Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (ly-. ing bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine -learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sen-sitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential

Solitons in a Grassmannian sigma-model Coupled to Chern-Simons Term

We propose an exactly solvable Grassmannian sigma-model coupled to the Chern-Simons theory. In the presence of a novel topological term our model admits exact self-dual vortex solutions which are identical to those of pure Grassmannian model, but the topological charge has a physical meaning as a magnetic flux since the gauge field is no longer auxiliary. We also extend the theory to a noncommutative plane and analyze the BPS solutions.Comment: 10+1 pages, No figure, LaTeX; Reference added, Minor changes, to appear in Phys. Rev.

Neutron Scattering Studies of Nanomagnetism and Artificially Structured Materials

Nanostructured magnetic materials are intensively studied due to their unusual properties and promise for possible applications. The key issues in these materials relate to the connection between their physical properties (transport, magnetism, mechanical, etc.) and their chemical-physical structure. In principle, a detailed knowledge of the chemical and physical structure allows calculation of their physical properties. Theoretical and computational methods are rapidly evolving so that magnetic properties of nanostructured materials might soon be predicted. Success in this endeavor requires detailed quantitative understanding of the magnetic structure and properties

A second-order cone cutting surface method: complexity and application

Second-order cone, Semidefinite inequality, Cutting plane techniques, Semidefinite programming,