Development of a Non-Reacting LES Solver for Unstructured Grid

The present thesis consists of development of an LES based explicit solver which could simulate non reacting flows. The numerical simulation is carried out using Dynamic k equation subgrid scale model. Along with solving the Navier- Stokes equation a convection diffusion equation for mass fraction is also solved which would correct the equivalent density.The length and time scales for the mesh and simulations are calculated based on the Kolmogorov’s hypothesis and the CFL number is calculated accordingly.An explicit solver is used because of the fact that the calculated CFL number is extremely lower than 1. Two cases were validated using the above developed code, first is the case of an axisymmetric turbulent jet of air entering a quiscent atmosphere and the second one is the case where a variable density fluid(here Helium) entering the same quiscent air. The development of the plumes are captured.The development of the plume structures of both the cases are discussed. The averaged velocity profiles are also discussed. The mean velocity , turbulent fluctuations and Reynolds stresses are plotted. A brief study on the parallelisation technique used in OpenFoam is also done. Finally using the fluctuating data from both simulations the energy spectrum graphs are plotted which ensures that the mesh is suitable for the present study

REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection

Technological advancements in web platforms allow people to express and share emotions towards textual write-ups written and shared by others. This brings about different interesting domains for analysis; emotion expressed by the writer and emotion elicited from the readers. In this paper, we propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. Within state-of-the-art NLP tasks, it is well understood that utilizing context-specific representations from transformer-based pre-trained language models helps achieve improved performance. Within this affective computing task, we explore how incorporating affective information can further enhance performance. Towards this, we leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention. For empirical evaluation, we procure a new dataset REN-20k, besides using RENh-4k and SemEval-2007. We evaluate the performance of our REDAffectiveLM rigorously across these datasets, against a vast set of state-of-the-art baselines, where our model consistently outperforms baselines and obtains statistically significant results. Our results establish that utilizing affect enriched representation along with context-specific representation within a neural architecture can considerably enhance readers' emotion detection. Since the impact of affect enrichment specifically in readers' emotion detection isn't well explored, we conduct a detailed analysis over affect enriched Bi-LSTM+Attention using qualitative and quantitative model behavior evaluation techniques. We observe that compared to conventional semantic embedding, affect enriched embedding increases ability of the network to effectively identify and assign weightage to key terms responsible for readers' emotion detection

REDAffectiveLM: leveraging affect enriched embedding and transformer-based neural languagemodel for readers’ emotion detection

Technological advancements in web platforms allow people to express and share emotions toward textual write-ups written and shared by others. This brings about different interesting domains for analysis, emotion expressed by the writer and emotion elicited from the readers. In this paper, we propose a novel approach for readers’ emotion detection from short-text documents using a deep learning model called REDAffectiveLM. Within state-ofthe-art NLP tasks, it is well understood that utilizing context-specific representations from transformer-based pre-trained language models helps achieve improved performance.Withinthis affective computing task, we explore how incorporating affective information can further enhance performance. Toward this, we leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention. For empirical evaluation, we procure a new dataset REN-20k, besides using RENh-4k and SemEval-2007. We evaluate the performance of our REDAffectiveLM rigorously across these datasets, against a vast set of state-of-the-art baselines, where our model consistently outperforms baselines and obtains statistically significant results. Our results establish that utilizing affect enriched representation along with context-specific representation within a neural architecture can considerably enhance readers’ emotion detection. Since the impact of affect enrichment specifically in readers’ emotion detection isn't well explored, we conduct a detailed analysis over affect enriched Bi-LSTM+Attention using qualitative and quantitative model behavior evaluation techniques.We observe that compared to conventional semantic embedding, affect enriched embedding increases the ability of the network to effectively identify and assign weightage to the key terms responsible for readers’ emotion detection to improve prediction

36-month clinical outcomes of patients with venous thromboembolism: GARFIELD-VTE

Background: Venous thromboembolism (VTE), encompassing both deep vein thrombosis (DVT) and pulmonary embolism (PE), is a leading cause of morbidity and mortality worldwide.Methods: GARFIELD-VTE is a prospective, non-interventional observational study of real-world treatment practices. We aimed to capture the 36-month clinical outcomes of 10,679 patients with objectively confirmed VTE enrolled between May 2014 and January 2017 from 415 sites in 28 countries.Findings: A total of 6582 (61.6 %) patients had DVT alone, 4097 (38.4 %) had PE +/- DVT. At baseline, 98.1 % of patients received anticoagulation (AC) with or without other modalities of therapy. The proportion of patients on AC therapy decreased over time: 87.6 % at 3 months, 73.0 % at 6 months, 54.2 % at 12 months and 42.0 % at 36 months. At 12-months follow-up, the incidences (95 % confidence interval [CI]) of all-cause mortality, recurrent VTE and major bleeding were 6.5 (7.0-8.1), 5.4 (4.9-5.9) and 2.7 (2.4-3.0) per 100 person-years, respectively. At 36-months, these decreased to 4.4 (4.2-4.7), 3.5 (3.2-2.7) and 1.4 (1.3-1.6) per 100 person-years, respectively. Over 36-months, the rate of all-cause mortality and major bleeds were highest in patients treated with parenteral therapy (PAR) versus oral anti-coagulants (OAC) and no OAC, and the rate of recurrent VTE was highest in patients on no OAC versus those on PAR and OAC. The most frequent cause of death after 36-month follow-up was cancer (n = 565, 48.6 %), followed by cardiac (n = 94, 8.1 %), and VTE (n = 38, 3.2 %). Most recurrent VTE events were DVT alone (n = 564, 63.3 %), with the remainder PE, (n = 236, 27.3 %), or PE in combination with DVT (n = 63, 7.3 %).Interpretation: GARFIELD-VTE provides a global perspective of anticoagulation patterns and highlights the accumulation of events within the first 12 months after diagnosis. These findings may help identify treatment gaps for subsequent interventions to improve patient outcomes in this patient population

Effects of once-weekly exenatide on cardiovascular outcomes in type 2 diabetes

BACKGROUND: The cardiovascular effects of adding once-weekly treatment with exenatide to usual care in patients with type 2 diabetes are unknown. METHODS: We randomly assigned patients with type 2 diabetes, with or without previous cardiovascular disease, to receive subcutaneous injections of extended-release exenatide at a dose of 2 mg or matching placebo once weekly. The primary composite outcome was the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The coprimary hypotheses were that exenatide, administered once weekly, would be noninferior to placebo with respect to safety and superior to placebo with respect to efficacy. RESULTS: In all, 14,752 patients (of whom 10,782 [73.1%] had previous cardiovascular disease) were followed for a median of 3.2 years (interquartile range, 2.2 to 4.4). A primary composite outcome event occurred in 839 of 7356 patients (11.4%; 3.7 events per 100 person-years) in the exenatide group and in 905 of 7396 patients (12.2%; 4.0 events per 100 person-years) in the placebo group (hazard ratio, 0.91; 95% confidence interval [CI], 0.83 to 1.00), with the intention-to-treat analysis indicating that exenatide, administered once weekly, was noninferior to placebo with respect to safety (P<0.001 for noninferiority) but was not superior to placebo with respect to efficacy (P=0.06 for superiority). The rates of death from cardiovascular causes, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, hospitalization for heart failure, and hospitalization for acute coronary syndrome, and the incidence of acute pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse events did not differ significantly between the two groups. CONCLUSIONS: Among patients with type 2 diabetes with or without previous cardiovascular disease, the incidence of major adverse cardiovascular events did not differ significantly between patients who received exenatide and those who received placebo