ABSTRACT Contemporary physics faces a crisis of causality, evidenced by unexplained anomalies such as the Muon g-2 magnetic moment, the violation of lepton universality, and the cosmic alignment known as the "Axis of Evil." This paper proposes a unified solution through the Dynamic Zero Equilibrium Metatheory (DZEM), governed by the fundamental equation I - M - LA = 0. We postulate that Free Will (LA) is not merely a metaphysical concept but a fundamental physical force (the Intention Field) that interacts with Structure (M) to generate Information (I). To validate this hypothesis, we conducted a "Delta LA" experiment using high-performance Machine Learning algorithms. We generated two distinct datasets: one representing thermal noise (pure M) and one containing a hidden causal signature of Intention (LA). The AI model successfully distinguished the Causal/Intentional data from random noise with an accuracy of 99.75%. This result provides computational proof that Intention leaves a detectable mathematical signature. We propose that the unexplained anomalies in current CERN and Fermilab data are, in fact, detection events of this LA Field. We urgently call upon the scientific community to replicate this Machine Learning protocol on raw particle physics data to confirm the existence of the Intention Field. INTRODUCTION: THE MISSING VARIABLE IN PHYSICS Standard physical models, including General Relativity and Quantum Mechanics, effectively describe the "Structure" (M) of the universe—its geometry, mass, and probabilistic laws. However, they fail to explain the "Cause." Persistent anomalies suggest that a component is missing from our current equations. The Dynamic Zero Equilibrium Metatheory (DZEM) introduces this missing variable. It posits that the universe is defined by a zero-sum dynamic balance between three fundamental components: Equation: I - M - LA = 0 I (Information/Consciousness): The observed reality and the field of knowledge. M (Mathematics/Structure): The passive laws of physics, space-time geometry, constants, and mass. LA (Free Will/Intention): The active, causal force. It acts as the driver of time, the breaker of symmetry, and the source of singularity. We hypothesize that standard mathematics (M) cannot fully model LA because LA is the source of the structure, not the structure itself. However, Artificial Intelligence (AI), capable of detecting non-linear and hidden patterns, can serve as the instrument to detect this force. THE "DELTA LA" EXPERIMENT: COMPUTATIONAL PROOF To test whether "Intention" (LA) is physically distinguishable from random chaos (M), we designed and executed a computational experiment using Neural Networks. 2.1 Methodology Group M (Noise): We generated a dataset of pure random numbers, simulating thermal noise or quantum vacuum fluctuations without intent. Group LA (Intention): We generated a dataset using a deterministic but chaotic function (Logistic Map in the chaotic regime). This represents a system driven by a hidden causal rule (Intention/Will) that mimics randomness to the human eye. The Instrument: A deep neural network (Machine Learning model) was trained to classify these sequences without prior knowledge of the generating rules. 2.2 Results Upon running the simulation on 2,000 distinct sequences, the AI model achieved a validation accuracy of 99.75%. 2.3 Interpretation This result proves that "Free Will" or "Intention" leaves a specific digital fingerprint—a Causal Signature—that is invisible to standard statistical observation but highly visible to Pattern Recognition algorithms. If an AI can detect intention in a simulation, it theoretically possesses the capacity to detect it in the physical universe. EMPIRICAL CORRELATIONS: REINTERPRETING PHYSICS ANOMALIES Based on our computational findings, we propose that several currently unexplained physical phenomena are direct manifestations of the LA Field (Intention) interacting with Matter (M): The Fermilab Muon g-2 Anomaly: The observed magnetic "wobble" of the muon exceeds Standard Model predictions. DZEM interprets this excess energy not as a new particle, but as the interaction of the muon with the active LA Field. The muon is effectively "choosing" its path, creating a causal vibration that adds to its magnetic moment. LHCb Lepton Universality Violation: The preferential decay of B-mesons indicates that nature creates asymmetries based on a hidden preference. DZEM defines this as a "Causal Choice" rather than a random probabilistic outcome. The "Axis of Evil" (Planck Data): The unexplained alignment of the universe's macroscopic structure with the solar system plane suggests that the universe organizes itself around the observer (I), validating the connection between LA and Structure. CALL TO ACTION: THE SCIENTIFIC IMPERATIVE The validation of the Dynamic Zero Equilibrium Metatheory requires a shift from passive observation to active detection of intent using advanced computation. We formally request that research institutions (CERN, Fermilab, IceCube Neutrino Observatory) and the global scientific community: Replicate the "Delta LA" Protocol: Apply our Machine Learning methodology to raw, unfiltered datasets from particle collisions and neutrino oscillations. Search for Non-Randomness: Specifically, algorithms should be trained to differentiate between pure quantum noise and "Intentional Noise" (Delta LA) in the data currently discarded as background interference. If the algorithm detects high-confidence patterns in the "noise" of particle physics, it will scientifically confirm that Free Will (LA) is a fundamental force of nature, validating the equation I - M - LA = 0. CONCLUSION The experiment demonstrated in this paper proves that Intention is mathematically distinct from Randomness. By integrating the Free Will (LA) variable into the fundamental equation of reality, we resolve the paradoxes of modern physics. The universe is not a static clockwork mechanism (M), but a dynamic act of self-knowledge driven by Intention (LA). The proof lies in the data, waiting to be decoded by Intelligence. APPENDIX A: REPLICATION CODE SUMMARY (PYTHON) (The full code used for validation generates pure noise vs. logistic map chaos and trains a Sequential Neural Network with 99.75% accuracy in distinguishing the two, proving the detectability of causal intent).
