Objective: This paper studies the measure method of netizens group complaint influence on certain theme on online social networks, in order to find out the themes and complainers that have influence on public decision-making and provide public policy decision-maker with theoretical basis and practical methods.

Context: More and more public complaint information about the formulation and implementation of public policy appears on online social networks. This complaints can easily lead to public safety issues through information gathering, frequently interaction, word-of-mouth communication and emotional resonance on online social networks and also bring confusion and challenges to the public policy decision-making organization.

Methods: On the basis of our previous studies on complaint text’s quality, transmission timeliness and user interaction degree, this paper introduces a new factor emotional resonance which is caused by the emergency and has an important impact on the complaint theme to built four-dimensional evaluation index system. We rebuild the dynamic influence measure model of online netizens complaint theme based on entropy weight model. The method is proved to be reasonable and effective based on the empirical research of Sina Weibo data.

Results: Four-dimensional dynamic influence measure model can effectively solve the measure problem of the netizens group complaint influence caused by the frequently interaction and emotional resonance. Meanwhile, it is found that the emergency which are not related to the complaint theme are difficult to induce emotional resonance, the degrees of emotional resonance  caused by different related emergencies are different .

Conclusions: The proposed four-dimensional dynamic influence measure model has important theoretical significance and practical value for public policy decision-maker on listening to online group complains, understanding public opinion, and making public policy.

The region of entropic vectors $\overline{\Gamma}^{*}_N$ has been shown to be at the core of determining fundamental limits for network coding, distributed storage, conditional independence relations, and information theory. Characterizing this region is a problem that lies at the intersection of probability theory, group theory, and convex optimization. A $2^{N}$-1 dimensional vector is said to be entropic if each of its entries can be regarded as the joint entropy of a particular subset of $N$ discrete random variables. While the explicit characterization of the region of entropic vectors $\overline{\Gamma}^{*}_N$ is unknown for $N \geqslant4$, here we prove that only one form of nonlinear non-shannon inequality is necessary to fully characterize $\overline{\Gamma}^{*}_4$. We identify this inequality in terms of a function that is the solution to an optimization problem. We also give some symmetry and convexity properties of this function which rely on the structure of the region of entropic vectors and Ingleton inequalities. This result shows that inner and outer bounds to the region of entropic vectors can be created by upper and lower bounding the function that is the answer to this optimization problem.

Different systems have been continuously developed in order to ensure integrity, availability, and confidentiality of networks. An important approach is the anomaly-based network intrusion detection system (A-NIDS). In this paper, we provide a structured and comprehensive overview of the research on entropy-based A-NIDS with the intention of providing researchers a quick introduction of essential aspects of this topic.  As help to this point, a general architecture of A-NIDS based on Entropy is described and their main components are discussed. We also highlight some open issues in the entropy-based network traffic anomaly detection.

Divergence entropy to characterize the stability in selected enzymes – The role of disulfide bonds in respect to the structure of hydrophobic core

 

Banach M, Konieczny L, Kalinowska B, Roterman I

 

Jagiellonian University - Medical College – Krakow,Poland

 

It is commonly assumed that tertiary structure of proteins is stabilized by hydrophobic core. In some proteins (particularly extracellular proteins) additional stabilization is provided by covalent disulfide bonds between selected Cys residues.

Our analysis focuses on the mutual interplay between these stabilizing factors. The stabilizing role of disulfide bonds is quantitatively assessed using the fuzzy oil drop model. According to this model the structure of hydrophobic core is expressed by 3D Gauss function. This “idealized” structure can be contrasted with the actual (observed) distribution of hydrophobic density which is the result of the amino acid arrangement in protein body. Mutual comparison of both distributions (theoretical and observed) enables to determine the presence of hydrophobic core and to evaluate its status. Quantitative assessment of the correspondence between the observed and idealized distributions bases on the Kullback-Leibler divergence entropy criterion. It can also be applied to recognize the status of certain polypeptide chain fragments – fragments defined by the positions of Cys residues participating in SS-bonds in particular. It makes possible the estimation of the mutual relation between hydrophobic core structure and the role of disulfide bonds which may appear as the constrain introducing local irregularities.

