• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• Dementia and Neurocognitive Disorders
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• v.21 no.4
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• pp.147-161
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• 2022

Background and Purpose: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline with normal performance on objective neuropsychological tests. SCD, which is the first help-seeking stage and the last stage before the clinical disease stage, can be considered to be the most appropriate time for prevention and treatment. This study aimed to compare characteristics between the amyloid positive and amyloid negative groups of SCD patients. Methods: A cohort study to identify predictors for the clinical progression to mild cognitive impairment (MCI) or dementia from subjective cognitive decline (CoSCo) study is a multicenter, prospective observational study conducted in the Republic of Korea. In total, 120 people aged 60 years or above who presented with a complaint of persistent cognitive decline were selected, and various risk factors were measured among these participants. Continuous variables were analyzed using the Wilcoxon rank-sum test, and categorical variables were analyzed using the χ² test or Fisher's exact test. Logistic regression models were used to assess the predictors of amyloid positivity. Results: The multivariate logistic regression model indicated that amyloid positivity on PET was related to a lack of hypertension, atrophy of the left temporal lateral and entorhinal cortex, low body mass index, low waist circumference, less body and visceral fat, fast gait speed, and the presence of the apolipoprotein E ε4 allele in amnestic SCD patients. Conclusions: The CoSCo study is still in progress, and the authors aim to identify the risk factors that are related to the progression of MCI or dementia in amnestic SCD patients through a two-year follow-up longitudinal study.

• Animal Bioscience
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• v.37 no.12
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• pp.2167-2177
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• 2024

Objective: Black soldier fly (BSF) as an animal protein feed source is currently becoming a hot research topic. This study investigated the effects of the BSF as a protein feed source for goats on slaughter performance, muscle nutrient composition, amino acids, fatty acids, minerals, and antioxidant levels. Methods: Thirty Qianbei Ma goats (20.30±1.09 kg) were randomly divided into three groups: the control group (GRP_C) supplemented with 10% full-fat soybean, treatment 1 (GRP_U) supplemented with 10% untreated BSF, and treatment 2 (GRP_T) supplemented with 10% heat-treated BSF. One-way analysis of variance among groups (with Fisher's least significant difference post hoc comparison) was used in this study. Results: The nutrients, amino acids, fatty acids, minerals, and antioxidants in muscle were analyzed. The results showed that there were no significant differences in the moisture, dry matter, crude protein, ash, amino acids, and mineral content of the muscles among the three feeding groups. The slaughter rate and carcass weight of the GRP_U and GRP_T groups were significantly lower (p<0.05). The overall meat quality of the GRP_U and GRP_T groups decreased (p<0.05). The individual unsaturated fatty acids and total unsaturated fatty acids in the GRP_U group were higher (p<0.05) than those in the GRP_C and GRP_T groups. Both GRP_U and GRP_T decreased (p<0.05) the antioxidant capacity of the meat. Conclusion: Therefore, the heat-treated BSF had a better effect on meat quality compared to untreated BSF, but there were greater negative effects on the meat quality of GRP_U and GRP_T than GRP_C.

• Animal Bioscience
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• v.37 no.12
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• pp.2101-2112
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• 2024

Objective: The current study investigated the effects of mulberry 1-deoxynijirimycin (DNJ) on the digestion ability, intestinal morphology, and intestinal barrier of rabbits. Methods: A total of 36 New Zealand White rabbits (male) about 45 days old (mean body weight of 1.05±0.04 kg) were reared and commercial diets were employed, and afterwards divided into three groups (n = 12) with different levels of DNJ extract additive in feed: T₀ (0 g/kg), T₁ (0.35 g/kg), T₂ (0.7 g/kg) for 28 d. Results: The results demonstrated that T₂ decreased the average daily gain (p<0.05). T₁ and T₂ decreased villus height and inflammatory factor levels as compared with T₀ (p<0.05). DNJ significantly decreased the content of valeric acid (p<0.05). The content of acetic acid, propionic acid, iso butyric acid, iso valeric acid in T₁ were higher than those in T₀ and T₂ (p<0.05). The content of butyric acid in T₂ was lower than it in T₀ and T₁ (p<0.05). The content of caproic acid was firstly improved then reduced as the DNJ concentration improved (p<0.05). T₂ significantly increased the abundance of dgA-11_gut_group and Christensenellaceae_R-7_group while decreased Bacteroide and Ralstonia as compared with T₀ (p<0.05). Compared with T₀, T₁, and T₂ significantly improved the gene expression of JAM2, JAM3, mucin4, mucin6 (p<0.05), T₁ significantly decreased the expression of occluding while T₂ significantly increased (p<0.05), T₂ significantly increased the expression of claudin1 and claudin2 (p<0.05). Conclusion: DNJ at high level changed microbiome compositions, inhibited inflammation, and improved intestinal barrier while it decreased the growth performance and shorted villus height in rabbit jejunum by regulating short chain fatty acid compositions in rabbits.

