콘크리트 구조물은 노후화에 따른 균열 발생으로 내구성이 저하되며, 유지보수 과정에서 경제적 비용이 발생한다. 이를 해결 하기 위해 박테리아를 이용한 자가치유 기술이 주목받고 있으나, 기존의 캡슐화 및 펠릿화 공정은 높은 제작 비용이 발생한다. 본 연구에서는 지속 가능한 폐기물 자원인 골재 분말(BAP) 내 Hydroxyaptite(HAp)를 박테리아의 보호 담체 및 반응 촉진제로 활용 하여 자가치유 효율을 최적화하고자 하였다. BAP의 혼입 조건에 따른 균열 치유 특성을 평가하였으며, SEM, EDS 및 XRD 분석을 통해 박테리아의 생체광물화에 의한   및 HAp 생성 여부와 미세구조적 치유 메커니즘을 규명하였다. 연구 결과, BAP는 박테리 아의 생존성을 확보함과 동시에 치유 성능을 유의미하게 향상시키는 것으로 나타났다. 본 연구는 BAP 용 자가치유 콘크리트의 최적 배합 도출을 통해 유지 보수 비용 절감 및 구조물 장수명화를 위한 기초 자료를 제공할 것으로 기대된다.

This study proposes a distributed integrated control architecture based on Direct Digital Control(DDC) as an alternative to conventional centralized Distributed Control System(DCS) structures for a Canadian oil sands pilot plant, and theoretically analyzes its control characteristics and operational optimization potential. The target process consists of production and circulation, separation, water treatment, partial upgrading, and utility systems, and exhibits complex characteristics such as multiphase flow, high viscosity, time delay, strong coupling, and operation under extreme environmental conditions. In this study, an integrated control architecture combining independent DDC nodes for each process unit with a supervisory control layer is presented. A control model considering the coupling relationships among production, separation, water treatment, and upgrading processes is formulated, along with an objective function for energy optimization. Furthermore, through literature-based comparison and system architecture analysis, it is demonstrated that the DDC-based structure is suitable for oil sands pilot plants in terms of responsiveness, scalability, fault isolation, and energy efficiency.

전 세계적인 물 부족 심화로 상수관망의 효율적 운영 및 유지보수(O&M) 중요성이 커지고 있다. 특히 정확한 수압 예측은 잠재적 문제의 사전 감지와 대응에 필수적이다. 이에 본 연구는 전처리된 데이터를 활용하여 현장 적용성이 높은 수압 예측 모델을 개발하는 것을 목표로 하였다. 이를 위해 8개 블록시스템(DMA)의 10분 단위 시계열 데이터와 4종류의 딥러닝 모델(LSTM, GRU, CNN-LSTM, CNN-GRU)을 활용하였으며, optuna를 통해 하이퍼파라미터를 최적화하고 배치 정규화 등을 적용해 학습 안정성을 확보하였다. 평가 결과, CNN-GRU 모델이 가장 우수한 성능을 나타냈다. 해당 모델을 기반으로 입력 조건에 따른 성능을 비교한 결과, 단변수 대비 다변수 입력 조건에서 예측 정확도가 향상됨을 확인하였다. 또한, 10분 선행 시점에서 최고 신뢰도(R2 0.9678, RMSE 0.0375)를 기록했으며, 지속성 모델의 성능이 점진적으로 하락하여 상대적인 저점을 형성하는 7시간 및 17시간 선행 시점에서 CNN-GRU 모델은 지속성 모델 대비 RMSE 기준 각각 48.0% 및 42.1%의 오차 개선을 달성하였다. 결론적으로, 본 연구에서 제안하는 전처리 및 하이퍼파라미터 통합 최적화 프로세스는 DMA별로 상이한 운영 환경에서도 안정적인 예측 성능을 확보할 수 있음을 입증하였다. 이는 현장 엔지니어의 데이터 분석 및 의사결정을 지원함으로써, 상수관망의 안정적인 운영과 유지보수 효율성 향상에 기여할 수 있을 것으로 기대된다.

To achieve competitive design, it is essential to develop an optimization method that ensures both high customer satisfaction and robustness for products with multiple criteria. While several studies have proposed optimization methods that integrate TOPSIS with Taguchi method or desirability function, no single study has yet combined all three methods into a unified optimization framework. Therefore, this study proposes an integrated optimization method that combines TOPSIS, Taguchi method and desirability function. The overall process of proposed method is based on the TOPSIS framework. To incorporate Taguchi method and desirability function into TOPSIS, we propose using desirability function for normalization, replacing the traditional vector normalization used in standard TOPSIS. In addition, Signal-to-Noise(S/N) ratios are calculated to evaluate the degree of customer satisfaction. To demonstrate the effectiveness of the proposed method, a hypothetical example is generated under specific conditions, and the resulting rankings are compared with those derived using the original TOPSIS approach. The comparison revealed that the rankings of design alternatives differed between the original TOPSIS and the proposed method. This difference is attributed to the influence of the desirability function’s threshold points, the specific type of desirability function applied (from Kano’s perspective), and the Taguchi S/N ratio used to assess satisfaction levels. These factors enabled a more nuanced evaluation of customer satisfaction and robustness, thereby validating the effectiveness of the proposed optimization method.

