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Article ; Online: Association of public health and social measures on the hand-foot-mouth epidemic in South Korea

Sukhyun Ryu / Changhee Han / Sheikh Taslim Ali / Chiara Achangwa / Bingyi Yang / Sen Pei

Journal of Infection and Public Health, Vol 16, Iss 6, Pp 859-

2023 Volume 864

Abstract: Background: School based-measures such as school closure and school holidays have been considered a viable intervention during the hand-foot-mouth disease (HFMD) epidemic. The aim of this study was to explore the association of nationwide public health ... ...

Abstract	Background: School based-measures such as school closure and school holidays have been considered a viable intervention during the hand-foot-mouth disease (HFMD) epidemic. The aim of this study was to explore the association of nationwide public health and social measures (PHSMs) including planned school vacation on the transmissibility and attack rate of the HFMD epidemic in South Korea. Methods: In this study, we used Korean national surveillance data on HFMD from 2014 to 2019 to estimate the temporal changes in HFMD transmissibility (instantaneous reproductive number, Rt). Furthermore, to assess the changes in the HFMD attack rate, we used a stochastic transmission model to simulate the HFMD epidemic with no school vacation and nationwide PHSMs in 2015 South Korea. Results: We found that school vacations and 2015 PHSMs were associated with the reduced Rt by 2–7 % and 13 %, respectively. Model projections indicated school vacations and 2015 PHSMs were associated with reduced HFMD attack rate by an average of 1.10 % (range: 0.38–1.51 %). Conclusions: PHSMs likely have a larger association with reduced HFMD transmissibility than school-based measures alone (i.e. school vacations). Preventive measures targeting preschoolers could be considered as potential options for reducing the future burden of HFMD.
Keywords	Hand-foot-and-mouth disease ; School holidays ; Vacation ; Nonpharmaceutical measure ; Public health and social measures ; Transmissibility ; Infectious and parasitic diseases ; RC109-216 ; Public aspects of medicine ; RA1-1270
Subject code	370
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023

Nirmalya Thakur / Kesha A. Patel / Audrey Poon / Rishika Shah / Nazif Azizi / Changhee Han

Future Internet, Vol 15, Iss 10, p

2023 Volume 346

Abstract: Exoskeletons have emerged as a vital technology in the last decade and a half, with diverse use cases in different domains. Even though several works related to the analysis of Tweets about emerging technologies exist, none of those works have focused on ...

Abstract	Exoskeletons have emerged as a vital technology in the last decade and a half, with diverse use cases in different domains. Even though several works related to the analysis of Tweets about emerging technologies exist, none of those works have focused on the analysis of Tweets about exoskeletons. The work of this paper aims to address this research gap by presenting multiple novel findings from a comprehensive analysis of about 150,000 Tweets about exoskeletons posted between May 2017 and May 2023. First, findings from temporal analysis of these Tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of Tweets, the lowest number of Tweets, Tweets with the highest number of hashtags, and Tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of Tweets posted per hour and the different characteristics of these Tweets. Third, the paper presents a multiple linear regression model to predict the number of Tweets posted per hour in terms of these characteristics of Tweets. The R 2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech, #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these Tweets was performed, and the results show that the percentages of positive, neutral, and negative Tweets were 46.8%, 33.1%, and 20.1%, respectively. To add to this, in the Tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by sentiments of joy, disgust, sadness, fear, and anger, respectively. Furthermore, hashtag-specific sentiment analysis revealed several novel insights. For instance, for almost all the months in 2022, the usage of #ai in Tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, lexicon-based ...
Keywords	Twitter ; data analysis ; big data ; exoskeletons ; data science ; text analysis ; Information technology ; T58.5-58.64
Subject code	400
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Yoongun Jung / Changhee Han / Dongwon Lee / Sungyoon Song / Gilsoo Jang

Applied Sciences, Vol 11, Iss 8979, p

2021 Volume 8979

Abstract: Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as ... ...

Abstract	Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.
Keywords	voltage unbalance ; volt–var curve control ; smart PV inverter ; multiagent proximal policy optimization ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	629
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dispatchable Substation for Operation and Control of Renewable Energy Resources

Yeuntae Yoo / Seungmin Jung / Sungwoo Kang / Sungyoon Song / Jaehyeong Lee / Changhee Han / Gilsoo Jang

Applied Sciences, Vol 10, Iss 7938, p

2020 Volume 7938

Abstract: Renewable generation in power systems has proved to be challenging for system operators owing to the increasing levels of penetration. The operation of power systems currently requires additional flexibility and reserves due to the intermittency and ... ...

