Search Results (3)

The purpose of this study is to propose and critically assess a sustainable urban regeneration model for the redevelopment of the former K-2 military airbase in Daegu, Korea. Large-scale idle military sites pose significant challenges in terms of ecological remediation, social integration, and economic transformation, but also offer opportunities for redefining urban identity and global competitiveness. To address this, we develop the concept of the “Global Contents City,” a planning framework that integrates cultural exchange, creative industries, education, and tourism within a sustainable urban ecosystem. The research employs a qualitative methodology that combines theoretical review, comparative analysis of international precedents (e.g., Munich-Riem, Tempelhof, Stapleton, and Toronto), and design-oriented masterplanning. The findings highlight design strategies that spatially interconnect cultural, educational, industrial, and ecological functions while reinforcing low-carbon infrastructure and green open space. By situating the Daegu K-2 case in an international context, the study demonstrates how lessons from post-military redevelopments can be adapted to Korea, contributing to both scholarly debates and practical frameworks for sustainable city-making. Full article

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost. Full article

In the distributed hydrological simulations for complex mountain areas, large amounts of meteorological input parameters with high spatial and temporal resolutions are necessary. However, the extreme scarcity and uneven distribution of the traditional meteorological observation stations in cold and arid regions of Northwest China makes it very difficult in meeting the requirements of hydrological simulations. Alternatively, regional climate models (RCMs), which can provide a variety of distributed meteorological data with high temporal and spatial resolution, have become an effective solution to improve hydrological simulation accuracy and to further study water resource responses to human activities and global climate change. In this study, abundant and evenly distributed virtual weather stations in the upper reaches of the Heihe River Basin (HRB) of Northwest China were built for the optimization of the input data, and thus a regional integrated environmental model system (RIEMS) based on RCM and a distributed hydrological model of soil and water assessment tool (SWAT) were integrated as a coupled climate–hydrological RIEMS-SWAT model, which was applied to simulate monthly runoff from 1995 to 2010 in the region. Results show that the simulated and observed values are close; Nash–Sutcliffe efficiency is higher than 0.65; determination coefficient (R²) values are higher than 0.70; percent bias is controlled within ±20%; and root-mean-square-error-observation standard deviation ratio is less than 0.65. These results indicate that the coupled model can present basin hydrological processes properly, and provide scientific support for prediction and management of basin water resources. Full article