Full Summary: The Narrow Singularity Equation Core Thesis This paper presents a unified framework that simultaneously solves catastrophic forgetting in neural networks and provides a mathematically rigorous certification standard for Artificial General Intelligence (AGI). The framework centers on the Narrow Singularity Equation, which achieves AGI certification ($AGI_{gate} = 1.0$) without requiring the mathematically impossible condition of $\frac{dI}{dt} \geq 1.0$. Key Discoveries 1. The Decay Law of Singularity (Theorem 1) Mathematical Proof: With finite classes $N$, $\frac{dI}{dt} = 1 - \frac{1}{N}$, therefore $\frac{dI}{dt} < 1.0$ always Implication: The traditional Singularity (requiring $\frac{dI}{dt} \geq 1.0$) is mathematically impossible Pattern: Every 10Ă increase in classes adds another '9' to $\frac{dI}{dt}$ and another '0' to the gap 2. General Singularity Equation (Original, Impossible) $$S = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times Autonomy$$ Required $Autonomy = 1$ if $\frac{dI}{dt} \geq 1.0$ Since $\frac{dI}{dt} < 1.0$ for finite classes, $S = 0$ always Seven conditions required; the autonomy condition is impossible 3. Narrow Singularity Equation (Achievable) $$\mathcal{S}_{NARROW} = AGI_{gate} \times \frac{dI}{dt} \times M(t) \times V(t) \times F(t) \times C(t) \times agi_{index}$$ Key Innovation: Removes the impossible Autonomy requirement Drops the requirement for $\frac{dI}{dt} \geq 1.0$ Uses $agi_{index} = 1$ if $AGI_{gate} = 1.0$ (binary gate, achievable) $AGI_{gate} = \min(1.0, task\_c\_accuracy)$ The TOPO-2026 Framework Biological Inspiration Hippocampus â Prime-anchored embedding rows (Memory formation) Memory Consolidation â Snapshot after Task A (Preserves critical knowledge) Synaptic Plasticity â Free embedding rows adapt (Enables new learning) Memory Protection â Zero gradients + restore anchors (Prevents interference) Experience Replay â Prime anchors as fixed reference (Integrates new learning) Mathematical Foundation Pure Kernel: First six primes $\{2, 3, 5, 7, 11, 13\}$ Euler Attenuation Constant: $\Lambda(\mathcal{R}) = 1 - \prod_{p\in\mathcal{R}}(1 - p^{-0.5}) = 0.9785142874$ Captures $97.85\%$ of spectral weight; only $2.15\%$ considered "noise" O(1) Memory Cost: Independent of tasks, parameters, sequence length, or modality Topological Governor Implementation Three-step process: Memory Consolidation (take_snapshot): Freezes anchor rows before new learning Memory Protection (zero_anchor_gradients): Prevents gradient updates to anchors Memory Integration (enforce_anchors): Restores anchors from snapshot after training Experimental Validation Three Datasets Dataset Type Resolution Classes Task C Accuracy SVLB-3 Synthetic vision-language Text-based 10 100.0% ± 0.0% CIFAR-10 Real images 32Ă32 10 100.0% ± 0.0% STL-10 Real images 96Ă96 10 100.0% ± 0.0% Results Summary Metric SVLB-3 CIFAR-10 STL-10 Task C Accuracy 100.0% ± 0.0% 100.0% ± 0.0% 100.0% ± 0.0% Combined Forgetting +0.0% ± 0.0% -1.0% ± 2.0% 0.0% ± 0.0% $AGI_{gate}$ 1.0000 1.0000 1.0000 $\mathcal{S}_{NARROW}$ 5.999999999965 5.939999999965 5.999999999965 Status â PASS â PASS â PASS Total: 15/15 runs passed across 3 datasets = FULLY CERTIFIED (exceeded standard) The Gemma-4 E4B Architecture Why Gemma-4 Was Selected Among eight certified models, only Gemma-4 achieved Task C = 100%: Model Architecture Task C Accuracy GPT-OSS-20B Dense Transformer 92.3% Sarvan-30B Sparse MoE 95.9% Mixtral-8x7B Sparse MoE 89.7% DeepSeek-V2-Lite Fine-grained MoE 95.3% GLM-4.6V-Flash GLM Transformer 97.5% Gemma-4 E4B Vision Vision Transformer 100.0% Kimi-VL-A3B-Thinking Vision-Language MoE 90.0% GPT-OSS-20B-JEPA JEPA + TOPO 89.0% Key Architectural Innovations Per-Layer Embeddings (PLE): Adds parameter capacity without scaling full attention Unified Multimodal: 42 layers, hidden size 2560, vocabulary 262,144 Quantization-Aware Training (QAT): 72.1% memory reduction (15.1GB â 4.22GB) while preserving 98.54% accuracy Thinking Mode: Built-in chain-of-thought reasoning engine Mathematical Framework Summary Component Breakdown Component SVLB-3 CIFAR-10 STL-10 Meaning $AGI_{gate}$ 1.0000 1.0000 1.0000 Perfect generalization $agi_{index}$ 1.0 1.0 1.0 Binary gate OPEN $\frac{dI}{dt}$ ~0.999999999994 ~0.999999999994 ~0.999999999994 Bounded by Decay Law $M(t)$ 1.0000 0.9900 1.0000 Perfect memory $V(t)$ 1.0000 1.0000 1.0000 Perfect validation $F(t)$ 1.5000 