• Smart Structures and Systems
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• v.11 no.5
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• pp.477-496
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• 2013

Due to their cost-effectiveness and ease of installation, wireless smart sensors (WSS) have received considerable recent attention for structural health monitoring of civil infrastructure. Though various wireless smart sensor networks (WSSN) have been successfully implemented for full-scale structural health monitoring (SHM) applications, monitoring of low-level ambient strain still remains a challenging problem for WSS due to A/D converter (ADC) resolution, inherent circuit noise, and the need for automatic operation. In this paper, the design and validation of high-precision strain sensor board for the Imote2 WSS platform and its application to SHM of a cable-stayed bridge are presented. By accurate and automated balancing of the Wheatstone bridge, signal amplification of up to 2507-times can be obtained, while keeping signal mean close to the center of the ADC span, which allows utilization of the full span of the ADC. For better applicability to SHM for real-world structures, temperature compensation and shunt calibration are also implemented. Moreover, the sensor board has been designed to accommodate a friction-type magnet strain sensor, in addition to traditional foil-type strain gages, facilitating fast and easy deployment. The wireless strain sensor board performance is verified through both laboratory-scale tests and deployment on a full-scale cable-stayed bridge.

• Safety and Health at Work
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• v.7 no.3
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• pp.185-193
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• 2016

Background: Management and workers in small and medium-sized enterprises (SMEs) often find it hard to comprehend the requirements related to controlling risks due to exposure to substances. An intervention study was set up in order to support 45 SMEs in improving the management of the risks of occupational exposure to chemicals, and in using the control banding tool and exposure model Stoffenmanager in this process. Methods: A 2-year intervention study was carried out, in which a mix of individual and collective training and support was offered, and baseline and effect measurements were carried out by means of structured interviews, in order to measure progress made. A seven-phase implementation evolutionary ladder was used for this purpose. Success and failure factors were identified by means of company visits and structured interviews. Results: Most companies clearly moved upwards on the implementation evolutionary ladder; 76% of the companies by at least one phase, and 62% by at least two phases. Success and failure factors were described. Conclusion: Active training and coaching helped the participating companies to improve their chemical risk management, and to avoid making mistakes when using and applying Stoffenmanager. The use of validated tools embedded in a community platform appears to support companies to organize and structure their chemical risk management in a business-wise manner, but much depends upon motivated occupational health and safety (OHS) professionals, management support, and willingness to invest time and means.

• Smart Structures and Systems
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• v.3 no.4
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• pp.455-474
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• 2007

A flexibility-based distributed computing strategy (DCS) for structural health monitoring (SHM) has recently been proposed which is suitable for implementation on a network of densely distributed smart sensors. This approach uses a hierarchical strategy in which adjacent smart sensors are grouped together to form sensor communities. A flexibility-based damage detection method is employed to evaluate the condition of the local elements within the communities by utilizing only locally measured information. The damage detection results in these communities are then communicated with the surrounding communities and sent back to a central station. Structural health monitoring can be done without relying on central data acquisition and processing. The main purpose of this paper is to experimentally verify this flexibility-based DCS approach using wired sensors; such verification is essential prior to implementation on a smart sensor platform. The damage locating vector method that forms foundation of the DCS approach is briefly reviewed, followed by an overview of the DCS approach. This flexibility-based approach is then experimentally verified employing a 5.6 m long three-dimensional truss structure. To simulate damage in the structure, the original truss members are replaced by ones with a reduced cross section. Both single and multiple damage scenarios are studied. Experimental results show that the DCS approach can successfully detect the damage at local elements using only locally measured information.

• Environmental Analysis Health and Toxicology
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• v.17 no.3
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• pp.245-252
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• 2002

Airborne pollutants in the subway facilities can be potentially harmful to the health of passengers. This study was designed to examine whether the suspended particulates have mutagenic or carcinogenic effect on the plant cell systems. Total suspended particulates were collected with a high volume air sampler, in the entrance, the waiting room, and the platform of each subway station. The biological end -points in this experiment were the pink mutations in stamen hairs and micronuclei in the pollen mother cells of Tradescantia. The exudates were collected by shaking the filter papers from the sampler in distilled water for 24 hours. All the plant cuttings exposed to the exudates resulted in positive responses. The micronucleus assay proved more reliable and sensitive to the test than the stamen hair assay. The results indicate that the air particulates can give an adverse effect on the health of subway passengers.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.456-462
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• 2011

