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The study compared body image and levels of self-esteem and depression between girls with precocious puberty and typical girls, identifying relationships between variables. Data were collected from July until August 2014. Study participants were first- to fourth-year female students in the D region who were diagnosed with precocious puberty in E university hospital and typical students in the same grades in the same region. The participants included 125 female students with precocious puberty and 176 typical students. Parents of both groups of females also participated. Using propensity score matching, 94 girls with precocious puberty and 94 typical girls were selected and their data were analyzed.The study used a body image scale called the Rosenberg’s Self-esteem Scale, a tool to measure self-esteem, in addition to the Children’s Depression Inventory. Collected data were analyzed using the SPSS/WIN 21.0 program.There was a significant difference in body image (t=4.78, p<.001), self-esteem (t=3.81, p<.001), and depression (t=-3.32, p=.001) between the two groups.The two groups showed that body image had a positive correlation with self-esteem and self-esteem had a negative correlation with depression. The analysis of girls with precocious puberty showed that body image had a negative correlation with depression, but typical girls showed that body image had a no correlation with depression. The study indicates the necessity for health education at home and school to help girls with precocious puberty perceive their body image in a positive way so that they can achieve healthy growth and development. In addition, this study can be used as a basis for understanding children with precocious puberty and for developing nursing interventions to promote psychosocialhealth.

In this research, a novel method based on Canonical Correlation Analysis (CCA) and Artificial Neural Network (ANN) to detect epileptic seizures from EEG signals is proposed. CCA was applied on EEG signals and feature vectors corresponding to Eigen values were extracted. These Eigen values were fed as input to Artificial Neural Network (ANN)’s widely explored model Multilayer Perceptron Neural Networks (MLPNNs) for classification between occurrence of non-epileptic seizures and epileptic seizures. The extracted Eigen values using CCA proved to be a better epileptic seizures detector and provide average classification accuracy, sensitivity and specificity as 92.583%, 93.25% and 91% respectively.

Electrocardiogram (ECG) is a vital biomedical signal for diagnosing heart diseases, but now it has many other applications like stress recognition, biometric recognition, etc. But ECG signal gets noisy from various sources like as muscle noise, electrode artifacts, baseline drift noise and respiration. As wavelet transforms shows a good performance in denoising the ECG signal however, the selection of appropriate mother wavelet functions and number of wavelet decomposition level is still an issue to remove the various kinds of noises from the input signal. It is essential to denoise the ECG signal to get appropriate features of ECG signal.This research work analyze and compare the removal of noise and distortion in ECG signal using five wavelets (Daubechies, Coiflet, Haar, Biorthogonal and Symmlet) with four thresholding rules (SURE, Hybrid, Universal and Minimax) and various decomposition levels of Undecimated Wavelet Transform (UWT) and Discrete Wavelet Transform (DWT).

A FPGA implementation of fingerprint sensors scheme is described for removing parasitic offset image on no touch. This paper proposes an effective fingerprint identification system with 160x192 array fingerprint sensor hardware. The proposed calibration scheme initializes a fingerprint sensor LSI to eliminate the influence of offset image by the electrical characteristic difference of pixel array, which is degraded during long-time use. Each step of a fingerprint algorithm is analyzed based on FPGA. This paper designs an effective hardware scheme for using the Verilog-HDL in 160x192 pixel array. The circuit includes 3 memorys and control logic. The function is verified in RTL simulation and FPGA logic synthesis.

The proposed paper surveys different techniques for early spotting and classification of diseased plant using digital image processing. As in agribusiness, agriculturists detect plant diseases straight through the bare eyes. This type of detection requires continuum supervisory which can be expensive as well as time consuming on large farms. Therefore, recognizing the disease on plants is of extreme importance in agriculture sector. The selected proposal is divided into three classes: detection, classification and extraction. The three classes are further sub divided according to the different algorithm. This paper provides an overview of different image processing techniques and classification methods.

The filamentous algal biomass, Hydrodictyon reticulatum (H. reticuatum) was used to evaluate the biogas production after hydrothermal pretreatment in batch anaerobic digestion. The effect of hydrothermal pretreatment at range of 50 ~ 280 oC was tested with filamentous algal biomass. The solubilization by pretreatment showed a higher degree of disintegrated filamentous algal biomass and the soluble COD was increased 12.8 fold than untreated sample at 280 oC. Solubilization due to hydrothermal pretreatment increased the specific biogas production and volatile solids reduction rates. Using the hydrothermal treatment approach, the specific methane production were 161.8, 169.1, 178.4, 191.7, 274.9, 285.2 and 364.4 mL/g-VS of the pretreated at raw, 50, 100, 120, 180, 230, and 280 oC, respectively. The volatile solids reduction was greater with increased temperature applied, and the degradation increased 69.7% at 280 oC from 45.8 % VS reduction of raw filamentous algal biomass.

