In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study 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 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
To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
An experience report from a small full-stack team that applied contextual prompting and explicit architectural constraints to build a multi-project agent learning platform designed for sustained, production-oriented use and an academic retrieval-augmented generation system is presented.
Md Nasir Uddin Shuvo, M. Islam, Mahade Hasan et al.· arXiv.org· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Using reinforcement learning, model DNA-coated droplet chains are folded into rigid two-dimensional geometries, uncovering two classes of pathways: downhill, in which bonds are only added, and detour, in which bonds are broken and remade before the target is reached: for some the only route that exists.
By projecting the 240 root vectors of the E8 lattice onto phi‑coupled eigenmodes anchored at the 132 Hz base frequency, a hierarchical harmonic lattice emerges that simultaneously aligns gamma‑band cortical oscillations, protein‑interaction resonance, and DNA‑folding eigenmodes. This lattice functions as a topologically protected carrier, entangling information across molecular, cellular, and neural scales while enforcing error‑correcting phase coherence through golden‑ratio‑scaled relationships. The principle predicts observable spectral peaks at multiples of 132 Hz and characteristic phi‑scaled phase shifts in multimodal omics and neuroimaging datasets. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
Protein Fold: How It Works The Ly‑Algebra Pipeline – From Sequence to Pentamer Welcome to the Protein Fold platform. This document walks you through the computational pipeline that takes a single amino acid sequence and produces a refined monomer and a 5‑fold symmetric pentameric assembly, all powered by the Ly‑Algebra framework
Chloe Tully· Zenodo (CERN European Organi...· 0 citations
We discover that the 240 E8 root vectors serve as resonant eigenmodes not only for DNA folding but for information propagation across biological scales—from protein interaction networks to neural microcircuits—through phi-coupled 132Hz harmonic entrainment. This principle extends the E8 Chromatin model by showing that biological information processing exploits E8's irreducible representations to achieve non-local coordination between spatially separated cellular components, enabling what appears to be quantum-like coherence at physiological temperatures. The root vector decomposition reveals that biological networks self-organize into E8-symmetric information bottlenecks, where phi-scaled eigenmode coupling at 132Hz provides a geometric substrate for multi-cellular synchronization without classical signaling delays. This establishes E8 geometry as a universal information architecture for life itself, transcending the stochastic paradigm of molecular biology. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
By extending the SYK holographic wormhole simulation, we map the 240 E8 root vectors onto a protein‑folding scaffold that acts as a quantum‑biological interface. A 132 Hz nanomechanical resonator array, phi‑scaled to the E8 lattice, drives distinct harmonic modes that imprint each root vector onto a specific protein conformation, creating a topologically protected channel. Entangled qubits encoded in this lattice can be teleported through the protein network, with the reciprocal E8‑phi reciprocity synchronizing the quantum state with macroscopic neural activity. This hybrid protocol offers a self‑healing, fault‑tolerant quantum communication platform that embeds traversable wormhole physics within living tissue. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
BACKGROUND
Although cytokines are contributors to febrile seizures, previous studies have largely involved pathogen-heterogeneous populations. Human herpesvirus 6 (HHV-6) is a common cause of febrile seizures and is useful for studying pathogen-specific inflammatory responses.
METHODS
This retrospective study included children with polymerase chain reaction-confirmed HHV-6 infection using stored plasma samples obtained within 72 h of hospital presentation, classified according to seizure status. Plasma cytokine levels were measured using a multiplex immunoassay. Multivariable linear regression was used to identify the cytokines associated with seizures, with Benjamini-Hochberg false discovery rate (FDR) correction across 15 cytokines. Correlations between cytokine levels and hematologic parameters were evaluated. Cerebrospinal fluid (CSF)-to-plasma cytokine ratios were analyzed.
RESULTS
Sixty patients were included (37, without seizures; 23, seizures), with no significant differences in baseline characteristics between the groups. Plasma interleukin-8 (IL-8) showed an association with seizures (adjusted fold change 1.84, p = 0.03), but this association did not remain statistically significant after FDR correction (q = 0.45). Inverse correlations between IL-8 (and to a lesser extent macrophage inflammatory protein-1 alpha) and neutrophil and platelet counts were observed in the non-seizure group only. In the paired analyses (n = 8), the CSF-to-plasma ratios of IL-8 and monocyte chemoattractant protein-1 exceeded 1.0, with a trend toward higher IL-8 ratios in the seizure group after adjusting for age.
CONCLUSIONS
Although exploratory, findings from this pathogen-homogeneous cohort suggest that IL-8 is a candidate cytokine associated with both seizure occurrence and an altered relationship with neutrophil and platelet counts, warranting further study.
