• /
• pp.335-346
• /
• 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.

• Composites Research
• /
• 제34권2호
• /
• pp.101-107
• /
• 2021

복합재료를 활용하여 설계되는 구조물은 각 부품들의 조립, 체결부를 갖게 된다. 이러한 연결 또는 조인트는 구조에서 잠재적으로 취약 부분이 될 수 있다. 복합재료 볼트 조인트의 파손모드는 구조 안전성을 위해 베어링 파손모드로 설계된다. 베어링 파손모드로 파괴되는 복합재료 볼트 조인트의 하중-변위 관계는 초기 파손 발생 후 비선형 거동을 보이며, 점진적인 파손을 보인다. 이러한 비선형적이고 점진적인 복합재료 볼트 조인트의 파손거동을 정확히 예측하기 위해 본 연구에서는 기존의 파손해석 모델에서 전단 손상변수 계산 과정에 수정을 수행하였다. 수정된 파손해석 모델을 이용하여 복합재료 볼트 조인트의 베어링 응력-베어링 변형률 결과를 예측하였으며, 기존 수정되지 않은 해석모델과 비교를 통해 수정된 모델의 유효성을 입증하였다.

• Safety and Health at Work
• /
• 제11권4호
• /
• pp.431-442
• /
• 2020

Background: Psychosocial risks are increasingly a type of risk analyzed in organizations beyond chemical, physical, and biological risks. To this type of risk, a greater attention has been given following the update of ISO 9001: 2015, more precisely the requirement 7.1.4 for the process operation environment. The update of this normative reference was intended to approximate OHSAS 18001: 2007 reference updated in 2018 with the publication of ISO 45001. Thus, the organizations are increasingly committed to achieving and demonstrating good occupational health and safety performance. Methods: The aim of this study was to characterize the psychosocial risks in a cryopreservation laboratory and to develop a predictive model for psychosocial risk management. The methodology followed to collect the information was the inquiry by questionnaire that was applied to a sample comprising 200 employees. Results: The results show that most of the respondents are aware of the psychosocial risks, identifying interpersonal relationships and emotional feelings as the main factors that lead to this type of risks. Furthermore, terms such as lack of resources, working hours, lab equipment, stress, and precariousness show strong correlation with psychosocial risks. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the psychosocial risks. Conclusion: This work presents the development of an intelligent system that allows identifying the weaknesses of the organization and contributing to the enhancement of the psychosocial risks management.

• 한국엔터테인먼트산업학회논문지
• /
• 제15권5호
• /
• pp.99-113
• /
• 2021

본 연구는 거시경제요인이 스포테인먼트 산업에 미치는 영향을 확인하여 그 활용 가치를 발견하기 위한 연구다. 연구를 위해 거시경제요인으로 DJIA, WTI, GP를 선택하였고, 스포테인먼트 산업을 대표할 만한 자료로 NIKE와 Adidas 주가를 선택하였으며, 20년 5,285일간의 거래 자료를 2단계 추출 과정을 거쳐 분석하였다. 분석 결과 첫째, 거시경제요인은 스포테인먼트 산업에 유의한 영향을 미치는 것으로 나타났다. 둘째 시간의 설정, 각 시기의 특성, 그리고 요인 간 관계에 따라 각기 다른 수준의 회귀식이 나타났다. 마지막으로, 시계열분석을 통한 미래 예측 방법인 Durbin-Watson 검증 결과 특정 시기의 특정 요인 간 회귀식은 미래 예측에 활용 가능한 것으로 나타났지만, 각 조건에 따라 각각 다른 결과가 관찰되어 향후 후속 연구가 필요하다 판단된다.

• 항공우주시스템공학회지
• /
• 제16권6호
• /
• pp.114-122
• /
• 2022

본 논문에서는 CFD 기법을 활용하여 전기체 형상의 투척식 무인기 형상에 대해 고속 회전하는 프로펠러와 그로 인해 생성된 후류 간섭 효과를 고려한 비정상 유동해석을 수행하였다. 또한 다양한 투척식 이륙 조건에서 롤 모멘트 평형에 요구되는 에일러론 타각을 정확하게 예측하기 위해 실제 조종면 회전을 고려한 유동해석이 수행되었다. 투척식 소형 무인기의 경우 초기 이륙상태에서 롤 안정성을 증대시키기 위해 적절한 초기 에일러론 설정을 활용하는 것이 유용한 방식이며, 구축된 공력 데이터베이스를 사용하여 다양한 이륙속도와 받음각 조건들에 대해 롤 모멘트를 상쇄시킬 수 있는 에일러론 타각 조건들이 빠르게 예측 가능함을 보였다.

• 한국농공학회논문집
• /
• 제64권3호
• /
• pp.45-52
• /
• 2022

Prediction of soil behavior should be interpreted based on the level of axial strain in the actual ground. Recently numerical methods have been carried out focus on the state of soil failure. However considered the deformation of soil the prior to failure, mostly the small strain occurring in the elastic range is considered. As a result of calculating the deformation modulus to 50% of the maximum unconfined compression strength, Deformation modulus (E50) showed a tendency to increase according to the degree of compaction by region. The Poisson's ratio during loading-unloading was 0.63, which was higher than the literature value of 0.5. For the unconfined compression test under cyclic loading for the measurement of permanent strain, the maximum compression strength was divided into four step and the test was performed by load step. Changes in permanent strain and deformation modulus were checked by the loading-unloading test for each stage. At 90% compaction, the permanent deformation of the SM sample was 0.21 mm, 0.37 mm, 0.6 mm, and 1.35 mm. The SC samples were 0.1 mm, 0.17 mm, 0.42 mm, and 1.66 mm, and the ML samples were 0.48 mm, 0.95 mm, 1.30 mm, and 1.68 mm.

• 한국물환경학회지
• /
• 제37권6호
• /
• pp.442-448
• /
• 2021

Seawater desalination is a technology through which salt and other constituents are removed from seawater to produce fresh water. While a significant amount of fresh water is produced, the desalination process is limited by the generation of concentrated brine with a higher salinity than seawater; this imposes environmental and economic problems. In this study, characteristics of seawater from three different locations in South Korea were analyzed to evaluate the feasibility of crystallization to seawater desalination. Organic and inorganic substances participating in crystal formation during concentration were identified. Then, prediction and economic feasibility analysis were conducted on the actual water flux and obtainable salt resources (i.e. Na2SO4) using membrane distillation and energy-saving crystallizer based on multi-stage flash (MSF-Cr). The seawater showed a rather low salinity (29.9~34.4 g/L) and different composition ratios depending on the location. At high concentrations, it was possible to observe the participation of dissolved organic matter and various ionic substances in crystalization. When crystallized, materials capable of forming various crystals are expected. However, it seems that different salt concentrations should be considered for each location. When the model developed using the Aspen Plus modular was applied in Korean seawater conditions, relatively high economic feasibility was confirmed in the MSF-Cr. The results of this study will help solve the environmental and economic problems of concentrated brine from seawater desalination.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.420-426
• /
• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.