INVESTIGATION OF DEFORESTATION USING MULTI-SENSOR SATELLITE TIME SERIES DATA IN NORTH KOREA

Department of Urban and Environmental Engineering(Environmental Science and Engineering)North Korea is very vulnerable to natural disasters such as floods and landslides due to institutional, technological, and other various reasons. Recently, the damage has been more severe and vulnerability is also increased because of continued deforestation. However, due to political constraints, such disasters and forest degradation have not been properly monitored. Therefore, using remote sensing based satellite imagery for forest related research of North Korea is regarded as currently the only and most effective method. Especially, machine learning has been widely used in various classification studies as a useful technique for classification and analysis using satellite images. The aim of this study was to improve the accuracy of forest cover classification in the North Korea, which cannot be accessed by using random forest model. Indeed, another goal of this study was to analyze the change pattern of denuded forest land in various ways. The study area is Musan-gun, which is known to have abundant forests in North Korea, with mountainous areas accounting for more than 90%. However, the area has experienced serious environmental problems due to the recent rapid deforestation. For example, experts say that the damage caused by floods in September 2016 has become more serious because denuded forest land has increased sharply in there and such pattern appeared even in the high altitude areas. And this led the mountain could not function properly in the flood event. This study was carried out by selecting two study periods, the base year and the test year. To understand the pattern of change in the denuded forest land, the time difference between the two periods was set at about 10 years. For the base year, Landsat 5 imageries were applied, and Landsat 8 and RapidEye imageries were applied in the test year. Then the random forest machine learning was carried out using randomly extracted sample points from the study area and various input variables derived from the used satellite imageries. Finally, the land cover classification map for each period was generated through this random forest model. In addition, the distribution of forest changing area to cropland, grassland, and bare-soil were estimated to the denuded forest land. According to the study results, this method showed high accuracy in forest classification, also the method has been effective in analyzing the change detection of denuded forest land in North Korea for about 10 years.ope

Applications and Major Achievements of Genome Editing in Vegetable Crops: A Review

The emergence of genome-editing technology has allowed manipulation of DNA sequences in genomes to precisely remove or replace specific sequences in organisms resulting in targeted mutations. In plants, genome editing is an attractive method to alter gene functions to generate improved crop varieties. Genome editing is thought to be simple to use and has a lower risk of off-target effects compared to classical mutation breeding. Furthermore, genome-editing technology tools can also be applied directly to crops that contain complex genomes and/or are not easily bred using traditional methods. Currently, highly versatile genome-editing tools for precise and predictable editing of almost any locus in the plant genome make it possible to extend the range of application, including functional genomics research and molecular crop breeding. Vegetables are essential nutrient sources for humans and provide vitamins, minerals, and fiber to diets, thereby contributing to human health. In this review, we provide an overview of the brief history of genome-editing technologies and the components of genome-editing tool boxes, and illustrate basic modes of operation in representative systems. We describe the current and potential practical application of genome editing for the development of improved nutritious vegetables and present several case studies demonstrating the potential of the technology. Finally, we highlight future directions and challenges in applying genome-editing systems to vegetable crops for research and product development

Requirements Analysis for Development of Off-Site Construction Project Management System: Focusing on Precast Concrete Construction

There has been increasing interest in the off-site construction (OSC) method in response to issues such as stagnant labor productivity, shortage of skilled workers, challenging site management, heightened safety and health-related regulations, and the push for carbon neutrality. Although efficient performance of an OSC project requires development of management techniques, and application of a management system that reflects the characteristics of the OSC projects, related technologies remain in their infancy. In this study, targeting precast concrete (PC) construction, which is one of the representative construction types of the OSC method, we derive the characteristics of OSC project management in six aspects: production place and time, production process, production method of construction, production method, production entity and facilities, and production environment. Based on this result, we further derived the requirements for developing an OSC project management system. Furthermore, based on the derived requirements, we constructed a system development scenario for the establishment of an installation plan and shipment requests. The managerial characteristics and requirements of the OSC project, presented in this study, provide the theoretical basis for developing OSC project management techniques, as well as guidance for the development of the OSC project management system in the future

Development and Application of an Integrated Management System for Off-Site Construction Projects

The off-site construction (OSC) method has attracted the interest of experts to resolve productivity stagnation and lack of skilled workforce and to reduce greenhouse gas emissions in the construction industry. Due to the unique characteristics of OSC projects, wherein building elements are produced in a factory, transported, and installed in the field, a management approach that differs from the management techniques of previous construction projects is required. Accordingly, with this study, we examined the characteristics of OSC projects and derived key management items through literature review, case analysis, and expert meetings to develop an integrated management system for OSC projects (OSC-IMS). The proposed system, OSC-IMS, integrates the entire supply chain of the OSC project. It includes the following functions: drawing management, scheduling and planning, site installation planning, production planning, production monitoring, shipping and transportation, delivery and inspection, site installation monitoring, and progress payment management. To verify the applicability and effectiveness of OSC-IMS, it was implemented in four projects. The application of the system to the case studies demonstrated the improvements in work efficiency and accuracy and decreased waste time in every work step. The findings indicate that the system can enhance project performance. This study contributes to the identification of the features and key elements of OSC management such that these factors can be linked with managing system development. This work describes the overall effect of the proposed system on real projects

Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors

The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011–2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets

BIM-Based Management System for Off-Site Construction Projects

Offsite construction (OSC) is one of the alternative methods for the various challenges that the construction sector faces today. This study developed a management system based on building information modeling (BIM) to execute OSC projects successfully. Because OSC differs from the conventional onsite building method, the authors studied and analyzed several project cases and interviewed the participants and stakeholders. The OSC method has unique characteristics in the aspects of the projects’ location and time, production process, flow, method, facility, and environment. Moreover, before the system development, we analyzed the OSC project management, usability, and system requirements. These requirements were turned into a BIM-based system following a waterfall model, with six management menus: drawing, schedule, production, logistics, installation and progress monitoring, and progress payment. This study implemented each menu’s vital functions within the system more effectively due to the BIM-based technological features, such as object-oriented data processing, visualization, high interoperability, linkage, and integration. The developed system was applied to four projects. The test resulted in a streamlined work process, improved activity, and less input time and workload than in a non-BIM-based management environment. These findings indicated that the proposed BIM-based system enabled OSC project management to perform better

Violence Victimization in Korean Adolescents: Risk Factors and Psychological Problems

We examined the risk factors for and psychological problems associated with violence victimization in a nationwide representative sample of Korean adolescents. Data from the 2016 Korean Youth Risk Behavior Web-based Survey was used. Participants were asked about their experience of being a victim of violence that required medical treatment during the past 12 months, as well as their perceived health, happiness, sleep satisfaction, stress, depressed mood, and suicidality. The 12-month prevalence of violence victimization requiring medical treatment was 2.4%. The results indicated that adolescents were at an increased risk for violence victimization if they were male, older, had parents of a foreign nationality, did not reside with their family, worked part time, resided in small cities or rural areas, were high or low in socioeconomic status (SES), exhibited high or low levels of academic performance, used alcohol or tobacco, and were sexually active. In addition, while violence victimization was negatively associated with perceived health and happiness, it was positively associated with perceived stress, depressed mood, and suicidality. The results indicate that a social disadvantage, involvement in risky behavior, and psychological problems are associated with violence victimization. Effective violence prevention efforts should thus target high-risk groups, and clinical attention is needed to address the psychological costs associated with violence victimization