Work Fatigue Determination of Nurses in Hospital of Hasanuddin University

This study aims to determine the effect of body mass index (BMI), sleep patterns, working years, physical workload, mental workload, and work time on work fatigue on nurses at Hasanuddin University Hospital Makassar. This research is a quantitative study with a cross-sectional design with a sample size of 56 people. Sampling using a simple random sampling method. Data collection was carried out by means of a questionnaire. Bodyweight is measured by weight scales, body temperature is measured by microtoice, and physical workload is measured by a pulse oximeter, which is by looking at the pulse of the nurse before and after work. Data analysis in this study used the SPSS program. The results of this study indicate that there is an influence between Body Mass Index (BMI) on work fatigue (p = 0.001), there is an effect of sleep patterns on work fatigue (p = 0.019), there is an effect of tenure on work fatigue (p = 0.017), the relationship between physical workload and work fatigue (p = 0.000), there is an effect of mental workload on work fatigue (p = 0.000) and there is an effect of length of work on work fatigue (p = 0.017). The physical workload is the variable with the greatest influence on work fatigue on nurses at Hasanuddin University Hospital Makassar with Exp. (B) = 14,526. The results of this study concluded that there was a significant influence between the Body Mass Index (BMI), sleep patterns, years of work, physical workload, mental workload, and length of work for nurses at Hasanuddin University Hospital Makassar.

Diagnostically Lossless Compression of Medical Images

Medical images are very important for diagnostics and therapy. However, digital imaging generates large amounts of data which need to be compressed, without loss of relevant information, to economize storage space and allow speedy transfer. In this research three techniques are implemented for medical image compression, which provide high compression ratios with no loss of diagnostic quality. Different image modalities are employed for experiments in which X-rays, MRI, CT scans, Ultrasounds and Angiograms are included. The proposed schemes are evaluated by comparing with existing standard compression techniques like JPEG, lossless JPEG2000, LOCOI and Huffman Coding. In a medical image only a small region is diagnostically relevant while the remaining image is much less important. This is called Region of Interest (ROI). The first approach compresses the ROI strictly losslessly and the remaining regions of the image with some loss. In the second approach an image is first compressed at a high compression ratio but with loss, and the difference image is then compressed losslessly. Difference image contain less data and is compressed more compactly than original. Third approach exploits inter-image redundancy for similar modality and same part of human body. More similarity means less entropy which leads to higher compression performance. The overall compression ratio is combination of lossy and lossless compression ratios. The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio. These techniques are based on self designed Neural Network Vector Quantizer (NNVQ) and Huffman coding. Their clever combination is used to get lossless effect. These are spatial domain techniques and do not require frequency domain transformation. i An overall compression ratio of 6-14 is obtained for images with proposed methods. Whereas, by compressing same images by a lossless JPEG2K and Huffman, compression ratio of 2 is obtained at most. The main contribution of the research is higher compression ratios than standard techniques in lossless scenario. This result will be of great importance for data management in a hospital and for teleradiology.