Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran · 8 authors
• Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. • Real-time data signing and blockchain submission using web3.py and EVM chain. • Smart contract for secure data logging, access control, and gas-efficient events. • Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. • Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the framework’s efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
Modern economic ecosystems require radical hazard management systems that may take care of big streams of statistics without compromising on regulatory compliance and business transparency. Conventional batch-based risk assessment models exhibit intrinsic shortcomings in addressing millisecond-level market turbulence and intricate network interdependencies that define new trading environments. Sophisticated artificial intelligence platforms embedded in distributed computing environments offer transformational possibilities for real-time risk sensing and mitigation. The suggested architecture develops end-to-end risk analytics capacity via ensemble machine learning algorithms, graph contagion analysis, and explainable AI features to meet strict regulatory demands. Complex data pipelines ingest heterogeneous finance streams from worldwide exchanges, payment networks, and blockchain ledgers in tandem. Tailored graph neural networks examine systemic risk transmission patterns in connected financial institutions while retaining dynamic relationship mapping capabilities. Explainable AI integration presents version interpretability and regulatory adherence through function attribution strategies and robust audit trail retention. Cloud-local infrastructure layout helps elastic scaling throughout multi-cloud environments using fault-tolerant distributed orchestration systems. Performance assessments display large upgrades in detection latency and predictive accuracy relative to standard batch-processing strategies. The design embodies a paradigm shift towards forward-looking, adaptive, and transparent risk management functionality critical to ensuring financial stability in progressively complex market conditions
Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.
Maksym Lazirko, Deniz Appelbaum, Miklos A. Vasarhelyi
Cryptocurrency exchanges face increasing pressure to demonstrate reserve adequacy following platform failures, yet current Proof of Reserves (PoR) systems suffer from incomplete verification approaches that examine either on-chain or off-chain assets separately. This study introduces the Double-Helix Framework, a verification methodology that integrates on-chain blockchain analysis with off-chain consensus algorithms to provide complete assessment of exchange financial positions. The framework employs parallel verification strands that simultaneously validate blockchain-recorded transactions and off-chain financial information, creating a unified assessment mechanism that addresses the verification gaps in existing PoR systems. The framework's integration of traditional auditing principles with distributed ledger verification creates new possibilities for regulatory compliance and investor protection in digital asset management. This framework has implications for accounting practice, suggesting that comprehensive cryptocurrency audits require verification approaches that extend to on-chain, off-chain, and intersecting transactions that have varying degrees of separation between ledgers.
Well construction in the oil and gas industry generates substantial emissions, necessitating precise tracking to meet environmental regulations and sustainability targets. This paper explores an innovative approach combining numerical modeling with distributed ledger technology (DLT) to monitor and manage emissions throughout the well construction process. Unlike traditional methods, which often rely on retrospective data collection, this method leverages real-time simulations and a decentralized data framework to provide actionable insights. By focusing on predictive modeling and data integration, we propose a system that enhances emissions accountability and supports operational efficiency. Case studies demonstrate its practical application, while the discussion addresses implementation challenges and future potential.
This paper presents a robust multi-period portfolio optimization framework that integrates interval analysis, entropy-based diversification, and downside risk control. In contrast to classical models relying on precise probabilistic assumptions, our approach captures uncertainty through interval-valued parameters for asset returns, risk, and liquidity—particularly suitable for volatile markets such as cryptocurrencies. The model seeks to maximize terminal portfolio wealth over a finite investment horizon while ensuring compliance with return, risk, liquidity, and diversification constraints at each rebalancing stage. Risk is modeled using semi-absolute deviation, which better reflects investor sensitivity to downside outcomes than variance-based measures, and diversification is promoted through Shannon entropy to prevent excessive concentration. A nonlinear multi-objective formulation ensures computational tractability while preserving decision realism. To illustrate the practical applicability of the proposed framework, a simulated case study is conducted on four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB). The model evaluates three strategic profiles based on investor risk attitude: pessimistic (lower return bounds and upper risk bounds), optimistic (upper return bounds and lower risk bounds), and mixed (average values). The resulting final terminal wealth intervals are [1085.32, 1163.77] for the pessimistic strategy, [1123.89, 1245.16] for the mixed strategy, and [1167.42, 1323.55] for the optimistic strategy. These results demonstrate the model’s adaptability to different investor preferences and its empirical relevance in managing uncertainty under real-world volatility conditions.
