In the context of the global shipping industry's transition towards high efficiency and low carbon emissions, energy conservation and drag reduction for ships have become core research directions in marine engineering. Container ships, as the backbone of international trade, experience a significant increase in wind resistance under extreme wind conditions of level 8 and above, which affects their navigation efficiency, energy consumption, and safety. Optimizing wind resistance is crucial for enhancing ship performance and reducing carbon emissions. The fairing can reduce the air resistance of ships by optimizing the flow field and suppressing vortex flows, presenting broad application prospects. However, existing research has primarily focused on conventional wind conditions, and further analysis is needed under extreme wind conditions. Given the typicality and harmfulness of level 8 winds, this paper takes large container ships as the research object. Based on Computational Fluid Dynamics (CFD) numerical simulations, by establishing key structural models, optimizing computational domains and grids, and selecting the Realizable k-ε turbulence model and Volume of Fluid (VOF) multiphase flow model, this study investigates the drag reduction effect of polygonal curved fairings under level 8 wind speeds. It analyzes parameters such as drag coefficient and flow field distribution, reveals the flow field regulation mechanism, and provides theoretical support and data reference for the optimal design and engineering application of fairings.

Against the backdrop of the rapid development of the global shipping industry and the deep advancement of “dual carbon” goals, energy transition, energy conservation, and emission reduction have become core issues in marine transportation. As a critical component of clean and renewable energy, the efficient development and utilization of wind energy are pivotal for achieving low-carbon shipping. Exhaust turbine sails, an innovative application of active suction control in marine aerodynamic propulsion, regulate boundary layer flow through active suction to enhance wind energy utilization efficiency, which has emerging as a research hotspot in the green transformation of modern shipping. This paper aims to synthesize research on exhaust turbine sails. First, based on fundamental fluid mechanics principles, it analyzes the impact of boundary layer separation on the aerodynamic characteristics of structural bodies. Second, through case studies, it summarizes flow control effects under different suction parameters. It further introduces combined blowing and suction control strategies to explore their influence on boundary layer management. Finally, it details the research progress of exhaust turbine sails, explaining their core principle: active suction control delays or prevents boundary layer separation, effectively suppressing vortex shedding, thereby significantly reducing ship navigation resistance and enhancing lift. The study reveals that the aerodynamic performance of exhaust turbine sails is jointly influenced by oncoming flow conditions, suction power, and structural parameters, necessitating multi-objective optimization to achieve energy efficiency balance. The paper concludes by addressing key challenges in their marine applications and envisioning future directions for integrating these sails with emerging technologies, providing practical implications for promoting the green and low-carbon transformation of the shipping industry.

Manganese dioxide, functioning as a cathode material for aqueous zinc-ion batteries (AZIBs), demonstrates a variety of benefits, such as elevated theoretical specific capacity, outstanding electrochemical performance, environmental compatibility, ample resource availability, and facile modification. These advantages make MnO2 one of the cathode materials that have attracted much attention for AZIBs. Nevertheless, manganese dioxide cathode in practical applications suffers from structural instability during the cycling process because of sluggish electrochemical kinetics and volume expansion, which hinder their large-scale application. Doping and compositing with conducting frameworks is an effective strategy for improving structural stability. Herein, homogeneously in situ growth of Yttrium-doped MnO2 nanorods on conductive reduced graphene oxide (Y-MnO2/rGO), were synthesized through a straightforward hydrothermal method. The Y-MnO2/rGO electrodes have an ultra-long cycle life of 179.2 mA h g− 1 after 2000 cycles at 1 A g− 1 without degradation. The excellent structural stability is attributed to the cooperative effect of yttrium doping and compositing with rGO, which is an effective approach to enhance the stability and mitigate the Jahn–Teller distortion associated with Mn ions.

With the increasing demand for energy conservation and emissions reduction in the shipping industry, suctionbased turbine sails have emerged as a novel wind energy utilization technology and have become a research hotspot. This study focuses on the aerodynamic performance of suction-based turbine sails with the aim of investigating the effects of suction intensity and suction port position on their aerodynamic characteristics. By employing Computational Fluid Dynamics (CFD) numerical simulations using the Re-Normalization Group (RNG) k–ε turbulence model and the SIMPLE algorithm, this study provides a detailed analysis of lift and drag coefficients, pressure distribution, and vorticity distribution under various combinations of suction intensity (γ) and suction port position (α). The results show that variations in suction intensity significantly affect the lift and drag characteristics of the turbine sail, while changes in the suction port position directly influence the attachment and separation behavior of airflow on the sail surface. Furthermore, a synergistic effect is observed between γ and α—their interaction not only alters the flow distribution but also plays a critical role in determining the overall performance of the turbine sail.By comprehensively considering the influence of these two factors, the study draws key conclusions for optimizing the design of suction-based turbine sail, providing valuable theoretical insights and technical guidance for their practical application in wind-assisted marine propulsion.

