Abstract
We present three epochs of early-time ultraviolet (UV) and optical HST/STIS spectroscopy of the young, nearby Type IIP supernova (SN) 2021yja. We complement the HST data with two earlier ...epochs of Swift UVOT spectroscopy. The HST and Swift UVOT spectra are consistent with those of other well-studied Type IIP SNe. The UV spectra exhibit rapid cooling at early times, while less dramatic changes are seen in the optical. We also present Lick/KAIT optical photometry up to the late-time tail phase, showing a very long plateau and shallow decline compared with other SNe IIP. Our modeling of the UV spectrum with the
TARDIS
radiative transfer code produces a good fit for a high-velocity explosion, a low total extinction
E
(
B
−
V
) = 0.07 mag, and a subsolar metallicity. We do not find a significant contribution to the UV flux from an additional heating source, such as interaction with the circumstellar medium, consistent with the observed flat plateau. Furthermore, the velocity width of the Mg
ii
λ
2798 line is comparable to that of the hydrogen Balmer lines, suggesting that the UV emission is confined to a region close to the photosphere.
Rapid changes in technology have led to an increasingly fast pace of product introductions. For long-life systems (e.g., planes, ships, and nuclear power plants), rapid changes help sustain useful ...life, but at the same time, present significant challenges associated with obsolescence management. Over the years, many approaches for forecasting obsolescence risk and product life cycle have been developed. However, gathering inputs required for forecasting is often subjective and laborious, causing inconsistencies in predictions. To address these issues, the objective of this research is to develop a machine learning-based methodology capable of forecasting obsolescence risk and product life cycle accurately while minimizing maintenance and upkeep of the forecasting system. Specifically, this new methodology enables prediction of both the obsolescence risk level and the date when a part becomes obsolete. A case study of the cell phone market is presented to demonstrate the effectiveness and efficiency of the new approach. Results have shown that machine learning algorithms (i.e., random forest, artificial neural networks, and support vector machines) can classify parts as active or obsolete with over 98% accuracy and predict obsolescence dates within a few months.
ABSTRACT
We describe the Period detection and Identification Pipeline Suite (pips) – a new, fast, and statistically robust platform for period detection and analysis of astrophysical time-series ...data. PIPS is an open-source Python package that provides various pre-implemented methods and a customizable framework for automated, robust period measurements with principled uncertainties and statistical significance calculations. In addition to detailing the general algorithm that underlies PIPS, this paper discusses one of PIPS’ central and novel features, the Fourier-likelihood periodogram, and compares its performance to existing methods. The resulting improved performance implies that one can construct deeper, larger, and more reliable sets of derived properties from various observations, including all-sky surveys. We present a comprehensive validation of PIPS against artificially generated data, which demonstrates the reliable performance of our algorithm for a class of periodic variable stars (RR Lyrae stars).
ABSTRACT A growing number of supernovae (SNe) are now known to exhibit evidence for significant interaction with a dense, pre-existing, circumstellar medium (CSM). SNe Ibn comprise one such class ...that can be characterized by both rapidly evolving light curves and persistent narrow He i lines. The origin of such a dense CSM in these systems remains a pressing question, specifically concerning the progenitor system and mass-loss mechanism. In this paper, we present multiwavelength data of the Type Ibn SN 2020nxt, including HST/STIS ultraviolet spectra. We fit the data with recently updated CMFGEN models designed to handle configurations for SNe Ibn. The UV coverage yields strong constraints on the energetics and, when combined with the CMFGEN models, offer new insight on potential progenitor systems. We find the most successful model is a ≲4 M⊙ helium star that lost its $\sim 1\, {\rm M}_\odot$ He-rich envelope in the years preceding core collapse. We also consider viable alternatives, such as a He white dwarf merger. Ultimately, we conclude at least some SNe Ibn do not arise from single, massive (>30 M⊙) Wolf–Rayet-like stars.
Over the last three decades, the salience of the ancient Greek poet Pindar’s Theban identity and its role in his poetics has found greater awareness among critics. Nevertheless, such discussions of ...Pindar’s “Thebanicity” have largely been focused on the poems that were performed at Thebes or were limited to his use of Theban myth. This thesis examines the role of Thebes in Pindar’s Fourth Pythian ode and, in so doing, shows that Pindar’s Theban poetics are not limited to his Theban poetry or mythic narratives. As an ode performed at Kyrēnē, Pythian 4 provides an excellent opportunity to examine the city’s significance in Pindar’s songworld, especially due to the unusual historical circumstances of the ode’s composition, in which Thebes plays a role. In Pythian 4, Pindar employs Thebes and Theban myth to realize his chief poetic objectives: repatriating the exile Damophilos and ending the threat of stasis at Kyrēnē. By drawing on an idealized mythopoetic Thebes, Pindar rehabilitates Damophilos in the eyes of Arkesilas, the king of Kyrēnē and the honoree of the poem, and promotes himself as a wise poetic advisor, allowing him to better assist the king in healing the rifts in the city. On the other hand, the poet exploits Theban mythohistorical ties with Kyrēnē to draw a series of exemplary parallels that serve as a warning to the king. These mythic connections allow Pindar to advance Thebes as Kyrēnē’s ultimate metropolis, which both heightens Theban prestige and enhances Pindar’s status as a consultatory figure. Accordingly, this thesis establishes that Thebanicity is an important structural motif within Pythian 4 and serves as the primary means by which Pindar achieves his poetic goals.
