환경 지속가능성에 대한 전 세계적인 관심이 고조되고 대체 단백질 공급원에 대한 수요가 증가하면서, 갈색거저리(Tenebrio molitor)는 중요 한 자원으로 인정받고 있다. 갈색거저리는 전 세계적으로 다양한 계통이 존재하며, 각 계통은 고유한 특성을 지니고 있어 특정 산업적 용도에 최적화 될 수 있는 잠재력을 가지고 있다. 그러나 갈색거저리 제품의 품질은 단순히 계통적 차이뿐만 아니라, 사육 후 관리(post-rearing) 및 생산 후 가공 (post-production processing) 과정에도 크게 영향을 받는다. 이러한 복합적인 요인들은 갈색거저리의 성공적인 산업화를 위한 해결 과제로 남아 있다. 본 종설은 갈색거저리 산업화의 최신 동향을 분석하고, 다양한 국제 사례 연구를 통해 국내 갈색거저리 산업이 직면하고 있는 현재의 한계점을 심층적으로 검토하였다. 이를 통해 국내 갈색거저리 산업의 지속적인 발전과 경쟁력 강화를 위한 방안을 모색하고자 한다.

This study develops and evaluates a prompt-driven large language model (LLM) agent for section design of doubly reinforced concrete (RC) beams. Using Google Gemini (Gems), an engineering “expert” that operates without fine-tuning by uploading ACI-318 provisions, sample design documents, and a database of prior beam designs was developed. The agent interprets code clauses, formulas, and constraints from these materials and retrieves similar design cases to propose an initial solution. It then incorporates user-specified natural-language constraints—most notably a strength-ratio cap (design strength ≤ 105% of required strength)—to iteratively refine toward safe and economical designs. Beyond reporting member size and reinforcement details, the agent provides step-by-step computational justifications for moment and shear checks, increasing verifiability and instructional value. We benchmark the LLM-generated designs against results from the commercial program MIDAS/Design+ and observe close agreement. In several scenarios, the constraint-guided LLM solutions are more material-efficient while remaining code-compliant. The workflow also supports batch processing from spreadsheet inputs, enabling practical automation across multiple beams. The approach requires no additional model training or coding making it accessible to non-developer practitioners. Results indicate that a general-purpose LLM, properly grounded with code documents and examples, can achieve practice-level performance with transparent reasoning. This demonstrates a viable approach to AI-assisted structural design that is explainable, interactive, and readily integrated with engineering workflows.

This study investigates the seismic fragility of nuclear power plant (NPP) auxiliary structures by incorporating material aging deterioration into machine learning–based response prediction models. An artificial neural network (ANN) was developed using 17 seismic and material parameters, achieving high predictive accuracy (R2 = 0.96) while significantly reducing computational demands compared with conventional finite element analyses. By combining the ANN with Monte Carlo simulations, fragility curves for Motor Control Center (MCC) cabinet anchors were derived at resonance frequencies of 10 Hz and 15 Hz. Results indicate that equipment with higher resonance frequency (15 Hz) exhibits lower seismic vulnerability due to reduced sensitivity to dominant low-frequency seismic components. When material deterioration was introduced, fragility curves shifted toward lower ground motion intensities, reflecting increased failure probabilities and approximately 20% reduction in median seismic capacity. These findings highlight the necessity of considering aging effects in probabilistic seismic risk assessments and demonstrate the efficiency of ML-based surrogate models for quantifying long-term safety margins of NPP structures.

This study presents the results of compression, drop impact, and vibration durability analyses conducted to evaluate the mechanical reliability of Battery Pack Cases (BPCs) in electric vehicle (EV) systems. BPCs are essential structural components that must endure compressive loads, impact forces, and vibrational fatigue. Finite Element Analysis (FEA) was applied to a representative BPC model to assess deformation, impact resistance, and vibration endurance. The results indicate that the BPC maintained integrity within yield strength limits under compressive loading and effectively absorbed energy under drop impact. Furthermore, Power Spectral Density (PSD) analysis identified stress concentration regions, providing insights for structural optimization. Overall, the findings support the development of lightweight and reliable BPC designs for advanced EV applications.

