Earticle

The purpose of this study was to identify the effect of different paddle blade types during forward stroke. Eight male elite kayak athletes participated. The EMG data of biceps femoris and biceps rectus muscles and kinematic data of knee joints and trunk were collected and frequency of strokes and driving distance per a stroke were calculated. The frequency decreased with type 2 blades from 1.00±0.15 to 0.87±0.12 stroke/sec (p=.03). The driving distance per a stroke increased with type 2 blade (2.76±0.16 to 2.92±0.38 m, p=.02). There was no significant result in other variables. The results of this study can be utilized for proper paddle blade fittings that may improve one’s overall rowing efficiency.

The need for clear ECG signals is increasing to reduce the probability of misdiagnosis regarding heart diseases. An algorithm was designed to diagnose patients with heart diseases using ECG signal analysis so that it can help physicians in the decision-making process. Before analyzing the ECG signal, noise of the low and high frequency components was removed through preprocessing. After preprocessing, pattern analysis detected important features on which diagnosis will be given. Then, the analysis was applied on the pure ECG signal to detect the patient's heart diseases. All feature points were extracted by using the proposed algorithm, called ‘Geometric Detection (GD)’. Results showed that performance was superior to others in standard error of the sample mean and variance. Data from CSE (Common Standards for Electrocardiography) database were used to test each algorithm except for GD, because patients’ ECG data was used to test the GD algorithm. Detection rate of the GD algorithm (se(%)) was 99.1% and we confirmed that the proposed algorithm is superior to the other algorithm in terms of stability and standard error of the sample mean. The result of the performance evaluation showed that the proposed algorithm produced higher accuracy and stability than the other algorithms.

The Korea Acute Myocardial Infarction Registry (KAMIR) dataset has been under construction at 41 Primary PCI Centers in Korea since November 2005. Many studies for the KAMIR have proceeded via analysis of statistical approaches: student’s t-test, χ2-test, and multivariate logistic regression analysis. However, there are problems, in that features tested are selected by domain experts according to the analysis conditions, that degrees of importance for features cannot be obtained, and that the huge numbers of features and instances involved incurs a high computation load and low processing speed. Thus, we considered novel feature selection methods using Gini-Index for prediction of the major features and reduction of feature space dimension. Unfortunately, only few studies on Gini-Index based nominal feature selection have as yet been completed, and problems in extracting representative features remain for 1) unbalanced dataset for classes, 2) instances having almost all of the features of the datasets, and 3) instances having almost all features with non-null values. Thus, for the datasets, the features selected are not discriminated for each class. In an effort to solve these problems and enable obtainment of good representative features for each class, we introduce here a novel Gini-Index feature selection algorithm for nominal datasets. We tested the algorithm for prediction of major features of AMI patients from the KAMIR. In the results, it can shows the degrees of importance for features with Gini values, and select the major features for given conditions without help by experts.

Shorea robusta is threatened these days due to poor regeneration potential. No systematic attempts were made in India, to understand dynamism of its natural regeneration and to suggest management inputs to encourage its regeneration. The present study deals with the natural regeneration with particular reference to crop composition and community structure in East Mandla Forest Division in Madhya Pradesh, India. The results indicated that the regeneration of Shorea robusta (seedlings per ha) varied from 916 to 21847. Besides this adequate regeneration of Shorea robusta, the distribution pattern of individuals of Shorea robusta trees in different girth classes was also seemed to be uninterrupted in most of the stands studied. This trend of uninterrupted distribution of Shorea robusta in different growth phases with plenty of established regeneration is the healthy sign of establishment and growth of Shorea robusta crop in the past in this area.

Fermented fish food products have been extensively studied in Asia's fermented food culture. We have attempted to categorize the different types of fermented fish products seen in some of the Asian countries as part of a cultural context indicator analysis. Categorization is available through two cultural context indicators: the macro cultural context indicator and micro cultural context indicator. The categorization of fermented fish products for this study is according to the macro cultural context indicator. Types and nomenclature of different fermented fish products found in different countries have been compared to ones found in Korea and categorized accordingly. East Asian countries' fermented fish food, fermented with salt, is categorized into jeot, jeotgal, paste, sauce, and sikhae in terms of form and ingredients. Two essential ingredients involved in the fermentation process are salt and rice. In other words, fermented fish food products were first introduced in cultures where rice is the staple cuisine. Salt is also used for preservation. The comparison and categorization of Asian fermented fish food as part of a cultural context indicator analysis provides an opportunity to understand its unique characteristics and qualities. It can eventually establish a fundamental frame of inherent properties of Asia's fermented food culture and provide cultural indicators that can measure them. The study can be utilized to understand the identity of the fermented food culture in East Asia.