Abstract	Renewable generation in power systems has proved to be challenging for system operators owing to the increasing levels of penetration. The operation of power systems currently requires additional flexibility and reserves due to the intermittency and unpredictability of renewable generators. However, it is difficult to precisely predict and control the stochastic nature of renewable sources; nevertheless, its capacity continues to increase. To monitor and control renewable generators efficiently, the entire system needs to be established in a hierarchical order. This study proposed the concept of a substation that is uniquely designed for renewable interconnection. The purpose of this substation is simple: to make the renewable generators dispatchable to operators such that each group of renewable generators is sufficiently stable to be considered as conventional generators. For this purpose, methods for sizing and controlling energy storage system are proposed based on forecasts and error distributions.
Keywords	DIR ; ESS ; forecast error ; renewable generator ; substation ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features

Erick Costa de Farias / Christian di Noia / Changhee Han / Evis Sala / Mauro Castelli / Leonardo Rundo

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 12

Abstract: Abstract Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the ... ...

Abstract	Abstract Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At $$2\times $$ 2 × SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at $$4\times $$ 4 × SR. We also evaluated the robustness of our model’s radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Lead Isotopic Constraints on the Provenance of Antarctic Dust and Atmospheric Circulation Patterns Prior to the Mid-Brunhes Event (~430 kyr ago)

Changhee Han / Laurie J. Burn / Paul Vallelonga / Soon Do Hur / Claude F. Boutron / Yeongcheol Han / Sanghee Lee / Ahhyung Lee / Sungmin Hong

Molecules, Vol 27, Iss 13, p

2022 Volume 4208

Abstract: A lead (Pb) isotopic record, covering the two oldest glacial–interglacial cycles (~572 to 801 kyr ago) characterized by lukewarm interglacials in the European Project for Ice Coring in Antarctica Dome C ice core, provides evidence for dust provenance in ... ...

Abstract	A lead (Pb) isotopic record, covering the two oldest glacial–interglacial cycles (~572 to 801 kyr ago) characterized by lukewarm interglacials in the European Project for Ice Coring in Antarctica Dome C ice core, provides evidence for dust provenance in central East Antarctic ice prior to the Mid-Brunhes Event (MBE), ~430 kyr ago. Combined with published post-MBE data, distinct isotopic compositions, coupled with isotope mixing model results, suggest Patagonia/Tierra del Fuego (TdF) as the most important sources of dust during both pre-MBE and post-MBE cold and intermediate glacial periods. During interglacials, central-western Argentina emerges as a major contributor, resulting from reduced dust supply from Patagonia/TdF after the MBE, contrasting to the persistent dominance of dust from Patagonia/TdF before the MBE. The data also show a small fraction of volcanic Pb transferred from extra-Antarctic volcanoes during post-MBE interglacials, as opposed to abundant transfer prior to the MBE. These differences are most likely attributed to the enhanced wet removal efficiency with the hydrological cycle intensified over the Southern Ocean, associated with a poleward shift of the southern westerly winds (SWW) during warmer post-MBE interglacials, and vice versa during cooler pre-MBE ones. Our results highlight sensitive responses of the SWW and the associated atmospheric conditions to stepwise Antarctic warming.
Keywords	lead isotopes ; EPICA Dome C ice core ; Mid-Brunhes Event ; dust and volcanic sources ; isotope mixing model ; southern westerly winds ; Organic chemistry ; QD241-441
Subject code	550
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Reliable Ultra Trace Analysis of Cd, U and Zn Concentrations in Greenland Snow and Ice by Using Ultraclean Methods for Contamination Control

Changhee Han / Heejin Hwang / Jung-Ho Kang / Sang-Bum Hong / Yeongcheol Han / Khanghyun Lee / Soon Do Hur / Sungmin Hong

Molecules, Vol 25, Iss 2519, p

2020 Volume 2519

Abstract: This study presents ultraclean procedures used in the challenging task of determining trace elements at or below the pg/g concentration level encountered in Greenland snow and ice. In order to validate these ultraclean procedures, recent snowfall and ... ...

