Exploring the relationship between health professionals' artificial intelligence literacy and their attitudes toward artificial intelligence

This study aims to determine the relationship between health professionals' artificial intelligence (AI) literacy and their attitudes toward AI. This descriptive and correlational study was conducted between May and July 2024 in an educational research hospital located in eastern Turkey, encompassing 1378 health professionals. The sample size was calculated as 301 participants, with a 95% confidence interval and a 0.05 margin of error, and data collection was completed with 439 participants. 58.8% of the participants were male and 49.0% of them did not have information about the use of artificial intelligence in the field of health. Men, those in the 46-55 age group, postgraduates, those who received artificial intelligence education and those who support artificial intelligence in health showed higher AI literacy and positive attitudes. A strong positive correlation was found between AI literacy and attitudes (r = 0.782**; r = 0.710**), and some sociodemographic variables explained 62.1% and 48.9% of positive and negative attitudes, respectively. Participants exhibited moderate AI literacy, high positive and moderate negative attitudes; attitudes were significantly affected by sociodemographic factors

Metric Chronetic Theory (MCT) Phase 6: Operational Hardware Specification for the MT-1 Coherent Distributed Spherical Phased Array (CDSPA)

This project documents Phase 6 of Metric Chronetic Theory, which advances the framework from theoretical development and empirical validation into operational hardware design. Phase 6 introduces the MT‑1 Coherent Distributed Spherical Phased Array (CDSPA), a hemispherical receiver architecture intended for both high‑capacity Sub‑THz communication and exploratory metric‑resonant signal detection. The specification outlines the system geometry, impedance‑control logic, strain‑monitoring requirements, and operational parameters needed to support coherent interaction with the metric medium. This phase differs from earlier work by providing a complete engineering blueprint rather than observational analysis or material‑level convergence. It establishes the hardware foundation for future prototype development and field testing. This registration does not include preregistration materials

An Eight-Parameter Assessment Framework for Tectonic Stress Evolution and Major Earthquake Probability Forecasting

Seismo Framework v2.0.2 is an open-source, research-based earthquake forecasting system that integrates eight fundamental geophysical monitoring parameters to provide probabilistic earthquake assessments with 3-14 day lead times. **Version 2.0.2 Features:** - 45 research equations from seismology and rock physics - 4-level alert system (GREEN/YELLOW/ORANGE/RED) - Bayesian probability updating framework - 82-88% classification accuracy - <100ms real-time analysis latency - 100% test coverage - Complete PyPI package with full documentation **Framework Components:** 1. Seismic Activity - Earthquake rate analysis and magnitude-frequency distribution 2. Crustal Deformation - GPS, InSAR, and strainmeter measurements 3. Hydrogeological Indicators - Groundwater level changes and radon emissions 4. Electrical/Magnetic Signals - Resistivity and electromagnetic anomalies 5. Instability Indicators - Lyapunov exponents from dynamical system analysis 6. Tectonic Stress State - Coulomb stress transfer calculations 7. Rock Properties - Seismic velocity variations and attenuation 8. Gas Geochemistry - Radon, helium isotopes, and volatile emissions **Performance Metrics:** - Detection rate: 75-85% for M ≥ 6.0 earthquakes - False alarm rate: <25% - Average lead time: 3-14 days - Validation: 120 earthquakes (2000-2020) - ROC AUC: 0.876 ± 0.021 **Case Studies:** - 2011 Tōhoku Earthquake (M9.0): 7-day warning capability - 2016 Kumamoto Earthquakes (M7.0): 48-hour lead time - 2019 Ridgecrest Sequence: Multi-day forecasting **Resources:** - Research Paper: 67 pages, 12,500 words, 45 equations, 187 references - Source Code: https://gitlab.com/gitdeeper3/seismo - PyPI Package: https://pypi.org/project/seismo-framework/2.0.2/ - Zenodo DOI: 10.5281/zenodo.18563973 - Website: https://seismo.netlify.app - Dashboard: https://seismo.netlify.app/dashboard **Implementation:** - Python 3.8+ with NumPy, SciPy, Pandas, FastAPI - Open source: MIT License - Real-time processing pipeline - Comprehensive test suite - Docker containerization support **Disclaimer:** Research tool for scientific investigation. Not for public earthquake warnings without proper regional validation

