There are analytical methods used for measuring activity when light photons are emitted for scintillating-based analytical application. When this electron returns to the original stable state, it releases its energy in the form of light emission (visible light or ultraviolet light), and this phenomenon is called scintillation. Scintillator is a general term for substances that emit fluorescence when exposed to radiation such as gamma-rays. Radioactivity is all around us and is unavoidable because of the ubiquitous existence of background radiations emitted by different sources. The scintillator contributes to these sensing, and it is expected that the inspection accuracy and limit of detection will be improved and new inspection methods will be developed in the future. Moreover, scintillators are chemical or nanomaterial sensors that can be used to detect the presence of chemical species and elements or monitor physical parameters on the nanoscale. In this study, it includes finding use in scintillating-based analytical sensing applications. A chemical and nanomaterial based sensors are self-contained analytical tools that could provide information about the chemical compositions or elements of their environment, that is, a liquid or even gas condition. Herein, we present an insightful review of previously reported research in the development of high-performance gamma scintillators. The major performance-limiting factors of scintillation are summed up here. Moreover, the 2D material has been discussed in the context of these parameters. It will help in designing a prototype nanomaterial based scintillators for radiation detection of gamma-ray.

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars (‘Arihyang’, ‘Jukhyang’, ‘Keumsil’, ‘Maehyang’, and ‘Seollhyang’). The coefficient of determination (R2) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R2 value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars ‘Jukhyang’, ‘Seolhyang’, and ‘Maehyang’ could be non-destructively predicted using the model developed in this study, as they had R2 values over 0.96. The cultivars ‘Arihyang’ and ‘Geumsil’ had slightly low R2 values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.

We performed a survey for flavivirus infection and distribution of Aedes albopictus that known as Zika and Dengue virus vector using black–light trap and BG-sentinel trap around urban area in Korea. Mosquitoes were collected in 27 cities during March to November (twice a month) year 2016. Total numbers of mosquitoes collected 102,102 including 19 species 8 genera during collecting period. Total 21,467 Ae. albopictus was collected that 20,961(24.3%) by BG-sentinel trap and 506 (3.2%) by Black-light trap in urban area. Trap index(trap/night) of Ae. albopictus was showed highest in Hamyang (TI:992.3) and lowest in Taebaek (TI:0.3) there was only collected by Black-light trap. A total of 894 pools from all collecting Ae. albopictus were performed a Flavivirus detection. Flavivirus was not detected during study period. This study may provide basic information for surveillance of imported diseases (include Zika virus) and vectors in Korea.

Herb extracts commercially used in Korea were screened for PPAR-γ agonist test and α-glucosidase inhibition assay. Total 16 herb plants had a PPAR-γ agonist activity. Specially, Alisma orientale Juz (108.41%), Ephedra sinica (98.22%), Sasa japonica Makino var. purpurascens Nakai (140.68%), Astragalus membranaceus Bunge (106.79%) and Cnidium officinale Makino (113.00%) showed high PPAR-γ agonist activity rate compared with rosiglitazone's (167.46%). And Cornus officinalis S. et Z. (90.3%), Cinnamomum cassia Blume (89.2%), Psoralea corylifolia L. (89.8%), Paeonia japonica (Makino) Miyabe (92.4%) and Paeonia suffruticosa Andr (93.2%), showed high α-glucosidase inhibition rates. These results support previous reports of the efficacy of Oriental medicinal plants used for diabetes mellitus.