PURPOSES : The purpose of this study is to derive dropout rates according to various international roughness index (IRI) specifications using ProVAL, develop a comparative methodology, and indirectly assess the level of road management in each country. METHODS : Based on a literature review, the IRI specifications for each country were collected, and the ProVAL analysis tool was used to compare and analyze dropout rates according to each specification. Thus, the dropout rate rankings for each country were calculated. Additionally, by analyzing the correlation between dropout rates according to each threshold, a model was created to convert the threshold between the most commonly used baseline distances of 100 m and 161 m. RESULTS : Dropout rates were derived according to the standards of each country and rankings were assigned. Comparing 51 standards, the IRI level of New Mexico appeared to be the highest, whereas the domestic specifications ranked 36th. A model was created to convert the threshold between the standard distances of 100 m and 161 m. CONCLUSIONS : This study objectively assessed the roughness standards in various countries using the dropout rate and IRI ranking specifications. The highest specification was found for the asphalt of New Mexico in the USA, with the domestic specification ranking 36th. A model that converts the thresholds between the most commonly used baseline distances of 100 m and 161 m was developed, with slight differences across sections. For a precise conversion, individual models may be required for each section.

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

PURPOSES : This study uses deep learning image classification models and vehicle-mounted cameras to detect types of pavement distress — such as potholes, spalling, punch-outs, and patching damage — which require urgent maintenance. METHODS : For the automatic detection of pavement distress, the optimal mount location on a vehicle for a regular action camera was first determined. Using the orthogonal projection of obliquely captured surface images, morphological operations, and multi-blob image processing, candidate distressed pavement images were extracted from road surface images of a 16,036 km in-lane distance. Next, the distressed pavement images classified by experts were trained and tested for evaluation by three deep learning convolutional neural network (CNN) models: GoogLeNet, AlexNet, and VGGNet. The CNN models were image classification tools used to identify and extract the combined features of the target images via deep layers. Here, a data augmentation technique was applied to produce big distress data for training. Third, the dimensions of the detected distressed pavement patches were computed to estimate the quantity of repair materials needed. RESULTS : It was found that installing cameras 1.8 m above the ground on the exterior rear of the vehicle could provide clear pavement surface images with a resolution of 1 cm per pixel. The sensitivity analysis results of the trained GoogLeNet, AlexNet, and VGGNet models were 93 %, 86 %, and 72 %, respectively, compared to 62.7 % for the dimensional computation. Following readjustment of the image categories in the GoogLeNet model, distress detection sensitivity increased to 94.6 %. CONCLUSIONS : These findings support urgent maintenance by sending the detected distressed pavement images with the dimensions of the distressed patches and GPS coordinates to local maintenance offices in real-time.

최근 다양한 유기재배 작물의 뿌리를 가해하는 굼벵이류의 피해가 증가하고 있으나, 굼벵이류는 토양 내 발생하는 특징으로 인해 발생시기 및 그 종류에 대한 확인이 어려운 해충이다. 피해를 끼치는 굼벵이의 발생을 파악하기 위해 고구마 유기재배지에 페로몬 트랩을 이용하여 굼벵이의 성충의 종류 및 발생 동향을 조사하였다. 조사지는 무안 유기재배농가와 국립농업과학원 완주군 포장에서 이루어졌다. 3종의 풍뎅이 페로몬 루어를 유인제로 사용하였으 며 6월 초부터 8월 말까지 조사지에 트랩을 설치하여 포획된 풍뎅이를 수집하여 동정을 하였다. 유기재배포장에서 포획된 종은 큰검정풍뎅이, 콩풍뎅이, 청동풍뎅이, 녹색콩풍뎅이, 별줄풍뎅이 등의 풍뎅이와 흰점박이꽃무지 등이 주로 채집되었다. 유기재배 고구마포장에서 풍뎅이 발생소장을 조사한 결과 최대로 발생한 시기는 7월초였다.

Japanese pine sawyer beetle, Monochamus alternatus Hope (Coleoptera: Cerambycidae) is considered as a serious pest in pine trees. To develop an eco-friendly strategy to manage this forest insect, we collected entomopathogenic fungi from Korean soil and assessed their virulence against the adults of the insect in laboratory conditions. As a result, two isolates with conidial suspension (1.0×107conidia/ml), showed 87% and 90% mortality 12 days after fungal treatment, respectively. We assessed the potential of the fungi-derived destruxin and protease as additives to the fungal isolates, and they showed insecticidal activity via feeding and spraying treatments. Finally, we produced fungal conidia in massive solid cultures and formulated wettable powders, and now studying optimal conditions of oil-based formulation with two isolates. Based on these results, we are evaluating the control efficacy of the fungal agents against M. alternatus in field conditions.

This study was carried out to investigate insect community structure from different habitats in Baengnyeong island.We performed day and night collection at two different habitats (mountain, rural area) of Baengnyeong island from Mayto September in 2015. A total of 2,879 individuals of 404 species, 81 families belonging to 10 orders were collectedand identified. A dominant species was Idisia ornata Pascoe (Tenebrionidae) despite a very low percentage (6.04%) ofthe species among the catches. Results of independent t-test showed a significant high (p<0.001) of species richness onmountain. Also, seasonal results of ANOVA (Analysis of variance) were significantly influential with species abundanceand species richness. The result of NMDS analysis showed that the community structure of the insects from the mountainis different with rural area.

