We examine the possible asymmetric relations between returns and changes in realized moments in the Bitcoin market by employing the quantile regression models (QRMs) which can account for investors’ heterogeneity. First, our findings confirm the existence of asymmetric return-volatility relation in the BTC market. Second, regarding the relations between returns and realized skewness, the negative and positive returns show larger impacts in lower and upper quantiles, respectively. Third, the relation between return and kurtosis exhibits similar asymmetric pattern to that of return-volatility. The empirical findings can be supported by behavioral theories including representative bias and affect heuristics.
G. N. Girish, Ashutosh Sahoo, Ajay Bhat, Akshay SP · 7 authors
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering. The anomaly ensemble combines a one-class reconstruction model with an isolation forest and applies graduated rather than binary penalties. On 1,073 labeled malicious wallets covering 124,638 transactions, the ensemble achieves 0.923 +/- 0.013 ROC-AUC, compared with 0.891 +/- 0.016 for the reconstruction model alone, when the isolation forest is trained on benign wallets. Training it on the pooled population reverses its polarity and removes the ensemble gain. Controlled simulations reduce adversarial reward capture by 30-90 percent while legitimate-user scenarios change by 1-8 percent. Live campaigns recorded a 56 percent reduction in sybil allocation, a 49 percent increase in quality-wallet participation, and a 50 percent reduction in sell pressure.
Blockchain is becoming an approachable data platform with several stakeholders having a shared history of transactions without being owned by an individual. The fundamental concepts of cryptographic hashing, peer-to-peer communication, and agreement protocols are sufficiently documented, and a lot of current research focuses on performance, security, privacy, and governance individually rather than as interacting dimensions. Recent blockchain research publications and deployments were structured based on a four-axis perspective that follows the dynamics of a system in terms of security, scalability, privacy, and governance. This lens was applied to consent mechanisms including proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, and to techniques such as sharding and layer-2 that are designed to enhance throughput. Basic throughput calculations using reported block sizes, transaction sizes, and block intervals indicate that configuration limits are usually much larger than the transaction rates achieved in practice, implying that protocol overheads and network behaviour require a major share of the budget. A survey of attacks and defences indicates that increases in speed and programmability often expand the attack surface at the consensus and smart contract layers, which motivates the development of better analysis and monitoring tools. The results are applied to draw design insights for domains of finance, supply chains, healthcare, identity, and smart city platforms, and to highlight remaining problems in benchmarking and cross-chain coordination. A practical mapping of major blockchain platforms, namely Bitcoin, Ethereum, and Hyperledger Fabric, onto the four-axis framework was also provided to demonstrate its utility for platform comparison and selection.
NEUROUNBOUND is a whitepaper and manifesto proposing a free, decentralized, and borderless research and educational ecosystem designed specifically for neurodivergent minds — individuals with ADHD, AuDHD, and Autism. Rather than replicating conventional academic structures, this framework is built around hyperfocus, non-linear reasoning, and proof-of-creation rather than standardized testing or rote memorization. Main Goal: To design an alternative educational and research model that removes the structural, sensory, and social barriers neurodivergent individuals face in traditional institutions, while channeling their cognitive strengths toward solving complex real-world problems across five core domains: Medicine & Neuroscience, Technology & Computing, Environment & Climate, Economy & Financial Systems, and Politics & Governance. Intended Audience: Neurodivergent students, independent researchers, educators, and institutions interested in inclusive, non-traditional models of education and talent development; also relevant to policy researchers and organizations working on neurodiversity, alternative credentialing, and decentralized education. Key Contributions / Outcomes: A four-stage adaptive admissions framework (Neurotype Verification, Hyperfocus Portfolio, Asynchronous Sandbox Challenge, Zero-Masking Async Interaction) designed to reduce social and sensory friction in evaluation. An educational methodology centered on "Proof of Creation" (a Magnum Opus artifact) rather than lectures and standardized grading. A self-sustaining funding and talent-retention model based on IP commercialization, open-source grants, and industry innovation contracts. This document is published as a formal, timestamped declaration of authorship and prior art for the NEUROUNBOUND concept, structure, and terminology, licensed under CC BY-NC-SA 4.0.