본 연구는 국내에서 사용되는 산란계 사육 형태에 따른 산란계의 생산성, 계란 품질, 혈액 성상, 동물복지 지표를 비교 평가하고자 하였다. 35주령의 Hy-Line Brown 256수를 대상으로 10주간 실험을 진행하였다. 세 처리구로 하였으며 무작위 배치하였다. Con(관행 배터리 케이지, 0.05 m2/bird), ConD+(개선된 밀도의 배터리 케이지, 0.075 m2/bird), FC(개선형 케이지, 0.075 m2/bird). 산란율과 산란량은 Con과 ConD+에서 FC보다 유의하게 높았고, 난중은 FC에서 유의하게 높았다. 계란 품질에서는 Haugh unit과 난백고는 유의한 차이가 없었으나, 난황색과 난각 두께, 난각 강도, 난각 밝기 등 일부 지표에서 유의한 차이가 나타났다. 혈청 생화학 지표는 대부분 유의한 차이가 없었으나, 포도당 농도는 FC에서 유의적으로 낮게 나타났다. Corticosterone 농도는 유의한 차이는 없었지만 FC에서 높은 경향을 보였으며, 깃털 손상도는 FC에서 유의적으로 가장 높은 값을 나타내었다. 본 연구 결과는 넓은 공간과 개선된 환경이 반드시 생산성과 복지를 동시에 향상시키지 않으며, 활동성 증가 및 사회적 상호작용에 따른 부정적 영향이 나타날 수 있음을 시사하며, 추후 연구가 필요할 것으로 사료된다.

Machine learning (ML) techniques have been increasingly applied to the field of structural engineering for the prediction of complex dynamic responses of safety-critical infrastructures such as nuclear power plant (NPP) structures. However, the development of ML-based prediction models requires a large amount of training data, which is computationally expensive to generate using traditional finite element method (FEM) time history analysis, especially for aging NPP structures. To address this issue, this study investigates the effectiveness of synthetic data generated using Conditional Tabular GAN (CTGAN) in training ML models for seismic response prediction of an NPP auxiliary building. To overcome the high computational cost of data generation, synthetic tabular data was generated using CTGAN and its quality was evaluated in terms of distribution similarity (Shape) and feature relationship consistency (Pair Trends) with the original FEM data. Four training datasets with varying proportions of synthetic data were constructed and used to train neural network models. The predictive accuracy of the models was assessed using a separate test set composed only of original FEM data. The results showed that models trained with up to 50% synthetic data maintained high prediction accuracy, comparable to those trained with only original data. These findings indicate that CTGAN-generated data can effectively supplement training datasets and reduce the computational burden in ML model development for seismic response prediction of NPP structures.

Reinforcement learning (RL) is successfully applied to various engineering fields. RL is generally used for structural control cases to develop the control algorithms. On the other hand, a machine learning (ML) is adopted in various research to make automated structural design model for reinforced concrete (RC) beam members. In this case, ML models are developed to produce results that are as similar to those of training data as possible. The ML model developed in this way is difficult to produce better results than the training data. However, in reinforcement learning, an agent learns to make decisions by interacting with an environment. Therefore, the RL agent can find better design solution than the training data. In the structural design process (environment), the action of RL agent represent design variables of RC beam. Because the number of design variables of RC beam section is many, multi-agent DQN (Deep Q-Network) was used in this study to effectively find the optimal design solution. Among various versions of DQN, Double Q-Learning (DDQN) that not only improves accuracy in estimating the action-values but also improves the policy learned was used in this study. American Concrete Institute (318) was selected as the design codes for optimal structural design of RC beam and it was used to train the RL model without any hand-labeled dataset. Six agents of DDQN provides actions for beam with, beam depth, bottom rebar size, number of bottom rebar, top rebar size, and shear stirrup size, respectively. Six agents of DDQN were trained for 5,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases that is not used for training. Based on this study, it can be seen that the multi-agent RL algorithm can provide successfully structural design results of doubly reinforced beam.

