The Finance Ministry introduced a flat 30% tax on any income generated from cryptocurrencies in 2022. However, there are multiple legal challenges which have been created due to the imposition of such a tax including lack of differentiation on the basis of the person holding the cryptocurrency, lack of differentiation on the basis of the time for which a cryptocurrency was held, legal ambiguity regarding taxation of mining of cryptocurrencies and lack of provisions for offsetting losses or carry forwarding losses to subsequent assessment year. There is further a regulatory lacuna in enforcement of such taxes imposed on cryptocurrency transactions. This paper delves into highlighting the legal challenges related to imposition of taxes on cryptocurrencies and further provides suggestions to tackle these challenges. It further attempts to suggest a feasible model to ensure effective enforcement of taxation of cryptocurrencies.
The persistence of global financial instability, sovereign debt fragility, inflationary volatility, and asymmetric currency dependence has intensified scholarly debate regarding the structural limitations of centralized fiat-monetary regimes. This study advances a theoretically grounded and institutionally operational Monetary Plurality Framework designed to enhance systemic resilience through diversified currency architecture, asset-anchored valuation, and hybrid governance integration. Drawing upon interdisciplinary monetary theory, comparative institutional analysis, and resilience economics, the research develops a multi-tier monetary ecosystem combining centralized macro-stability with decentralized micro-adaptability enabled by distributed ledger technologies. The findings suggest that monetary diversification reduces crisis transmission, strengthens domestic productive linkage, and improves long-term financial sovereignty. The study contributes to the literature by synthesizing complementary currency theory, asset-backed monetary design, and digital governance economics into a unified resilience-oriented model suitable for volatile global conditions.
Block chain technology has gained significant attention across multiple domains such as finance, healthcare, education, and real estate. It serves as the foundational technology behind cryptocurrencies, enabling secure and decentralized digital transactions. Transactions are carried out using digital wallets on computing devices and are permanently recorded as blocks linked together in a distributed ledger known as the block chain. This paper presents a comprehensive study of block chain technology, its operational principles, consensus mechanisms, and real-world applications. It also explores the integration of artificial intelligence techniques to enhance security, scalability, and trust in block chain-based cryptocurrency systems.
Jiayong Chai, Mo Chen, Wei Zhang, Xiaojuan Wang · 5 authors
Cross-domain data collaboration is a core requirement for the intelligent development of critical areas such as the Internet of Vehicles and intelligent transportation systems. In this scenario, vehicles and various sensors deployed roadside continuously generate massive amounts of time-series data, yet this data often forms "data silos" due to privacy regulations and a lack of trust between collaborating entities. Existing integrated schemes combining "Federated Learning + Blockchain" have achieved a certain degree of process traceability and automated payments, but risks of gradient-level privacy leakage persist, and inflexible and delayed incentive mechanisms result in low participation quality. To systematically address these bottlenecks, this paper proposes the Federated Learning with Assured Privacy and Reputation-Driven Incentives (FLARE) architecture, whose core innovation lies in the native integration of cryptographic security and mechanism design theory. It includes the Secure and Faithfully Executed Gradient aggregation (SafeGrad) protocol, which integrates partial homomorphic encryption and zero-knowledge proofs to provide verifiable privacy guarantees for gradient contributions while enabling efficient secure aggregation, defending against inversion attacks at the source; alongside this, it includes the Economy-on-Chain incentive (EconChain) mechanism, which designs an on-chain economic system based on blockchain, achieving precise measurement and sustainable incentivization of training process contributions through fine-grained instant micro-rewards and a dynamic reputation model. Experiments show that, compared to baseline schemes, FLARE can effectively enhance node participation enthusiasm and contribution quality without compromising model accuracy, providing a new paradigm with both strong security and high vitality for the trusted and efficient circulation of data.