Counterfeit medicines, fragmented record management, limited end-to-end visibility, and dependence on centralized databases create security and traceability challenges in pharmaceutical supply chains. This article presents Pharma-Chain, a blockchain-enabled pharmaceutical supply chain management system developed to record medicine registration, stakeholder interactions, ownership transfer, and authenticity verification on an Ethereum blockchain. The system integrates a React.js frontend with Web3.js, MetaMask wallet authentication, Solidity smart contracts, Truffle deployment tools, and Ganache for local blockchain development and validation. Manufacturers register medicine batches with product and lifecycle information; distributors and retailers verify blockchain records before accepting and transferring ownership; and customers retrieve medicine details and transaction history before purchase. The implementation was tested module by module and as an integrated application in a local Ethereum environment. Testing confirmed successful authentication and role-based access, medicine registration, ownership transfer, transaction validation, blockchain record retrieval, and medicine verification. Each successful supply-chain operation generated a blockchain transaction record, providing an immutable ownership history and improving transparency and auditability. The project demonstrates a practical decentralized approach for secure pharmaceutical tracking while identifying QR-code verification, IoT monitoring, AI-assisted analytics, mobile access, and deployment on public or enterprise blockchain networks as future extensions.
Open access
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Blockchain Technology Applications and Security
Pharmaceutical Quality and Counterfeiting
Physical Unclonable Functions (PUFs) and Hardware Security
This bilingual engineering report presents a practical framework for designing reliable AI agent systems in production environments. It focuses on explicit tool and permission boundaries, memory governance, observability, evaluation, human approval, failure recovery, and auditability. The report connects these concerns with the operational requirements of FinTech real-time systems and Web3 transaction workflows. It introduces the Controlled Action Protocol (CAP-1), a report-defined design proposal that binds an action proposal, policy decision, human approval, and execution record through identifiers, payload hashes, evidence, policy versions, and expiration windows. CAP-1 is not presented as a validated industry standard or a claim of industry-first novelty. The report also includes reference architectures, implementation guidance, risk analysis, and production-readiness checklists derived from the author's engineering practice from 2024 to 2026.本双语工程报告提出了一套面向生产环境的 AI Agent 可靠性工程框架,重点讨论工具与权限边界、记忆治理、可观测性、评估、人工审批、故障恢复和审计能力,并结合 FinTech 实时系统与 Web3 交易工作流的工程要求。报告定义受控行动协议 CAP-1,将行动提案、策略判定、人工授权和执行记录通过标识符、载荷 Hash、证据、策略版本和有效期绑定为可核验链条。CAP-1 被定位为本报告的设计提案,不被表述为已验证的行业标准,也不主张具有“行业首创”地位。报告同时给出参考架构、实施建议、风险分析和生产就绪检查清单,内容来源于作者 2024—2026 年间的工程实践总结。This is an author-directed technical report. Generative AI tools assisted under the author's direction with drafting, translation, language editing, diagram production, and document formatting; Pengpeng Han (Corn Han / 韩朋朋) retains responsibility for the report's factual accuracy, technical claims, source review, and final approval. Repository publication and DOI registration provide persistent identification and citation infrastructure; they do not imply peer review, third-party endorsement, or independent validation of the claims.本报告为由作者指导并负责的技术报告。生成式 AI 工具在作者指导下用于辅助起草、翻译、语言编辑、图表制作和文档排版;韩朋朋(Corn Han)对报告的事实准确性、技术主张、来源审核和最终批准承担责任。存储库发布及 DOI 注册用于提供持久标识和标准引用,不代表同行评审、第三方背书或对报告主张的独立验证。
Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasingly use deep learning and treat blockchain activity as a quasi-linguistic sequence. However, complex sequence models are computationally expensive for real-time monitoring, and their reported performance may be inflated by label leakage from high-signal smart contracts. We ask whether and how organic users, Sybil bots, and MEV bots differ in the structural complexity of their transaction histories; whether sequential models outperform tree-based tabular models once leakage is reduced; whether transaction order or timing provides the stronger behavioral signal; and whether the resulting models are practical for low-latency deployment. Our approach to leakage-aware Sybil bot detection consists of a Blind-Spot protocol and a Transaction Grammar representation of wallet behavior. The former eliminates shortcuts associated with high-signal contracts, whereas the latter models wallets using rhythm, EVM execution structure, and intent. We evaluate this approach on Ethereum actor classification by comparing Transformer and BiLSTM sequence models against XGBoost and SVM baselines. We contribute a framework for leakage-aware Ethereum actor classification and a Transaction Grammar representation of wallet behavior. Our results demonstrate that, under leakage-aware evaluation, XGBoost outperforms Transformer-based sequence models while providing lower latency and estimated energy use.