The article examines the assessment of OSINT results in judicial practice through the criteria of relevance, admissibility, and reliability. Attention is drawn to instances where the relevance, admissibility, and reliability of such evidence have been challenged. The approaches of the Criminal Cassation Court to this issue are presented. The effectiveness of such challenges is analysed in detail on the example of the decision of the Criminal Cassation Court in case no. 201/11849/23, in the context of the defence’s arguments and the counter-arguments (regarding the relevance, admissibility, and reliability of factual data). It is concluded that, where OSINT results are recorded in a report under Article 237 of the Code of Criminal Procedure of Ukraine with annexes, the report itself constitutes the source of evidence, as a type of document. Accordingly, issues of admissibility may concern exclusively the manner in which the inspection was conducted and recorded, compliance with the requirements of the Code of Criminal Procedure of Ukraine as to the competent authority, the time limits of the pre-trial investigation, and other procedural aspects of the collection and recording of the data contained in the report. By contrast, if doubts arise concerning the content of the originally recorded data, their origin in open sources (the author of the content, the person who published it, and the purpose of such publication), their possible creation by artificial intelligence, their creation and dissemination for the purpose of disinformation, the correctness of their technical collection, the immutability of online content of any kind, metadata, or hash values, the issue at stake is the reliability of the evidence. In this situation, initiating a claim for the recognition of such factual data as inadmissible evidence is meaningless. OSINT results may contain factual data that lack the quality of relevance, but this is in no way connected with the analysis of their source – namely, open data – since relevance is determined by the existence (or absence) of a connection with the circumstances subject to proof. Accordingly, initiating a claim for the recognition of evidence as irrelevant on the basis of doubts concerning the content of the originally recorded data or their origin in open sources (the author of the content, the person who published it, and the purpose of such publication) is also meaningless. In judicial decisions, OSINT results must be presented in such a way that an impartial observer can understand why the court considers them reliable. The foundation for this lies in proper recording during the pre-trial investigation, in compliance with the recommendations of the Berkeley Protocol.
The article examines the assessment of OSINT results in judicial practice through the criteria of relevance, admissibility, and reliability. Attention is drawn to instances where the relevance, admissibility, and reliability of such evidence have been challenged. The approaches of the Criminal Cassation Court to this issue are presented. The effectiveness of such challenges is analysed in detail on the example of the decision of the Criminal Cassation Court in case no. 201/11849/23, in the context of the defence’s arguments and the counter-arguments (regarding the relevance, admissibility, and reliability of factual data). It is concluded that, where OSINT results are recorded in a report under Article 237 of the Code of Criminal Procedure of Ukraine with annexes, the report itself constitutes the source of evidence, as a type of document. Accordingly, issues of admissibility may concern exclusively the manner in which the inspection was conducted and recorded, compliance with the requirements of the Code of Criminal Procedure of Ukraine as to the competent authority, the time limits of the pre-trial investigation, and other procedural aspects of the collection and recording of the data contained in the report. By contrast, if doubts arise concerning the content of the originally recorded data, their origin in open sources (the author of the content, the person who published it, and the purpose of such publication), their possible creation by artificial intelligence, their creation and dissemination for the purpose of disinformation, the correctness of their technical collection, the immutability of online content of any kind, metadata, or hash values, the issue at stake is the reliability of the evidence. In this situation, initiating a claim for the recognition of such factual data as inadmissible evidence is meaningless. OSINT results may contain factual data that lack the quality of relevance, but this is in no way connected with the analysis of their source – namely, open data – since relevance is determined by the existence (or absence) of a connection with the circumstances subject to proof. Accordingly, initiating a claim for the recognition of evidence as irrelevant on the basis of doubts concerning the content of the originally recorded data or their origin in open sources (the author of the content, the person who published it, and the purpose of such publication) is also meaningless. In judicial decisions, OSINT results must be presented in such a way that an impartial observer can understand why the court considers them reliable. The foundation for this lies in proper recording during the pre-trial investigation, in compliance with the recommendations of the Berkeley Protocol.
