Dynamic patterns of flow in the workplace: characterizing within-individual variability using a complexity science approach

As a result of the growing interest in studying employee well-being as a complex process that portrays high levels of within-individual variability and evolves over time, this present study considers the experience of flow in the workplace from a nonlinear dynamical systems approach. Our goal is to offer new ways to move the study of employee well-being beyond linear approaches. With nonlinear dynamical systems theory as the backdrop, we conducted a longitudinal study using the experience sampling method and qualitative semi-structured interviews for data collection; 6981 registers of data were collected from a sample of 60 employees. The obtained time series were analyzed using various techniques derived from the nonlinear dynamical systems theory (i.e., recurrence analysis and surrogate data) and multiple correspondence analyses. The results revealed the following: 1) flow in the workplace presents a high degree of within-individual variability; this variability is characterized as chaotic for most of the cases (75%); 2) high levels of flow are associated with chaos; and 3) different dimensions of the flow experience (e.g., merging of action and awareness) as well as individual (e.g., age) and job characteristics (e.g., job tenure) are associated with the emergence of different dynamic patterns (chaotic, linear and random).

Modelling, control and supervision for a class of hybrid systems

The aim of this thesis is to narrow the gap between two different control techniques: the continuous control and the discrete event control techniques DES. This gap can be reduced by the study of Hybrid systems, and by interpreting as Hybrid systems the majority of large-scale systems. In particular, when looking deeply into a process, it is often possible to identify interaction between discrete and continuous signals. Hybrid systems are systems that have both continuous, and discrete signals. Continuous signals are generally supposed continuous and differentiable in time, since discrete signals are neither continuous nor differentiable in time due to their abrupt changes in time. Continuous signals often represent the measure of natural physical magnitudes such as temperature, pressure etc. The discrete signals are normally artificial signals, operated by human artefacts as current, voltage, light etc. Typical processes modelled as Hybrid systems are production systems, chemical process, or continuos production when time and continuous measures interacts with the transport, and stock inventory system. Complex systems as manufacturing lines are hybrid in a global sense. They can be decomposed into several subsystems, and their links. Another motivation for the study of Hybrid systems is the tools developed by other research domains. These tools benefit from the use of temporal logic for the analysis of several properties of Hybrid systems model, and use it to design systems and controllers, which satisfies physical or imposed restrictions. This thesis is focused in particular types of systems with discrete and continuous signals in interaction. That can be modelled hard non-linealities, such as hysteresis, jumps in the state, limit cycles, etc. and their possible non-deterministic future behaviour expressed by an interpretable model description. The Hybrid systems treated in this work are systems with several discrete states, always less than thirty states (it can arrive to NP hard problem), and continuous dynamics evolving with expression: with Ki ¡ Rn constant vectors or matrices for X components vector. In several states the continuous evolution can be several of them Ki = 0. In this formulation, the mathematics can express Time invariant linear system. By the use of this expression for a local part, the combination of several local linear models is possible to represent non-linear systems. And with the interaction with discrete events of the system the model can compose non-linear Hybrid systems. Especially multistage processes with high continuous dynamics are well represented by the proposed methodology. Sate vectors with more than two components, as third order models or higher is well approximated by the proposed approximation. Flexible belt transmission, chemical reactions with initial start-up and mobile robots with important friction are several physical systems, which profits from the benefits of proposed methodology (accuracy). The motivation of this thesis is to obtain a solution that can control and drive the Hybrid systems from the origin or starting point to the goal. How to obtain this solution, and which is the best solution in terms of one cost function subject to the physical restrictions and control actions is analysed. Hybrid systems that have several possible states, different ways to drive the system to the goal and different continuous control signals are problems that motivate this research. The requirements of the system on which we work is: a model that can represent the behaviour of the non-linear systems, and that possibilities the prediction of possible future behaviour for the model, in