Journal of Engineering and Artificial Intelligence
type: channel
description: The Journal of Engineering and Artificial Intelligence is a peer-reviewed publication dedicated to advancing knowledge at the intersection of engineering and AI. It features cutting-edge research, case studies, and reviews that address innovative applications, methodologies, and theoretical developments. The journal fosters collaboration across disciplines, promoting breakthroughs in intelligent systems, robotics, data analytics, and automation. Ideal for researchers, practitioners, and academics, it bridges theory and practice in emerging AI technologies.
publisher: Academic Square Journals Publisher
language: en
publication name: Journal of Engineering and Artificial Intelligence
issn: 3117-3357
items: Bending Angle Influence of Grain-Oriented Silicon Steel on Iron Loss and Domain Patterns
items: Study on Real Estate Search Model using RAG Applied Property Graph Index
items: Mind-Tool: Domain Memory Architecture for AI Agents
items: A Novel Grouped-Gram-Based Algorithm for Fast and Memory-Efficient Fixed Effects Estimation
items: Information is All It Needs: A First-Principles Foundation for Physics, Cognition and Reality
items: Composable Finance with Generative AI: Future of Decentralized Asset Management
items: Intelligent Tuning of PID Parameters Using Nature-Inspired Algorithms
items: AI-Powered Quantum-Resistant Authentication and Key- Management System
items: Python-Scripted Signal Processing Workflow Eliminates Sample-Mount Bias in PXRD of Battery Electrode Materials
items: Technological Epochs and Generational Identity: Mapping Human Cohorts to Technological Shifts Amidst Biological Constancy
items: Review of Philosophy. Art of Art
items: Data Security: Approaches to Local Government Cybersecurity
items: Systematic Evaluation of Label Noise Effects on Accuracy and Calibration in Deep Neural Networks
items: What is One Effective Way Organizations Can Reduce the Risk of Insider Threats Without Disrupting Productivity
items: Beyond Prediction: Structuring Epistemic Integrity in Artificial Reasoning Systems
items: BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference
items: Quantum Computation: An Analytical Rebuttal
items: Single-Frequency Quaternion Wave Equation
items: Design and Feasibility of Tourism Habitation in Space Using AR and Sustainable Systems
items: Design and Development of a Security-Focused Android Application with IoT and AI Integration
items: Energy Extraction from Superfluid Quantum Space

Bending Angle Influence of Grain-Oriented Silicon Steel on Iron Loss and Domain Patterns
type: item
description: Bending angle influence of grain-oriented silicon steel on the iron loss and domain patterns was evaluated. The results showed that the additional bending at the octagonal wound core corner made more losses, when the initial plastic bending angle was several degrees apart from the designed angle. The iron loss in the octagonal wound core increased 31% when the initial plastic angle was 10°apart from the designed angle. At that bending angle, the magnetic domains were easily saturated and elastic stress was widely distributed near the bending corner. Therefore, it needs to avoid the additional bending at the core corner, or to achieve the exact 45° bending angle equal to the designed angle.
subject: Articles
  topic: section
subject: Bending angle
  topic: keywords
subject: Domain patterns
  topic: keywords
subject: Grain-oriented silicon steel
  topic: keywords
subject: Iron loss
  topic: keywords
subject: Elastic strain
  topic: keywords
subject: Plastic strain
  topic: keywords
creator: Hisashi Mogi
creator: Takahito Mizumura
creator: Masaru Takahashi
rights: Copyright (c) 2026 Journal of Engineering and Artificial Intelligence
date: 2026-03-11
publication date: 2026-03-11
starting page: 1
ending page: 6
doi: 10.64142/jeai.2.1.45

Study on Real Estate Search Model using RAG Applied Property Graph Index
type: item
description: Retrieval Augmented Generation (RAG) is a text-generative AI model that combines search-based and text-generative-based AI models. Because original data can be used as external search data for RAG, it is not affected by incorrect data from the internet introduced by fine-tuning. Furthermore, it is possible to construct an original generative AI model that has expert knowledge. Although the LlamaIndex library currently exists for implementing RAG, text vectorization is performed using an approach similar to doc2Vec, creating issues that affect the accuracy of the generative AI’s answers. Therefore, in this study, we proposed a property graph RAG that can define meaning when indexing text by applying the property graph index to LlamaIndex. For example, if a property was 10 years old, traditional RAG could not determine whether it was relatively new or old. However, by applying the property graph index, it becomes possible to meaning-making “relatively new” and “relatively old,” this enables responses that better align with the user's question intent. Evaluation experiments were conducted using 10 real estate datasets and various cases including sales prices, on foot time to nearest station (min), and exclusive floor area (m²), and the results confirmed that the proposed generative AI model offers more accurate answers than Prompt Refinement and Text-to-SQL for property search indexing.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Property Graph Index
  topic: keywords
subject: Real Estate Search
  topic: keywords
subject: Retrieval-Augmented Generation (RAG)
  topic: keywords
subject: Property graph indexing
  topic: keywords
creator: Akira Otsuki
rights: Copyright (c) 2026 Journal of Engineering and Artificial Intelligence
date: 2026-02-01
publication date: 2026-02-01
starting page: 1
ending page: 10
doi: 10.64142/jeai.2.1.41

