arXiv subjects

Diogo Sousa

Publications and source records attributed to Diogo Sousa.

3 recordsLinked to original sources

arXiv2026

A Physics-Informed Statistical Learning Model for Long-Term Fragmentation Cloud Propagation

This paper introduced a Hierarchical Generative Density Model (HGDM) for the long-term propagation of orbital fragmentation clouds. Validation against high-fidelity Monte Carlo simulations showed that the proposed surrogate accurately reproduces the dominant multidimensional structures of propagated clouds while consistently outperforming classical band-formation approximations based on independent angular variables. Accurate cloud reconstructions were obtained using only a few hundred to a few thousand propagated fragments, yielding reductions exceeding two orders of magnitude in computational cost and three orders of magnitude in storage requirements; future work will investigate its application to large-scale debris-environment evolution and collision-cascade simulations associated with the Kessler syndrome.

math.NA↗

arXiv2025

A Dopamine-Serotonin Theory of Consciousness

This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework developed herein demonstrates that consciousness exists as a continuous, non-monotonic function across a high-dimensional neurochemical space, with dopamine serving as the primary intensity regulator and serotonin (5-HT2A) as the complexity modulator. This work offers mechanistic explanations for the full spectrum of conscious states, from deep sleep and psychosis to the ultimate collapse in neural death. The theory explains paradoxical phenomena such as prefrontal cortex hypoactivity during seizures, the evolutionary persistence of psychosis-prone individuals, and why controlled administration of classical 5-HT2A agonists shows a comparatively low incidence of serious medical events (< 0.01 % in modern clinical trials), while dopaminergic excess proves rapidly lethal. The framework is tested using 70,290 sleep nights from 242 Parkinson's disease patients, using disease severity (UPDRS) as a proxy for system integrity and medication (LEDD) as a proxy for dopaminergic input. The analysis reveals a significant LEDD x UPDRS interaction (beta=-1.7, p<.0001), confirming the model's prediction of state-dependent, non-linear dynamics.

q-bio.NC↗

arXiv2025

Performance of Large Language Models in Supporting Medical Diagnosis and Treatment

The integration of Large Language Models (LLMs) into healthcare holds significant potential to enhance diagnostic accuracy and support medical treatment planning. These AI-driven systems can analyze vast datasets, assisting clinicians in identifying diseases, recommending treatments, and predicting patient outcomes. This study evaluates the performance of a range of contemporary LLMs, including both open-source and closed-source models, on the 2024 Portuguese National Exam for medical specialty access (PNA), a standardized medical knowledge assessment. Our results highlight considerable variation in accuracy and cost-effectiveness, with several models demonstrating performance exceeding human benchmarks for medical students on this specific task. We identify leading models based on a combined score of accuracy and cost, discuss the implications of reasoning methodologies like Chain-of-Thought, and underscore the potential for LLMs to function as valuable complementary tools aiding medical professionals in complex clinical decision-making.

cs.CL↗