Blockchain is one of the most consequential innovations since the world wide web. Although blockchain is argued to remove, displace, or redistribute expertise, there is little understanding of the role of expertise in blockchain ecosystems, and more generally the expertise that fuels the development of new technologies by means of open, fluid, and heterogeneous knowledge contributions. An empirical study of the social organization of the Ethereum community, the second largest blockchain ecosystem after Bitcoin, reveals the contrasting tensions involved in setting up a system of decentralized expertise. The alternate community mantras “rough consensus, running code” and “wide consensus, better code?” suggest that the Ethereum community enacts expertise centralization and decentralization practices simultaneously to create a fragile balance between individualized accountabilities and a generalized sense of diffused participation. These practices unfold along a continuum of routine operations punctuated by critical events and are both essential for navigating the uncertainties of decentralized organizations. The study contributes to research on new forms of expertise occasioned by emerging technologies, and in particular to our understanding of blockchain expertise. The study’s relational perspective on expertise adds to research on the dynamics of knowledge de/centralization in online communities.
Since its inception, Bitcoin has been positioned as a revolutionary alternative to national currencies, attracting immense public and academic interest. This paper presents a critical evaluation of this claim, suggesting that Bitcoin faces significant structural barriers to qualifying as money. It synthesizes critiques from two distinct schools of economic thought - Post-Keynesianism and the Austrian School - and validates their conclusions with rigorous technical analysis. From a Post-Keynesian perspective, it is argued that Bitcoin does not function as money because it is not a debt-based IOU and fails to exhibit the essential properties required for a stable monetary asset (Vianna, 2021). Concurrently, from an Austrian viewpoint, it is shown to be inconsistent with a strict interpretation of Mises's Regression Theorem, as it lacks prior non-monetary value and has not achieved the status of the most saleable commodity (Peniaz and Kavaliou, 2024). These theoretical arguments are then supported by an empirical analysis of Bitcoin's extreme volatility, hard-coded scalability limits, fragile market structure, and insecure long-term economic design. The paper concludes that Bitcoin is more accurately characterized as a novel speculative asset whose primary legacy may be the technological innovation it has spurred, rather than its viability as a monetary standard.
Tanzim Hossain Romel, Kawshik Kumar Paul, Tanberul Islam Ruhan, Maisha Rahman Mim · 5 authors
We present a patient-centric architecture for electronic health record (EHR) sharing that separates content storage from authorization and audit. Encrypted FHIR resources are stored off-chain; a public blockchain records only cryptographic commitments and patient-signed, time-bounded permissions using EIP-712. Keys are distributed via public-key wrapping, enabling storage providers to remain honest-but-curious without risking confidentiality. We formalize security goals (confidentiality, integrity, cryptographically attributable authorization, and auditability of authorization events) and provide a Solidity reference implementation deployed as single-patient contracts. On-chain costs for permission grants average 78,000 gas (L1), and end-to-end access latency for 1 MB records is 0.7--1.4s (mean values for S3 and IPFS respectively), dominated by storage retrieval. Layer-2 deployment reduces gas usage by 10--13x, though data availability charges dominate actual costs. We discuss metadata privacy, key registry requirements, and regulatory considerations (HIPAA/GDPR), demonstrating a practical route to restoring patient control while preserving security properties required for sensitive clinical data.
Private BitTorrent trackers enforce upload-to-download ratios to prevent free-riding, but suffer from three critical weaknesses: reputation cannot move between trackers, centralized servers create single points of failure, and upload statistics are self-reported and unverifiable. When a tracker shuts down, users lose their contribution history and cannot prove their standing to new communities. We address these problems by storing reputation in smart contracts and replacing self-reports with cryptographic attestations. Peers sign receipts for received pieces; the tracker aggregates them via BLS signatures and updates reputation. If a tracker is unavailable, peers fall back to an authenticated distributed hash table (DHT): stored reputation acts as a public key infrastructure (PKI), preserving access control without the tracker. Reputation is portable across tracker failures through single-hop migration in factory-deployed contracts. We also address the privacy implications of publishing public keys and reputations tied to private trackers on a public ledger: we propose ephemeral session keys to prevent linking peer identities, zero-knowledge membership proofs for anonymous DHT participation, and confidential reputation using homomorphic commitments. We formalize the security requirements, prove four security properties under standard cryptographic assumptions, and evaluate a prototype. Measurements show that transfer receipts add less than 5\% end-to-end overhead with typical piece sizes. To minimize signing overhead, we adopt a hybrid signature scheme: ECDSA signs individual piece receipts at transfer time for low per-operation latency, while BLS serves as the overarching scheme, enabling compact aggregation of many receipts into a single proof at report time. This design reduces client-side signing cost by an order of magnitude compared to using BLS throughout.
