Eun Jong Cha

Hypersensitivity to RF fields emitted from CDMA cellular phones: A provocation study

With the number of cellular phone users rapidly increasing, there is a considerable amount of public concern regarding the effects that electromagnetic fields (EMFs) from cellular phones have on health. People with self-attributed electromagnetic hypersensitivity (EHS) complain of subjective symptoms such as headaches, insomnia, and memory loss, and attribute these symptoms to radio frequency (RF) radiation from cellular phones and/or base stations. However, EHS is difficult to diagnose because it relies on a persons subjective judgment. Various provocation studies have been conducted on EHS caused by Global System for Mobile Communications (GSM) phones in which heart rate and blood pressure or subjective symptoms were investigated. However, there have been few sham-controlled provocation studies on EHS with Code Division Multiple Access (CDMA) phones where physiological parameters, subjective symptoms, and perception of RF radiation for EHS and non-EHS groups were simultaneously investigated. In this study, two volunteer groups of 18 self-reported EHS and 19 non-EHS persons were tested for both sham and real RF exposure from CDMA cellular phones with a 300 mW maximum exposure that lasted half an hour. We investigated not only the physiological parameters such as heart rate, respiration rate, and heart rate variability (HRV), but also various subjective symptoms and the perception of EMF. In conclusion, RF exposure did not have any effects on physiological parameters or subjective symptoms in either group. As for EMF perception, there was no evidence that the EHS group better perceived EMF than the non-EHS group.

Assessment of The Cleft Nasal Deformity Using a Regression Equation

Objective: The purpose of this study is to propose an objective and simple method for assessing surgical outcomes of cleft nasal deformity using two-dimensional digital images. Design: Five plastic surgeons evaluated slides of 20 normal and 65 repaired cleft nasal deformities using the full range of values from 0 to 100 with 10-point intervals. Five laypersons rated 12 parameters per image from the same data set using the developed assessment tool. Correlation coefficients between the parameter values and the surgeons’ grades were obtained to find the best matches, and a regression equation was formulated using those. The reproducibility of the proposed method used by the laypersons was compared with that of the subjective grades made by the surgeons. Results: The mean correlation coefficient among the evaluated grades by the five laypersons using the proposed method was higher (.90) than that for the subjectively determined grades from the five plastic surgeons (.80). The grade reproducibility of the patients by the laypersons (9.6%) was also better than that by the surgeons (14.6%). Conclusions: The proposed assessment tool, including the regression equation, allows laypersons as well as surgeons to perform simple, reproducible, quantitative, and objective assessments of the surgical outcomes for cleft nasal deformity using two-dimensional photographs.

Classification Technique of Human Motion Context based on Wireless Sensor Network

The fusion technology of small sensor and wireless communication was followed by various application examples of the embedded system, where the social infrastructural facilities and ecological environment were wirelessly monitored. In the paper, new monitoring and classifying method of human motion context was proposed by using 2-axial MEMS accelerometer and 916 MHz short range data communication technology. During four types of subjects motion, waveform changes of the accelerometer data was acquired by wireless sensor network, then analyzed by principal component analysis (PCA) and support vector machine (SVM) method for clustering the first and second principal components. To classify the subjects motion type, supervised learning method was used for segmentation algorithm. The present study showed that the developed algorithm could classify four types correctly. Therefore, human motion context during daily life could be monitored and classified by using wireless sensor network

Consensus protocol design for discrete-time networks of multiagent with time-varying delay via logarithmic quantizer

This article considers the problem of consensus for discrete-time networks of multiagent with time-varying delays and quantization. It is assumed that the logarithmic quantizer is utilized between the information flow through the sensor of each agent, and its quantization error is included in the proposed method. By constructing a suitable Lyapunov-Krasovskii functional and utilizing matrix theory, a new consensus criterion for the concerned systems is established in terms of linear matrix inequalities LMIs which can be easily solved by various effective optimization algorithms. Based on the consensus criterion, a designing method of consensus protocol is introduced. One numerical example is given to illustrate the effectiveness of the proposed method.

Physiological effects of RF exposure on hypersensitive people by a cell phone

Persons with electromagnetic hypersensitivity (EHS) complain of subjective symptoms such as headaches, insomnia, memory loss etc. resulting from radio frequency (RF) radiation by cellular phones. There have been various EHS provocation studies on heart rate, blood pressure, and subjective symptoms using GSM phones. However, there are few provocation studies on case-control study investigating simultaneously physiological parameters from CDMA phones. In this study, two volunteer groups of 18 self-declared EHS and 19 controls were exposed to both sham and real RF exposures by a CDMA cellular phone for half an hour each. We investigated the physiological parameters such as heart rates, respiration rates, and hear rate variability (HRV). In conclusion, the RF exposure by a CDMA cellular phone did not have any effects on the physiological parameters for both groups.

