Following the previous study, which investigated the pharmacological properties of the Technekitty injection (Tc-99m), the toxicity of a single intravenous administration of the Technekitty injection (Tc-99m) and the side effects that may occur at the diagnostic dose were confirmed. The Technekitty injection (Tc-99m) was administered intravenously once at a dose of 0, 0.67, 2.0, and 6.0 mCi/kg to 5 male and female rats per group. Mortality, general symptom observation, and weight measurement were performed for 2 weeks, followed by observation of autopsy findings. There were no deaths, and no statistically significant weight change was observed. No abnormal systemic signs related to the Technekitty injection (Tc-99m) were observed. These results confirmed that Technekitty injection (Tc-99m) can be safely administered intravenously at doses up to 6.0 mCi/kg. Additionally, technetium-99m at an average dose of 2 mCi (74 MBq) has been verified as a diagnostic dose without adverse effects, allowing the Technekitty injection (Tc-99m) to be used safely without side effects at this dosage. This study demonstrates that the Technekitty injection (Tc-99m) has a wide safety margin, supporting its potential for clinical application. Moreover, these findings align with the nonclinical safety standards for radiopharmaceuticals, reinforcing its utility in veterinary medicine. The Technekitty injection (Tc-99m) is expected to be applicable for clinical diagnosis as a veterinary drug in Korea.

This study investigated the relationship between teacher support in the areas of learner autonomy, relatedness, and competence, and motivational beliefs regarding achievement goal orientations and perceived task value in Chinese EFL college classrooms based on self-determination theory. A questionnaire was administered to 712 college students, and the data were analyzed using SPSS 26.0 and AMOS 24.0. The findings indicated that teacher support for autonomy, relatedness, and competence was positively correlated with mastery goal orientation and performance-approach orientation, while it was negatively correlated with performance-avoidance orientation. Furthermore, these forms of teacher support positively influenced students’ perceived task value. Achievement goal orientation mediated the relationship between the three types of teacher support and student engagement. However, perceived task value only mediated the relationship between teacher autonomy support and student engagement. This study offers valuable insights for enhancing English teaching practices by promoting student motivation through addressing their psychological needs in EFL college classrooms.

A multi-criteria decision-making(MCDM) method allows the decision makers to systematically evaluate the alternatives based on a predefined set of decision criteria. The most commonly used MCDM methods include Analytic Hierarchy Process(AHP), Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS), Weighed Aggregated Sum Product Assessment(WASPAS), Preference Selection Index(PSI), etc. In MCDM Problems, it is common that performance ratings for different criteria are measured by different units. Normalization is thus used to convert performance ratings into commensurable data. There are many normalization techniques that can be used for MCDM problems. Much effort has been made for comparative studies on the suitability of normalization techniques used in MCDM methods. However, most studies present normalization methods suitable for specific MCDM problems based on specific data samples. Therefore, this study proposes the most suitable normalization method for each MCDM method under consideration using extensive data samples. A wide range of MCDM problems with various measurement scales are generated by simulation for comparative study. The experimental results show that vector normalization method is best suited for all MCDM methods considered in this study.

Thyroid scanning using technetium-99m (99mTc) is the gold standard for diagnosing feline hyperthyroidism. In cats with an overactive thyroid, a thyroid scan is the most appropriate imaging technique to detect and localize any hyperfunctional adenomatous thyroid tissue. In this study, the pharmacological properties of the Technekitty injection (Tc-99m), developed as a diagnostic agent for feline hyperthyroidism using 99mTc as an active ingredient, were tested in FRTL-5 thyroid follicular cell line and ICR mice. The percentage of cell uptake of the Tc-99m in FRTL-5 thyroid cells was 0.182 ± 0.018%, which was about 6 times higher compared to Clone 9 hepatocytes. This uptake decreased by 38.2% due to competitive inhibition by iodine (sodium iodide). In tissue distribution tests by using ICR mice, the highest distribution was observed in the liver, kidneys, spleen, lungs, and femur at 0.083 hours after administration, and this distribution decreased as the compound was excreted through the kidneys, the primary excretory organ. Maximum distribution was confirmed at 1 hour in the small intestine, 6 hours in the large intestine, and 2 hours in the thyroid gland. Additionally, the total amount excreted through urine and feces over 48 hours (2 days) was 78.80% of the injected dose, with 37.70% (47.84% of the total excretion) excreted through urine and 41.10% (52.16% of the total excretion) through feces. In conclusion, the Tc-99m has the same mechanism of action, potency, absorption, distribution, metabolism, and excretion characteristics as 99mTc used for feline hyperthyroidism in the United States, Europe, and other countries, because the Technekitty injection (Tc-99m) contains 99mTc as its sole active ingredient. Based on these results, the Technekitty injection (Tc-99m) is expected to be safely used in the clinical diagnosis of feline hyperthyroidism.