Millions of young Americans set out to play football each year, and as we come to understand more about the effects of repetitive head impacts (RHI) on the developing brain, the mitigation of these ...effects become increasingly important.It is well understood the relationship between RHIs and later life cognitive decline, however, not much is known about why some former players exhibit worse symptoms than others. One proposed reason for these differences is resiliency, the ability of the brain to resist pathological changes, most commonly referred to as cognitive reserve (CR).To help us better understand cognitive reserve and how it is impacted by the age of first exposure to American football, we propose the Chronic Traumatic Encephalopathy-Cognitive Reserve (CTE-CR) score which we define as the difference between clinical neuropsychological presentation and expected pathological burden. The CTE-CR score is unique in that it defines CR not in its traditional way through proxies, but as a quantifiable and measurable score. This score can be used to further research CR, while the results of our proposed study will aid in the research of CTE pathogenesis and help influence the future of age restrictions in American tackle football.
Any product can become obsolete. Products becoming obsolete causes increased costs to organizations due to the interruption of the usual flow of business. These interruptions can be seen in product ...and component shortages, stockpiling, and forced redesigns. To minimize the impact of product obsolescence, businesses must integrate forecasting methods into business processes to allow for proactive management. Proactive management enables organizations to maximize the time to make a decision and increases the number of viable decisions. This dissertation seeks to demonstrate new obsolescence forecasting methods and techniques, along with frameworks for applying these to business processes. There are four main types of product obsolescence: (1) technical, (2) functional, (3) style, and (4) legislative. In technical obsolescence, old products can no longer compete with the specifications of newer products. In functional obsolescence, products can no longer perform their original task due to aging factors. The third is style; many products no longer are appealing visually and become obsolete due to changes in fashion (e.g., wood paneling on cars). The last is legislative; if a government passes laws that forbid certain materials, components, chemicals in manufacturing process, or creates new requirements in a market, this can often lead to many products and designs becoming obsolete. The focus in this research is on technical and functional obsolescence. Technical obsolescence is especially prolific in highly competitive consumer electronic markets such as cell phones and digital cameras. This work seeks to apply traditional machine learning methods to predict obsolescence risk levels and assign a status of “discontinued” or “procurable” depending on the availability in the market at a given time. After these models have been investigated, a new framework is proposed to reduce the cost impact of mislabeled products in the forecasting process. Current models pick a threshold between the “discontinued” and “procurable” status that minimizes the number of errors rather than the error cost of the model. The new method proposes minimizing the average error cost of status misclassifications in the forecasting process. The method looks at the cost to the organization from false positives and false negatives; it varies the threshold between the statuses to reduce the number of more-costly errors by trading off less-costly errors using Receiver Operating Characteristic (ROC) analysis. This technique draws from practices in disease detection to decide the tradeoff between telling a healthy person they have a disease (false positive) and telling a sick person they do not have a disease (false negative). Early work and a case study using this method have demonstrated an average reduction of 6.46% in average misclassification cost in the cell phone market and a reduction of 20.27% in the camera market. Functional obsolescence management has grown with the increased connectivity of products, equipment, and infrastructure. In the alternative energy industry, as solar farms and wind turbines transform from emerging technologies to a fully developed one, these industries seek to better monitor the performance of aging equipment. Similar techniques to forecasting technical obsolescence is applied to predict whether equipment is functioning properly. A case study is used to predict functional obsolescence of wind turbines.Obsolescence forecasting models are quite useful when applied to aid business decisions. In business, the classic tradeoff is between longer life cycles and lower costs. This research seeks to create a generalized model to estimate the total life cycle cost of a component and the life cycle of a product before it becomes obsolete. These models will aid designers and manufacturers in better understanding how changes in usage and manufacturing factors shift the life cycle and total cost. A genetic optimization algorithm is applied to find the minimal cost given a desired life cycle. An alternative model is developed to find the maximum life cycle given a set cost level.
The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that ...help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.
Rapid changes in technology have led to an increasingly fast pace of product introductions. New components offering added functionality, improved performance and quality are routinely available to a ...growing number of industry sectors (e.g., electronics, automotive, and defense industries). For long-life systems such as planes, ships, nuclear power plants, and more, these rapid changes help sustain the useful life, but at the same time, present significant challenges associated with managing change. Obsolescence of components and/or subsystems can be technical, functional, related to style, etc., and occur in nearly any industry. Over the years, many approaches for forecasting obsolescence have been developed. Inputs to such methods have been based on manual inputs and best estimates from product planners, or have been based on market analysis of parts, components, or assemblies that have been identified as higher risk for obsolescence on bill of materials. Gathering inputs required for forecasting is often subjective and laborious, causing inconsistencies in predictions. To address this issue, the objective of this research is to develop a new framework and methodology capable of identifying and forecasting obsolescence with a high degree of accuracy while minimizing maintenance and upkeep. To accomplish this objective, current obsolescence forecasting methods were categorized by output type and assessed in terms of pros and cons. A machine learning methodology capable of predicting obsolescence risk level and estimating the date of obsolescence was developed. The machine learning methodology is used to classify parts as active (in production) or obsolete (discontinued) and can be used during the design stage to guide part selection. Estimates of the date parts will cease production can be used to more efficiently time redesigns of multiple obsolete parts from a product or system. A case study of the cell phone market is presented to demonstrate how the methodology can forecast product obsolescence with a high degree of accuracy. For example, results of obsolescence forecasting in the case study predict parts as active or obsolete with a 98.3% accuracy and regularly predicts obsolescence dates within a few months.
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