Abstract	This study presents ultraclean procedures used in the challenging task of determining trace elements at or below the pg/g concentration level encountered in Greenland snow and ice. In order to validate these ultraclean procedures, recent snowfall and Holocene ice from northwest Greenland were analyzed for Cd, U, and Zn concentrations. The total procedural blanks brought through the entire measurement procedure proved to be negligible, compared to trace element concentrations, measured in snow and ice samples. This validates the overall practicality of the proposed ultraclean procedures, thereby ensuring the reliable measurements of ultra-trace analysis. A comparison between our study and published data shows that improper procedures employed throughout all stages, from field sampling to analysis to elevate the concentrations by several orders of magnitude, relative to the reliable concentration ranges. The risk of contamination exposure for selected trace elements appears to increase in the order of U < As ≤ Pb < Cd < Zn. Reliable measurements of Cd, U, and Zn concentrations in snow and ice allowed us to interpret the data in terms of seasonal variations in the inputs of crustal and anthropogenic sources to Greenland ice sheet.
Keywords	ultraclean procedure ; ultralow trace elements ; contamination ; Greenland snow and ice ; seasonal variations ; anthropogenic inputs ; Organic chemistry ; QD241-441
Subject code	910
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MADGAN

Changhee Han / Leonardo Rundo / Kohei Murao / Tomoyuki Noguchi / Yuki Shimahara / Zoltán Ádám Milacski / Saori Koshino / Evis Sala / Hideki Nakayama / Shin’ichi Satoh

BMC Bioinformatics, Vol 22, Iss S2, Pp 1-

unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction

2021 Volume 20

Abstract: Abstract Background Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers ... ...

Abstract	Abstract Background Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. Results We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 $$\ell _1$$ ℓ 1 loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average $$\ell _2$$ ℓ 2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. Conclusions Similar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen ...
Keywords	Generative adversarial networks ; Self-attention ; Unsupervised anomaly detection ; Brain MRI reconstruction ; Various disease diagnosis ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Pb concentrations and isotopic record preserved in northwest Greenland snow

Kang, Jung-Ho / Heejin Hwang / Changhee Han / Soon Do Hur / Seong-Joong Kim / Sungmin Hong

Chemosphere. 2017 Nov., v. 187

2017

Abstract: We present high-resolution lead (Pb) concentrations and isotopic ratios from a northwest Greenland snow pit covering a six-year period between 2003 and 2009. Pb concentrations ranged widely from 2.7Â pgÂ gâ1 to 97.3Â pgÂ gâ1, with a mean ... ...

Abstract	We present high-resolution lead (Pb) concentrations and isotopic ratios from a northwest Greenland snow pit covering a six-year period between 2003 and 2009. Pb concentrations ranged widely from 2.7Â pgÂ gâ1 to 97.3Â pgÂ gâ1, with a mean concentration of 21.6Â pgÂ gâ1. These values are higher than those recorded for the pre-industrial period. Pb concentrations exhibit seasonal spikes in winterâspring layers. Crustal Pb enrichment factors (EF) suggest that the northwest Greenland snow pit is highly enriched with Pb of predominantly anthropogenic origin. The 206Pb/207Pb ratios ranged from 1.144 to 1.169 with a mean value of 1.156, which fall between less radiogenic Eurasian-type and more radiogenic Canadian-type signatures. This result suggests that several potential source areas of Pb impact on northwest Greenland. Abrupt changes in Pb concentrations and Pb isotope ratios were observed and related to seasonal shifts in source regions of aerosol transport. The 206Pb/207Pb isotope ratio increased gradually between 2003 and 2009. The similarity of the three-isotope plot (206Pb/207Pb versus 208Pb/207Pb) between some of our samples and Chinese urban aerosols suggests a steadily increasing contribution of Chinese Pb to northwest Greenland snow.
Keywords	aerosols ; isotopes ; lead ; snow ; Greenland
Language	English
Dates of publication	2017-11
Size	p. 294-301.
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2017.08.101
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: The Mixed Ontology Building Methodology Using Database Information

Minyoung Ra / Donghee Yoo / Sungchun No / Jinhee Shin / Changhee Han

Lecture Notes in Engineering and Computer Science, Vol 2195, Iss 1, Pp 68-

2012 Volume 73

Keywords	Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
Language	English
Publishing date	2012-03-01T00:00:00Z
Publisher	Newswood and International Association of Engineers
Document type	Article ; Online
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