As the ARRA enacted recently in the United States, the interest in EHR systems have been increased in the field of medical industry. The passage of the ARRA presents a program that provides incentives to office-based physicians and hospitals adapting the EHR systems to guarantee interoperability with various medical standards. Thanks to the incentive program, a great number of EHR systems have been developed and lots of office-based physicians and hospitals have adapted the EHR systems certified by CCHIT. Keeping pace with the rapid changes in the market of healthcare, some enterprises try to push in to the United States healthcare market based on the experience acquired by developing EHR systems for hospitals in Korea. However, the developed system must be customized because of the different medical environment between Korea and the United States. In this paper, therefore, we design and develop an integrated EHR platform to guarantee the interoperability between different medical information systems based on medical standard technologies. In the developed platform, an integrated system has been composed by integrating various basic techniques such as data transmission standards and its methods, medical standard terminologies and its usage, and knowledge management for medical decision-making support. Moreover, medical data can be processed electronically by adapting an HL7 interface engine and the terminologies for exchanging medical information and the standardization of medical information. We develop SeniCare, an EHR system for supporting ambulatory care of the office-based physicians, based on the platform, and we verify the usability of the platform by confirming whether SeniCare satisfies the criteria of "meaningful use" issued by CMS or not.

• Korean Journal of Adult Nursing
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• v.27 no.4
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• pp.459-471
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• 2015

Purpose: The purpose of this study was to develop a smartphone application and evaluate it's effectiveness and usability for promoting bone health in Korean young adult women. Methods: A smartphone application was developed covering exercise, nutrition, and health habits related to bone health. A pilot testing was conducted using a one-group pre/posttest design. A total of 22 female college students with low bone mass participated in the 8-week use of a pilot testing. Results: The "Strong bone Fit body (SbFb)" application was developed using Android platform. In the pilot testing, bone mineral density, serum CTX, and 25 (OH)Vit.D were significantly decreased. There is no significant change in serum osteocalcin and calcium. Serum phosphorus concentration, nutrient intakes (fiber, VitB6, and pantotenic acid), and all bone composition profiles were significantly increased. Bone-health knowledge was significantly increased but no change in health belief and self-efficacy. Conclusion: The results of this pilot testing suggest the effectiveness of the SbFb application to enhance and promote self-managing abilities for bone health. It can provide continuous feedback and high-quality bone health information. However, some contents and working methods of the application need to be modified and its effectiveness on bone health requires further investigations.

• Journal of Korean society of Dental Hygiene
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• v.23 no.1
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• pp.43-53
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• 2023

Objectives: We developed an integrated cognitive function improvement program comprising cognitive, emotional, and physical domains, and remotely applied it to middle-aged adults to investigate its effects on oral health, cognitive function, and mental health improvement. Methods: The experimental group underwent the program remotely, using the Zoom platform. A total of 24 participants were recruited and divided into 12 experimental and 12 control groups. The program comprised cognitive, emotional, and physical activities. The sessions lasted 90 min and were performed twice a week for 6 weeks from April to May 2022. Results: Cognitive function, arousal, physical, and mental stress were significantly improved in the experimental group after the intervention than at the baseline (p<0.05). Regarding oral health, tongue plaque decreased 1.34-fold (p<0.01) and saliva increased 1.04-fold (p<0.05) in the experimental group after the intervention than at the baseline. Moreover, the experimental group showed significant improvements in tongue plaque and saliva than the control group (p<0.05 for tongue plaque and p<0.01 for saliva). Regarding mental health, social support significantly increased 11.67-fold (p<0.05) in the experimental group than at the baseline. The experimental group also showed significantly improved social support than the control group (p<0.01). Conclusions: The non-face-to-face integrated cognitive function improvement program for middle-aged adults improved their cognitive function and oral and mental health. Based on these findings, this program may be a useful health program tool for middle-aged individuals.

• Journal of Environmental Health Sciences
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• v.15 no.2
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• pp.1-9
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• 1989

A pilot study was conducted in order to measure indoor and outdoor formaldehyde levels during August 3 - 22, 1988 in several underground spaces in Seoul. Formaldehyde concentrations were monitored during 1 week in selected sampling areas (subway station, underground shopping center, underpass, tunnel, underground parking lot) using passive formaldehyde monitors. In order to investigate a relationship between respiratory prevalence and levels of formaldehyde, each subject was asked to answer respiratory questions. The mean formaldehyde concentrations were 60.1 ppb in subway station, 122.2 ppb in underground shopping stores, 72.1 ppb in underpasses, 39.7 ppb in tunnel, and 75.9 ppb in underground parking lots, respectively. The mean indoor formaldehyde concentrations in underground environments varied from 28.6 ppb to 118.7 ppb. Generally, the mean formaldehyde concentrations in ticketing office in subway stations appeared higher than those level measured in platform. The mean formaldehyde concentrations of underground shopping center in Gangnam Terminal were higher than any other areas and it exceeded 100 ppb of the American Ambient Air Quality Standards of formaldehyde. Prevalence rates of respiratory symptoms of dwellers seemed to be related to higher indoor formaldehyde levels.