• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.9-21
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• 2024

Deep learning has been developing rapidly in recent years, with many researchers working to utilize large language models in various domains. However, there are practical difficulties that developing and utilizing language models require massive data and high-performance computing resources. Therefore, in-context learning, which utilizes prompts to learn efficiently, has been introduced, but there needs to be clear criteria for effective prompts for learning. In this study, we propose a methodology for enhancing prompt-based learning performance by improving the PET technique, which is one of the contextual learning methods, to select PVPs that are similar to the context of existing data. To evaluate the performance of the proposed methodology, we conducted experiments with 30,100 restaurant review datasets collected from Yelp, an online business review platform. We found that the proposed methodology outperforms traditional PET in all aspects of accuracy, stability, and learning efficiency.

• Animal Bioscience
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• v.37 no.2
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• pp.184-192
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• 2024

Objective: This study aims to investigate the genetic structure and characteristics of the Angus cattle population in Hungary. The survey was performed with the assistance of the Hungarian Hereford, Angus, Galloway Association (HHAGA). Methods: Genetic parameters of 1,369 animals from 16 Angus herds were analyzed using the genotyping results of 12 microsatellite markers with the aid of PowerMarker, Genalex, GDA-NT2021, and STRUCTURE software. Genotyping of DNA was performed using an automated genetic analyzer. Based on pairwise identity by state values of animals, the Python networkx 2.3 library was used for network analysis of the breed and to identify the central animals. Results: The observed numbers of alleles on the 12 loci under investigation ranged from 11 to 18. The average effective number of alleles was 3.201. The overall expected heterozygosity was 0.659 and the observed heterozygosity was 0.710. Four groups were detected among the 16 Angus herds. The breeders' information validated the grouping results and facilitated the comparison of birth weight, age at first calving, number of calves born and productive lifespan data between the four groups, revealing significant differences. We identified the central animals/herd of the Angus population in Hungary. The match of our group descriptions with the phenotypic data provided by the breeders further underscores the value of cooperation between breeders and researchers. Conclusion: The observation that significant differences in the measured traits occurred among the identified groups paves the way to further enhancement of breeding efficiency. Our findings have the potential to aid the development of new breeding strategies and help breeders keep the Angus populations in Hungary under genetic supervision. Based on our results the efficient use of an upcoming genomic selection can, in some cases, significantly improve birth weight, age at first calving, number of calves born and the productive lifespan of animals.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• Physical Therapy Rehabilitation Science
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• v.13 no.2
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• pp.152-162
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• 2024

Objective: This study was conducted to analyze the effect of wearable Electromyography-controlled functional electrical stimulation (EMG-controlled FES) System on Gait Function and cardiopulmonary metabolic efficiency during walking in older adults. Design: Cross-section study Methods: Total 22 older adult participants suitable to selection criteria of this study participated in this study. The EMG-controlled FES System, which functions as a wearable physical activity assist FES system was used. All participations performed randomly assigned two conditions (Non-FES assist [NFA], FES assist [FA]) of walking. In all conditions, spatio-temporal parameters and kinematics and kinetics parameters during walking was collected via 3D motion capture system and 6 minutes walking test (6MWT) and metabolic cost during walking and stairs climbing was collected via a portable metabolic device (COSMED K5, COSMED Srl, Roma, Italy). Results: In Spatio-temporal parameters aspects, The EMG-controlled FES system significantly improved gait functions measurements of older adults with sarcopenia at walking in comparison to the NFA condition (P<0.05). Hip, knee and ankle joint range of motion increased at walking in FA condition compared to the NFA condition (P<0.05). In the FA condition, moment and ground reaction force was changed like normal gait during walking of older adults in comparison to the NFA condition (P<0.05). The EMG-controlled FES system significantly reduced net cardiopulmonary metabolic energy cost, net energy expenditure measurement at stairs climbing (P<0.05). Conclusions: This study demonstrated that EMG-controlled FES is a potentially useful gait-assist system for improving gait function by making joint range of motion and moment properly.

• Safety and Health at Work
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• v.15 no.2
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• pp.228-235
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• 2024

Background: Glove reuse poses risks, as chemicals can persist even after cleaning. Decontamination methods like thermal aeration, recommended by US OSHA, vary in effectiveness. Some studies show promising results, while others emphasize the importance of considering both permeation and tensile strength changes. This research advocates for informed glove reuse, emphasizing optimal thermal aeration temperatures and providing evidence to guide users in maintaining protection efficiency. Methods: The investigation evaluated Neoprene and Nitrile gloves (22 mils). Permeation tests with toluene and acetone adhered to American Society for Testing Materials (ASTM) F739 standards. Decontamination optimization involved aeration at various temperatures. The experiment proceeded with a maximum of 22 re-exposure cycles. Tensile strength and elongation were assessed following ASTM D 412 protocols. Breakthrough time differences were statistically analyzed using t-test and ANOVA. Results: At room temperature, glove residuals decreased, and standardized breakthrough time (SBT)₂ was significantly lower than SBT₁, indicating reduced protection. Higher temperature decontamination accelerated residual removal, with ∆SBT (SBT₂/SBT₁) exceeding 100%, signifying restored protection. Tensile tests showed stable neoprene properties postdecontamination. Results underscore thermal aeration's efficacy for gloves reuse, emphasizing temperature's pivotal role. Findings recommend meticulous management strategies, especially post-breakthrough, to uphold glove-protective performance. Conclusions: Thermal aeration at 100℃ for 1 hour proves effective, restoring protection without compromising glove strength. The study, covering twenty cycles, suggests safe glove reuse with proper decontamination, reducing costs significantly. However, limitations in chemical-glove combinations and exclusive focus on specific gloves caution against broad generalization. The absence of regulatory directives on glove reuse highlight the importance of informed selection and rigorous decontamination validation for workplace safety practices.