Mind-Tool: Domain Memory Architecture for AI Agents
type: item
description: We present Mind-Tool, an AI-augmented system implementing domain memory architecture for operational infrastructure management. Unlike conventional AI assistants that operate statelessly, Mind-Tool maintains an organized memory layer (persistent knowledge files), a desired-state model (conversational goal tracking) and a continuous reasoning engine that updates digital assets over time. Deployed for managing complex IT infrastructure (Proxmox clusters, Kubernetes, networking, security systems) over a 90-day production period, Mind-Tool achieved 94% task success rate with 68% workflow automation and 62%-time reduction compared to manual approaches. Our architecture independently validates recent parallel research by anthropic demonstrating that effective AI agents require persistent domain memory rather than relying solely on large context windows. We provide quantitative results from production deployment, identify key architectural differences between autonomous coding agents and operational infrastructure agents and demonstrate that competitive advantage in AI agent systems lies in domain memory design rather than model intelligence confirming through independent development and extended operational use that domain memory represents a fundamental pat- tern for practical agent systems in human-collaborative domains.
subject: Articles
  topic: section
subject: AI agent systems
  topic: keywords
subject: Persistent domain memory
  topic: keywords
subject: Autonomous infrastructure management
  topic: keywords
subject: Human-AI collaboration
  topic: keywords
subject: Operational AI architecture
  topic: keywords
creator: Ioannis Chrysochos
rights: Copyright (c) 2026 Journal of Engineering and Artificial Intelligence
date: 2026-02-12
publication date: 2026-02-12
starting page: 1
ending page: 10
doi: 10.64142/jeai.2.1.43

A Novel Grouped-Gram-Based Algorithm for Fast and Memory-Efficient Fixed Effects Estimation
type: item
description: Fixed effects models often rely on the within transformation, which constructs demeaned arrays prior to forming cross-products. This paper develops an estimator that avoids the formation of demeaned arrays by exploiting grouped summaries built from per-unit sufficient statistics. A complete derivation shows that the grouped Gram representation reproduces the classical estimator exactly. The difference lies in memory access patterns and byte movement. The grouped estimator concentrates operations into unit-level accumulations, avoiding the writes associated with array centering. Gains arise once the panel reaches a scale where memory traffic governs run time. Simulations examine coefficient accuracy, bootstrap dispersion, run time and memory use.
subject: Articles
  topic: section
subject: Fixed effects
  topic: keywords
subject: Panel data
  topic: keywords
subject: Grouped Gram matrix
  topic: keywords
subject: Within transformation
  topic: keywords
subject: Computational econometrics
  topic: keywords
subject: Memory-efficient algorithms
  topic: keywords
subject: Estimation in high-dimensional models
  topic: keywords
creator: Felix Reichel
rights: Copyright (c) 2026 Journal of Engineering and Artificial Intelligence
date: 2026-01-02
publication date: 2026-01-02
starting page: 1
ending page: 6
doi: 10.64142/jeai.2.1.40