Context: AI-assisted software development can speed up coding and review, but it also makes accountability harder to establish. Developers may submit insufficiently verified code, reviewers may approve changes with limited inspection, and centralized reputation records may be difficult to audit. Objectives: This study introduces TrustChain-Review, a framework that combines verifiable evidence, strategic incentives, and risk-sensitive governance to support more trustworthy code review. Methods: The framework includes a blockchain-based evidence layer, a three-player game-theoretic model for developers, reviewers, and the platform, and a rule that applies stronger governance when the expected benefit justifies its cost. The evaluation uses a controlled simulation calibrated with the Diff Quality Estimation dataset. Six governance configurations are compared over 30 independent runs using reputation accuracy, trust convergence, malicious-review detection, superficial-review detection, net platform utility, governance cost, and cost-efficiency. Results: The full-evidence configuration produces the strongest reputation, trust, and detection results, but it also has the highest governance cost. The risk-adaptive configuration lowers this cost and improves cost-efficiency by applying stronger controls selectively, although its trust and detection results are lower than those of the full-evidence setting. Conclusion: Strong evidence-based governance is most appropriate for high-risk or audit-sensitive changes. For routine or lower-risk contributions, selective governance offers a more practical balance between trustworthiness and operational cost.
Sui's object-centric data model enables parallel transaction execution, but realised performance is fundamentally bounded by workload contention. Prior empirical analyses of Sui have relied on "read+write" (R+W) conflict graphs inherited from account-based blockchains. Because Sui's engine serialises only on mutable shared access, R+W graphs contain spurious edges, bounding contention from above. In this paper, we adopt a complementary "write-set-only" (W-only) model in which every edge represents a real write-serialisation event, providing a lower bound on contention. Together, the two models bracket Sui's true execution-dependency structure. Applying the W-only analysis to Sui mainnet data through 2025 yields three primary findings. First, removing read-only dependencies (notably the system clock) causes previously reported "hub-and-spoke" structures to collapse. The remaining contention topology is highly assortative and clique-dominated, with the W-only bound shaving roughly $30$--$40\%$ off the R+W estimate of Sui's optimal-parallelism headroom. Second, via union-find object grouping, we isolate DeepBook (Sui's native central limit order book). While it dominates contention by volume, its underlying logic does not impose disproportionate sequential bottlenecks. Finally, we quantify the economic cost of contention, showing that $10$--$50\%$ of the network's USD-denominated value flows through sequentially constrained execution paths, exposing it to potential ordering effects.
Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive? We show this can be answered "no" by a conspiracy that is nonetheless profitable. Consider bidding agents that couple only through the joint distribution of their unexplained bid components, leaving every agent's own bid law exactly at the competitive law. Any test whose input is a single agent's price or bid history then has power exactly equal to its false-positive rate, for every coupling strength up to comonotonicity. The published detection methodology is therefore blind to this conduct by construction rather than underpowered, and no sample size repairs it. Three empirical results follow. First, the mechanism appears in real language-model agents: twenty models from nineteen independent developers, three deployment prompts each, show residual correlation of $+0.053$ between two deployments of one model against $+0.0001$ across models, with a 95% interval clustered by developer of $[0.030, 0.078]$, under an auditor that sees every order feature and is fitted out of sample. Second, the coupling falls monotonically as sampling temperature rises ($p=0.002$), turning a deployment parameter into a candidate mitigation. Third, on 24 days of Ethereum block-building auction data covering 77,684 bids from 39 bidders, the honest population of bidder pairs is itself so dependent that a screen held at a 5% false-positive rate must sit above a floor of $+0.50$ to $+0.81$, which is 20 to 32 times the family-wise sampling threshold and does not fall as the audit window grows. Since lawful multi-identity operation and conspiracy are behaviourally indistinguishable here, the tractable regulatory target is not detection but counting: resolving 40 bidding identities into 23 operators raises the Herfindahl index by 247.5%, and adding behavioural clusters from public bid streams reaches 324.5%.
