A Video Analysis of Intra- and Inter-professional Leadership Behaviours Within “The Burns Suite”: Identifying Key Leadership Models

Objective Leadership is particularly important in complex highly interprofessional health care contexts involving a number of staff, some from the same specialty (intraprofessional), and others from different specialties (interprofessional). The authors recently published the concept of “The Burns Suite” (TBS) as a novel simulation tool to deliver interprofessional and teamwork training. It is unclear which leadership behaviors are the most important in an interprofessional burns resuscitation scenario, and whether they can be modeled on to current leadership theory. The purpose of this study was to perform a comprehensive video analysis of leadership behaviors within TBS. Methods A total of 3 burns resuscitation simulations within TBS were recorded. The video analysis was grounded-theory inspired. Using predefined criteria, actions/interactions deemed as leadership behaviors were identified. Using an inductive iterative process, 8 main leadership behaviors were identified. Cohen’s κ coefficient was used to measure inter-rater agreement and calculated as κ = 0.7 (substantial agreement). Each video was watched 4 times, focusing on 1 of the 4 team members per viewing (senior surgeon, senior nurse, trainee surgeon, and trainee nurse). The frequency and types of leadership behavior of each of the 4 team members were recorded. Statistical significance to assess any differences was assessed using analysis of variance, whereby a p < 0.05 was taken to be significant. Leadership behaviors were triangulated with verbal cues and actions from the videos. Results All 3 scenarios were successfully completed. The mean scenario length was 22 minutes. A total of 362 leadership behaviors were recorded from the 12 participants. The most evident leadership behaviors of all team members were adhering to guidelines (which effectively equates to following Advanced Trauma and Life Support/Emergency Management of Severe Burns resuscitation guidelines and hence “maintaining standards”), followed by making decisions. Although in terms of total frequency the senior surgeon engaged in more leadership behaviors compared with the entire team, statistically there was no significant difference between all 4 members within the 8 leadership categories. This analysis highlights that “distributed leadership” was predominant, whereby leadership was “distributed” or “shared” among team members. The leadership behaviors within TBS also seemed to fall in line with the “direction, alignment, and commitment” ontology. Conclusions Effective leadership is essential for successful functioning of work teams and accomplishment of task goals. As the resuscitation of a patient with major burns is a dynamic event, team leaders require flexibility in their leadership behaviors to effectively adapt to changing situations. Understanding leadership behaviors of different team members within an authentic simulation can identify important behaviors required to optimize nontechnical skills in a major resuscitation. Furthermore, attempting to map these behaviors on to leadership models can help further our understanding of leadership theory. Collectively this can aid the development of refined simulation scenarios for team members, and can be extrapolated into other areas of simulation-based team training and interprofessional education.

Determining day length and temperature regulation of flowering: a molecular and modelling approach

Thesis (Ph.D.)--University of Washington, 2016-08

Adsorption Calorimetry for Energy Conversion Technologies: Applications in Organic Photovoltaics, Catalysis, and Atomic Layer Deposition

Thesis (Ph.D.)--University of Washington, 2016-07

Influence of environmental variability and age on the body condition of small pelagic fish in the Gulf of Lions

Endogenous and environmental variables are fundamental in explaining variations in fish condition. Based on more than 20 yr of fish weight and length data, relative condition indices were computed for anchovy and sardine caught in the Gulf of Lions. Classification and regression trees (CART) were used to identify endogenous factors affecting fish condition, and to group years of similar condition. Both species showed a similar annual cycle with condition being minimal in February and maximal in July. CART identified 3 groups of years where the fish populations generally showed poor, average and good condition and within which condition differed between age classes but not according to sex. In particular, during the period of poor condition (mostly recent years), sardines older than 1 yr appeared to be more strongly affected than younger individuals. Time-series were analyzed using generalized linear models (GLMs) to examine the effects of oceanographic abiotic (temperature, Western Mediterranean Oscillation [WeMO] and Rhone outflow) and biotic (chlorophyll a and 6 plankton classes) factors on fish condition. The selected models explained 48 and 35% of the variance of anchovy and sardine condition, respectively. Sardine condition was negatively related to temperature but positively related to the WeMO and mesozooplankton and diatom concentrations. A positive effect of mesozooplankton and Rhone runoff on anchovy condition was detected. The importance of increasing temperatures and reduced water mixing in the NW Mediterranean Sea, affecting planktonic productivity and thus fish condition by bottom-up control processes, was highlighted by these results. Changes in plankton quality, quantity and phenology could lead to insufficient or inadequate food supply for both species.