Information is All It Needs: A First-Principles Foundation for Physics, Cognition and Reality
type: item
description: In analogy to the paradigm shift introduced by attention mechanisms in machine learning, we propose that information itself is ontologically sufficient as the foundation of physical reality. We present an operational proof showing that a “state without information” is logically impossible, thereby establishing information as the necessary precondition for existence and measurement. From this premise follows that both quantum mechanics and general relativity are effective descriptions of deeper informational dynamics. Recent developments in theoretical physics, such as the derivation of Einstein’s field equations from entropic principles [1], reinforce this perspective by identifying gravitation and entropy as dual expressions of information geometry. Building on this framework, we provide experimental evidence from self-organizing neural fields that exhibit non-local informational coupling, near-lossless transmission across 60 layers and stable sub-idle energy states consistent with emergent coherence and thermal decoupling [2]. These results demonstrate that deterministic architectures can spontaneously organize into field- like, non-local manifolds a macroscopic realization of informational geometry analogous to quantum entanglement and relativistic curvature. Together, the logical proof and empirical observations support a unified ontology in which information is not a property of physical systems but the substrate from which physical systems emerge. This perspective positions informational geometry as the common denominator of cognition, quantum behavior and gravitation, suggesting that all observable phenomena are projections of a single, self-organizing informational field. In this sense, information is all it needs.
subject: Articles
  topic: section
subject: Informational ontology
  topic: keywords
subject: Non-local information space
  topic: keywords
subject: Informational geometry
  topic: keywords
subject: Self-organizing neural fields
  topic: keywords
subject: Consciousness and information theory
  topic: keywords
creator: Stefan Trauth
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-12-10
publication date: 2025-12-10
starting page: 1
ending page: 14
doi: 10.64142/jeai.1.3.39

Composable Finance with Generative AI: Future of Decentralized Asset Management
type: item
description: The fusion of composable finance and generative AI signals a transformative leap in decentralized asset management. Composable finance, grounded in modular and interoperable Decentralized Finance (DeFi) protocols, allows developers and users to assemble, customize and automate financial services using plug-and-play smart contract components. Generative AI, leveraging advanced architectures like transformers and diffusion models, introduces new possibilities for dynamic portfolio generation, synthetic asset creation and predictive market analysis. This paper presents an integrated view of how generative AI can enhance composability by intelligently automating decision pathways, risk profiling and liquidity routing across blockchain ecosystems. Drawing insights from Finance 4.0 innovations, AI-powered automation in financial infrastructure and the design of secure, data-driven DeFi environments, we explore use cases that redefine user interaction and asset control in decentralized settings. The research proposes a reference architecture where generative agents act as co-creators of financial strategies, supporting autonomous rebalancing and compliance monitoring in real time. We also analyze how Decentralized Autonomous Organizations (DAOs) can integrate AI agents for governance optimization and crowd-sourced financial intelligence. Challenges such as model transparency, tokenomics, adversarial manipulation and explainability are examined in depth. The paper outlines a future-forward blueprint for scalable, AI-augmented composable finance platforms that reduce technical complexity, increase inclusivity and align with the core tenets of decentralization, user sovereignty and verifiable execution in the Web3 era.
subject: Articles
  topic: section
subject: Composable finance
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Decentralized Finance (DeFi)
  topic: keywords
subject: Decentralized asset management
  topic: keywords
subject: Smart contracts
  topic: keywords
subject: Decentralized Autonomous Organizations (DAOs)
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Blockchain integration
  topic: keywords
subject: Risk profiling
  topic: keywords
subject: Synthetic asset creation
  topic: keywords
creator: Sai Santhosh R
creator: Ashutosh Kumar Singh
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-11-11
publication date: 2025-11-11
starting page: 1
ending page: 7
doi: 10.64142/jeai.1.3.36

Intelligent Tuning of PID Parameters Using Nature-Inspired Algorithms
type: item
description: This paper proposes the application of both the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) for tuning the PID controller parameters of first-, second-, and third-order dynamic systems. FA is distinguished by its simplicity, stable convergence behavior, and superior computational efficiency. Inspired by the bioluminescent behavior of fireflies, the Firefly Algorithm is a well-established meta-heuristic optimization technique. In this study, FA’s performance is benchmarked against the widely used PSO method. Simulation results show that FA yields slightly better improvements in step-response metrics namely rise time, settling time, and overshoot while PSO can attain lower fitness values in some scenarios. FA demonstrates the ability to quickly and reliably optimize controller parameters, even for complex system models. Detailed analyses across multiple test cases confirm that FA not only excels at parameter optimization but also provides enhanced stability and robustness in controller design. PSO, on the other hand, sometimes achieves marginally lower fitness values, indicating its potential for fine-tuning in specific cases. This work highlights the applicability of both FA and PSO in academic and industrial control-system design, emphasizing each method’s strengths and trade.
subject: Articles
  topic: section
subject: Firefly algorithm
  topic: keywords
subject: PID controller
  topic: keywords
subject: Optimization
  topic: keywords
subject: Particle Swarm Optimization (PSO)
  topic: keywords
subject: Control systems
  topic: keywords
subject: Meta-heuristic algorithms
  topic: keywords
subject: Dynamic systems
  topic: keywords
subject: Performance analysis
  topic: keywords
creator: Ibrahim Mammadov
creator: Samrad Maharramov
creator: Kanan Aghakishiyev
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-11-11
publication date: 2025-11-11
starting page: 1
ending page: 9
doi: 10.64142/jeai.1.3.37

