Research Papers
Application of digital twins for the low-voltage electricity grid–Challenges and opportunities of Distribution Grid Analytics in India
Singh Ajeet Kumar, Kumar Mukesh , Schopf Michael
| 2025
India anticipates significant growth in both population and energy demand. A substantial portion of this growth in energy demand is projected to be met through non-fossil-based resources, with ambitious plans to expand capacity to 500 GW by 2030. This ransition necessitates extensive grid infrastructure expansion to accommodate increasing demand and supply fluctuations. However, urban areas, experiencing rapid population growth, offer limited space for large-scale electricity network infrastructure development.
Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803
Bhagat Namrata , Gupta Guddu Kumar , Minhas Amritpreet Kaur , Chhabra Deepak , Shukla Pratyoosh
| 2025
Natural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high light intensity, affect the pigment accumulations in comparison to the control conditions.
Efficient nutrient sequestration and biomolecule production by Chlorella Sorokiniana MSP1 cultivated in industrial wastewater
Kalwani Mohneesh, Minhas Amritpreet Kaur, Shukla Pratyoosh, Pabbi Sunil
The generation of industrial wastewater poses a hazardous impact on the environment. Hence, this study aims to evaluate the performance of a new isolate Chlorella sorokiniana MSP1 in sequestering nutrients: nitrogen and phosphorus from wastewater to produce protein, carbohydrate, chlorophyll, carotenoid and lipids.Industrial wastewater supplemented with different percentage of BG-11 media i.e.
Sustainable recovery of Rare Earth Elements (REEs) from coal and coal ash through urban mining: A Nature Based Solution (NBS) for circular economy
Agrawal Ruchi , Ragauskas Arthur J.
The demand for rare earth elements (REEs) has surged in recent years, driven by their crucial role in various industrial applications and their uneven geological distribution. As a result, urban mining from secondary resources, particularly coal and coal ash, has gained traction as a sustainable solution within a circular economy framework. This study highlights the significant presence of REEs in coal and coal ash, revealing that certain samples contain REE concentrations that rival traditional ores.
Chemical characterization and biological activity of Curvularia Lunata, an endophytic fungus isolated from lemongrass (Cymbopogon citratus). Braz J Microbiol
Kaur Mehak, Mishra Rahul C., Lahane Vaibhavi, Kumari Anita, Yadav Akhilesh K., Garg Monika, Barrow Colin J., Goel Mayurika
Exploration of medicinal plants for bioactive-producing endophytic fungi is a relatively unmapped source of pharmaceuti¬cally important compounds. In this study, the endophytic fungus Curvularia lunata AREF029 isolated from the medicinal plant Cymbopogon citratus (known as lemongrass) was assessed for its biological activity. The methanolic extract of AREF029 had minimum inhibition concentration (MIC) ranging from 38 to 174 μg/ml against phytopathogenic fungi Alteranria solani, Fusarium oxysporum and Rhizoctonia solani.
An Overview of Pigmented Bioactive Products from the Genus Cordyceps
Kaur Mehak , Goel Mayurika
| 2025
The demand for natural colorants in the food, textile, cosmetic, pharmaceutical, and other industries has increased due to the adoption of environmentally friendly alternatives to non-sustainable resources. Microorganisms are preferred over other biological resources because of their high stability, water solubility, year-round availability, and cost-effectiveness. Fungi are prolific producers of a myriad of pigments with therapeutic benefits.
Artificial Intelligence in Climate Resilience: Evaluating Contributions to SDG 13
Sharma Reeta, Shekar Alpana C
| 2024
In recent years, there has been a significant increase in artificial intelligence (AI) approaches for Sustainable Development Goals (SDGs), particularly SDG 13: Climate Action. Several AI technologies, such as machine learning, deep learning neural networks, and big data analytics present new tactics to tackle the complex problems of climate change.
Spatial variation of biochar production potential from surplus crop residues in India
Datta Arindam , Dutta Sutapa , Sharma Shivani , Rahman Md.Hafizur
| 2025
India faces environmental issues due to large-scale seasonal in situ burning of crop residues, leading to air pollution and nutrient loss. Biochar application can increase soil carbon content, moisture, and nutrient content while reducing air pollution. India produces 156 Mt. of annual in situ surplus crop residues from ten major crops, with the highest potential for rice residue biomass in Sangrur, Punjab. Biochar could reduce greenhouse gas emissions by 405 Tg annually and its application to soil could sequester 7.5 Tg of carbon.
GIS and ML-Driven Insights into Forest Vulnerability and Climate Hotspots in Assam, India
Ghosh Sayanta , Warman Aakash , Chauhan Pranjul , Soni Aniruddh , and Sharma Jitendra Vir
| 2025
This study assesses the impact of regional climate variability on forest vulnerability in Assam using a GIS and Machine Learning (ML)-based approach. A grid-based Forest Vulnerability Index (FVI) was developed using eight key indicators, and climate change hotspots were mapped using temperature and precipitation anomalies. The results revealed that 87 forested grids are highly vulnerable, with significant overlaps between climate hotspots and biodiversity risk zones.
Remote Sensing and Machine Learning-based assessment of Forest Biomass Changes in Pench Tiger Reserve, Maharashtra, India
Ghosh, S., Zaidi, A., Chauhan, P., Soni, A., & Vir Sharma, J. V
| 2024
This study evaluates spatio-temporal variations in forest biomass in the Pench Tiger Reserve, Maharashtra, India, using a combination of remote sensing, field-based observations, and machine learning approaches. Fractional Vegetation Cover (FVC) layers were generated using the Linear Spectral Unmixing (LSU) Algorithm applied to multi-temporal LANDSAT data (2001-2022). A Linear Regression Model (LRM) was developed to estimate forest biomass across three forest strata: Very Dense Forest (VDF), Moderate Dense Forest (MDF), and Open Forest (OF), based on field sampling plots.