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ABSTRACT
001
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Relationship between air pollution and
outpatient visits for diabetes mellitus type 2
Yi-Horng Lai
Department of Health Care Administration, Oriental Institute of
Technology, New Taipei City, Taiwan
Objectives: Diabetes mellitus is a chronic disease that has
been recognized as an important health issue in many
countries. The prevalence rate of diabetes is very high in
Taiwan that poses a serious threat to the health of people.
Diabetes mellitus is a group of metabolic disorders charac-
terized by hyperglycemia resulting characterized by hyper-
glycemia resulting from a lack of insulin effect. According
to pervious study, insulin secretion has a negative relation-
ship with long term exposure to polluted air. The purpose
of this study is to examine the association between air pol-
lution and the occurrence of diabetes mellitus type 2
(DMT2) in Taiwan.
Methods: This study investigates the impact of air pollu-
tion on DMT2 based on the data from outpatient visits.
The research focuses on the significance of air pollution's
impact on ocular health. Data for outpatient visits were
obtained from the National Health Insurance Research
Database of Taiwan. Ambient air monitoring data were
obtained from the Taiwan Environmental Protection
Administration air pollution monitoring stations. Six regu-
lated air pollutants were included in the studies. This study
used symbolic data analysis with Julia 0.6.4 to investigate
the association between daily air pollutant concentrations
and the number of outpatient visits for DMT2 as well as
the relationship between climate factors and air pollutants.
Results: The result of this study shows that the air pollu-
tant, CO (t‐value = 2.730, P‐value = 0.008) and PM2.5 (t‐
value = 2.292, P‐value = 0.025) is significantly related to
DMT2. The more the increase of CO and PM2.5, the more
the cases of DMT2 occur. As for the relationship between
climate factors and air pollutants, it can be found that daily
temperature is also positively correlated with CO and
PM2.5. The air pollutant, CO and PM2.5 can increase
chances of outpatient visits, suggesting that is a possible
cause for DMT2. This study also shows that a favorable
air‐pollution‐monitoring infrastructure combined with the
establishment of public health data can provide valuable
information for an examination of adverse health effects
related to air pollution.
Conclusions: This study combined and integrated air pol-
lution and health insurance data to investigate associations
between outpatient visits for DMT2 and air pollution
levels. It has found that the air pollutant, CO and PM2.5
can increase chances of outpatient visits, suggesting that it
is a possible cause for DMT2.5. Furthermore, this study
has also shown that a favorable air‐pollution‐monitoring
infrastructure combined with established public health data
can provide valuable information for an examination of
adverse health effects related to air pollution.
002
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D‐ConvNet: Deep learning model for
enhancement of brain MR images
Kathiravan Srinivasan
1
; Vishal Sharma
2
; Dushantha Nalin
K. Jayakody
3
; Durai Raj Vincent
1
1
School of Information Technology and Engineering. Vellore Institute of
Technology, Vellore, India, E‐mail: kathiravan.srinivasan@vit.ac.in;
2
Department of Information Security Engineering, Soonchunhyang
University‐Asan Si, The Republic of Korea;
3
School of Computer Science
& Robotics, National Research Tomsk Polytechnic University, Russia
Objectives: In clinical and medical imaging, the magnetic
resonance (MR) images obtained, generally do not have a
very high resolution because of the several factors like
patient's comfort, scanning time, scanning equipment limi-
tations, long sampling times, and so on. However, the MR
imaging is always favored by physicians as one of the most
trusted modes for clinical pathology, disease diagnosis, and
treatment. Therefore, the enhancement of low‐resolution
MR image to a high‐resolution MR image is critical for
precise and effective clinical diagnosis. Furthermore, sin-
gle‐image super‐resolution is an inverse problem because
of its ill‐posed characteristics. This problem can be sur-
passed by using deep learning models such as deep convo-
lutional neural networks (D‐ConvNet).
Methods: In this research, the D‐ConvNet is employed for
enhancing the resolution of the low‐resolution MR image.
The D‐ConvNet model possesses a single deconvolution
layer, several convolution layers and output of each layer
is sent through a Leaky Rectified Linear Unit. The high‐
resolution MR image is obtained by from a sequence of
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©2018 The Authors. Basic & Clinical Pharmacology & Toxicology © 2018 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).
