A comparative study of Sinhalese literature in the eleventh and twelfth centuries.

The thesis consists of six chapters. The first chapter deals with the historical background of the period (eleventh and twelfth centuries A.D.). It sums up the results of the Cola invasion and examines the nature and the functions of intellectual awakening after the re-establishment of Sinhalese rule with special reference to the age of Parakramabahu I. The second chapter treats briefly almost all the works of the period with their respective authors. It includes various categories of works', literary and otherwise, written in Sinhalese, Pali and Sanskrit. The third chapter concerns the concept of bhakti and its special significance in the context of devotional literature. It also shows how this concept was transferred to the Buddha by later Buddhist devotee writers. The fourth chapter makes a comparative study of the works of Gurulugomi and Vidyacakravarti, It examines their inspirations and influences from such Buddhist works as Avadanas as well as Sanskrit poetic traditions and some Sanskrit prose works. It also assesses the literary quality of each work. The fifth chapter contains a comparative study of the Sasadavata and the Muvadevdavata. It deals with the concept of poetry with special reference to the Siyabaslakara, and examines the inspiration of these Sinhalese poems from Buddhist literary traditions in addition to their influence from Sanskrit poetic traditions. It also approaches each work aesthetically following the rasa theory as postulated by Anandavardhana and Visvanatha. The sixth chapter deals with the problem of literary translation. It examines the translating processes put into practice by Ceylonese writers from early times. It pays special attention to the translating processes practised by Gurulugomi and Vidyacakravarti in their renderings of verse and prose

Development of a machine learning approach for local-scale ozone forecasting: application to Kennewick, WA

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O(3) episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O(3) forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O(3) observation data from Kennewick as training datasets. The meteorology datasets are from the Weather Research and Forecasting (WRF) meteorological model forecasts produced daily by the University of Washington. Our ozone forecasting system consists of two ML models, ML1 and ML2, to improve predictability: ML1 uses the random forest (RF) classifier and multiple linear regression (MLR) models, and ML2 uses a two-phase RF regression model with best-fit weighting factors. To avoid overfitting, we evaluate the ML forecasting system with the 10-time, 10-fold, and walk-forward cross-validation analysis. Compared to AIRPACT, ML1 improved forecast skill for high-O(3) events and captured 5 out of 10 unhealthy O(3) events, while AIRPACT and ML2 missed all the unhealthy events. ML2 showed better forecast skill for less elevated-O(3) events. Based on this result, we set up our ML modeling framework to use ML1 for high-O(3) events and ML2 for less elevated O(3) events. Since May 2019, the ML modeling framework has been used to produce daily 72-h O(3) forecasts and has provided forecasts via the web for clean air agency and public use: http://ozonematters.com/. Compared to the testing period, the operational forecasting period has not had unhealthy O(3) events. Nevertheless, the ML modeling framework demonstrated a reliable forecasting capability at a selected location with much less computational resources. The ML system uses a single processor for minutes compared to the CTM-based forecasting system using more than 100 processors for hours

Evaluating emission factors of PM2.5, selected PAHs and phenols from wheat and Kentucky Bluegrass stubble burning in Eastern Washington and Northern Idaho

Thesis (Ph.D.), Department of Civil and Environmental Engineering, Washington State Universit

Quantifying the COVID Clean-up of Air Quality in Colombo

This insight (3p.) was disseminated along with 6 infographics. Copyright © 2021 Verité Research Pvt Ltd. All Rights Reserved.This Insight quantifies the improvements in air quality in Colombo during periods of Covid19 related restrictions and reveals that the Covid19 restrictions have led to a 60% reduction in the average duration of time that people in Colombo were exposed to air that was unhealthy. To quantify the Covid-19 clean-up of air quality in Colombo, the average air quality in the months of November to February, which is usually the worst months for air quality in the city, from 2017 to 2020, has been compared against the air quality observed in November 2020 to February 2021. Furthermore, the main contributor of air pollution in Colombo has been identified as vehicle emissions