• Journal of Animal Reproduction and Biotechnology
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• v.39 no.1
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• pp.40-47
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• 2024

Background: Sperm quality and the number of sperm introduced into the uterus during artificial insemination (AI) are pivotal factors influencing pregnancy outcomes. However, there have been no reports on the relationship between sperm concentration at AI and sperm quality in Hanwoo cattle. In this study, we examined sperm quality and pregnancy rates after AI using sperm inseminated at different concentrations. Methods: We evaluated the motility, viability, and acrosomal membrane integrity of sperm at different concentrations (10, 15, 18, and 20 million sperm/straw) in 0.5-mL straws. Subsequently, we compared the pregnancy rates after AI with different sperm concentrations. Results: After freeze-thawing, sperm at the assessed concentrations showed similar viability and acrosomal membrane integrity. After AI, cattle in the 10 million group had significantly lower pregnancy rates compared to those in the 18 and 20 million groups. Conversely, there were no statistically significant variances observed between cattle in the 10 and 15 million groups. Conclusions: Sperm at concentrations of 10, 15, 18 and 20 million per straw exhibited comparable motility, viability, and acrosomal membrane integrity. However, a concentration of at least 18 million sperm per straw is required to achieve a consistent rate of pregnancy rate in Hanwoo cattle after AI.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.5
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• pp.243-250
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• 2018

In this paper, design technique using genetic algorithms(GA) for design optimization of composite leaf springs is presented here. After the initial design has been validated by the car plate spring as a finite element model, the genetic algorithm suggests the process of optimizing the number of layers of composite materials and their angles. Through optimization process, the weight reduction process of leaf springs and the number of repetitions are compared to the existing algorithm results. The safety margin is calculated by organizing a finite element model to verify the integrity of the structure by applying an additive sequence optimized through the genetic algorithm to the structure. When GA is applied, layer thickness and layer angle of complex leaf springs have been obtained, which contributes to the achievement of minimum weight with appropriate strength and stiffness. A reduction of 65.6% original weight is reached when a leaf steel spring is replaced with a leaf composite spring under identical requirement of design parameters and optimization.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.3
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• pp.332-340
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• 2016

A design optimization is performed for the double-bolted joint in cylindrical composite structures by using a simplified analytical method. This method uses failure criteria for the major failure modes of the bolted composite joint. For the double-bolted joint with a zigzag arrangement, it is necessary to consider an interaction effect between the bolt arrays. This paper proposes another failure mode which is determined by angle and distance between two bolts in different arrays and define a failure criterion for the failure mode. The optimal design for the double-bolted joint is carried out by considering the interactive net-tension failure mode. The genetic algorithm (GA) is adopted to determine the optimized parameters; bolt spacing, edge distance, and stacking sequence of the composite laminate. A purpose of the design optimization is to maximize the burst pressure of the cylindrical structures by ensuring structural integrity. Also, a progressive failure analysis (PFA) is performed to verify the results of the optimal design for the double-bolted joint. In PFA, Hashin 3D failure criterion is used to determine the ply that would fail. A stiffness reduction model is then used to reduce the stiffness of the failed ply for the corresponding failure mode.

• Genomics & Informatics
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• v.11 no.4
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• pp.164-173
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• 2013

Genomic instability, which occurs through both genetic mechanisms (underlying inheritable phenotypic variations caused by DNA sequence-dependent alterations, such as mutation, deletion, insertion, inversion, translocation, and chromosomal aneuploidy) and epigenomic aberrations (underlying inheritable phenotypic variations caused by DNA sequence-independent alterations caused by a change of chromatin structure, such as DNA methylation and histone modifications), is known to promote tumorigenesis and tumor progression. Mechanisms involve both genomic instability and epigenomic aberrations that lose or gain the function of genes that impinge on tumor suppression/prevention or oncogenesis. Growing evidence points to an epigenome-wide disruption that involves large-scale DNA hypomethylation but specific hyper-methylation of tumor suppressor genes, large blocks of aberrant histone modifications, and abnormal miRNA expression profile. Emerging molecular details regarding the modulation of these epigenetic events in cancer are used to illustrate the alterations of epigenetic molecules, and their consequent malfunctions could contribute to cancer biology. More recently, intriguing evidence supporting that genetic and epigenetic mechanisms are not separate events in cancer has been emerging; they intertwine and take advantage of each other during tumorigenesis. In addition, we discuss the collusion between epigenetics and genetics mediated by heterochromatin protein 1, a major component of heterochromatin, in order to maintain genome integrity.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2547-2555
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• 2021

