Influence of neutron irradiation on magnetic field sensors

Parameters of modern experimental set-ups depend on the precision of the magnetic field monitoring under real experimental conditions. As a rule, the conditions of modern experiments (ATLAS, CMS, ALISE, LRC-B) have their special requirements to radiation hardness of the magnetometric apparatus, Specialized magnetic-calibration stands have been manifactured to investigate magnetic field sensors for radiation hardness at the Joint Institute for Nuclear Research (JINR) and at the State University "Lviv Politechnic" (SULP). Characteristics of different magnetic field sensors were studied before and after exposure. The sensors were irradiated at the IBR-2 reactor, JINR, by fast neutrons with the mean energy much less than E much greater than=1.35 MeV up to the fluence of 10(19) n/m(2).</p

Influence of neutron irradiation on magnetic field sensors

Parameters of modern experimental set-ups depend on the precision of the magnetic field monitoring under real experimental conditions. As a rule, the conditions of modern experiments (ATLAS, CMS, ALISE, LRC-B) have their special requirements to radiation hardness of the magnetometric apparatus, Specialized magnetic-calibration stands have been manifactured to investigate magnetic field sensors for radiation hardness at the Joint Institute for Nuclear Research (JINR) and at the State University "Lviv Politechnic" (SULP). Characteristics of different magnetic field sensors were studied before and after exposure. The sensors were irradiated at the IBR-2 reactor, JINR, by fast neutrons with the mean energy much less than E much greater than=1.35 MeV up to the fluence of 10(19) n/m(2)

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset