http://210.212.227.212:8080/xmlui/handle/123456789/19 Sun, 17 May 2026 00:00:57 GMT 2026-05-17T00:00:57Z http://localhost:8080/xmlui/bitstream/id/219a070f-1808-48f9-afa6-1b580e9ca054/journal.jpg http://210.212.227.212:8080/xmlui/handle/123456789/19 http://210.212.227.212:8080/xmlui/handle/123456789/128 Drying and Atterberg limits of Cochin marine clay Amal, Azad Sahib Marine clays are reported to undergo an irreversible change in plasticity on drying. Various reasons such as the presence of halloysite and allophane minerals that change their structure, the presence of sesquioxides or organic matter that undergo cementation and the presence of salinity that generates strong attractive forces leading to aggregation on drying have been reported. In this study, the properties of Cochin marine clay, obtained from Cochin, India, in air-dried and oven-dried conditions were evaluated. The study shows that the plasticity characteristics get altered on drying, but can be reversed by proper dispersion using dispersion tools. Sat, 28 Dec 2019 00:00:00 GMT http://210.212.227.212:8080/xmlui/handle/123456789/128 2019-12-28T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/118 Air quality in five major cities of India induced by the COVID-19 pandemic lockdown Priya, K L; Adarsh, S This discussion paper looks into the COVID-19 induced lockdown on the air quality of the five most polluted cities in India. There were significant reductions in the concentrations of particulate matter, 2.5 mm and 10 mm, NO2, and CO during lockdown compared to that in 2019. Even then, the levels of particulate matter never reached the target specified by the World Health Organization. There was an increase in the concentrations of O3 at some cities, which may be attributed to the alterations in the photostationary cycle due to change in the concentration of nitrogen oxides and volatile organic carbons. Fri, 01 Jan 2021 00:00:00 GMT http://210.212.227.212:8080/xmlui/handle/123456789/118 2021-01-01T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/117 Modeling future irrigation water demands in the context of climate change: a case study of Jayakwadi command area, India Adarsh, S In the present study, the downscaled future climate data from the General Circulation Model (GCM), CanESM2 has been used to calculate the monthly crop water requirements of the major crops cultivated in the Jayakwadi command area, Maharashtra, India. Statistical downscaling was carried out using the statistical downscaling model and the future irrigation demands were estimated using the CROPWAT model. Statistical downscaling of the CanESM2 GCM model and prediction of the future temperature and precipitation was done for two representative concentration pathways (RCP) scenarios namely the RCP 4.5 and RCP 8.5. Further, the future irrigation demands were estimated under the RCP 4.5 and 8.5 scenarios for the period 2011–2100 with three-time spells of 30 years centered on the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). The results indicated an increase in temperature and precipitation over time spells when compared to the base period (1961–2005). The annual average temperature has been projected to increase by 0.306 °C and 0.358 °C by the 2080s when compared to the base period under the RCP 4.5 and RCP 8.5 scenarios, respectively. The annual average precipitation has been projected to increase from 856.58 mm in the base period to 1410.11 mm and 1784.06 mm under RCP 4.5 and RCP 8.5, respectively. The average reference evapotranspiration ( ETo) values showed an increase from 5.41 mm/day to 5.45 mm/ day, 5.53 mm/day, and 5.57 mm/day for the periods 2020s, 2050s and 2080s respectively in the RCP 8.5 scenario. The average annual irrigation demand showed a reduction of 14.07% and 14.72% for RCP 4.5 and RCP 8.5 scenarios respectively. The estimated variations in demand values can be used for optimal irrigation planning in the culturable command area of the Jayakwadi reservoir. Sun, 30 Aug 2020 00:00:00 GMT http://210.212.227.212:8080/xmlui/handle/123456789/117 2020-08-30T00:00:00Z http://210.212.227.212:8080/xmlui/handle/123456789/115 Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia Adarsh, S The peak period of an energy-generating wave is one of the most important parameters that describe the spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (TP) forecast model is constructed using a suite of statistically significant lagged inputs based on the partial auto-correlation function with an extreme learning machine model developed and its predictive utility is benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy relative to deep learning models in selected coastal study zones. The study establishes the practical usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and sustainable energy resource management systems. Sun, 12 Dec 2021 00:00:00 GMT http://210.212.227.212:8080/xmlui/handle/123456789/115 2021-12-12T00:00:00Z