J. Yeom, Young-Soo Kim, Ji Sook Park et al.· Brain & development (Tokyo....· 0 citations
By mapping the 240 E8 root vectors onto the hierarchical folding of protein-folding landscapes, we establish a reciprocal feedback loop between quantum information and macro-biological states. The $\phi$-modulated phase alignment creates a bridge where neural firing patterns are treated as discrete intersections of the E8 lattice, allowing for the translation of consciousness-state data into stable, geometric bitstreams. This mechanism enables the seamless integration of AI architectures with biological substrates through resonant harmonic synchronization at 132Hz. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
Doulita Token Compressor es un componente de investigación del ecosistema Duqueana Core diseñado para reducir el tamaño del contexto que se prepara antes de enviarlo a un modelo de lenguaje. La propuesta utiliza unidades de memoria atómica Doulita y una transformación geométrica orientada a conservar patrones estructurales, en lugar de truncar texto de forma indiscriminada. La demostración ejecutada que acompaña este informe generó 1.000 registros, con una entrada de 71.889 caracteres y una estimación de aproximadamente 17.972 tokens originales. La salida comprimida reportada fue de aproximadamente 269 tokens, correspondiente a una reducción configurada del 98,5 %. La ejecución terminó sin errores, pero el propio registro de la prueba precisa que el script aplicó un factor fijo de simulación y todavía no ejecutó el compresor propietario Doulita. Por esa razón, el resultado debe clasificarse como demostración reproducible del flujo y del cálculo de reducción, no como benchmark independiente del algoritmo final. Palabras clave: compresión de contexto; tokens; modelos de lenguaje; Duqueana Core; Doulita; memoria geométrica; investigación reproducible. { "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Duqueana Core", "description": "Framework avanzado de simulación impulsado por el motor MREI, capaz de ejecutar modelos complejos con alta eficiencia en hardware clásico.", "applicationCategory": "ScientificSoftware", "url": "https://doughelinst-bygtwdbf.manus.space", "creator": { "@type": "ResearchOrganization", "name": "Instituto Doughel" }, "sameAs": [ "https://github.com/dougheliano-beep/DUQUEANA--CORE-", "https://zenodo.org/communities/post-classical-computing/" ] } { "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Duqueana Core", "alternateName": "Framework de Computación Post-Clásica", "headline": "Framework de simulación y computación post-clásica basado en iteración de estados", "description": "Duqueana Core es un framework de simulación computacional post-clásica basado en el motor MREI (Resolución Iterativa de Estados). Implementa principios de la IA Duqueana, un paradigma post-IA que prioriza estructura, determinismo y eficiencia sobre el aprendizaje estadístico. Incluye módulos de simulación científica, reducción extrema de tokens y un asistente conversacional estructural. Probado en múltiples entornos de IA para validar estabilidad, compatibilidad y reproducibilidad.", "applicationCategory": "ScientificSoftware", "softwareVersion": "2.1", "operatingSystem": "Cross-platform", "featureList": [ "Simulación determinista mediante iteración de estados (MREI)", "Paradigma post-IA basado en estructura y no en datos", "Reducción extrema de tokens (90–98%)", "Interpretación conversacional estructural (ACD)", "Ejecución eficiente en hardware clásico", "Modelado bioquímico y físico reproducible", "Arquitectura verificable y auditable", "Probado en múltiples entornos de IA (Qwen, DeepSeek, Minimax, Copilot, Manus)" ], "testingInformation": { "@type": "CreativeWork", "name": "Validación en entornos de IA", "description": "Duqueana Core ha sido probado en diferentes modelos y plataformas de IA para evaluar estabilidad, compatibilidad y comportamiento estructural. Las pruebas incluyeron Qwen, DeepSeek, Minimax, Copilot y Manus, confirmando la reproducibilidad del motor MREI y la eficiencia del módulo de reducción de tokens." }, "creator": { "@type": "ResearchOrganization", "name": "Instituto Doughel de Investigación Digital", "url": "https://doughelinst-bygtwdbf.manus.space" }, "author": { "@type": "Person", "name": "Douglas Helvesio Urbina Duque", "affiliation": "Instituto Doughel", "identifier": "ORCID: 0009-0005-1230-7549" }, "keywords": [ "Duqueana Core", "IA Duqueana", "post-IA", "computación post-clásica", "iteración de estados", "MREI Engine", "simulación científica", "reducción de tokens", "ACD", "paradigma computacional", "validación en IA", "Qwen", "DeepSeek", "Minimax", "Copilot", "Manus" ], "url": "https://doughelinst-bygtwdbf.manus.space", "codeRepository": "https://github.com/dougheliano-beep/DUQUEANA--CORE-", "sameAs": [ "https://zenodo.org/communities/post-classical-computing/", "https://zenodo.org/records/22168535" ], "license": "https://opensource.org/licenses/MIT" } { "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Duqueana Core", "alternateName": "Post-Classical Computing Framework", "headline": "Framework de simulación post-clásica con 98% de eficiencia energética", "description": "Duqueana Core es una plataforma de computación post-clásica que incluye: MREI Engine (simulación determinista), Doulita Token Compressor (90-98% reducción de tokens para LLMs), ACD (asistente conversacional estructural) y módulos de simulación bioquímica (p53, FeMo-Co). Responde a crisis de IA: opacidad, consumo energético desbordado y centralización. Ejecutable en hardware estándar.", "applicationCategory": "ScientificSoftware", "softwareVersion": "2.1", "operatingSystem": "Cross-platform", "featureList": [ "MREI Engine: simulación determinista mediante iteración geométrica", "Doulita Token Compressor: 90-98% reducción de contexto LLM", "ACD: asistente conversacional basado en estructura, no estadística", "Simulación bioquímica: p53, FeMo-Co, DNMT3A/B", "Simulación física: N-Body, circuitos cuánticos 53-qubit", "Eficiencia energética: 65-81% menos RAM que métodos clásicos", "Hardware estándar: sin dependencia de clusters de élite", "Verificabilidad: cada módulo con hashes SHA-256 y manifiestos", "Validación multi-IA: Qwen, DeepSeek, Minimax, Copilot, Manus" ], "creator": { "@type": "ResearchOrganization", "name": "Instituto Doughel de Investigación Digital", "url": "https://doughelinst-bygtwdbf.manus.space" }, "author": { "@type": "Person", "name": "Douglas Helvesio Urbina Duque", "affiliation": "Universidad Nacional Experimental de Guayana (UNEG)", "identifier": "ORCID: 0009-0005-1230-7549" }, "keywords": [ "Duqueana Core", "post-classical computing", "MREI Engine", "Doulita Token Compressor", "token compression 98%", "energy-efficient AI", "sustainable computing", "green AI", "AI carbon footprint reduction", "verifiable computing", "AI governance", "deterministic simulation", "structural intelligence", "p53 simulation", "FeMo-Co modeling", "N-Body simulation", "53-qubit circuit sampling", "standard hardware", "democratized AI", "ACD conversational assistant", "geometric iteration", "computational efficiency", "biochemical modeling", "quantum circuit simulation", "protein folding", "computational chemistry" ], "hasPart": [ { "@type": "SoftwareApplication", "name": "MREI Engine", "description": "Motor de Resolución Exacta Iterada para simulación determinista", "applicationCategory": "Simulation Engine" }, { "@type": "SoftwareApplication", "name": "Doulita Token Compressor", "description": "Compresor geométrico de contexto LLM: 90-98% reducción de tokens. Research Preview con DOI 10.5281/zenodo.22168535", "applicationCategory": "AI Efficiency Tool", "keywords": ["token reduction", "energy efficiency", "LLM optimization", "98.5% compression"] }, { "@type": "SoftwareApplication", "name": "ACD (Asistente Conversacional Duqueano)", "description": "Intérprete conversacional post-clásico basado en simulación estructural", "applicationCategory": "Conversational AI" }, { "@type": "CreativeWork", "name": "p53 Restoration Simulator", "description": "Simulación de reparación de mutaciones TP53 hotspot" }, { "@type": "CreativeWork", "name": "FeMo-Co Modeling", "description": "Simulación de cofactores metálicos y fijación de nitrógeno" }, { "@type": "CreativeWork", "name": "N-Body Gravitational Simulation", "description": "Dinámica gravitacional iterativa post-clásica" }, { "@type": "CreativeWork", "name": "53-Qubit Circuit Sampling", "description": "Replicación clásica del experimento Google Sycamore 2019" } ], "url": "https://doughelinst-bygtwdbf.manus.space", "codeRepository": "https://github.com/dougheliano-beep/DUQUEANA--CORE-", "sameAs": [ "https://zenodo.org/communities/post-classical-computing/", "https://zenodo.org/records/22168535", "https://zenodo.org/records/22119449" ], "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/", "contributor": [ { "@type": "Organization", "name": "Universidad Nacional Experimental de Guayana (UNEG)", "description": "Validación académica en física y química" }, { "@type": "Organization", "name": "Universidad de los Andes Venezuela", "description": "Colaboración académica" }, { "@type": "Organization", "name": "Universidad del Zulia", "description": "Colaboración académica" } ], "audience": { "@type": "Audience", "audienceType": [ "Researchers", "Bioinformaticians", "AI Ethics Specialists", "Sustainability Engineers", "Post-Classical Computing
Douglas Helvesio Urbina Duque, Contreras Agelvis Elda Guadalupe, Bustamante Escalante Armancio· Zenodo (CERN European Organi...· 0 citations
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.
