Abstract: Traditional social models overlook interdependence, the defining characteristic of social behavior, in favor of the least entropy production (LEP) from cooperation (e.g., [2] [3] [4]). Teams organized like distributed processing [5] over-look the benefits from the interdependence of multitasking [11]). In our model, the entropy of interdependence reduces degrees of freedom like quantum entanglement, allowing maximum entropy production (MEP) to solve problems. Evolution in Nature (viz., ) demonstrates MEP [6] from intelligent choices [7]. Exploiting interdependence improves team intelligence [8]; forced cooperation dis-organizes it; e.g., China is reducing its social intelligence [9]. In our model, competition self-organizes those willing to sort through the noise for the choices that improve social welfare. Social systems organized around competition (checks and balances) better control a society than authoritarian regimes (e.g., on China’s inability, see [12]). Authoritarians are inefficient in sizing teams to solve problems; e.g., Sinopec oil company uses about 548 thousand employees to produce about 4.4 million barrels of oil per day whereas Exxon uses about 82 thousand employees to produce about 5.3 million barrels of oil per day [10]. Overall, the density of MEP directed at solving problems in a society able to freely self-organize its labor and capital is denser.

Keywords: interdependence; maximum entropy production; teams; multitasking;

The electronic transport through a nano-scale device is an interesting topic for both experimental and theoretical physicists, addressing development of new nano-devices. Among the synthesized carbon nanostructures as the materia prima for development of nano-devices, graphene sheets has attracted a lot of attention among researchers. Exceptional properties including carriers with extremely large mobilities and truly two dimensional geometry have made graphene as a promising candidate for replacing semiconductors in the future of nanotechnology. It seems that for a more comprehensive insight of electronic transport properties of the graphene, we could use new frames. The electronic properties of graphene can be well described using a 2-D tight-binding model. The tight-binding is a quantum model to describe electron motion within an atomic lattice, so graphene and other synthesized carbon nanostructure dynamics can be well studied through quantum chaos theory. Present study discusses different regimes of conductivity in a 2-D tight-binding model of a graphene sheet. For this purpose, we apply quantum chaos theory. Spectral statistics of energy levels are used to get consecutive level spacing distribution as an identifier of electronic properties of the device. In order to find best configuration of the crystal for metallic regime, different arrays of onsite energies and hopping constants are analyzed. Also, we discuss the obtained results through multi-fractal analysis. Our results can report different regimes of conductivity and transition between metallic and insulator phases.

The article presents only the author’s view on the formation of the notion of information. The basic idea is that the information comes into existence with the emergence of organic molecular structures of increasing complexity. In the prebiotic physico-chemical systems, emerging molecules are carriers of structural inert information that reflects the fixed coordinates of the atoms in the molecule being in a stable state. Emerging irregular polymers (RNA world) acquire the potential of the digital encoding and transmission of information messages. Their structural information contained in the sequence of nucleotides evolved into symbolic (prescriptive) information in the process of molecular semiosis. It would be more accurate to say that these molecules as a result of natural evolution and selection for survival become operational. The meaning of this term is that the memory fixed in a sequence of monomers forming these molecules can now be used not only for the reproduction of its own structure, but also in a symbolic form can be employed in the biosynthesis of another class of polymers (proteins).

The Zaire Ebola virus Makona variant (ZEBOV-Makona) is the cause of the 2014-2015 high-mortality epidemic of Ebola virus disease (EVD). The viral glycoprotein (GP) mediates cell binding and internalization of the virus. Information entropy (H) determined on ZEBOV-Makona GP sequences (downloaded 6/18/2015) isolated from humans with EVD identified three amino positions (82, 230 and 371) with H values significantly greater than those of all the other 673 GP amino acids. These three large H value amino acid positions served as reference positions for detection of three disjoint, complete but non-interconnected MI-based cliques of amino acids. These three cliques contained subsets of five, ten and six amino acids, respectively. Fifteen of the cliqued amino acids were in random coils, four in helices and two in extended sheets. However, the following metadata applied to all 21 of the MI-cliqued GP amino acids: each wild type, cliqued amino acid was of one of the essential amino acids VAL, THR or ILE. Since the essential amino acids are not synthesized by humans, use of these essential amino acids by the MI-based cliques may reflect host factors, e.g., diet and nutritional status. that influence occurrence and survival of the ZEBOV-Makona GP mutations.

Papillomavirus are circular double-stranded DNA viruses that specifically infect the skin epithelium and mucocutaneous of mammals, reptiles and birds causing asymptomatic infections, benign and malignant lesions. The discovery of new viral types in the Papillomaviridae family is very relevant since they have different pathological characteristics. The classification of papillomaviruses is based on L1 gene sequence identity. However, several studies on Human papillomavirus (HPV) diversity make use of only 450 bp fragment in L1 gene in order to classify novel HPV types, subtypes, and variants. It has been observed that this L1 fragment is not appropriated for detection and genotyping based on molecular biology methods and topological and statistical aspects of phylogenetic tree. So, the identification of novel genomic markers is relevant to develop more effective diagnostic methods. Therefore, the aim of this study was to develop and apply a novel computational tool based on entropy in order to identify phylogenetic informative genomic regions that could be used as markers for the detection and genotyping of HPV. In order to develop the method, a comparative analysis was performed to assess the genetic variability of L1 gene sequences from Alphapapillomavirus, Betapapillomavirus and Gammapapillomavirus genera. Shannon entropy was calculated for each site in L1 sequence alignment. Informative sites were identified by using a cutoff of 1.0 bits of information. Phylogenetic trees were constructed based on those informative sites with maximum likelihood method. The tree topology and bootstrap values were compared. Once the markers were identified, the method uses the entropy measure to determine the best genomic regions to establish degenerate primers. The locations of forward and reverse primers are established around the selected region, sorted by their entropy values. The results showed that it was possible to identify regions in HPV genome that provide robust phylogenetic topologies, and good statistical support. Simulations showed that the primers were capable of detecting several HPV types. In order to confirm their efficacy, the primers were tested experimentally and they successfully detected HPV DNA. So, the entropy measure presented itself as a good approach to identify phylogenetic informative genomic regions, which is important to correctly position novel HPV types in a phylogenetic tree, relevant to genotype these viruses. In addition, the entropy based method could efficiently design degenerate primers that are able to amplify phylogenetic informative regions, increasing the sensitivity and specificity of the HPV diagnosis.

Mechanical wear and defective bearings can cause machinery to reduce its reliability, safety and efficiency. Therefore it is very important to take care of bearings during maintenance and detect their faults in an early stage in order to assure safe and efficient operation. We present a new technique for an early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. After normalization and the wavelet transform of vibration signals, the logarithmic energy entropy as a measure of the degree of order/disorder is extracted in a few sub-bands of interest. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection is performed by a quadratic classifier and the fault diagnosis by another two quadratic classifiers. Accuracy of the new technique was tested on the ball bearing data recorded at the Case Western Reserve University Bearing Data Center. In total four classes of the vibrations signals were studied, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique can be used to increase productivity and energy efficiency by preventing unexpected faulty operation of machinery bearings.

There are many parasite species with very different antiparasite drugs susceptibility. Computational methods in biology and chemistry prediction of the biological activity based on Quantitative Structure-Activity Relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model (ms-QSAR). In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a ms-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using entropy type indices. The data was processed by Artificial Neural Network (ANN) classifying drugs as active or non-active against the different tested parasite species. The best ANN found was MLP 23:23-18-1:1. Overall model classification accuracy was 85.65% (211/244 cases) in training. Validation of the model was carried out by means of external predicting series. In this serie, the model classified correctly 81.85% (275/357 cases).

Entropy is a well-established measure of population variability and already used in contingency in life table analysis. Such an entropy, denoted H, is a measure of heterogeneity of the distribution of deaths in a cohort and consist of a touchstone to compare life strategies in different populations or species. Since environmental change affects directly life history traits of populations, entropy as crude demographic parameter may be used to quantitative such trends. Particularly for poikilotherms, entropy may serve as highly quantitative predictor of species net reproductive success under optimum and/or non favorable biotic conditions for growth and development. Nevertheless, entropy has been also used as a more general dynamic measure of species fitness and adaptation to variable ecological conditions. In principle, such demographic-population entropy is an analogue of the Gibbs–Boltzmann entropy in statistical mechanics, H=-Σpilnpi. Here, pi represents the probability density function of the age of reproducing individuals and therefore maximization of entropy is equivalent to maximization of the uncertainty of age reproduction. In such a context, population entropy consist of a dynamic measure and maximization of H under various constrains yield to different distributions of reproduction and survivorship. Moreover, considering that demographic dynamics are formally equivalent to the dynamics of a Markov chain, demographic entropy can be further used under an expression of the classical Leslie model by means of stationary Markov chains to estimate the convergence rate of population transitions to stable age distributions and demographic equilibriums. Populations may differ by terms of robustness captured by evolutionary entropy: the rate at which populations return to demographic equilibrium after a certain perturbation. Hence, under the hypothesis that resource abundance is unlimited and that the only factor affecting population dynamics are inherent properties of species, population entropy may be used to predict the selective advantage among different populations according to the entropic principle. From an applied population-ecological standpoint, considering that that entropy may differ among genera, species and populations, the capacity of each organism to adapt to new environments may be quantified. From an environmental management standpoint, demographic properties of a population do constrain to the rate of which species adapt to human disturbed environments. The adaptive value of a population can be interpreted as distance measure between the variability of the mortality distribution, where no environmental forces interfere and the conditional entropy, estimated given the known unperturbed reference mortality. Repeated use of pesticides for instance, can cause undesirable changes in the gene pool leading of a species due to artificial selection. Through this operation, populations with the favored demographic properties gradually develop resistance to the pesticide showing fitness advantages in the presence of the artificial selection factor. Thus, reproductive trends captured by demographic entropy may reflect macroevolutionary changes such as adaptation and extinction under variable conditions.

This paper presents an anomaly-based methodology for reliable detection of integrity attacks in cyber-physical critical infrastructures. Such malicious events compromise the smooth operation of the infrastructure while the attacker is able to exploit the respective resources according to his/her purposes. Even though the operator may not understand the attack, since the overall system appears to remain in a steady state, the consequences may be of catastrophic nature with a huge negative impact. Here, we apply a deep learning technique and more specifically reservoir networks. They follow the supervised learning principle for recurrent neural networks, while the fundamental logic is to steer a random, large, fixed recurrent neural network with the input signal to the desired direction (class, probability, etc.). Their great advantage is the fact that the only part in need of training is the output layer which is a linear combination of all of the response signals. In addition we consider both temporal and functional dependencies existing among the elements of an infrastructure. The experimental platform includes a simulator of both a power grid and a cyber-network of the IEEE-9 bus model. Subsequently we implemented a wide range of integrity attacks (replay, ramp, pulse, scaling, and random) with different intensity levels. A thorough evaluation procedure is carried out while the results demonstrate the ability of the proposed method to produce a desired result in terms of false positive rate, false negative rate and detection delay.

A number of recent reports in the peer-reviewed literature have discussed irreproducibility of results in biomedical research. Some of these articles suggest that the inability of independent research laboratories to replicate published results has a negative impact on the development of, and confidence in, the biomedical research enterprise. To get more resilient data and to achieve higher reproducible result, we present an adaptive and learning system reference architecture for smart learning system interface. To get deeper inspiration, we focus our attention on mammalian brain neurophysiology. In fact, from a neurophysiological point of view, neuroscientist LeDoux finds two preferential amygdala pathways in the brain of the laboratory mouse. The low road is a pathway which is able to transmit a signal from a stimulus to the thalamus, and then to the amygdala, which then activates a fast-response in the body. The high road is activated simultaneously. This is a slower road which also includes the cortical parts of the brain, thus creating a conscious impression of what the stimulus is (to develop a rational mechanism of defense for instance). To mimic this biological reality, our main idea is to use a new input node able to bind known information to the unknown one coherently. Then, unknown "environmental noise" or/and local "signal input" information can be aggregated to known "system internal control status" information, to provide a landscape of attractor points, which either fast or slow and deeper system response can computed from. In this way, ideal cybernetics system interaction levels can be matched exactly to practical system modeling interaction styles, with no paradigmatic operational ambiguity and minimal information loss. The present paper is a relevant contribute to classic cybernetics updating towards a new General Theory of Systems, a post-Bertalanffy Systemics.