The frequency of reactor coolant leakage is expected to increase over the lifetime of a nuclear power plant owing to degradation mechanisms, such as flow-acceleration corrosion and stress corrosion cracking. When loss of coolant accidents (LOCAs) occur, several parameters change rapidly depending on the size and location of the cracks. In this study, leak flow during LOCAs is predicted using a deep fuzzy neural network (DFNN) model. The DFNN model is based on fuzzy neural network (FNN) modules and has a structure where the FNN modules are sequentially connected. Because the DFNN model is based on the FNN modules, the performance factors are the number of FNN modules and the parameters of the FNN module. These parameters are determined by a least-squares method combined with a genetic algorithm; the number of FNN modules is determined automatically by cross checking a fitness function using the verification dataset output to prevent an overfitting problem. To acquire the data of LOCAs, an optimized power reactor-1000 was simulated using a modular accident analysis program code. The predicted results of the DFNN model are found to be superior to those predicted in previous works. The leak flow prediction results obtained in this study will be useful to check the core integrity in nuclear power plant during LOCAs. This information is also expected to reduce the workload of the operators.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Molecules and Cells
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• v.42 no.4
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• pp.363-375
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• 2019

Fungal sectorization is a complex trait that is still not fully understood. The unique phenotypic changes in sporadic sectorization in mutants of CpBck1, a mitogen-activated protein kinase kinase kinase (MAPKKK) gene, and CpSlt2, a mitogen-activated protein kinase (MAPK) gene, in the cell wall integrity pathway of the chestnut blight fungus Cryphonectria parasitica have been previously studied. Although several environmental and physiological factors cause this sectoring phenotype, genetic variants can also impact this complex morphogenesis. Therefore, RNA sequencing analysis was employed to identify candidate genes associated with sectorization traits and understand the genetic mechanism of this phenotype. Transcriptomic analysis of CpBck1 and CpSlt2 mutants and their sectored progeny strains revealed a number of differentially expressed genes (DEGs) related to various cellular processes. Approximately 70% of DEGs were common between the wild-type and each of CpBck1 and CpSlt2 mutants, indicating that CpBck1 and CpSlt2 are components of the same MAPK pathway, but each component governs specific sets of genes. Functional description of the DEGs between the parental mutants and their sectored progenies revealed several key pathways, including the biosynthesis of secondary metabolites, translation, amino acid metabolism, and carbohydrate metabolism; among these, pathways for secondary metabolism and translation appeared to be the most common pathway. The results of this comparative study provide a better understanding of the genetic regulation of sector formation and suggest that complex several regulatory pathways result in interplays between secondary metabolites and morphogenesis.

• Smart Structures and Systems
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• v.33 no.2
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• pp.165-175
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• 2024

Rotating beams play a crucial role in representing complex mechanical components that are prevalent in vital sectors like energy and transportation industries. These components are susceptible to the initiation and propagation of cracks, posing a substantial risk to their structural integrity. This study presents a two-stage methodology for detecting the location and estimating the size of an open-edge transverse crack in a rotating Euler-Bernoulli beam with a uniform cross-section. Understanding the dynamic behavior of beams is vital for the effective design and evaluation of their operational performance. In this regard, modal parameters such as natural frequencies and eigenmodes are frequently employed to detect and identify damages in mechanical components. In this instance, the Frobenius method has been employed to determine the first two natural frequencies and corresponding eigenmodes associated with flapwise bending vibration. These calculations have been performed by solving the governing differential equation that describes the motion of the beam. Various parameters have been considered, such as rotational speed, beam slenderness, hub radius, and crack size and location. The effect of the crack has been replaced by a rotational spring whose stiffness represents the increase in local flexibility as a result of the damage presence. In the initial phase of the proposed methodology, a damage index utilizing the slope of the beam's eigenmode has been employed to estimate the location of the crack. After detecting the presence of damage, the size of the crack is determined using a Genetic Algorithm optimization technique. The ultimate goal of the proposed methodology is to enable the development of more suitable and reliable maintenance plans.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.1
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• pp.28-36
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• 2011

To investigate the genetic diversity of six Chinese indigenous pig breeds in Shandong province (Laiwu Black, Dapulian Black, Licha Black, Yantai Black, Yimeng Black and Wulian Black), explain their genetic relationship and assess their integrity and degree of admixture with three Western commercial breeds (Landrace, Yorkshire and Duroc), 303 individuals from these breeds were genotyped for 26 microsatellite markers. In general, high genetic diversity (observed heterozygosity ranging from 0.5495 to 0.7746) and large breed differentiation ($F_{ST}$ = 0.188) were observed. The indigenous pig breeds in Shandong exhibited consistently higher levels of genetic diversity than the three Western breeds. However, compared with the Western breeds, which have an $F_{ST}$ value of 0.252, the indigenous breeds in Shandong have smaller $F_{ST}$ value of 0.145. The analysis of breed relationship indicated that the six indigenous breeds are classified into two groups. One includes four breeds, Licha, Yantai, Yimeng and Wulian, which have experienced large gene introgression of the Western breeds through progressive crossbreeding as well as gene flow among themselves. The other includes Laiwu and Dapulian, which are less influenced by the Western breeds and other indigenous breeds in Shandong in the recent past. The results show that some measures must be taken to effectively protect these indigenous pig breeds in Shandong.