Stability of Intercelular Exchange of Biochemical Substances Affected by Variability of Environmental Parameters

Communication between cells is realized by exchange of biochemical substances. Due to internal organization of living systems and variability of external parameters, the exchange is heavily influenced by perturbations of various parameters at almost all stages of the process. Since communication is one of essential processes for functioning of living systems it is of interest to investigate conditions for its stability. Using previously developed simplified model of bacterial communication in a form of coupled difference logistic equations we investigate stability of exchange of signaling molecules under variability of internal and external parameters.Comment: 11 pages, 3 figure

Comparison of the properties of biogenic wine by-products stabilized biocomposites compounded with a miniaturized single-screw extruder and a co-rotating twin-screw extruder

Bioplastics research is hindered by high material prices and limited availability of biopolymers. For conventional compounding, even on lab-scale, large quantities of material are required. In this study, an alternative process for compounding biocomposites was evaluated to investigate the potential of wine-derived biogenic by-products as functional fillers. Formulations based on poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and wine grape pomace (WP) with filler contents up to 10 wt.-% were prepared. The materials were processed with a modified miniaturized single-screw extruder (MSE) and compared to a lab-scale twin-screw extruder (TSE). Thermal and rheological properties of the materials were determined using GPC, MFR, DSC, TGA and OIT. The mixing quality of both extruders was evaluated by optical microscopy imaging. The results revealed that the MSE represents an efficient alternative for research purposes, but differences in the dominant degradation mechanisms during processing must be considered. Thermal analysis showed that WP successfully suppressed the thermo-oxidative degradation of PHBV

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Short-time homomorphic wavelet estimation

Successful wavelet estimation is an essential step for seismic methods like impedance inversion, analysis of amplitude variations with offset and full waveform inversion. Homomorphic deconvolution has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. Associated disadvantages like phase unwrapping and restrictions of sparsity in the reflectivity function limit its application. We explore short-time homomorphic wavelet estimation as a combination of the classical homomorphic analysis and log-spectral averaging. The introduced method of log-spectral averaging using a short-term Fourier transform increases the number of sample points, thus reducing estimation variances. We apply the developed method on synthetic and real data examples and demonstrate good performance.Comment: 13 pages, 5 figures. 2012 J. Geophys. Eng. 9 67

El Gas Natural en Bolivia Diagn´ostico y Perspectivas

La industria de gas natural se caracteriza por el alto grado de verticalizaci´on a lo largo de toda la cadena gas´ıfera, tambi´en llamada cadena de valordel gas natural

Quantum encoding is suitable for matched filtering

Matched filtering is a powerful signal searching technique used in several employments from radar and communications applications to gravitational-wave detection. Here we devise a method for matched filtering with the use of quantum bits. Our method's asymptotic time complexity does not depend on template length and, including encoding, is $\mathcal{O}(L(\log_2L)^2)$ for a data with length $L$ and a template with length $N$, which is classically $\mathcal{O}(NL)$. Hence our method has superior time complexity over the classical computation for long templates. We demonstrate our method with real quantum hardware on 4 qubits and also with simulations.Comment: 4 pages + 3 figures. Comments are welcom

On the Form Factor for the Unitary Group

We study the combinatorics of the contributions to the form factor of the group U(N) in the large $N$ limit. This relates to questions about semiclassical contributions to the form factor of quantum systems described by the unitary ensemble.Comment: 35 page