• Journal of Plant Biotechnology
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• v.33 no.4
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• pp.309-313
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• 2006

Discovery of single nucleotide polymorphisms (SNPs), including small insertions and deletions, is one of the hot topics in genetic research. The most common type of sequence variant consists of single base differences or small insertions and deletions at specific nucleotide positions. Significance of SNPs in rice is increasing for genetic research, positional cloning and molecular breeding. $F_2$ 170 lines and $F_3$ 194 lines derived from Sangjuchalbyeo/HR13721-53-3-1-3-3-2-2 Were used for Searching SNP markers related to bacterial blight resistance. Sangjuchalbyeo is susceptible to bacterial blight, but HR13721-53-3-1-3-3-2-2 has Xa1 gene resistant to bacterial blight. Individual lines were inoculated with $K_1$ race of bacterial blight and resistant or susceptible was evaluated after 3 weeks from inoculation. The genotypes of population were analysed by PCR-RFLP for SNP marker developing. The segregation of $F_2\;and\;F_3$ population showed almost 3:1, 1:1 ratio, respectively. Analysis of genotype using SNP marker is capable of confirming resistance for $K_1$ race and genotype through amplifying the gene using 16PFXal primer and digested the PCR product with Eco RV. There were close relation between resistance test for $K_1$ race and SNP marker genotype. Especially, DNA analysis using SNP marker is capable of judging homozygote/heterozygote in $F_2$ population compared with resistant test for Kl race. So, it seems to improve the selection efficiency in disease resistant breeding.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Korean Society of Forest Science
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• v.86 no.2
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• pp.241-250
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• 1997

The study was conducted to make a map for selecting righteous tree species for each site by digital terrain analysis. We set an algorithmic value for each tree species' characteristics with distribution pattern analysis, and the soil types were digitized from data indicated on soil map. Mean altitude, slope, aspect and micro-topography were estimated from the digital map for each block which had been calculated by regression equations with altitude. The results obtained from the study could be summarized as follows 1. We could develope a method to select righteous tree species for a given site with concern of soil, forest condition and topographic factors on Muju-Gun in Chonbuk province(2,500ha) by the terrain analysis and multi-variate digital map with a personal computer. 2. The brown forest soils were major soil types for the study area, and 29 tree species were occurred with Pinus densiflora as a dominant species. The differences in site condition and soil properties resulted in site quality differences for each tree species. 3. We tried to figure out the accuracy of a basic program(DTM.BAS) enterprised for this study with comparing the mean altitude and aspect calculated from the topographic terrain analysis map and those from surveyed data. The differences between the values were less than 5% which could be accepted as a statistically allowable value for altitude, as well as the values for aspect showed no differences between both the mean altitude and aspect. The result may indicate that the program can be used further in efficiency. 4. From the righteous-site selection map, the 2nd group(R, $B_1$) took the largest area with 46% followed by non-forest area (L) with 23%, the 5th group with 7% and the 4th group with 5%, respectively. The other groups occupied less than 6%. 5. We suggested four types of management tools by silvicultural tree species with considering soil type and topographic conditions.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.52 no.4
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• pp.429-438
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• 2007

Early growth vigor(EGV) is one of the physiological characteristics that may contribute to the increase of genetic yield potential and radiation use efficiency by closing the canopy earlier. To estimate the genotypic variation of EGV, determine the relationships among the related traits, and identify the rapidly growing genotypes and indirect indicator for selection in breeding program, the evaluation of EGV and EGV-related traits was conducted for a total of 140 rice varieties consisting of 101 Korean, 25 Northern China and 14 IRRI-bred rice varieties in a serial sowing experiment in plastic rain shelter and plastic-covered nursery bed in 2003. EGV defined as the amount of leaf area and/or dry weight produced early in the season and the EGV-related traits such as length and breadth of the $2^{nd}\;and\;3^{rd}$ leaves showed highly significant positive correlation with the embryo and seed weight. Especially, the genotypic variation in the length of the third leaf was explained over 90% of genotypic variation in the seed weight. Owing to a large effect of seed size on EGV and its related traits, vigor measurements were adjusted based on their linear or exponential relationships with seed weight for excluding the seed weight effect. EGV and its related-traits adjusted for seed weight also showed big variation among genotypes. Increased EGV was genetically correlated with increases in breadth and length of early leaves. The broad-sense heritability for EGV was significantly high(81%), but lower than those of leaf breadth(90% for the $2^{nd}$ leaf and 93% for the $3^{rd}$ leaf) and length(87% for the $2^{nd}$ leaf and 89% for the $3^{rd}$ leaf). Significantly positive genetic correlations were found between EGV and the breadth and length of early leaves. The high heritability of early leaf breadth and length coupled with their strong genetic correlation with EGV indicated that the breadth and length of the $2^{nd}\;and\;3^{rd}$ leaf would be used as good indirect indicators for EGV selection in rice breeding program.