Harmful exposure to erythemally-effective ultraviolet radiation (UVR) poses high health risks such as malignant keratinocyte cancers and eye-related diseases. Delivering short-term forecasts of the solar ultraviolet index (UVI) is an effective way to advise UVR exposure information to the public at risk. This research reports on a novel framework built to forecast UVI, integrating antecedent lagged memory of cloud statistical properties and the solar zenith angle (SZA). To produce the forecasts at multi-step horizon we design a 3-phase hybrid convolutional long short-term memory network (W-O-convLSTM) model, validated with Queensland-based datasets. Our approach in optimizing the performance entails a robust selective filtering method using the BorutaShap algorithm, data decomposition with stationary wavelet transformation and hyperparameter optimization using the Optuna algorithm. We assess the performance of the proposed W-O-convLSTM model alongside the baseline and benchmark models. The captured results, through statistical metrics and visual infographics, elucidate the superior performance of the objective model in shortterm UVI forecasting. For instance, at a 10-minute forecast horizon, our objective model yields a relatively high correlation coefficient of ~0.961 in the autumn, 0.909 in the summer, 0.926 in the spring and 0.936 in the winter season. Overall, the proposed O-convLSTM model outperforms its competing counterpart models for all forecast horizons with the lowest absolute forecast error. The robustness of our newly proposed model avers its practical utility in delivering accurate sun-protection behavior recommendations to mitigate UVexposure-related public health risk. In accordance with our findings, we recommend that future integration of aerosol and ozone effects with cloud cover data may further enhance our UVI forecasting framework.INDEX TERMS Ultraviolet index forecasting, cloud effects, convolutional long short-term memory network, stationary wavelet transform.
Diminishing fossil fuel reserves, rising market price for diesel and the need to combat greenhouse gas emissions have led to the development of a crucial area of research into alternative fuels for diesel engines. In this work, a hybrid fuel was prepared for the first time by blending Pongamia oil, hydrated ethanol (95% purity) and butanol (as a surfactant). To eliminate engine modification and reduce injector clogging in the diesel engine, degummed Pongamia oil was utilized for preparing hybrid fuels. The results show that the density and viscosity of Pongamia oil reduced considerably after blending with ethanol and was brought closer to that of diesel. The gross calorific values were comparable with that of diesel. The brake thermal efficiencies of using hybrid fuels on a compression ignition engine were very similar to that of diesel. The emissions characteristics of hybrid fuels show reduced emissions of CO 2 , NO x and SO 2. The hybrid fuel blends E 22 B 27 DPO 51 and E 17 B 16 DPO 67 prepared with degummed Pongamia oil show the lowest emissions. Thus, these hybrid fuels have the potential to substitute diesel to run diesel powered inter-island shipping vessels, fishing boats and smaller power plants for household electricity in remote and outer islands of developing countries.
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