Understanding knowledge coordination dynamics in traditional and fast-response IT organizations

This research takes a dynamic view on the knowledge coordination process, aiming to explain how the process is affected by changes in the operating environment, from normal situations to emergencies in traditional and fast-response organizations, and why these changes occur. We first conceptualize the knowledge coordination process by distinguishing between four dimensions - what, when, how and who - that together capture the full scope of the knowledge coordination process. We use these dimensions to analyze knowledge coordination practices and the activities constituting these practices, in the IT functions of traditional and fast-response (military) organizations where we distinguish between "normal" and "emergency" operating conditions. Our findings indicate that (i) inter-relationships between knowledge coordination practices change under different operating conditions, and (ii) the patterns of change are different in traditional and fast-response organizations.

Advocacy coalitions:influencing the policy process in the EU

The aim of this contribution is to critically evaluate one of the theoretical approaches used to study the European Union (EU) political system and interest groups activity: the advocacy coalition framework (ACF). ACF considers that the outcome of legislative procedures is influenced by the alignment and role played by advocacy coalitions. This contribution assesses the impact of ACF on our understanding of the influences on the EU policy processes, highlighting the strengths and weaknesses of the approach. The main argument is that the ACF, although very useful in studying the EU political system, shows shortcomings when applied to the study of EU interest groups' performance. The contribution ends with a consideration of future directions for theoretical and empirical ACF research, alone and as part of wider integrated theoretical approaches to understanding the dynamics of influence in the EU. © 2013 Copyright Taylor and Francis Group, LLC.

Does start-up financing influence start-up speed? Evidence from the panel study of entrepreneurial dynamics

Why are some entrepreneurs able to start a new firm more quickly than others in the venture creation process? Drawing on pecking order and agency theory, this study investigates how start-up capital structure influences the time to either new firm founding or quitting the start-up process. The temporal aspect of the start-up process is one that is often discussed, but rarely studied. Therefore, we utilize competing risk and Cox regression event history analysis on a nationally representative sample of US entrepreneurs to investigate how start-up capital structure impacts the time in gestation to particular kinds of start-up outcomes. Our findings suggest that external equity has an appreciable impact on new firm emergence over time, and that the percentage of ownership held by the founders attenuates the benefits of external equity.

Immunological synapse: a mathematical model of the bond formation process:mathematical modelling of the immature synapse

The cell:cell bond between an immune cell and an antigen presenting cell is a necessary event in the activation of the adaptive immune response. At the juncture between the cells, cell surface molecules on the opposing cells form non-covalent bonds and a distinct patterning is observed that is termed the immunological synapse. An important binding molecule in the synapse is the T-cell receptor (TCR), that is responsible for antigen recognition through its binding with a major-histocompatibility complex with bound peptide (pMHC). This bond leads to intracellular signalling events that culminate in the activation of the T-cell, and ultimately leads to the expression of the immune eector function. The temporal analysis of the TCR bonds during the formation of the immunological synapse presents a problem to biologists, due to the spatio-temporal scales (nanometers and picoseconds) that compare with experimental uncertainty limits. In this study, a linear stochastic model, derived from a nonlinear model of the synapse, is used to analyse the temporal dynamics of the bond attachments for the TCR. Mathematical analysis and numerical methods are employed to analyse the qualitative dynamics of the nonequilibrium membrane dynamics, with the specic aim of calculating the average persistence time for the TCR:pMHC bond. A single-threshold method, that has been previously used to successfully calculate the TCR:pMHC contact path sizes in the synapse, is applied to produce results for the average contact times of the TCR:pMHC bonds. This method is extended through the development of a two-threshold method, that produces results suggesting the average time persistence for the TCR:pMHC bond is in the order of 2-4 seconds, values that agree with experimental evidence for TCR signalling. The study reveals two distinct scaling regimes in the time persistent survival probability density prole of these bonds, one dominated by thermal uctuations and the other associated with the TCR signalling. Analysis of the thermal fluctuation regime reveals a minimal contribution to the average time persistence calculation, that has an important biological implication when comparing the probabilistic models to experimental evidence. In cases where only a few statistics can be gathered from experimental conditions, the results are unlikely to match the probabilistic predictions. The results also identify a rescaling relationship between the thermal noise and the bond length, suggesting a recalibration of the experimental conditions, to adhere to this scaling relationship, will enable biologists to identify the start of the signalling regime for previously unobserved receptor:ligand bonds. Also, the regime associated with TCR signalling exhibits a universal decay rate for the persistence probability, that is independent of the bond length.

