본 연구는 네트워크 이상 감지 및 예측을 위해 벡터 자기회귀(VAR) 모델의 사용을 비교 분석한다. VAR 모 델에 대한 간략한 개요를 제공하고 네트워크 이상 체크로 사용 가능한 두 가지 버전을 검토하며 두 종류의 VAR 모델을 통한 경험적인 평가를 제시한다. VAR-Filtered moving-common-AR 모델이 단일 노드 이상 감지 성능에서 우수하며, VAR-Adaptive Learning 버전은 몇 개의 노드 간 이상을 효과적으로 식별하는 데 특히 효 과적이며 두 가지 주요VAR 모델의 전반적인 성능 차이에 대한 근본적인 이유도 분석한다. 각 기술의 장단점 을 개요로 제공하고 성능 향상을 위한 제안도 제시하고자 한다.

본 연구에서는Stable Diffusion 프레임워크를 활용하여 게임 스타일의 스케치, 특히 도시 장면을 생성하는 방법 을 소개한다. 확산 기반의 모델인Stable Diffusion은 쉬운 접근성과 뛰어난 성능으로 많은 연구자와 일반인들에 게 선호되며, 텍스트-스케치, 이미지-스케치의 생성이 가능하다. Stable Diffusion의 몇 가지 문제는 이미지의 국 소성 보존 문제 및 미세 조정인데, 이를ControlNet과DreamBooth를 사용하여 해결한다. 결과적으로, 본 연구를 통 해 게임 제작에 사용될 수 있는 텍스트-스케치, 이미지-스케치 생성이 가능하며, 더 나아가 아티스트를 돕는 툴 로도 활용될 수 있다.

본 연구는 테이블 탑 보드게임 개발 방법론인 CDR 모델을 제안하여 보드게임 개발과정에서의 어려움을 일 부 해소하고 나아가 디자이너가 개발 경험을 지속할 수 있는 토대를 마련하는 것이 목적이다. 이를 위해 보 드게임 개발 방법론인 CDR 모델을 설계하고 보드게임 개발 경험이 있는 전문가 패널 15명을 대상으로 인터 뷰를 진행하였다. 연구 결과 CDR 모델이 기존의 개발 방법론보다 체계적인 개발이 가능하도록 설계되어 개 발 시 단계별 작업 내용을 확인하고, 오류를 줄이면서 누락내용 없이 개발이 가능한 것으로 나타났다. 본 연 구에 의한 CDR 모델을 통해 보드게임을 만들고자 하는 디자이너는 보드게임을 디자인하는 데 있어서 가장 중요한 게임요소와 그것을 어떻게 부각할지에 대한 고민을 해결하고 개발을 지속할 수 있는 토대를 마련하 는 데 도움이 될 것으로 기대한다.

Effects-Based Operations (EBO) refers to a process for achieving strategic goals by focusing on effects rather than attrition-based destruction. For a successful implementation of EBO, identifying key nodes in an adversary network is crucial in the process of EBO. In this study, we suggest a network-based approach that combines network centrality and optimization to select the most influential nodes. First, we analyze the adversary’s network structure to identify the node influence using degree and betweenness centrality. Degree centrality refers to the extent of direct links of a node to other nodes, and betweenness centrality refers to the extent to which a node lies between the paths connecting other nodes of a network together. Based on the centrality results, we then suggest an optimization model in which we minimize the sum of the main effects of the adversary by identifying the most influential nodes under the dynamic nature of the adversary network structure. Our results show that key node identification based on our optimization model outperforms simple centrality-based node identification in terms of decreasing the entire network value. We expect that these results can provide insight not only to military field for selecting key targets, but also to other multidisciplinary areas in identifying key nodes when they are interacting to each other in a network.

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

In this study, we developed Rapid Enrichment Broth for Vibrio parahaemolyticus (REB-V), a broth capable enriching V. parahaemolyticus from 100 CFU/mL to 106 CFU/mL within 6 hours, which greatly facilitates the rapid detection of V. parahaemolyticus. Using a modified Gompertz model and response surface methodology, we optimized supplement sources to rapidly enrich V. parahaemolyticus. The addition of 0.003 g/10 mL of D-(+)- mannose, 0.002 g/10 mL of L-valine, and 0.002 g/10 mL of magnesium sulfate to 2% (w/v) NaCl BPW was the most effective combination of V. parahaemolyticus enrichment. Optimal V. parahaemolyticus culture conditions using REB-V were at pH 7.84 and 37oC. To confirm REB-V culture efficiency compared to 2% (w/v) NaCl BPW, we assessed the amount of enrichment achieved in 7 hours in each medium and extracted DNA samples from each culture every hour. Real-time PCR was performed using the extracted DNA to verify the applicability of this REB-V culture method to molecular diagnosis. V. parahaemolyticus was enriched to 5.452±0.151 Log CFU/mL in 2% (w/v) NaCl BPW in 7 hours, while in REB-V, it reached 7.831±0.323 Log CFU/mL. This confirmed that REB-V enriched V. parahaemolyticus to more than 106 CFU/mL within 6 hours. The enrichment rate of REB-V was faster than that of 2% (w/v) NaCl BPW, and the amount of enrichment within the same time was greater than that of 2% (w/v) NaCl BPW, indicating that REB-V exhibits excellent enrichment efficiency.

Velvet antler is widely used as a traditional medicine, and numerous studies have demonstrated its tremendous nutritional and medicinal values including immunity-enhancing effects. This study aimed to investigate different deer velvet extracts (Sample 1: raw extract, Sample 2: dried extract, and Sample 3: freeze-dried extract) for proximate composition, uronic acid, sulfated glycosaminoglycan, sialic acid, collagen levels, and chemical components using ultra-performance liquid chromatography-quadrupole-time-of-light mass spectrometry. In addition, we evaluated the cytotoxic effect of the deer velvet extracts on BV2 microglia, HT22 hippocampal cells, HaCaT keratinocytes, and RAW264.7 macrophages using the cell viability MTT assay. Furthermore, we evaluated acute toxicity of the deer velvet extracts at different doses (0, 500, 1000, and 2000 mg/kg) administered orally to both male and female ICR mice for 14 d (five mice per group). After treatment, we evaluated general toxicity, survival rate, body weight changes, mortality, clinical signs, and necropsy findings in the experimental mice based on OECD guidelines. The results suggested that in vitro treatment with the evaluated extracts had no cytotoxic effect in HaCaT keratinocytes cells, whereas Sample-2 had a cytotoxic effect at 500 and 1000 μg/mL on HT22 hippocampal cells and RAW264.7 macrophages. Sample 3 was also cytotoxic at concentrations of 500 and 1000 μg/mL to RAW264.7 and BV2 microglial cells. However, the mice treated in vivo with the velvet extracts at doses of 500–2000 mg/kg BW showed no clinical signs, mortality, or necropsy findings, indicating that the LD50 is higher than this dosage. These findings indicate that there were no toxicological abnormalities connected with the deer velvet extract treatment in mice. However, further human and animal studies are needed before sufficient safety information is available to justify its use in humans.