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.

본 연구는 원통형 종이포트를 활용한 토마토 육묘시, 염스트레스를 활용하여 고온기 도장 억제가능성을 검토하기 위하여 수행되었다. 시험구는 K2SO4, KCl과 KH2PO4을 각 5, 10 dS·m-1로 처리하였고, 또한, 토마토 모종에 고염도의 칼륨을 처리하여 수분 및 저온스트레스 환경에서의 적응성 및 생존성을 조사하였다. 조사결과, 처리 농도가 높아질수록 지상·지하부 건물중, 옆면적, 순동화율 (NAR)이 감소하고, 경경과 충실도는 증가하였다. 수분 스트레스 처리 이후, 대조구는 심한 위조현상을 보였지만, KCl처리구는 양호하였다. 상대수분함량은 대조구에서 23%, KCl처리구에서 8% 감소 하였다. 또한, 대조구에 비하여 KCl 처리구는 저장시(9, 12 및 15°C) 모종의 손상 비율이 낮았다. 이와 같은 결과로 보아, KCl과 같은 고농도의 칼륨 처리가 원통형 종이포트 토마토 육묘의 도장 억제에 효과적이며 환경 스트레스 내성을 향상시키는 것으로 판단된다.

Lysophosphatidic acid (LPA) is a lipid messenger mediated by G protein-coupled receptors (LPAR1-6). It is involved in the pathogenesis of certain chronic inflammatory and autoimmune diseases. In addition, it controls the self-renewal and differentiation of stem cells. Recent research has demonstrated the close relationship between periodontitis and various diseases in the human body. However, the precise role of LPA in the development of periodontitis has not been studied. We identified that LPAR1 was highly expressed in human periodontal ligament stem cells (PDLSCs). In periodontitis-mimicking conditions with Porphyromonas gingivalis -derived lipopolysaccharide (Pg-LPS) treatment, PDLSCs exhibited a considerable reduction in the cellular viability and osteogenic differentiation potential, in addition to an increase in the inflammatory responses including tumor necrosis factor-α and interleukin-1β expression and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation. Of the various LPAR antagonists, pre-treatment with AM095, an LPAR1 inhibitor, showed a positive effect on the restoration of cellular viability and osteogenic differentiation, accompanied by a decrease in NF-κB signaling, and action against Pg-LPS. These findings suggest that the modulation of LPAR1 activity will assist in checking the progression of periodontitis and in its treatment.