order to apply an supervisor which decides the optimal and secure action to drive the system toward the goal. Specific problems can be determined by the use of this kind of hybrid models are: - The unity of order. - Control the system along a reachable path. - Control the system in a safe path. - Optimise the cost function. - Modularity of control The proposed model solves the specified problems in the switching models problem, the initial condition calculus and the unity of the order models. Continuous and discrete phenomena are represented in Linear hybrid models, defined with defined eighth-tuple parameters to model different types of hybrid phenomena. Applying a transformation over the state vector : for LTI system we obtain from a two-dimensional SS a single parameter, alpha, which still maintains the dynamical information. Combining this parameter with the system output, a complete description of the system is obtained in a form of a graph in polar representation. Using Tagaki-Sugeno type III is a fuzzy model which include linear time invariant LTI models for each local model, the fuzzyfication of different LTI local model gives as a result a non-linear time invariant model. In our case the output and the alpha measure govern the membership function. Hybrid systems control is a huge task, the processes need to be guided from the Starting point to the desired End point, passing a through of different specific states and points in the trajectory. The system can be structured in different levels of abstraction and the control in three layers for the Hybrid systems from planning the process to produce the actions, these are the planning, the process and control layer. In this case the algorithms will be applied to robotics ¡V a domain where improvements are well accepted ¡V it is expected to find a simple repetitive processes for which the extra effort in complexity can be compensated by some cost reductions. It may be also interesting to implement some control optimisation to processes such as fuel injection, DC-DC converters etc. In order to apply the RW theory of discrete event systems on a Hybrid system, we must abstract the continuous signals and to project the events generated for these signals, to obtain new sets of observable and controllable events. Ramadge & Wonham¡¦s theory along with the TCT software give a Controllable Sublanguage of the legal language generated for a Discrete Event System (DES). Continuous abstraction transforms predicates over continuous variables into controllable or uncontrollable events, and modifies the set of uncontrollable, controllable observable and unobservable events. Continuous signals produce into the system virtual events, when this crosses the bound limits. If this event is deterministic, they can be projected. It is necessary to determine the controllability of this event, in order to assign this to the corresponding set, , controllable, uncontrollable, observable and unobservable set of events. Find optimal trajectories in order to minimise some cost function is the goal of the modelling procedure. Mathematical model for the system allows the user to apply mathematical techniques over this expression. These possibilities are, to minimise a specific cost function, to obtain optimal controllers and to approximate a specific trajectory. The combination of the Dynamic Programming with Bellman Principle of optimality, give us the procedure to solve the minimum time trajectory for Hybrid systems. The problem is greater when there exists interaction between adjacent states. In Hybrid systems the problem is to determine the partial set points to be applied at the local models. Optimal controller can be implemented in each local model in order to assure the minimisation of the local costs. The solution of this problem needs to give us the trajectory to follow the system. Trajectory marked by a set of set points to force the system to passing over them. Several ways are possible to drive the system from the Starting point Xi to the End point Xf. Different ways are interesting in: dynamic sense, minimum states, approximation at set points, etc. These ways need to be safe and viable and RchW. And only one of them must to be applied, normally the best, which minimises the proposed cost function. A Reachable Way, this means the controllable way and safe, will be evaluated in order to obtain which one minimises the cost function. Contribution of this work is a complete framework to work with the majority Hybrid systems, the procedures to model, control and supervise are defined and explained and its use is demonstrated. Also explained is the procedure to model the systems to be analysed for automatic verification. Great improvements were obtained by using this methodology in comparison to using other piecewise linear approximations. It is demonstrated in particular cases this methodology can provide best approximation. The most important contribution of this work, is the Alpha approximation for non-linear systems with high dynamics While this kind of process is not typical, but in this case the Alpha approximation is the best linear approximation to use, and give a compact representation.

