세계 100대 악성 침입외래종인 유리알락하늘소(Anoplophora glabripennis)와 근연종인 노랑알락하늘소(가 칭, Anoplophora horsfieldii (Hope, 1843))가 2019년 제주도에서 처음 발견된 후 2023년까지 지속적으로 확인되었 다. 본 연구는 MaxEnt 알고리즘을 기반으로 하는 생물종 분포 모델을 이용하여 19개의 기후변화 변수에 노랑알락 하늘소(가칭) 먹이식물 5종(차나무, 팽나무, 멀구슬나무, 종가시나무, 비술나무)의 변수를 추가하여 외래해충인 노랑알락하늘소(가칭)의 현재·미래의 분포 가능지역에 대한 공간적 분포 특성을 규명하고 국가적 확산을 대응 하고자 한다. 모델 예측 정확도(AUC)는 0.983으로 출현지점을 정확하게 예측하는 비율이 매우 높다고 할 수 있다. 모델 예측 정확도의 증감에 영향을 주는 환경변수 중 먹이식물의 기여도가 70%를 상회하는 것으로 나타났다. 현재 75% 이상 분포 가능지역은 전라남도 진도군 일대와 경상북도 포항시 일대로 나타났으나 2050년에는 서해안을 따라 태안군까지 동해안을 따라 북한의 고성군까지 분포가 가능한 것으로 나타났다. 또한 75% 이상 분포 가능 면적은 현재 423㎢에서 2050년에는 9,270㎢로 약 대한민국 면적의 1/10 정도 확산될 것으로 예측된다.

기후변화는 연안지역에 심각한 영향을 미치고 있으며 그 영향이 점점 증가할 것이라고 예상되는 바, 최근 기후변화 적응 및 리스크 평가에 있어 많은 연구들이 취약성과 함께 회복탄력성 개념을 이용하고 있다. 본 연구의 목적은 기후변화 적응을 위한 연안재해 회복탄력성 측정 모형을 개발하는 것이다. 측정 모형 개발에 앞서 연안재해 회복탄력성에 대한 광범위한 문헌검토를 통해 취약성과 회복 탄력성에 대한 조작적 정의와 함께 여러 피드백 메커니즘이 포함된 개념적 프레임워크를 작성하였다. 연안재해 회복탄력성 측정 모형은 네 가지 측정값(MRV, LRV, RTSPV, ND)과 연안재해 회복탄력성 복합 지수(CRI)를 포함하고 있으며, 개발된 지수는 국내 연안침식 사례에 적용되었다. 또한 지수 등급에 따른 지역적 분석이 수행되었다. 연구 결과, 네 가지 회복탄력성 측정값을 통해 각 지점이 가지는 연안침식 회복탄력성의 다양한 특성을 파악할 수 있음을 확인하였다. 연안 회복탄력성 복합 지수의 매핑 결과 서해안 및 남해안 지역에 비해 동해 안 지역들은 연안침식 회복탄력성이 상대적으로 떨어지는 것으로 나타났다. 본 연구의 회복탄력성 측정 모형은 적응 이후의 이행전략에 대한 논의를 제공하는 도구로 활용될 수 있으며, 서로 다른 취약 지역 그룹 간 정책지원에 대한 우선순위를 결정하는 데 이용 가능하다.

기후변화에 따른 서식지 감소는 생물다양성의 커다란 위협 요소 중 하나이고 생물종이 서식하는 공간적 분포에 대한 이해는 멸종위기종 관리, 생태계 복원 등 다양한 분야에서 매우 중요하다. 본 연구는 남한지역에서 서식하는 멸종위기종 Ⅰ급으로 지정된 붉은점모시나비를 대상으로 기후변화에 따른 서식분포변화를 분석하고자 한다. 이 를 위해서 최근 보전생물학, 동물생태학 등 다양한 분야에서 널리 활용되는 MaxEnt 모델을 현재시기와 미래시기 의 생물기후변수에 적용하여 잠재적 서식지 분포 변화를 평가하였다. 붉은점모시나비는 미래시기에 서식지가 감소하는 경향으로 예측되었고, 기온보다 강수량에 의한 영향이 크고, 특히 강수량 계절성에 영향이 가장 클 것으 로 분석되었다. 분석결과는 국내 생물다양성 증진에 필요한 기초자료로서 활용할 수 있을 것으로 기대된다.