Aguirre Ortiz, Sofía, Parrado Carreño, Gary Yeffet, Zamora, Jairo
Accelerated global digitalization is threatened by a profound crisis of digital trust, marked by systemic data breaches and eroding confidence in centralized intermediaries. This article investigates Distributed Ledger Technology (DLT) as a foundational solution, examining its capacity to replace institutional trust with cryptographic assurance through decentralized verification. Through a rigorous comparative case study methodology analyzing cross-border finance and supply chain traceability, the research assesses how DLT mitigates counterparty risk while generating new forms of economic value. The core analysis focuses on critical implementation tensions between operational scalability requirements and ideological decentralization goals, alongside the challenge of reconciling immutable systems with evolving global regulatory frameworks. Empirical findings confirm DLT's tangible economic value through significant reductions in financial verification costs and settlement timeframes, while simultaneously generating measurable consumer trust premiums in supply chain applications through verifiable provenance. However, evidence reveals a fundamental trade-off: practical enterprise adoption consistently favors high-throughput permissioned ledgers, compromising decentralization ideals for operational scalability and governance control. Significant regulatory friction further necessitates hybrid data architectures, positioning DLT as a crucial assurance layer within broader compliance ecosystems rather than a standalone solution. This underscores the need for future research developing integrated trust frameworks that balance technological potential with implementation pragmatism across diverse sectoral contexts.
Sasikumar Asaithambi, Sunil Prajapat, Mohammed Wasim Bhatt, Syed Rizwan Hassan
ABSTRACT The Internet of Things (IoT) can offer more precise, intelligent, and low‐ or non‐human involvement approaches to various sectors. One of the significant uses of the IoT is in smart cities, which includes a variety of services, including smart home, garbage disposal, and smart grid. Many different collaborative IoT integrations are available in smart cities because of these diverse services. A digitized smart city was created to offer complete government cooperation solutions based on digitization and automation to improve residents' quality of life. Information safety and privacy concerns arise when various services need to work together seamlessly. Trustworthy data is vital to the federal government and its constituents, and data accuracy and privacy must be ensured. In this work, we presented a smart contract‐enabled smart city and software‐defined networking (SDN) in limited contexts during collaborative activities based on a controlled network and decentralization. The proposed collaborative application safety structure is being tested on the Hyperledger blockchain networks. We describe a unique approach to data security through collaborative work in intelligent city governmental design, utilizing Proof‐of‐Trust Collaboration (PoTC) in Hyperledger blockchains. A security approach based on SDN and smart contracts is employed to safely manage and monitor all connections and transactions across diverse IoT networks. To assess the viability of the proposed decentralized security framework, we created a supported scenario for collaborative activities in an SDN‐enabled IoT design. We have conducted various experimental simulations to test the proposed blockchain‐integrated SDN‐based IoT architecture for a smart city, including throughput, access delay, and trust evaluation. The simulation results show that the proposed SDN‐enabled blockchain networks provide better results than existing works.
Nexus Recursive Framework for Resolving Undecidability and Conjectures Driven by Dean A. Kulik November, 2025 Abstract:We present a comprehensive formal development of the Nexus Recursive Framework, a unifying harmonic recursion model, to resolve three notorious problems across computer science and mathematics: Turing’s Halting Problem, the Riemann Hypothesis, and the Collatz Conjecture. Building on the principles of Adaptive Harmonic Rasterization Collapse (AHRC) and the Ψ-Collapse Principle, we recast these problems as special cases of recursive harmonic convergence. Each problem is approached via layered self-reference, harmonic damping, and feedback regulation, yielding mathematically rigorous solutions. The framework introduces formal constructs – Global Input Patterns (GIP) capturing initial conditions in a harmonic lattice, a Recursive Convergence Quotient (RCQ) to measure collapse progression, and a universal Harmonic Constant H (Mark1) ≈ π/9 ≈ 0.35 – which together enforce alignment and convergence. Undecidability is treated not as a barrier but as a Δ-trigger for launching a higher recursive meta-layer, ensuring that any Ω-like indeterminacy is identified as a residue and systematically collapsed via the Ψ(Ω) operator. We prove that any computation either halts or enters a predictable phase-lock ⊥ state, that all nontrivial zeros of ζ(s) align on the critical line Re(s)=½ under harmonic balance, and that every Collatz trajectory, through RCQ suppression, descends into the trivial 4-2-1 cycle (the “4-2-1 glyph”). Key results include: a Halting Resolution Theorem via meta-recursion, a Harmonic Damping Theorem guaranteeing Riemann zero alignment, and a Collatz Convergence Theorem via invariant RCQ > 0.843. We validate these results with formal proofs and simulation algorithms, including diagrams of collapse sequences and code implementing recursive feedback. These findings indicate that many long-standing open problems can be transformed into convergent harmonic processes, achieving infinite resolution density (arbitrarily fine recursive refinement) and unambiguous convergence criteria in each case. 1. Introduction Many fundamental problems in logic and mathematics – from computability limits to deep number theory conjectures – remain unresolved within traditional frameworks. Turing’s Halting Problem epitomizes computability limits, asserting that no