This article is devoted to the general legal characteristics of smart contracts as a means of disposing of digital assets. Smart contracts are a key mechanism for transferring digital assets, but their legal regime remains controversial and unexplored. The main goal. To make a general civil law qualification of smart contracts as a special form of concluding transactions by order of the Central Bank and to justify their recognition as full-fledged legal means of achieving a legal result. The problems under consideration are the differentiation of the IC from related legal phenomena: computer programs, methods of securing, executing and concluding a transaction. The methods used are: formal-legal, comparativelegal, historical-legal, systematic. Conclusions. The IC is a special digital form of the transaction, which has the highest degree of reliability and accuracy of recording the will of the parties in the presence of objective guarantees of its execution.
Uncertainty plays a significant role in shaping investment decisions, both directly and indirectly. In an uncertain economic environment, investors’ motivations for decision-making may vary. While some investors tend to seek safe-haven assets, others may engage in speculative behavior. Therefore, uncertainty can influence financial instruments through various mechanisms. One of these instruments is Bitcoin, which is often regarded as the “gold” of cryptocurrencies. Compared to traditional financial investment instruments, Bitcoin exhibits higher volatility and is among the primary assets that may be affected by uncertainty. However, an important question is whether this effect is temporary or permanent. The main objective of this study is to address this question by examining the causal nexus between Global Economic Policy Uncertainty (GEPU) and Bitcoin by employing a frequency-domain causality approach. In this context, the causal relationships between GEPU and BTC prices are examined for the entire period and for different sub-periods. Although the study's findings show no causal relationship between the variables over the entire period, the analyses for the short-, medium-, and long-run indicate a causal relationship from GEPU to BTC in the medium run. Accordingly, GEPU can be considered one of the factors affecting BTC price; however, its impact does not appear to be persistent.
Cathedral Arkhe is an attempt to build a research programme whose every claim is attached to amechanism that can refute it.The programme has three layers. The first is a speculative physical framework — the CathedralWave Framework — that models self-referential systems as standing waves on a non-orientablemanifold, and derives from that geometry a catalogue of 43 numbered predictions, 37 equations,14 paradoxes and 21 costed experimental proposals. The second is an operational shell — AEGIS— a typed hypergraph that stores every prediction, equation, experiment and falsification resultas a first-class object with explicit provenance, governed by a human-in-the-loop operator and anappend-only evidence bus. The third is an infrastructure layer — Cathedral-PoUW — a proposalfor a decentralized network in which the useful work performed by participants is the executionof the framework’s own simulations, and in which the correctness of that work is established bymechanism rather than by reputation.The three layers are deliberately unequal in epistemic standing, and the whitepaper is organizedto keep that inequality visible. Layer 1 claims are mathematical and can be machine-checked.Layer 2 claims restate established physics. Layer 3 claims are speculative extensions that willprobably be wrong, and the document says which experiments would show it. A fourth category— infrastructure — is engineering, carries no physical content, and is evaluated on whether itcompiles and whether it holds under adversarial assumptions.Three findings drive the design.First, verification does not remove uncertainty; it relocates it. A framework with no formal verification has uncertainty distributed everywhere and nowhere in particular. A frameworkwith formal verification has uncertainty concentrated in a small, enumerable set of unproven assumptions — what this document calls orphan axioms. The total quantity of uncertainty may notdecrease. Its extent does, and extent is what makes uncertainty actionable.Second, the naive proposal that miners submit zero-knowledge proofs of scientific simulations is not viable with 2026 technology, and the correct alternative is not morecryptography but refereed delegation. Published measurements place cryptographic proofoverhead at roughly four orders of magnitude over native execution; refereed delegation withreproducible operators achieves correctness guarantees at under one order of magnitude, conditional on at least one honest participant. For partial differential equation simulations with millionsof degrees of freedom, this difference is decisive.Third, the binding constraint on verifiable scientific computation is not proof systemsbut floating-point reproducibility. Two honest participants running the same simulation ondifferent hardware will disagree in the low-order bits. Any verification scheme that comparesoutputs bit-for-bit therefore requires deterministic operator implementations before it requiresproofs. This document treats reproducible numerics as a prerequisite, not a detail.The whitepaper’s most important section may be its self-assessment. The Casimir operator atthe centre of the physical framework is constrained but undefined. The heartbeat frequency thatappears in the framework’s most distinctive equation has no independent physical identification,which makes that equation a reparametrization rather than a prediction. One concept — thephoton as a Nambu–Goldstone mode of a broken discrete symmetry — appears to violate thestandard Goldstone theorem and is flagged as high-risk pending retraction or repair. These arestated plainly, in the body, with the conditions under which each would be resolved.
