Accurate estimation of vehicle exhaust emissions at urban intersections is essential to assess environmental impacts and support sustainable traffic management. Traditional emission models often rely on aggregated traffic volumes or measures of average speed that fail to capture the dynamic behaviors of vehicles such as acceleration, deceleration, and idling. This study presents a methodology that leverages video data from smart intersections to estimate vehicle emissions at microscale and in real time. Using a CenterNet-based object detection and tracking framework, vehicle trajectories, speeds, and classifications were extracted with high precision. A structured preprocessing pipeline was applied to correct noise, missing frames, and classification inconsistencies to ensure reliable time-series inputs. Subsequently, a lightweight emission model integrating vehicle-specific coefficients was employed to estimate major pollutants including CO and NOx at a framelevel resolution. The proposed algorithm was validated using real-world video data from a smart intersection in Hwaseong, Korea, and the results indicated significant improvements in accuracy compared to conventional approaches based on average speed. In particular, the model reflected variations in emissions effectively under congested conditions and thus captured the elevated impact of frequent stopand- go patterns. Beyond technical performance, these results demonstrate that traffic video data, which have traditionally been limited to flow monitoring and safety analysis, can be extended to practical environmental evaluation. The proposed algorithm offers a scalable and cost-effective tool for urban air quality management, which enables policymakers and practitioners to link traffic operations with emission outcomes in a quantifiable manner.

Smart factory technology, a core component of the Fourth Industrial Revolution, demonstrates significant disparities in technological development across countries. To quantitatively assess these international technology gaps, this study proposes an integrated analytical framework that combines text mining-based topic modeling and social network analysis (SNA), using global smart factory-related patent data from 2017 to 2023. Approximately 4,300 patent documents (titles and abstracts) were collected through the GPASS system and preprocessed. Through Latent Dirichlet Allocation (LDA) modeling with optimized hyperparameters, major technology topics were identified. Semantic interpretation using ChatGPT and expert review enabled the assignment of precise topic labels, which were further mapped to CPC (Cooperative Patent Classification) codes to construct a standardized technology taxonomy. Subsequently, the network structures of topic and classification nodes were analyzed by country (China, the United States, and South Korea), and the relative importance of key technology areas was evaluated using centrality metrics such as degree, closeness, betweenness, and eigenvector centrality. The analysis revealed that, globally, the most central technology areas include manufacturing process management and control, IoT and data-driven decision making, and facility-based process optimization. At the national level, China showed a strategic focus on technologies related to product quality improvement and cost reduction, South Korea emphasized IoT-enabled technologies and equipment-level optimization, while the United States prioritized control systems and data-driven project management. By utilizing patent-based textual data, this study offers a novel methodology for quantitatively diagnosing structural differences in national technological capabilities. The proposed framework provides valuable insights for country-specific R&D planning and strategic decision-making in the field of smart manufacturing.

This study aims to identify and prioritize the key factors essential for transforming a traditional port into a smart port using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) based on Chang’s extent analysis method. Based on the insights of 30 experts from Vietnam and South Korea, the research framework comprises three main dimensions, namely Policy, Operation, and Environment, which are further divided into ten sub-factors. The analysis revealed that Policy and Operation were perceived as the most critical dimensions, while Environment received relatively less emphasis. At the factor level, Automation & Intelligent Infrastructure ranked highest, followed by Investment & Financial Support, Productivity, and Regulatory Frameworks. In contrast, environmental factors such as Water & Waste Management and Emission Control were ranked lowest. Notably, Vietnamese and Korean experts all value the importance of advanced technology and investment capacity but still have some differences in prioritizing the other factors, reflecting differing national contexts and developmental stages. These findings offer strategic guidance for policymakers and port authorities in tailoring smart port development strategies to local conditions and priorities.

Small and medium-sized manufacturing enterprises(SMEs) have traditionally relied on skilled labor to support multi-variety, small-batch production. However, demographic changes such as low birth rates and aging populations have led to severe labor shortages, prompting increased interest in collaborative robots(cobots) as a viable alternative. Despite this necessity, many SMEs continue to face significant challenges in implementing such technologies due to technical, organizational, and environmental(TOE) constraints. While prior research has mainly focused on technology adoption from the perspective of user organizations, this study adopts a differentiated approach by analyzing adoption factors from the perspective of smart factory experts—specifically, evaluators/mentors and solution providers—who play a critical role in Korea’s policy-driven smart manufacturing environment. Using the Analytic Hierarchy Process(AHP), the study evaluates the relative importance and prioritization of adoption factors across three dimensions: technology, organization, and environment. Survey data collected from 20 smart factory experts indicate that top management support, relative advantage, and safety are key determinants in cobot adoption. Furthermore, the findings reveal that organizational readiness and technical effectiveness have greater influence on implementation decisions than external pressures such as partner pressure. This study provides new insights by incorporating expert perspectives into the adoption framework and offers practical policy and managerial implications to support cobots implementation in the SMEs.

