Проектирование и исследование режимов работы ГИН в среде Micro-Cap

Выпускная квалификационная работа объемом 90 страниц, 42 рисунок, 14 таблиц, 15 использованных источников, 3 приложения.Final qualifying work of 90 pages, 42 picture, 14 tables, 15 sources used, 3 applications

Critical exponents of a three dimensional O(4) spin model

By Monte Carlo simulation we study the critical exponents governing the transition of the three-dimensional classical O(4) Heisenberg model, which is considered to be in the same universality class as the finite-temperature QCD with massless two flavors. We use the single cluster algorithm and the histogram reweighting technique to obtain observables at the critical temperature. After estimating an accurate value of the inverse critical temperature \Kc=0.9360(1), we make non-perturbative estimates for various critical exponents by finite-size scaling analysis. They are in excellent agreement with those obtained with the $4-\epsilon$ expansion method with errors reduced to about halves of them.Comment: 25 pages with 8 PS figures, LaTeX, UTHEP-28

Numerical Modelling of Caseless Ammunition with Coreless Bullet in Internal Ballistics

In the search of a new weapon for combat in short range, it is proposed the use of a new experimentally designed 7.62 mm calibre ammunition with a lighter weight (caseless-coreless). This can be used in carbine assault rifles with short barrel or pistols. In this work, the compressible gases flowing through the gun barrel caused by the proposed ammunition were experimentally and numerically analysed. The Large Eddy Simulation was used for the numerical simulation, considering a compressible and turbulent flow, with the chemical species transport model and a complete conversion of the propellant reaction. Variations in pressure and temperature were compared with the results obtained from a conventional 7.62 mm full metal jacket (FMJ) ammunition. Results of ballistic experimental tests and numerical simulations were similar than those of the 9 mm x 19 mm FMJ ammunitions, showing feasibility for the development of new weapons intended for operations of short range shots.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.203-207, DOI: http://dx.doi.org/10.14429/dsj.65.851

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

Mean Field Behavior of Cluster Dynamics

The dynamic behavior of cluster algorithms is analyzed in the classical mean field limit. Rigorous analytical results below $T_c$ establish that the dynamic exponent has the value $z_{sw}=1$ for the Swendsen-Wang algorithm and $z_{uw}=0$ for the Wolff algorithm. An efficient Monte Carlo implementation is introduced, adapted for using these algorithms for fully connected graphs. Extensive simulations both above and below $T_c$ demonstrate scaling and evaluate the finite-size scaling function by means of a rather impressive collapse of the data.Comment: Revtex, 9 pages with 7 figure

Toward food waste reduction at universities

Food waste is a serious problem, which undermines the achievement of many sustainable development goals (SDGs), despite their consideration in the agendas of many countries and companies. Notoriously, food waste (FW) causes different kinds of pollution that affect public health and social justice, while contributing to economic losses. This waste phenomenon has causes, drivers, and impacts that require rigorous assessments and effective approaches to mitigate its noxious effects, which are a serious concern for universities. Within these institutions, reducing food waste becomes a circular economy strategy, which is being utilized to assist in promoting sustainable development. However, there is a need for urgent attention to the specific causes of food waste and for consistent actions to reduce it, while boosting awareness in the campus community and triggering a change in students’ eating habits. The purpose of this study is to analyze what can be done to reduce the levels of food waste at universities. To achieve this, a review of the theme’s state of the art, which is inclusive of an overview of food waste production at universities around the world, is presented. The study employed a qualitative methodology where a comprehensive review of the literature and case studies analyses from selected world regions were considered. The data indicate that a broad variance exists in producing food waste among universities, from 0.12 to 50 kg/capita/day. More factors influence the problem (e.g., gender, age, season, consumer behavior), as well as strategies to solve and prevent it (e.g., composting, recycling, new designs of packages, trayless meals, education), and benefits leading toward food waste reductions from 13 to 50%. Also, four priority actions were identified to reduce food waste at universities, and these consist of planning and awareness, food preparation and storage, services, and direct waste reuse. With appropriate adaptations, these recommended actions should be deployed as means for reducing food waste at universities around the world, while expanding learning and education in sustainability