This study investigates the effect of machine translation (MT) use on the writing performance of Korean EFL students, focusing on complexity, accuracy, and fluency (CAF). Six participants completed a series of writing tasks in which they first translated their L1 writing into L2 manually and then used MT to revise their L2 drafts. This process was repeated across ten different writing topics. Participants’ drafts were analyzed using CAF measures to assess MT’s impact on their writing performance and observe changes over tasks. The results show that MT significantly improved accuracy and fluency. However, gains in syntactic and lexical complexity were less evident. While group-level analysis showed consistent progress, individual trajectories varied widely, indicating diverse patterns of development. Overall, the findings suggest that MT enhances writing accuracy and fluency among Korean EFL students, although its impact on syntactic and lexical complexity is limited. These results indicate that MT can serve as a valuable tool for self-directed learning, helping students independently improve their writing accuracy and fluency and develop essential self-editing skills. This study highlights the potential of MT as a supplementary tool to support EFL students’ writing development, along with traditional instruction.

This study examined the feeding behavior and growth performance of 31 Hanwoo steers (10 months old; 278 ± 36.13 kg) within a precision livestock farming system using Roughage Intake Control (RIC) units for real-time data collection. Feeding behavior traits were derived from RIC database data using R software, with subsequent analysis conducted using SAS software. The results indicated that the steers visited the feed stations every 31.12 ± 11.99 minutes, averaging 11 ± 3.37 visits/day. Each feeding session lasted an average of 5.90 ± 1.55 minutes, resulting in a feed intake rate of 77.98 ± 22.53 g/min. Mean daily feed intake was 4.76 ± 1.36 kg, and body weight increased consistently, reaching an average of 412 ± 43.44 kg, with an average daily gain (ADG) of 1.26 ± 0.38 kg. Pearson correlation analysis revealed a strong relationship between daily feed intake and visit duration (r² = 0.621; p < 0.01) and an inverse correlation between daily feed intake and feed intake duration (r² = −0.445; p < 0.05), indicating behavioral adaptation. These findings highlight the importance of monitoring feeding behavior traits concerning growth performance, enhancing our understanding of individual animal behavior and its implications for productivity while emphasizing the role of advanced technologies in optimizing feed utilization in confined livestock systems.

This study focuses on the effectiveness of regional business support programs funded by South Korea's Balanced National Development Special Account, one of the policies designed to address regional imbalances and promote local autonomy. Using the analytical approach including DEA (Data Envelopment Analysis) methodology, This study analyzed the efficiency of 76 star companies in the Jeonbuk region based on their performance from 2018 to 2023. This study was designed to improve previous studies limitations, which only analyzed simple input-output efficiency in the short term, by using six years of mid-term data to comprehensively evaluate input variables in both R&D and Non-R&D sectors. The main purpose of this study is to analyze the effectiveness of the expiring Star Company Development Program by evaluating efficiency of supported company groups using DEA and to propose support models and policy suggestions for upcoming regional specialized industries support program by identifying the features of both optimal and inefficiency models. For this, employments along with financial indicators such as sales revenue, operating profit, and total assets were set as output variables, with R&D and non-R&D support amounts were set as input variables for analysis. According to the results, the optimal efficiency model group has strong intellectual property acquisition capabilities, and continuous R&D investment. It shows that continuous innovation activities are a key factor for improving the effectiveness of support. This study found that, from a mid․long term perspective, policy support programs should be customized by unique characteristics of each industry field, Based on this, it was suggested that upcoming regional specialized industry support programs in the Jeonbuk region should include policy planning and support program design to complement the weaknesses of each industry field.