DOI: 10.1111/bcpt.13173
Basic Clin Pharmacol Toxicol. 2018;124 (Suppl. 2), 3–119. wileyonlinelibrary.com/journal/bcpt
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3
Conclusion: Tai Chi exercise as an adjunctive treatment
may potentially improve smooth ventilating blood, smooth
emotion, strong bones and strong bones of Cervical
Spondylosis patients as well as quality of life. Tai Chi
exercise could be tentatively prescribed for Neck Type Cer-
vical Spondylosis in combination with the conventional
rehabilitation program to quicken the process of recovery.
Acknowledgements: This work was supported by The
Ministry of education of Humanities and Social Science
project (17YJA890025); The Fundamental Research Funds
for the Central Universities (WUT: 2018VI014).
060
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Concentration level monitoring in
education and healthcare
Abraham Varghese
1
; Ali Al Musawi
2
; Sunil Jacob
3
; Jibin Lukose
3
1
Information Technology Department, Higher college of Technology,
Muscat, Sultanate of Oman;
2
Department of Instructional and Learning
Technologies (ILT), Sultan Qaboos University, Muscat, Oman;
3
SCMS
Centre for Robotics, SCMS School of Engineering and Technology,
Ernakulam, Kerala, India
Background: During the learning process, whether stu-
dents remain attentive throughout the session influences
their learning capability. If teachers can identify whether
students are attentive they can be notified to remain
focused, thus resulting in improving their learning capabil-
ity. Traditional methods require, teachers observe students’
facial expressions to identify whether they are attentive
during the session. However, this method is often inaccu-
rate and increases the burden on teachers. The number of
Alzheimer's patient and resulting deaths is increasing every
year mainly due to delay in the early detection so as to
take necessary measures and treatments to overcome it.
With the development of electroencephalography (EEG)
detection tools, a window has opened for developing an
effective equipment to aid this cause.
Objective: The aim is to develop a device that can check
the concentration level, memory capacity and arithmetic
levels of students and for the early detection of the Alzhei-
mer's disease. Teachers can put on remedial methods for
weaker students by offering them with special attention and
care. Early detection of Alzheimer's disease will help in
taking necessary measured to get cured or control the
extent of it.
Methods: Every subject undergoes a session of trials for
computing the subject‐specific threshold to assess his/her
attention and concentration level to perform specified tasks.
The subject will be sitting in an upright posture wearing
the EEG device, presented with the some objects or a sheet
of patterns. Session has 3 phases such as preparation, con-
centration and relaxed phases. During idle phase active
concentration to object or point on the pattern is avoided.
In the concentration phase, user actively concentrated at a
specified object or part of the pattern. In Relaxed phase
subjects are free to divert their focus from the objects and
patterns. Analysis of the data was done to estimate the val-
ues of the EEG signal extracted during the active concen-
tration period. The average data of the trials is taken as the
threshold. The threshold and the extracted raw data are
compared and analyzed. The resulting data is used for the
brain activity analysis which would help in the effective
detection of Alzheimer's in its early stages.
Results: Effective EEG signal extraction is achieved. Effi-
ciently analyzed concentration levels of students thereby
helping the staffs to analyse their concentration levels and
employing necessary measures to enhance the brain activity
and concentration skills of the students. In healthcare early
detection of the Alzheimer's was very supportive to help
the patient and provide necessary treatment and care at the
early stages itself.
Conclusion: A more user friendly, compact and better por-
table system will be designed such that it could be easily
used in education institutions. The easy portable design
will also be very helpful in hospitals aiding the patient to
move around and interact by wearing the device.
061
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Study and design of physical health
management system for civil servants in Hubei
province of China
Qi Luo
College of Sports Engineering and Information Technology, Wuhan Sports
University, Wuhan, 430079, China
Objectives: The purpose of this paper is to construct a
complete civil servant health assessment index system
(adding psychological and social adaptation indicators), and
to take civil servants in Hubei province as the research
object, aiming at comprehensive, objective and accurate
evaluation of the physical health status of civil servants in
Hubei province. On the basis of seizing the factors affect-
ing the health of the civil servants, we searched the opin-
ions of the collective health experts, designed targeted
exercise prescriptions, and finally developed and designed
the Hubei province civil servant health management inte-
grated system (including database, expert database and
website), which support for the practice of promoting the
health of civil servants. This project is the need to imple-
ment the scientific concept of development, strengthen the
quality of civil servants, and build a civil servant team that
is lean, efficient, diligent, honest and pragmatic, so that
civil servants can understand their physical health and seize
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ABSTRACTS
