Multi-mesh gear dynamics program evaluation and enhancements

A multiple mesh gear dynamics computer program was continually developed and modified during the last four years. The program can handle epicyclic gear systems as well as single mesh systems with internal, buttress, or helical tooth forms. The following modifications were added under the current funding: variable contact friction, planet cage and ring gear rim flexibility options, user friendly options, dynamic side bands, a speed survey option and the combining of the single and multiple mesh options into one general program. The modified program was evaluated by comparing calculated values to published test data and to test data taken on a Hamilton Standard turboprop reduction gear-box. In general, the correlation between the test data and the analytical data is good

Supersonic quantum communication

When locally exciting a quantum lattice model, the excitation will propagate through the lattice. The effect is responsible for a wealth of non-equilibrium phenomena, and has been exploited to transmit quantum information through spin chains. It is a commonly expressed belief that for local Hamiltonians, any such propagation happens at a finite "speed of sound". Indeed, the Lieb-Robinson theorem states that in spin models, all effects caused by a perturbation are limited to a causal cone defined by a constant speed, up to exponentially small corrections. In this work we show that for translationally invariant bosonic models with nearest-neighbor interactions, this belief is incorrect: We prove that one can encounter excitations which accelerate under the natural dynamics of the lattice and allow for reliable transmission of information faster than any finite speed of sound. The effect is only limited by the model's range of validity (eventually by relativity). It also implies that in non-equilibrium dynamics of strongly correlated bosonic models far-away regions may become quickly entangled, suggesting that their simulation may be much harder than that of spin chains even in the low energy sector.Comment: 4+3 pages, 1 figure, some material added, typographic error fixe

Dynamic Energy Management

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar

A synoptic view of ionic constitution above the F-layer maximum

Ionic composition above F layer maximum from Ariel I satellite ion mass spectromete

Relation between socioeconomic deprivation and death from a first myocardial infarction in Scotland: population based analysis

No abstract available

Population structures in the SARA and SARB reference collections of Salmonella enterica according to MLST, MLEE and microarray hybridization

In the 1980's and 1990's, population genetic analyses based on Multilocus Enzyme Electrophoresis (MLEE) provided an initial overview of the genetic diversity of multiple bacterial species, including Salmonella enterica. The genetic diversity within S. enterica subspecies enterica according to MLEE is represented by the SARA and SARB reference collections, each consisting of 72 isolates, which have been extensively used for comparative analyses. MLEE has subsequently been replaced by Multilocus Sequence Typing (MLST). Our initial MLST results indicated that some strains within the SARB collection differed from their published descriptions. We therefore performed MLST on four versions of the SARB collection from different sources and one collection of SARA, and found that multiple isolates in SARB and SARA differ in serovar from their original description, and other SARB isolates differed between different sources. Comparisons with a global MLST database allowed a plausible reconstruction of the serovars of the original collection. MLEE, MLST and microarrays were largely concordant at recognizing closely related strains. MLST was particularly effective at recognizing discrete population genetic groupings while the two other methods provided hints of higher order relationships. However, quantitative pair-wise phylogenetic distances differed considerably between all three methods. Our results provide a translation dictionary from MLEE to MLST for the extant SARA and SARB collections which can facilitate genomic comparisons based on archival insights from MLEE

Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Analytical model of brittle destruction based on hypothesis of scale similarity

The size distribution of dust particles in nuclear fusion devices is close to the power function. A function of this kind can be the result of brittle destruction. From the similarity assumption it follows that the size distribution obeys the power law with the exponent between -4 and -1. The model of destruction has much in common with the fractal theory. The power exponent can be expressed in terms of the fractal dimension. Reasonable assumptions on the shape of fragments concretize the power exponent, and vice versa possible destruction laws can be inferred on the basis of measured size distributions.Comment: 10 pages, 3 figure