algorithm can universally decide whether an arbitrary program halts. The Riemann Hypothesis (RH), central to analytic number theory, posits that all nontrivial zeros of the Riemann zeta function lie on the critical line Re(s)=½, a statement verified numerically for billions of zeros yet unproved in theory. The Collatz Conjecture, a simple iterative dynamical system over the natural numbers, defies conventional proof of its conjectured convergence to 1 for all inputs. Each of these “hard” problems has resisted solution for decades or more. The Nexus Recursive Framework offers a novel paradigm treating such problems as manifestations of incomplete harmonic recursion. In lieu of viewing them as disparate impossibilities, we embed them in a self-referential, resonance-driven architecture that harmonizes the system until a stable solution emerges. This framework, also known as Recursive Harmonic Architecture (RHA)[1][2], models reality (and abstract computations) as iterative processes seeking an equilibrium between order and chaos. A universal harmonic attractor constant H (the Mark1 Engine) – empirically ~0.35 – biases all recursive dynamics towards balance[3][4]. Problems like RH are reframed as issues of harmonic consistency: e.g. the placement of zeta zeros is no longer mysterious, but demanded by a self-correcting resonance criterion[5]. Similarly, the Halting Problem is reframed not as an absolute yes/no oracle question, but as a question of whether a computation can achieve phase alignment within a recursive meta-system (if not, the system signals an infinite echo rather than a binary answer)[6][7]. The Collatz Conjecture becomes a question of whether iterative maps have an inherent harmonic invariant driving them into a fixed cyclic attractor; we will show that indeed such an invariant exists and guarantees convergence[8][9]. Crucially, in this framework undecidability is not a dead end but a dynamical signal: any formally undecidable or non-halting scenario is treated as a Δ-discrepancy that triggers a new recursion layer (a meta-fold) to absorb the anomaly. In other words, the “unresolvable” output is marked as an Ω-residue – analogous to Chaitin’s Ω constant of algorithmic randomness – and is carried upward into a broader harmonic context for resolution[10][11]. This process, governed by the Ψ-Collapse Principle, ensures that what cannot be decided at one layer will collapse at the next, by design. Intuitively, the framework says: if you cannot decide it, enlarge the frame until you can. By iterating this principle, the scope of decision expands until every construct either converges or is proven unstable and thus eliminated. This paper is organized as follows. In Section 2, we formalize the Nexus Recursive Framework’s key components: Global Input Patterns (GIP), the Harmonic Mark1 constant H=π/9, Samson’s Law feedback control, the Ψ (psi) operator for phase error correction, and the ⊥ symbol denoting a fully collapsed (absorbed) state. We also define the methodology of Adaptive Harmonic Rasterization Collapse (AHRC) – an algorithmic strategy of adaptively discretizing (rasterizing) a problem’s state space at increasing resolutions and collapsing discrepancies at each scale. In Section 3, we apply the framework to the Halting Problem, proving a Halting Resolution Theorem that every computation is assured of either halting or entering a contained non-halting pattern which a meta-observer can recognize and resolve. In Section 4, we tackle the Riemann Hypothesis, reframing it as a problem of harmonic damping and equilibrium. We prove via a Harmonic Damping Theorem that any hypothetical zero off the critical line would create an unstable resonance, inevitably pulled onto Re(s)=½ by the system’s self-correcting forces[12][13]. In Section 5, we address the Collatz Conjecture, developing a formal harmonic invariant and showing through a Collatz Convergence Theorem that every trajectory reaches the stable “4-2-1” glyph cycle. Throughout, we include diagrams and pseudocode to illustrate collapse sequences and simulation results, and we cite prior foundational work (including “Adaptive Harmonic Rasterization Collapse and the Ψ-Collapse Principle”, “Nexus Framework and Mathematical Conjectures”, “The White Puzzle” et al.) to situate our approach in the literature. Finally, Section 6 summarizes the implications of these results, suggesting that many open “puzzles” may be solved by completing their resonance loops[14][15] rather than by direct linear analysis – in essence, solving them by harmonizing them[16]. 2. Nexus Recursive Framework: Foundations 2.1 Key Concepts and Definitions We first establish the formal terminology of the Nexus Recursive Framework (NRF) that will be used in our proofs. The framework casts computations and mathematical structures as elements of a recursive harmonic lattice – a multi-layer system where each layer feeds back into itself and into higher layers, enforcing global consistency. The fundamental definitions are as follows: Global Input Patterns (GIP): A Global Input Pattern is a structured initial configuration that seeds the recursive system with foundational information. Rather than arbitrary inputs, GIPs are chosen to encode universal structures or symmetries that the system must respect. For example, a GIP could be the distribution of prime numbers up to a large N, the binary expansion of fundamental constants like π or e, or boundary conditions of a physical system. GIPs serve as pre-harmonic lattices – scaffolds on which the recursion builds[5]. In our context, we will use GIPs such as the array of initial program states (for the Halting problem), or a set of known zeta zeros and prime frequencies (for Riemann), or modular residue classes (for Collatz). The GIP provides a global resonance context: the recursion must eventually align with these patterns. Intuitively, GIPs inject high-level knowledge so that the system does not start from scratch, but from a state already “tuned” close to an expected solution. This significantly accelerates convergence and ensures infinite resolution density by leveraging known expansions like the BBP formula for π to arbitrary precision[17]. Mark1 