ObjectivesVitamin D deficiency is increasing worldwide. However, few studies have attempted to examine the vitamin D status of wage workers and the correlation between vitamin D deficiency and working conditions. Hence, we aimed to evaluate the prevalence of vitamin D deficiency and the association between occupational conditions and vitamin D deficiency among Korean wage workers.MethodsWage workers aged 20–65 years from the 5th Korea National Health and Nutrition Examination Survey (KNHANES 2010–2012; n = 5409) were included in our analysis. We measured the prevalence of vitamin D deficiency and identified the correlations with the working conditions of these subjects.ResultsThe prevalence of vitamin D deficiency in male and female subjects was 69.5% and 83.1%, respectively. Among the male subjects, a significant correlation between vitamin D deficiency and working conditions was observed among shift workers, office workers, and permanent workers. No significant correlation with any type of working conditions was observed among female subjects.ConclusionThe prevalence of vitamin D deficiency among Korean wage workers was very high and was found to correlate significantly with working conditions, likely because of insufficient exposure to sunlight associated with certain types of work. Wage workers require more frequent outdoor activity and nutrition management to maintain sufficient vitamin D level.
For ship detection, X-band synthetic aperture radar (SAR) imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN). The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL)-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image.
ObjectivesThe prevalence of aged individuals in the Korean workforce continues to increase. This research determined the health and working conditions of Korean older wage workers and confirmed the effects of factors on the health-related quality of life of Korean older workers.MethodsOf the 25,534 persons surveyed in the fifth Korea National Health and Nutrition Examination Survey, 1368 older (>55 years of age) wage workers without missing variables were selected. Their general characteristics, health status (cardiovascular disease, musculoskeletal disease, and mental health), working conditions (type of occupation, employment status, full- or part-time work, weekly average working hours, and shift work), and health-related quality of life assessed by the EQ-5D questionnaire were examined.ResultsThe mean values of the EQ-5D index of the male and female older workers were 0.956 ± 0.087 and 0.917 ± 0.124, respectively (p < 0.001). The factors that caused statistically significant differences in the EQ-5D index for all subjects were age, education, household income, cerebro-cardiovascular event, osteoarthritis, musculoskeletal pain, stress, occupation type, employment status, and working hours. In logistic regression analysis, the factors that associated with perceived problems in each EQ-5D dimensions were age, musculoskeletal pain, stress, diabetes, smoking, occupation type, employment status, and working hours.ConclusionsTo eventually raise the quality of life of older workers through health maintenance and management, it is necessary to manage related factors that include of musculoskeletal pain and diseases, stress, diabetes, smoking, occupation, employment status, and working hours.
Fire service personnel and ambulance paramedics suffer musculoskeletal disorders as they lift and carry patients while performing Emergency Medical Services (EMS). The objective of the current study was performed to examine the association between working environment and musculoskeletal disorders of 119 paramedics and to analysis the EMS activities for them through basic survey (including task characteristics, risk factors, symptoms and illnesses). Observational job analysis of EMS activities indicated the squatting posture during first-aid performed on floor and the abrupt use of force during carrying heavy load including stretcher with patients on as hazard factors, and excessive low back twisting and bending during stairway transfer was observed. In addition, work-physiological assessment revealed various but rather high lumbar muscle usage rate among the study subjects, being 14.6~32.8% compared with Maximum Voluntary Contraction (MVC) during patients transfer work. Resting heart rate showed 65/min, on the other hand, heart rate on mobilization indicated maximum 124~156/min. Therefore, the results of analysis to the EMS activities, rescuer activities and medical tasks were accompanied with high possibility of accident and musculoskeletal disorders. Also, EMS activities indicated high muscle fatigue and energy consumption, and accumulated muscle fatigue with during continued work.
Synthetic aperture radar (SAR) has been widely used to detect oil-spill areas through the backscattering intensity difference between oil and background pixels. However, since the signal is similar to that produced by other phenomena, positive identification can be challenging. In this study we developed an algorithm to effectively analyze large-scale oil spill areas in SAR images by focusing on optimizing the input layer to artificial neural network (ANN) through removal the factor of lowering the accuracy. An ANN algorithm was used to generate probability maps of oil spills. Highly accurate pixel-based data processing was conducted through false or un-detection element reduction by normalizing the image or applying a non-local (NL) means filter and median filter to the input neurons for ANN. In addition, the standard deviation of co-polarized phase difference (CPD) was used to reduce false detection from the look-alike with weak damping effect. The algorithm was validated using TerraSAR-X images of an oil spill caused by stranded oil tanker Volganefti-139 in the Kerch Strait in 2007. According to the validation results of the receiver operating characteristic (ROC) curve, the oil spill was detected with an accuracy of about 95.19% and un-detection or false detection by look-alike and speckle noise was greatly reduced.
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