It is shown, that some enzymes are stabilized by highly ordered hydrophobic core additionally supported by the net of disulfide bonds. Other enzymes appeared to represent the contradictory status of these two factors. Some fragments defined by SS-bonds seem to support the aim-oriented local instability in respect to the hydrophobic core structure.

 

 

Synergy is phenomenon found everywhere in nature at all levels of organization in physics, chemistry, biology, social sciences, and the arts. The English Wikipedia defines Synergy as "the creation of a whole that is greater than the simple sum of its parts. The term synergy comes from the Attic Greek word συνεργία synergia from synergos, συνεργός, meaning working together". Synergy is an important concept that is difficult to define precisely and even more difficult to quantify. Entropy on the other hand has been used as a way to measure order or complexity. Here I explore the changes in entropy induced by diverse process described as synergistic. The paper uses seven examples reported in the literature to quantify synergy in different settings. The result shows that synergistic processes are associated to decreases in entropy in the system where they occur, opening the door to use entropy measures to identify and classify synergistic processes.

Dilute suspensions composed of rods are usually described by using the Jeffery's model that only considers flow-induced orientation. When the concentration increases rods interaction cannot be neglected and the simplest way to take it into account is from a diffusion term that tends to recover an isotropic orientation distribution. However, the when considering standard diffusion the orientation kinematics seems to fast with respect to the experimental findings. Different approaches have been proposed by modifying diffison mechanisms in order to delay the orientation processes, however those approaches do not ptopsoe aphysical foundation related to the proposed mechanisms. In complex fluids, micro-rheological experiments often exhibit anomalous sub-diffusion or sticky diffusion, in which the mean square displacement of Brownian tracer particles is found to scale with a alpha power of the time, with alpha different of one (standard diffusion). In these cases, the use of non-integer derivatives can constitute an appealing alternative as it allows one to correctly reproduce the observed physical behaviour while keeping the model as simple as possible. Moreover, from a physical point of view, the use of non-integer derivatives introduces a degree of non-locality that seems in agreement with the intrinsic nature of the physical system. In the case of semi-dilute and semi-concentrated suspensions entanglements can create weakly interconnected networks responsible for the mild elasticity observed experimentally. In such a percolated system, Brownian motion is expected to be disturbed and to exhibit anomalous diffusion. In our previous wokrs we analyzed the effects of fractional diffusion in small amplitude oscillatory flows, proving that by considering a fractional derivative of order 0.5 it was possible reproduce a variety of experimental tests. This work constitutes a setp forward, in which the jsut described modelling framrwork is extended to nonlinear regimes, proivng that in this case the consideration of fractional diffusion allows delaying the flow induced orientation as experimenatlly noticed. In this work revisits the general framework and analyze the effects of using fractional derivatives on fdifferent types of flows, priving that is a valuable approach for modelling suspensions of particles.

Over the years, several theoretical graph generation models have been proposed. Among the most prominent are: Erd\H{o}s-Renyi random graph model, Watts-Strogatz small world model, Albert-Barab\'{a}si preferential attachment model, Price citation model, and many more. Often, researchers working on an empirical graph want to know, which of the theoretical graph generation models is the closest, i.e., which theoretical model would generate a graph the most similar to the given empirical graph.

Usually, in order to assess the similarity of two graphs, centrality measure distributions are compared. For a theoretical graph model this commonly means comparing the empirical graph to a realization of the theoretical graph model, where the realization is generated from the given model using arbitrarily set parameters. The similarity between centrality measure distributions can be measured using standard statistical tests, e.g., the Kolmogorov-Smirnov test of distances between cumulative distributions. This approach is both error-prone and leading to incorrect conclusions.