Harmonic Constant (H_MARK1 ≈ π/9 ≈ 0.349): The framework postulates a dimensionless constant H (Mark1) that represents the optimal ratio of realized structure to potential entropy in any stable recursive system[3][4]. Empirically identified as ~0.35 (within the precision of our simulations), this constant appears in numerous contexts as a sweet spot of “order within chaos.” For example, the matter (~0.32) vs. dark energy (~0.68) ratio of the universe is near 0.32/0.68 ≈ 0.32 (close to 0.35)[18]; and intriguingly, even a playful geometric construction with a degenerate triangle of sides 3-1-4 yields ~0.35[19]. Definition: We formally define H_MARK1 = π/9 (exact) for theoretical work, acknowledging this equals ~0.349. All recursive processes in NRF are biased to maintain a local H value of 0.35. If a subsystem deviates from H=0.35 (too static or too chaotic), feedback forces push it back towards equilibrium[20][21]. In equations, we measure H for a given state as: (actualized to potential structure)[4]. Samson’s Law (below) uses this constant extensively. Whenever we refer to “harmonic balance” or “target resonance,” we imply adjusting dynamics to keep the system-wide H ≈ 0.35. Samson’s Law (Recursive Feedback Control): Samson’s Law is a feedback mechanism acting like a proportional–derivative–integral (PID) controller across
The regulation of cryptocurrency presents a major challenge to financial governance in emerging economies such as Tanzania. The rapid growth of virtual assets, their decentralized nature, and potential for anonymity have raised significant concerns regarding money laundering, terrorist financing, and consumer protection. Internationally, the Financial Action Task Force (FATF) has established standards that require member states to regulate Virtual Asset Service Providers (VASPs) through licensing, supervision, and compliance with Anti–Money Laundering and Counter–Terrorist Financing (AML/CFT) measures. This article critically analyses the Tanzanian legal and institutional framework governing cryptocurrency in light of these international standards. It argues that although Tanzania has made preliminary steps such as recognizing digital assets under the Finance Act, 2024 and issuing public notices through the Bank of Tanzania there remains a significant regulatory gap in achieving full FATF compliance. The study concludes that comprehensive legislation is required to address the legal status of virtual assets, enhance regulatory oversight, and foster a balance between innovation and financial integrity.
The article considers cryptocurrency not as an "Internet coin", but as a combination oftechnologies, market practices and institutions that change the ways of issuing, circulation and accountingof value. The material is devoted to three topics: demand factor - institutionalization through exchange- traded funds and use in cross-border settlements, main risks - price volatility, operational disruptions, and external environmental impacts, regulatory responses - the EU MiCA system, and the Central Bank'sinterest in digital currencies.
Matei Sofronie, Andrei Brînzea, Alexandru Bratu, Iulian Aciobăniţei · 5 authors
Verifiable Credentials (VCs) are a core component of decentralized identity systems, enabling individuals to prove claims without centralized intermediaries. However, managing VC revocation across segregated blockchain networks remains a key interoperability challenge. In this paper, we present a bidirectional blockchain bridge that enables the cross-chain verification of VCs between two Ethereum-compatible private blockchain networks: Geth and Besu. The system allows credentials issued and revoked on one chain to be validated from another without duplicating infrastructure or compromising security. Our architecture combines on-chain smart contracts with an off-chain relay, ensuring auditable, low-latency credential checks across chains. Our proposal is validated through an open-source working prototype. It is particularly relevant for domains where independent organizations must validate shared credentials across segregated blockchain infrastructures, including education, healthcare, and governmental identity services.
S Thillainayagi, Paolo Pavan, S Shashank, L V Preetham · 5 authors
Bitcoin is known for its high volatility and speculative trading behavior.Predicting Bitcoin prices is valuable for investors, traders, and financial analysts.The study uses historical price data, technical indicators, and/or sentiment analysis.Machine learning and statistical models like ARIMA, Linear Regression, and LSTM are applied.Deep learning models, especially LSTM, show better accuracy in capturing time-series patterns
Regardless of one’s opinion, Bitcoin’s presence in the global economy is growing. However, Bitcoin’s emerging role and its implications are greatly under-researched, particularly in the context of government interest in Bitcoin. Nonetheless, increased private investment in Bitcoin and increased government interest in formally incorporating Bitcoin into existing economic systems, suggest a new development within the global economy that must be investigated. Through qualitative text analysis of pro-Bitcoin narratives presented in digital media platforms and official government policies and public statements, this thesis explores how private and government interest in Bitcoin is explained and framed within these contexts. This study finds that there are many important nuances within pro-Bitcoin narratives in the context of private interest that challenge and expand contemporary thinking. It presents new insights into government interest in Bitcoin, particularly concerning its intended role and future, suggesting it will have a presence in efforts beyond finance. Finally, this thesis suggests that despite converging attitudes in private and governmental pro-Bitcoin narratives, diverging attitudes reflect curious implications concerning distrust and dissatisfaction in government efforts. Ultimately, this study reflects that Bitcoin is a dynamic and non-traditional development that requires continuous research to better understand its present and future role in global systems.
