Raman-scattering and weak-ferromagnetism studies in Eu2CuO4

We show that there is a subtle instability of the T' structure for the R(2)CuO(4) (R = rare earth) compounds at the center of the R series with the boundary at Eu2CuO4. Crystals grown in Pt crucibles and PbQ flux show weak ferromagnetism (WF) and two strongly temperature-dependent forbidden Raman peaks. However crystals grown in alumina crucibles and CuO flux do not show WF and the forbidden Raman peaks are much less intense. The observation of WF and forbidden Raman peaks for Eu2CuO4 compounds suggests that the instability of the T' structure may be associated with O(1) displacement in the CuO2 planes.53283784

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

Sensor technologies play an essential part in the agricultural community and many other scientific and commercial communities. Accelerometer signals and Machine Learning techniques can be used to identify and observe behaviours of animals without the need for an exhaustive human observation which is labour intensive and time consuming. This study employed random forest algorithm to identify grazing, walking, scratching, and inactivity (standing, resting) of 8 Hebridean ewes located in Cheshire, Shotwick in the UK. We gathered accelerometer data from a sensor device which was fitted on the collar of the animals. The selection of the algorithm was based on previous research by which random forest achieved the best results among other benchmark techniques. Therefore, in this study, more focus was given to feature engineering to improve prediction performance. Seventeen features from time and frequency domain were calculated from the accelerometer measurements and the magnitude of the acceleration. Feature elimination was utilised in which highly correlated ones were removed, and only nine out of seventeen features were selected. The algorithm achieved an overall accuracy of 99.43% and a kappa value of 98.66%. The accuracy for grazing, walking, scratching, and inactive was 99.08%, 99.13%, 99.90%, and 99.85%, respectively. The overall results showed that there is a significant improvement over previous methods and studies for all mutually exclusive behaviours. Those results are promising, and the technique could be further tested for future real-time activity recognition

The role of anti-aquaporin 4 antibody in the conversion of acute brainstem syndrome to neuromyelitis optica

Background: Acute brainstem syndrome (ABS) may herald multiple sclerosis (MS), neuromyelitis optica (NMO), or occur as an isolated syndrome. The aquaporin 4 (AQP4)-specific serum autoantibody, NMO-IgG, is a biomarker for NMO. However, the role of anti-AQP4 antibody in the conversion of ABS to NMO is unclear. Methods: Thirty-one patients with first-event ABS were divided into two groups according to the presence of anti-AQP4 antibodies, their clinical features and outcomes were retrospectively analyzed. Results: Fourteen of 31 patients (45.16 %) were seropositive for NMO-IgG. The 71.43 % of anti-AQP4 (+) ABS patients converted to NMO, while only 11.76 % of anti-AQP4 (-) ABS patients progressed to NMO. Anti-AQP4 (+) ABS patients demonstrated a higher IgG index (0.68 ± 0.43 vs 0.42 ± 0.13, p < 0.01) and Kurtzke Expanded Disability Status Scale (4.64 ± 0.93 vs 2.56 ± 0.81, p < 0.01) than anti-AQP4 (-) ABS patients. Area postrema clinical brainstem symptoms occurred more frequently in anti-AQP4 (+) ABS patients than those in anti-AQP4 (-) ABS patients (71.43 % vs 17.65 %, p = 0.004). In examination of magnetic resonance imaging (MRI), the 78.57 % of anti-AQP4 (+) ABS patients had medulla-predominant involvements in the sagittal view and dorsal-predominant involvements in the axial view. Conclusions: ABS represents an inaugural or limited form of NMO in a high proportion of anti-AQP4 (+) patients