Objective:
To scientifically validate the probiotic and prebiotic qualities of traditional fermented foods—especially Vidarbha’s mango pickle—and promote them as sustainable, immunity-boosting alternatives requiring no refrigeration or artificial processing.

Scientific Basis:

• Probiotic Potential: Lactic Acid Bacteria (LAB) foster gut health
  • Ref: Ray & Sivakumar, 2009 – Traditional Fermented Foods of India
• Prebiotic Ingredients: Raw mango, mustard seeds, fenugreek, asafoetida
  • Ref: Pandey et al., 2015 – Journal of Food Science and Technology

Expected Impact:

• Improved understanding of gut-health practices based on indigenous diets
• Promotion of low-cost, natural food preservation for nutrition security
• Rural entrepreneurship models based on pickle-making and fermentation

Data and Knowledge Generation:

• Microbial diversity profiling of fermented foods
• LAB identification and quantification
• Nutritional value estimation

Shelf-life and bioavailability studies

Objective:
To explore and validate socio-cultural health practices, such as clan-based marriage restrictions and Ekadashi fasting, through genetics, metabolism, and behavioral science.

Focus Areas:

• Maternal Clan of Bride and Groom Should not be Same Maternal Clan Restriction (Marathi Tradition मुलाचे आणि मुंलीचे मामकुळ एक नको, नाडी ? ): Genetic rationale against consanguinity
• Ekadashi Fasting: Study of metabolic reset, hormonal regulation, immune response

Expected Impact:

• Re-establishing trust in ancestral practices via scientific validation
• Health models for disease prevention through non-pharmacological interventions
• Socio-genetic risk mapping for future public health strategies

Data and Knowledge Generation:

• Community-based biometric and genetic data
• Metabolic and hormonal profiling during fasting

Ethnographic data on fasting patterns and health outcomes

Objective:
To revive and scientifically assess ancient water management (JalTara) and farming practices suitable for today’s climate and environmental challenges.

Focus Areas:

• Indigenous irrigation systems
• Organic nutrient cycles and pest control
• Crop resilience and soil enrichment methods

Expected Impact:

• Scalable low-input farming systems
• Enhanced soil carbon and biodiversity indices
• Eco-restoration using traditional engineering

Data and Knowledge Generation:

• Soil quality, water retention, and biodiversity analysis
• GIS and remote sensing data for traditional water body mapping

Crop yield and ecological footprint comparisons

Objective:
To revitalize Indian pedagogy through experiential frameworks like Shravan, Manan, Nididhyasan, and Sakshatkar, integrating them into competency-based NEP-aligned learning.

Pedagogical Framework:

• Shravan: Listening from authority via storytelling and demonstration
• Manan: Critical contemplation and filtration of knowledge
• Nididhyasan: Deep assimilation into one’s consciousness
• Sakshatkar: Spontaneous, internalized realization and intuitive recall

Expected Impact:

• Development of emotionally intelligent, reflective learners
• Culturally rooted, future-ready education practices
• Skilling ecosystem based on ancient Indian epistemology

Data and Knowledge Generation:

• Qualitative and quantitative assessment of learning outcomes
• AR/VR-based experiential module performance tracking

Comparative studies with traditional and modern pedagogy models

Objective:
To convert traditional diagnostic methods—such as Naadi Pariksha, facial tone, and voice cues—into validated, sensor-based tools using AI and signal analytics.

Focus Areas:

• AI modeling of health cues from traditional diagnostics
• Wearable sensor development for preventive rural healthcare
• Mobile platforms for self-screening based on IKS

Expected Impact:

• Rural-accessible early disease screening tools
• Preservation and modernization of traditional health wisdom
• Creation of health-tech startups rooted in Indian systems

Data and Knowledge Generation:

• Biosignal repositories (pulse, voice, skin tone, etc.)
• Diagnostic algorithm training and validation datasets

Case studies linking traditional signs with clinical markers