The rapid expansion of bridge and tunnel infrastructure has resulted in a growing incidence of wind-induced traffic accidents occurring at bridge approaches and tunnel portals. These accidents not only inflict direct damage on vehicles but also lead to substantial social and economic losses, stemming from roadway infrastructure repair and maintenance costs, as well as elevated logistics expenses due to traffic delays and congestion. In this study, a theoretical expression for the lateral displacement of vehicles as a function of wind speed was derived. Subsequently, lateral displacement and lateral wind force were analyzed and compared across vehicle types, considering both straight and curved roadway sections. An analysis of prevailing wind directions at each site revealed that, for passenger cars, the maximum lateral force and displacement on straight sections occurred at a wind incidence angle of 45°, whereas on curved sections with a pier curvature of 90°, the critical wind direction ranged from 0° to 120°. These results demonstrate that vehicle stability can be significantly compromised during high-speed travel under crosswind conditions. Based on departure trajectories of vehicles under varying wind speeds, a risk-assessment scale for wind-induced accidents was developed. In addition, design guidelines were proposed for the strategic placement of windbreak barriers to enhance driving safety under strong wind conditions.

The 3T irregular shape structure is used for designing wind loads in high-rise buildings. Among them, the Tapered shape is a shape with a cross-section that changes throughout the entire floor. Recently, various advanced Tapered shapes have been applied, such as having a cross-section that varies only in part of the height or combining different shapes. In this study, an analysis model was selected by applying three types of Tapered part locations(Bottom, Middle, Top) and angles as design variables. Equivalent static seismic loads and historical earthquake records were applied to compare and analyze the seismic response of the Tapered models with regular-shaped models. As a result of the analysis, positioning the partial taper in the middle shows the lowest seismic response. Additionally, a larger taper angle decreased the story drift ratio, top-story displacement, shear wall shear force, and column bending moment, while increasing absolute acceleration and column axial force.

Automated structural design methods for reinforced concrete (RC) beam members have been widely studied with various techniques to date. Recently, artificial intelligence has been actively applied to various engineering fields. In this study, machine learning (ML) is adopted to make automated structural design model for RC beam members. Among various machine learning methods, a supervised learning was selected. When a supervised learning is applied to development of ML-based prediction model, datasets for training and test are required. Therefore, the datasets for rectangular and t-shaped RC beams was constructed by commercial structural design software of MIDAS. Five supervised learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost) were used to develop the automated structural design model. Design moment (Mu), design shear force (Vu), beam length, uniform load (wu) were used for inputs of structural design model. Width and height of the designed section, diameter of top and bottom bars, number of top and bottom bars, diameter of stirrup bar were selected for outputs of structural design model. Performance evaluation of the developed structural design models was conducted using metrics sush as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). This study presented that random forest provides the best structural design results for both rectangular and t-shaped RC beams.

In this study, static and dynamic analyses were conducted on three atypical building models to evaluate the displacement response reduction performance based on the outrigger system installation location in a atypical building that incorporated both tapered and twisted shapes. Three 60-story models were developed with a fixed 3-degree taper and twist angles of 1, 2, and 3 degrees per story. Outrigger systems were installed at 10-story intervals and additionally between the 20th and 40th floor at 1-story intervals. The results indicated that, although there were variations depending on the seismic loads, the displacement response reduction performance was generally most effective when the outriggers were installed in the upper stories (41st to 60th floors) of the analytical models.

Lactic acid bacterial (LAB) fermentation is frequently used to enhance the nutritional and functional properties of natural products. Oysters (Crassostrea gigas), a marine bivalve mollusc, have long been used in food applications. In the present study, we explored the effects of LAB fermentation on the physiological activity of C. gigas. To identify new starter strains, we isolated and screened LAB from local specialties in Sacheon, South Korea. Eighteen LAB strains were identified by 16S rRNA gene sequencing, four of which exhibited protease activity. All the four isolates were identified as Latilactobacillus curvatus. Fermentation was carried out in a medium containing C. gigas powder for three days. After incubation, the antioxidant activity in the culture supernatant of fermented C. gigas with L. curvatus GH-118-24 increased by approximately 139.2% compared with that of the non-fermented control. Additionally, the extract of fermented C. gigas for three days showed significant improvements in anti-inflammatory and anti-diabetic effects, with increases of over 71.2% and 253.8%, respectively, compared to the non-fermented extract. These results suggested that the selected LAB strains have potential as starters capable of enhancing the bioactive properties of food, thus highlighting the importance of genetic resources in South Korea.

