Transfer function concept for ultrasonic characterization of material microstructures

The approach given depends on treating material microstructures as elastomechanical filters that have analytically definable transfer functions. These transfer functions can be defined in terms of the frequency dependence of the ultrasonic attenuation coefficient. The transfer function concept provides a basis for synthesizing expressions that characterize polycrystalline materials relative to microstructural factors such as mean grain size, grain-size distribution functions, and grain boundary energy transmission. Although the approach is nonrigorous, it leads to a rational basis for combining the previously mentioned diverse and fragmented equations for ultrasonic attenuation coefficients

Towards Understanding and Harnessing the Potential of Clause Learning

Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths and limitations of the technique. This paper presents the first precise characterization of clause learning as a proof system (CL), and begins the task of understanding its power by relating it to the well-studied resolution proof system. In particular, we show that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution (RES) satisfying a natural property. These include regular and Davis-Putnam resolution, which are already known to be much stronger than ordinary DPLL. We also show that a slight variant of CL with unlimited restarts is as powerful as RES itself. Translating these analytical results to practice, however, presents a challenge because of the nondeterministic nature of clause learning algorithms. We propose a novel way of exploiting the underlying problem structure, in the form of a high level problem description such as a graph or PDDL specification, to guide clause learning algorithms toward faster solutions. We show that this leads to exponential speed-ups on grid and randomized pebbling problems, as well as substantial improvements on certain ordering formulas

Stability of mode-locked kinks in the ac driven and damped sine-Gordon lattice

Kink dynamics in the underdamped and strongly discrete sine-Gordon lattice that is driven by the oscillating force is studied. The investigation is focused mostly on the properties of the mode-locked states in the {\it overband} case, when the driving frequency lies above the linear band. With the help of Floquet theory it is demonstrated that the destabilizing of the mode-locked state happens either through the Hopf bifurcation or through the tangential bifurcation. It is also observed that in the overband case the standing mode-locked kink state maintains its stability for the bias amplitudes that are by the order of magnitude larger than the amplitudes in the low-frequency case.Comment: To appear in Springer Series on Wave Phenomena, special volume devoted to the LENCOS'12 conference; 6 figure

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics

Fractional and unquantized dc voltage generation in THz-driven semiconductor superlattices

We consider the spontaneous creation of a dc voltage across a strongly coupled semiconductor superlattice subjected to THz radiation. We show that the dc voltage may be approximately proportional either to an integer or to a half- integer multiple of the frequency of the applied ac field, depending on the ratio of the characteristic scattering rates of conducting electrons. For the case of an ac field frequency less than the characteristic scattering rates, we demonstrate the generation of an unquantized dc voltage.Comment: 6 pages, 3 figures, RevTEX, EPSF. Revised version v3: corrected typo

Escape from a zero current state in a one dimensional array of Josephson junctions

A long one dimensional array of small Josephson junctions exhibits Coulomb blockade of Cooper pair tunneling. This zero current state exists up to a switching voltage, Vsw, where there is a sudden onset of current. In this paper we present histograms showing how Vsw changes with temperature for a long array and calculations of the corresponding escape rates. Our analysis of the problem is based on the existence of a voltage dependent energy barrier and we do not make any assumptions about its shape. The data divides up into two temperature regimes, the higher of which can be explained with Kramers thermal escape model. At low temperatures the escape becomes independent of temperature.Comment: 4 pages 5 figure

Satisfiability by Maxwell-Boltzmann and Bose-Einstein Statistical Distributions

Recent studies in theoretical computer science have exploited new algorithms and methodologies based on statistical physics for investigating the structure and the properties of the Satisfiability (SAT) problem. We propose a characterization of the SAT problem as a physical system, using both quantum and classi-cal statistical-physical models. We associate a graph to an SAT instance and we prove that a Bose-Einstein condensation occurs in the instance with higher probability if the quantum distribution is adopted in the gen-eration of the graph. Conversely, the fit-get-rich behavior is more likely if we adopt the Maxwell-Boltzmann distribution. Our method allows a comprehensive analysis of the SAT problem based on a new definition of entropy of an instance, without requiring the computation of its truth assignments. The entropy of an SAT instance increases in the satisfiability region as the number of free variables in the instance increases. Finally, we develop six new solvers for the MaxSAT problem based on quantum and classical statistical dis-tributions, and we test them on random SAT instances, with competitive results. We experimentally prove that the performance of the solvers based on the two distributions depends on the criterion used to flag clauses as satisfied in the SAT solving process

Fractional diffusion in periodic potentials

Fractional, anomalous diffusion in space-periodic potentials is investigated. The analytical solution for the effective, fractional diffusion coefficient in an arbitrary periodic potential is obtained in closed form in terms of two quadratures. This theoretical result is corroborated by numerical simulations for different shapes of the periodic potential. Normal and fractional spreading processes are contrasted via their time evolution of the corresponding probability densities in state space. While there are distinct differences occurring at small evolution times, a re-scaling of time yields a mutual matching between the long-time behaviors of normal and fractional diffusion

Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling

Drought has promoted large‐scale, insect‐induced tree mortality in recent years, with severe consequences for ecosystem function, atmospheric processes, sustainable resources and global biogeochemical cycles. However, the physiological linkages among drought, tree defences, and insect outbreaks are still uncertain, hindering our ability to accurately predict tree mortality under on‐going climate change. Here we propose an interdisciplinary research agenda for addressing these crucial knowledge gaps. Our framework includes field manipulations, laboratory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects interactions between conifer trees and bark beetles. We build upon existing theory and examine several key assumptions: 1) there is a trade‐off in tree carbon investment between primary and secondary metabolites (e.g. growth vs. defence); 2) secondary metabolites are one of the main component of tree defence against bark beetles and associated microbes; and 3) implementing conifer‐bark beetle interactions in current models improves predictions of forest disturbance in a changing climate. Our framework provides guidance for addressing a major shortcoming in current implementations of large‐scale vegetation models, the under‐representation of insect‐induced tree mortality