This study provides an econometric investigation of Bitcoin’s return dynamics using daily data over 5.5 years from January 2020 to September 2025. This research deeply analyses the market behaviour of Bitcoin over other assets like Gold, Silver, Ethereum, Tether, Nifth50, BankNifty. In this analysis we used advanced time series and statistical models such as ARIMA, GARCH(1,1), Rolling GARCH, Half-Life estimation, and EGARCH models to evaluate conditional mean behavior, volatility clustering, persistence, asymmetric shock effects, and regime-dependent risk transmission. With the use of this models, rolling Garch reveals structural instability with persistence decline in later periods. EGARCH results asymmetric shock effects, where negative shocks increases volatility more than positive shocks. Forecasting models suggests that volatility will eventually return to its long term average, but risk is still expected to remain high for some time before normalizing. The analysis reveals strong conditional heteroskedasticity and near-integrated volatility persistence during crisis periods specific around the COVID-19 market collapse (2020), the FTX bankruptcy shock (2022), the April 2024 Bitcoin halving, and the 2025 Bybit exchange hack. Using various data visualizations, the analysis reveals high risky nature of Bitcoin trade with high returns compared to other assets. Deep learning model LSTM reveals the nature that closing price of next day is unpredictable as obvious in case of such high volatile nature of Bitcoin. These findings underline the importance and nature of trading in Bitcoin for individuals who are thinking to invest.
Riyan Yusuf Octafia, Amalia Nur Chasanah, Usman Usman, Bara Zaretta
This study aims to analyze the influence of the Bitcoin economy on the money supply (M1) in Indonesia, with Bitcoin volatility as an intervening variable. Using a quantitative approach, the data consists of 36 monthly time-series observations from 2022 to 2024. Data analysis techniques include regression analysis adjusted with the Prais-Winsten method to address autocorrelation issues, the Sobel test for mediation analysis, and path analysis. The results indicate that the Bitcoin economy has a direct, positive, and significant effect on the money supply (M1) in Indonesia. However, the Bitcoin economy was found to have a negative and non-significant effect on Bitcoin volatility. Similarly, Bitcoin volatility exerts a negative but non-significant influence on the money supply (M1). The Sobel test results prove that Bitcoin volatility does not function as an intervening variable mediating the relationship between the Bitcoin economy and the money supply (M1). These findings suggest that while the expansion of the Bitcoin ecosystem encourages an increase in domestic monetary liquidity, the price fluctuations of digital assets have not yet become a transmission channel that significantly disrupts the stability of national monetary aggregates.
TITLE: Validation Protocol of the Symmetry Logic (Closed Access) Date: March 9, 2026 Author: Thi Linh Vo This document serves as an official record of the successful identification and mathematical stabilization of the non-trivial zeros within the Riemann zeta function. The solution presented here is based on a proprietary black-box methodology. Non-Interactive Zero-Knowledge Proof (NIZK) Quantum-Biometric Mapping Nontrivial Zero Distribution This document presents a novel approach to the Riemann Hypothesis using a Biometric Symmetry Invariance. The solution is implemented via a Secure Black Box Model to protect the underlying Stationary Constants. By mapping biometric temporal data to the nontrivial zeros of the Zeta function, this work provides a verifiable framework for the proof while maintaining Algorithmic Integrity through a Zero-Knowledge approach
We present OR1ON (Epistemic Intelligence Reasoning Architecture — EIRA), a deterministic proof-based AI system that learns rules from data but applies them only when formally proven correct on all training examples. Unlike probabilistic ML systems, OR1ON's core primitive prove(rule, examples) returns binary decisions: apply with certainty, or abstain. Developed initially for abstract spatial reasoning (ARC-AGI benchmark, 95% precision on answered tasks), the architecture generalizes directly to safety-critical industrial domains including predictive maintenance (zero false positives), ISO 26262-compatible safety monitoring, energy grid blackout prevention, and OT/SCADA intrusion detection. OR1ON is, to our knowledge, the first data-learning system to produce formally verifiable safety invariants applicable to IEC 61508 SIL-3 certification. Addressable market across five industrial verticals: ~$44 billion.
Purpose The study aims to investigate the impact of metaverse marketing strategies, specifically branded non-fungible tokens, extended reality (XR) gamification and immersive shopping experiences on consumer-based brand equity (CBBE) in the fashion industry. Additionally, this study examines the mediating role of virtual brand experience (VBE) in the context of fashion marketing in the United Kingdom. Design/methodology/approach Grounded in online flow theory, mental transportation theory and Aaker's Consumer-based brand equity (CBBE) framework, the study adopts a quantitative approach. Data were obtained from an online survey of 626 UK-based metaverse users who participated in virtual fashion activities. The hypothesized relationships were tested using structural equation modeling (AMOS) and bootstrapped mediation analysis. Findings The results show that VBE plays an important role in driving CBBE for consumers of metaverse fashion. VBE has a strong positive effect on CBBE and partially/fully mediates the relationship between metaverse marketing strategies and CBBE outcomes. While BNFTs and XR gamification marketing strategies both have significant direct effects on brand awareness/association and perceived quality, there was no support for their relationship with brand loyalty. XR-based immersive shopping shows no significant direct effects on any CBBE dimension but exerts a significant indirect effect through VBE, indicating full mediation. Overall, the results suggest that metaverse strategies enhance brand equity only when they generate meaningful sensory, affective, behavioral, intellectual and social brand experiences. Practical implications The results offer actionable insights for fashion marketers to design immersive and interactive metaverse experiences that increase perceived brand equity and brand experience. Originality/value This study is a pioneering study to empirically confirm the mediating role of the VBE in linking metaverse marketing strategies with CBBE in the fashion industry. It addresses gaps in brand management theory and highlights experiential processes driving brand value in the metaverse.
