UAVNet: A Mobile Wireless Mesh Network Using Unmanned Aerial Vehicles

We developed UAVNet, a framework for the autonomous deployment of a flying Wireless Mesh Network using small quadrocopter-based Unmanned Aerial Vehicles (UAVs). The flying wireless mesh nodes are automatically interconnected to each other and building an IEEE 802.11s wireless mesh network. The implemented UAVNet prototype is able to autonomously interconnect two end systems by setting up an airborne relay, consisting of one or several flying wireless mesh nodes. The developed software includes basic functionality to control the UAVs and to setup, deploy, manage, and monitor a wireless mesh network. Our evaluations have shown that UAVNet can significantly improve network performance.

Role of milk and meat products as vehicles for antibiotic-resistant bacteria

This subject is reviewed under the following headings: Microbial contamination of raw meat and raw milk; Antibiotic resistance of food-borne pathogens; Antibiotic resistance of commensal and potentially pathogenic bacteria as a new threat in food microbiology; Antibiotic-resistant staphylococci in fermented meat and [in] milk products; Antibiotic-resistant Enterococcus sp. in fermented meat and [in] milk products; Enterococci in farm animals and meat; Enterococci in fermented food; Molecular characterization of resistance of food-borne enterococci; and Further ecological and epidemiological considerations of resistant live bacteria in food. It is concluded that further research is needed, particularly into the possible transfer of the resistance of bacteria consumed in meat or milk products to the indigenous bacteria of the human consumer.

The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides

The application of pesticides and fertilizers in agricultural areas is of crucial importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Weather conditions, such as the intensity and direction of the wind while spraying, add further complexity to the problem of maintaining control. In this paper, we describe an architecture to address the problem of self-adjustment of the UAV routes when spraying chemicals in a crop field. We propose and evaluate an algorithm to adjust the UAV route to changes in wind intensity and direction. The algorithm to adapt the path runs in the UAV and its input is the feedback obtained from the wireless sensor network (WSN) deployed in the crop field. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.

Nonperturbative results for two-index conformal windows

Via large and small N c relations we derive nonperturbative results about the conformal window of two-index theories. Using Schwinger-Dyson methods as well as four-loops results we estimate subleading corrections and show that naive large number of colors extrapolations are unreliable when N c is less than about six. Nevertheless useful nonper-turbative inequalities for the size of the conformal windows, for any number of colors, can be derived. By further observing that the adjoint conformal window is independent of the number of colors we argue, among other things, that: the large N c two-index conformal window is twice the conformal window of the adjoint representation (which can be determined at small N c) expressed in terms of Dirac fermions; lattice results for adjoint matter can be used to provide independent information on the conformal dynamics of two-index theories such as SU(N c) with two and four symmetric Dirac flavors.

Norm enforcement in the city revisited: An international field experiment of altruistic punishment, norm maintenance, and broken windows

In laboratory experiments, people are willing to sanction norms at a cost—a behavioral tendency called altruistic punishment. However, the degree to which these findings can be generalized to real-world interactions is still debated. Only a small number of field experiments have been conducted, and initial results suggest that punishment is less frequent outside of the lab. This study replicates one of the first field experiments on altruistic punishment and builds ties to research on norm compliance and the broken windows theory. The original study addressed the enforcement of the anti-littering norm in Athens. We replicate this study in Bern, Zurich, and New York City. As an extension, we investigate how the experimental context (clean vs littered) impacts social norm enforcement. As a second extension, we investigate how opportunity structure impacts the maintenance of the anti-littering norm. Findings indicate that norms are universally enforced, although significantly less than in the standard laboratory experiment,and that enforcement is significantly more common in Switzerland than in New York. Moreover, individuals prefer more subtle forms of enforcement to direct punishment. We also find that enforcement is less frequent in littered than in clean contexts, suggesting that broken windows might not only foster deviant behavior but also weaken informal social control. Finally, we find that opportunity structure can encourage people to maintain norms, as indicated by the fact that people are more likely to voluntarily pick up litter when it is closer to a trash bin.