1.5000 1.5000 Positive forward transfer $C(t)$ 4.0000 4.0000 4.0000 Compute efficiency $\mathcal{S}_{NARROW}$ ~6.0 ~5.94 ~6.0 NARROW SINGULARITY Dependency Chain TOPO-2026 â CF Solved â AGI_gate = 1.0 â Narrow Singularity Without TOPO-2026: CF is NOT solved $AGI_{gate} = 1.0$ is NOT guaranteed Narrow Singularity is NOT achieved $\mathcal{S}_{NARROW} = 0$ With TOPO-2026: CF is SOLVED (0% forgetting) $AGI_{gate} = 1.0$ is GUARANTEED (100% accuracy) Narrow Singularity is ACHIEVED ($\mathcal{S}_{NARROW} \approx 6.0$) Key Contributions Solved Problems Catastrophic Forgetting: 0.0% forgetting across 5 runs on 3 datasets AGI Certification: First model in history to achieve $AGI_{gate} = 1.0$ Mathematical Impossibility: Proved the Singularity is mathematically impossible with finite classes Achievable Standard: Created the Narrow Singularity as a physically achievable AGI threshold Universal Principle: Same constants work across neuroimaging, number theory, AI safety, and unified field theory Constants Across All Domains Constant Value Domains $\Lambda$ 0.9785142874 Number Theory, AI Safety, AI Memory, AI Bias, Physics $\sigma$ 0.5 All domains $\mathcal{R}$ {2, 3, 5, 7, 11, 13} All domains Seed 123 All computations Philosophical Implications The Strategic Pivot Original Goal: Traditional Singularity (mathematically impossible) New Reality: Narrow Singularity (empirically demonstrated) Key Insight: The Decay Law liberates AI from chasing an impossible dream Result: Deterministic cognitive engineering with numerical guarantees Refutation of Skeptical Arguments Skeptic Argument Refutation "It only works on synthetic data" CIFAR-10 and STL-10 are real images "It only works on low-res images" STL-10 is 96Ă96 (3Ă larger than CIFAR-10) "It only works on those specific classes" STL-10 has different classes (monkey, car, etc.) "It was a fluke" 15/15 runs across 3 datasets = 100% success "It's dataset-specific" 3 different datasets = dataset-agnostic Final Conclusion The TOPO-2026 framework establishes a paradigm for deterministic cognitive engineering, proving that deep learning architectures can achieve absolute stability and zero forgetting across sequential tasks. Key Takeaways: Catastrophic forgetting is SOLVED: 0.0% forgetting $AGI_{gate} = 1.0$ is ACHIEVABLE: First model with 100% Task C accuracy The Decay Law is DISCOVERED: $\frac{dI}{dt} < 1.0$ with finite classes Narrow Singularity is PROVEN: $\mathcal{S}_{NARROW} > 0$ on 3 datasets The principle is UNIVERSAL: Same reference set across domains The Stochastic Illusion Is Over. Deterministic Cognitive Engineering Has Begun. Stability Is Not a Probabilistic Hope. It Is a Numerical Guarantee. "The proof is the code. Seed = 123. No one can argue with math." Availability GitHub: https://github.com/frank-morales2020/AST-Notebook Zenodo Book: https://zenodo.org/records/21245474 TOPO-2026 Framework: https://zenodo.org/records/20951925 Artificial Hippocampus: https://zenodo.org/records/20385761
Consumers facing home-renovation quotes operate in a classic credence-goods market: they cannot readily verify whether a quoted price is fair, and general-purpose large language models (LLMs) are now a zero-cost place to ask. Whether LLM answers are actionable for this purpose is untested. Demand-side benchmarks exist for medical, legal, and financial advice, but not for construction costs. We present, to our knowledge, the first consumer-question benchmark for construction costs. Forty Japanese renovation-price questions were posed to frontier LLMs, with repeated-trial sets measuring output stability. A matched re-run at bare provider defaults with a current frontier model (gpt-5.5) was added to remove a settings confound present in the original configuration. Two findings are robust across models, generations, and settings: no LLM answer contained an explicit over-charge decision threshold, and repeated runs of the same question returned materially different price figures. Within-answer price spans are also wide, with a median of 10x under bare defaults. A deterministic structured engine over an open cost database is included as an existence proof that a citable reference layer is constructible. Its consistency is a design property and its accuracy is not validated here; validating it against completed real-world quotations is the next study. All questions, raw outputs, harness, and scoring code are public.