As the usage of mobile device is increased, it is started to appear various mobile service such as mobile payment, e-healthcare, etc. Identification and authentication are essential functions to use secure mobile service. As a means of user identification and authentication, password based method is used for long periods. However, password based method has some disadvantages like difficulties for remembering, leakage to others, etc. To solve these problems, biometrics technologies began to receive attention in virtue of user convenience. In this paper, we describe some mobile biometric authentication model and examine threats. On the basis of this, we summarize possible countermeasures to provide secure telebiometric service. Then, we show our telebiometric system and analyze that system’s security.

Purpose: This study was performed to determine the effects of heated humidified anesthetic gases, via a heated and humidified circuit (HHC), on body temperature and shivering. Methods: A quasi-experimental design with pretest and posttest measures was used. Data was collected from 71 patients who underwent musculoskeletal surgery for clavicle or upper arm fractures under general anesthesia from June 4 to September 31, 2009. The experimental group consisted of 34 patients who received heated and humidified gas through a HHC, which was set at 40 ˚C, and continued to be warm during the period from the endotracheal tube intubation to extubation. The control group was made up of 37 patients who were provided with gas through a standard breathing circuit system with no added heat and humidity. The collected data was analyzed by chi-square test and t-test. Results: The esophageal temperature was significantly higher in the experimental group at 20, 30 and 40 minutes after intubation compared with those of the control group. However, there were no differences in the tympanic membrane temperature and in the level of shivering between the two groups. Conclusion: The finding partially supports evidence for applying HHC in patients undergoing surgery under general anesthesia to prevent hypothermia. However, further studies are required to measure the effects of HHC on body temperature and shivering over time considering different results among researches.

Practical nursing training includes the learning of knowledge, skills, and attitude that are applied in clinical situations. The Objective Structured Clinical Examination (OSCE) method is an effective tool for evaluating the clinical nursing skills of nursing students. Depression is an important stress factor that can reduce academic achievement. However, self-efficacy with respect to the ability to provide patient care is vital for nurses who may be required to initiate treatments. The clinical field also requires that nurses use problem solving strategies (PSS) effectively. In this study, we conducted a survey to examine the relationship between the OSCE, Depression Cognitive Scale (DCS), self-efficacy (SE) and PSS in sophomore nursing students. We collected data data from 90 sophomore students at Namseoul University The data were analyzed using a t-test, ANOVA test, Mann-Whitney, Kruskal-Wallis, and stepwised multiple regression using SPSS 18.0. The stepwise multiple regression analysis shows that DCS(p=<.001), SE(p=.014), and PSS(p=.004) had significant effects on OSCE performance. Lower depression (B=-.164), higher self-efficacy (B=.025), and higher PSS (B=.071) appeared to be related to higher OSCE performance. DCS explains 64.3% of the total variation in OSCE, DCS and SE explained 66.3%. of the total variation and the addition of PSS increased this to 69.1%. Finally, the hit ratio of DCS, SE, and Pss was 69.1%.

Currently, remote sensing technology is used in various ways in research on vegetation change detection or temperature variant detection in inaccessible areas or extensive areas. Especially, it is very useful for target area needing very continuous change detection. This study detected temperature variant using remote sensing technology in Cheonji lake in the highest mountain Mt. Baekdu, compared water temperature data in the tide station and temperature extracted from images to investigate the extracted temperature. As a result of temperature variant detection in Cheonji lake, from July 2000 to July 2010, it was presented that water temperature continuously increased, and in the future, hourly measurement and management of Cheonji lake water temperature will greatly contribute to the prediction and probability related research Mt. Baekdu volcanic eruption.

Protoperidinium spp. are heterotrophic dinoflagellates in the world ocean. Although most Protoperidinium species are known to feed on diatoms and bacteria, several species feed on red tide dinoflagellate. The samples were collected from 17 locations in the neritic areas of Korea from 1990 to 2010. In the thecate dinoflagellates, the thecal plate pattern has been considered to be important criteria in their classifications. Thecate dinoflagellates with distinct epitheca and hypotheca separated by a girdle which is usually equatorial and may or may not be displaced with or without overhang. Due to this fact the shape of the theca and the arrangement of the theca were studied. The shape of first apical plate is considered an important factor. To identify the genus Protoperidinium, the shape of the first apical plate and the second anterior intercalary plate were used. Depending the shape of first apical plate, will be designated as meta(4), meta(5), para(4), para(5) or para(6). From this point of view, the genus Protoperidinium as a group of planktonic protist in the coastal area was classified.