The rapid development of precise diagnosis and treatment of diabetes has imposed higher requirements for the sensitivity, selectivity, and stability of glucose sensors. Given the bottlenecks of traditional carbon nanotubes in electrochemical sensing applications, such as low purity, numerous structural defects, and poor biocompatibility, this paper systematically reviews the mechanism of glucose detection, preparation and purification of high-purity carbon nanotubes, and the preparation methods and advantages of carbon nanotube-metal nanoparticle composite electrodes. To address these critical limitations, this review focuses on three interconnected aspects of CNT-based glucose sensing technology. First, the catalyst regeneration, dynamic process control and green carbon source substitution have effectively overcome the problems of high energy consumption, low purity and environmental burden of traditional methods. Second, the purification and innovative functionalization of carbon nanotubes have significantly improved their purity and electrochemical performance. Finally, the preparation method of a carbon nanotube-metal nanoparticle composite electrode is described. It not only achieves the precise spatial positioning of the catalytic active center, but also significantly enhances the long-term stability of the electrode through the synergistic regulation of chemical bonding strength and interface electronic structure. These advancements lay a theoretical foundation for the development of a new generation of wearable sensors with antibiofouling properties and resistance to complex physiological interferences.

This study of a high-entropy alloy (HEA) explored two strategies to simultaneously satisfy two mechanical properties, ultimate tensile strength (UTS) and total elongation. The first strategy used inverse design based on a conditional variational autoencoder (CVAE), and the second employed multi-objective Bayesian optimization. Using a dataset of 501 literature-based HEAs, three models were trained with alloy composition and experimental conditions as inputs. Among these, extreme gradient boosting (XGBoost) exhibited the highest predictive performance for both properties and was selected as the final prediction model. CVAE was employed to generate 1,000 new samples from the latent space under the condition that both UTS and total elongation exceeded their mean values. Of these, 310 physically feasible compositions were validated using the XGBoost model, and approximately 17.7 % satisfied the target properties. Next, expected hypervolume improvement (EHVI)-based Bayesian optimization, beginning with 130 initial compositions that demonstrated superior properties, proposed five recommended candidates. These samples were found to differ in compositional characteristics from the existing dataset, which can be interpreted as exploration driven by the uncertainty of the probabilistic machine learning model. The candidate compositions generated by both methods were predicted by the XGBoost model to have the potential to achieve the target properties.

The slow cathodic oxygen reduction rate (ORR) of microbial fuel cells (MFCs) is still one of the main bottlenecks in its industrialization. As an ORR catalyst, metal oxides are expected to significantly enhance ORR efficiency by providing active sites, regulating reaction pathways, and enhancing stability. In this paper, four bimetallic oxide catalysts, CuO/Co3O4, CuO/ MnO2, CuO/NiO, and CuO/Fe2O3, were synthesized by sol–gel method, and their structural characteristics were characterized. The results showed that CuO/Co3O4 exhibited the largest specific surface area and optimized pore structure, and the synergistic effect of Cu and Co significantly improved the electrochemical performance. As the cathode catalyst of MFCs, CuO/Co3O4 shows high ORR catalytic activity, low charge transfer resistance, and good stability. In MFCs application, CuO/ Co3O4 catalyst achieved the maximum power density of 227 mW m− 2. In the five-cycle test, the output voltage is stable at about 240 mV, and the COD removal rate reaches 91.9%, which shows great application potential in wastewater treatment.

The loss of soil available nutrients may affect soil quality and crop growth. Biochar can form a multi-level fixed network because of its rich pore structure and surface functional groups, which can effectively fix available nutrients in soil and maintain nutrient utilization rate. Because it is difficult to directly prepare biochar materials with good adsorption characteristics through experimental results. This study employed an XGBoost machine learning prediction model to determine the optimal nutrient-rich biochar preparation conditions. The R2 value ranged from 0.97 to 0.99. The results indicated that specific surface area was the primary factor influencing ammonium nitrogen adsorption, with a feature importance of 56.13%. Production conditions (hydrothermal temperature and time) significantly affected the adsorption of nitrate nitrogen and available phosphorus, with feature importances of 75.91% and 81.54%, respectively. Mean pore diameter was negatively correlated with potassium ion adsorption characteristics. Biochar prepared under hydrothermal conditions at 202.50–251.25 °C for 3 h exhibited favorable adsorption characteristics for multiple soil available nutrients. This study provides new insights into biochar’s application in the field of soil nutrient adsorption through data analysis. It is helpful to avoid the waste in the process of energy utilization from biomass to biochar.