목적 : 본 연구의 목적은 Coronavirys Disease(COVID-19) 팬데믹을 거친 고령자의 사회적 건강의 수준의 변화궤적에 대한 잠재계층을 분류하고 잠재 계층별 특성을 분석함에 있다. 또한 이러한 사회적 건강에 영향을 미치는 예측요인을 파악하여 고령자의 사회적 건강을 증진을 위한 기초자료를 마련하고자 한다. 연구방법 : 국내 고령자의 사회적 친밀도에 따른 사회적 건강 유형을 파악하기 위해서 한국복지패널의 3 년차 종단자료를 토대로 분석하였으며, 연구대상자는 세 시점 모두를 응답한 2845명의 고령자를 대상으 로 하였다. 대상자중심접근인 성장혼합모형(Growth Mixture Model; GMM)을 적용하여 변화궤적에 따 른 잠재계층을 분석 하였고, 도출된 각 잠재유형별 특성을 파악하기 위해 χ2 분석, 분산분석을 실시하 였으며, 계층 간 차이를 유발하는 요인을 파악하기 위해 다항로지스틱 회귀분석을 실시하였다. 결과 : GMM 적용결과, 사회적 건강의 변화궤적에 대한 잠재계층은 최종 4개의 집단으로 저수준 감소-증 가 집단, 중수준 유지-증가 집단, 고수준-감소 집단, 고수준 유지’집단으로 분류되었다. 또한 사회적 건강 수준에 따라 여가만족도에서 차이가 나타나는 것으로 드러났으며, 그 외에도 연령 차이가 존재하였 다. 잠재계층분류에 영향을 미치는 영향변인을 검증한 결과, 특히 여성일수록, 종교를 가지고 있을수록, 여가만족도와 전반적 만족도가 모두 높을수록 고수준 유지 집단에 속할 확률이 높은 것으로 나타났다. 결론 : 국내 고령자의 사회적 건강은 시간이 지남에 따라 감소하는 궤적을 보이는 것으로 나타났다. 변화 궤적에 따라 4개의 집단으로 구분 지을 수 있으며, 각 잠재 유형별 연령과 여가 만족도 부분에서 집단별 차이가 드러났다.

본 논문은 Schumpeter의 기술변화 3단계 개념에 기초해서 기술진보가 실질 경제성장에 기여하는 정도를 측정하는 새로운 방법을 제시한다. 통계청 광업제조업조사 마이크로데이터를 이용해서 2003-2018년 기간 동안 기술진보의 실질 성장기여 정도를 총요소 생산성 성장률(성장회계방법), R&D투자 기여율, 슘페테리안 혁신성장률로 각각 측정하고 비교한 결과는 다음과 같다. 첫째, 성장회계의 총요소생산성 성장률과 슘페테리안 혁신 성장률에 의한 기술진보의 실질 성장기여에 대한 측정에서 상반된 결과를 나타낸다. 둘째, 생산성장률이 감소추세에 있으면 증가추세에 있을 때와 비교해서 생산성장률과 총요소생산 성성장률 차이가 커지고, 반대로 증가추세에 있으면 감소추세에 있을 때와 비교에서 생산성 장률과 총요소생산성성장률의 차이가 작아진다. 셋째, 혁신성장률에 영향을 미치는 기술기회, 즉, 어느 한 분야의 연구개발과 그 인접분야까지의 연구개발 유인이 혁신성장에 미치는 기여도는 3.3%에 불과하다. 이 결과가 기존의 기술진보의 성장기여에 대한 인식과 다른 이유는 기술 진보라는 동일한 용어를 측정하면서 서로 다른 실체를 측정하고 있는 것에 기인한다. 따라서 총요소생산성 성장률은 거시적 경제효율성, R&D투자는 신기술공급의 효과성, 슘페테리안 혁신성장률은 기술진보의 경제적 영향을 측정하는 데 사용해야 한다. 본 논문 연구 결과의 정책적 함의는 다음과 같다: ① 기술공급일변도의 정책에서 기술공급과 신기술수요지원의 융합정책으로 전환, ② 임무지향형 R&D 정책과 국가 R&D와 민간 R&D가 연계되는 R&D 정책, ③ 신지식체화 정도를 반영한 자본재의 재분류.

