The recent boom in 21cm astronomy has piqued the interest of many, especially cosmologists realizing its applications in their field. This study aims to determine if 21cm astronomy is a good tool for testing the validity of keV WDM models, as well as creating a code capable of simulating keV WDM models from an existing CDM code. The assumed WDM models vary only in dark matter particle mass and assume a single-species dark matter case as well as totally thermalized particles, such as gravitinos. Small-scale simulations of approximately 183Mpc3, as well as large-scale simulations of approximately 1503Mpc3-1793Mpc3, indicated that the CDM code was successfully modified to simulate WDM through the total dark matter power spectrum and the halo-mass function. Additionally, the large-scale simulations hinted at a positive use of 21cm astronomy for the restriction of keV WDM models, due to the existence of a distinct CDM HI power spectrum as compared to the 0.5keV WDMHI power spectrum. A discernable difference between CDM and keV WDM galactic HI power spectra would provide a step towards a confirmation or falsification of keV WDM models if supplied with a large-scale galactic HI power spectrum study

Biomechanical testing of cadaveric spinal segments forms the basis for our current understanding of healthy, pathological, and surgically treated spinal function. Over the past 40 years there has been a substantial amount of data published based on a spinal biomechanical testing regimen known as the flexibility method. This data has provided valuable clinical insights that have shaped our understanding of low back pain and its treatments. Virtually all previous lumbar spinal flexibility testing has been performed at room temperature, under very low motion rates, without the presence of a compressive follower-load to simulate upper body weight and the action of the musculature. These limitations of previous work hamper the applicability of published spinal biomechanics data, especially as researchers investigate novel ways of treating low back pain that are intended to restore the spine to a healthy biomechanical state. Thus, the purpose of this thesis work was to accurately characterize the rate-dependent flexibility of the lumbar spine at body temperature while in the presence of a compressive follower-load. A custom spine simulator with an integrated environmental chamber was developed and built as part of this thesis work. Cadaveric spinal motion segments were tested at 12 different rates of loading spanning the range of voluntary motion rates. The testing methodology allowed for comparison of spinal flexibility at room and body temperatures in the three primary modes of spinal motion, both with and without a compressive follower-load. Additionally, the work developed a stochastic model for rate-dependent spinal flexibility that allows for accurate prediction of spinal flexibility at any rate within the range of voluntary motion, based on a single flexibility test. In conclusion, the biomechanical response was significantly altered due to testing temperature, loading-rate, and application of a compressive follower-load. The author emphasizes the necessity to simulate the physiological environment during ex vivo biomechanical analysis of the lumbar spine in order to obtain a physiological response. Simplified testing procedures may be implemented only after the particular effect is known.

Let F be a field, let G be an undirected graph on n vertices, and let SF(G) be the set of all F-valued symmetric n x n matrices whose nonzero off-diagonal entries occur in exactly the positions corresponding to the edges of G. Let MRF(G) be defined as the set of matrices in SF(G) whose rank achieves the minimum of the ranks of matrices in SF(G). We develop techniques involving Z-hat, a process termed nil forcing, and induced subgraphs, that can determine when diagonal entries corresponding to specific vertices of G must be zero or nonzero for all matrices in MRF(G). We call these vertices nil or nonzero vertices, respectively. If a vertex is not a nil or nonzero vertex, we call it a neutral vertex. In addition, we completely classify the vertices of trees in terms of the classifications: nil, nonzero and neutral. Next we give an example of how nil vertices can help solve the inverse inertia problem. Lastly we give results about the inverse eigenvalue problem and solve a more complex variation of the problem (the λ, µ problem) for the path on 4 vertices. We also obtain a general result for the λ, µ problem concerning the number of λ’s and µ’s that can be equal.

The main aim of this paper is to explore preschol teachers' perspectives on the implementation of gender goals as set by the Swedish national curriculum for preschools. This qualitative research endeavours to investigate the way preschool teachers implement the curriculum and what their feelings are when implementing it. Finally, the research also seeks to explore the teachers' views based on their experience on ways to improve the curriculum. Six preschool teachers, who teach in international preschools in Sweden were interviewed;three female pedagogues and three male ones. A thematic analysis was conducted on the transcribed material of the interviews and three primary themes emerged: I) Implementation of the curriculum through everyday engagement with the students, II) mixed feelings when implementing gender-equality pedagogy and III) satisfaction about the gender goals set by the curriculum but beliefs that it can be improved. In conclusion, this paper by closely examining the preschool teachers' perspectives and opinions, seeks to aid the discussion on gender teaching to young children in preschools.

