Mauremys reevesii (Reeves’ turtle) is an endemic freshwater turtle species found throughout East Asia. Due to a rapid population decline, the International Union for Conservation of Nature (IUCN) and the Korean government have classified this species as Endangered (EN). The reported largest population size of M. reevesii in the Republic of Korea was previously estimated to be approximately 20-30 individuals. Our study assessed the population size and structure of M. reevesii at Geumho Reservoir, Republic of Korea, using a capture-recapture data. A total of 433 M. reevesii were incidentally captured during a 35-week trapping process conducted from March to October 2023. The sex ratio of the captured population exhibited a male bias of 1.3 : 1. Sexual size dimorphism was observed only in body weight. Individuals were recaptured up to 11 times during the study period, with males and females being recaptured at an average of 2.1±2.0 times and 1.5±0.9 times, respectively. The estimated population size of M. reevesii in Geumho Reservoir was approximately 891 turtles. The absence of notable sexual size dimorphism and significant sex ratio differences suggests that the population in this area may have been established relatively recently. Compared to previous records, the population in Geumho Reservoir represents the largest single population of M. reevesii, both within the Republic of Korea and globally.

Thermoelectric materials have been the focus of extensive research interest in recent years due to their potential in clean power generation from waste heat. Their conversion efficiency is primarily reflected by the dimensionless figure of merit, with higher values indicating better performance. There is a pressing need to discover materials that increase output power and improve performance, from the material level to device fabrication. This review provides a comprehensive analysis of recent advancements, such as Bi2Te3-based nanostructures that reduce thermal conductivity while maintaining electrical conductivity, GeTe-based high entropy alloys that utilize multiple elements for improved thermoelectric properties, porous metal-organic frameworks offering tunable structures, and organic/hybrid films that present low-cost, flexible solutions. Innovations in thermoelectric generator designs, such as asymmetrical geometries, segmented modules, and flexible devices, have further contributed to increased efficiency and output power. Together, these developments are paving the way for more effective thermoelectric technologies in sustainable energy generation.

Additive manufacturing makes it possible to improve the mechanical properties of alloys through segregation engineering of specific alloying elements into the dislocation cell structure. In this study, we investigated the mechanical and microstructural characteristics of CoNi-based medium-entropy alloys (MEAs), including the refractory alloying element Mo with a large atomic radius, manufactured via laser-powder bed fusion (L-PBF). In an analysis of the printability depending on the processing parameters, we achieved a high compressive yield strength up to 653 MPa in L-PBF for (CoNi)85Mo15 MEAs. However, severe residual stress remained at high-angle grain boundaries, and a brittle μ phase was precipitated at Mo-segregated dislocation cells. These resulted in hot-cracking behaviors in (CoNi)85Mo15 MEAs during L-PBF. These findings highlight the need for further research to adjust the Mo content and processing techniques to mitigate cracking behaviors in L-PBF-manufactured (CoNi)85Mo15 MEAs.

This study developed conductive inks composed of carbon black (CB) and silver nanowires (Ag NWs) for cost-effective screen-printing on fabrics. The Ag NW density within the CB matrix was precisely controlled, achieving tunable electrical conductivity with minimal Ag NW usage. The resulting inks were successfully patterned into shapes such as square grids and circles on textile surfaces, demonstrating excellent conductivity and fidelity. Adding 19.9 wt% Ag NWs reduced sheet resistance by ~92% compared to CB-only inks, highlighting the effectiveness and potential of this hybrid approach for cost-effective, high-performance textile-based electronics. The one-dimensional morphology of Ag NWs facilitated the formation of conductive percolation networks, creating efficient electron pathways within the CB matrix even at low loadings. This work advances the field of CB-based conductive inks and provides a scalable and practical method for producing functional, patterned electronic textiles.

This study confirmed the fungal community of rice makgeolli sold in the eastern part of Jeollanam-do using ITS 2 sequence-based metagenome analysis. A total of 18 fungi were found in six makgeolli samples, with Saccharomyces cerevisiae being dominant in all samples at high rates ranging from 96.61~99.96%. The six makgeolli samples were classified into three groups based on the PCoA and UPGMA tree analysis results using the Jaccard distance matrix. Network analysis of the relationships among the 18 identified fungal species helped identify a fungus that demonstrated either a positive or negative correlation with the dominant species, Saccharomyces cerevisiae. This study provides important foundational data for understanding the fungal composition in the makgeolli fermentation process.

Malaria remains a significant public health issue, particularly in regions such as the Korean Demilitarized Zone (DMZ). Effective malaria control and prevention require precise prediction of mosquito density across both monitored and unmonitored areas. This study aimed to develop predictive models to estimate the abundance of malaria vector mosquitoes by integrating meteorological and geographical data. Data from mosquito surveillance sites and NASA MODIS land cover datasets acquired between 2009 and 2022 were utilized. Two predictive models, the Gradient Boosted Model (GBM) and Principal Component Regression (PCR), were employed and evaluated. Model performance was assessed using the coefficient of determination (R²). Results showed that PCR outperformed GBM in predictive accuracy, suggesting that PCR is more robust in handling multicollinearity among variables. However, both models did not show practically-usable level of prediction performance. This study provides a preliminary but foundational framework for extending predictive modeling to broader regions, thereby supporting malaria prevention efforts through improved risk mapping.

