Integrating PV-based energy production utilizing the existing infrastructure of MRT-6 at Dhaka, Bangladesh.

In a very dense urban landscape, incorporating renewables becomes challenging due to a lack of space, planning, and mindset. Utilization of already existing large infrastructures in combination with existing technology and necessary adaptation can create the right synergy for harnessing renewables like solar. This paper proposes the installation of a solar power plant in Dhaka, Bangladesh, using available space on Metro Rail Line 6 to meet the increasing demand for clean and renewable energy. The proposed system involves the selection of suitable space, and PV panels, the calculation of annual electricity generation, and performing financial and environmental analyses. The proposed on-grid/grid-tied system offers the advantage of reducing dependence on power supplied to the grid, resulting in lower energy costs, and promoting the use of green energy. The system has a payback period of 7.7 years and a return on investment of 45.7 %. It is estimated that the system saves 14,053.203 tons of CO2 emissions per year and 281,064.06 tons of CO2 emissions over its lifetime. Also, the grid life cycle emission is 584 gCO2/kWh, and the system life cycle emission is 39,119.4 tCO2, which further proves that it is a feasible solution to meeting energy demands while reducing the dependency on fossil fuels and promoting sustainable energy utilization. The results of simulations run using PVsyst and HOMER confirm the economic viability of the proposed solar power station, supporting its viability. The levelized energy cost (LCOE), as projected by PVsyst, is $0.09 per kWh, nearly matching HOMER's prediction of $0.0835. This convergence of results from several simulation tools supports the solar power plant's predicted cost-effectiveness, demonstrating its potential as a key player in the effort to create a greener and more affordable energy landscape.

Characterizing the Aging Process of the Human Eye: Tear Evaporation, Fluid Dynamics, Blood Flow, and Metabolism-Based Comparative Study.

Eye temperature and intraocular pressure are two measurable parameters that can be monitored as a health index with aging. Deviations from the normal range of intraocular pressure and temperature lead to the formation of many diseases. This study has been carried out to evaluate the relations between the physiological and anatomical changes of the eye with aging using mathematical modeling. 2D computer-aided design of the human eye has been developed for two major groups: 21 to 30 years and 41 to 50 years. The computer simulation has been carried out to determine the effects of physiological changes of tear evaporation, fluid dynamics, blood flow, and metabolism of eye tissues with aging. The simulation has been carried out in the standing and the supine position of a human body. The rate of temperature change is - 0.0075 K per year in the standing position and - 0.007 K per year in the supine position because of the modeled anatomical and physiological effects. All the three simulation parameters of this study, the temperature of the human eye, the intraocular pressure, and the aqueous humor flow velocity, have been compared with the recent practical and simulation-based experiments to validate our results.

Sequential transfer learning based on hierarchical clustering for improved performance in deep learning based food segmentation.

Accurately segmenting foods from optical images is a challenging task, yet becoming possible with the help of recent advances in Deep Learning based solutions. Automated identification of food items opens up possibilities of useful applications like nutrition intake monitoring. Given large variations in food choices, Deep Learning based solutions still struggle to generate human level accuracy. In this work, we propose a novel Sequential Transfer Learning method using Hierarchical Clustering. This novel approach simulates a step by step problem solving framework based on clustering of similar types of foods. The proposed approach provides up to 6% gain in accuracy compared to traditional network training and generated a robust model performing better in challenging unseen cases. This approach is also tested for segmenting foods in Danish school children meals for dietary intake monitoring as an application.