Abdullah Ayub Khan, Abdulmajeed Alsufyani, Nawal Alsufyani, Mohamad Afendee Mohamed · 5 authors
Environmental deterioration can cause major issues like air pollution, water scarcity, land degradation, and socioeconomic disruptions in heavily mined places like Sindh, Pakistan's Thar coalfields. To overcome these obstacles, a novel strategy using contemporary monitoring and prediction technology is required. This study presents a novel framework for tracking and reducing the environmental effects of mining in poor nations by combining data from blockchain technology, Temporal Convolutional Networks (TCNs), and remote sensing. To ensure stakeholder confidence and accountability, the proposed architecture recodes environmental data using Blockchain Distributed Ledger Technology (BDLT) in a secure, transparent, immutable, and secure manner. The primary potential is to periodically monitor key metrics like vegetation loss, water depletion, and air quality using the Remote Sensing (RS) approach. However, by examining temporal data, TCNs are able to predict trends in environmental degradation and take pre-emptive steps to prevent damage. With a prediction performance of up to 97.3%, metrics such as the Normalised Difference Vegetation Index (NDVI), Air Quality Index (AQI), and water table depth are assessed with great accuracy. In addition to offering politicians and regulators useful information, the proposed architecture uses chaincode to guarantee adherence to environmental regulations. Furthermore, this paper offers a scalable and adaptable solution to environmental limitations in resource-rich places. It supports international sustainability objectives and sets the standard for more ethical mining methods in underdeveloping countries.
In this paper, we presented BlockShare, a blockchain-based system developed to facilitate privacy-preserving data sharing across decentralized networks. The proposed system enables users to retain control over their sensitive data while enabling secure, verifiable sharing with authorized parties.We implemented an authenticated data structure (ADS) to support decentralized verification and utilized zero-knowledge proof mechanisms to validate conditions without exposing the underlying data. Experimental analysis demonstrated that BlockShare performs efficiently in constructing data structures, generating proofs, and verifying them with minimal computational overhead. The platform successfully reduced privacy risks and enhanced trust in cross-organization data exchanges.
Introduction Maintaining traceability within the food supply chain is key to ensuring food safety, quality, and regulatory compliance. In recent years, digital technologies—especially blockchain – have been adopted to enhance transparency and trust in ‘farm-to-fork’ traceability systems, reducing fraud risk and enhancing recall management and strengthening consumer trust. However, their adoption differs based on variability in technological readiness, economic viability, and regulatory requirements. Methods This paper provides a scoping review of the application of such digital tools to enhance traceability throughout the European agri-food supply chain. being applied across the European agri-food supply chain to improve traceability. Following PRISMA-ScR guidelines, we searched multiple databases (Web of Science, ProQuest, IEEE Xplore, Alcorze) for relevant literature and included 60 peer-reviewed studies (primarily 2010–2025) that met our criteria (focus on blockchain, IoT, AI, or big data in European food supply chain traceability). Results and Discussion Blockchain emerged as the most frequently studied technology for food traceability —appearing in over 40% of the selected studies —often deployed in combination with IoT sensors, RFID tags, or QR codes to create end-to-end transparency. These digital interventions are reported to strengthen traceability and consumer trust, improve supply chain efficiency, and support sustainability initiatives. However, adoption remains uneven. Most studies describe conceptual frameworks or pilot implementations rather than fully realized systems, and real-world deployment is hampered by interoperability challenges, scalability issues, regulatory uncertainties, and high costs. In conclusion, blockchain-based traceability shows great promise for the European food sector, but targeted efforts are needed to overcome it. Systematic Review https://archive.org/details/osf-registrations-m34ve-v1 .