This research measured the change in mechanical characteristics of a sample obtained by finishing a metal coating to an engineering plastic manufactured using a 3D printer to satisfy both lightweight and quality characteristics. High-Temp material, which can be applied to space thermal environments with large temperature fluctuations, was applied as the engineering plastic material, and Stereolithography(SLA) method, which has relatively higher precision than Fused Film Fabrication(FFF) method, was selected as the manufacturing method. Electroless & electroplating were performed by metal coating on the surface to satisfy the characteristics of products requiring electrical conductivity. Tensile and bending tests were conducted to verify a change in the mechanical characteristics of a sample completed with a metal coating, and an adhesion test of the metal coating was also added.

Understanding the light environment in greenhouse cultivation and the light utilization characteristics of crops is important in the study of photosynthesis and transpiration. Also, as the plant grows, the form of light utilization changes. Therefore, this study aims to develop a light extinction coefficient model reflecting the plant growth. To measure the extinction coefficient, five pyranometers were installed vertically according to the height of the plant, and the light intensity by height was collected every second during the entire growing season. According to each growth stage in the early, middle, and late stages, the difference between the top and bottom light intensity tended to increase to 69%, 72%, and 81%. When leaf area index and plant height increased, the extinction coefficient decreased, and it showed an exponential decay relationship. Three-dimensional model reflecting the two growth indexes, the paraboloid had the lowest RMSE of 1.340 and the highest regression constant of 0.968. Through this study, it was possible to predict the more precise light extinction coefficient during the growing period of plants. Furthermore, it is judged that this can be utilized for predicting and analyzing photosynthesis and transpiration according to the plant height.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

9% nickel steel has remarkable mechanical properties in a cryogenic condition and is widely used in storage containers for LNG fueled ships. Demand for laser welding rather than conventional arc welding has grown to increase manufacturing efficiency. However there are various types of heat sources which are suggested by other researchers. With that, it is difficult to select a proper heat source shape for welding conditions. The author proposed a representative heat source model that can cover most of suggested heat source models through previous studies. Welding power was fixed at 4kW and the speed was changed to 1.0m/min, 1.5m/min, and 2.0m/min respectively. The shapes of the welding heat sources were derived, and the tendency of the main parameters was also deducted. It was observed that the width and depth of the weld bead decreased as the welding speed increased through welding experiment, parameters of welding heat source are changed linearly. Based on this study, it is expected that it will be possible to estimate the shape of the heat source under untested welding conditions.

PURPOSES : The surface distress of asphalt pavements is one of the major factors affecting the safety of road users. The aim of this study was to analyze the factors influencing the occurrence of surface distress and statistically predict its annual change to contribute to more reasonable asphalt pavement management using the data periodically collected by the national highway pavement data management system. METHODS : In this study, the factors that were expected to influence the surface distress were determined by reviewing the literature. The normality was secured by changing the forms of the variables to make the distribution of the variables got closer to normal distribution. In addition, min-max normalization was performed to minimize the effect of the unit and magnitude of the candidate independent variables on the dependent variable. The final candidate independent variables were determined by analyzing the correlation between the annual surface distress change and each candidate independent variable. In addition, a prediction model was developed by performing data grouping and multi-regression analysis. RESULTS : An annual surface distress change prediction model was developed using present surface distress, age, and below 0 ℃ days as the independent variables. As a result of sensitivity analysis, the surface distress affected the annual surface distress change the most. The positive correlation between the dependent variable and each independent variable demonstrated engineering and statistical meaningfulness of the prediction model. CONCLUSIONS : The surface distress in the future can be predicted by applying the annual surface distress prediction model to the national highway asphalt pavement sections with survey data. In addition, the prediction model can be applied to the national highway pavement condition index (NHPCI) evaluating the national highway asphalt pavement conditions to be used in the prediction of future NHPCI.

PURPOSES : Rut depth of asphalt pavements is a major factor that affects the maintenance of pavements as well as the safety of drivers. The purpose of this study was to analyze the factors influencing rut depth, using data collected periodically on national highways by the pavement management system and, consequently, predict annual rut depth change, to contribute to improved asphalt pavement management. METHODS : The factors expected to influence rut depth were determined by reviewing relevant literature, and collecting the related data. Further, the correlations between the annual rut depth change and the influencing factors were analyzed. Subsequently, the annual rut depth change model was developed by performing regression analysis using age, present rut depth, and annual average maximum temperature as independent variables. RESULTS : From the sensitivity analysis of the developed model, it was found that age affected the annual rut depth change the most. Additionally, the relationship between the dependent and independent variables was statistically significant. The model developed in this study could reasonably predict the change in the rut depth of the national highway asphalt pavements. CONCLUSIONS : In summary, it was verified that the model developed in this study could be used to predict the change in the National Highway Pavement Condition Index (NHPCI), which represents comprehensive conditions of national highway pavements. Development of other models that predict changes in surface distress as well as international roughness index is required to predict the change in NHPCI, as they are the independent variables of the NHPCI prediction model.