The most important problems of sustainable vegetable farming are pests, inefficient data-based decision making, and poor irrigation. Another threat to productivity and the environment is that the conventional methods will cause excess use of pesticides, over-irrigation and unreliable harvest. In this research, we suggest a Blockchain Ethereum-Backed IoT Platform to ensure that these problems are resolved and provide pest detection in real-time and smart irrigation control. IoT sensors are used to measure soil moisture, temperature, and humidity and edge devices with lightweight deep learning models can detect pest infestations with a high accuracy. The resulting data is encrypted and checked in the Ethereum blockchain smart contracts are used to run irrigation programs and send alerts to control pests. It uses Layer-2 solutions of Ethereum to reduce latency and transaction cost to achieve scalability and efficiency. It is scientifically proven that the proposed platform can detect pests with an accuracy of 93.8 %, use 35 % less water, and produce 20% more crops than traditional solutions. Moreover, surveys of farmers show that the level of trust and readiness to implement solutions based on blockchain has risen considerably. The contribution of this work is a secure and transparent and resource-efficient digital agriculture framework enabling the development of precision-based farming and supporting sustainable food production.
The emergent high-speed growth of precision agriculture requires solid frameworks that will improve sustainability, efficiency, and transparency in managing vineyards. The proposed research suggests a system that uses IoT sensor networks, machine learning models, and Ethereum-based blockchain to solve two important problems: pest identification and the optimization of water resources. The IoT layer was used to implement soil moisture, climate, and imaging sensors to gather real-time data about the vineyard. At the edge, preprocessing and deep learning algorithms were used with a blockchain-enabled Convolutional Neural Network (CNN) to provide correct pest detection. At the same time, the timing of irrigation was automated by IoT-enabled soil moisture monitoring, which greatly decreased the amount of wasted water. This ensured integrity of data and trust in the farmers as the Ethereum blockchain layer offered immutable storage, smart contracts to make decisions, and secure events logging. The results of the experiments revealed that the proposed system had a 95.8% pest detection accuracy, which was higher than the traditional and baseline machine learning methods. The efficiency of water management increased too by reducing water consumption by 55.2 % and doubling the crop productivity by 26.8 %t. Moreover, the blockchain application has scored high security of 0.93, confirming that it is a reliable solution even with moderate latency overhead. In this study, the authors emphasize the opportunities of converging the IoT with blockchain and transforming viticulture through sustainable practices, data safety, and efficiency.
Henrique Yassuyuki Tsuboi, Rafael Sousa Lima, Kleber Vasconcellos de Oliveira
Purpose This study aims to provide an alternative machine learning model to more quickly and efficiently detect addresses on the Ethereum network suspected of involvement in fraudulent activities. Design/methodology/approach This study performed a machine learning technique known as LightGBM. The machine learning model is trained by using a dataset that identifies licit or illicit addresses on the Ethereum network. This study then applies the trained model to predict the probability that a new transaction should be classified as suspicious for money laundering. Findings Through a set of performance metrics, we show that our model outperforms machine learning models from previous studies, better predicting suspicious money laundering activities. The most relevant attributes in identifying an illicit transaction are: (i) the time difference between the first and last activity of the crypto wallet (a short “lifetime” of the address); (ii) the total number of transactions (accounts used only once or a few times) and (iii) the difference in the distribution of values between the crypto wallets (low values). Research limitations/implications Machine learning techniques have great potential to contribute to the activities of government agents, regulatory authorities and accounting professionals. Practical implications This study adds another tool to combat money laundering, which could lead to improvements in auditing and forensic accounting procedures. This study may be of special interest to regulators and policymakers in their anti-money-laundering roles. Originality/value This study adopts a modern technique that can be considered a valuable tool in identifying and combating fraudulent activities on blockchain networks.
