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Abstract Large language models (LLMs) demonstrate strong reasoning and planning capabilities in static textual contexts, yet they struggle significantly with dynamic decision-making tasks involving spatial elements, such...

Anatomical education presents ongoing challenges in helping students master complex content while integrating it with clinical relevance. Neuroanatomy is particularly difficult due to its intricacy and abstract nature. T...

IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or hand...

The provision of accurate and explainable captions and answers to visual questions about remote sensing images is essential in areas such as disaster management, land-use supervision, and military analysis, where system ...

IntroductionThe complex interplay between environmental dynamics, biodiversity loss, and public health necessitates advanced methodologies for quantifying and interpreting their interactions. Respiratory health, highly s...

Dairy farms increasingly deploy sensor networks that generate high-frequency behavioral and spatial data, yet translating these multimodal streams into actionable welfare insights remains a key challenge. This study pres...

Abstract Lung cancer remains the primary cause of cancer-related deaths throughout the world. The main reason behind this is late diagnosis and the restrictions in the manual interpretation of imaging data. In these days...

The perception systems for Traffic Sign Recognition (TSR) and Lane Line Recognition (LLR) are foundational pillars for the safe and effective operation of Advanced Driver-Assistance Systems (ADAS) and fully autonomous ve...