The development of hydrogen energy is crucial for achieving global dual-carbon strategic goals, namely "carbon peak" and "carbon neutrality." Photocatalytic water splitting, powered by solar energy, presents a promising approach to hydrogen production. Advancing this technology requires the development of photocatalysts that are cost-effective, highly active, and stable. As a non-metallic semiconductor, g-C3N4 stands out for its potential in sustainable energy and environmental remediation technologies, garnering considerable interest for its efficiency in harnessing light-driven reactions. Although g-C3N4 exhibits promising characteristics, its practical application is significantly hindered by the rapid recombination of photogenerated charge carriers and its limited light absorption range. This review highlights various strategies employed to improve the photocatalytic hydrogen production efficiency of g-C3N4, including heteroatom doping, microstructure control, co-catalyst modification, defect engineering, and heterojunction construction. These strategies enhance active site density, light absorption capacity, and photogenerated charge separation in g-C3N4, thereby boosting electron migration rates and improving photocatalytic hydrogen production. Additionally, we explore the potential of integrating cutting-edge AI technology with advanced instrumentation for the prediction, design, preparation, and in-situ characterization of g-C3N4-based photocatalytic systems. This review aims to offer key insights into the design, development, and practical application of innovative, high-performance carbon-based catalysts.

This study aims to develop an AI-based analysis system that aligns with the international trend of AI legislation, including the EU's AI Act, while also addressing the analytical needs of the public sector. The focus is on providing timely and objective information to policymakers and specialized researchers by exploring advanced analytical methodologies. As the complexity and volume of data rapidly increase in the modern policy environment, these methods have become essential for governments to obtain the objective information needed for critical decision-making. To achieve this, the study integrates machine learning, natural language processing (NLP), and Large Language Models (LLM) to create a system capable of meeting the analytical demands of government entities. The target dataset consists of “quantum” field data collected from South Korea's National R&D Information System (NTIS). Machine learning was applied to this data to assess the validity of the analysis, while BERTopic, a natural language analysis package, was used for text analysis. With the introduction of LLMs, the extracted information from machine learning and natural language analysis was not merely listed but also connected in meaningful ways to provide policy insights. This approach enhanced the transparency and reliability of AI analysis, minimizing potential errors or distortions in the data analysis process. In conclusion, this study emphasizes the development of a system that enables rapid and accurate information provision while maintaining compatibility with international AI regulations such as the AI Act. The use of LLMs, in particular, contributed to enhancing the system’s capabilities for deeper and more multifaceted analysis.

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the “drone” keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI’s power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

식용곤충은 미래식량 자원으로써 우수한 가치를 지니고 있어 해외에서는 사육자동화, IoT 및 AI 기술적용, 수직재배시스템 구축 등 많은 연구가 진행되고 있지만 국내에서는 대규모 사육농가나 곤충스마트팜 기술개발 이 부족하여 이를 위한 AI/빅데이터 인프라 구축이 시급한 실정이다. 학습용 인공지능 데이터는 식용곤충으로 활용되고 있는 장수풍뎅이, 흰점박이꽃무지, 갈색거저리, 백강잠, 메뚜기, 풀무치의 생애 주기별 총 6종의 RGB 사진데이터와 분광이미지 데이터 408,000장을 구축하였으며 온도, 습도, CO,, 암모니아, 조도, 수분 등 환경 데이 터 200,000세트를 수집하였다. 수집된 데이터는 원시데이터 수집, 원천데이터 가공, 라벨링 데이터 결합, 가공데 이터 검수 등을 통해 만들어졌으며 관련 데이터는 AI Hub(www.aihub.or.kr)에서 다운받을 수 있다. 확보된 식용곤 충 6종의 데이터는 곤충 종별 성장단계, 환경 변수에 따른 최적의 사육환경 조성, 생산시기 예측, 스마트대량사육 시스템 개발, 제품 가공시 추적이력제 도입, 식용곤충 스마트팜 기술 개발 및 연구 등 다양한 분야에 활용될 수 있을 것으로 예상된다.

Truck no-show behavior has posed significant disruptions to the planning and execution of port operations. By delving into the key factors that contribute to truck appointment no-shows and proactively predicting such behavior, it becomes possible to make preemptive adjustments to port operation plans, thereby enhancing overall operational efficiency. Considering the data imbalance and the impact of accuracy for each decision tree on the performance of the random forest model, a model based on the Borderline Synthetic Minority Over-Sampling Technique and Weighted Random Forest (BSMOTE-WRF) is proposed to predict truck appointment no-shows and explore the relationship between truck appointment no-shows and factors such as weather conditions, appointment time slot, the number of truck appointments, and traffic conditions. In order to illustrate the effectiveness of the proposed model, the experiments were conducted with the available dataset from the Tianjin Port Second Container Terminal. It is demonstrated that the prediction accuracy of BSMOTE-WRF model is improved by 4%-5% compared with logistic regression, random forest, and support vector machines. Importance ranking of factors affecting truck no-show indicate that (1) The number of truck appointments during specific time slots have the highest impact on truck no-show behavior, and the congestion coefficient has the secondhighest impact on truck no-show behavior and its influence is also significant; (2) Compared to the number of truck appointments and congestion coefficient, the impact of severe weather on truck no-show behavior is relatively low, but it still has some influence; (3) Although the impact of appointment time slots is lower than other influencing factors, the influence of specific time slots on truck no-show behavior should not be overlooked. The BSMOTE-WRF model effectively analyzes the influencing factors and predicts truck no-show behavior in appointment-based systems.