In this paper we show that the generalization error of AdaBoost is $Θ\big(\tfrac{d\ln(nγ^{2}/d)}{nγ^2}+\tfrac{\ln(1/δ)}{n}\big)$, where $γ$ is the advantage guaranteed by the weak learner, $d$ is the VC-dimension of the class containing the weak hypotheses, $n$ is the sample size, and $δ$ is the confidence parameter. The contribution of this paper is the upper bound; the matching lower bound follows from prior work. The upper bound proof follows by combining the known fact that AdaBoost outputs a voting classifier whose voting function has zero empirical $γ/2$-margin loss with what is, to the best of our knowledge, a new margin-based generalization bound for voting classifiers.
Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
Paper T14B tests whether an internal triadic state can supply the actual information used by later partner condensation and relational phase transport. The upstream stage constructs a symmetric candidate cost field from the cuboctahedral registry and evaluates global mutual pairings. A sanitized handoff then passes each directional cost row, a diffuse normalized candidate distribution, and the resulting radial phases into downstream calculations while withholding explicit winner, antipode, target, and expected answer fields. The downstream condensation map does not independently discover a new ordering. It conserves unit weight and deterministically sharpens the ranking already contained in the transmitted directional cost state. The production audit separately records the global matching partner, the directional row minimum, the second minimum, the row cost gap, and their agreement with the downstream endpoint. In every native case, the global partner, directional minimum, and condensed endpoint agreed. Scrambling the association between costs and physical positions caused the downstream endpoint to follow the altered upstream ranking rather than independently reconstruct the physical antipode. Flattened costs remained unresolved, while the construction-enforced no-exclusivity control retained a multipartner state. The actual radial phases associated with the selected endpoint were passed into a local transport ledger. Exact ledger accounting preserved the incoming relation as an algebraic identity, whereas omitted, quantized, and delayed ledger information produced substantial additional phase error. Separate optimized and fixed-setting phase witnesses measured the remaining ensemble coherence. The revised production run used sixty seeds per grid cell and three workers, completed all expected evaluator, handoff, condensation, and transport records, passed every scientific and evidence-completeness gate, and issued a supported decision. The results establish an intervention-sensitive computational handoff from an upstream directional candidate-cost state to a target-free condensed endpoint and from the resulting radial phases to a locally ledgered relation. They do not derive the candidate cost law, the one-radial-capacity rule, the condensation map from a microscopic action, a local replacement for global matching, carrier-resolved separation, or distributed Bell-outcome formation.
This paper analyzes the institutional constraints on the effective implementation of the LEADER/Community-Led Local Development (CLLD) approach in Bulgaria and proposes a concrete institutional model for overcoming them. The study proceeds from the premise that the approach holds substantial potential as a territorially oriented instrument for local development, but that its effectiveness is limited by a structural contradiction between the decentralized conceptual logic of LEADER/CLLD and the centralized institutional framework of its national implementation in Bulgaria. The analysis shows that the current distribution of functions between the Ministry of Agriculture and Food, as Managing Authority, and the State Fund “Agriculture” – Paying Agency creates a structural asymmetry, whose measurable consequences are systemic delays in contracting and disbursing project funds, limited procedural flexibility, and insufficient specialized administrative capacity. As an institutional response to these deficits, the paper proposes the creation of a specialized executive agency under the Rural Development Programme, with functions covering the administrative management of projects, methodological support for Local Action Groups (LAGs), monitoring and evaluation, and coordination of multi-fund financing. Two options for the institutional positioning of the agency are examined — as an independent structure under the Ministry, or as a specialized unit within the existing administration — and these are compared with established European practices (Austria, Germany, Ireland). The study concludes that overcoming the structural contradiction requires not a change in the conceptual philosophy of the approach, but a targeted institutional reconfiguration aimed at increasing the autonomy of Local Action Groups.