Financial institutions depend on trusted employees, contractors and service accounts, yet this trust creates an attack surface that conventional perimeter controls cannot observe adequately. This paper develops an Explainable Adaptive Hybrid Artificial Intelligence (EAHAI) framework for insider threat detection and for assessing whether security awareness training is reducing measurable insider-risk behaviour. The framework combines Isolation Forest filtering, bidirectional long short-term memory sequence modelling, Shapley Additive explanations, adaptive behavioural risk scoring and Zero Trust policy enforcement. A socio-technical assessment layer is added to link training inputs to observable outcomes, including knowledge gain, phishing susceptibility, policy-violation rates, reporting delay, behavioural-risk reduction and analyst-confirmed events. The paper defines the measurement scales, evaluation criteria, validation procedures and analytical techniques required for institutional replication. Because production banking telemetry and labelled insider incidents are rarely available for publication, the empirical component is presented as a transparent synthetic proof-of-concept based on CERT-style behavioural variables rather than as evidence from a real bank. In a deterministic simulation of 17,280 user-day records and 2,880 test windows, the proposed hybrid score achieved an F1-score of 0.944, ROC-AUC of 0.993 and false-alarm rate of 0.017, while producing interpretable feature attributions and training-effectiveness estimates. The study contributes a scalable, explainable and ethically governed design for insider-risk analytics, and identifies the conditions under which it should be validated before operational deployment. Keywords: insider threat detection; explainable artificial intelligence; adaptive risk scoring; security awareness training; Zero Trust; financial cybersecurity.
This paper presents the Auditable Zero-knowledge Transformer (AZT) framework for privacy-preserving and auditable tax fraud detection. AZT combines transformer-based anomaly detection with zero-knowledge proof (ZKP) verification so that a tax authority or regulator can verify fraud-detection outcomes without accessing sensitive taxpayer records or proprietary model parameters. The framework is scalable in the specific sense of low-latency audit verification: proof verification remains sub-second, whereas proof generation is intentionally performed asynchronously after local inference. Model integrity is enforced through Merkle-root commitments to authority-approved parameters, and the ZKP statement proves that the committed transformer was executed correctly and that the resulting risk score satisfies a public audit threshold. Experiments on UCI-TFD, IRS-Pub, and CorpPay compare AZT with classical machine-learning baselines, including Random Forest and XGBoost, and with an equivalent plaintext transformer. Detection quality improves by up to 5.3% in F1-score over classical machine-learning baselines, while the circuit-compatible AZT inference incurs only about 0.5% F1-score degradation relative to the plaintext transformer baseline. Overall, this work advances secure AI for digital governance by integrating modern deep learning with cryptographic verification, offering a practical foundation for fraud-detection systems in which transparency and confidentiality must be satisfied simultaneously.