Inverse problems in dynamic cognitive modeling

Inverse problems for dynamical system models of cognitive processes comprise the determination of synaptic weight matrices or kernel functions for neural networks or neural/dynamic field models, respectively. We introduce dynamic cognitive modeling as a three tier top-down approach where cognitive processes are first described as algorithms that operate on complex symbolic data structures. Second, symbolic expressions and operations are represented by states and transformations in abstract vector spaces. Third, prescribed trajectories through representation space are implemented in neurodynamical systems. We discuss the Amari equation for a neural/dynamic field theory as a special case and show that the kernel construction problem is particularly ill-posed. We suggest a Tikhonov-Hebbian learning method as regularization technique and demonstrate its validity and robustness for basic examples of cognitive computations.

Delays versus performance of visually guided systems

The paper presents an overview of dynamic systems with inherent delays in both feedforward and feedback paths and how the performance of such systems can be affected by such delays. The authors concentrate on visually guided systems, where the behaviour of the system is largely dependent on the results of the vision sensors, with particular reference to active robot heads (real-time gaze control). We show how the performance of such systems can deteriorate substantially with the presence of unknown and/or variable delays. Considered choice of system architecture, however, allows the performance of active vision systems to be optimised with respect to the delays present in the system.

Mathematical modeling and control of population systems: Applications in biological pest control

The aim of this paper is to apply methods from optimal control theory, and from the theory of dynamic systems to the mathematical modeling of biological pest control. The linear feedback control problem for nonlinear systems has been formulated in order to obtain the optimal pest control strategy only through the introduction of natural enemies. Asymptotic stability of the closed-loop nonlinear Kolmogorov system is guaranteed by means of a Lyapunov function which can clearly be seen to be the solution of the Hamilton-Jacobi-Bellman equation, thus guaranteeing both stability and optimality. Numerical simulations for three possible scenarios of biological pest control based on the Lotka-Volterra models are provided to show the effectiveness of this method. (c) 2007 Elsevier B.V. All rights reserved.

Management of vomplex systems: Modeling the biological pest control

The aim of this paper is to study the cropping system as complex one, applying methods from theory of dynamic systems and from the control theory to the mathematical modeling of the biological pest control. The complex system can be described by different mathematical models. Based on three models of the pest control, the various scenarios have been simulated in order to obtain the pest control strategy only through natural enemies' introduction. © 2008 World Scientific Publishing Company.

Dynamic Models under Uncertainty for Large-Scale Enterprise Systems

Thesis (Ph.D.)--University of Washington, 2016-06

Modelling the input-output behaviour of piezoelectric structural health monitoring systems for composite plates

This paper investigates the input-output characteristics of structural health monitoring systems for composite plates based on permanently attached piezoelectric transmitter and sensor elements. Using dynamic piezoelectricity theory and a multiple integral transform method to describe the propagating and scattered flexural waves an electro-mechanical model for simulating the voltage input-output transfer function for circular piezoelectric transmitters and sensors adhesively attached to an orthotropic composite plate is developed. The method enables the characterization of all three physical processes, i.e. wave generation, wave propagation and wave reception. The influence of transducer, plate and attached electrical circuit characteristics on the voltage output behaviour of the system is examined through numerical calculations, both in frequency and the time domain. The results show that the input-output behaviour of the system is not properly predicted by the transducers' properties alone. Coupling effects between the transducers and the tested structure have to be taken into account, and adding backing materials to the piezoelectric elements can significantly improve the sensitivity of the system. It is shown that in order to achieve maximum sensitivity, particular piezoelectric transmitters and sensors need to be designed according to the structure to be monitored and the specific frequency regime of interest.

Understanding the dynamics of information technology implementation: The case of clinical information systems

The ultimate intent of this dissertation was to broaden and strengthen our understanding of IT implementation by emphasizing research efforts on the dynamic nature of the implementation process. More specifically, efforts were directed toward opening the "black box" and providing the story that explains how and why contextual conditions and implementation tactics interact to produce project outcomes. In pursuit of this objective, the dissertation was aimed at theory building and adopted a case study methodology combining qualitative and quantitative evidence. Precisely, it examined the implementation process, use and consequences of three clinical information systems at Jackson Memorial Hospital, a large tertiary care teaching hospital.^ As a preliminary step toward the development of a more realistic model of system implementation, the study proposes a new set of research propositions reflecting the dynamic nature of the implementation process.^ Findings clearly reveal that successful implementation projects are likely to be those where key actors envision end goals, anticipate challenges ahead, and recognize the presence of and seize opportunities. It was also found that IT implementation is characterized by the systems theory of equifinality, that is, there are likely several equally effective ways to achieve a given end goal. The selection of a particular implementation strategy appears to be a rational process where actions and decisions are largely influenced by the degree to which key actors recognize the mediating role of each tactic and are motivated to action. The nature of the implementation process is also characterized by the concept of "duality of structure," that is, context and actions mutually influence each other. Another key finding suggests that there is no underlying program that regulates the process of change and moves it form one given point toward a subsequent and already prefigured end. For this reason, the implementation process cannot be thought of as a series of activities performed in a sequential manner such as conceived in stage models. Finally, it was found that IT implementation is punctuated by a certain indeterminacy. Results suggest that only when substantial efforts are focused on what to look for and think about, it is less likely that unfavorable and undesirable consequences will occur. ^

A systems approach to describing the process of individual adoption of IT innovations

