u/Ashd9909

The people around him forced him to include her name illegally.

Hindustan Aeronautics Limited has signed agreements with Adani Defence Systems and Technologies and BEML for manufacturing fuselage structures of the Prachand Light Combat Helicopter.

BEML will make 48 fuselages

Adani Defence will make 42 fuselages

This is being called the first time private industry is being integrated into helicopter fuselage production in India.

HAL has already started building the 156 Prachand LCHs ordered by the Indian Armed Forces at its new facility in Tumakuru, Karnataka. These partnerships will create two additional production lines to speed up deliveries.

HAL CMD Ravi K said it’s a significant step in strengthening India’s aerospace ecosystem. The move is also expected to open more work for Indian MSMEs and suppliers in the domestic aerospace supply chain.

What do you guys think good move for capacity building, or should HAL have kept more of this in-house?

City2Graph is a Python library I built that turns geospatial data into analysis-ready graphs (for spatial analysis, network analysis, and Graph Neural Networks as GeoAI), and the paper describing it has just been published, so I wanted to share it here.

Repository: https://github.com/c2g-dev/city2graph

import city2graph as c2g

# buildings + street segments -> heterogeneous morphological graph
nodes, edges = c2g.morphological_graph(buildings, segments)

# straight into PyTorch Geometric
data = c2g.gdf_to_pyg(nodes, edges)

What it covers:

• Morphology: graphs of buildings, streets, and tessellated urban fabric from OpenStreetMap and Overture Maps
• Transportation: GTFS and GBFS feeds loaded through DuckDB, with GTFS aggregated into stop-to-stop transit graphs
• Mobility: OD matrices and flow data (migration, bike-sharing, pedestrian counts) as weighted spatial graphs
• Proximity and contiguity: KNN, Delaunay, Gilbert, Waxman, plus queen/rook contiguity, under Euclidean, Manhattan, or network distances
• Heterogeneous graphs and metapaths: several node and edge types in one graph, with metapath-derived edges composing relations across them
• Conversion: round trips between GeoDataFrames, NetworkX, rustworkx, and PyTorch Geometric Data/HeteroData, with geometries and attributes kept intact

It sets out why urban data is better treated as heterogeneous graphs than as flat feature tables, how the morphological, transport, mobility, and proximity constructions relate to each other, and how the library keeps geometry and graph structure consistent across conversions. If you use the library in research, that is the citation.

Paper

Sato, Y., Pietrostefani, E., Mahabir, R., & Arribas-Bel, D. (2026). City2Graph: A Python library for Heterogeneous Graph Neural Networks and spatial analysis in urban systems. Computers, Environment and Urban Systems, 130, 102492.

Happy to answer questions about the design, and issues or PRs are very welcome. I am especially keen to hear which data sources people want supported next.

Almost all major infrastructure in Varanasi (airport road, multimodal terminal, ropeway, Kashi Vishwanath Corridor, Smart City projects, sewers, solar, PNG, ring road, etc.) was built after 2014 — many items 100% post-2014.Yet the winning margin dropped. Some are saying voters preferred Ajay Rai because he’s upper caste while Modi is OBC. Development vs caste — what do you think?