Crumpling of a stiff tethered membrane

first-principles numerical simulation model for crumpling of a stiff tethered membrane is introduced. In our model membranes, wrinkles, ridge formation, ridge collapse, as well as the initiation of stiffness divergence, are observed. The ratio of the amplitude and wave length of the wrinkles, and the scaling exponent of the stiffness divergence, are consistent with both theory and experiment. We observe that close to the stiffness divergence there appears a crossover beyond which the elastic behavior of a tethered membrane becomes similar to that of dry granular media. This suggests that ridge formation in membranes and force-chain network formation in granular packings are different manifestations of a single phenomenon.Comment: For full resolution figures, please send us an emai

Spin wave dispersion softening in the ferromagnetic Kondo lattice model for manganites

Spin dynamics is calculated in the ferromagnetic (FM) state of the generalized Kondo lattice model taking into account strong on-site correlations between e_g electrons and antiferromagnetic (AFM) exchange among t_{2g} spins. Our study suggests that competing FM double-exchange and AFM super-exchange interaction lead to a rather nontrivial spin-wave spectrum. While spin excitations have a conventional Dq^2 spectrum in the long-wavelength limit, there is a strong deviation from the spin-wave spectrum of the isotropic Heisenberg model close to the zone boundary. The relevance of our results to the experimental data are discussed.Comment: 6 RevTex pages, 3 embedded PostScript figure

Some Practical Applications of Dark Matter Research

Two practical spin-offs from the development of cryogenic dark matter detectors are presented. One in materials research, the other in biology.Comment: 8 pages,4 figure

Childhood negative dental experiences and tooth loss in later life:A 25-year longitudinal study in Sweden

OBJECTIVE: To explore the association between childhood NDEs and changes in tooth loss over 25 years among Swedish older adults, and the role of dental visits in explaining such an association.METHODS: We used data from 6154 adults, members of a cohort study that started in 1992 when participants were 50 years old. All data were self-reported through postal questionnaires (6 in total, one every 5 years). Information on childhood NDEs was collected at baseline only. Tooth loss was the repeated outcome measure. Mixed effects logistic regression models were used to test the association between childhood NDEs and tooth loss adjusting for confounders.RESULTS: Childhood NDEs was positively associated with greater odds of experiencing tooth loss and its rate of change over the 25-year period. Although having a dental visit within the past year was positively associated with childhood NDEs and inversely associated with incidence of tooth loss, it explained very little of the association between childhood NDEs and tooth loss in later life.CONCLUSION: The findings underscore the long-lasting damaging effects of early life NDEs on adult oral health.CLINICAL SIGNIFICANCE: A positive patient-dentist relationship starts early in life. Early visits to the dentist are essential to build an enduring relationship of trust between people and healthcare providers.</p

A Single-Loop DC Motor Control System Design with a Desired Aperiodic Degree of Stability

The application of the original analytical approach for Pi-controller synthesis of a stable second-order plant is considered. This approach allows finding controller parameters without any intensive computing by using the direct expressions. The plant model is obtained on the basis of identification, which is based on the automated real-interpolation method. The results of natural experiments are given

Resilient Parameter-Invariant Control With Application to Vehicle Cruise Control

This work addresses the general problem of resilient control of unknown stochastic linear time-invariant (LTI) systems in the presence of sensor attacks. Motivated by a vehicle cruise control application, this work considers a first order system with multiple measurements, of which a bounded subset may be corrupted. A frequency-domain-designed resilient parameter-invariant controller is introduced that simultaneously minimizes the effect of corrupted sensors, while maintaining a desired closed-loop performance, invariant to unknown model parameters. Simulated results illustrate that the resilient parameter-invariant controller is capable of stabilizing unknown state disturbances and can perform state trajectory tracking

Ball on a beam: stabilization under saturated input control with large basin of attraction

This article is devoted to the stabilization of two underactuated planar systems, the well-known straight beam-and-ball system and an original circular beam-and-ball system. The feedback control for each system is designed, using the Jordan form of its model, linearized near the unstable equilibrium. The limits on the voltage, fed to the motor, are taken into account explicitly. The straight beam-and-ball system has one unstable mode in the motion near the equilibrium point. The proposed control law ensures that the basin of attraction coincides with the controllability domain. The circular beam-and-ball system has two unstable modes near the equilibrium point. Therefore, this device, never considered in the past, is much more difficult to control than the straight beam-and-ball system. The main contribution is to propose a simple new control law, which ensures by adjusting its gain parameters that the basin of attraction arbitrarily can approach the controllability domain for the linear case. For both nonlinear systems, simulation results are presented to illustrate the efficiency of the designed nonlinear control laws and to determine the basin of attraction

Active Learning in Persistent Surveillance UAV Missions

The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this important problem by considering model uncertainty in a multi-agent Markov Decision Process (MDP) and using an active learning approach to quickly learn transition model parameters. We build on previous research that allowed UAVs to passively update model parameter estimates by incorporating new state transition observations. In this work, however, the UAVs choose to actively reduce the uncertainty in their model parameters by taking exploratory and informative actions. These actions result in a faster adaptation and, by explicitly accounting for UAV fuel dynamics, also mitigates the risk of the exploration. This paper compares the nominal, passive learning approach against two methods for incorporating active learning into the MDP framework: (1) All state transitions are rewarded equally, and (2) State transition rewards are weighted according to the expected resulting reduction in the variance of the model parameter. In both cases, agent behaviors emerge that enable faster convergence of the uncertain model parameters to their true values