Performance-Based Seismic Design (PBSD) is an approach that evaluates how structures will perform under different levels of seismic activity. It focuses on ensuring that buildings not only withstand earthquakes but also meet specific performance objectives, such as minimizing damage or maintaining functionality after the event. Unlike traditional methods, PBSD allows for more tailored, cost-effective designs by considering varying degrees of acceptable damage based on the structure's importance and use. PBSD was introduced in Korea in 2016 to replace elastic design, which is inevitable to over-design to cope with all variables such as earthquakes and winds. When PBSD is applied to the structural design new building, One of the challenges of PBSD is the complexity involved in creating accurate inelastic analysis models. The process requires significant time and effort to analyze the results, as it involves detailed simulations of how structures will behave under seismic stress. Additionally, organizing and interpreting the analysis data to meet performance objectives can be labor-intensive and technically demanding. In order to solve this problem, a post-processor program was developed in this study. A post-processor was developed based on Excel program using Visual Basic for Applications(VBA). Because analysis outputs of Perform-3D, that is a commercial software for structural analysis and design, are very complicated, generation of tables and graphs for report is significant time and effort consuming task. When the developed post-processor is used to make the seismic design report, the required task time is significantly reduced.

The diagrid structural system has a braced frame that simultaneously resists lateral and vertical loads, and is being applied to many atypical high-rise buildings for aesthetic effects. In this study, a 60-story structure with twisted degrees of 0° to 180° was selected to determine seismic response control performance of twisted high-rise structures whether the diagrid system was applied and according to the reduction of braced frame material quantity. For this purpose, ‘Nor’ model without the diagrid system and the ‘DS’ model with the diagrid system, which was modeled by reducing braced frame member section to 700~400, were modeled. As a result, the 'DS' model showed an seismic response control effect in all Twisted models even when the quantity was reduced, and especially, the Twisted shape model was found to have an superior response control effect compared to the regular structure. In addition, the ‘600DS’ analysis model, which matched the ‘Nor’ model by 99.0% in quantity, showed an increase in seismic response control performance as the rotation angle increased.

This study is to deal with the cause analysis and improvement ideas for breakage to hydraulic pipes mounted on self-propelled howitzers. Hydraulic piping is one of the core components of a hydraulic system. This is because in the case of devices that use hydraulic pressure as a power source, hydraulic oil is supplied through hydraulic piping to operate. Compared to the main hydraulic assembly, its importance is low, so there are not many studies or failure analysis cases on it. However, contrary to this, cases of hydraulic pipe failure account for a significant proportion of the total number of failures, requiring in-depth technical review. In this study, we aim to analyze the causes of failures in hydraulic pipes of self-propelled guns operated by the military and propose improvement measures. It is expected that this study will aid as a reference for problem solving when similar failures occur in the future.

High-rise buildings are equipped with TMD (Tuned Mass Damper), a vibration control device that ensure the stability and usability of the building. In this study, the seismic response control performance was evaluated by selecting the design variables of the TMD based on the installation location of the twisted irregular building. To this end, we selected analysis models of 60, 80, and 100 floors with a twist angle of 1 degree per floor, and performed time history analysis by applying historical seismic loads and resonant harmonic loads. The total mass ratio of TMDs was set to 1.0%, and the distributed installation locations of TMDs were selected through mode analysis. The analysis results showed that the top-floor displacement responses of all analysis models increased, but the maximum story drift ratio decreased. In order to secure the seismic response control performance by distributed installation of TMDs in twisted irregular buildings, it is judged that the mass ratio distribution of TMDs will act as a key variable.

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson’s ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using 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 analysis results show that neural networks present good prediction performance considering aging deterioration.

This study identifies the possibility of alignment discrepancies during mortar firing when using inactive fuzes, which make it impossible to visually observe impact points. To address this issue, we studied a quality assurance method for Sight Alignment after firing. To establish a baseline, we analyzed the pre-firing Sight Alignment and the impact group status during firing for 00 mortars and 000 shells. Based on this analysis, we derived the alignment position information range after firing for 36 mortars, distinguishing between 68% and 95% confidence interval. Finally, considering data characteristics, inspection time requirements, and non-conforming data, we selected the Sight Alignment range after firing based on the 95% confidence interval. This study is expected to contribute to the development of quality assurance methods for munitions by serving as an example of quality assurance in the mass production stage of mortars.