Proposal reviewer recommendation system based on big data for a national research management institute

National research management organizations need to ensure that research proposals are reviewed fairly and efficiently, which requires the selection of suitable reviewers. In particular, reviewing research proposals in a particular area necessitates the selection of a group with the most reasonable standard for recommending an expert in that area. In this study, we develop an automatic matching system that matches a research proposal with a reviewer who can evaluate it most effectively, using keywords with fuzzy weights based on databases in the corresponding field of research. All functions that we developed were based on the MapReduce framework created by Hadoop, which was verified to enhance matching performance and ensure expandability. This enabled us to select suitable researchers from existing research projects, papers and research reviewer databases. Our system can influence the operation of the national research management system and contribute to academic development.

Terahertz dielectric response of ferroelectric Ba x Sr 1¿x TiO 3 thin films

Terahertz time-domain spectroscopy has been used to investigate the dielectric and optical properties of ferroelectric Ba_xSr_1-xTiO₃ thin films for nominal x-values of 0.4, 0.6, and 0.8 in the frequency range of 0.3 to 2.5 THz. The ferroelectric thin films were deposited at approximately 700 nm thickness on [001] MgO substrate by pulsed laser deposition. The measured complex dielectric and optical constants were compared with the Cole-Cole relaxation model. The results show that the Cole-Cole relaxation model fits well with the data throughout the frequency range and the dielectric relaxation behavior of ferroelectric Ba_xSr_1-xTiO₃ thin films varies with the films compositions. Among the compositions of Ba_xSr_1-xTiO₃ films with different Ba/Sr ratios, Ba_0.6Sr_0.4TiO₃ has the highest dielectric constants and the shortest dielectric relaxation time.

Expression levels of FGFR3 as a prognostic marker for the progression of primary pT1 bladder cancer and its association with mutation status

The present study examined the utility of fibroblast growth factor receptor 3 (FGFR3) mutation status and gene expression as a prognostic marker in primary pT1 bladder cancer (BC). A total of 120 patients with primary pT1 BC were enrolled. FGFR3 mutation status was determined by direct sequencing and FGFR3 mRNA expression level was determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. The results were compared with the clinicopathological parameters, and the prognostic value of FGFR3 was evaluated by Kaplan-Meier analysis and a multivariate Cox regression test. FGFR3 mutations were identified in 48/120 (40.0%) patients with pT1 BC. FGFR3 mRNA expression level was significantly higher in those with BC harboring FGFR3 mutations (P<0.001). Low FGFR3 expression level was associated with high-grade tumors and cancer progression (P=0.006 and P=0.001), whereas FGFR3 mutation status was not associated with cancer progression. Kaplan-Meier analysis revealed a similar result (log-rank, P<0.001). Multivariate analysis identified low FGFR3 expression level (odds ratio, 3.300; 95% confidence interval, 1.310-8.313; P=0.011) as an independent predictor of cancer progression. Stratification by exon site of FGFR3 mutations yielded significant differences in mRNA expression level. None of the patients with BC harboring FGFR3 mutations in exon 9 demonstrated disease progression. The mRNA expression level of the FGFR3 gene may be used to precisely identify subsets of patients with pT1 BC that have a relatively better prognosis. The prognostic influences of FGFR3 mutations may be modulated by the exon site of FGFR3 mutations.

System Framework for Cardiovascular Disease Prediction Based on Big Data Technology

Amid growing concern over the changing climate, environment, and health care, the interconnectivity between cardiovascular diseases, coupled with rapid industrialization, and a variety of environmental factors, has been the focus of recent research. It is necessary to research risk factor extraction techniques that consider individual external factors and predict diseases and conditions. Therefore, we designed a framework to collect and store various domains of data on the causes of cardiovascular disease, and constructed a big data integrated database. A variety of open source databases were integrated and migrated onto distributed storage devices. The integrated database was composed of clinical data on cardiovascular diseases, national health and nutrition examination surveys, statistical geographic information, population and housing censuses, meteorological administration data, and Health Insurance Review and Assessment Service data. The framework was composed of data, speed, analysis, and service layers, all stored on distributed storage devices. Finally, we proposed a framework for a cardiovascular disease prediction system based on lambda architecture to solve the problems associated with the real-time analyses of big data. This system can be used to help predict and diagnose illnesses, such as cardiovascular diseases.

Classification of Closed Frequent Patterns Improved by Feature Space Transformation

In some real-world applications, the predefined features are not discriminative enough to represent well the distinctiveness of different classes. Therefore, building a more well-defined feature space becomes an urgent task. The main goal of feature space transformation is to map a set of features defined in a space into a new more powerful feature space so that the classification based on the transformed data can achieve performance gain compared to the performance in the original space. In this paper, we introduce a feature transformation method in which the feature transformation is conducted using the closed frequent patterns. Experiments on real-world datasets show that the transformed features obtained from combining the closed frequent patterns and the original features are superior in terms of classification accuracy than the approach based solely on closed frequent patterns.