스마트계약은 자동화된 이행을 특징으로 한다. 이는 스마트계약을 계약의 일 종으로 이해하든, 컴퓨터 프로그램이라고 하는 정의하든 마찬가지이다. 스마트 계약에 의한 계약의 집행에서는 “코드가 법”이어서 “실행을 통제하거나 실행 에 영향을 주는 중재자 또는 제3자”가 필요없다. 이러한 스마트 계약을 부동산 거래에 적용할 수 있는지에 관해서 다양한 검토가 행해지고 있다. 외국에서는 스마트계약을 통한 부동산 거래가 시도되기도 했으며, 우리 정부도 이를 부동 산 거래에 도입하는 것을 추진하고 있다. 이 논문에서는 스마트계약을 통한 부 동산 거래의 가능성과 그 한계에 관하여 검토하였다. 스마트계약을 통해 부동 산 매매계약이 집행된다면 동시이행의 확보가 분명하고 효율적으로 달성될 것 이다. 거래의 효율화와 집행용이화는 거래계에서 흔히 볼 수 있는 일반적 흐름 이므로, 스마트계약이 이에 활용될 수 있다면 거래계에서 자연스럽게 채용될 것이다. 그러나 블록체인 상의 스마트계약을 통해서 부동산 거래를 할 필요성 이 있는지, 그러한 방식이 효율적이고 바람직한지에 대하여는 의문이 있다. 우 선 블록체인에서 거래하려면 이를 대체불가능토큰(NFT)화 해야 할 것인데, 부대체의 특정 자산인 부동산을 디지털 자산화 할 필요는 없을 것으로 생각된다. 또한 부동산 거래의 전 과정을 코드화 하려면 그 효용보다 비용이 더 클 것이 므로, 코드화 할 수 있는 것보다 코드화 할 수 없는 부분이 더 많을 것이다. 더 나아가 코드에 의해 이행된 경우에도 법적 분쟁이 발생하지 않는 것은 아니 며, 스마트계약의 불변성은 오히려 법적 해결에 걸림돌로 작용할 수 있을 것이 다. 더 나아가 탈중앙화라는 블록체인의 이상 자체가 부동산 거래에는 적합하 지 않다. 또한 블록체인 기술의 장점으로 평가되는 기록의 정확성과 불변성은 국가가 관리하는 등기부를 통해서도 충분한 정도로 달성될 수 있다. 따라서 블 록체인 스마트계약을 통해서 부동산거래를 할 수 있도록 제도를 마련할 필요 성은 없다고 생각된다.

This study aims to present a methodology and the corresponding results of an economic analysis, incorporating both costs and benefits, to assess the feasibility of introducing a smart on-board truck scale.The cost estimation was conducted based on direct expenditures associated with the installation and operation of smart on-board truck scales. The benefit analysis was performed by evaluating the reduction in social costs resulting from the mitigation of overloading, including transportation infrastructure maintenance costs, traffic accident costs, and environmental costs. The economic analysis outlines the variables required for each phase of the smart on-board truck scale implementation, along with their reasonable value ranges. In consideration of the uncertainty regarding the effectiveness of the smart on-board truck scales, a quantitative assessment of the impact of individual variables on the economic indicators was carried out through scenario analysis, focusing on key variables. The influence of the vehicle service life, the service life of the smart on-board truck scale, and personnel expenses—each related to installation and operation—on the benefit-cost ratio (B/C) and net present value (NPV) was determined to be limited. In contrast, the overload crackdown rate exhibited the most significant impact on the B/C and NPV, as it directly increased the number of vehicles contributing to measurable benefits. Notably, an increase in the discount rate led to a decrease in the values of both economic indicators. This outcome is expected, as the discount rate reduces the present value of future costs and benefits by increasing the denominator in the calculation. The introduction of smart on-board truck scales enables the achievement of economic feasibility in preemptive overload enforcement. Therefore, progressively expanding the number of vehicles equipped with smart on-board truck scales is essential to maximize their effectiveness in the near term.

In this study, the quality of hydroponically grown leafy vegetables (batavia and butterhead) from smart farms was compared with that of soil-grown vegetables. The quality characteristics (weight, size, color, water content, pH, texture, bitter compounds, functional ingredients, antioxidant capacity, microorganisms, and sensory properties) of the two leafy vegetables were analyzed. Significant differences were observed in fresh weight, shear force, and functional ingredients between the two cultivation methods. With regard to weight measurement, hydroponically grown leafy vegetables were lighter compared to soil-grown vegetables (batavia: hydroponic 127.15–138.26 g, soil-grown 219.30 g; butterhead: hydroponic 107.48–127.66 g, soil-grown 237.23 g; p<0.05). In addition, hydroponically grown vegetables had significantly lower shear force values (batavia: 32–82%, butterhead: 49–70%) than soil-grown vegetables, except for one hydroponically grown batavia sample (p<0.05). Analysis of functional ingredients showed that both total polyphenol and total flavonoid contents were significantly higher in the soil-grown vegetables (p<0.05). However, no differences related to the growth system were observed in plant size, color, pH, bitter taste compounds, antioxidant capacity, and the presence of microorganisms between the two cultivation methods. This study provides a database of quality differences between hydroponically grown and soil-grown leafy vegetables, which is valuable for improving the quality of hydroponically grown products.

In this study, we target demand-responsive smart mobility, i.e., a bus-type rural transportation model, that has recently been activated to target public transportation-vulnerable areas in urban-rural integrated cities, and empirically analyze the effects of travel time and service factors on user satisfaction with the transportation mode. An ordered logit model was used for the empirical analysis of a field survey of 449 passengers regarding their usage status and satisfaction with demand-responsive smart mobility in rural areas across the country. As access and travel times increased, bus user satisfaction decreased. Particularly, access time was approximately 1.6 times more important than travel time. Meanwhile, satisfaction with demand-responsive smart mobility was found to increase as drivers were kind and drove safely, vehicles were convenient and ran on time, lines and stops were appropriate, fares were satisfactory, and information on schedules and how to use them was available. Among these service elements, the kindness of the driver was analyzed as the most important variable. This suggests that to activate the use of demand-responsive smart mobility, considering the selection of pick-up and drop-off locations to reduce access time and to make efforts to increase the kindness of drivers is important. The essential flexible schedule of demand-responsive smart mobility, i.e., the use of demand-responsive smart mobility, can be activated only when an operating environment is created that reduces access time and in-vehicle travel time. In other words, it is difficult to revitalize the use of demand-responsive smart mobility if it operates on a fixed route and schedules similar to those of existing buses.