Non-human primates, due to their high genetic similarity to humans, are used as laboratory animals in biotechnology researches. The growing demand has recently led to a shortage of primate resources, which has become a significant issue both domestically and internationally. This shortage has been further exacerbated by the COVID-19 pandemic. Consequently, the importance of resource conservation through effective primate management is increasing. This requires the establishment of proper quarantine procedures and infectious disease control. Quarantine is an important process that protects not only animal health but also public health significance. Non-human primate quarantine procedures were organized in order. We compared the differences in quarantine procedures not only in Korea but also in various countries such as the US, EU, and Australia. In addition, the etiology, clinical symptoms, diagnosis, and treatment methods of representative infectious diseases of quarantine concern (tuberculosis, monkeypox, monkey immunodeficiency virus, salmonellosis, and shigellosis) were summarized. A literature review of nonhuman primate quarantine procedures in other countries revealed minimal differences in the basic structure. The quarantine periods were similar around 30 days, but we found some differences in details such as legal requirements, documentation forms, and quarantine authorities. These findings are expected to contribute to the development of strategies for improving methods to prevent the spread of infectious diseases and enhancing quarantine management methods.

This study proposes a weight optimization technique based on Mixture Design of Experiments (MD) to overcome the limitations of traditional ensemble learning and achieve optimal predictive performance with minimal experimentation. Traditional ensemble learning combines the predictions of multiple base models through a meta-model to generate a final prediction but has limitations in systematically optimizing the combination of base model performances. In this research, MD is applied to efficiently adjust the weights of each base model, constructing an optimized ensemble model tailored to the characteristics of the data. An evaluation of this technique across various industrial datasets confirms that the optimized ensemble model proposed in this study achieves higher predictive performance than traditional models in terms of F1-Score and accuracy. This method provides a foundation for enhancing real-time analysis and prediction reliability in data-driven decision-making systems across diverse fields such as manufacturing, fraud detection, and medical diagnostics.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

정보 기술이 교육에서 지속적으로 응용됨에 따라 ‘혼합형’ 수업 교육 모델이 대학 교의 개방 교육에 점차 융합되고 있다. 중국의 많은 대학교들은 시대의 조류를 따라 현대 정보 기술과 원격 개방 교육의 결합을 통해 온라인 핵심 과정, 온라인 학습 공 간, 온라인 교수진, 온라인 학습 서비스, 온라인 학습 평가, 온라인 교수 관리 등을 중심으로 한 ‘혼합형’ 교수 모델을 구축하고 있다. 본 연구는 중국 개방대학의 ‘응용 한국어’ 전공 교재 편찬에서 나타나는 특징을 분석하였다. 특히, 대학생을 중심으로 한 인터넷 플랫폼을 활용한 자율 학습을 강조하는 하는 교재 편찬 사고방식을 바탕으로, 온라인 교육 시대에 적합한 교재의 내용 및 디자인 방안을 논의하였다. 연구는 한국어 발음, 전문 용어, 한국어 문법 및 문법 프로젝트, 평가 방안 등 교재 편찬에 있어 중요한 요소들을 살펴보고, 각 항목 별로 제시되는 편찬 순서를 구체적으로 소 개하였다.

Afghanistan is a producer of high-quality pistachios among the top ten producers in the World. Nowadays, these edible nuts are losing their quality and quantity due to hemipteran insect infestation and their diversity during all growth phases in the country borderline. The expansion of the pistachio pest in Iran has caused concerns for Afghanistan, especially in the provinces of Herat, Badghis, and Takhar which are close to the Iran borders. Hence, this survey was initiated to primarily detect the phytophagous insect pest presence in the pistachio open forest areas and to know what management tools were effectively used in alleviation of the pest infestation. Our findings indicated that galling aphids, aphids, thrips, and root beetles received the maximum positive responses in the Herat province, but psyllids, aphids, root beetles, leave miners, fruit borers and thrips received the highest responses in Badghis province. In addition, Takhar province depicted plant bugs, twig borer moths, galling aphids, leaf miners, and leafhoppers got the most positive responses accordingly. In the meantime, the data showed that management outcomes were not satisfactory despite the use of sanitation, cultural practices, and chemical application. The survey presented significant differences in the prevalence of pest and in the application of pest control methods among the survey regions. Finally, it is essential to develop the strategy of integrated pest management in order to stop the phytophagous insects from turning into actual pests. This will reduce the pistachio loss annually and improve the nut culture quality in Afghanistan.