Time series anomaly detection receives increasing research interest given the growing number of data-rich application domains. Recent additions to anomaly detection methods in research literature include deep learning algorithms. The nature and performance of these algorithms in sequence analysis enable them to learn hierarchical discriminating features and time-series temporal nature. However, their performance is affected by the speed at which the time series arrives, the use of a fixed threshold, and the assumption of Gaussian distribution on the prediction error to identify anomalous values. An exact parametric distribution is often not directly relevant in many applications and it’s often difficult to select an appropriate threshold that will differentiate anomalies with noise. Thus, implementations need the Prediction Interval (PI) that quantifies the level of uncertainty associated with the Deep Neural Network (DNN) point forecasts, which helps in making a better-informed decision and mitigates against false anomaly alerts. To achieve this, a new anomaly detection method is proposed that computes the uncertainty in estimates using quantile regression and used the quantile interval to identify anomalies. Similarly, to handle the speed at which the data arrives, an online anomaly detection method is proposed where a model is trained incrementally to adapt to the concept drift that improves prediction. This is implemented using a window-based strategy, in which a time series is broken into sliding windows of sub-sequences as input to the model. To adapt to concept drift, the model is updated when changes occur in the new arrival instances. This is achieved by using anomaly likelihood which is computed using the Q-function to define the abnormal degree of the current data point based on the previous data points. Specifically, when concept drift occurs, the proposed method will mark the current data point as anomalous. However, when the abnormal behavior continues for a longer period of time, the abnormal degree of the current data point will be low compared to the previous data points using the likelihood. As such, the current data point is added to the previous data to retrain the model which will allow the model to learn the new characteristics of the data and hence adapt to the concept changes thereby redefining the abnormal behavior. The proposed method also incorporates feature extraction to capture structural patterns in the time series. This is especially significant for multivariate time-series data, for which there is a need to capture the complex temporal dependencies that may exist between the variables. In summary, this thesis contributes to the theory, design, and development of algorithms and models for the detection of anomalies in both static and evolving time series data. Several experiments were conducted, and the results obtained indicate the significance of this research on offline and online anomaly detection in both static and evolving time-series data. In chapter 3, the newly proposed method (Deep Quantile Regression Anomaly Detection Method) is evaluated and compared with six other prediction-based anomaly detection methods that assume a normal distribution of prediction or reconstruction error for the identification of anomalies. Results in the first part of the experiment indicate that DQR-AD obtained relatively better precision than all other methods which demonstrates the capability of the method in detecting a higher number of anomalous points with low false positive rates. Also, the results show that DQR-AD is approximately 2 – 3 times better than the DeepAnT which performs better than all the remaining methods on all domains in the NAB dataset. In the second part of the experiment, sMAP dataset is used with 4-dimensional features to demonstrate the method on multivariate time-series data. Experimental result shows DQR-AD have 10% better performance than AE on three datasets (SMAP1, SMAP3, and SMAP5) and equal performance on the remaining two datasets. In chapter 5, two levels of experiments were conducted basis of false-positive rate and concept drift adaptation. In the first level of the experiment, the result shows that online DQR-AD is 18% better than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results in the second level of the experiment show that the online DQR-AD method has better performance than five counterpart methods with a relatively 10% margin on six out of the seven NAB datasets. This result demonstrates how concept drift adaptation strategies adopted in the proposed online DQR-AD improve the performance of anomaly detection in time series. / Petroleum Technology Development Fund (PTDF)

Ultracold neutral plasmas provide a platform for studying transport properties in an idealized environment. In this dissertation, transport properties in a Ca$^+$/Yb$^+$ dual species ultracold neutral plasma and a Ca$^+$ magnetized ultracold neutral plasma are studied. In dual species plasmas, we study ion-ion temperature relaxation. We compare measured relaxation rates with atomistic simulations and a range of popular theories. Our work validates the assumptions and capabilities of molecular dynamic simulations and invalidates theoretical models in this regime. This work illustrates an approach for precision determinations of detailed material properties in Coulomb mixtures across a wide range of conditions. We also study plasma expansion in single species plasma in the presence of a strong uniform magnetic field. We find that the asymptotic expansion velocity falls exponentially with magnetic field strength, which disagrees with a previously published ambipolar diffusion model. In the parallel direction, plasma expansion is driven by electron pressure. However, in the perpendicular direction, no plasma expansion is observed at large magnetic field strengths.

This paper focuses on the use of English epicene pronouns in discourse which most individuals are exposed to on an everyday basis. Gender-based linguistic reforms have been implemented since the beginning of the 1970s, to achieve a more non-sexist language. In the present study Corpus linguistics has been applied to investigate the use of personal pronoun he and the personal pronoun singular they in relationship with these linguistic reforms. The present study uses data from both written and spoken English between the 1930s and 2019. The data is collected from corpora COHA and COCA to diachronically study patterns of the use of generic he and singular they when used as anaphoric reference to an antecedent of unknown gender. To narrow the searches in corpora, indefinite compound pronouns are used in collocation with the epicene pronouns as linguistic features. Systematic sampling and mapping of the data lay the ground for the analysis that determines if patterns in the language can be related to the gender-based linguistic reforms. The evidence from the data collected for the present study shows that there are changes in use of male biased pronouns before and after the gender-based linguistic reforms. From the data conducted for the present study the evidence shows trends in the figures that the neutralization strategy can be related to the changes, to some extent. The diachronic development is discussed in connection with reforms, guidelines and previous conducted studies in more detail and suggestion on further studies are presented.

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