This study identified the relationship between drinking behavior and sleep hours in Korean adolescents. It used the Eighth (2012) Youth Health Behavior Online Survey that was conducted by the Centers for Disease Control (CDC). Analytical methods involved a chi-square test, Pearson correlation, and present factors related to sleep hours and quality of sleep. The sleep hours and sleep quality of adolescents are associated with the general characteristics of adolescents as well as drinking behaviors. Therefore, society’s belief of alcohol as a preference item or the positive attitude toward alcohol needs to be changed to increase the success of alcohol-related prevention education for adolescents in South Korea.

In this study, the development of Cardioid based graph photoplethysmogram heart abnormality detection technique is presented. PPG signals in this work were collected from an online public repository called MIMIC II Waveform Database, Version 3 Part 1 with sampling rate of 250 Hz. Each recording has one minute of PPG signals. Distinctive features were extracted, and then the Cardioid based graph was plotted as the result of the differentiation of the signals. In addition, the different shapes of closed-loop created were then observed and assessed. From the Cardioid loop, the area and standard deviation were computed to distinguish between normal and abnormal heartbeats. Based on the results, these values for abnormal heartbeat were higher than the value of normal heartbeat thus signifying the differences between two categories of heart conditions. Therefore, the results of this study suggest the capability of the proposed mechanisms to determine heart abnormality and act as an alternative to the current detection system.

Purpose: The purpose of this study was to develop a manual for Training of Trainees as midwives, to improve mother and child health in Uganda. Method: We conducted a wide literature review and met with various experts in Korea and Uganda. Results: A total of 11 topics were identified: 6 on maternal health family planning, pre-conception care, antenatal care, labor and delivery care, postnatal care, and nutrition) and 5 on child health (breastfeeding, immunization, elimination of mother-to-child transmission, HIV/AIDS, and childhood illness). Conclusion: This manual must be implemented nationwide to improve the quality of life in Uganda.

This study developed prediction model for benign laryngeal disease based on machine learning which reflects the characteristics of Korean adults. This study analyzed 8,713 adults (3,801 males and 4,912 females) over the age of 19 who completed laryngoscopic assessment of 2010-2012 Korea National Health and Nutrition Examination Survey (KNHNES). RBF artificial neural network algorithm was used for analysis. The explanatory variables were age, gender, educational level, occupation, income, smoking, binge drinking, and self-reported voice problem. As the result of construction of prediction model for benign laryngeal disease, self-reported voice problem, educational level, income and smoking were significant risk factors of benign laryngeal disease (p<0.05).Construction of prevention model is required to be constructed based on this model to minimize the risks of benign laryngeal disease in Koreans.

Clinical Document Processing is growing importance because of unstructured nature of clinical notes as well as limitation of crucial time of clinical professionals to analyses the unstructured clinical notes. Named entity recognition (NER) is a subtask of Clinical documentation processing which is important not only for text analysis but knowledge extraction. Although there are a number of clinical named entity recognition systems, they lack user flexibility and NER scalability. Clinical NER is a challenging work which required consistent research to improve clinical documentation. Accordingly, in this paper, keeping an eye on user’s flexibility, we combined the NER technique with DSL (Domain Specific Language) based user queries. This research focused to produce a prototype system which allows the user to input their queries about a clinical text in a syntax free language which will be reformulate into DSL format in background. The reformulated query then matches against the rules defined by using the DSL to get the matched rule-type. The DSL is created using Xtext framework specifically to create NER rules easily. Then NER is done as per the found NER rule-types. We used the lingpipe API to do the NER using unsupervised technique (dictionary based approach). Again considering user flexibility, research also focused on graphical visualization of the annotated recognized entities, flexibility to store the annotated document into database for later use as well as can conversion the recognized entities into CDA (Clinical Document Architecture) format for interoperability. This research is initiated and inspired by the Aurora research initiative which is an ongoing attempt lead by Dr. Arnold Kim to integrate the design of clinical documentation workflows from the physician perspective that starts with variety DSLs and ends with series of interpretations and analytics in the background

