Design of K-W-NET model of turbulence based on K-W/V2-F models with the neural network component

The subject of this article is the models of turbulence based on introduction of neural network components into the widespread standard semi-empirical models. It is stated that such technique allows achieving significant acceleration of calculation while maintaining sufficient accuracy and stability, by training neural network components based on the data acquires with the use of fairly accurate and advanced models, as well as replacing and complementing separate fragments of the initial models with such components. An overview is give on the existing classical approaches towards modeling of turbulence, which allows determining the V2-F model suggested by Durbin as one of the most advanced, and thereby promising, with regards to subsequent neural network modifications. The author offers the new model of turbulence based on K-W models paired with a neural network component trained in accordance with the V2-F Durbin model. All necessary ratios are provided. The properties of the obtained model are examined in terms of the numerical experiment on the flow over of a single obstacle. The results are compared with data acquired from other semi-empirical models (K-E, K-W), as well as via direct neural network model. It is demonstrated that the proposed model, with less computational labor output in comparison with other models (excluding direct neural network, which, however, is less accurate), provides high precision close to precision of the Durbin model.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in an history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed an automated workflow that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.One Sentence SummarySystematic and automated frameworks for implementing robust history-dependent genetic programs in cellular populations.

Estimating the Interval of Failure Probability Fluctuation Under Uncertain Input Variable Distributions

Structural reliability analysis often faces the problem that the input variable distributions are uncertain and thus the interval for reliability measures must be determined. A Monte Carlo simulation consists in estimating the failure probability for several sets of random realizations of the distributions, thus implying a huge computational labor, much higher than in conventional Monte Carlo. In this paper a method for drastically simplifying this task is proposed. The method exploits the ordering statistics representation property of the reliability plot, which is shown to approximately obey an orthogonal hyperbolic pattern. Accordingly a two-level FORM approach is used to derive the polar vectors for building two plots, one for the input variable space and another for the uncertain distribution parameter space. It is demonstrated that the extrema of the failure probability are contained amongst the samples located in extreme sectors of the parameter plot as pointed out by the hyperbolae.

Single DSP Implementation of Realtime 3D Sound Synthesis Algorithm

This paper describes a single DSP implementation of a realtime 3D sound synthesis algorithm dedicatedly for mobile applications. This approach is distinctive in that the audible frequency band is divided into three subbands in such a way that, given a head-related transfer function (HRTF), the portion of the functional behavior in each subband can be realized efficiently by a distinct digital filtering structure. The whole digital filters are implemented on a commercially available 16-bit fixed-point DSP, taking account of three major design factors of memory size, calculation accuracy, and computational complexity. As a result, the realtime 3D sound synthesis is achieved by employing a single DSP with the low computational labor of 51.3 MIPS and the small memory space of 4.6 k words.

Abuse of statistical packages: the case of the general linear model

Since their introduction in the 1960s, packaged computer programs have freed researchers from much drudgery and painstaking computational labor. Before the distribution of these packages, many meaningful projects could not be attempted, simply because the data analysis phase would have been too labor intensive. However, the very ease of use of these packages can lead to their abuse. In particular, performing all possible t tests among numerous groups, calculating many correlations and circling the ones with P values less than 0.05, and performing stepwise regression all can easily lead to spurious statistical conclusions. The danger of these three practices is illustrated with three simple computer runs using random data. In each run statistically significant results were found for the random data.

THE APPLICATION OF FINITE FORWARD DIFFERENCES IN THE RESISTIVITY COMPUTATIONS OVER A LAYERED EARTH

The paper describes a new method of computing the infinite series which arise in the calculation of standard graphs in the resistivity method. It is shown that, by using finite forward differences of higher orders and repeated summations by parts, the convergence of the potential and resistivity series can be improved appreciably. The procedure allows calculations with any degree of accuracy and with a substantial savings in computational labor. A correct estimate of the truncation errors involved may easily be determined.

SIMPLER FORMULAE FOR THE THERMAL DIFFUSION FACTOR OF BINARY GAS MIXTURES

Simpler formulae for the second approximation to the thermal diffusion factor of a binary gas mixture are derived according to both the approximation procedures of Chapman and Cowling, and Kihara. These formulae are much simpler than the rigorous theoretical formulae from the view point of computational labor. These new formulae are derived by expanding the rigorous expressions in powers of the molecular weight ratio, M, and deleting all those terms which contain explicitly the power of M higher than two. The accuracies of these simpler formulae are demonstrated by numerical calculations on gas systems as a function of both temperature as well as composition.