Simon Fraser University Undergraduate Collection

Based largely upon diaries and memoirs, this case study of Poland questions when various peoples in Poland understood what the “Final Solution” was. By January 1942, the “Final Solution” had been finalized as extermination, mainly through the gas chambers. As violence to the Jews grew more extreme, the populace started to take notice of events. As Poland was the center of the “Final Solution” and the extermination centers. This article analyses when people in Poland understood the deadly nature of the “Final Solution.” Through analysis based largely on diaries, this article details how those in Poland noticed the horrors of “Final Solution” within the first year of its enaction.

When smallpox began spreading through Montreal in 1885, many refused the vaccine that may have spared 3,000 lives and prevented 19,905 other cases of the disease. Thus far, the literature on the epidemic has linked anti-vaccination sentiment to the linguistic and ethnic identity of Montreal’s French-Canadian Catholic population. Using Rob Nixon's concept of slow violence, this thesis argues that the rejection of public health measures by working-class French Canadians was an anti-establishment reaction to the interventions of a city whose industrialization and urbanization had worsened and ignored other pressing environmental and health concerns.

In stochastic biological systems, it is difficult to predict how the state of the system will evolve in response to a dynamic environment. Various attempts have been proposed in different literature. Some papers contain extreme simplicity in the system or suggest a potentially misleading method. In this study, we propose methodology to infer properties of the environment in which an observed system may have evolved: “reverse ecology”. Here, the system can be a cell, and the environment can be everything else other than the cell. We aim to understand the success of a given system compared to all other possible systems in the given environment. From this, we infer the environment that is the most likely one for the system to have arisen in. This Bayesian approach is applied as an inference method that is different from the existing methods. Two different model systems, Poisson distributions and negative binomial distributions, are applied to infer the evolutionary environment from an observed system.

We investigate a model that allows us to look at electron transfer in the fast-hopping regime. Using recent developments in the study of non-equilibrium processes, we compute optimal protocols which minimize the excess work required to drive the system from one control parameter value to another. Using these protocols, we evolve the system using Fokker-Planck dynamics to calculate how successful these protocols are over a variety of parameter values. We find that in using these protocols there is a trade-off between reducing the dissipation and successfully transferring the electron.

The standard cosmological model, known as the ΛCDM model, is the simplest model that accurately describes a variety of aspects of the Universe, including the cosmic microwave background (CMB), large scale structure, and accelerated expansion. Independent observational data, including data from the CMB, baryon acoustic oscillations (BAO), and supernovae type Ia (SNe Ia) provide significant statistical support for the ΛCDM model. Despite this support the Hubble expansion rate determined from these observations is inconsistent with direct measurements, presenting a tension of over 4σ. In this examination we attempt to alleviate this tension by looking at an important assumption of the ΛCDM model, the assumption that the energy densities of the different cosmic fluid components evolve independently. To test this we consider pairwise interactions between dark sector cosmic fluid components by introducing terms which allow for energy exchange between components to the right hand side of the Friedmann fluid equations. Making use of Markov Chain Monte Carlo methods we find that energy exchange between cold dark matter (CDM) and dark energy can correct for the discrepancy between CMB measurements of the Hubble expansion rate and direct measurements, but that adding the BAO measurements to the analysis prevents this tension from being fully alleviated. Our findings suggest dark sector cosmic fluid interactions are a strong candidate for physics beyond ΛCDM and warrant additional research.

Cellular networks in biological systems are complex and as such, identifying the molecular interactions that give rise to the complex behavior observed can require an immense amount of data. Often, statistical and machine learning techniques are used to analyze this data and extract a global picture of network dynamics. One of the challenges of network analysis in systems biology is finding the connections between genes, proteins, or both, and predicting additional ones that have not yet been detected experimentally. This problem is easily mappable to the inverse problem of statistical physics: inferring the microscopic particle-particle interactions given macroscopic observations of a system. In particular, the focus of this work is to investigate whether perturbations can be introduced into the system so as to improve the output data quality. Specifically, we explore how perturbations in the form of magnetic field can be used to improve the inference of interactions for a three-spin Ising system. Utilizing a maximum likelihood approach, we empirically show that there exists an optimal field where learning is most ecient. Such a field seems to counteract the individual interactions between spins, allowing for optimal inference.

A stochastic system under the influence of a stochastic environment will become correlated with both present and future states of the environment. Such a system can be seen as a predictive model of future environmental states. The non-predictive model complexity in such a model has been shown in a recent paper to be fundamentally equivalent to thermodynamic dissipation. In this dissertation, this abstract result is explored in concrete models in order to illustrate how it emerges in realistic systems. In steady-state, this model complexity is found to be the dominant form of dissipation when the system is strongly driven and quick to relax back to equilibrium. Model complexity being the dominant form of dissipation is shown to be equivalent to the rate at which the system learns about its environment being large compared to the heat dissipation.

Biological processes are inherently stochastic, and to achieve directionality, consume free energy. In most cases, the total free energy for a single cycle is fixed. We investigate how this fixed free energy can be allocated throughout different states to maximize the probability flux. We adopt a formalism based on the master equation to find exact solutions to the probability distribution for a specific state at a given time in Laplace space. With this analytical expression for probability distributions, we can calculate the probability flux for a 2-state and 3-state systems using two different energy landscapes, forward=labile, and reverse-labile schemes. We find the optimal allocation of free energy is unevenly distributed to compensate for slower transitions rates in the cycle.

To study how gene-gene interactions may be controlled, and driven toward particular gene states, an Ising model has been proposed to model genes as binary interacting spins. To determine the effect of ‘clamping’ the states of particular genes requires accounting for the other interactions in the network through the renormalization scheme proposed in this thesis.