Purpose: To determine the incidence of anatomic variations of the hook of hamate and to evaluate its association with the development of carpal tunnel syndrome (CTS) in Korean patients. Materials and Methods: Carpal tunnel views of 916 hands (458 patients) that received out-patient treatment for tingling hands, from March 2008 to November 2009. The sex distribution was 69 men and 389 women and the average age was 52.7 years old. The frequency of appearance of variations of the hamate and the ratio of each variation were identified. In addition, patients with variant hooks of hamate with CTS were compared to patients with variant hooks of hamate who had no evidence of CTS. The counting variant age and the correlation of the variant of the hooks of hamate and the occurrence of CTS were analyzed. Results: Variations of the hook of hamate were found in 48 of 916 hands. The ratio of width to height averaged 0.97 with a standard deviation of 0.32. Twenty-five right hands and twenty-three left hands were studied. Four men and forty four women were found with variations but there was no statistical significance to this finding. CTS was diagnosed in 42 of 48 patients. This group accounted for 6.1% of all patients diagnosed with CTS. Out of 42 patient cases 9 were bipartite hook cases, 27 were hypo plastic cases, and 6 were aplastic cases. Six cases, which made up 2.5% of the patients, were not diagnosed with CTS. These cases consisted of two bipartite cases, three hypo plastic cases, and one aplastic case. When the two groups were analyzed by Chi-square test & Fisher’s exact test, there was a higher incidence of variation with the group that was diagnosed with CTS than with the others (P=0.0282). Conclusion: The variations of the hook of hamate are found in 5.2% of the patients in this study. This variation appears to be one of the causal factors of CTS.

Staphylokinase and its variant (Staphylokinase-Hirulog), the third generation thrombolytic molecule plays an significant role in treating the acute myocardial infarction and stroke which are leading causes of morbidity and mortality across the globe. In refolding of the staphylokinase and its variant from industrially feasible pET28a+, gradual decrease in concentration of urea was used to solubilise the protein. But urea is a chaotropic denaturant, which unravel the structure of proteins by destabilizing internal, non-covalent bonds between atoms. In this study, we selected the expression system to have the increased soluble fraction of sak and its variant from IPTG inducible expression host Escherichia coli BL21(DE3). The expression level and solubility of recombinant sak and its variant from cold shock expression vector pCOLDI was induced at the low temperature (15°C) was highly contrast to the induction process at 37 °C from pET28a+, which gives insoluble fractions. Recombinant sak and its variant were expressed primarily as soluble protein using pCOLDI, later purified with Nickel-chelating resin (Ni-NTA) and the quantity of the purified protein was 913 mg/L. Soluble fractions of purified sak variant having the fibrinolytic activity of 21,825 U/ml and specific anti-thrombin activity of 1,200 ATU/mg. For the first time soluble fractions was achieved instead of inclusion bodies without compromising the quantity and activity of the protein and working with urea was excluded.

This paper focuses ocular artifacts separation of EEG signals using Noise Assisted Bi-variate adaptive based filtering. In order to facilitate clinical diagnosis and/or implement so-called brain computer interface (BCI), detecting the rhythmic activity from EEG data recorded in a noisy environment is crucial. The pre-processing of EEG signal is mandatory due to highly interference with the EEG signal. Electro-oculogram (EOG) is the most important interference that misinterpret significantly of the EEG signal for brain activity measurements. To suppress EOG data, we have used a newly developed model with empirical mode decomposition (EMD) named as noise assisted EMD (NEMD). Because the complex signals have a mutual dependence between the real and imaginary parts, so it is possible to analyses both parts simultaneously using NEMD. Here, the EEG signal and white Gaussian noise (reference signal) are combined to produce complex signal which is decomposed using NEMD to extract complex intrinsic mode functions (IMFs). Then the low frequency trend (EOG) and high frequency components (purified EEG) of recorded EEG signals are obtained partial reconstruction on the basis of the energy distribution of their intrinsic mode functions. The experimental results show that the NEMD based data adaptive filtering technique performs better.