Recently, N-terminal pro-brain natriuretic peptide (NTproBNP) has been widely used in the areas of diagnosis, monitoring treatment efficiency, and prognosis for various heart diseases, especially heart failure (HF). In this paper, we try to estimate the prognostic significance of NT-proBNP as a risk evaluation marker in Non-ST-segment Elevation Myocardial Infarction (NSTEMI) patients. We selected NSTEMI patients who underwent percutaneous coronary intervention (PCI) primarily using a drug-eluting stent within 24 h after the onset of chest pain. We compared incidences of major adverse cardiac events (MACE) including death, myocardial infarction (MI), stent thrombosis (ST), and target vessel revascularization (TVR) in two patient groups according to a high or low serum concentration of NT-proBNP, which was measured in the emergency room (ER). We intend to minimize selection bias selecting comparing groups, considering covariate of observed variables together using propensity score matching (PSM) and propensity score weighting (PSW) based on propensity score (PS) to control the difference in baseline characteristics between high- and low NT-proBNP groups. We found that as the log NT-proBNP value increases by 1 through a hazard function of COX’s analysis, the risk of MACE increases by 1.312 times. This result indicated that the NT-proBNP level on ER admission can be used as a significant prognostic indicator to estimate 1 year of MACE in NSTEMI patients who were treated with PCI within 24 h after the onset of chest pain.

The spotted-wing drosophila Drosophila suzukii (Diptera: Drosophilidae) is an Asian species introduced into North America and Europe. It damages a wide variety of thin-skinned fruits. We sequenced the complete mitochondrial genome (mitogenome) of D. suzukii to better understand the mitogenomic characteristics of this species and understand phylogentic relationships of Drosophila. The 16,230-bp complete mitogenome of the species consists of a typical set of genes, including 13 protein-coding genes (PCGs), two rRNA genes, and 22 tRNA genes, and one major non-coding A+T-rich region, with an arrangement typical of insects. Twelve PCGs began with the typical ATN codon, whereas the COI began with TCG, which has been designated as the start codon for other Drosophila species. The 1,525-bp A+T-rich region is the second longest in Drosophila species for which the whole mitogenome has been sequenced, after D. melanogaster. Phylogenetic analysis with the 13 PCGs of the Drosophila species using Bayesian Inference and Maximum likelihood methods both placed D. suzukii at the basal lineage of the previously defined Melanogaster group, with a strong support.

Entomopathogenic fungi are facultative microorganisms, dwelling in soil or infecting host insects, and some of the genera have been used as biological control agents worldwide. Collection of fungal isolates should be a platform for the development of highly effective resources, thus in this work we constructed a fungal library using a mealworm pathogenecity-based fungal collection method and further characterized some isolates with high virulence. A phylogenetic three was generated, and of the isolates 17 isolates’ biological features were characterized, such as morphology, spectrum of virulence, cultural characteristics, thermo-stability of fungi, production of biologically active materials, such as enzymes. This work reports an attractive entomopathogenic fungal library including the information of effective isolates in pest management.

This study was carried out to investigate insect community structure from different habitats. We performed day and night collection at three different habitats (mountain, coast and rural area) of island Deokjeok, island Soya and island Mungap from May to September in 2014. A total of 3,482 individuals of 725 species, 119 families belonging to 10 orders were collected and identified. A dominant species was Corymbia rubra (Cerambycidae) despite a very low percentage of the species among the catches. Results of ANOVA test showed a significant effect of habitats typeon species diversity. Also, combination of seasons and habitats types were significantly influential with species abundance and species diversity. Indicator species analysis (ISA) result identified 121 significant (p < 0.05) indicator species; one species for the habitats cluster, 93 species for the season cluster and 27species for combination of habitats with seasons.

An insect faunal survey was carried out to investigate insect community structure along the vegetation community to monitor insect species in forest ecosystem. We performed day and night collections from June to August along three vegetation communities of Is. Nam-hae in 2014: the first stand with Pinus thunbergii, the second P. thunbergii with Quercus serrata and the third P. thunbergii with various Quercus species. In total 2,259 individuals of 532 species, 99 families, 13 orders are identified. Cluster Analyses (CA) showed that all three vegetation communities were relatively similar between vegetation community types. According to indicator species analysis (ISA) result, nine significant indicator species were identified (p < 0.05); five species were found to be affected by the vegetation cluster and four species the month cluster.

The world is in the process of a structural change related to ICT convergence knowledge industries. ICT is leading to the creation of new products and services, and is making our lives more convenient, safer, and more efficient. In advanced countries, many studies have been conducted with the goal of developing new business models converged with ICT, and this is also the case in the food industry. Korea possesses world-leading ICT, and if this ICT is applied to the food industry, a world-class new business model can be developed. The u-Food System, which is in the process of development in Korea, is a next-generation food system that can allow food providers, consumers, and distributors to access various types of information about food products, including traceability, distribution, safety, quality, and freshness, and manage this information. It is a future food system that converges ICT, biotechnology and sensing technology with food. Based on the u-Food System, this paper will introduce the status of current smart quality distribution technologies that converge ICT (such as sensor tag, sensor network, LBS, GIS, and CDMA) with food technologies (such as traceability, quality, distribution management) to manage the safety and quality of fresh food in the distribution process.