Efficiency of low versus high airline pressure in stunning cattle with a pneumatically powered penetrating captive bolt gun

The efficiency of stunning cattle was assessed in 443 animals (304 pure Zebu and 139 crossbred cattle), being mainly mature bulls and cows. Cattle were stunned using a Jarvis pneumatically powered penetrating captive bolt gun operating with low (160–175 psi, N = 82) and high (190 psi, N = 363) airline pressure, which was within the manufactures specifications. Signs of brain function and the position of the shots on the heads were recorded after stunning. Velocity of the captive bolt and its physical parameters were calculated. Cattle shot with low pressures showed more rhythmic respiration (27 vs. 8%, P < 0.001), less tongue protrusion (4 vs. 12%, P = 0.03) and less masseter relaxation (22 vs. 48%, P < 0.001). There was an increased frequency of shots in the ideal position when cattle were shot with the low compared to high airline pressures (15.3 vs. 3.1%). Bolt velocity and its physical parameters were significantly (P < 0.01) higher when using high pressure. Airline pressures below 190 psi are inappropriate when shooting adult Zebu beef cattle with pneumatically powered penetrating captive bolt guns

All-strain based valley filter in graphene nanoribbons using snake states

A pseudo-magnetic field kink can be realized along a graphene nanoribbon using strain engineering. Electron transport along this kink is governed by snake states that are characterized by a single propagation direction. Those pseudo-magnetic fields point towards opposite directions in the K and K' valleys, leading to valley polarized snake states. In a graphene nanoribbon with armchair edges this effect results in a valley filter that is based only on strain engineering. We discuss how to maximize this valley filtering by adjusting the parameters that define the stress distribution along the graphene ribbon.Comment: 8 pages, 6 figure

What are the Best Hierarchical Descriptors for Complex Networks?

This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.Comment: 9 pages, 4 figure