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Hong Hien Pham,Thi Vuong Pham,Jong Kyu Kim,Yoo Han Song 한국응용곤충학회 2008 한국응용곤충학회 학술대회논문집 Vol.2008 No.10
Root zone application of several systemic insecticides was tested for control of the brown planthopper, Nilaparvata lugens (Stål), in Vietnam and Korea. In Vietnam, the results indicated that carbofuran showed the highest nymphal mortality in all experiments, followed by imidacloprid and carbosulfan. When the insecticides were applied on 10-day old rice, carbofuran was shown almost 100% N. lugens mortality at six days after treatment and the efficacy was extended to twelve days after application. In Korea, various root-zone application methods were tested with carbofuran and carbosulfan. The results showed that carbofuran was the most active in reducing the egg hatching rates. When root-zone treated on 40-50 day-old rice in a greenhouse, no nymphs were hatched in carbofuran treated pots, while average of 20 nymphs were emerged in carbofuran broadcasting pots. Especially the number of nymphs emerged in carbosulfan foliar spray was 54 nymphs per pot even at the eight day after application, which was higher than in control pots. This is the first study ever demonstrated the high egg mortality of N. lugens on rice due to the root-zone application of insecticides.
Hong Hien Pham,Jong Kyu Kim,Yoo Han Song 한국응용곤충학회 2009 한국응용곤충학회 학술대회논문집 Vol.2009 No.05
The root zone applications of a systemic insecticide, carbofuran, were evaluated for their impacts on the brown planthopper, Nilaparvata lugens (Stål), and spider populations in the greenhouse and rice paddy fields. In the green house experiments, no BPH nymphs were hatched at root zone treated on 40 to 50 day-old rice, while around 20 to 54 nymphs per pot were emerged in broadcasting and foliar spray treatments. This indicates that the root zone treatment can kill the eggs of BPH effectively. This is the first study ever demonstrated the high egg mortality of BPH due to the root-zone application. In the field experiments, the density of BPH in root zone treated plots were four to six times lower than in broadcasting and foliar spray plots at the 21 days after application. The BPH outbreaks and hopper-burns were observed at all treatments except the root zone treated plot at the 28 days after application. The root-zone application did not impact on the spider population, while foliar spray killed most of all spiders just one day after application. The densities of spider in foliar spray plots were always lower than in root-zone treated and control plots. The results indicated that the root-zone application of carbofuran can control BPH effectively without adverse effects to the spiders inhabited on the paddy field.
Hong Thi Bich Truong,Hiep Nghia Bui,Hieu Trung Nguyen,Thanh-Luu Pham,Duy Ngoc Nguyen,Yuan-Shing Perng,Linh Thi My Lam,Thi-Dieu-Hien Vo,Van-Truc Nguyen,Ha Manh Bui 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.4
Electron-beam (EB) irradiation was employed to degrade enrofloxacin (ENR) in an aqueous solution. Thealgal growth inhibition test revealed that ENR exhibited low toxicity against the cyanobacterium Arthrospira sp., with anEC50-96 h value of 5.17mg/L. The Taguchi design also involved finding the best optimum for ENR treatment using EB. Results revealed that the high-efficiency removal of ENR in an aqueous solution was approximately 98.53% under theoptimum conditions of an absorbed dose of 5 kGy, a pH of 5.0, and an initial ENR concentration of 10 mg/L and anH2O2 concentration of 2mM. The ERR degradation under a couple of EB irradiation and H2O2 followed pseudo-firstorderkinetics, with an R2 of ~0.970. The major degradation pathways of ENR were suggested by density functional theory,natural bond orbital calculations, and liquid chromatography-tandem mass spectrometry (LC/MS/MS) analysis. Lifecycle assessment (LCA) was also performed to evaluate the impact of the EB on removing ENR; the industrial processwas designed based on laboratory tests aimed with the ReCiPe tool. The obtained results indicated that energy consumptionand H2O2 affect environmental impacts with order human health, ecology systems, and natural resource. The LCAalso proved that EB could be a green and efficient method for eliminating pharmaceutical contaminants in water.
Improve object recognition using UWB SAR imaging with compressed sensing
The Hien Pham,Ic-Pyo Hong 한국전기전자학회 2021 전기전자학회논문지 Vol.25 No.1
In this paper, the compressed sensing basic pursuit denoise algorithm adopted to synthetic aperture radar imaging is investigated to improve the object recognition. From the incomplete data sets for image processing, the compressed sensing algorithm had been integrated to recover the data before the conventional back- projection algorithm was involved to obtain the synthetic aperture radar images. This method can lead to the reduction of measurement events while scanning the objects. An ultra-wideband radar scheme using a stripmap synthetic aperture radar algorithm was utilized to detect objects hidden behind the box. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to transmit and receive signal data of two conductive cylinders located inside the paper box. The results confirmed that the images can be reconstructed by using a 30% randomly selected dataset without noticeable distortion compared to the images generated by full data using the conventional back-projection algorithm.
( Hai The Pham ),( Hien Thi Tran ),( Linh Thuy Vu ),( Hien The Dang ),( Thuy Thu Thi Nguyen ),( Thu Ha Thi Dang ),( Mai Thanh Thi Nguyen ),( Huy Quang Nguyen ),( Byung Hong Kim ) 한국미생물·생명공학회 2019 Journal of microbiology and biotechnology Vol.29 No.7
In this study, we investigated the potential of using sediment bioelectrochemical systems (SBESs) for in situ treatment of the water and sediment in brackish aquaculture ponds polluted with uneaten feed. An SBES integrated into a laboratory-scale tank simulating a brackish aquaculture pond was established. This test tank and the control (not containing the SBES) were fed with shrimp feed in a scheme that mimics a situation where 50% of feed is uneaten. After the SBES was inoculated with microbial sources from actual shrimp pond sediments, electricity generation was well observed from the first experimental week, indicating successful enrichment of electrochemically active bacteria in the test tank sediment. The electricity generation became steady after 3 weeks of operation, with an average current density of 2.3 mA/㎡ anode surface and an average power density of 0.05 mW/㎡ anode surface. The SBES removed 20-30% more COD of the tank water, compared to the control. After 1 year, the SBES also reduced the amount of sediment in the tank by 40% and thus could remove approximately 40% more COD and approximately 52% more nitrogen from the sediment, compared to the control. Insignificant amounts of nitrite and nitrate were detected, suggesting complete removal of nitrogen by the system. PCR-DGGE-based analyses revealed the dominant presence of Methylophilus rhizosphaerae, Desulfatitalea tepidiphila and Thiothrix eikelboomii, which have not been found in bioelectrochemical systems before, in the bacterial community in the sediment of the SBES-containing tank. The results of this research demonstrate the potential application of SBESs in helping to reduce water pollution threats, fish and shrimp disease risks, and thus farmers’ losses.
Indoor Environment Drone Detection through DBSCAN and Deep Learning
Ha Tran Thi,Hien Pham The,Yun-Seok Mun,Ic-Pyo Hong 한국전기전자학회 2023 전기전자학회논문지 Vol.27 No.4
In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.