Commercial condom advertisements usually emphasize the sexual pleasure of branded products, which leads to controversial public views. Some people agree that commercial condom advertisements can also benefit public health, whereas others disapprove of such commercial condom advertisements because their contents are usually offensive, low-tasted, and pornographic. Despite controversy over commercial condom advertising, we know little about the spillover effect of commercial condom advertisement. On one hand, sexual-related content in the commercial condom advertisement may have an arousal effect. That is, it can evoke sexual arousal, leading to more sex intercourses. More frequent sex behaviors, especially casual sex behaviors, may then lead to a risk of contracting sexually transmitted diseases (STDs). On the other hand, commercial condom advertisements may have an educational effect. That is, it can persuade people to use condoms, helping people get in the habit of using condoms, thus reducing the STD trends. In the short term when condom commercials are aired, the arousal effect and educating effect coexists, which motivates the net short-term effect as an open empirical question. In the long-term when the condom commercial no longer aired, the educating effect remains, which decreases the STD trends.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

This study assessed the utility of netted melon ‘Top Earl’s’ and cantaloupe melon ‘Alex’ as functional fruits by analysing their moisture content, vitreous sugar, folic acid, citric acid, and beta-carotene levels. High-performance liquid chromatography (HPLC) was used to analyse the free sugar, folic acid, citric acid, and beta-carotene levels. The moisture content was not significantly different between ‘Top Earl’s’ and ‘Alex.’ The glucose, sucrose, and fructose contents were three, two, and one-and-a-half fold higher in ‘Alex’ than in ‘Top Earl’s.’ Moreover, citric acid was approximately three times higher in ‘Alex’ than that in Top Earl’s.’ However, the folic acid content was higher in ‘Top ‘Earl’s’ than ‘Alex,’ and the amount was 124 μg / 100 g FW and 112 μg / 100 g FW respectively. ‘Beta-carotene was undetectable in ‘Top Earl’s,’ whereas it was 1000 μg / 100 g FW in ‘Alex.’ β-carotene, a substance that is converted in the body into vitamin A and acts as an antioxidant, is an important component in healthy food. These results suggested that the cantaloupe melon ‘Alex’ has a higher free sugar content and functional ingredients, such as antioxidants, including citric acid and beta carotene, than the netted melon ‘Top Earl’s.’

Explainable AI (XAI) is an approach that leverages artificial intelligence to support human decision-making. Recently, governments of several countries including Korea are attempting objective evidence-based analyses of R&D investments with returns by analyzing quantitative data. Over the past decade, governments have invested in relevant researches, allowing government officials to gain insights to help them evaluate past performances and discuss future policy directions. Compared to the size that has not been used yet, the utilization of the text information (accumulated in national DBs) so far is low level. The current study utilizes a text mining strategy for monitoring innovations along with a case study of smart-farms in the Honam region.

다양한 동물 모델이 인간 질병, 의약품의 효능 및 작용 메커니즘을 연구하는 데 사용되고 있다. Zebrafish(Danio rerio)는 여러 가지 장점이 있어 인간 질병에 대한 중개 연구의 모델로 점점 더 폭넓게 활용되고 있다. 본 논문은 Pubmed, Google Scholar, Scopus에서 2020년 12월까지 최근 10년간 zebrafish 모델, 천연물(한약), in vivo 스크리닝의 키워드를 사용하여 저널에 게재된 논문을 검토하여 필 요한 정보를 얻었다. 이 리뷰에서 우리는 천연물(한약) 연구에 대한 다양한 제브라피쉬 질병 모델의 최 근 경향에 대해 논의하였다. 특히, 암, 안질환, 혈관 질환, 당뇨병 및 합병증, 피부질환에 중점을 두었고, zebrafish 배아를 사용하여 이들 질병에 대한 의약품의 분자 작용 메커니즘에 관해 언급하였다. Zebrafish는 실험실에서 임상 연구까지의 격차를 줄이는 데 중추적 역할을 할 수 있는 중요한 동물 모 델이다. Zebrafish는 의약품이나 화장품 개발, 질병의 병인론을 이해하기 위해 사용되고, 이로 인해 생의 학 연구에서 설치류의 사용을 줄이는 데 크게 기여하고 있다.