A deterministic approximation algorithm for computing the permanent of a 0, 1 matrix

We consider the problem of computing the permanent of a n by n matrix. For a class of matrices corresponding to constant degree expanders we construct a deterministic polynomial time approximation algorithm to within a multiplicative factor ( 1 + ∈)[superscript η] for arbitrary∈ > 0. This is an improvement over the best known approximation factor e[superscript η] obtained in Linial, Samorodnitsky and Wigderson (2000), though the latter result was established for arbitrary non-negative matrices. Our results use a recently developed deterministic approximation algorithm for counting partial matchings of a graph (Bayati, Gamarnik, Katz, Nair and Tetali (2007)) and Jerrum–Vazirani method (Jerrum and Vazirani (1996)) of approximating permanent by near perfect matchings

Non-contractible loops in the dense O(n) loop model on the cylinder

A lattice model of critical dense polymers $O(0)$ is considered for the finite cylinder geometry. Due to the presence of non-contractible loops with a fixed fugacity $\xi$, the model is a generalization of the critical dense polymers solved by Pearce, Rasmussen and Villani. We found the free energy for any height $N$ and circumference $L$ of the cylinder. The density $\rho$ of non-contractible loops is found for $N \rightarrow \infty$ and large $L$. The results are compared with those obtained for the anisotropic quantum chain with twisted boundary conditions. Using the latter method we obtained $\rho$ for any $O(n)$ model and an arbitrary fugacity.Comment: arXiv admin note: text overlap with arXiv:0810.223

Obtaining of Maghemite Containing Red Mud for Effective As(V) Adsorption

This paper describes the studies of the use of red muds as adsorbents for cleaning solutions from As(V). The red mud is a waste that contains a large amount of iron oxides and hydroxides, which are excellent adsorbents of arsenic, especially those possessing magnetic properties and large specific surface area. The purpose of the experiment was to study the possibility of obtaining an effective adsorbent by direct extraction of alumina from bauxite using the caustic alkali fusion method and optimization of the process. The main iron-containing phase of the red muds obtained by fusing bauxite with caustic alkali was maghemite, which has a large specific surface area. Arsenic adsorption experiments were carried out using red muds obtained through bauxite alkali fusing at different temperatures and time of fusion, as well as the mass ratio of caustic alkali to bauxite. The red muds obtained by fusing bauxite with caustic alkali at 400∘ C and NaOH to bauxite mass ration 1.5 within 70 minutes have the highest effectiveness removing arsenic. Their As(V) uptake capacity was over than 37 mg/g. Keywords: red mud, maghemite, nanoparticles, As(V) adsorption, optimizatio

New approaches for boosting to uniformity

The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known as boosting. This paper explores several novel boosting methods that have been designed to produce a uniform selection efficiency in a chosen multivariate space. Such algorithms have a wide range of applications in particle physics, from producing uniform signal selection efficiency across a Dalitz-plot to avoiding the creation of false signal peaks in an invariant mass distribution when searching for new particles.National Science Foundation (U.S.) (Grant PHY-1306550

Leaching kinetics of arsenic sulfide-containing materials by copper sulfate solution

The overall decrease in the quality of mineral raw materials, combined with the use of arsenic-containing ores, results in large amounts of various intermediate products containing this highly toxic element. The use of hydrometallurgical technologies for these materials is complicated by the formation of multicomponent solutions and the difficulty of separating copper from arsenic. Previously, for the selective separation of As from copper–arsenic intermediates a leaching method in the presence of Cu(II) ions was proposed. This paper describes the investigation of the kinetics of arsenic sulfide-containing materials leaching by copper sulfate solution. The cakes after leaching of arsenic trisulfide with a solution of copper sulfate were described using methods such as X-ray diffraction spectrometry (XRD), X-ray fluorescence spectrometry (XRF), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy analysis (EDS). The effect of temperature (70–90 °C), the initial concentration of CuSO4 (0.23–0.28 M) and the time on the As recovery into the solution was studied. The process temperature has the greatest effect on the kinetics, while an increase in copper concentration from 0.23 to 0.28 M effects an increase in As transfer into solution from 93.2% to 97.8% for 120 min of leaching. However, the shrinking core model that best fits the kinetic data suggests that the process occurs by the intra-diffusion mode with the average activation energy of 44.9 kJ/mol. Using the time-to-a-given-fraction kinetics analysis, it was determined that the leaching mechanism does not change during the reaction. The semi-empirical expression describing the reaction rate under the studied conditions can be written as follows: 1/3ln(1 − X) + [(1 − X) − 1/3 − 1] = 4,560,000Cu3.61e−44900/RT t. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.10.7347.2017/8.9Russian Science Foundation, RSF: 18-19-00186Funding: The research was funded by the Russian Science Foundation, grant number 18-19-00186. The SEM/EDS analyses were funded by State Assignment, grant number 10.7347.2017/8.9