• Journal of the Korean Geotechnical Society
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• v.40 no.5
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• pp.113-129
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• 2024

This study reclassifies the risk levels of cut-slopes and addresses the limitations inherent in existing evaluation methods using road slope maintenance data. Conventional risk assessment predominantly relies on subjective expert judgment, resulting in issues of consistency and reliability. To mitigate these limitations, this study applies dimensionality reduction techniques, specifically Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), followed by K-means clustering, to classify new risk levels. The clustering results using PCA demonstrated more distinct cluster separation compared to LDA, and also showed superior performance in terms of the silhouette coefficient and other clustering metrics. This suggests that the existing risk level labels may not adequately capture the underlying data structure. Furthermore, the inconsistency observed between LDA-based clustering results and current risk labels indicates potential reliability issues in the present labeling approach. To resolve this, new risk levels were assigned using PCA and K-means clustering, with cluster risk levels evaluated based on risk scores. A quantitative analysis of key risk factors was also conducted to establish criteria for risk classification and assess the impact of each variable on the different risk levels. This study proposes a data-driven, objective, and quantitative approach to risk level evaluation, aiming to improve the efficiency and reliability of road slope management.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.517-528
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• 2024

This paper reports the results of a recent acoustic survey conducted with the objective of providing on the acoustic conditions of school classrooms in Korea. The measurements included both occupied speech and noise levels during 27 active classes and unoccupied data on the acoustical conditions, and sound insulation performance in 16 classrooms in 4 schools. The effects various parameters on the speech and noise levels in occupied classrooms has been examined. The impact of room acoustic design on speech and noise levels in active classrooms has been also investigated. The speech and noise levels of the elementary school are approximately 4 dBA to 5 dBA higher than those of the other three schools (junior high, high, and special), likely due to the nature of activities involved in group work and the age of the students. A notable 19 dBA difference is observed between the quietest and noisiest classroom activities and the classrooms in which students were observed working in groups with discussion had the highest noise levels. Both occupied and unoccupied data have enabled the establishment of a comprehensive picture of the acoustic conditions in classrooms and have highlighted the necessity of introducing acoustic standards for improving the acoustic environment in Korean schools.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.139-160
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• 2017

Technology commercialization creates effective economic value by linking the company's R & D processes and outputs to the market. This technology commercialization is important in that a company can retain and maintain a sustained competitive advantage. In order for a specific technology to be commercialized, it goes through the stage of technical planning, technology research and development, and commercialization. This process involves a lot of time and money. Therefore, the duration and cost of technology commercialization are important decision information for determining the market entry strategy. In addition, it is more important information for a technology investor to rationally evaluate the technology value. In this way, it is very important to scientifically estimate the duration and cost of the technology commercialization. However, research on technology commercialization is insufficient and related methodology are lacking. In this study, we propose an evaluation model that can estimate the duration and cost of R & D technology commercialization for small and medium-sized enterprises. To accomplish this, this study collected the public data of the National Science & Technology Information Service (NTIS) and the survey data provided by the Small and Medium Business Administration. Also this study will develop the estimation model of commercialization duration and cost of R&D performance on using these data based on the market approach, one of the technology valuation methods. Specifically, this study defined the process of commercialization as consisting of development planning, development progress, and commercialization. We collected the data from the NTIS database and the survey of SMEs technical statistics of the Small and Medium Business Administration. We derived the key variables such as stage-wise R&D costs and duration, the factors of the technology itself, the factors of the technology development, and the environmental factors. At first, given data, we estimates the costs and duration in each technology readiness level (basic research, applied research, development research, prototype production, commercialization), for each industry classification. Then, we developed and verified the research model of each industry classification. The results of this study can be summarized as follows. Firstly, it is reflected in the technology valuation model and can be used to estimate the objective economic value of technology. The duration and the cost from the technology development stage to the commercialization stage is a critical factor that has a great influence on the amount of money to discount the future sales from the technology. The results of this study can contribute to more reliable technology valuation because it estimates the commercialization duration and cost scientifically based on past data. Secondly, we have verified models of various fields such as statistical model and data mining model. The statistical model helps us to find the important factors to estimate the duration and cost of technology Commercialization, and the data mining model gives us the rules or algorithms to be applied to an advanced technology valuation system. Finally, this study reaffirms the importance of commercialization costs and durations, which has not been actively studied in previous studies. The results confirm the significant factors to affect the commercialization costs and duration, furthermore the factors are different depending on industry classification. Practically, the results of this study can be reflected in the technology valuation system, which can be provided by national research institutes and R & D staff to provide sophisticated technology valuation. The relevant logic or algorithm of the research result can be implemented independently so that it can be directly reflected in the system, so researchers can use it practically immediately. In conclusion, the results of this study can be a great contribution not only to the theoretical contributions but also to the practical ones.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.4
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• pp.333-339
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• 2016

The objective of this study was to optimize analytical methods for ochratoxin A (OTA) and zearalenone (ZEA) in rice straw silage and winter forage crops using ultra-high performance liquid chromatography (UHPLC). Samples free of mycotoxins were spiked with $50{\mu}g/kg$, $250{\mu}g/kg$, or $500{\mu}g/kg$ of OTA and $300{\mu}g/kg$, $1500{\mu}g/kg$, or $3000{\mu}g/kg$ of ZEA. OTA and ZEA were extracted by acetonitrile and cleaned-up using an immunoaffinity column. They were then subjected to analysis with UHPLC equipped with a fluorescence detector. The correlation coefficients of calibration curves showed high linearity ($R^2{\geq_-}0.9999$ for OTA and $R^2{\geq_-}0.9995$ for ZEA). The limit of detection and quantification were $0.1{\mu}g/kg$ and $0.3{\mu}g/kg$, respectively, for OTA and $5{\mu}g/kg$ and $16.7{\mu}g/kg$, respectively, for ZEA. The recovery and relative standard deviation (RSD) of OTA were as follows: rice straw = 84.23~95.33%, 2.59~4.77%; Italian ryegrass = 79.02~95%, 0.86~5.83%; barley = 74.93~97%, 0.85~9.19%; rye = 77.99~96.67%, 0.33~6.26%. The recovery and RSD of ZEA were: rice straw = 109.6~114.22%, 0.67~7.15%; Italian ryegrass = 98.01~109.44%, 1.65~4.81%; barley = 98~113.53%, 0.25~5.85%; rye = 90.44~108.56%, 2.5~4.66%. They both satisfied the standards of European Commission criteria (EC 401-2006) for quantitative analysis. These results showed that the optimized methods could be used for mycotoxin analysis of forages.