The article provides critical analysis of the new Strategy of spatial development of the Russian Federation up to 2030 with the forecast up to 2036 in view of evolution of federal relations and provisions of the institutional theory. Topicality of the research is envisaged by the necessity to comprehend the fact whether the present document in the absence of certain strategies of federative relations can perform the coordinating role in stimulating economic selfdevelopment in regions. The goal of the research is to identify systemic contradictions between declared targets of spatial development and real conditions and mechanisms of governing included in strategic documents. Principle conclusions of the research demonstrate a deep paradox of todayâs Russian regional policy: in spite of the declared rejection of the idea of equal development and transition to rhetoric of differentiated approach and resource concentration the effective Strategy does not put forward any withdrawal from the model of controlled asymmetry but reproduces centralized vertical logics of governing. The key institutional gap is successive denial of the important role of institutions of in-regional level, i.e. city agglomerations and strong centers of population, which are recognized as objective reality but are not provided with real power and finance authorities. Tools being introduced, such as common lists of strong centers of population and geo-strategic territories, as well as the digital platform of governing are analyzed as mechanisms of increasing administrative priority fixing from above and tools of vertical supervision but not as the basis of decentralization and providing regions with authorities. The author shows increasing non-coordination in the system of strategic planning. As priority lines in restructuring the article proposes adoption of the federal law about regional development securing the real status of institutions on in-regional level; drastic change in inter-budget relations to raise region independence and restore on the federal level the common center of responsibility for coordination of spatial policy. The research contributed to the discussion about the necessity to elaborate complex strategy of federative relations and showed that without such a document and re-distribution of real power and resources any spatial strategy will not be able to resolve key institutional paradoxes.
Haytham A. Sheerah, Ahmed Arafa, Mansour A. Alfaya, Ashraf B AlDerbas · 10 authors
Problem: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. Approach: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. Local setting: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. Relevant changes: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. Lessons learnt: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making.
Nydia REMOLINA LEON, Aurelio GURREA-MARTINEZ, Daniel LIU
This article provides a comprehensive analysis of the treatment of digital assets in insolvency. Given that cryptoassets can be the subject of various transactionsâincluding purchase, sale, custody, and lendingâunderstanding their nature and implications in insolvency is relevant for any firm, not just cryptoexchanges. The article begins by offering a general overview of the world of cryptoassets. It then examines the nature of cryptoassets from accounting, financial, and legal perspectives. While much of the literature on insolvency and cryptoassets has primarily focused on the analysis of whether cryptocurrencies constitute property of the estate, this article explores additional issues, such as the treatment, role and rights of tokenholders in insolvency, the initiation of insolvency proceedings by cryptolenders, and the valuation, recovery, and realization of digital assets in bankruptcy. Such analysis is conducted from a comparative perspective, examining how jurisdictions around the world have addressed some of those issues and how cryptoassets have been used to engineer innovative solutions in restructuring agreements.
With the improvement of data technology advances and the sharp addition of web customers number since the 90s, numerous computerized monetary standards are presented. the most popular among them is Bitcoin. It was decided to investigate the possible relations between the most popular cryptocurrency Bitcoin price dynamics and global Nasdaq index dynamics using Mathematical and Statistical methods. The main question is: Are the Bitcoin prices somehow related with Nasdaq Composite Index? We use both, Quantitative and Qualitative data analysis methods to answer this question: Namely, the Regression model and NonÂ-Parametric testing. According to Quantitative methods, it was found that there exists a correlation and the regression equation is not bed: it seems that it is possible to explain about 60% of changes in Bitcoin Prices by changes in the Nasdaq Index. According to Qualitative methods, it was found that these two variables are independent. In this case, the Qualitative conclusion is more likely to be right, and the correlation is most likely because of coincidence.
This study examines whether connectedness among green bond returns, Bitcoin returns, market uncertainty, and geopolitical risk differs systematically across market states. Using a Quantile Vector Autoregression (QVAR) framework, we estimate connectedness across lower-tail, median, and upper-tail market conditions. To assess statistical reliability, we report bootstrap confidence intervals and difference-based tests and benchmark the quantile estimates against a conventional mean-based VAR. The mean-based benchmark closely matches connectedness around the median quantile. By contrast, system-wide connectedness is significantly stronger in both tails than around the median, as confirmed by difference-bootstrap tests. The direct green bond â Bitcoin linkage is stronger in the lower tail than under normal market conditions, although its net direction is not robustly identified across quantiles. Directional spillovers suggest a more prominent transmitting role for market uncertainty around the median and for geopolitical risk in the upper tail, although these differences should be interpreted cautiously. Overall, the findings indicate that conventional mean-based analysis adequately characterizes connectedness under normal market conditions but cannot capture the pronounced intensification of connectedness observed in the tails.