The rapid expansion of the fast fashion industry has led to a dramatic increase in textile waste, posing significant environmental and systemic challenges. Although approximately 95% of discarded clothing is technically recyclable, current recycling system remains inefficient due to fragmented collection, manual sorting, limited recycling capabilities, and a lack of integrated data management. This study investigates the structural limitations of Korea’s waste clothing recycling system and proposes optimization strategies grounded in circular economy principles. These strategies, if implemented, have the potential to significantly improve the efficiency and effectiveness of Korea’s textile waste recycling system. Through a comparative analysis of international models― including government-led Extended Producer Responsibility (EPR) systems, digital platform-based collection services, and brand-driven recycling initiatives―the study identifies key bottlenecks in Korea’s current system. The findings highlight the need for a unified and monitored collection infrastructure, the deployment of AI-based automated sorting technologies, and the development of fiber-to-fiber (F2F) recycling processes supported by standardized classification codes and centralized databases. Furthermore, the study emphasizes the importance of real-time data integration across all stages of the recycling chain to enable transparent tracking and performance evaluation. Drawing on successful PET bottle recycling cases, the research outlines a roadmap for transitioning Korea’s textile waste management to a scalable, sustainable circular economy. The study concludes by calling for robust institutional support, legal clarity, and most importantly, cross-sector collaboration. This collaboration is crucial to ensure effective implementation of EPR and long-term resource circulation, and it will require the collective efforts of environmental policymakers, waste management professionals, industry stakeholders, and researchers.

Camellia japonica L. is highly valued for its ornamental and industrial applications. However, existing limitations in conventional seed and cutting propagation necessitate the development of a stable and efficient mass propagation system. This study systemically optimized each critical stage of in vitro culture—including shoot and root development, multiple shoot induction, rooting, and acclimatization —and quantitatively assessed the overall efficiency using integrated indices. Shoot growth was most vigorous on Woody Plant Medium (WPM) without the addition of indole-3-butyric acid (IBA), while root development was notably promoted by Murashige and Skoog (MS) medium supplemented with IBA. The highest number of multiple shoots was produced using basal explants cultured on MS medium containing 0.5 mg/L thidiazuron (TDZ), yielding an average of 2.67 shoots per explant. Optimal root induction was observed following a 15-min pulse treatment with 500 mg/L IBA (producing 24,33 roots), whereas the root elongation was maximized by a 5-min treatment with 1000 mg/L IBA (2.10 cm). Acclimatization successfully resulted in 100% survival in both tested substrates (A: peat moss, perlite, and cocopeat mixed in a 3:1:1 ratio; B: peat moss, perlite, and vermiculite mixed in a 1:1:1 ratio), with substrate B promoting a greater increase in plant height. Normalized growth parameters were averaged to calculate the Camellia Micropropagation Index (CMI). Integrated analysis identified the most efficient treatments as: WPM without IBA (shoot growth), MS with IBA (root growth), MS + 0.5 mg/L TDZ with basal explants (multiple shoots), 1000 mg/L IBA for 5 min (rooting), and substrate B (acclimatization). Despite these optimal conditions, considerable variation within treatments suggests that further fine-tuning or long-term evaluation is necessary to improve reliability. These findings provide a robust guideline for establishing a successful in vitro mass micropropagation system for C. japonica.

Carbon nanotube (CNT) has promising applications in several fields due to their excellent thermal, electrical, mechanical, and biocompatible properties. However, the complexity of its structure leads to the problems of computationally intensive and inefficient synthetic characterization optimization and prediction by traditional research methods, which seriously restricts the development process. Machine learning (ML), as an emerging technology, has been widely used in CNT research due to its ability to reduce computational cost, shorten the development cycle, and improve the accuracy. ML not only optimizes the synthetic control parameters for precise structural control, but also combines various imaging and spectroscopic techniques to significantly improve the accuracy and efficiency of characterization. In addition, ML helps to improve the performance of CNT devices at the optimization and prediction levels, and achieve accurate performance prediction. However, ML in CNT research still faces challenges such as algorithmic processing of complex data situations, insufficient space for algorithmic combined optimization, and lack of model interpretability. Future research can focus on developing more efficient ML algorithms and unified standardized databases, exploring the deep integration of different algorithms, further improving the performance of ML in CNT research, and promoting its application in more fields.