International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Modern geospatial systems increasingly require search that is both where-aware and meaning-aware. Traditional spatial indexes (e.g., R-tree, Quad/Oct-tree, S2/Geohash) excel at geometric predicates and topological filtering, yet fall short when users ask semantic questions (“ports similar to Rotterdam,” “neighborhoods with transit-oriented development like X”). In parallel, embedding models for text and imagery enable powerful semantic retrieval but typically ignore spatial topology, containment, and scale.
This paper introduces a hybrid spatial–vector search architecture that unifies spatial predicates with embedding similarity for GIS-scale data. The proposed approach involves: (i) a two-stage retrieval process that initially prunes candidates using spatial cells (such as R-tree or S2 indexing) before ranking results with approximate nearest neighbour (ANN) search over embeddings (for example, HNSW or IVF methods); (ii) cell-aware vector indexes that co-partition embeddings according to space-filling curves, thereby reducing cross-cell probes; (iii) a cost-based query planner designed to jointly optimise spatial selectivity and vector recall; and (iv) a multi-modal Retrieval-Augmented Generation (RAG) layer, which integrates map features, textual data, and remote-sensing image embeddings to produce grounded responses. Evaluation is conducted on public geo-text and satellite imagery datasets, with results reported on latency/recall trade-offs, spatial bias effects, and robustness across heterogeneous scales and coordinate reference systems.
Results demonstrate that hybrid indexing delivers more than tenfold lower latency at fixed recall compared to vector-only baselines for spatially selective queries, while maintaining geometric correctness through predicate pushdown. Integration pathways with mainstream GIS and spatial SQL systems (such as PostGIS combined with pgvector) are explored, and ongoing challenges are identified in areas including geodesic distance metrics, CRS normalization, privacy, and reproducible benchmarking. These findings provide a practical blueprint for AI-empowered geospatial search that addresses both the spatial characteristics of locations and the semantic aspects of meaning.

AI models are reshaping software performance testing. Machine Learning and Deep Learning algorithms automate test scenario generation, enable real-time monitoring, and predict performance issues. They facilitate dynamic load balancing, anomaly detection, testing automation, and offer performance optimization recommendations.

In recent years, artificial intelligence (AI)-enhanced educational games have become powerful tools for people with disabilities. These games use AI algorithms to customize and personalize the learning experience to meet learners’ unique needs and abilities. This comparative analysis aims to evaluate and compare different AI-powered educational games specifically developed for people with disabilities. This article attempts to provide insights so that developers, educators, and people with disabilities can use AI to select appropriate educational games. Highlights how AI-powered educational games can improve accessibility, engagement, and learning
outcomes for people with disabilities.

We are witnessing the massive and impressive penetration of artificial intelligence (AI) into many areas of human activity. This process is expected to intensify in the next few decades. In most technical fields, there will be a preponderance of the so-called narrow artificial intelligence with clearly defined tasks and functions. It is usually a coherent set of neural networks trained to solve specific problems. The advantage of narrow AI is that it is entirely controllable and, at the same time, has excellent capabilities. This publication aims to outline guidelines for applying narrow artificial intelligence in investigating the impact of climate change on transport infrastructure. After a brief introduction to narrow artificial intelligence and climate change, various possible areas suitable for AI modeling are explored. Directions and preparatory tasks for collecting climate-sensitive local data on the condition and changes in the transport infrastructure in Bulgaria necessary for AI training are identified.