A new method of neutron electric dipole moment (EDM) searching is proposed. It is based on the spin dependence of the Pendellosung phase of a neutron diffracted by a non-centrosymmetric crystal. A two-crystal set-up is proposed and analysed to get the most luminosity possible. A strong interplanar electric field of the crystal and a sufficiently long time for the neutron passage through the crystals for Bragg angles close to n/2 makes it possible to exceed the sensitivity achieved with the ultra cold neutron (UCN) method.

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As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.

We have measured the neutron electric dipole moment using spin rotation in a non-centrosymmetric crystal. Our result is d n = (2.5 ± 6.5 stat ± 5.5 syst ) · 10 −24 ecm. The dominating contribution to the systematic uncertainty is statistical in nature and will reduce with improved statistics. The statistical sensitivity can be increased to 2·10 −26 ecm in 100 days data taking with an improved setup. We state technical requirements for a systematic uncertainty at the same level.