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To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.

This study integrated a focus on geographical, physical, and commercial characteristics to explore the commercial gentrification phenomenon and its related statistical summaries in the area of Garosu-gil in Seoul’s Sinsa-dong ward. In particular, this study first collected parcel and building data and corresponding attribute information and mapped the resulting datasets in a geographic information system (GIS) environment. We then examined gentrification issues per building and conducted statistical analyses to investigate spatial patterns of commercial gentrification, which were used to develop criteria for determining degrees of gentrification. Third, this study conducted correlation and regression analyses to quantify the strength of the linear relationship between pairs of variables associated with primary factors contributing to commercial gentrification, and used a geographically weighted regression model (GWR) to help understand and predict spatial relationships between significant variables. The results showed positive correlations between several variables and commercial gentrification in the study area, namely neighborhood-convenience facilities, building ages, store rents, new franchise and restaurant businesses, distance to subways, and the presence of multiple roads. Based on its finding, there are key contributions of this study as follows. The first significant contribution of this study is developing measurement of gentrification levels that can be used by policy makers at each of four stages of the gentrification process. Furthermore, this paper develops a comprehensive approach for spatially identifying gentrifying neighborhoods across multiple time periods in 2- and 3-dimensions. It eventually helps urban planners implement preventative or supportive programs to protect lower-income residents and small businesses and thereby engender more sustainable community development.

Pursuing innovations in sustainable architectural solutions, this study examines the impact of a climate-adaptive building envelope with dynamic photovoltaic integrated shading devices (PVSDs) on building performance. A major challenge in designing PVSDs is the lack of established guidelines for geometry and operations. We delve into the complexities and potential benefits of integrating dynamic PVSD designs into building performance simulations, particularly considering their time-varying geometric and operational aspects. This research assesses a range of similar PVSD design options with differing patterns, emphasizing their effects on solar energy potential, daylighting, and thermal efficiency. We conducted tests on south-oriented PVSDs (featuring two-axis rotation) in Houston, Texas, focusing on variables such as panel count (4 or 36), rotation angle range, and operational patterns (synchronized or individual). Regarding solar potential, the four-panel synchronized PVSD option outperformed static shading by 2.1 times. For daylighting and thermal performance, the 36-panel synchronized option with a wide rotation range and the four-panel individual option proved superior to other PVSD configurations, improving up to an average of 36% (sDA300/50%) and 1.5 °C, respectively. Our findings emphasize the critical role of integrating geometric design and operational patterns in PVSDs for enhanced system effectiveness and highlight PVSD design and application limitations. Our findings emphasize the critical role of integrating geometric design and operational patterns in PVSDs for enhanced system effectiveness. Furthermore, they shed light on the limitations in the PVSD design process and practical applications.

Embedded sensors are widely employed for the structural health monitoring of structures constructed with concrete or mortar. Despite embedded sensors being actively used, there has been no study on whether or not the sensor probe placement within structures made of concrete or mortar influences their structural stability. The strength of small structures in particular could be affected by sensor probes embedded within them. To address the lack of research in this area, this study analyzed the effect of embedding positions of sensor probes on the compressive strength development of mortar. After the production of mortar specimens with the depth of the embedded sensor being controlled by the developed mold, compressive strength tests were conducted, and then test results were verified through finite element analysis. For testing, copper–nickel-plated sensor probes were embedded within the mortar because these sensor probes are popular commercial probes. The test results show that the compressive strength was 7.1 MPa when the sensor probe was embedded at a depth of 5 mm. In contrast, the compressive strength was 28.2 MPa at a depth of 30 mm. Since the compressive strength without the embedded sensor probe was 29.8 MPa, considering the results of this study, it is highly recommended that copper–nickel-plated sensor probes be embedded at least 30 mm from the surface of mortar structures.

This study explored geospatial technologies to support efforts of community organizations and of the Chicago Gun Violence Research Collaboration to reduce gun-related crimes. It entailed (1) identification of spatial trends in gun-related crimes during 2012 to 2017 in each of the following four areas: Austin, East Garfield, North Lawndale, and Englewood; (2) investigation of changes in crime patterns near safe school zones in the areas before and after the establishment of the city’s safe passage routes in 2009 to protect the youth from street violence when traveling to school; and (3) development of a web-enabled mobile application to provide researchers and residents with spatial information on local crime incidents and to enable community members to collect and share their information in a GIS environment. The results of this research revealed that, although the number of safe passage routes has increased in these areas over the past several years, hotspot trends for gun-related crimes have intensified in most of the communities in these areas, which include school zones and safe passage routes. Accordingly, it turned out that GIS can serve as an ideal platform supporting collaborative efforts between communities and researchers.

Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This paper proposes a vehicle routing problem considering reconnaissance and transportation (VRPCRT) for wartime troop movements. The VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements and reconnaissance, and transportation vehicle routes were determined simultaneously in the VRPCRT. For this paper, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed, and computational experiments were conducted to compare the ACO algorithm’s performance and that of the mixed-integer programming model. The performance of the ACO algorithm was shown to yield excellent results even for the real-size problem. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.

Effective change order management is very important in maintaining the financial sustainability of various stakeholders related to construction projects by minimizing cost overruns. In this study, we propose a zero-cost risk management approach based on the collar option model in order to control for the loss caused by change orders, the main cause of cost overruns in construction projects. We apply this model to actual projects for empirical analysis. The analysis, based on 237 projects, indicates that insurance buyers benefit from the collar option model in 46% of the cases, while insurance sellers do so in 53% of the cases. In most cases, the insurance buyer is the owner. According to the model, the owner experiences a loss when the cost overrun caused by change orders is lower than what was expected. In such cases, it is appropriate to conclude that the loss is not caused by the collar option model, but by the absence of additional revenue. However, the insurance seller suffers a loss if the cost overrun is higher than the strike price of the call option. Thus, the insurance seller needs to have expertise in construction management.

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.