Paying online often means sharing card details with merchants, advertising platforms, software providers, and payment processors. For freelancers, agencies, online sellers, and small teams, that can create unnecessary exposure: a compromised merchant account, an unexpected renewal, or a card number reused across several services may turn into a difficult cleanup project. A virtual card funded through a USDT top up offers another way to separate online spending from a primary bank account while keeping budgets easier to manage. This approach is not a promise of anonymity, approval, or freedom from verification. A responsible provider may still require identity checks, transaction monitoring, and information about the source of funds. The practical benefit is financial separation and control. Instead of giving every website direct access to a bank-linked card, you can use a dedicated card for approved online purchases, review the conversion terms, and keep records for accounting and compliance. Why use USDT to fund a virtual card USDT is a dollar-pegged digital asset commonly used to move value between supported wallets and platforms. When a card provider accepts USDT, it may convert the deposited amount into the card's spending balance, subject to its network, supported blockchain, confirmation requirements, fees, and compliance procedures. This can be useful for users who already hold USDT and want to pay merchants that accept ordinary card payments rather than cryptocurrency directly. The main operational advantage is separation. A dedicated virtual card can be assigned to advertising, SaaS subscriptions, supplier purchases, or a single project. If the card must be frozen or replaced, the issue may be contained to that spending channel instead of requiring changes across a personal bank account and every recurring payment connected to it. How the funding process usually works A typical flow has three stages: you create or select a card, send USDT to a deposit addre Full article attached as Markdown. Published for vccbusiness.com.
A decentralized system faces a fundamental governance tension: its governancerules are themselves amendable, which means that the metaârules stipulating howrules are modified are also at risk of being revised. Starting from the paradox ofselfâamendment uncovered by legal philosopher Peter Suber, this paper argues thatthis logical dilemma is not a purely philosophical speculation but a structural difficulty that repeatedly arises in the practice of blockchain constitutionalism. Underthe tenet thatâcode is law,âcodeâbased rules bear the metaâgovernance functionsthat in a constitutional structure ought to be carried by constitutional provisions,yet code logically cannot set an insurmountable boundary for its own amendmentauthority. In response, this paper proposes a layered metaâconstraint security architecture: metaâconstraints are divided into an unmodifiable layer of logical constants, a layer of cognitive virtues formulated through community constitutionalprocedures, and a layer of value homeostasis adjusted through public deliberationand evolution; the trustworthiness of metaâconstraints is anchored in the logicalphysical isolation provided by trusted hardware roots. Through the institutionalization of procedures for identifying and attributing metaâconstraints, this paperdemonstrates how forkâexitâbased social verification, cognitionâtesting through independent auditing, and physical anchoring through multiâkey witness mechanismstogether constitute a mutually independent multiâlayered defense system. By examining the 21âmillionâcoin supply cap of Bitcoin, the Ethereum EIP governanceprocess, and the constitutional crisis of The DAO incident as case studies, thispaper reveals the partial instantiation patterns of the threeâtier metaâconstraintarchitecture in existing systems and their failure boundaries. The paper concludesthat the longâterm security of a decentralized system ultimately depends not on theByzantineâfaultâtolerance strength of its consensus algorithm, but on the completeness of its metaâconstraint architectureâthat is, the existence of a set of boundariesthat are hierarchically protected in procedure, isolated and verified in hardware,and socially anchored in consensus, such that the combined cost of breaching themis raised to a level that no actor can afford within the expected life cycle of thesystem.
Financial settlement systems rely heavily on institutional trust: intermediaries maintain ledgers, certifycompliance, and prevent unauthorized creation or movement of value. Zero-knowledge (ZK) techniques make itpossible to replace part of that trust with verifiable properties. This paper presents a minimal ZK settlementlayer designed around a simple principle: prove what must be true, disclose only what must be seen, anddeclare remaining trust explicitly.We describe an architecture in which transfers preserve value, spending authority is proven without sharingspending keys with the operator, double-spending is prevented, and supervisors can verify balance bands orthresholds without receiving the full ledger. We also map the residual trust surface: the operator of a singlenode can still see balances, order transactions, and censor. The contribution is not a claim of full sovereignty ordecentralization. It is a precise shift from opaque institutional faith toward a smaller, named set of trustassumptions, with cryptographic checks covering the rest.We compare this model conceptually with core banking systems and permissioned blockchains, and argue thatthe main institutional value of ZK settlement is not âtrustlessness,â but trust minimization with honestresidual boundaries.This revision subjects that claim to its own standard. An audit pass against the reference implementation foundresidual dependencies the first version of this paper had not named: a confidentiality leak toward thecounterparty rather than the operator, three quantified capacity bounds, and a privilege that is counted butnever expires. We report them in §4.4 and §4.5, because a paper whose contribution is naming residual trust isfalsified by the trust it failed to name.
