Journal of Engineering and Artificial Intelligence
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.
Information is All It Needs: A First-Principles Foundation for Physics, Cognition and Reality
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.
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Composable Finance with Generative AI: Future of Decentralized Asset Management
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.
Decentralized Autonomous Organizations (DAOs)
Intelligent Tuning of PID Parameters Using Nature-Inspired Algorithms
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.
AI-Powered Quantum-Resistant Authentication and Key- Management System
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-regulating neural systems and non-local information spaces, as introduced in our previous studies and integrates established cryptographic, quantum-resistant and AI-driven security concepts. Here, the numbers refer to the full references listed in the References section at the end of this preprint. Together, these sources provide the scientific basis for the proposed architecture and situate it within the state-of-the-art at the intersection of neural computation and secure information management.
Python-Scripted Signal Processing Workflow Eliminates Sample-Mount Bias in PXRD of Battery Electrode Materials
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.
Technological Epochs and Generational Identity: Mapping Human Cohorts to Technological Shifts Amidst Biological Constancy
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.”
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 politically motivated competitions yield to more meaningful evaluation axes, fostering deep theoretical engagement and creative autonomy. The Architects "Yue" Organisation Group a collaborative, family-like entity aims to reduce overtime, enhance intellectual productivity and strengthen resilience in cutting-edge design and manufacturing. Anticipated pilot projects across diverse climates will validate universal principles of dynamic aesthetics. An open science platform will disseminate design genes, algorithms and control software to catalyse interdisciplinary co-creation. By positioning environmentalists as central producers and supporting Ph.D. level research in robust manufacturing, this framework aspires to embed "living beauty" in future cities and architecture.
Data Security: Approaches to Local Government Cybersecurity
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 the legacy environment. The paper analyses successful and unsuccessful initiatives at municipal cybersecurity based on a case study of Los Angeles and Gloucester City. The secure security tools based on the cloud, available to the towns in Los Angeles and real-time monitoring of the threats are a good example of the benefits of periodic modernisation of information technology infrastructure. In the meantime, the experience of Gloucester City’s ransomware attack demonstrates the disastrous outcomes of non-observance of cybersecurity planning and the importance of funds spent on security. Lastly, the writer points out that cybersecurity is an issue that local governments should prioritise through research, development and implementation of the latest technology, employee education and the adoption of best practices to maintain the confidentiality of information. In so doing, cities can employ cyber resilience to achieve market trust and maintain key services’ business continuity.
Systematic Evaluation of Label Noise Effects on Accuracy and Calibration in Deep Neural Networks
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.
What is One Effective Way Organizations Can Reduce the Risk of Insider Threats Without Disrupting Productivity
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 reduce the risk of insider threats while ensuring a productive workplace, thus reinforcing both security and trust within the workforce [6,7].
Beyond Prediction: Structuring Epistemic Integrity in Artificial Reasoning Systems
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.
BEWA: A Bayesian Epistemology-Weighted Artificial Intelligence Framework for Scientific Inference
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.
Quantum Computation: An Analytical Rebuttal
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.
Single-Frequency Quaternion Wave Equation
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.
Design and Feasibility of Tourism Habitation in Space Using AR and Sustainable Systems
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.
Design and Development of a Security-Focused Android Application with IoT and AI Integration
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.
Energy Extraction from Superfluid Quantum Space
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.