Abstract 1. Introduction 2. Related Work 3. Materials and Methods 3.1. Preprocessing 3.2. Cluster Analysis 3.3. Max. Clique Identification 4. Experimental Setup and Data Preparation 5. Result and Discussion 6. Conclusions References

Accurate diagnosis of acute appendicitis is a difficult problem in practice especially if the patient is too young or pregnant women in that radiological test have high risk. Thus, ultrasonographic image analysis is a good way to reduce the difficulty. However, studies show that there are few reliable common predictors and the decision is usually made by medical expert's naked eye examination since there is yet a reliable software tools to use in decision making. In this paper, we propose a new intelligent method to extract appendix from abdomen ultrasonography as a basic building block of developing such intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy binarization to the organ area in order to extract appendix accurately. The experiment verifies that this two-phase image analysis is effective in extracting appendix.

Recent advancement in microarray technologies and large high throughput data generated has made it very challenging to decipher and draw a feasible biological conclusion from current microarray experiments. The difficulty arises when the number of samples available for analysis is smaller than the huge numbers of genes that need to be considered. Currently, pathway analysis is a preferable tool in extracting and understanding the biological information obtained from high throughput experiments. It is essential to analyze microarray experiments along with their biological information to represent the underlying structure of the biological network. Currently, there are numerous software developed for pathway analysis available with the same goal of mining the information from the microarray experiments with biological relevance over the extensive amounts of data. This paper discusses the comparisons between pathway analysis software in terms of their performance, advantages and limitations as well as the available pathway databases in terms of their data availability and organization. The aim of this review is to provide a better understanding of the capabilities of these software and helps to select the tools most suited for a particular purpose.

This paper intents to assess the application of artificial neural network in predicting the presence of heart disease, mainly the angina in patients. The prediction and detection of angina are significant in determining the most appropriate form of treatment for these patients. The development of the application involves three main phases. The first phase is the development of Heart Disease Management Information System (HDMIS) for data collection and patient management. Then followed by the second phase, which is the development of Neural Network Simulator (NNS) using back propagation neural network for training and testing. The final phase is the development of Prediction System (PS) for prediction on new patient’s data. The best network model produced prediction accuracy of 88.89 percent. Apart from proving the ability of neural network technology in medical diagnosis, this study also shown how the neural network could be incorporated into the hospital information system as a prediction tool. As the pilot project, the application developed could be used as the starting point in building a medical decision support system, particularly in diagnosing the heart disease.

Grassfire method for spot matching is proposed based on similarity comparison of topological patterns for neighbor spots. Grassfire method is an algorithm where spot matching is performed as if fire spreads all around on grass. Spot matching starts from a seed spot pair confirmed as a matched pair of spots and spot matching spreads to the direction where the best matching result is produced. In this paper, the simple type of grassfire method where a seed spot pair is manually selected and spot matching is conducted under the circumstances without outlier spots is studied to examine the potential of grassfire method. The proposed method outperforms matching methods by random combination of spots in terms of speed and accuracy.

The present study describes the preliminary evaluation of the composition and beauty cosmetic biological activities of Viscum album L. extracts. The 8 types of phenolic acids and flavonoids components in both hot water and ethanol extracts were identified. And, flavonoid components were identified 3 types in hot water , and 4 types in ethanol extract. The DPPH radical scavenging activities of hot water and ethanol extracts were measured as 62.55±0.035% and 72.0±0.024% respectively. Tyrosinase inhibitory activity of hot water and ethanol extracts were 63.94±0.0249% and 62.61±0.0124%, respectively. The Inhibition of NO(nitric oxide) production on the hot water and 95% ethanol extracts gradually increased with increasing concentration. In the case of V. album extracted by hot water, Hela, AGS and HT-29 cells showed very good growth in a 60㎍/㎖ solution, but cell growth was inhibited in a 100㎍/㎖ solution. In the case of V. album extracted by 95% ethanol, Hela cell growth gradually increased with increasing concentration. But AGS and HT-29 cell growth increased in 20㎍/㎖. On the other hand, cell growth tended to decrease in higher than 60㎍/㎖ concentrations. The microbial growths of S. aureus, E. coli, P. aeruginosa and S. typhimurium tested with hot water extract increased dependent on concentration. But growth of K. pneumoniae bacteria has increased until 300㎍/㎖, but antimicrobial activity started to appear at higher than 300㎍/㎖ concentrations. In these case of 95% ethanol extract, microbial growth of 5 strains growth depended on the concentration of the extract, and cell growth not inhibited in treated concentration(1,500㎍/㎖) of this study.

