Dissipation patterns of acrinathrin and metaflumizone in Aster scaber

Abstract The establishment of preharvest residue limits (PHRLs) is important to minimize damage to producer and consumers caused by agricultural products which pesticide residue exceeds maximum residue limits (MRLs). Dissipation patterns of acrinathrin and metaflumizone in Aster scaber in greenhouse were studied during 10days in order to determine a pre-harvest interval after application. Acrinathrin and metaflumizone were applied in two different greenhouse, located in Taean-gun (field 1) and Gwangyang-si (field 2). Samples were collected at 0, 1, 2, 3, 5, 7, and 10days after insecticides application. The recoveries of two insecticides analyzed by LC–MS/MS and HPLC–DAD were ranged from 77.1 to 111.3%. The half-lives of acrinathrin and metaflumizone residues respectively were 3.8 and 5.9days in field 1 and 9.2 and 4.5days in field 2. The PHRLs 10days before harvesting A. scaber were 0.610mg/kg (field 1), 0.946mg/kg (field 2) for acrinathrin, and 5.930mg/kg (field 1), 5.147mg/kg (field 2) for metaflumizone. This results can be used as basic data for the establishment of PHRL in A. scaber

Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

수자원 일원화를 위한 무인 보트를 이용한 수량과 수질 동시 측정 기술 개발

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Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions

Design and analysis of a low-profile 28 GHz beam steering antenna solution for Future 5G cellular applications

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Interruption Cost Evaluation by Cognitive Workload and Task Performance in Interruption Coordination Modes for Human–Computer Interaction Tasks

Interruption is a widespread phenomenon in human–computer interaction in modern working environments. To minimize the adverse impact or to maximize possible benefits of interruptions, a reliable approach to evaluate interruption cost needs to be established. In this paper, we suggest a new approach to evaluate the interruption cost by cognitive workload and task performance measures. The cognitive workload is assessed by pupil diameter changes and National Aeronautics and Space Administration (NASA) task load index. Task performance includes task completion time and task accuracy in a series of controlled laboratory experiments. This integrated approach was applied to three interruption coordination modes (i.e., the immediate, the negotiated, and the scheduled modes), which were designed based on McFarlane’s interruption coordination modes. Each mode consists of cognitive and skill tasks depending on the degree of mental demands providing four different task sets of interruptive task environments. Our results demonstrate that the negotiated mode shows a lower interruption cost than other modes, and primary task type and task similarity between primary and peripheral tasks are crucial in the evaluation of the cost. This study suggests a new approach evaluating interruption cost by cognitive workload and task performance measures. Applying this approach to various interruptive environments, disruptiveness of interruption was evaluated considering interruption coordination modes and task types, and the outcomes can support development of strategies to reduce the detrimental effects of unexpected and unnecessary interruptions

Dissipation Kinetics and Risk Assessment of Spirodiclofen and Tebufenpyrad in <i>Aster scaber</i> Thunb

The dissipation kinetics of spirodiclofen and tebufenpyrad after their application on Aster scaber Thunb were studied for 10 days, including the pre-harvest intervals. Spirodiclofen and tebufenpyrad were used in two greenhouses in Taean-gun, Chungcheongnam province (Field 1) and Gwangyang-si, Jeollanam province (Field 2), Republic of Korea. Samples were taken at 0, 1, 3, 5, 7, and 10 days after pesticide application. The method validations were performed utilizing liquid chromatography (LC)-tandem mass spectrometry (MS/MS). The recoveries of the studied pesticides ranged from 82.0–115.9%. The biological half-lives of spirodiclofen and tebufenpyrad were 4.4 and 3.8 days in Field 1, and 4.5 and 4.2 days in Field 2, respectively. The pre-harvest residue limits (PHRLs; 10 days before harvesting) of Aster scaber were 37.6 mg/kg (Field 1) and 41.2 mg/kg (Field 2) for spirodiclofen, whereas the PHRLs were 7.2 (Field 1) and 3.6 (Field 2) for tebufenpyrad. The hazard quotient for both pesticides at pre-harvest intervals was less than 100% except in the case of spirodiclofen (0 day)