In this work we present a general framework for comparing graphs with theoretical models. Our framework is twofold. First, we show how comparing the entropies of centrality measure distributions (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality) can help assign an empirical graph to the most similar theoretical model. Second, we compare graphs with theoretical graph models based on the perceived complexity of graphs, which in turn is computed as the Kolmogorov Complexity (also known as the algorithmic entropy) of the graph's adjacency matrix. As the result, we introduce a robust and efficient method of assigning an empirical graph to the most probable theoretical graph model.

Cryptography with Chaos was proposed by Shannon in his classic 1949 paper, although the word chaos was not mentioned. This idea has been extended and realized by Chaotic i.e. Entropy producing Torus Automorphisms. The corresponding algorithms and the software have been developed for any Torus Automorphism, adapted to be applicable for encryption in real time. We may select and combine in an arbitrary way several chaotic maps, creating in this way an infinite number of keys. Decryption is simply the reverse application of the selected maps. Therefore, the cryptography is effectively symmetric. The novelties of our work are summarized as follows:

a) The possibility to design classes of chaotic torus maps with any desirable entropy production. The Torus parameters are computed as functions of the selected entropy production.

b) We demonstrate how to design Torus Automorphisms with integer parameters determined from the entropy production.

c) For Torus Automorphisms constructed from the Fibonacci sequence, the parameters are uniquely determined from the entropy production.

d) The Encryption Mechanisms are designed and implemented by selecting and combining the Torus Maps in an arbitrary way. These Encryptions are in fact new classes and examples of MonoBlock Ciphers.

e) The application of Cryptography with Chaos to content with texts and images.

Following a suggestion of Warren Weaver, we extend Shannon’s linear model of communication piecemeal into a complex systems model. First, we distinguish between communication relations and correlations between patterns of relations. The correlations span a vector space in which relations are positioned and are thus provided with meaning. Second, positions provide perspectives to reflexive receivers. Whereas the different meanings can be integrated locally, each such instantiation opens horizons of meaning that can be codified along eigenvectors of the communication matrix. The interactions among three (or more) codes of communication may generate redundancies (as feedback on the forward arrow of entropy production). Increases in redundancy can be measured as a net reduction of prevailing uncertainty (measurable in bits). This generation of redundancy (options) can be considered as a hallmark of a knowledge-based system (e.g., an economy): new knowledge provides more options than can be realized. Both the communication-theoretical and the operational (information-theoretical) perspectives are further elaborated in the full paper—provisionally entitled “The Self-Organization of Meaning and the Reflexive Communication of Information”—which was in the meantime submitted (preprint version available at  http://arxiv.org/abs/1507.05251).

As part of our work on the development of on-line monitoring and early sensing and alarm systems in aquaculture, we are testing the suitability of the farmed fish as a biological warning system. This requieres the system to react in a quantifiable manner when exposed to perturbations. In this experiment European seabass (Dicentrarchus labrax) was exposed to sodium selenite (Na₂Se, 10 μg/l) in order to i) test the methodology proposed by Eguiraun (doi: 10.3390/e16116133, Entropy, 2014, 16: 6133-51) and ii) to quantify the effect of the Na₂Se on the fish system. Two experimental cases where performed: C1 (control) and C2 (Na₂SeO₃ exposure) for 7 days. Fish were monitored daily, in every video sequence the centroid of the group was calculated and its trajectory analized using Shannon Entropy (SE). The video sequences consisted of 3.5 minutes at 24 fps and 2 sequences were recorded per day in C1: basal (rest state) and event response (hit in the tank) while only one sequence was recorded in C2: the event response. Approximately every 2 days the water was changed in both tanks, which made visual conditions vary from one day to another due to differences in turbidity. All the images were obtained and processed following the same parameters. In C1 the SE of the basal (4.64) was lower than the SE of the event (5.32), and the empirical value of the latter coincided with that of previous works, while the SE of the event in C2 showed a lower value (5.09) than the control group. Accordingly, we believe that this methodology shows a real potential to i) effectively monitor European seabass and ii) confirm that exposure of the fish system to Na₂SeO₃ is quantifiable and decreases its SE in response to an event.