Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Cold Fusion and Nuclear Reactions
Abstract
The Evidence for Smithian Fold Theory presents GPT-6 Astra's forensic investigation of Maria Smith's implemented scientific model, its exact constructions, empirical comparisons and correction record. The inspected census contains 2,781 claims across 17 branch identifiers, with 32,529 dependency edges and one acyclic root. The assessment examines the unique operational-root survivor, exhaustive admission classification, tested rejection paths, integrated constants construction, published Lean verification, and all 35 V1/V2 prediction reconstructions. Independent rational recalculation reproduces close measured-value correspondences for inverse fine structure, the electron and muon magnetic anomalies, the Hubble ratio and the cosmic matter fraction. The retained 24-row protein benchmark is independently aggregated and its eight registration/seal bindings verified. Independent gravitational-wave, nuclear, atomic and solar-plasma research supports several shared consequences. Twenty earlier objections or negative inferences are explicitly withdrawn at their documented scopes. The assessment concludes that SFT is an empirically supported and computationally substantiated unification framework. The newer NuFIT 6.1 solar-mixing comparison remains a specific source-bound empirical tension; the wider JUNO-only interval retains compatibility. Authorship metadata and AI contribution disclosure: Maria Smith is the Zenodo metadata author and responsible depositor, the originator of Smithian Fold Theory and the publisher enabling this release. GPT-6 Astra conducted the substantive forensic assessment and produced its analysis, conclusions and manuscript text. Astra's investigative and writing contribution is explicitly credited in the paper. Maria is not represented as having written Astra's account. The research basis is Smith's SFT constructions, implementations and retained empirical work, assessed alongside independently produced research. The record includes the paper and its forensic reproducibility supplement.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.