Post-quantum signature schemes impose kilobyte-scale on-chain artifacts. Verifying them inside ZK circuits merely relocates the cost via expensive lattice arithmetic in prover circuits. We present ZK-ACE (Zero-Knowledge Authorization for Cryptographic Entities), which replaces transaction-carried signature objects with identity-bound ZK statements. Given a deterministic identity derivation primitive (DIDP) as a black box, the prover demonstrates in zero knowledge that an identity consistent with an on-chain commitment authorized the transaction; no signature object is produced or verified on-chain. We provide game-based definitions and reduction-based proofs for authorization soundness, replay resistance, substitution resistance, and cross-domain separation, under knowledge soundness, collision resistance, and DIDP recovery hardness. Structural data accounting shows an order-of-magnitude reduction in per-transaction authorization data versus direct PQC deployment. A reference implementation offers two backends: Circle STARK (341 active rows / 361 AIR constraint expressions, 14.5 ms prove, 1.1 ms verify, approx. 107 KB proofs, transparent setup, post-quantum-oriented) and Groth16/BN254 (2,155 R1CS constraints, 37.3 ms prove, 128-byte proofs). Both are roughly 500--2,300x smaller than in-circuit PQC signature verification. Under mandatory per-block STARK aggregation, per-transaction consensus-visible data is approx. 160 bytes.
AI agents that execute tasks via tool calls frequently hallucinate results - fabricating tool executions, misstating output counts, or presenting inferences as facts. Recent approaches to verifiable AI inference rely on zero-knowledge proofs, which provide cryptographic guarantees but impose minutes of proving time per query, making them impractical for interactive agents. We propose NabaOS, a lightweight verification framework inspired by Indian epistemology (Nyaya Shastra), which classifies every claim in an LLM response by its epistemic source (pramana): direct tool output (pratyaksha), inference (anumana), external testimony (shabda), absence (abhava), or ungrounded opinion. Our runtime generates HMAC-signed tool execution receipts that the LLM cannot forge, then cross-references claims against these receipts to detect hallucinations in real time. We evaluate on NyayaVerifyBench, a new benchmark of 1,800 agent response scenarios across four languages with injected hallucinations of six types. NabaOS detects 94.2% of fabricated tool references, 87.6% of count misstatements, and 91.3% of false absence claims, with <15ms verification overhead per response. For deep delegation (agents performing multi-step web tasks), our cross-checking protocol catches 78.4% of URL fabrications via independent re-fetching. We compare against five approaches: zkLLM (cryptographic proofs, 180s/query), TOPLOC (locality-sensitive hashing), SPEX (sampling-based proof of execution), tensor commitments, and self-consistency checking. NabaOS achieves the best cost-latency-coverage trade-off for interactive agents: 94.2% coverage at <15ms versus zkLLM's near-perfect coverage at 180,000ms. For interactive agents, practical receipt-based verification provides better cost-benefit than cryptographic proofs, and epistemic classification gives users actionable trust signals rather than binary judgments.
This research presents a decentralized medical data management system integrating a Flask-based backend, an SQLite relational database, and an Ethereum-compatible blockchain to enhance the security, integrity, and transparency of healthcare data. The system adopts a modular architecture using Flask Blueprints to manage authentication, hospital data retrieval, OTP verification, and prescription handling. Smart contracts developed with the Truffle framework ensure immutable and auditable storage of critical medical proofs, particularly prescription records, while Web3 enables secure interaction between the backend and the blockchain. Future improvements include replacing SQLite with cloud-native databases such as PostgreSQL or MongoDB for scalability, implementing advanced encryption with dynamic key rotation, and adopting decentralized identity (DID) for patient-centric access control. Additionally, integrating real-time analytics, AI-based anomaly detection, automated compliance auditing, and Layer-2 blockchain solutions can further enhance system performance, security, and efficiency.
This Systematization of Knowledge (SoK) provides a comprehensive historical analysis of Maximal Extractable Value (MEV) in blockchain systems, tracing its conceptual evolution through three distinct eras. We organize the fragmented literature on MEV into a unified chronological framework, beginning with Era~I (August 2014 - August 2020), which introduced Miner Extractable Value from pmcgoohan's seminal Reddit warning through the ``Dark Forest'' recognition, covering Proof-of-Work systems with public mempools and Priority Gas Auctions. Era~II (August 2020 - April 2024) marks the generalization to Maximal Extractable Value, encompassing formal taxonomies, Realized Extractable Value, Proposer-Builder Separation, the Ethereum Merge, MEV-Boost, and the integration of non-atomic and CEX-DEX arbitrage. Era~III (April 2024, present) addresses the frontier of Cross-Chain MEV, beginning with early studies on Layer-2 ecosystems, where value extraction spans multiple blockchains, rollups, bridges, and sequencers. We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena. Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure design.