This project introduces "X-142's Blockchain," a novel multi-layer architecture designed to resolve key bottlenecks in existing distributed ledger technologies. Traditional Order-Execute (OE) and Execute-Order-Verify (EOV) transaction models introduce severe sequential processing delays under peak loads, while standard Proof-of-Authority (PoA) consensus mechanisms remain vulnerable to malicious validator collusion. To address these limitations, our methodology implements a dual-layer network infrastructure consisting of a Main Layer and a Subordinate Layer governed by a "Guardian" node tracking reputation metrics to neutralize routing attacks. Furthermore, the framework integrates cryptographic trust via Zero-Knowledge Proofs (ZKP), utilizing an optimized Nova IVC-PloNK framework to guarantee fixed-size proof validation regardless of off-chain network states.Experimental benchmarks demonstrate that the proposed architecture achieves linear throughput degradation with increasing validator counts while maintaining strong Byzantine Fault Tolerance (BFT) consensus properties. The multi-layer anchoring system successfully enforces data sovereignty and real-time network visibility without penalizing nominal network throughput. Future development will focus on optimizing the SN-PoA PPO selectors using real operational traces, replacing current consensus components with quantum-resistant cross-layer proofs, and validating the framework across heterogeneous, multi-organizational Wide Area Network (WAN) deployments.
With the advent of digital services, various vulnerabilities in centralized identity management systems such as Single Point of Failure (SPOF), data leakage, and user privacy invasion are evident. In this paper, an architecture framework for designing Decentralized Identity Management System (DIDMS) by leveraging blockchain technologies is proposed for privacy preserving authentication in digital age. The DIDMS framework makes use of Decentralized Identifier (DID) with Verifiable Credentials (VC) and Zero Knowledge Proof (ZKP) techniques to implement the concept of self-sovereign identity. Four phases of framework including registration, credential issuance, authentication and storage is designed and tested. The performance results obtained from the experimentation indicate that the proposed framework gives an authentication latency of 320 ms, storage overhead reduction of 42%, and verification accuracy of 98.6% with 31% gas optimization over conventional smart contract approaches. The comparative evaluation with existing frameworks indicates improvement in the areas of privacy protection, scalability and user control.
Scaling Up the Internet of Things (IoT) Safely Using Smart Cryptography The Big Picture Problem: The Traffic Jam of Smart Devices Imagine a world where your smart fridge, your fitness watch, your car, and the security cameras at your local hospital all need to talk to each other securely. To trust each other, they use a Blockchain—a digital, un-hackable ledger that keeps track of every device's true identity. Here is the catch: traditional blockchains are notoriously slow. If thousands of smart devices try to log in, update their status, or check their permissions at the exact same second, the system gets clogged. It creates a massive digital traffic jam. The Proposed Solution: "The Digital Carpool" (ZK-Rollups) This research introduces a framework that fixes this traffic jam using two concepts: Rollups and Zero-Knowledge Proofs. What is a Rollup? Instead of every single IoT device sending its identity data directly to the main blockchain one by one, a Rollup groups thousands of these transactions together off the main chain, bundles them into a single neat package, and sends just that one package back to the main blockchain. It’s like forcing 50 individual drivers to get into a single bus—suddenly, the highway clears up. What is Zero-Knowledge (ZK)? When you bundle all those devices together, how does the main blockchain know nobody cheated or snuck a fake device into the bundle? Usually, the blockchain would have to unpack the bundle and check everything, which defeats the purpose of saving time. A Zero-Knowledge Proof is a mathematical certificate attached to the bundle. It proves mathematically that every single transaction inside the bundle is valid, without actually revealing the private data of the devices inside. How the Framework Works (Step-by-Step) Device Action: Your smart smartwatch or factory sensor wants to verify its identity. Off-Chain Bundling: Instead of bothering the main blockchain, the device sends its request to a side-processor (the Rollup). The Rollup collects thousands of these requests. Generating the Proof: The system creates a