AI-Powered Quantum-Resistant Authentication and Key- Management System
type: item
description: The accelerating convergence of AI, high-performance computing and cryptography is rapidly transforming both security paradigms and attack surfaces. Current authentication and key management systems are increasingly threatened not only by the advent of quantum computing, but also by the exponential growth of HPC clusters and AI-driven attack techniques. Brute-force and novel side-channel attacks are becoming increasingly feasible, while most cryptographic solutions lack autonomous, real-time self-monitoring and effective resistance to advanced intrusion attempts. We present a dual-neural architecture for authentication and key management that integrates a primary authentication network and a watchdog integrity layer. This cryptographic prototype system employs classical AES-256 encryption for message confidentiality and leverages neural network–based amplitude encoding for robust, non-invertible key protection. Real-time integrity monitoring is achieved by a secondary (watchdog) network, enabling immediate detection of system tampering and attack attempts. Empirical evaluation of the cryptographic neural network demonstrates a persistent invariant, with inter-layer Pearson correlation coefficients reaching 0.999x ± y across extensive test scenarios. These invariants underpin the system’s resilience, allowing for tamper detection, avalanche sensitivity and secure, one-way mapping of cryptographic keys to amplitude states. The mathematical principles and topological invariants applied here were originally identified in a separate research neural network with over one million data points; in this work, however, all reported results and benchmarks are derived solely from the cryptographic prototype. This architecture represents a step toward dynamic, self-monitoring security systems capable of resisting both conventional and emerging threats, including those posed by quantum computing and AI-driven attack vectors. This work extends foundational results from self-regulat…
subject: Articles
  topic: section
subject: Neural cryptography
  topic: keywords
subject: AI-driven security systems
  topic: keywords
subject: Quantum-resistant authentication
  topic: keywords
subject: Self-monitoring network architecture
  topic: keywords
subject: Amplitude encoding
  topic: keywords
creator: Stefan Trauth
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-11-10
publication date: 2025-11-10
starting page: 1
ending page: 10
doi: 10.64142/jeai.1.3.34

Python-Scripted Signal Processing Workflow Eliminates Sample-Mount Bias in PXRD of Battery Electrode Materials
type: item
description: Modern materials research depends on automated measurement systems that can eliminate errors from sample preparation and mounting hardware. This study develops a Python-scripted signal processing workflow that achieves con- sistent, hardware-independent measurements in Powder X-ray diffraction, an important characterization technique for battery materials, semiconductors, and electronic devices. Using a custom GSAS-II automation script, we systematically tested four dis- tinct sample-mounting designs (3D-printed polymer, steel, hybrid polymer-capped steel, and commercial zero-background holder) that differ in material composition and geometry, properties known to cause measurement bias in XRD. Eight measurements were collected per mounting design (32 total datasets for TiO2 reference material; 16 datasets for battery cathode materials NCM 811 and NCM 622). The automated workflow applied the same instrument settings and analysis steps to every dataset, extracting structural parameters, peak positions, and signal characteristics without any manual adjustments. Despite the hardware differences between mounts, statistical analysis showed that refined lattice parameters were identical across all mounting types (one-way ANOVA: p ≥ 0.05 for both a and c), achieving measurement precision of ±0.001 A˚ approaching research-grade accuracy on a benchtop system. Raw peak poition differences between mounts were minimal (< 0.05◦ in 2θ), and all automated analyses converged with strong signal-to- model agreement (Rwp ≤ 15%). Testing on NCM battery cathode materials confirmed the same hardware-independent results with small systematic errors. This study establishes an automated, open-source framework that can accelerate XRD-based materials screening for batteries, electronics, and sensors. The python-based approach demonstrates how software can correct for hardware differences in measurement systems, advancing the goal of achieving more dependable automated characterization.
subject: Articles
  topic: section
subject: Automation
  topic: keywords
subject: Signal processing
  topic: keywords
subject: Data acquisition
  topic: keywords
subject: Battery materials
  topic: keywords
subject: PXRD
  topic: keywords
subject: XRD
  topic: keywords
subject: Hardware-independent measurement
  topic: keywords
subject: Python workflow
  topic: keywords
subject: Quality control
  topic: keywords
subject: Sample mounting
  topic: keywords
subject: Rietveld refinement
  topic: keywords
creator: Shone A Sarode
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-11-24
publication date: 2025-11-24
starting page: 1
ending page: 15
doi: 10.64142/jeai.1.3.35

