On the Spectral Lags and Peak-Counts of the Gamma-Ray Bursts Detected by the RHESSI Satellite

A sample of 427 gamma-ray bursts from a database (February 2002 - April 2008) of the RHESSI satellite is analyzed statistically. The spectral lags and peak-count rates, which have been calculated for the first time in this paper, are studied completing an earlier analysis of durations and hardness ratios. The analysis of the RHESSI database has already inferred the existence of a third group with intermediate duration, apart from the so-called short and long groups. First aim of this article is to discuss the properties of these intermediate-duration bursts in terms of peak-count rates and spectral lags. Second aim is to discuss the number of GRB groups using another statistical method and by employing the peak-count rates and spectral lags as well. The standard parametric (model-based clustering) and non-parametric (K-means clustering) statistical tests together with the Kolmogorov-Smirnov and Anderson-Darling tests are used. Two new results are obtained: A. The intermediate-duration group has similar properties to the group of short bursts. Intermediate and long groups appear to be different. B. The intermediate-duration GRBs in the RHESSI and Swift databases seem to be represented by different phenomena.Comment: 41 pages, 10 figures, 9 tables, accepted to be published in The Astrophysical Journa

Cosmology with Gamma-Ray Bursts Using k-correction

In the case of Gamma-Ray Bursts with measured redshift, we can calculate the k-correction to get the fluence and energy that were actually produced in the comoving system of the GRB. To achieve this we have to use well-fitted parameters of a GRB spectrum, available in the GCN database. The output of the calculations is the comoving isotropic energy E_iso, but this is not the endpoint: this data can be useful for estimating the {\Omega}M parameter of the Universe and for making a GRB Hubble diagram using Amati's relation.Comment: 4 pages, 6 figures. Presented as a talk on the conference '7th INTEGRAL/BART Workshop 14 -18 April 2010, Karlovy Vary, Czech Republic'. Published in Acta Polytechnic

Exploring Physically-Motivated Models to Fit Gamma-Ray Burst Spectra

We explore fitting gamma-ray burst spectra with three physically-motivated models, and thus revisit the viability of synchrotron radiation as the primary source of GRB prompt emission. We pick a sample of 100 bright GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM), based on their energy flux values. In addition to the standard empirical spectral models used in previous GBM spectroscopy catalogs, we also consider three physically-motivated models; (a) a Thermal Synchrotron model, (b) a Band model with a High-energy Cutoff, and (c) a Smoothly Broken Power Law (SBPL) model with a Multiplicative Broken Power Law (MBPL). We then adopt the Bayesian information criterion (BIC) to compare the fits obtained and choose the best model. We find that 42% of the GRBs from the fluence spectra and 23% of GRBs from the peak-flux spectra have one of the three physically-motivated models as their preferred one. From the peak-flux spectral fits, we find that the low-energy index distributions from the empirical model fits for long GRBs peak around the synchrotron value of -2/3, while the two low-energy indices from the SBPL+MBPL fits of long GRBs peak close to the -2/3 and -3/2 values expected for a synchrotron spectrum below and above the cooling frequency.Comment: arXiv admin note: text overlap with arXiv:2103.1352

PRODUCT ATTRIBUTE PREFERENCES – A MULTIDISCIPLINARY APPROACH

The basis of buyers’ preferences are the differences of goods. Revealed preferences can be deducted from the market behaviour of the consumers, that is from their choices. In marketing consumer preferences are defined as the subjective tastes, as measured by utility, of various bundles of goods. They permit the consumer to rank these bundles of goods according to the levels of utility they give the consumer. In an expert brainstorming process we have identified eight factors that can determine the perception of product attributes: attribute strengths, preference interval, stability, product complexity, consumer task, lifelikeness, environment and experience. Our series of research plans to analyse the perception of product attributes and the system of the parameters of preferences related to them in a complex way. We aim to investigate preference systems that relate to the system of attributes with a multidisciplinary, multifocus, hierarchic series of surveys. As a first stage in our experimental study we are investigating intransitivity occurring in participants’ preferences during selection between simple, medium complex, and complex products. The participants’ task is to make pair-wise comparisons of preference between specific realizations of each product group. There are two possible versions to show up the pairs of virtual products to the subjects. We show up to the subject those attributes, which are not different, then only those that are different from each other. Using a computer based experimental design every participant has the personalized attribute set

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks

Cellular forgetting, desensitisation, stress and aging in signalling networks. When do cells refuse to learn more?

Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and forgetting of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Aging occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and aging may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-\k{appa}B signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g., helping to overcome drug-resistance.Comment: 19 pages, 4 figure

Network strategies to understand the aging process and help age-related drug design

Recent studies have demonstrated that network approaches are highly appropriate tools to understand the extreme complexity of the aging process. The generality of the network concept helps to define and study the aging of technological, social networks and ecosystems, which may give novel concepts to cure age-related diseases. The current review focuses on the role of protein-protein interaction networks (interactomes) in aging. Hubs and inter-modular elements of both interactomes and signaling networks are key regulators of the aging process. Aging induces an increase in the permeability of several cellular compartments, such as the cell nucleus, introducing gross changes in the representation of network structures. The large overlap between aging genes and genes of age-related major diseases makes drugs which aid healthy aging promising candidates for the prevention and treatment of age-related diseases, such as cancer, atherosclerosis, diabetes and neurodegenerative disorders. We also discuss a number of possible research options to further explore the potential of the network concept in this important field, and show that multi-target drugs (representing "magic-buckshots" instead of the traditional "magic bullets") may become an especially useful class of age-related future drugs.Comment: an invited paper to Genome Medicine with 8 pages, 2 figures, 1 table and 46 reference

A Model to Assess the Risk of Ice Accretion Due to Ice Crystal Ingestion in a Turbofan Engine and its Effects on Performance

The occurrence of ice accretion within commercial high bypass aircraft turbine engines has been reported under certain atmospheric conditions. Engine anomalies have taken place at high altitudes that were attributed to ice crystal ingestion, partially melting, and ice accretion on the compression system components. The result was one or more of the following anomalies: degraded engine performance, engine roll back, compressor surge and stall, and flameout of the combustor. The main focus of this research is the development of a computational tool that can estimate whether there is a risk of ice accretion by tracking key parameters through the compression system blade rows at all engine operating points within the flight trajectory. The tool has an engine system thermodynamic cycle code, coupled with a compressor flow analysis code, and an ice particle melt code that has the capability of determining the rate of sublimation, melting, and evaporation through the compressor blade rows. Assumptions are made to predict the complex physics involved in engine icing. Specifically, the code does not directly estimate ice accretion and does not have models for particle breakup or erosion. Two key parameters have been suggested as conditions that must be met at the same location for ice accretion to occur: the local wet-bulb temperature to be near freezing or below and the local melt ratio must be above 10%. These parameters were deduced from analyzing laboratory icing test data and are the criteria used to predict the possibility of ice accretion within an engine including the specific blade row where it could occur. Once the possibility of accretion is determined from these parameters, the degree of blockage due to ice accretion on the local stator vane can be estimated from an empirical model of ice growth rate and time spent at that operating point in the flight trajectory. The computational tool can be used to assess specific turbine engines to their susceptibility to ice accretion in an ice crystal environment