Decoherence models for discrete-time quantum walks and their application to neutral atom experiments

We discuss decoherence in discrete-time quantum walks in terms of a phenomenological model that distinguishes spin and spatial decoherence. We identify the dominating mechanisms that affect quantum-walk experiments realized with neutral atoms walking in an optical lattice. From the measured spatial distributions, we determine with good precision the amount of decoherence per step, which provides a quantitative indication of the quality of our quantum walks. In particular, we find that spin decoherence is the main mechanism responsible for the loss of coherence in our experiment. We also find that the sole observation of ballistic-instead of diffusive-expansion in position space is not a good indicator of the range of coherent delocalization. We provide further physical insight by distinguishing the effects of short- and long-time spin dephasing mechanisms. We introduce the concept of coherence length in the discrete-time quantum walk, which quantifies the range of spatial coherences. Unexpectedly, we find that quasi-stationary dephasing does not modify the local properties of the quantum walk, but instead affects spatial coherences. For a visual representation of decoherence phenomena in phase space, we have developed a formalism based on a discrete analogue of the Wigner function. We show that the effects of spin and spatial decoherence differ dramatically in momentum space.

Chaotic mixing of a competitive-consecutive reaction

The evolution of a competitive-consecutive chemical reaction is computed numerically in a two-dimensional chaotic fluid flow with initially segregated reactants. Results from numerical simulations are used to evaluate a variety of reduced models commonly adopted to model the full advection-reaction-diffusion problem. Particular emphasis is placed upon fast reactions, where the yield varies most significantly with Peclet number (the ratio of diffusive to advective time scales). When effects of the fluid mechanical mixing are strongest, we find that the yield of the reaction is underestimated by a one-dimensional lamellar model that ignores the effects of fluid mixing, but overestimated by two other lamellar models that include fluid mixing.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Development of novel therapeutics and in vitro models for spinal cord injury