Research on the adoption of innovations by individuals has been criticized for focusing on various factors that lead to the adoption or rejection of an innovation while ignoring important aspects of the dynamic process that takes place. Theoretical process-based models hypothesize that individuals go through consecutive stages of information gathering and decision making but do not clearly explain the mechanisms that cause an individual to leave one stage and enter the next one. Research on the dynamics of the adoption process have lacked a structurally formal and quantitative description of the process. ^ This dissertation addresses the adoption process of technological innovations from a Systems Theory perspective and assumes that individuals roam through different, not necessarily consecutive, states, determined by the levels of quantifiable state variables. It is proposed that different levels of these state variables determine the state in which potential adopters are. Various events that alter the levels of these variables can cause individuals to migrate into different states. ^ It was believed that Systems Theory could provide the required infrastructure to model the innovation adoption process, particularly applied to information technologies, in a formal, structured fashion. This dissertation assumed that an individual progressing through an adoption process could be considered a system, where the occurrence of different events affect the system's overall behavior and ultimately the adoption outcome. The research effort aimed at identifying the various states of such system and the significant events that could lead the system from one state to another. By mapping these attributes onto an “innovation adoption state space” the adoption process could be fully modeled and used to assess the status, history, and possible outcomes of a specific adoption process. ^ A group of Executive MBA students were observed as they adopted Internet-based technological innovations. The data collected were used to identify clusters in the values of the state variables and consequently define significant system states. Additionally, events were identified across the student sample that systematically moved the system from one state to another. The compilation of identified states and change-related events enabled the definition of an innovation adoption state-space model. ^

Emotional Theory of Rationality

In recent decades, it has been definitely established the existence of a close relationship between the emotional phenomena and rational processes, but we still do not have a unified definition, or effective models to describe any of them well. To advance our understanding of the mechanisms governing the behavior of living beings we must integrate multiple theories, experiments and models from both fields. In this paper we propose a new theoretical framework that allows integrating and understanding, from a functional point of view, the emotion-cognition duality. Our reasoning, based on evolutionary principles, add to the definition and understanding of emotion, justifying its origin, explaining its mission and dynamics, and linking it to higher cognitive processes, mainly with attention, cognition, decision-making and consciousness. According to our theory, emotions are the mechanism for brain function optimization, besides being the contingency and stimuli prioritization system. As a result of this approach, we have developed a dynamic systems-level model capable of providing plausible explanations for some psychological and behavioral phenomena, and establish a new framework for scientific definition of some fundamental psychological terms.

Credit assignment: The memory representation of dynamic task events

The experiment examined the influence of memory for prior instances on aircraft conflict detection. Participants saw pairs of similar aircraft repeatedly conflict with each other. Performance improvements suggest that participants credited the conflict status of familiar aircraft pairs to repeated static features such as speed, and dynamic features such as aircraft relative position. Participants missed conflicts when a conflict pair resembled a pair that had repeatedly passed safely. Participants either did not attend to, or interpret, the bearing of aircraft correctly as a result of false memory-based expectations. Implications for instance models and situational awareness in dynamic systems are discussed.

On the Hopf bifurcation in control systems with a bounded nonlinearity asymptotically homogeneous at infinity

The paper studies existence, uniqueness, and stability of large-amplitude periodic cycles arising in Hopf bifurcation at infinity of autonomous control systems with bounded nonlinear feedback. We consider systems with functional nonlinearities of Landesman-Lazer type and a class of systems with hysteresis nonlinearities. The method is based on the technique of parameter functionalization and methods of monotone concave and convex operators. (C) 2001 Academic Press.

Differential inclusions and robust control theory

Uncontrolled systems (x) over dot is an element of Ax, where A is a non-empty compact set of matrices, and controlled systems (x) over dot is an element of Ax + Bu are considered. Higher-order systems 0 is an element of Px - Du, where and are sets of differential polynomials, are also studied. It is shown that, under natural conditions commonly occurring in robust control theory, with some mild additional restrictions, asymptotic stability of differential inclusions is guaranteed. The main results are variants of small-gain theorems and the principal technique used is the Krasnosel'skii-Pokrovskii principle of absence of bounded solutions.

Should optional properties be used in conceptual modelling? A theory and three empirical tests

An important feature of some conceptual modelling grammars is the features they provide to allow database designers to show real-world things may or may not possess a particular attribute or relationship. In the entity-relationship model, for example, the fact that a thing may not possess an attribute can be represented by using a special symbol to indicate that the attribute is optional. Similarly, the fact that a thing may or may not be involved in a relationship can be represented by showing the minimum cardinality of the relationship as zero. Whether these practices should be followed, however, is a contentious issue. An alternative approach is to eliminate optional attributes and relationships from conceptual schema diagrams by using subtypes that have only mandatory attributes and relationships. In this paper, we first present a theory that led us to predict that optional attributes and relationships should be used in conceptual schema diagrams only when users of the diagrams require a surface-level understanding of the domain being represented by the diagrams. When users require a deep-level understanding, however, optional attributes and relationships should not be used because they undermine users' abilities to grasp important domain semantics. We describe three experiments which we then undertook to test our predictions. The results of the experiments support our predictions.