Diversity for Texts Builds in Language L(MT) II: Indexes Based in Abundances

One saw previously that indications of diversity IT and the one of Shannon permits to characterize globally by only one number one fundamental aspects of the text structure. However a more precise knowledge of this structure requires specific abundance distributions and the use, to represent this one, of a suitable mathematical model. Among the numerous models that would be either susceptible to be proposed, the only one that present a real convenient interest are simplest. One will limit itself to study applied three of it to the language L(MT): the log-linear, the log-normal and Mac Arthur's models very used for the calculation of the diversity of the species of ecosystems, and used, we believe that for the first time, in the calculation of the diversity of a text written in a certain language, in our case L(MT). One will show advantages and inconveniences of each of these model types, methods permitting to adjust them to text data and in short tests that permit to decide if this adjustment is acceptable.

Transcriptional network dynamics in macrophage activation

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes. (c) 2006 Elsevier Inc. All rights reserved.

Pattern-based reuse of successful designs: usability ofsafety-critical systems

Users of safety-critical systems are expected to effectively control or monitor complex systems, with errors potentially leading to catastrophe. For such systems, safety is of paramount importance and must be designed into the human-machine interface. While many case studies show how inadequate design practice led to poor safety and usability, concrete guidance on good design practices is scarce. The paper argues that the pattern language paradigm, widely used in the software design community, is a suitable means of documenting appropriate design strategies. We discuss how typical usability-related properties (e.g., flexibility) need some adjustment to be used for assessing safety-critical systems, and document a pattern language, based on corresponding "safety-usability" principles

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

Understanding policy implementation processes as self-organizing systems

Implementation studies and related research in organizational theory can be enhanced by drawing on the field of complex systems to understand better and, as a consequence, more successfully manage change. This article reinterprets data previously published in the British Journal of Management to reveal a new contribution, that policy implementation processes should be understood as a self-organizing system in which adaptive abilities are extremely important for stakeholders. In other words, national policy is reinterpreted at the local level, with each local organization uniquely mixing elements of national policy with their own requirements making policy implementation unpredictable and more sketchy. The original article explained different paces and directions of change in terms of traditional management processes: leadership, politics, implementation and vision. By reinterpreting the data, it is possible to reveal that deeper level processes, which are more emergent, are also at work influencing change, which the authors label possibility space. Implications for theory, policy and practice are identified.

A methodology for analysis and control of discrete event dynamic systems

The rapid developments in computer technology have resulted in a widespread use of discrete event dynamic systems (DEDSs). This type of system is complex because it exhibits properties such as concurrency, conflict and non-determinism. It is therefore important to model and analyse such systems before implementation to ensure safe, deadlock free and optimal operation. This thesis investigates current modelling techniques and describes Petri net theory in more detail. It reviews top down, bottom up and hybrid Petri net synthesis techniques that are used to model large systems and introduces on object oriented methodology to enable modelling of larger and more complex systems. Designs obtained by this methodology are modular, easy to understand and allow re-use of designs. Control is the next logical step in the design process. This thesis reviews recent developments in control DEDSs and investigates the use of Petri nets in the design of supervisory controllers. The scheduling of exclusive use of resources is investigated and an efficient Petri net based scheduling algorithm is designed and a re-configurable controller is proposed. To enable the analysis and control of large and complex DEDSs, an object oriented C++ software tool kit was developed and used to implement a Petri net analysis tool, Petri net scheduling and control algorithms. Finally, the methodology was applied to two industrial DEDSs: a prototype can sorting machine developed by Eurotherm Controls Ltd., and a semiconductor testing plant belonging to SGS Thomson Microelectronics Ltd.

T-cell epitope prediction and immune complex simulation using molecular dynamics:state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

Convergence and knowledge processing in multi-agent systems

Multi-agent systems are complex systems comprised of multiple intelligent agents that act either independently or in cooperation with one another. Agent-based modelling is a method for studying complex systems like economies, societies, ecologies etc. Due to their complexity, very often mathematical analysis is limited in its ability to analyse such systems. In this case, agent-based modelling offers a practical, constructive method of analysis. The objective of this book is to shed light on some emergent properties of multi-agent systems. The authors focus their investigation on the effect of knowledge exchange on the convergence of complex, multi-agent systems.

Hydrophenylation of internal alkynes with boronic acids catalysed by a Ni–Zn hydroxy double salt-intercalated anionic rhodium(III) complex

[Rh(OH)6]3− intercalated Ni–Zn mixed basic salt (Rh/NiZn) acts as an efficient catalyst for the hydrophenylation of internal alkynes with arylboronic acids under mild conditions. The turnover number per Rh site approached 740 in the reaction between 4-octyne and phenylboronic acid. The catalytic monomeric Rh(III) complex is stabilised within the NiZn interlayers, attributable to a strong electrostatic interaction, promoting its re-use.