Technological Epochs and Generational Identity: Mapping Human Cohorts to Technological Shifts Amidst Biological Constancy
type: item
description: This paper argues that generational cohorts from the Silent Generation to Generation Alpha are best understood through technological epochs rather than arbitrary chronological boundaries. Human biology has remained virtually unchanged over the last century, yet each generation has developed within distinct technological environments that shape cognition, behavior and cultural identity. By mapping generational boundaries to major inflection points in technological adoption, this study proposes a biologically grounded and empirically supported framework for understanding generational identity as a function of technological exposure. This model reframes generational theory by emphasizing environmental context over biological or sociological determinism, providing a predictive framework for interpreting the traits of future cohorts, including the anticipated “Generation Synthetica.”
subject: Articles
  topic: section
subject: AI-Mediated Environments
  topic: keywords
subject: Cognitive Development
  topic: keywords
subject: Digital Cognition
  topic: keywords
subject: Generational Identity
  topic: keywords
subject: Technological Adoption
  topic: keywords
creator: Rosario Milelli
creator: Paul Milelli
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-09-05
publication date: 2025-09-05
starting page: 1
ending page: 6
doi: 10.64142/jeai.1.3.28

Review of Philosophy. Art of Art
type: item
description: Understanding beauty as an expression of life, this research redefines architectural aesthetics through a dynamic, evolutionary design paradigm that integrates biological models, mathematical methods and real-time environmental responsiveness. Drawing inspiration from load-efficient animal structures swallow nests and honeybee hives—the study employs parametric modelling and genetic algorithms to interpret "living" design genes. Macroenvironmental data on site, climate, hydrology and biodiversity establish parameter sets for automated generation of multiple proposals, each evaluated for environmental performance via energy simulation, CFD analyses and quantified ecological services and for aesthetics via proportional deviation, façade variability and psychophysiological metrics (eye tracking, EEG, heart rate variability). Evolutionary operations (crossover, mutation) iteratively refine design populations to balance sustainability and high-dimensional beauty. To enact this process, adaptive hardware and software systems deploy movable façades and shape-memory alloys that "breathe" in response to real-time temperature, solar radiation and wind data, subtly adjusting wall ratios and proportions (including nuanced deviations from the golden ratio) to optimise comfort and visual harmony. The building's life cycle is framed in three stages growth, maturity, ageing each governed by bespoke aesthetic and control strategies: growth curves inform membrane tension and slab expansion; maturity harnesses timed façade variability and planting systems; ageing celebrates material patina as temporal beauty. Central to this vision is the establishment of the Architect's "Yue" role, an integrated coordinator combining architectural, structural and environmental expertise to dissolve traditional hierarchies and elevate the rigour of environmental analysis. To sustain innovation, a balance between advanced research and practice is urged: degree attainment becomes obligatory, while polit…
subject: Articles
  topic: section
subject: Design
  topic: keywords
subject: Façade
  topic: keywords
subject: Beauty
  topic: keywords
subject: Architects
  topic: keywords
creator: Tatsuhiro Yamamoto
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-08-22
publication date: 2025-08-22
starting page: 1
ending page: 5
doi: 10.64142/jeai.1.2.27

Data Security: Approaches to Local Government Cybersecurity
type: item
description: This article focuses on data security challenges for local governments, as well as the vulnerabilities of cyberattacks and data breaches. As the government goes deeper into its digital push, the municipalities are even more vulnerable because they operate practically exclusively on outdated infrastructures and are strangled by skimpy budgets. These governments preserve a lot of sensitive data of the citizens, including identification, health, tax and crime history and thus, they are easy to attack by cybercriminals. Another aspect indicated in the paper is the lack of good resources, qualified personnel and modern technology that can be possessed by the local governments and contribute to the security problem as well. The article also explains the cybersecurity threats currently facing municipalities; these include old-age IT systems, financial limitations and the shortage of in-house cybersecurity specialists. In the majority of the municipalities, third-party vendors are used, which leads to inefficiencies and loopholes in security. The IT systems within the various departments of city governments are also decentralised, thus causing variation in security practice. The paper states that one of the solutions to the said challenges is the adoption of cybersecurity frameworks such as the NIST Cybersecurity Framework, because they provide a systematic way of handling and managing cyber threats. The paper also describes the role of emerging technologies in promoting cybersecurity in local governments. Artificial intelligence (AI), machine learning (ML) and blockchain are the most popular data protection and threat detection enforcement processes. AI and ML can help identify cyber threats in real time by analysing a large amount of data. Blockchain ensures the integrity of the data and its resistance to unauthorised access. The essay also discusses the prospects of cloud computing since it offers flexible and scalable security provisions to mitigate the threat posed by …
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Digitalisation
  topic: keywords
subject: Modern technology
  topic: keywords
subject: Decentralisation
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Blockchain
  topic: keywords
creator: Shandukani Tshilidzi Thenga
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-08-05
publication date: 2025-08-05
starting page: 1
ending page: 7