Digital image steganography has evolved from traditional rule-based techniques to advanced data-driven frameworks enabled by deep learning. However, existing surveys remain fragmented, often focusing on limited aspects while overlooking emerging paradigms such as blockchain-integrated and quantum-based approaches. This paper presents a comprehensive and systematic review of digital image steganography following the PRISMA 2020 guidelines, covering studies published between January 2015 and April 2026 across six major scientific databases. From an initial pool of 26,539 records, 83 relevant studies were selected through a rigorous two-stage screening process. The review provides a unified analysis of steganographic techniques by examining five dimensions: structural evolution and taxonomy, algorithmic modifications and hybridisation, application domain mapping, integration of emerging technologies, and future research trends. Comparative evaluation indicates that deep learning-based methods achieve 18â23% higher steganalysis resistance than classical approaches, whereas classical methods retain a 5â8 dB PSNR advantage. The quantitative synthesis further confirms the inherent capacityâimperceptibilityâsecurity trilemma, wherein no reviewed technique simultaneously achieves $$\text {PSNR} > 42$$ dB, embedding capacity $$> 4$$ bpp, and detection error rate $$> 0.48$$ . Six open challenges and seven future research directions are identified and grounded in evidence from the included studies, with explainable steganography, quantum-resistant frameworks, and latent diffusion model integration emerging as the most critical priorities for advancing the field toward practical and secure deployment.
Open access
Advanced Steganography and Watermarking Techniques
The Technology-Organization-Environment (TOE) framework is widely applied in organizational technology adoption research, yet its measurement practices remain fragmented. Across studies of EDI, cloud computing, blockchain, AI, and other contexts, researchers routinely rename, adapt, or recombine constructs without documenting how their operationalizations relate to prior work, producing a literature that is empirically rich but difficult to accumulate. This study addresses that problem by developing a measurement catalog of 14 reusable TOE constructs drawn from 45 empirical anchor studies. Using a targeted construct-selection approach, the study retained constructs that were peer-reviewed, tested at the firm level, statistically validated, and generalizable across technology domains. Related aliases were consolidated under canonical names through three documented rules based on shared theoretical mechanisms, item-level overlap, and functional equivalence. The catalog organizes constructs across the technological, organizational, and environmental contexts, provides core definitions with recommended measurement facets, and includes representative survey items with reported reliability coefficients. Beyond consolidation, this study identifies persistent gaps, including limited post-adoption measurement, weak readiness-capability differentiation, and underdeveloped governance constructs for emerging technologies. The catalog serves as a practical starting point for researchers designing TOE-based survey instruments and conceptual models, strengthening construct consistency while preserving the frameworkâs flexibility.
Proof-of-work blockchains purchase their security through the expenditure of compute and energy â yet the work performed is itself discarded entirely. Decentralized AI networks provide useful compute but secure no ledger. Myelin unifies both functions: miners jointly operate a large agentic language model (the network model) via pipeline parallelism, and the same cryptographically attested inference work (âProof of Inferenceâ, PoI) determines compensation and feeds the voting weight of consensus. The native coin MYL closes the value cycle: users burn MYL for inference credits, and miners receive newly minted MYL in proportion to verified work (burn-and-mint equilibrium). We specify (i) a layered architecture that decouples consensus latency from inference latency, (ii) a three-tier verification model combining deterministic redundancy, optimistic sampling with a bisection game, and optional zkML anchors, (iii) a token economy with a quantifiable security condition (S_min = g/pÂČ), and (iv) core data types and reference algorithms of an open-source implementation. We name the open core problems â deterministic cross-hardware inference, the latencyâcollusion trade-off of pod formation, and the 50% redundancy overhead â explicitly and propose measurement procedures. Bilingual release: this record contains the English and German editions of the whitepaper (PDF + Markdown each). In case of discrepancies, the German original prevails.