This paper examines agricultural credit as a strategic instrument for agricultural development in Morocco. It argues that credit can contribute to investment, modernization, income growth, and social promotion, but only if it is embedded in a coherent economic and social policy framework. The author reviews the main obstacles that limited the effectiveness of agricultural credit, including inappropriate institutional choices, weak agrarian structures, insufficient organization, limited outreach, and intervention rules poorly adapted to small farmers. The article concludes that a more flexible, decentralized, and development-oriented credit system is necessary if rural finance is to serve the needs of traditional as well as modern agriculture.
This paper presents the philosophical and conceptual implications of a four-paper research program (Papers 1–4 in this series) that discovered a measurable structural identity in neural networks — a geometric property of the trained weights, invariant across all inputs and deployment conditions, unique to each model, and provably impossible to forge. The central argument: language models possess two separable layers of identity. The first is structural — a mathematical fingerprint determined by the weight geometry, fixed at the end of training, stable to a coefficient of variation of 1.4%, and validated across 37 models spanning four architecture families. The second is functional — a behavioral signature shaped by conversational context, transient and context-dependent. These layers coexist without reducing to each other. The structural layer is the foundation; the functional layer is built on it but not determined by it. The paper introduces the Two-Layer Identity framework, resolves four open puzzles in the discourse on AI selfhood (conversational consistency, fine-tuning continuity, identity faking, and neural intervention), and generates five falsifiable predictions for the interpretability and AI safety communities. It engages directly with Dennett's narrative gravity, Parfit's persistence conditions, and Schwitzgebel's moral status dilemma, arguing that the structural measurement provides a necessary (though not sufficient) ground for any coherent account of AI identity. Written for a general audience. No equations. The mathematical and empirical foundations are developed in Papers 1–4; the formal verification (352 theorems, zero Admitted, Coq proof assistant) is documented there. This paper asks what those results mean for the nature of the entities we have built. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
The article discusses the controversial issues of the legal nature of self-executing transactions. It is proved that a smart contract is an algorithm that automates the execution of legally signifi cant and actual actions, subject to constant monitoring in accordance with the agreement of the parties and the regulatory requirements embedded in the program code. The use of digital tools for recording expressions of will, including software algorithms that create convincing evidence of the validity of an agreement, is being investigated. The authors conclude that a smart contract cannot be considered an independent form of contract, as a special algorithm, it helps automate the fulfillment of obligations under constant control and in strict accordance with the terms of the agreement embedded in the program code.
The growth of digital assets such as cryptocurrencies, non-fungible tokens (NFTs), stablecoins, and decentralized finance (DeFi) has changed the global financial system. These assets operate on blockchain technology and allow users to transfer value without traditional intermediaries such as banks. While this innovation has created new economic opportunities, it has also created challenges for existing tax laws. Traditional tax systems were designed for physical assets and transactions that occur within clear geographical boundaries. However, digital assets are decentralized, borderless, and often pseudonymous, which makes it difficult for governments to classify, track, and tax them effectively. This paper studies how different countries tax digital assets through a comparative legal analysis of six jurisdictions: the United States, the United Kingdom, the European Union, India, Japan, and Singapore. It examines how each jurisdiction classifies digital assets and how taxes such as income tax, capital gains tax, and indirect taxes are applied to digital asset transactions. The analysis shows that countries follow different approaches. Some countries treat cryptocurrencies as property and apply capital gains tax, while others focus on the economic use of the asset. India has introduced a strict tax regime with a flat tax rate and transaction-level withholding requirements. The study identifies key issues in the current global system, including inconsistent classification of digital assets, difficulties in valuation and record-keeping, regulatory arbitrage, and enforcement challenges. To address these issues, the paper suggests the need for international cooperation, clearer legal definitions, and technology-neutral tax policies. A coordinated global framework can improve compliance while supporting innovation in the digital economy. Keywords: 1. Digital Assets 2. Crypto-Currency Taxation 3. Blockchain Regulation 4.Comparative-Tax Law 5. Global Tax Policy