In this study, an simultaneous LC-MS/MS multi-residue analytical method was developed and validated for the residues of six neonicotinoid insecticides (acetamiprid, clothianidin, dinotefuran, imidacloprid, thiacloprid, and thiamethoxam) in honey. Sample preparation included a combination of QuEChERS extraction kit and liquid-liquid extraction method to effectively extract pesticide components from the honey matrix and optimized analytical conditions to achieve high sensitivity and selectivity. The limits of detection (LOD) and the limits of quantitation (LOQ) were set in the range of 6-15 ng/mL and 19-44 ng/mL, respectively and the correlation coefficient (R²) was greater than 0.99, confirming good linearity. In addition, the intra-day recoveries for each pesticide were 75-104%, and the coefficient of variation (CV) was less than 20%, which met the guideline recommended by the Ministry of Food and Drug Safety. The LC-MS/MS method developed in this study is expected to be used as a multi-residue analysis method for 6 neonicotinoid pesticides in honey.

This study aimed to address the time, cost, and ethical issues associated with traditional animal experiment-based observational methods by utilizing in silico Physiologically Based Pharmacokinetic modeling to predict veterinary drug residues in livestock products and validate them against observational data. Using PK-sim software, we modeled the physiological conditions of pigs to predict the depletion of ceftiofur and spiramycin. We evaluated the ceftiofur (3 mg, 6 mg) and spiramycin models by comparing them with observational data using residuals, MSE, and R-squared values. Specifically, the R-squared values for the ceftiofur models were all negative, indicating poor predictive power. For Ceftiofur (3 mg), the R-squared value was <0 with MSE of 611.3764, and for Ceftiofur (6 mg), it was <0 with MSE of 2447.982, highlighting significant discrepancies. Similar shortcomings were observed in the spiramycin models, with an R-squared value of <0 . These discrepancies can be attributed to inaccuracies in literature data, limited physicochemical data, inadequate consideration of inter-individual differences, mismatches between experimental and model conditions, and limitations of benchmark observational experiments. This underscores the critical importance of enhancing data quality and refining modeling approaches. Future research should focus on validating in silico techniques across diverse animal models and drugs to broaden their applicability in safety assessments. Ultimately, leveraging in silico techniques is crucial for establishing a scientifically robust safety management system for livestock products, overcoming the constraints of current observational experimental methods.

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.

본 연구에서는 source face와 target face를 합성해서 게임 캐릭터의 얼굴을 효과적으로 생성 하기 위한 방법으로 target face의 특징을 제거한 nullface를 생성한 다음 source face의 얼굴 의 특징을 적용해서 새로운 얼굴을 합성하는 기법을 제안한다. 이를 위해서 본 연구는 2 가지 모듈을 이용해서 수행된다. 첫 번째 모듈은 nullface를 생성하며, 두번째 모듈은 nullface에 source face의 특징을 적용해서 새로운 얼굴을 합성한다. 이 두 과정 모두 diffusion model에 기반한 방법론을 적용함으로써 효과적인 얼굴 합성 기법을 구현한다.

Myxomatous mitral valve disease (MMVD) in dogs is a heart disease that is characterized by histopathologic changes in cardiomyocytes, which ultimately result in valve degeneration and blood regurgitation due to structural changes in the heart valves. A number of studies have been conducted with the objective of identifying prognostic factors that may influence the prognosis of dogs with MMVD. Nevertheless, there is a paucity of research examining the factors that predict MMVD stage progression as defined by the American College of Veterinary Internal Medicine. The objective of this study was to examine whether there are factors associated with stage progression within one year of diagnosis in dogs diagnosed with subclinical MMVD (stage B1 or B2) using physical examination findings, clinicopathologic biomarkers, and echocardiographic markers. This is a retrospective study of veterinary practice performed at Chungbuk National University Animal Hospital. The electronic medical record of the hospital was searched to obtain clinical records of canine patients diagnosed with subclinical MMVD over an 11-year period. For each patient cohort, a logistic regression analysis was conducted. The variables were initially selected using the backward elimination method, and the optimal logistic regression model was determined by removing the independent variables with the largest variance inflation factor. Among the independent variables examined in this study, heart murmur intensity was identified as a statistically significant predictor of stage progression within one year for subclinical MMVD, a finding that aligns with those of previous studies. No other independent variables were found to be significantly associated with subclinical MMVD stage progression. This is the inaugural exploratory study to concentrate on blood test results, a relatively straightforward and quantifiable test result that can be readily obtained in primary care veterinary clinics, among the factors that may be associated with the progression of subclinical MMVD stages.

The AlSi10Mg alloy has garnered significant attention for its application in laser powder bed fusion (L-PBF), due to its lightweight properties and good printability using L-PBF. However, the low production speed of the L-PBF process is the main bottleneck in the industrial commercialization of L-PBF AlSi10Mg alloy parts. Furthermore, while L-PBF AlSi10Mg alloy exhibits excellent mechanical properties, the properties are often over-specified compared to the target properties of parts traditionally fabricated by casting. To accelerate production speed in L-PBF, this study investigated the effects of process parameters on the build rate and mechanical properties of the AlSi10Mg alloy. Guidelines are proposed for high-speed additive manufacturing of the AlSi10Mg alloy for use in automotive parts. The results show a significant increase in the build rate, exceeding the conventional build rate by a factor of 3.6 times or more, while the L-PBF AlSi10Mg alloy met the specifications for automotive prototype parts. This strategy can be expected to offer significant cost advantages while maintaining acceptable mechanical properties of topology-optimized parts used in the automobile industry.