Langtidsovervåking av miljøkvaliteten i kystområdene av Norge. Kystovervåkingsprogrammet. Hydrografi/hydrokjemi/plankton. Datarapport 2004

Rapporten gjengir hydrografiske/hydrokjemiske observasjoner fra 5 stasjoner i kystvannet mellom svenskegrensen og Lista i 2004 og planktontellinger fra en stasjon utenfor Arendal (St. 2). Det er gjennomført 10 tokt til Færder, 22 tokt til Jomfruland, 22 tokt til Arendal St. 2, 12 tokt til Arendal St. 3 og 12 tokt til Lista, jevnt fordelt over året.Statens forurensningstilsy

Light-convolution dense selection u-net (Lds u-net) for ultrasound lateral bony feature segmentation

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement

Galaxy Cluster Abell 2744

THE NORTH DAKOTA SPACE GRANT CONSORTIUM COLLECTION NASA initiated the National Space Grant College and Fellowship Program, also known as Space Grant, in 1989. This national network of colleges and universities works to expand opportunities for Americans to understand and participate in NASA\u27s aeronautics and space projects by supporting and enhancing science and engineering education, research, and public engagement efforts. The  North Dakota NASA Space Grant Consortium (NDSGC)  was established in February of 1991. The North Dakota NASA Space Grant Consortium fulfills the Space Grant mission by involving North Dakota faculty, students, and K‐12 teachers and students in multi‐institutional, collaborative, NASA‐relevant research and education projects, while also educating the North Dakota citizenry about NASA, its purpose, and its missions. The North Dakota Space Grant Consortium has a collection of printed posters, photos, and art available to the public. Pieces can be collected at the North Dakota Space Grant Consortium office (Clifford Hall, Room 270) on the UND campus. Image text: Galaxy Cluster Abell 2744 National Aeronautics and Space Administration NASAhttps://commons.und.edu/ndsgc-posters/1024/thumbnail.jp