PURPOSES: This study aimed to evaluate the performance of a model developed for road surface temperature change pattern in reflecting specific road characteristics. Three types of road sections were considered, namely, basic, tunnel, and soundproof tunnel. METHODS: A thermal mapping system was employed to collect actual road surface temperature and locational data of the survey vehicle. Data collection was conducted 12 times from 05:30 am to 06:30 am on the test route, which is an uninterrupted flow facility. A total of 9010 road surface temperature data were collected, and half of these were selected based on a random selection process. The other half was used to evaluate the performance of the model. The model used herein is based on machine learning algorithms. The mean absolute error (MAE) was used to evaluate the accuracy of the estimation performance of the model. RESULTS: The MAE was calculated to determine the difference between the estimated and the actual road surface temperature. A MAE of 0.48℃ was generated for the overall test route. The basic section obtained the smallest error whereas that of the tunnel was relatively high. CONCLUSIONS: The road surface temperature change is closely related to the air temperature. The process of data pre-processing is very important to improve the estimation accuracy of the model. Lastly, it was difficult to determine the influence of the data collection date on the estimation of the road surface temperature change pattern due to the same weather conditions.

Invasive pests have posed an ecological threat as climate change has been accelerated, suggesting early prediction ofinvasive pests is required to minimize damages by them. As one of predictive tools, CLIMEX has been effectively usedin a few regions, including US, Australia, and Europe. It allows us to predict a species distribution on a local area inresponse to climatic conditions: and thus, potential distribution of invasive species, risk assessment of agricultural pests,and suitability of biological control agents have been tested by CLIMEX. In this study, we introduced how to use CLIMEXfor predicting a species distribution differed by climate change in terms of its functions, required data, and examplesof its application.

Climate change is the biggest concern of the 21st century. Greenhouse gas (GHG) emissions from various sectors are attracting attention as a cause of climate change. The DeNitrification-DeComposition (DNDC) model simulates GHG emissions from cropland. To study future GHG emissions using this simulation model, various factors that could change in future need to be considered. Because most problems are from the agricultural sector, DNDC would be unable to solve the factor-changing problem itself. Hence, it is necessary to link DNDC with separate models that simulate each element. Climate change is predicted to cause a variety of environmental disasters in the future, having a significant impact on the agricultural environment. In the process of human adaptation to environmental change, the distribution and management methods of farmland will also change greatly. In this study, we introduce some drawbacks of DNDC in considering future changes, and present other existing models that can rectify the same. We further propose some combinations with models and development sub-models.

This study aims to offer basic data to effectively preserve and manage pine forests using more precise pine forests’ distribution status. In this regard, this study predicts the geographical distribution change of pine forests growing in South Korea, due to climate change, and evaluates the spatial distribution characteristics of pine forests by age. To this end, this study predicts the potential distribution change of pine forests by applying the MaxEnt model useful for species distribution change to the present and future climate change scenarios, and analyzes the effects of bioclimatic variables on the distribution area and change by age. Concerning the potential distribution regions of pine forests, the pine forests, aged 10 to 30 years in South Korea, relatively decreased more. As the area of the region suitable for pine forest by age was bigger, the decreased regions tend to become bigger, and the expanded regions tend to become smaller. Such phenomena is conjectured to be derived from changing of the interaction of pine forests by age from mutual promotional relations to competitive relations in the similar climate environment, while the regions suitable for pine forests’ growth are mostly overlap regions. This study has found that precipitation affects more on the distribution of pine forests, compared to temperature change, and that pine trees’ geographical distribution change is more affected by climate’s extremities including precipitation of driest season and temperature of the coldest season than average climate characteristics. Especially, the effects of precipitation during the driest season on the distribution change of pine forests are irrelevant of pine forest’s age class. Such results are expected to result in a reduction of the pine forest as the regions with the increase of moisture deficiency, where climate environment influencing growth and physiological responses related with drought is shaped, gradually increase according to future temperature rise. The findings in this study can be applied as a useful method for the prediction of geographical change according to climate change by using various biological resources information already accumulated. In addition, those findings are expected to be utilized as basic data for the establishment of climate change adaptation policies related to forest vegetation preservation in the natural ecosystem field.