José León

Ontic Zero and the Resolution of Foundational Underdetermination in Physics A Meta-Axiomatic Framework Author: Jose Jorge Leon Gavonel Date: December 30, 2025, Status: for formal review (Philosophy of Physics category) Abstract Modern physics operates across two distinct structural regimes: ordinal structures, encoding order and hierarchy without magnitude, and cardinal structures, encoding metric extension, continuity, and infinite dimensionality. While these regimes coexist formally within mathematics, their coupling in physics is pragmatic rather than ontologically grounded. This underdetermination manifests in persistent boundary pathologies such as singularities, divergences, fine-tuning, and unbounded infinities. This paper introduces Ontic Zero, a meta-axiomatic framework that establishes ordinal differentiation as ontically primary and cardinal extension as conditionally admissible. Rather than rejecting continua or infinite dimensional formalisms, Ontic Zero provides explicit criteria governing their ontological legitimacy. Minimal asymmetry (δ) is shown to be a transcendental necessity for any extension, while Gödelian incompleteness is reframed as a structural feature of extensional regimes. Ontic Zero does not replace physical theories or generate new empirical predictions; instead, it functions as a foundational constraint system that explains why existing theories succeed where they do and fail at their boundaries.

Latin America struggles to reach advanced Artificial Intelligence (AI) development due to limited resources and talent retention. However, different public indexes indicate the region’s potential to become an AI hub. Our analysis of research databases indicates a growing trend towards NeuroAI — an interdisciplinary field emerging from neuroscience and artificial intelligence — suggesting that this area of study may be a driving force behind the potential for Latin America. This paper explores how NeuroAI could enable Latin America to bridge the development gap with global AI leaders, leveraging its potential to overcome resource constraints. We analyze strategies for integrating NeuroAI advancements into the private sector, which could provide essential financial support for highimpact scientific research and foster regional innovation.

Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson’s disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson’s disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.

This study aimed to examine the relationship between anxiety, depression, subjective well-being, and academic performance in Peruvian university health science students with COVID-19-infected relatives. Eight hundred two university students aged 17–54 years ; 658 females and 144 males ; who completed the Patient Health Questionnaire-2, Coronavirus Anxiety Scale, Subjective Well-being Scale, and Self-reporting of Academic Performance participated. A partial unregularized network was estimated using the ggmModSelect function. Expected influence values were calculated to identify the central nodes and a two-tailed permutation test for the difference between the two groups. The results reveal that a depression and well-being node presents the highest relationship. The most central nodes belonged to COVID-19 anxiety, and there are no global differences between the comparison networks; but at the local level, there are connections in the network of COVID-19-infected students that are not in the group that did not present this diagnosis. It is concluded that anxious–depressive symptomatology and its relationship with well-being and evaluation of academic performance should be considered in order to understand the impact that COVID-19 had on health sciences students.