Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers.

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three-axis neck-mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross-validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three-axis accelerometers mounted to a collar.

Structure of laboratory ball lightning.

Trajectories of self-sustained laboratory ball lightning, generated by arc discharges with silicon, are investigated for understanding the possibility of buoyant flight. Extremely low apparent densities are found, nearly approaching that of standard air. The freely buoyant balls are observed to survive for about 0.1 s, with significantly buoyant balls surviving for several seconds. These ball lightning objects are found to have a density and size that can easily allow them to be carried by a gentle breeze of a few meters per second. The results are interpreted by a model that is an extension of that first proposed by Abrahamson and Dinniss [J. Abrahamson and J. Dinniss, Nature (London) 403, 519 (2000)]. The buoyant behavior of ball lightning seen in our experiments is believed to arise as a result of the formation of a nanoparticle oxide network growing from a molten silicon core.