Association rule mining is now widely used in many fields such as commerce, telecom, insurance, and bioinformatics. Although association rule mining has improved in performance, the real commerce database has also grown in its size and dimension to a point of creating millions of association rules. One of the biggest problems of association rule mining is that it frequently produces large numbers of rules, and this makes it difficult for users to select those that are of interest. We proposed the Common Sense Measure (CSM) so that only interesting knowledge can be selected in order to resolve the problem resulting from a large quantity of rules. The CSM is an interestingness measure that evaluates how closely rules match the common sense knowledge. We developed an algorithm of rule matching method with common sense knowledge using the common sense network (CSN).

In the present work Al (99.7) matrix nano composites were fabricated and characterized with the aid of stir casting. Ilmenite(FeTiO3) nano particles synthesized by top down approach using high-energy wet ball milling process. Al (99.7) matrix reinforced with the Ilmenite(FeTiO3) nano particles at 1 to 5 weight percent were fabricated with the help of stir casting. XRD characterization was carried out on nano particles showing the least particle size. SEM images of cast composites shows the uniform distribution of nano particles in the Al matrix. The results were analyzed and found that the composites reinforced with 5 wt percent nano particles cast at 850◦C have homogeneity in micro structures and exhibit’s increased mechanical properties like hardness and tensile strength.

Object recognition is usually processed based on region segmentation algorithm. Region segmentation in the IT field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on Susceptibility Weighted Imaging (SWI) within the magnetic resonance (MR) theory. When we do pre-processing, proposed method was composed of SWI process. And then we do the Gray-white matter segmentation by Active Contour Model(ACM) in post-processing. In this study, the experiment had been conducted using images including the brain region and by getting up contrast enhancement image of SWI for segmentation to extract region (white matter) segmentation even when the border line was not clear. As a result, an average area difference of 8.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

In this study, a new diagnosis model based on multiple serum biomarkers and meno-pausal information was developed for the early detection of ovarian cancer. A total of 254 serum samples from 202 patients with benign tumors and 52 patients with ovarian cancer were used. The concentrations of 22 ovarian-cancer-specific serum biomarkers were de-termined using Luminex, and premenopausal and postmenopausal status was binary mapped as 0 and 1. To identify the best biomarker combinations effectively differentiating ovarian cancer from benign tumors, all combinations with 2 and 3 biomarkers were eval-uated according to the area under the receiver operating characteristic curve (AUC). Menopausal information was included in the combinations of 2 and 3 biomarkers that showed the best classification performance, with AUC values of 84.53 and 87.01, respec-tively. The diagnosis model for ovarian cancer developed in this study based on the best biomarker combinations proved that combinations of biomarkers together with menopau-sal information show more accurate performance than mere combinations of biomarkers alone.

Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.

Data mining is the process to extract hidden patterns from enormous amount of data that is commonly used in a range of areas including marketing, fraud detection, scientific discovery as well as health care. The study was conducted to ensure high accuracy in assessing of elderly depression and to build useful decision rules by developing a very reliable evidence based decision support model with the combination of statistical analysis and decision tree algorithms. A large data set of 2008 Korean Elderly Survey (KES) was used consisted of 14,970 elderly data. Having depression as target variable, input variables were demographic, health related and socioeconomic characteristics of the Korean elderly population. Statistical analysis was conducted as a feature selection procession that includes the Chi-square, Fisher’s exact test, the Mann-Whitney U-test and Wald logistic regression Using the C5.0 decision tree algorithm of Clementine 12.0, the final decision support models were built and C5.0 tree showed a high accuracy level of 81.6%. The decision model developed in this study can improve healthcare providers’ ability in making decisions, increasing vigilance with suspected depression in elderly population.

Finite element analysis was performed using 3-dimensional model to analyze the biomechanical effects on the hip and knee joint after unilateral total hip arthroplasty. Clinical studies have reported that hip and knee osteoarthritis (OA) often occurred on the contralateral (non-implanted) side after unilateral total hip arthroplasty (THA). Both local and systemic stress-shielding occur on the lower extremities when used various material property of THA implants. It demonstrated that the force reaction with unilateral THA surgery was carried more in ipsilateral side (implanted side) than contralateral side, while total deformation was higher in contralateral side and the maximum stress value was higher on contralateral side of femoral condyles than ipsilateral side.