This study advances the knowledge of developmental patterns in affiliation with delinquent peers and delinquency during adolescence; data were obtained from waves 1-5 (2003-2007) of the Korea Youth Panel Survey. Semi-parametric group-based modeling (SGM) identified 3 affiliative trajectories of delinquent peers from age 13 to 16: rarely or never, persistently affiliative, and declining groups; and five developmental trajectories of delinquency: non-offending, late onset, low-level continuous, desisting, and chronic groups. A joint trajectory analysis predicted the membership of delinquency trajectories conditional on delinquent peer trajectories. Persistently affiliative group was more likely than others to follow chronic trajectory of delinquency; the rarely or never affiliative group was more likely to be non-offending. This study may help reconcile different theoretical models such as influence, selection, and enhancement models with respect to the role of delinquent peers in delinquency. The distinct theoretical models are equally valid, albeit each model pertains to a specific aspect of longitudinal patterns of affiliation with delinquent peers. Implications of this study for youth welfare were discussed to reduce increased risks for both affiliation with delinquent peers and delinquency.
This study presents a systematic literature review aimed at integrating and analyzing the research findings regarding the components of various self-care intervention programs conducted for individuals with chronic obstructive pulmonary disease (COPD) both domestically and internationally. The paper selection criteria were established based on materials from PubMed, CINAHL, EMBASE, /MEDLINE, RISS, DBpia, KISS, and KMbase databases, covering the period from January 1, 2000, to September 30, 2022. The inclusion criteria included controlled pre-post experimental study designs. A total of 23 studies were included in this systematic review. The intervention-related factors examined included the sample size, type of intervention mediator, intervention frequency, and duration. Additionally, factors associated with intervention effects, such as measurement tools and variables, were analyzed. The findings of the analysis support the notion that self-care intervention programs for COPD can contribute to the prevention of symptom exacerbation and hospital readmissions among patients. Consequently, the development of a tailored self-care intervention program specifically designed for COPD patients in South Korea is warranted, considering aspects such as patient accessibility, convenience, and motivation for long-term sustainability.
Proceedings of the Korean Society of Crop Science Conference
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2022.10a
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pp.90-90
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2022
Nicaragua is located in Central America, climatic conditions are considered tropical dry forest. Statistics reflex that in Nicaragua exits 24,000 rice farmers. National rice production only covers 73% of the national consumption. It exists two sowing system: irrigation and rainfed. Varieties used in both systems are mid-late maturity (120-135 days), there are 14 released varieties for irrigation, eight for rainfed, and eight landraces used in rainfed. The current breeding system (introduction of lines from Colombia) has increased the national production, however, has some limitation due to the lack of enough variability, reducing the proability of finding good genotypes and therefore the possibility of satisfying 100% of the demand. The purpose of this study was to analyze the problems that must be resolved in the short and long term to improve rice productivity in Nicaragua. In this paper we explain some proposal for an improvement plan. The selection of varieties with high adaptability to various cultivation environmental conditions it is necessary, also to thoroughly manage seed purity to supply certified seeds. In rice cultivation technology, it needs to improve seedling standing and weeding effect by improving soil leveling and water-saving cultivation technology. Also, proper fertilization and planting density must be established in irrigated and rain-fed areas. Furthermore, capacity must be strengthened by collecting and training with the most recent agricultural technology information, as well as by revitalizing the union rather than the individual farmer. It is necessary to develop varieties highly adaptable to the Nicaraguan cultivation environment, as well as to expand irrigation facilities and cultivation technology suitable for weather conditions in rain-fed areas. Last, it is necessary to maintain the consistency of agricultural policy for continuous and stable rice production in response to climate change events such as drought or intermittent heavy rain.
ASEAN is emerging as the next-generation market following BRICs. Korea is also an important economic cooperation partner as a second trading partner and third target for overseas investment. ASEAN is attracting attention as an attractive business place for many companies as a future investment area in the future. Therefore, the Korean government is strongly promoting a "New Southern Policy(NSP)" to develop cooperative relations with ASEAN. As ASEAN has recently emerged as a central area for shipping and logistics development, development cooperation and support for the shipping and logistics sector in the ASEAN region of neighboring countries are also active in entering the new southern region and the government is supporting it. In order to enter these countries, it is necessary to accurately understand the investment attraction system, strategy, and market for entering the business in other countries. Among the various methods of entering the overseas market, it is essential to understand the business selection and establishment method suitable for localization strategies such as foreign direct investment and establishment of foreign corporations. In order to understand the Overseas Investment Act and the Corporate Establishment Act of shipping and logistics-related companies who want to enter marine ASEAN countries, we will study the overseas investment method and the establishment method according to the type of company.