The purpose of this study is to develop a timely fall detection system aimed at improving elderly care, reducing injury risks, and promoting greater independence among older adults. Falls are a leading cause of severe complications, long-term disabilities, and even mortality in the aging population, making their detection and prevention a crucial area of public health focus. This research introduces an innovative fall detection approach by leveraging Mediapipe, a state-of-the-art computer vision tool designed for human posture tracking. By analyzing the velocity of keypoints derived from human movement data, the system is able to detect abrupt changes in motion patterns, which are indicative of potential falls. To enhance the accuracy and robustness of fall detection, this system integrates an LSTM (Long Short-Term Memory) model specifically optimized for time-series data analysis. LSTM's ability to capture critical temporal shifts in movement patterns ensures the system's reliability in distinguishing falls from other types of motion. The combination of Mediapipe and LSTM provides a highly accurate and robust monitoring system with a significantly reduced false-positive rate, making it suitable for real-world elderly care environments. Experimental results demonstrated the efficacy of the proposed system, achieving an F1 score of 0.934, with a precision of 0.935 and a recall of 0.932. These findings highlight the system's capability to handle complex motion data effectively while maintaining high accuracy and reliability. The proposed method represents a technological advancement in fall detection systems, with promising potential for implementation in elderly monitoring systems. By improving safety and quality of life for older adults, this research contributes meaningfully to advancements in elderly care technology.

Recently, with the development of industrial technology and the increase of young consumers, engine monitoring devices for small ships are rapidly changing from analog devices to LCD-based digital devices. In addition, consumers’ product selection criteria are gradually increasing in favor of luxurious and emotional products rather than price attractiveness. Therefore, in order to develop differentiated products in marketing, it is necessary to find and improve emotionally attractive quality elements. The purpose of this study is to collect 11 customer requirements related to the emotional quality of DGP (Digital Gauge Panel) for small ships through customer interviews and to find attractive quality elements among the emotional qualities of DGP for small ships. 17 design elements were derived by applying QFD to the collected customer requirements, and they were classified into one-dimensional quality, must be quality, and attractive quality through Kano model analysis, and 6 attractive quality elements were confirmed using Timko customer satisfaction index.

This study analyzed inductive and deductive instructional approaches for teaching grammar within a Presentation-Practice-Production grammar lesson. The participants of this study included 119 Korean university students enrolled in an English as a foreign language class, with approximately half receiving deductive instruction and the other half receiving inductive grammar instruction. The analysis involved comparing learning gains as well as student perceptions of the two approaches via Mann Whitney U tests. The results showed no statistical difference in terms of the immediate or delayed learning gains for each grammatical topic, nor when all grammatical topics were aggregated. However, the analysis of student perceptions indicated that students found inductive instruction to be moderately more effective, interesting, and easier than deductive instruction. The study concludes with a discussion of the implications of these findings related to instructional practices in foreign language classes that utilize the Presentation- Practice-Production model as well as suggestions for future research concerning deductive and inductive instructional approaches.

This study aimed to investigate the effects of various washing pre-treatments of native Codium fragile as a feed additive on in vitro ruminal fermentation and CH4 production in ruminants. Seaweed was included at 0.5% dry matter (DM) based on the experimental feed (forage : concentrate = 3:7). Treatment groups were classified as follows: experimental feed (C), no washing (T1), washing at 0°C (T2), washing at 22°C (T3) and washing at 70°C (T4) each immersed for 6 minutes in distilled water. The pH consistently fell within the ruminal stability range. In vitro dry matter digestibility was significantly highest in T2, T3, T4 and C, T4 was the lowest at 48 h (p<0.05). NH3-N concentration was significantly highest in T4 at 48 h (p<0.05). Total gas production at 48 h was 19% lower in T4 compared to C (p<0.01). CH4 production (mL/g DM) at 48 h was lower in all treatment groups compared to C, with T3 showing a 31% reduction (p<0.01). Similarly, CH4 production (mL/g dry matter degradability, DMD) at 48 h was 39% lower for T3 compared to C (p<0.01). At 24 h, total VFA was significantly highest in T1 and T4 (p<0.05). The proportions of acetate was significantly highest in C and T3 was the lowest at 48 h (p<0.01). The proportions of propionate was significantly highest in T3 and C was the lowest at 48 h (p<0.01). The acetate to propionate ratio was singnificantly highest in C at 48 h (p<0.01). The proportions of butyrate at 24 h was lower for T3 compared to C (p<0.05). Therefore, this study confirms that Codium fragile can reduce CH4 production when used as a feed additive for ruminants and this effect is not significantly influenced by the washing pre-treatment. However, if washing process is necessary, washing at 22°C is the most appropriate method to remove foreign objects.