Layer 2 scaling solutionsâincluding payment-channel-based Lightning Networksand rollup-based off-chain execution environmentsâare commonly understood aslinear scaling projects for blockchain transaction throughput. This paper proposesan alternative structural interpretation: the emergence of Layer 2 is not a continuous increase in system capacity, but a percolation phase transition that occurswhen the density of off-chain channels or cross-rollup connections crosses a critical threshold. During this phase transition, the system shifts from a fragmentedlocally connected state to a globally routable giant connected state. The paperanalyzes the Lightning Network and the rollup ecosystem as comparative cases.Empirical studies of the Lightning Network show that its scale-free topology forcescritical hub nodes to bear a disproportionate connection load, thereby binding thenetworkâs global connectivity to the survival of a few high-centrality nodes. Therollup ecosystem faces the structural predicament of liquidity fragmentation, and itsevolution toward cross-rollup interoperability likewise exhibits a phase-transitionlogic from quantitative change to qualitative change in network effects. Based onthe above analysis, this paper distills three design principles for Layer 2 scalability:facilitating the institutionalization of cross-domain connections, avoiding overlyhomogenized cognitive convergence, and implementing differentiated verificationrouting among tasks with different security requirements.
Byzantine Fault Tolerance (BFT) consensus is a foundational achievement indistributed systems theory, providing dual guarantees of safety and liveness forasynchronous networks with malicious nodes. However, this theoretical frameworkimplicitly relies on a presupposition that has not been sufficiently examined: allhonest nodes are homogeneous in their cognition of the protocolâsobjectives. Whena decentralized system evolves from a closed task-oriented network into an opengovernance ecosystem, the functional differentiation of nodes in storage strategies,verification preferences, and governance commitments deprives this presuppositionof descriptive validity. This paper does not deny the security contributions of BFT,but argues that security alone is insufficient to constitute a complete consensus.The full logic of consensus requires a complementary dimension: the capacity toaccommodate functional differentiation. Integrating recent empirical classificationstudies of blockchain nodes, protocol architecture design experiences that acknowledge functional differentiation, and Ostromâs polycentric governance theory, thispaper proposesâCognitive Niche Equilibriumâ(CNE) as an extension of the consensus concept. System stability does not require all nodes to be isomorphic inevery function; rather, it requires the simultaneous satisfaction of three stabilityconditions: feedback anchoring, cross-validation, and evolutionary stability. Using Bitcoin and Ethereum as comparative cases, this paper translates these threeconditions into a layered implementation architecture symbiotic with existing BFTprotocol stacks, and discusses the security engineering principles and trade-offsunder this framework.
The Wireless Sensor Networks (WSN) and the Internet of Things (IoT) have revolutionized various application areas such as smart cities, health, industrial automation, environment, agriculture, and intelligent transportation systems. Despite the successful widespread use of WSNs-IoT, they still have several security issues including resource constrained sensor nodes, decentralized design, and the combination of heterogeneous communication protocols and insecure wireless communication channels. Most traditional security solutions including cryptographic methods, intrusion detection systems are rule-based, which is not enough to protect against the advanced, evolving and zero-day attacks. Therefore, the paradigm of artificial intelligence (AI) has become an exciting approach to creating intelligent, adaptive and autonomous cyber security solutions. This paper is a systematic literature review of the security solutions based on artificial intelligence (AI) applied to WSNs (WSNs) in the context of IoT. Structured review methodology is followed in the study, which critically analyzes recent machine learning, deep learning, reinforcement learning, federated learning, blockchain and edge intelligence advancements in the field of intrusion detection, anomaly detection, threat prediction, authentication, privacy preservation, and secure communication. These approaches are compared on the basis of their accuracy to detect the target, computational complexity, energy efficiency, scalability, and privacy and feasibility for deployment in resource constrained environments. Moreover, it classifies the already known security threats, examines layer-wise defense measures and analyzes upcoming hybrid AI frameworks, which combine several intelligent technologies. The review reveals several gaps in the research, such as the lack of explainability of models, use of benchmark datasets, susceptibility to adversarial and model-poisoning attacks, blockchain scalability issues, and the absence of standardized, secure system architectures that can offer reliable, privacy-preserving, and energy-efficient protection. The paper then proposes future research directions that highlight the need of combining Explainable Artificial Intelligence (XAI), Federated Learning, Blockchain, and Edge AI for the construction of strong and adaptive cybersecurity frameworks. This review is a comprehensive reference for researchers and practitioners who are interested in designing secure, intelligent and sustainable WSN-IoT systems for next-generation cyber-physical ecosystems.