D-Theory: Field Theory via Dimensional Reduction of Discrete Variables

A new non-perturbative approach to quantum field theory --- D-theory --- is proposed, in which continuous classical fields are replaced by discrete quantized variables which undergo dimensional reduction. The 2-d classical O(3) model emerges from the (2+1)-d quantum Heisenberg model formulated in terms of quantum spins. Dimensional reduction is demonstrated explicitly by simulating correlation lengths up to 350,000 lattice spacings using a loop cluster algorithm. In the framework of D-theory, gauge theories are formulated in terms of quantum links --- the gauge analogs of quantum spins. Quantum links are parallel transporter matrices whose elements are non-commuting operators. They can be expressed as bilinears of anticommuting fermion constituents. In quantum link models dimensional reduction to four dimensions occurs, due to the presence of a 5-d Coulomb phase, whose existence is confirmed by detailed simulations using standard lattice gauge theory. Using Shamir's variant of Kaplan's fermion proposal, in quantum link QCD quarks appear as edge states of a 5-d slab. This naturally protects their chiral symmetries without fine-tuning. The first efficient cluster algorithm for a gauge theory with a continuous gauge group is formulated for the U(1) quantum link model. Improved estimators for Wilson loops are constructed, and dimensional reduction to ordinary lattice QED is verified numerically.Comment: 15 pages, LaTeX, including 9 encapsulated postscript figures. Contribution to Lattice 97 by 5 authors, to appear in Nuclear Physics B (Proceeding Supplements). Requires psfig.tex and espcrc2.st

Critical Exponents of the Classical 3D Heisenberg Model: A Single-Cluster Monte Carlo Study

We have simulated the three-dimensional Heisenberg model on simple cubic lattices, using the single-cluster Monte Carlo update algorithm. The expected pronounced reduction of critical slowing down at the phase transition is verified. This allows simulations on significantly larger lattices than in previous studies and consequently a better control over systematic errors. In one set of simulations we employ the usual finite-size scaling methods to compute the critical exponents $\nu,\alpha,\beta,\gamma, \eta$ from a few measurements in the vicinity of the critical point, making extensive use of histogram reweighting and optimization techniques. In another set of simulations we report measurements of improved estimators for the spatial correlation length and the susceptibility in the high-temperature phase, obtained on lattices with up to $100^3$ spins. This enables us to compute independent estimates of $\nu$ and $\gamma$ from power-law fits of their critical divergencies.Comment: 33 pages, 12 figures (not included, available on request). Preprint FUB-HEP 19/92, HLRZ 77/92, September 199

A General Limitation on Monte Carlo Algorithms of Metropolis Type

We prove that for any Monte Carlo algorithm of Metropolis type, the autocorrelation time of a suitable ``energy''-like observable is bounded below by a multiple of the corresponding ``specific heat''. This bound does not depend on whether the proposed moves are local or non-local; it depends only on the distance between the desired probability distribution $\pi$ and the probability distribution $\pi^{(0)}$ for which the proposal matrix satisfies detailed balance. We show, with several examples, that this result is particularly powerful when applied to non-local algorithms.Comment: 8 pages, LaTeX plus subeqnarray.sty (included at end), NYU-TH-93/07/01, IFUP-TH33/9

Social interaction, noise and antibiotic-mediated switches in the intestinal microbiota

The intestinal microbiota plays important roles in digestion and resistance against entero-pathogens. As with other ecosystems, its species composition is resilient against small disturbances but strong perturbations such as antibiotics can affect the consortium dramatically. Antibiotic cessation does not necessarily restore pre-treatment conditions and disturbed microbiota are often susceptible to pathogen invasion. Here we propose a mathematical model to explain how antibiotic-mediated switches in the microbiota composition can result from simple social interactions between antibiotic-tolerant and antibiotic-sensitive bacterial groups. We build a two-species (e.g. two functional-groups) model and identify regions of domination by antibiotic-sensitive or antibiotic-tolerant bacteria, as well as a region of multistability where domination by either group is possible. Using a new framework that we derived from statistical physics, we calculate the duration of each microbiota composition state. This is shown to depend on the balance between random fluctuations in the bacterial densities and the strength of microbial interactions. The singular value decomposition of recent metagenomic data confirms our assumption of grouping microbes as antibiotic-tolerant or antibiotic-sensitive in response to a single antibiotic. Our methodology can be extended to multiple bacterial groups and thus it provides an ecological formalism to help interpret the present surge in microbiome data.Comment: 20 pages, 5 figures accepted for publication in Plos Comp Bio. Supplementary video and information availabl