Spinal cord injury (SCI) is a devastating condition, which results from trauma to the cord, resulting in a primary injury response which leads to a secondary injury cascade, causing damage to both glial and neuronal cells. Following trauma, the central nervous system (CNS) fails to regenerate due to a plethora of both intrinsic and extrinsic factors. Unfortunately, these events lead to loss of both motor and sensory function and lifelong disability and care for sufferers of SCI. There have been tremendous advancements made in our understanding of the mechanisms behind axonal regeneration and remyelination of the damaged cord. These have provided many promising therapeutic targets. However, very few have made it to clinical application, which could potentially be due to inadequate understanding of compound mechanism of action and reliance on poor SCI models. This thesis describes the use of an established neural cell co-culture model of SCI as a medium throughput screen for compounds with potential therapeutic properties. A number of compounds were screened which resulted in a family of compounds, modified heparins, being taken forward for more intense investigation. Modified heparins (mHeps) are made up of the core heparin disaccharide unit with variable sulphation groups on the iduronic acid and glucosamine residues; 2-O-sulphate (C2), 6-O-sulphate (C6) and N-sulphate (N). 2-O-sulphated (mHep6) and N-sulphated (mHep7) heparin isomers were shown to promote both neurite outgrowth and myelination in the SCI model. It was found that both mHeps decreased oligodendrocyte precursor cell (OPC) proliferation and increased oligodendrocyte (OL) number adjacent to the lesion. However, there is a difference in the direct effects on the OL from each of the mHeps; mHep6 increased myelin internode length and mHep7 increased the overall cell size. It was further elucidated that these isoforms interact with and mediate both Wnt and FGF signalling. In OPC monoculture experiments FGF2 treated OPCs displayed increased proliferation but this effect was removed when co-treated with the mHeps. Therefore, suggesting that the mHeps interact with the ligand and inhibit FGF2 signalling. Additionally, it was shown that both mHeps could be partially mediating their effects through the Wnt pathway. mHep effects on both myelination and neurite outgrowth were removed when co-treated with a Wnt signalling inhibitor, suggesting cell signalling mediation by ligand immobilisation and signalling activation as a mechanistic action for the mHeps. However, the initial methods employed in this thesis were not sufficient to provide a more detailed study into the effects the mHeps have on neurite outgrowth. This led to the design and development of a novel microfluidic device (MFD), which provides a platform to study of axonal injury. This novel device is a three chamber device with two chambers converging onto a central open access chamber. This design allows axons from two points of origin to enter a chamber which can be subjected to injury, thus providing a platform in which targeted axonal injury and the regenerative capacity of a compound study can be performed. In conclusion, this thesis contributes to and advances the study of SCI in two ways; 1) identification and investigation of a novel set of compounds with potential therapeutic potential i.e. desulphated modified heparins. These compounds have multiple therapeutic properties and could revolutionise both the understanding of the basic pathological mechanisms underlying SCI but also be a powered therapeutic option. 2) Development of a novel microfluidic device to study in greater detail axonal biology, specifically, targeted axonal injury and treatment, providing a more representative model of SCI than standard in vitro models. Therefore, the MFD could lead to advancements and the identification of factors and compounds relating to axonal regeneration.

Parameterization of quasigeostrophic eddies in primitive equation ocean models

A parameterization of mesoscale eddy fluxes in the ocean should be consistent with the fact that the ocean interior is nearly adiabatic. Gent and McWilliams have described a framework in which this can be approximated in L-coordinate primitive equation models by incorporating the effects of eddies on the buoyancy field through an eddy-induced velocity. It is also natural to base a parameterization on the simple picture of the mixing of potential vorticity in the interior and the mixing of buoyancy at the surface. The authors discuss the various constraints imposed by these two requirements and attempt to clarify the appropriate boundary conditions on the eddy-induced velocities at the surface. Quasigeostrophic theory is used as a guide to the simplest way of satisfying these constraints.

Analysis of the four wave mixing effect (FWM) in a dispersion decreasing fiber (DDF) for a WDM system

We did a numerical investigation of the propagation of short light pulses in the region of 1.55 mu m and the conversion efficiency (CE) for the four wave mixing generation (FWM) of ordinary and dispersion decreasing fibers for use in wavelength division multiplexing (WDM) systems, Our simulations studies three different profiles, linear, hyperbolic. and constant, One conclude that for all the profiles there is decrease of the conversion efficiency with the increase in the channel separation. The hyperbolic profile present a higher efficiency of around 1000 above in magnitude compared with the others profiles at 0.2 nm of channel separation. We calculate the conversion efficiency versus the fiber length for the three profiles. The conversion efficiency for the hyperbolic profile is higher when compared to the constant and linear profiles. The other interesting point of the hyperbolic profile is that the increase of the CE in the beginning of the fiber does not show my oscillation in the CE value (log eta), which was observed for the constant and linear profiles. For all the profiles there is an increase of the conversion efficiency with the increase of the pump power. The compression factor C-i for the generated FWM signal at omega(3) was measured along the DDF's and the constant profile fibers. One can conclude that with the use of decreasing dispersion profile (DDF) fibers one can have a control of the (CE) conversion efficiency and the compression factor of the four wave mixing (FWM) generation in WDM systems. (c) 2005 Elsevier B.V. All rights reserved.

