Social Money: Literary Engagements with Economics in Early Modern English Drama

This thesis investigates the impact of economic philosophy and history on sixteenth- and seventeenth-century English drama. It focuses primarily on the ways in which emergent mercantilist theories, new labour models, and changing class structures informed literary production. The significant influence exerted on the English public by financial developments during the early modern period suggests that economic concerns were of preeminent relevance to public discourse. As a result, playwrights cognizant of these worries produced plays that incorporated the distinctive language and character of economic thought and engaged their audiences through tableaus representative of select aspects of London’s financial landscape. In my first chapter, I use historical studies of Jacobean England’s engagement with slavery to read Shakespeare’s The Tempest as a political debate over the delineations among slaves, servants, and subjects within English institutions of servitude. Chapter Two examines Walter Mountfort’s The Launching of the Mary as a piece of early modern economic propaganda, with particular emphasis on its confluence of economic dialogue and the use of the female body as political imagery. Chapter Three is a rereading of Shakespeare’s 1 Henry VI; I argue that the play, which has chiefly been read as a dramatization of political history, is also an allegorical and moralized narrative of England’s transition from feudalism to mercantilism. Chapter Four addresses the personifications of greed in the medieval morality plays Everyman and The Castle of Perseverance and in Marlowe’s The Jew of Malta, with specific attention paid to the models of ideological morality and institutional discipline promoted by these displays. The considerable perspectives offered by economic criticism produce meaningful engagements with these plays and their literary, historical, and philosophical frameworks

Deep Representation of a Normal Map for Screen-Space Fluid Rendering

We propose a novel method for addressing the problem of efficiently generating a highly refined normal map for screen-space fluid rendering. Because the process of filtering the normal map is crucially important to ensure the quality of the final screen-space fluid rendering, we employ a conditional generative adversarial network (cGAN) as a filter that learns a deep normal map representation, thereby refining the low-quality normal map. In particular, we have designed a novel loss function dedicated to refining the normal map information, and we use a specific set of auxiliary features to train the cGAN generator to learn features that are more robust with respect to edge details. Additionally, we constructed a dataset of six different typical scenes to enable effective demonstrations of multitype fluid simulation. Experiments indicated that our generator was able to infer clearer and more detailed features for this dataset than a basic screen-space fluid rendering method. Moreover, in some cases, the results generated by our method were even smoother than those generated by the conventional surface reconstruction method. Our method improves the fluid rendering results via the high-quality normal map while preserving the advantages of the screen-space fluid rendering methods and the traditional surface reconstruction methods, including that of the computation time being independent of the number of simulation particles and the spatial resolution being related only to image resolution

Ultra-Processed Food Intakes Are Associated with Depression in the General Population: The Korea National Health and Nutrition Examination Survey

Depression is the most common mental illnesses worldwide. The consumption of ultra-processed food (UPF) has increased globally due to its affordability and convenience; however, only a few studies have investigated the link between UPF intake and depression in the general population. We investigated the associations between UPF and depression using the Korea National Health and Nutrition Examination Survey. A total of 9463 individuals (4200 males and 5263 females) aged above 19 years old participated in this study. The prevalence of depression was identified using the Patient Health Questionnaire-9. Dietary intake was assessed through a 24-h recall interview. The percentage of energy from UPFs was ascertained based on the NOVA classification. The associations between the quartile ranges of UPF intake and depression were estimated using logistic regression models. Individuals in the highest quartile had a 1.40 times higher likelihood of having depression, with marginal significance (95% confidence intervals (CIs) = 1.00–1.96). In a sex-specific stratification, only females demonstrated a significant association (odds ratio (OR) = 1.51, 95% CI 1.04–2.21), even after adjusting for confounders (p-value for trend = 0.023). Our findings revealed a significant association between higher UPF intake and depression among females but not among males in the Korean general population

Designing a public engagement process for long-term urban park development project.

Gathering public consensus about long-term urban open space development is more difficult than ever, even though public engagement is crucial for sustainable long-term policymaking. Routine evaluation of public awareness is important for retaining project momentum and designing appropriate public engagement processes for the future. This study focuses on the Yongsan Park Development Project, which has been in progress for more than three decades. An online survey of 2,000 respondents was conducted and analyzed to evaluate the current public awareness and ask questions about respondents' expectations for public engagement. The results of this study reveal that 1) a hybrid methodology is needed to effectively approach different age groups; 2) an online survey can offer new insights for projects that repurpose U.S. army base and military sites into urban open spaces; 3) the survey results will enable us to design a better public participation process that is appropriate for post-pandemic society, in which virtual meetings and socially distanced communications are part of the new norm

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

BackgroundImmunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.MethodsWe developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.ResultsWe trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.ConclusionThe results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.11Nsciescopu