Anisotropic opinion dynamics

We consider the process of opinion formation in a society of interacting agents, where there is a set B of socially accepted rules. In this scenario, we observed that agents, represented by simple feed-forward, adaptive neural networks, may have a conservative attitude (mostly in agreement with B) or liberal attitude (mostly in agreement with neighboring agents) depending on how much their opinions are influenced by their peers. The topology of the network representing the interaction of the society's members is determined by a graph, where the agents' properties are defined over the vertexes and the interagent interactions are defined over the bonds. The adaptability of the agents allows us to model the formation of opinions as an online learning process, where agents learn continuously as new information becomes available to the whole society (online learning). Through the application of statistical mechanics techniques we deduced a set of differential equations describing the dynamics of the system. We observed that by slowly varying the average peer influence in such a way that the agents attitude changes from conservative to liberal and back, the average social opinion develops a hysteresis cycle. Such hysteretic behavior disappears when the variance of the social influence distribution is large enough. In all the cases studied, the change from conservative to liberal behavior is characterized by the emergence of conservative clusters, i.e., a closed knitted set of society members that follow a leader who agrees with the social status quo when the rule B is challenged.

Dynamics of spatial heterogeneity in a landfill with interacting phase densities:a stochastic analysis

A landfill represents a complex and dynamically evolving structure that can be stochastically perturbed by exogenous factors. Both thermodynamic (equilibrium) and time varying (non-steady state) properties of a landfill are affected by spatially heterogenous and nonlinear subprocesses that combine with constraining initial and boundary conditions arising from the associated surroundings. While multiple approaches have been made to model landfill statistics by incorporating spatially dependent parameters on the one hand (data based approach) and continuum dynamical mass-balance equations on the other (equation based modelling), practically no attempt has been made to amalgamate these two approaches while also incorporating inherent stochastically induced fluctuations affecting the process overall. In this article, we will implement a minimalist scheme of modelling the time evolution of a realistic three dimensional landfill through a reaction-diffusion based approach, focusing on the coupled interactions of four key variables - solid mass density, hydrolysed mass density, acetogenic mass density and methanogenic mass density, that themselves are stochastically affected by fluctuations, coupled with diffusive relaxation of the individual densities, in ambient surroundings. Our results indicate that close to the linearly stable limit, the large time steady state properties, arising out of a series of complex coupled interactions between the stochastically driven variables, are scarcely affected by the biochemical growth-decay statistics. Our results clearly show that an equilibrium landfill structure is primarily determined by the solid and hydrolysed mass densities only rendering the other variables as statistically "irrelevant" in this (large time) asymptotic limit. The other major implication of incorporation of stochasticity in the landfill evolution dynamics is in the hugely reduced production times of the plants that are now approximately 20-30 years instead of the previous deterministic model predictions of 50 years and above. The predictions from this stochastic model are in conformity with available experimental observations.

Dynamics of vector rogue waves in a fiber laser with a ring cavity

A pulse–pulse interaction that leads to rogue wave (RW) generation in lasers was previously attributed either to soliton–soliton or soliton–dispersive-wave interaction. The beating between polarization modes in the absence of a saturable absorber causes similar effects. Accounting for these polarization modes in a laser resonator is the purpose of the distributed vector model of laser resonators. Furthermore, high pump power, high amplitude, and short pulse duration are not necessary conditions to observe pulse attraction, repulsion, and collisions and the resonance exchange of energy between among them. The regimes of interest can be tuned just by changing the birefringence in the cavity with the pump power slightly higher than the laser threshold. This allows the observation of a wide range of RW patterns in the same experiment, as well as to classify them. The dynamics of the interaction between pulses leads us to the conclusion that all of these effects occur due to nonlinearity induced by the inverse population in the active fiber as well as an intrinsic nonlinearity in the passive part of the cavity. Most of the mechanisms of pulse–pulse interaction were found to be mutually exclusive. This means that all the observed RW patterns, namely, the “lonely,” “twins,” “three sisters,” and “cross,” are probably different cases of the same process.