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Quantum Field Theory and the Universal Law of Balance: A Systems-Level Interpretation for Scientific Outreach
No full textQuantum Field Theory (QFT) is among the most successful theoretical frameworks in modern science, providing extremely accurate predictions for particle interactions and underpinning much of contemporary technology. Despite this success, QFT remains conceptually incomplete, relying on mathematical procedures such as renormalization and probabilistic interpretation without a unified explanatory foundation. This paper presents a systems-level interpretation of QFT using the Universal Formula, grounded in three natural laws: system integrity, balance, and feedback mechanisms. Rather than replacing QFT, this framework explains why QFT works and why its conceptual difficulties persist. The analysis reframes quantum phenomena as lawful processes of balance regulation within natural systems
Trust in AI-Generated Ethical Advice: The Role of Advice Quality and Source Disclosure
No full textStage 2 registered report for https://osf.io/6fpw
AI-Enabled Decision Support Systems for Patient Triage: A Scoping Review
No full textBackground and Rationale:
Nurse-led triage in Emergency Departments (ED) is a high-stakes gatekeeping process where rapid prioritization is essential to patient safety. However, the process remains prone to human variability and mistriage. While AI-enabled Clinical Decision Support Systems (CDSS) have emerged as a potential solution to standardize and enhance this process, there is a lack of synthesized evidence regarding how these systems integrate into real-world nursing workflows and their tangible impact on clinical outcomes.
Purpose and Objectives:
The primary purpose of this scoping review is to map the rapidly evolving landscape of AI and Machine Learning (ML) applications within CDSS specifically designed for nurse-led triage. The review seeks to:
Identify the technological architectures (Traditional ML vs. Generative AI) currently in use.
Evaluate model performance and the effectiveness of multimodal data fusion (text, vitals, imaging).
Identify the socio-technical barriers to implementation, including clinician trust and the "Explainability Paradox."
Uncover research gaps concerning the quantification of "soft skills" and non-verbal cues in AI models.
Methodology:
This review follows the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) framework. A systematic search was conducted across six major databases - Embase, MEDLINE, CINAHL, PubMed, Scopus, and PsycINFO - supplemented by snowballing techniques for the period of 2018–2025
Ergodicity, Fluid Dynamics, and the Complex Entropy General Equation
No full textClassical fluid dynamics and statistical mechanics are usually treated as separate domains: the former describes velocity fields and vortices, while the latter introduces the ergodic hypothesis to justify entropy and ensemble averages. This paper proposes a unified structural view based on the Complex Entropy General Equation. In this framework, the real part of entropy corresponds to diffusive relaxation and ergodic mixing, whereas the imaginary part captures vortical, phase-coherent, and non-ergodic structures. We show that the core equations of fluid dynamics can be reinterpreted as the dynamics of the imaginary component of a complex entropy field, while the ergodic hypothesis appears as the limiting regime in which the real part dominates and the imaginary part becomes negligible. This perspective clarifies why turbulence, coherent vortices, and long-lived structures escape classical ergodic assumptions, and suggests that low-speed physical systems share a common complex-entropy-based mechanism underlying both diffusion and vortex dynamics
Effects of retrieval practice and sentence generation on vocabulary learning in middle-school children
No full textRetrieval practice improves learning, yet classroom evidence on vocabulary learning in children and comparisons with elaborative activities remain limited. We conducted two classroom experiments with Brazilian sixth graders learning pseudoword to Brazilian Portuguese word pairs. Using a within-subject design, each student completed three study-practice cycles under retrieval practice, sentence generation, and copying, followed by a final cued-recall test after a brief distractor. In Experiment 1 (N = 100), retrieval practice with immediate corrective feedback produced higher final recall than sentence generation and copying. In Experiment 2 (N = 100), feedback was removed and a dictation measure indexed writing skill, both retrieval practice and sentence generation outperformed copying, but did not differ from each other, and writing skill did not moderate effects. These findings support low-cost active techniques for classroom vocabulary learning in public schools and highlight feedback as a key factor for maximizing retrieval benefits
Testing Measurement invariance of the National Student Survey in Russell Group institutions
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Military Veterans’ Self-Perceptions of Wellbeing Across International Settings: A Scoping Review
No full textSensor Memory Desynchronization Theory of Déjà Vu with the Multisensory Asynchrony Model (MAM)
No full textDéjà vu is the experience of perceiving a present event as if it has occurred before despite no explicit memory. This paper proposes the Sensor Memory Desynchronization Theory, suggesting that déjà vu occurs when one or more sensory memories are temporarily delayed and later reintegrated with ongoing perception. The Multisensory Asynchrony Model (MAM) further proposes that attentional prioritization can create temporary buffering of secondary sensory streams. When buffered sensory signals reintegrate with live perception, the brain may misattribute the moment as familiar, producing the déjà vu experience. Together, these models provide a mechanistic, multisensory, and experimentally testable explanation for déjà vu