Nishimori point in random-bond Ising and Potts models in 2D

We study the universality class of the fixed points of the 2D random bond q-state Potts model by means of numerical transfer matrix methods. In particular, we determine the critical exponents associated with the fixed point on the Nishimori line. Precise measurements show that the universality class of this fixed point is inconsistent with percolation on Potts clusters for q=2, corresponding to the Ising model, and q=3Comment: 11 pages, 3 figures. Contribution to the proceedings of the NATO Advanced Research Workshop on Statistical Field Theories, Como 18-23 June 200

Nitrides as ammonia synthesis catalysts and as potential nitrogen transfer reagents

In this article, an overview of the application of selected metal nitrides as ammonia synthesis catalysts is presented. The potential development of some systems into nitrogen transfer reagents is also described

Scattering and duality in the 2 dimensional OSP(2|2) Gross Neveu and sigma models

We write the thermodynamic Bethe ansatz for the massive OSp(2|2) Gross Neveu and sigma models. We find evidence that the GN S matrix proposed by Bassi and Leclair [12] is the correct one. We determine features of the sigma model S matrix, which seem highly unconventional; we conjecture in particular a relation between this sigma model and the complex sine-Gordon model at a particular value of the coupling. We uncover an intriguing duality between the OSp(2|2) GN (resp. sigma) model on the one hand, and the SO(4) sigma (resp. GN model) on the other, somewhat generalizing to the massive case recent results on OSp(4|2). Finally, we write the TBA for the (SUSY version of the) flow into the random bond Ising model proposed by Cabra et al. [39], and conclude that their S matrix cannot be correct.Comment: 41 pages, 27 figures. v2: minor revisio

MAESTRI Toolkit for Industrial Symbiosis: overview, lessons learnt and implications

This paper presents a structured approach to support the development of self-organized industrial symbiosis, the Toolkit for Industrial Symbiosis. Developed within MAESTRI project, it provides a set of tools and methods to help companies gain value from wasted resources and contributes to MAESTRI goal of advancing the sustainability of European manufacturing and process industry. A participatory approach was taken for its development. The ultimate objective of this work is to encourage companies to change their attitude and consider waste as a resource and potential source for value creation

A novel knowledge repository to support industrial symbiosis

The development of tools and methods supporting the identification of Industrial Symbiosis opportunities is of utmost importance to unlock its full potential. Knowledge repositories have proven to be powerful tools in this sense, but often fail mainly due to poor contextualization of information and lack of general applicability (out of the boundaries of specific areas or projects). In this work, a novel approach to the design of knowledge repositories for Industrial Symbiosis is presented, based on the inclusion and categorization of tacit knowledge as well as on the combination of mimicking and input-output matching approaches. The results of a first usability test of the proposed tool are also illustrated

Shunting operations at flat yards : retrieving freight railcars from storage tracks

In this paper, we study the railcar retrieval problem (RRT) where specified numbers of certain types of railcars have to be withdrawn from the storage tracks of a flat yard. This task arises in the daily operations of workshop yards for railcar maintenance. The objective is to minimize the total cost of shunting via methods such as minimizing the usage of shunting engines. We describe the RRT formally, present a mixed-integer program formulation, and prove the general case to be NP-hard. For some special cases, exact algorithms with polynomial runtimes are proposed. We also analyze several intuitive heuristic solution approaches motivated by observed real-world planning routines. We evaluate their average performances in simulations with different scenarios and provide their worst-case performance guarantee. We show that although the analyzed heuristics result in much better solutions than the naive planning approach, they are still on average 30%-50% from the optimal objective value and may result in up to 14 times higher costs in the worst case. Therefore, we conclude that optimization should be implemented in practice in order to save valuable resources. Furthermore, we analyze the impacts of yard layout and the widespread organizational routine of presorting on the railcar retrieval cost

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures

Characterising B cell numbers and memory B cells in HIV infected and uninfected Malawian adults

BACKGROUND: Untreated human immunodeficiency virus (HIV) disease disrupts B cell populations causing reduced memory and reduced naïve resting B cells leading to increases in specific co-infections and impaired responses to vaccines. To what extent antiretroviral treatment reverses these changes in an African population is uncertain. METHODS: A cross-sectional study was performed. We recruited HIV-uninfected and HIV-infected Malawian adults both on and off antiretroviral therapy attending the Queen Elizabeth Central hospital in Malawi. Using flow cytometry, we enumerated B cells and characterized memory B cells and compared these measurements by the different recruitment groups. RESULTS: Overall 64 participants were recruited - 20 HIV uninfected (HIV-), 30 HIV infected ART naïve (HIV+N) and 14 HIV-infected ART treated (HIV+T). ART treatment had been taken for a median of 33 months (Range 12-60 months). Compared to HIV- the HIV+N adults had low absolute number of naïve resting B cells (111 vs. 180 cells/μl p = 0.008); reduced memory B cells (27 vs. 51 cells/μl p = 0.0008). The HIV+T adults had B-cell numbers similar to HIV- except for memory B cells that remained significantly lower (30 vs. 51 cells/μl p = 0.02). In the HIV+N group we did not find an association between CD4 count and B cell numbers. CONCLUSIONS: HIV infected Malawian adults have abnormal B-cell numbers. Individuals treated with ART show a return to normal in B-cell numbers but a persistent deficit in the memory subset is noted. This has important implications for long term susceptibility to co-infections and should be evaluated further in a larger cohort study