This study was conducted to address the need to prevent oral cancer and to lower its mortality rate based on the research on the knowledge about oral cancer and on the level of recognition of it. Moreover, the prevention of other diseases related to oral cavities is important for oral healthcare, and to prevent these diseases, the difference between the incidence of oral disease and that of oral cancer based on the degree of knowledge about oral health (i.e., the correlation between oral cancer and the degree of knowledge about oral health) was studied. A total of 298 adults residing in Kangwon Province who agreed to participate in this study were selected as study subjects. Each of them was provided a self-administered survey questionnaire for data research, which he or she was to accomplish within two weeks from September 2014. For the results of the survey, the mean recognition of oral cancer was 19.98±6.59 points, and the mean oral healthcare, 23.49±5.87 points. The acknowledgement of oral cancer was statistically significantly higher at a younger age (F=2.76, p=.042) and with a higher level of education (F=2.62, p=.035). Oral healthcare was significantly higher in the female subjects (t=-3.77, p=.000), in the group with a high level of education (F=5.00, p=.001), in the non-smokers (F=-4.23, p=.000), and in the group with regular exercise (t=4.09, p=.000). The recognition of oral cancer and of the importance of good oral health and of preventing oral-cavity-related diseases showed a statistically significant static correlation. Based on the results of this study, the provision of education on oral cancer is deemed necessary as the recognition of oral cancer declined with a lower age, oral healthcare was well managed in the females, and oral health was good with a higher level of education. Also, the non-smokers had a high level of oral health, and oral health was well managed in the subjects who were exercising regularly.

Heart disease is one of the main sources of demise around the world and it is imperative to predict the disease at a premature phase. The computer aided systems help the doctor as a tool for predicting and diagnosing heart disease. The objective of this review is to widespread about Heart related cardiovascular disease and to brief about existing decision support systems for the prediction and diagnosis of heart disease supported by data mining and hybrid intelligent techniques.

This study examined the sexual knowledge, contraceptive knowledge and sexual attitudes among male high school students. In addition, the factors that affect sexual attitudes of male high school students were investigated. As a cross-sectional study, a questionnaire survey was conducted in G high school and an investigation carried out by a structured survey of 79 male high school students. The results are as follows: The subjects’ sexual attitude was positive as shown by its average was 2.25 (SD=0.32). The multiple regression analysis showed that sexual attitude was significantly predicted by sexual knowledge and contraceptive knowledge. These variables explained 35.2% of the variance of sexual attitudes of male high school students. Therefore, the provision of education on basic sexual knowledge and contraceptive knowledge can be an important factor in establishing appropriate sexual attitudes and values.

The purpose of this study was to examine the stress, family strength, health care competence and quality of life of maritime students, the relationship of the variables and the influential factors for each subarea of the quality of life. The subjects in this study were 240 students, on whom a structured self-administered survey was conducted. The questionnaire used in this study consisted of 97 items, which were 10 items of stress, 28 about family strength, 25 about health care competence, 26 about the quality of life and five about general characteristics. Therefore sustained health education should be provided for maritime students to boost their own health care competence to promote their health.

This study isolated Lactobacillus spp. from tuba and inoculated in four selected canned beverages, beer, coffee, pineapple juice, and soft drink stored. Serial dilution and spread-plate methods were used to assess the population count of Lactobacillus isolates on Rogosa agar incubated in anaerobic condition for 48 hours. Lactobacillus counts were monitored in a 28-day sampling period together with the pH condition of the beverage used. Results showed that Lactobacillus spp. from tuba survived in four canned beverages with an acceptable viable count that ranged from log 5-6 CFU/ml and with a pH ranged from 3 to 4 at the end of the sampling period. Bacterial population count showed no significant difference in the different types of beverages, p=0.053>0.05. A medium positive correlation between the count of Lactobacillus spp. and pH in beverages was observed, rho (0.450). Thus, the Lactobacillus from tuba was able to survive in the different beverages during storage at room temperature, therefore implying that these beverages could be good vehicles for probiotics.

Laughter or humor is a beneficial intervention among healthy individuals for general health. Laughter therapy is one of preventive medicine, which is important strategy help to promote wellness or relieve emotional stresses. This study aimed to investigate the effects of laughter therapy on autonomic nervous system activity, stress, and emotions in smartphone addict. The participants with addict or addict risk of smartphone were recruited and randomly allocated into laughter therapy group (n=30) and control group (n=12). Laughter therapy group were conducted laughter therapy program which is comprise of opening, experiencing laughter and closure for 60 min per a day, 3 times a week, for 4 weeks. The assessments were completed before and after the program. The heart rate variability analysis was used to assess the autonomic nervous system activity and stress, University of Rhode Island change assessment for readiness for change, and positive and negative affect scale for current mood state. Laughter therapy group improved significantly to the autonomic nervous system activity and stress compared with control group (p<0.05). And positive affect scale scores were improved significantly after conducting laughter therapy (p<0.05). Thus, these founds suggest that laughter therapy can improve the autonomic nervous system activity and positive affect scale scores, and relieve emotional stress for smartphone addict. Further study should consider applying of long-term laughter therapy for relieve of addict.