MULTI-BAND BOSE HUBBARD MODELS AND EFFECTIVE THREE-BODY INTERACTIONS

Experiments with ultracold atoms in optical lattice have become a versatile testing ground to study diverse quantum many-body Hamiltonians. A single-band Bose-Hubbard (BH) Hamiltonian was first proposed to describe these systems in 1998 and its associated quantum phase-transition was subsequently observed in 2002. Over the years, there has been a rapid progress in experimental realizations of more complex lattice geometries, leading to more exotic BH Hamiltonians with contributions from excited bands, and modified tunneling and interaction energies. There has also been interesting theoretical insights and experimental studies on “un- conventional” Bose-Einstein condensates in optical lattices and predictions of rich orbital physics in higher bands. In this thesis, I present our results on several multi- band BH models and emergent quantum phenomena. In particular, I study optical lattices with two local minima per unit cell and show that the low energy states of a multi-band BH Hamiltonian with only pairwise interactions is equivalent to an effec- tive single-band Hamiltonian with strong three-body interactions. I also propose a second method to create three-body interactions in ultracold gases of bosonic atoms in a optical lattice. In this case, this is achieved by a careful cancellation of two contributions in the pair-wise interaction between the atoms, one proportional to the zero-energy scattering length and a second proportional to the effective range. I subsequently study the physics of Bose-Einstein condensation in the second band of a double-well 2D lattice and show that the collision aided decay rate of the con- densate to the ground band is smaller than the tunneling rate between neighboring unit cells. Finally, I propose a numerical method using the discrete variable repre- sentation for constructing real-valued Wannier functions localized in a unit cell for optical lattices. The developed numerical method is general and can be applied to a wide array of optical lattice geometries in one, two or three dimensions.

Development of Planning And Evaluation Models For Superstreets

Despite the extensive implementation of Superstreets on congested arterials, reliable methodologies for such designs remain unavailable. The purpose of this research is to fill the information gap by offering reliable tools to assist traffic professionals in the design of Superstreets with and without signal control. The entire tool developed in this thesis consists of three models. The first model is used to determine the minimum U-turn offset length for an Un-signalized Superstreet, given the arterial headway distribution of the traffic flows and the distribution of critical gaps among drivers. The second model is designed to estimate the queue size and its variation on each critical link in a signalized Superstreet, based on the given signal plan and the range of observed volumes. Recognizing that the operational performance of a Superstreet cannot be achieved without an effective signal plan, the third model is developed to produce a signal optimization method that can generate progression offsets for heavy arterial flows moving into and out of such an intersection design.

Estimation of leaf area in grapevine cv. Syrah using empirical models

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment.

Mixing, Chemical Reactions, and Combustion in Supersonic Flows

Experiments were conducted at the GALCIT supersonic shear-layer facility to investigate aspects of reacting transverse jets in supersonic crossflow using chemiluminescence and schlieren image-correlation velocimetry. In particular, experiments were designed to examine mixing-delay length dependencies on jet-fluid molar mass, jet diameter, and jet inclination.

The experimental results show that mixing-delay length depends on jet Reynolds number, when appropriately normalized, up to a jet Reynolds number of 500,000. Jet inclination increases the mixing-delay length, but causes less disturbance to the crossflow when compared to normal jet injection. This can be explained, in part, in terms of a control-volume analysis that relates jet inclination to flow conditions downstream of injection.

In the second part of this thesis, a combustion-modeling framework is proposed and developed that is tailored to large-eddy simulations of turbulent combustion in high-speed flows. Scaling arguments place supersonic hydrocarbon combustion in a regime of autoignition-dominated distributed reaction zones (DRZ). The proposed evolution-variable manifold (EVM) framework incorporates an ignition-delay data-driven induction model with a post-ignition manifold that uses a Lagrangian convected 'balloon' reactor model for chemistry tabulation. A large-eddy simulation incorporating the EVM framework captures several important reacting-flow features of a transverse hydrogen jet in heated-air crossflow experiment.

Developing real option game models

By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a ‘‘game’’ between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been ‘‘solved’’ by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.