Towards probabilistic synchronization of local controllers

The traditional use of global and centralised control methods, fails for large, complex, noisy and highly connected systems, which typify many real world industrial and commercial systems. This paper provides an efficient bottom up design of distributed control in which many simple components communicate and cooperate to achieve a joint system goal. Each component acts individually so as to maximise personal utility whilst obtaining probabilistic information on the global system merely through local message-passing. This leads to an implied scalable and collective control strategy for complex dynamical systems, without the problems of global centralised control. Robustness is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, can be implemented adaptively and opens a systematic rich way to information sharing. This paper opens the foreseen direction and inspects the proposed design on a linearised version of coupled map lattice with spatiotemporal chaos. A version close to linear quadratic design gives an initial insight into possible behaviours of such networks.

A tudásérték meghatározása minőségügyi szempontból, hálózatelemzési módszerekkel (The knowledge value in terms of quality with network analysis methods)

A minőségügy egyik kulcsfeladata, hogy azonosítsa az értékteremtés szempontjából kritikus tényezőket, meghatározza ezek értékét, valamint intézkedjen negatív hatásuk megelőzése és csökkentése érdekében. Az értékteremtés sok esetben folyamatokon keresztül történik, amelyek tevékenységekből, elvégzendő feladatokból állnak. Ezekhez megfelelő munkatársak kellenek, akiknek az egyik legfontosabb jellemzője az általuk birtokolt tudás. Mindezek alapján a feladat-tudás-erőforrás kapcsolatrendszer ismerete és kezelése minőségügyi feladat is. A komplex rendszerek elemzésével foglalkozó hálózatkutatás eszközt biztosíthat ehhez, ezért indokolt a minőségügyi területen történő alkalmazhatóságának vizsgálata. Az alkalmazási lehetőségek rendszerezése érdekében a szerzők kategorizálták a minőségügyi hálózatokat az élek (kapcsolatok) és a csúcsok (hálózati pontok) típusai alapján. Ezt követően definiálták a multimodális (több különböző csúcstípusból álló) tudáshálózatot, amely a feladatokból, az erőforrásokból, a tudáselemekből és a közöttük lévő kapcsolatokból épül fel. A hálózat segítségével kategóriákba sorolták a tudáselemeket, valamint a fokszámok alapján meghatározták értéküket. A multimodális hálózatból képzett tudáselem-hálózatban megadták az összefüggő csoportok jelentését, majd megfogalmaztak egy összefüggést a tudáselem-elvesztés kockázatának meghatározására. _______ The aims of quality management are to identify those factors that have significant influence on value production, qualify or quantify them, and make preventive and corrective actions in order to reduce their negative effects. The core elements of value production are processes and tasks, along with workforce having the necessary knowledge to work. For that reason the task-resource-knowledge structure is pertinent to quality management. Network science provides methods to analyze complex systems; therefore it seems reasonable to study the use of tools of network analysis in association with quality management issues. First of all the authors categorized quality networks according to the types of nodes (vertices) and links (edges or arcs). Focusing on knowledge management, they defined the multimodal knowledge network, consisting of tasks, resources, knowledge items and their interconnections. Based on their degree, network nodes can be categorized and their value can be quantified. Derived from the multimodal network knowledge-item network is to be created, where the meaning of cohesive subgroups is defined. Eventually they proposed a formula for determining the risk of knowledge loss.

A framework for transforming, analyzing, and realizing software designs in unified modeling language

Unified Modeling Language (UML) is the most comprehensive and widely accepted object-oriented modeling language due to its multi-paradigm modeling capabilities and easy to use graphical notations, with strong international organizational support and industrial production quality tool support. However, there is a lack of precise definition of the semantics of individual UML notations as well as the relationships among multiple UML models, which often introduces incomplete and inconsistent problems for software designs in UML, especially for complex systems. Furthermore, there is a lack of methodologies to ensure a correct implementation from a given UML design. The purpose of this investigation is to verify and validate software designs in UML, and to provide dependability assurance for the realization of a UML design.^ In my research, an approach is proposed to transform UML diagrams into a semantic domain, which is a formal component-based framework. The framework I proposed consists of components and interactions through message passing, which are modeled by two-layer algebraic high-level nets and transformation rules respectively. In the transformation approach, class diagrams, state machine diagrams and activity diagrams are transformed into component models, and transformation rules are extracted from interaction diagrams. By applying transformation rules to component models, a (sub)system model of one or more scenarios can be constructed. Various techniques such as model checking, Petri net analysis techniques can be adopted to check if UML designs are complete or consistent. A new component called property parser was developed and merged into the tool SAM Parser, which realize (sub)system models automatically. The property parser generates and weaves runtime monitoring code into system implementations automatically for dependability assurance. The framework in the investigation is creative and flexible since it not only can be explored to verify and validate UML designs, but also provides an approach to build models for various scenarios. As a result of my research, several kinds of previous ignored behavioral inconsistencies can be detected.^

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Control efficacy of complex networks

W.-X.W. was supported by NSFC under Grant No. 11105011, CNNSF under Grant No. 61074116 and the Fundamental Research Funds for the Central Universities. Y.-C.L. was supported by ARO under Grant W911NF-14-1-0504

Community control in cellular protein production : consequences for amino acid starvation

The authors thank M. C. Romano, I. Stansfield, L. Ciandrini, A. Kort, and M. Rehberg for helpful discussions. This work was funded by BBSRC grants BB/F00513/X1 and BB/G010722, and the Scottish Universities Life Science Alliance (SULSA).