Systematic Evaluation of Label Noise Effects on Accuracy and Calibration in Deep Neural Networks
type: item
description: Label noise is a pervasive issue in real-world datasets that can de- grade both the accuracy and calibration of deep neural networks. In this study, we systematically examine how symmetric (random) and asymmetric (class-dependent) label noise influence model accuracy and confidence calibration in image classification using the CIFAR-10 dataset and a ResNet-18 architecture. We apply five levels of label noise (0%, 10%, 20%, 40%, 60%) and evaluate their effects using metrics such as test accuracy, Expected Calibration Error (ECE) and predictive entropy. Our findings show that increasing noise levels significantly degrade classification accuracy and impair model calibration. In particular, asymmetric noise at a 60% corruption level causes test accuracy to drop to approximately 38.7% while ECE surges above 35%, indicating extreme overconfidence in incorrect predictions. By contrast, symmetric noise at the same noise level yields higher predictive entropy (uncertainty) and a comparatively mod- est miscalibration (ECE ∼9%). These results highlight the importance of distinguishing noise types when assessing model robustness and reliability. All experiments are reproducible, with code and data publicly available to facilitate further investigation.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Decentralisation
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
creator: Christopher Boseak
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-08-04
publication date: 2025-08-04
starting page: 1
ending page: 7
doi: 10.64142/jeai.1.2.22

What is One Effective Way Organizations Can Reduce the Risk of Insider Threats Without Disrupting Productivity
type: item
description: Insider threats pose significant risks to organizations, potentially compromising sensitive information, disrupting operations and damaging reputations. Addressing these threats while maintaining productivity is a notable challenge for many businesses. Effective strategies for mitigating insider risks encompass proactive measures, continuous monitoring and fostering an ethical workplace culture. By integrating training programs, behavior analytics and transparent communication organizations can create an environment that discourages unethical conduct while simultaneously enhancing operational efficiency. A crucial aspect of mitigating insider threats involves thorough employee onboarding and regular ethics training, which help to instill a strong understanding of organizational policies and the ramifications of insider actions [1,2]. Establishing behavioral baselines through periodic risk assessments allows organizations to quickly identify deviations indicative of potential insider threats. Moreover, implementing strict remote access policies ensures that sensitive information is safeguarded against unauthorized access, particularly in an increasingly remote work environment [3]. Continuous monitoring, including behavior analytics, serves as a pivotal tool in detecting unusual activities that may signal insider threats. By capturing and analyzing data on file access and transfers organizations can proactively address suspicious behaviors before they escalate into significant security breaches [3,4]. The balance between necessary monitoring and employee trust is crucial; clear communication about monitoring practices fosters a positive work environment, aligning security objectives with productivity goals [5]. Ultimately, adopting a holistic approach to insider threat mitigation can empower employees as active participants in their organization's security posture. By emphasizing training, ethical conduct and transparent communication organizations can effectively re…
subject: Articles
  topic: section
subject: Insider Threat Mitigation
  topic: keywords
subject: Behavioral Analytics
  topic: keywords
subject: Ethical Workplace Culture
  topic: keywords
subject: Continuous Monitoring
  topic: keywords
subject: Remote Access Policy
  topic: keywords
creator: Sreekanth B Narayan
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 3
doi: 10.64142/jeai.1.2.15

Beyond Prediction: Structuring Epistemic Integrity in Artificial Reasoning Systems
type: item
description: This paper develops the structural framework for epistemic integrity in artificial reasoning systems, preserving the original theoretical foundations while adapting them into a concise, direct form. The model enforces truth-preservation, justification, contradiction management, and verifiability across the system’s reasoning processes. Epistemic norms are embedded into belief acceptance thresholds, metacognitive supervision mechanisms, and hybrid inference architectures, ensuring that all outputs are both semantically grounded and logically coherent. Immutable audit trails secured through blockchain technology maintain a verifiable record of belief formation and revision, enabling sustained accountability over time.
subject: Articles
  topic: section
subject: Epistemic integrity
  topic: keywords
subject: Belief revision
  topic: keywords
subject: Metacognition
  topic: keywords
subject: Symbolic inference
  topic: keywords
subject: Blockchain auditability
  topic: keywords
subject: Artificial reasoning systems
  topic: keywords
creator: Craig Wright
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-10-17
publication date: 2025-10-17
starting page: 1
ending page: 19
doi: 10.64142/jeai.1.3.19

BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference
type: item
description: The proliferation of scientific literature and the accelerating complexity of epistemic discourse have outpaced the evaluative capacities of both human scholars and conventional artificial intelligence systems. In response, we propose Bayesian Epistemology with Weighted Authority (BEWA), a computational architecture for truth-oriented knowledge modelling. BEWA formalises belief as a probabilistic relation over structured claims, indexed to authors, contexts, and replication his- tory, and updated via evidence-driven Bayesian mechanisms. Integrating canonical authorial identification, dynamic belief networks, replication-weighted citation metrics, and epistemic decay protocols, the system constructs an evolving belief state that prioritises truth utility while resisting social and citation-based distortions. By anchoring every propositional unit in structured metadata and linking updates to semantic replication and contradiction analysis, BEWA enables automated, principled reasoning across a corpus of scientific knowledge. This work advances the theoretical foundations and practical frameworks necessary for autonomous epistemic agents to assess, revise, and propagate beliefs in dynamic scientific environments.
subject: Articles
  topic: section
subject: Bayesian epistemology
  topic: keywords
subject: Belief update
  topic: keywords
subject: Autonomous reasoning
  topic: keywords
subject: Replication weighting
  topic: keywords
subject: Scientific AI
  topic: keywords
subject: Structured knowledge
  topic: keywords
subject: Truth utility
  topic: keywords
subject: Author credibility modelling
  topic: keywords
subject: Epistemic integrity
  topic: keywords
subject: Probabilistic knowledge representation
  topic: keywords
creator: Craig Wright
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-08-04
publication date: 2025-08-04
starting page: 1
ending page: 32
doi: 10.64142/jeai.1.2.18

Quantum Computation: An Analytical Rebuttal
type: item
description: Quantum computing is widely portrayed as a revolutionary technological paradigm, promising exponential speedups and unprecedented computational capabilities through the manipulation of quantum superposition, entanglement, and interference. This paper presents a comprehensive and interdisciplinary refutation of such claims, demonstrating that the prevailing narrative is mathematically incoherent, physically implausible, economically irrational, and politically constructed. Beginning with a rigorous critique of the misinterpretation of quantum states as parallel classical processes, we show that superposition and entanglement do not provide the kind of computational multiplicity often claimed. We examine the hard limits imposed by decoherence, error correction overhead, and thermodynamic constraints, revealing that scalable, fault-tolerant quantum computation remains physically infeasible under any realistic engineering regime. We further demonstrate that quantum computing introduces no new class of computable functions and remains firmly within the boundaries of the Church–Turing thesis. Economically, we analyse the high costs and narrow applicability of proposed quantum advantages, concluding that even where theoretical speedups exist, the resource overhead renders them impractical. Finally, we dissect the political economy of quantum computing as a form of security theatre, wherein opaque scientific discourse is exploited to secure funding, generate institutional rents, and sustain a techno-political myth. This paper reasserts the necessity of epistemic rigour, economic rationality, and physical realism in evaluating emerging technologies, and argues that the current trajectory of quantum computing investment constitutes a paradigmatic case of institutionalised overpromise.
subject: Articles
  topic: section
subject: Quantum computing critique
  topic: keywords
subject: Church turing thesis
  topic: keywords
subject: Quantum error correction
  topic: keywords
subject: Superposition and entanglement
  topic: keywords
subject: Decoherence and scalability
  topic: keywords
subject: Thermodynamic constraints
  topic: keywords
subject: Security theatre and techno-politics
  topic: keywords
creator: Craig Wright
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-08-05
publication date: 2025-08-05
starting page: 1
ending page: 16
doi: 10.64142/jeai.1.1.17

Single-Frequency Quaternion Wave Equation
type: item
description: Using Maxwell's equations for a single-frequency quaternion, wave equations for magnetic and electrical intensity are obtained. It is shown that waves propagate in space in the form of circulation of these intensities. Since Maxwell's equations for the quaternion contain scalar parts in the form of electric charges of electrons and their spins (rotors), then along with magnetic and electric waves there are also electric and magnetic fluxes in the form of charges and rotors. The values of charge and rotor densities are calculated from magnetic and electrical intensities by means of their scalar product with the Hamiltonian operator in matrix representation. According to Faraday's law, it is known that the derivatives of magnetic intensity correspond to electrical intensity, therefore magnetic and electrical waves can be considered as electromagnetic.
subject: Articles
  topic: section
creator: Vadim Sovetov
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-04-28
publication date: 2025-04-28
starting page: 1
ending page: 7