In 2024, the South Korean government’s research and development budget cuts sparked significant concerns in the scientific community, prompting increased interest in international research funding opportunities. In this regard, South Korea’s upcoming participation as an Associated Country in the European Union’s (EU’s) Horizon Europe offers a timely opportunity. Horizon Europe is the EU’s flagship research and innovation program, running from 2021 to 2027 with a budget of €95.5 billion. It is structured on three key pillars: 1) excellent science; 2) global challenges and European industrial competitiveness; and 3) innovative Europe. South Korea’s direct benefits will focus on Pillar II, which emphasizes global challenges across six clusters, including health, climate, and digital innovation. It should be noted that participation in the program mandates international collaborations, typically involving consortia with diverse expertise. Meanwhile, the National Contact Points network has been expanded to support Korean researchers, offering the necessary resources to facilitate engagement with EU counterparts. By leveraging these opportunities, South Korean researchers aim to collaboratively address global challenges, thus enhancing the nation’s scientific standing.

This study analyzes the impact of ESG (Environmental, Social, and Governance) activities on Corporate Financial Performance(CFP) using machine learning techniques. To address the linear limitations of traditional multiple regression analysis, the study employs AutoML (Automated Machine Learning) to capture the nonlinear relationships between ESG activities and CFP. The dataset consists of 635 companies listed on KOSPI and KOSDAQ from 2013 to 2021, with Tobin's Q used as the dependent variable representing CFP. The results show that machine learning models outperformed traditional regression models in predicting firm value. In particular, the Extreme Gradient Boosting (XGBoost) model exhibited the best predictive performance. Among ESG activities, the Social (S) indicator had a positive effect on CFP, suggesting that corporate social responsibility enhances corporate reputation and trust, leading to long-term positive outcomes. In contrast, the Environmental (E) and Governance (G) indicators had negative effects in the short term, likely due to factors such as the initial costs associated with environmental investments or governance improvements. Using the SHAP (Shapley Additive exPlanations) technique to evaluate the importance of each variable, it was found that Return on Assets (ROA), firm size (SIZE), and foreign ownership (FOR) were key factors influencing CFP. ROA and foreign ownership had positive effects on firm value, while major shareholder ownership (MASR) showed a negative impact. This study differentiates itself from previous research by analyzing the nonlinear effects of ESG activities on CFP and presents a more accurate and interpretable prediction model by incorporating machine learning and XAI (Explainable AI) techniques.

본 논문에서는 히니가 어떻게 저장소로서의 의미와 아일랜드의 집단적 기억이 퇴적된 공간으로서의 의미를 가지고 있는 늪의 이미지를 통해 북아일랜드에서 일어나는 폭력에 대해 사유했는지 보여주고 있다. 늪 시편에서 시인은 고고학자가 되 어 늪을 발굴하면서 늪에 있는 희생자를 다시 끄집어내어 이들이 자신의 행위에 대한 심판을 받게 한다. 이러한 발견의 과정은 또한 히니가 1974년 에세이 “Feeling into Words”에서 썼듯 시적 창작 과정에 대한 은유이기도 하다. “The Tollund Man,” “Punishment,”와 “Kinship”이라는 시에서 히니는 과거와 현재의 폭력을 나란히 비교하 면서 어떻게 두 시대에서 (더 이상 유효하지 않은) 믿음에 기반한 폭력이 자행되는지 보여준다.

As the Fourth Industrial Revolution advances, smart factories have become a new manufacturing paradigm, integrating technologies such as Information and Communication Technology (ICT), the Internet of Things (IoT), Artificial Intelligence (AI), and big data analytics to overcome traditional manufacturing limitations and enhance global competitiveness. This study offers a comprehensive approach by evaluating both technological and economic performance of smart factory Research and Development (R&D) projects, addressing gaps in previous studies that focused narrowly on either aspect. The research combines Latent Dirichlet Allocation (LDA) topic modeling and Data Envelopment Analysis (DEA) to quantitatively compare the efficiency of various topics. This integrated approach not only identifies key research themes but also evaluates how effectively resources are utilized within each theme, supporting strategic decision-making for optimal resource allocation. Additionally, non-parametric statistical tests are applied to detect performance differences between topics, providing insights into areas of comparative advantage. Unlike traditional DEA methods, which face limitations in generalizing results, this study offers a more nuanced analysis by benchmarking efficiency across thematic areas. The findings highlight the superior performance of projects incorporating AI, IoT, and big data, as well as those led by the Ministry of Trade, Industry, and Energy (MOTIE) and small and medium-sized enterprises (SMEs). The regional analysis reveals significant contributions from non-metropolitan areas, emphasizing the need for balanced development. This research provides policymakers and industry leaders with strategic insights, guiding the efficient allocation of R&D resources and fostering the development of smart factories aligned with global trends and national goals.