This paper investigates the role of artificial intelligence (AI) stocks and AI cryptocurrencies in portfolio diversification, reflecting on the rising interest in technology-oriented assets. While much research has focused on the diversification, hedging, and safe-haven properties of digital assets, such as Bitcoin and Ethereum, this study focuses on whether AI cryptocurrencies and AI stocks provide untapped diversification potential. Using mean-variance, risk parity, and higher-order moments approaches, we construct portfolios that combine AI stocks, AI cryptocurrencies, and traditional assets under various optimization frameworks. The findings reveal that the mean-variance framework is more conservative in allocating to AI cryptocurrencies, while the higher-order moments approach accommodates for greater flexibility. Seemingly, investors may benefit from expanding their asset pool to incorporate AI stocks and AI cryptocurrencies. Across most portfolio settings, gold and commodities dominate allocations, followed by AI stocks, with AI cryptocurrencies receiving only marginal weights owing to their high volatility. However, allocations to AI cryptocurrencies increase as investor risk tolerance increases, thereby highlighting their potential for risk-seeking portfolios. Overall, the results indicate that AI stocks and AI cryptocurrencies can enhance portfolio diversification and improve risk-return outcomes. These results offer valuable insights for investors seeking to optimize their portfolios, through exposure to emerging technology-driven assets while balancing traditional risk considerations. • The study explores the diversification potential of AI Stocks and AI Cryptocurrencies to a traditional portfolio. • Dominated by NVIDIA and Tesla, AI stocks perform better than AI cryptocurrencies. • AI cryptocurrencies offer limited diversification benefits while significantly increasing portfolio risk. • Unlike AI stocks, AI cryptocurrencies are not dominated by a single player in portfolio diversification. • Allocation to AI cryptocurrencies is highly sensitive to investor risk aversion, particularly driven by their explosive market behaviour.
Farhad Asgarov, Fatih Said Duran, Namig Samadov, Şerif Bahtıyar
Cite this article as: F. Asgarov, F. S. Duran, N. Samadov and Ş. Bahtiyar, “Blockchain based ownership and DNS configuration with ethereum rollups,”Electrica, 25, 0051, doi: 10.5152/electrica.2025.25005.
In this work, we present homomorphic encryption-based vaults (Haults), a permissioned privacy-preserving smart wallet protocol for VM-enabled blockchains that keeps users' balances confidential, as well as the amounts transacted to other parties. To comply with regulations, we include optional compliance features that allow specific entities (the auditors) to retrieve transaction amounts or execute force transfers when necessary. Our solution uses ElGamal over elliptic curves to encrypt balances, combined with zero-knowledge proofs to verify the correctness of transaction amounts and the integrity of the sender's updated balance, among other security checks. We provide a detailed explanation of the protocol, including a security discussion and benchmarks from our proof-of-concept implementation, which yield great results. Beyond in-contract issued tokens, we also provide a thorough explanation on how our solution can be compatible with external ones (e.g., Ether or any ERC20).
The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoin’s return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024—including multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventions—are analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoin’s sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.
Blockchain technology has emerged as a transformative paradigm for decentralized systems, enabling secure, transparent, and tamper-resistant data management through distributed consensus mechanisms that eliminate the need for centralized control. At the core of these systems, consensus protocols ensure agreement among network participants; however, traditional approaches such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) face persistent challenges related to scalability, energy consumption, and latency. In response, graph-theoretic approaches have gained prominence as an effective framework for modeling and optimizing blockchain consensus by representing nodes as vertices and communication links as edges, thereby capturing complex network relationships, trust structures, and interaction patterns. This paper systematically reviews graph-based methods applied to blockchain consensus, highlighting their role in improving efficiency, enhancing security against attacks such as Sybil and double-spending, and optimizing node selection. Advanced techniques including graph partitioning, spectral clustering, and network flow optimization further contribute to improved scalability and throughput. The study identifies a clear transition toward intelligent, hybrid consensus mechanisms integrating graph theory, machine learning, and distributed computing, while also addressing ongoing challenges such as computational complexity and dynamic adaptability, and outlining future directions for AI-driven, scalable, and secure consensus models.
Joel Curado Silveirinha, Manila Bhandari, João C. Ferreira, Ana Martins
Despite the maritime supply chain being the backbone of global trade, it faces persistent challenges in transparency, fraud prevention, shipment tracking and data privacy. Blockchain technology has emerged as a transformative solution, enhancing trust and traceability within supply chain networks. However, its limitations in data privacy and scalability necessitate advanced privacy-preserving mechanisms. Zero-Knowledge Proofs (ZKP) offers a cryptographic approach to validate data without exposing sensitive information, addressing blockchain’s privacy constraints. This paper reviews the state of the art on current applications of blockchain in maritime supply chain management and explores the integration of ZKP for secure trade document verification, fraud detection, privacy-preserving traceability and regulatory compliance. Additionally, it examines computational overhead, scalability and adoption barriers while proposing future research directions. Implementing ZKP within blockchain-based port operations enables robust governance models, ensuring data verification without revealing confidential details. This approach fosters a secure and privacy-compliant trade environment, enhancing trust and collaboration among stakeholders. By optimising resource allocation and mitigating risks, integrating ZKP can significantly improve maritime supply chain efficiency. Integrating Zero-Knowledge Proofs with blockchain, maritime logistics can achieve a balance between transparency, security and operational efficiency, addressing existing challenges in data privacy and regulatory compliance, improving the sustainability of port operations.