ZK-Proof—a cryptographic receipt that says: "We checked all 1,000 devices, they are all authentic, and here is the math to prove it." Final Verification: The main blockchain receives just the receipt. Because the math is undeniable, the blockchain approves all 1,000 devices instantly in a fraction of a second. Why This Matters Massive Speed (High Throughput): Instead of handling maybe 15 device checks per second, the system can now handle thousands per second. The traffic jam is gone. Bank-Grade Security: Because it relies on advanced mathematics (Zero-Knowledge), hackers cannot forge a device identity or trick the system, even though the heavy lifting is done off the main blockchain. Low Cost: Smart devices usually have weak batteries and low computing power. By moving the heavy math away from the devices and onto the Rollup system, the devices save energy and operational costs. Conclusion So, we don't have to choose between speed and security. By bundling IoT data and verifying it with modern mathematical shortcuts, we can build a future where billions of smart devices connect instantly, safely, and without crashing the system.
This study investigates two questions relating to cryptocurrency market dynamics. First, whether a composite skew measure derived from MicroStrategy (MSTR) trading activity can predict future Bitcoin (BTC) and Ethereum (ETH) volatility. Second, whether Ethereum volatility exhibits reproducible structural properties consistent with established theories of volatility persistence and cascading shock dynamics. Using rolling out-of-sample testing, autocorrelation-adjusted significance testing, regime classification, shock-decay modelling, return-interval analysis, and earthquake-inspired cascade frameworks, the study finds no evidence that MSTR composite skew provides a useful forecasting signal. More broadly, no forecasting model tested outperforms naive benchmark models beyond horizons of approximately three to five days. However, several descriptive properties of Ethereum volatility appear robust, including volatility persistence, regime structure, extreme-event clustering, non-simple shock decay, and partially transferable aftershock dynamics. In particular, while Omori-style decay and the productivity law are supported, Bath's Law fails consistently, suggesting cryptocurrency volatility cascades may differ fundamentally from those observed in traditional financial markets. The findings contribute to the understanding of volatility organisation in digital asset markets while highlighting the difficulty of extracting persistent predictive signals from historical OHLCV
Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance. Current methods of Sybil actor detection include constructing graphs, which requires token transfers between examined wallets. Machine learning algorithms have been employed as well, but they treat the task as a closed-set classification problem, making them vulnerable to frequent changes in attack strategies or evasion tactics. We address the following questions: can compression-based similarity differentiate Sybil bots, organic users, and arbitrage bot wallets without direct financial links? What is the effect of high-signal contracts on the discovery of Sybils, and how robust are behavioral graphs under temporal drift and adversarial perturbations? Our approach synthesizes a symbolic Transaction Grammar from EVM (Ethereum Virtual Machine) traces, capturing separately transaction rhythm, execution structure, and functional intent. The high-signal contracts are filtered with our own protocol, called the Blind-Spot Protocol. Gzip-based NCD is used to construct a behavioral graph for Sybil discovery. We validate this framework against supervised machine learning baselines, a temporal split, and synthetic camouflage stress tests. Ultimately, we contribute a leakage-aware behavioral framework for Sybil candidate discovery. Its core NCD primitive requires no supervised training and can expand suspicious seed wallets without explicit funding links. We position the method as a training-free local discovery primitive for open-world blockchain audits, rather than as a formal open-set recognition system.
Q-PROOF is an experimental blockchain architecture based on adaptive consensus, aperiodic topology, and quadratic governance. The model integrates Aperiodic Consensus Relaxation (ACR), distributed reputation, Sybil attack defense, coordinated attack detection, and post-quantum migration pathways. This technical white paper outlines the core consensus engine, mathematical modeling of system tension, correlation-aware consensus mechanics, and benchmark comparisons demonstrating enhanced finality and resilience against coordinated network capture.