Studies on the seaweeds of Andaman and Nicobar Group of Islands

The importance of marine algae, often referred to as seaweeds, has been felt over a long time and is appreciated more and more in modern times. The economic value of marine algae is understood both indirectly and directly. The indirect benefit is due to the role of ma rine phytoplankton as well as the benthic macrophyte biomass along the shore and in the continental shelf, in primary production of the sea. Direct benefit includes the use of ma rine algae as food, feed, fertilizer and as source of various products of commercial importance such as agar and alginic acid

Light Quality Effects on Cellular Organelles, Metabolism and Function of Acid-Base Balance in Ulva pertusa Kjellman

連合農学研究科博士論文(水産学) ; 学位取得日: 平成10年3月3

Effect of light quality on the cell integrity in marine alga <i style="">Ulva pertusa </i>(Chlorophyceae)

21-25A floating sterile mutant of Ulva pertusa was grown in the laboratory under various light conditions: White light (as reference), broadband isoquantic red (600 – 700 nm) and blue (400 – 500 nm) light. The morphological variation of cell organelles of white (WLC), blue (BLC) and red light cultures (RLC) was studied by electron microscopy after 15 days of light treatment with 14: 10 h light and dark photoperiod. The results indicate that blue light is more efficient than the red light in developing the thylakoid architecture of individual cell organelles. The cell maintenance (integrity) process, which is most essential for the cell division and growth, was found to be positively controlled by blue light. Furthermore, the cell morphology of Ulva was relatively well developed in BLC than in RLC and also comparable with WLC. The results suggest that the red part of the light, though not by itself able to support the growth, is not inhibitory either to growth or to the maintenance of cell integrity. However, the energy is not substantial to run other essential metabolic process

Solution-processed Ga-TiO₂ electron transport layer for efficient inverted organic solar cells

To boost the charge transport and collection processes in inverted organic solar device, gallium (Ga) was added to conventional TiO₂ film to form the Ga-TiO₂ film. Compared to the pristine film, the Ga-TiO₂ film showed better energy level alignment, which is in good agreement with the heightened open-circuit voltage and fill factor. Attributed to the superior electron transport, the Ga-TiO₂ based solar cell achieved paramount power conversion efficiency (PCE) of 7.72%, while the pristine TiO₂ based solar cell showed lower PCE of 6.65%.Ministry of Education (MOE)The research is supported by AcRF Tier1 grant (MOE2019-T1- 002-087) from Singapore Ministry of Education

Prediction of ionospheric TEC by LSTM and OKSM during M class solar flares occurred during the year 2023

Advancements in space weather forecasting have become crucial for understanding and mitigating the impacts of solar activity on Earth’s ionosphere. This research focuses on the prediction of Total Electron Content (TEC) during M-class solar flare events in 2023. TEC is a vital parameter for satellite communications and navigation, making accurate forecasting imperative. Two prediction models, Long Short-Term Memory (LSTM) neural networks and Surrogate Models based on Ordinary Kriging (OKSM), are employed. LSTM, known for capturing temporal dependencies, is contrasted with OKSM, a geostatistical interpolation technique capturing spatial autocorrelation. The study utilizes TEC measurements from the Hyderabad (HYDE) GPS station for model training and evaluation along with solar and geomagnetic parameters. The performance metrics for both models across various solar flare dates are measured using Root Mean Square Error (RMSE), Normalized RMSE, Correlation Coefficient (CC), and Symmetric Mean Absolute Percentage Error(sMAPE). The research interprets the results, highlighting the strengths and limitations of each model. Notable findings include LSTM’s proficiency in capturing temporal variations and OKSM’s unique spatial perspective. Different solar flare intensities are analyzed separately, demonstrating the model’s adaptability to varying space weather conditions. The average performance metrics during M 4.65 SF events for the OKSM model, in terms of Root Mean Square Error is 5.61, Normalized RMSE is 0.14, Correlation Coefficient is 0.9813, and Symmetric Mean Absolute Percentage Error is 14.90. Similarly, for LSTM, the corresponding averages are 10.03, 0.24, 0.9313, and 28.64. The research contributes valuable insights into the predictive capabilities of LSTM and OKSM for TEC during solar flare events. The outcomes aid in understanding the applicability of machine learning and geostatistical techniques in space weather prediction. As society’s reliance on technology susceptible to space weather effects grows, this research is pivotal for enhancing space weather forecasts and ensuring the robustness of critical technological infrastructure on Earth.</p