Algorithmic issues in queueing systems and combinatorial counting problems

Includes bibliographical references (leaves 111-118).Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.(cont.) However, these randomized algorithms can never provide proven upper or lower bounds on the number of objects they are counting, but can only give probabilistic estimates. We propose a set of deterministic algorithms for counting such objects for three classes of counting problems. They are interesting both because they give an alternative approach to solving these problems, and because unlike MCMC algorithms, they provide provable bounds on the number of objects. The algorithms we propose are for special cases of counting the number of matchings, colorings, or perfect matchings (permanent), of a graph.Multiclass queueing networks are used to model manufacturing, computer, supply chain, and other systems. Questions of performance and stability arise in these systems. There is a body of research on determining stability of a given queueing system, which contains algorithms for determining stability of queueing networks in some special cases, such as the case where there are only two stations. Yet previous attempts to find a general characterization of stability of queueing networks have not been successful.In the first part of the thesis, we contribute to the understanding of why such a general characterization could not be found. We prove that even under a relatively simple class of static buffer priority scheduling policies, stability of deterministic multiclass queueing network is, in general, an undecidable problem. Thus, there does not exist an algorithm for determining stability of queueing networks, even under those relatively simple assumptions. This explains why such an algorithm, despite significant efforts, has not been found to date. In the second part of the thesis, we address the problem of finding algorithms for approximately solving combinatorial graph counting problems. Counting problems are a wide and well studied class of algorithmic problems, that deal with counting certain objects, such as the number of independent sets, or matchings, or colorings, in a graph. The problems we address are known to be #P-hard, which implies that, unless P = #P, they can not be solved exactly in polynomial time. It is known that randomized approximation algorithms based on Monte Carlo Markov Chains (MCMC) solve these problems approximately, in polynomial time.by Dmitriy A. Katz-Rogozhnikov.Ph.D

Leaching kinetics of sulfides from refractory gold concentrates by nitric acid

The processing of refractory gold-containing concentrates by hydrometallurgical methods is becoming increasingly important due to the depletion of rich and easily extracted mineral resources, as well as due to the need to reduce harmful emissions from metallurgy, especially given the high content of arsenic in the ores. This paper describes the investigation of the kinetics of HNO3 leaching of sulfide gold-containing concentrates of the Yenisei ridge (Yakutia, Russia). The effect of temperature (70–85 °C), the initial concentration of HNO3 (10–40%) and the content of sulfur in the concentrate (8.22–22.44%) on the iron recovery into the solution was studied. It has been shown that increasing the content of S in the concentrate from 8.22 to 22.44% leads to an average of 45% increase in the iron recovery across the entire range temperatures and concentrations of HNO3 per one hour of leaching. The leaching kinetics of the studied types of concentrates correlates well with the new shrinking core model, which indicates that the reaction is regulated by interfacial diffusion and diffusion through the product layer. Elemental S is found on the surface of the solid leach residue, as confirmed by XRD and SEM/EDS analysis. The apparent activation energy is 60.276 kJ/mol. The semi-empirical expression describing the reaction rate under the studied conditions can be written as follows: 1/3ln(1 - X) + [(1 - X)-1/3 - 1] = 87.811(HNO3)0.837(S)2.948e-60276/RT·t. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.Funding: The research was funded by the Russian Science Foundation, grant number 18-19-00186. The SEM/EDS and microprobe analysis were funded by State Assignment, grant number 11.4797.2017/8.9

Nitric acid leaching of polymetallic middlings of concentration

Investigations into the nitric acid leaching of polymetallic middlings with the purpose of the maximal recovery of copper and zinc into the solution are performed. Using methods of mathematical planning of the experiment, the optimal process parameters are determined: ratio L: S = 5, the consumption of nitric acid is 80 cm3 per 20 g of the charge, and the process duration is 120 min. © 2013 Allerton Press, Inc

Effect of preliminary alkali desilication on ammonia pressure leaching of low-grade copper–silver concentrate

Ammonia leaching is a promising method for processing low-grade copper ores, especially those containing large amounts of oxidized copper. In this paper, we study the effect of Si-containing minerals on the kinetics of Cu and Ag leaching from low-grade copper concentrates. The results of experiments on the pressure leaching of the initial copper concentrate in an ammonium/ammonium-carbonate solution with oxygen as an oxidizing agent are in good agreement with the shrinking core model in the intra-diffusion mode: in this case, the activation energies were 53.50 kJ/mol for Cu and 90.35 kJ/mol for Ag. Energy-dispersive X-ray spectroscopy analysis (EDX) analysis showed that reagent diffusion to Cu-bearing minerals can be limited by aluminosilicate minerals of the gangue. The recovery rate for copper and silver increases significantly after a preliminary alkaline desilication of the concentrate, and the new shrinking core model is the most adequate, showing that the process is limited by diffusion through the product layer and interfacial diffusion. The activation energy of the process increases to 86.76 kJ/mol for Cu and 92.15 kJ/mol for Ag. Using the time-to-a-given-fraction method, it has been shown that a high activation energy is required in the later stages of the process, when the most resistant sulfide minerals of copper and silver apparently remain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.Russian Science Foundation, RSF: 0836-2020-0020Funding: This work was financially supported by the Russian Science Foundation Project No. The SEM-EDX analyses were funded by State Assignment, grant number 0836-2020-0020