The amount of damage and the area of damage to forest fires are increasing globally, and the effectiveness analysis of the restoration method after the damage is performed insufficient. This study calculated the area of forest fire damage was calculated using Sentinel-2B satellite images and stack map and the intensity of forest fire damage is analyzed according to the forest type. In addition, the vegetation index was calculated using various wavelength bands. Based on the results, the vegetation resilience by the restoration method was quantitatively. As results, areas with a high proportion of coniferous forests suffered high intensity forest fire damage, and areas with a relatively high ratio of mixed and broad-leaved forests tended to have low forest fire damage. Also, artificial forests showed a recovery of about 92.7% compared to before forest fires and natural forests showed a recovery of about 99.6% from the result of analyzing vegetation resilience in artificial and natural forests after forest fires. Accordingly, it was confirmed that natural forests after forest fire damage had superior vegetation resilience compared to artificial forests. It can be proposed that this study is meaningful in providing important information for efficiently restoring the affected target site and the selection criteria for trees to reduce forest fire damage through the evaluation of vegetation resilience by the intensity of forest fire damage and restoration methods.
Kim, Min-Ji;Jang, Rae-ik;Yoo, Young-jae;Lee, Jun-Won;Song, Eui-Geun;Oh, Hong-Shik;Sung, Hyun-Chan;Kim, Do-kyung;Jeon, Seong-Woo
Journal of the Korean Society of Environmental Restoration Technology
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v.26
no.5
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pp.19-32
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2023
The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.
KIPS Transactions on Computer and Communication Systems
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v.12
no.10
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pp.291-298
/
2023
In this paper, we study whether the programming questions of the Informatics·Computer recruitment tests were suitable for selecting teachers with required programming skills. The average points of the programming questions constituted 38%(20.8 points) of the total scores for the entire curriculum based on the results from analyzing the previous questions in the past 5 years. Moreover, the distribution of points for each evaluation criteria within programming and data structure, two exam subjects which have a high proportion of programming questions, demonstrated a large deviation ranging from 0% to 47% and 0% to 53% respectively. In this study, a questionnaire survey was conducted on 31 teachers to examine if the previous programming questions were suitable for measuring teachers' competency in programming abilities required in the actual teaching experience. Computational thinking ability was ranked the highest at 58% in response to the area that needs to be evaluated in the recruitment test. In response to the relevance of previous questions, problem solving ability was ranked the highest at 2.84 on a 5-point scale, but the overall appropriateness was deemed low. C language and Python were regarded as the computer languages suitable to be tested for programming questions with each ranked 55% and 45%. The finding confirms that teachers preferred Python and the incumbent C language to others. Based on the results of the questionnaire, we recommend changes in the programming questions to improve the selection criteria.
The purpose of this study was to identify conserved metabolic pathways and conserved genes in 122 archaeal species. Using the Clusters of Orthologous Groups of Proteins (COG) database of conserved genes, we analyzed whether 122 species had 63 COG metabolic pathways, the 822 COGs that compose them, and a total of 4,877 COGs. Archaeal ribosomal proteins were the most conserved in metabolic pathways. 46 COGs in seven COG pathways among 63 COG pathways and 20 COGs in others were conserved in 122 species. Some genes involved in cell wall and extracellular matrix synthesis, replication, transcription, translation, and protein metabolism were common to all 122 species. When the distance value of the phylogenetic tree was analyzed at the phylum level or class level, the average was the lowest at the class Halobacteria of the phylum Euryarchaeota. Standard deviation was high for the class Nitosospharia of the phylum Thaumarchaeota, the unclassified members of phylum Thaumarchaeota, the class Halobacteria of the phylum Euryarchaeota, the class Thermoprotei of the phylum Crenarchaeota, and other archaea. Furthermore, the phylogenetic tree analysis revealed six commonalities. The results of this study, along with data on conserved genes, could be used for drug development and gene selection for strain improvement.
Suho Bak;Heung-Min Kim;Youngmin Kim;Inji Lee;Miso Park;Seungyeol Oh;Tak-Young Kim;Seon Woong Jang
Korean Journal of Remote Sensing
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v.39
no.6_1
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pp.1195-1210
/
2023
Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.
Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.
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