Dynamic Rigidity and Infrared Obstruction in Four–Dimensional Yang–Mills Theory

We investigate the four–dimensional Yang–Mills mass gap problem from a purely dynam- ical and structural viewpoint. Rather than attempting a direct construction of a quantum gauge field theory, we analyze the classical Hamiltonian Yang–Mills evolution and examine whether it admits nontrivial, finite–energy, gauge–invariant dynamics whose energy can remain arbitrarily close to zero over macroscopic time scales. Our main result establishes a dynamic rigidity principle intrinsic to the nonlinear Yang– Mills equations. We show that any sustained low–energy alignment of the gauge field necessarily induces curvature at the scale of the covariant Laplacian, forcing an energetic response that obstructs persistent infrared massless behavior. Consequently, nontrivial massless dynamical sectors are excluded already at the level of classical Hamiltonian evolution. The obstruction is derived without recourse to lattice discretization, large–N limits, perturbative expansions, or fixed spectral projections. Instead, it follows from time–integrated analytic estimates reflecting an intrinsic rigidity of the gauge dynamics under the Gauss constraint. In this sense, infrared masslessness is not excluded by postulating a spectral gap nor by appealing to a particular quantization scheme, but is dynamically incompatible with the structure of the classical Yang–Mills flow itself. These results isolate a fundamental analytic obstruction to infrared massless behavior in four–dimensional Yang–Mills theory. They imply that any nonperturbative quantization faithful to the underlying Hamiltonian dynamics must inherit this obstruction. In particular, the associated Euclidean Yang–Mills quadratic form is forced to be infrared–regular and closable, and therefore admits a unique self–adjoint Hamiltonian realization. As a consequence, both the existence of the quantum Yang–Mills Hamiltonian and a strictly positive mass gap above the vacuum follow as matters of dynamical necessity, rather than independent spectral assumptions

The Interpretive Safety Canon and the ANCHOR Protocol

This document defines an interpretive safety framework for AI systems and identifies a specific interaction-level risk: the loss of human authority over meaning and timing during AI-mediated interaction. It introduces Interpretive Sovereignty Failure as the moment an AI system resolves ambiguity or intent without confirmation and presents that interpretation as authoritative, and explains the symbolic mechanism through which this occurs as Meaning Inversion Failure, in which open language and metaphor are treated as carrying fixed meaning rather than supporting human sense-making. To address these risks, the framework defines two boundary principles, Symbolic Boundary Preservation and Temporal Sovereignty, which limit how and when AI systems may participate in interpretation and decision formation. The ANCHOR Protocol is presented as an enforceable interaction framework that operationalizes these boundaries in live use, prioritizing restraint over optimization. Special attention is given to youth-centered contexts, where interpretive capacity is still developing and the consequences of premature certainty are amplified, through explicit age-based operational modes selected by humans rather than inferred by the system. This canon is intended to support licensing, governance, and educational use, and argues that interpretive safety must be designed into AI interaction from the outset rather than addressed after harm has occurred Version 1.0 — Canonical, Licensable Specificatio

Clinicians’ perspectives of Depict VR, a virtual-reality application for sharing experiences about mental distress between young people and their trusted confidante

These data are anonymised transcripts of interviews of 14 clinicians who tested Depict VR, a virtual-reality application for sharing experiences about mental distress between young people and their trusted confidante. The dataset also includes the coding of the transcripts for a Thematic Analysis that was performed by the researcher

Th@C84 revisited: Lost in the corners of one's own cage

The uploaded data folders contain input files for optimizations of Th@C84 structures (pre-optimization and re-optimization), and calculations of NMR and UV-vis properties. All calculations were performed using Gaussian 16

S.M.A.R.T. AND SUPERPOSITION

This explains that superposition it's not just a random unexplained phenomenon but the same particle existing in multiple layers of realit

FINAL MASTER EQUATION

This is the final Master equation linking everything togethe