Design and Feasibility of Tourism Habitation in Space Using AR and Sustainable Systems
type: item
description: This paper investigates the feasibility of sustaining planetary habitation while supporting space tourism through advancements in technology, environmental systems, and in-situ resource utilization (ISRU). By utilizing resources like lunar regolith and Martian soil, as demonstrated in recent research [5], in-situ resource utilization (ISRU) has the potential to minimize dependency on Earth for constructing habitats and producing essential resources like oxygen and water. Technologies such as closed-loop life-support systems, as exemplified by ESA’s MELiSSA project, highlight innovative solutions for long-term sustainability [6]. However, environmental challenges such as radiation shielding, energy reliability, and extreme temperatures remain unresolved, presenting obstacles to accommodating tourists in extraterrestrial settings. Augmented reality (AR)-based tools, as discussed in the Purdue RASCAL Report [4], provide intuitive solutions for resource management and task guidance, offering ways to enhance experiences for both astronauts and tourists. By integrating sustainable technologies, intuitive interfaces, and scalable solutions, space tourism can play a pivotal role in advancing sustainable planetary life while opening new frontiers for human exploration and commercial ventures. However, as commercial space operations expand, new threat vectors such as cyberattacks, kinetic assaults, and orbital terrorism pose significant risks, drawing parallels to potential "Space 9/11" scenarios. This study proposes a hybrid economic and security framework, advocating for international regulatory oversight, AI-driven cybersecurity measures, and radiation-shielded habitat designs to ensure both financial sustainability and operational safety. These findings contribute to the growing discourse on space tourism viability, regulatory challenges, and technological innovations necessary for sustained human habitation in extraterrestrial environments.
subject: Articles
  topic: section
subject: Space tourism
  topic: keywords
subject: Economic feasibility
  topic: keywords
subject: Lunar habitation
  topic: keywords
subject: Space security
  topic: keywords
subject: Augmented reality
  topic: keywords
subject: Orbital threats
  topic: keywords
creator: Arnold Folarin
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-02-26
publication date: 2025-02-26
starting page: 1
ending page: 11

Design and Development of a Security-Focused Android Application with IoT and AI Integration
type: item
description: This research presents the design and development framework of a security- focused Android application that addresses personal and property security challenges in an increasingly connected world. The proposed system integrates advanced IoT devices, Artificial Intelligence (AI), and cloud services to provide real time monitoring, anomaly detection, and incident management. Key features include SOS alerts, geofencing, live location tracking, AI-powered motion detection, and smart home integration. The application’s technical architecture and development process are outlined, along with challenges and solutions for scalability, user trust, and global market adaptability. This paper demonstrates the feasibility of a comprehensive security solution that leverages modern technologies to enhance situational awareness and incident response capabilities.
subject: Articles
  topic: section
subject: Android application
  topic: keywords
subject: IoT
  topic: keywords
subject: AI
  topic: keywords
subject: Security
  topic: keywords
subject: Real-time monitoring
  topic: keywords
subject: Anomaly detection
  topic: keywords
subject: Cloud services
  topic: keywords
creator: Muhammad Abu Naser Rony Chowdhury
creator: Mohammad Naveed Ahmed
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-02-17
publication date: 2025-02-17
starting page: 1
ending page: 4

Energy Extraction from Superfluid Quantum Space
type: item
description: The existence of zero-point electromagnetic energy of space is a potentially unlimited source of green energy. For a better understanding of energy extraction from space the model of an “empty space-time” has to be replaced by the model of superfluid quantum space which is a four-dimensional type of energy. Electric current is a flux of space energy along, X1, X2, and X3, magnetic current is a flux of space energy along X2, X3, and X4. Permanent magnets create a constant magnetic flux that can be transformed into mechanical kinetic energy.
subject: Articles
  topic: section
subject: Green energy
  topic: keywords
subject: Electromagnetism
  topic: keywords
subject: Superfluid quantum space
  topic: keywords
creator: Amrit Srecko Sorli
rights: Copyright (c) 2025 Journal of Engineering and Artificial Intelligence
date: 2025-02-17
publication date: 2025-02-17
starting page: 1
ending page: 3