This paper introduces a novel multi-objective optimization framework for the portfolio rebalancing problem, incorporating return, risk, and liquidity as the central financial objectives. Unlike static models, our approach captures market dynamics by allowing periodic reallocation of assets and explicitly modeling transaction costs. To address uncertainty in key financial parameters such as expected returns, volatility, and asset liquidity, we employ interval arithmetic, offering a flexible representation without requiring distributional assumptions. The framework models risk using semi-absolute deviation, which better reflects downside exposure compared to traditional variance. A distinctive feature of the model is the integration of nonlinear transaction costs, ensuring higher realism in trading scenarios. The optimization problem is formulated with interval coefficients and solved under multiple decision-making strategies: pessimistic, optimistic, and mixed (via convex combination). To validate the model, we conduct a case study on a cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, covering the period January–March 2025. The numerical simulations demonstrate the adaptability of the proposed methodology under different investor attitudes and market conditions. Our findings show that the interval-based, multi-objective framework provides robust, diversified portfolio allocations and valuable strategic insights for decision-makers operating under uncertainty.
Олександр Кузнецов, Anton Yezhov, Kateryna Kuznetsova, Oleksandr Domin
This study presents a comprehensive theoretical and empirical analysis of Patricia tries, the fundamental data structure underlying Ethereum's state management system. We develop a probabilistic model characterizing the distribution of path lengths in Patricia tries containing random Ethereum addresses and validate this model through extensive computational experiments. Our findings reveal the logarithmic scaling of average path lengths with respect to the number of addresses, confirming a crucial property for Ethereum's scalability. The study demonstrates high precision in predicting average path lengths, with discrepancies between theoretical and experimental results not exceeding 0.01 across tested scales from 100 to 100,000 addresses. We identify and verify the right-skewed nature of path length distributions, providing insights into worst-case scenarios and informing optimization strategies. Statistical analysis, including chi-square goodness-of-fit tests, strongly supports the model's accuracy. The research offers structural insights into node concentration at specific trie levels, suggesting avenues for optimizing storage and retrieval mechanisms. These findings contribute to a deeper understanding of Ethereum's fundamental data structures and provide a solid foundation for future optimizations. The study concludes by outlining potential directions for future research, including investigations into extreme-scale behavior, dynamic trie performance, and the applicability of the model to non-uniform address distributions and other blockchain systems.
Portfolio optimization is the art and science of constructing investment portfolios to strike a balance between risk and return. Traditional models, like Modern Portfolio Theory (MPT) and the Capital Asset Pricing Model (CAPM), have long served as the foundation for portfolio management. However, these methods often struggle to account for the intricacies of real financial markets. This study explores cutting-edge portfolio optimization techniques, incorporating unconventional assets such as cryptocurrencies and ESG investments to bolster diversification. Leveraging machine learning and artificial intelligence, we aim to improve asset selection, risk assessment, and allocation, accommodating the dynamic and non-linear nature of markets. Furthermore, we evaluate how these models perform in various market conditions through empirical analyses of historical data. Our findings indicate that adopting a more adaptable portfolio optimization framework can help investors navigate changing market dynamics more effectively, ultimately achieving a more efficient risk-return trade-off. These insights are invaluable for both individual and institutional investors, enabling them to construct portfolios that adapt to evolving market realities while optimizing wealth preservation and growth. In essence, this research contributes to the ongoing discourse on portfolio optimization, offering potential enhancements for investment strategies in today's financial landscape.
Zibin Zheng, Jianzhong Su, Jiachi Chen, David Lo · 6 authors
The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports from 29 security teams. In total, we identified 9,154 weaknesses and developed two distinct datasets, i.e., DAPPSCAN-SOURCE and DAPPSCAN-BYTECODE. The DAPPSCAN-SOURCE dataset comprises 39,904 Solidity files, featuring 1,618 SWC weaknesses sourced from 682 real-world DApp projects. However, the Solidity files in this dataset may not be directly compilable for further analysis. To facilitate automated analysis, we developed a tool capable of automatically identifying dependency relationships within DApp projects and completing missing public libraries. Using this tool, we created DAPPSCAN-BYTECODE dataset, which consists of 6,665 compiled smart contract with 888 SWC weaknesses. Based on DAPPSCAN-BYTECODE, we conducted an empirical study to evaluate the performance of state-of-the-art smart contract weakness detection tools. The evaluation results revealed sub-par performance for these tools in terms of both effectiveness and success detection rate, indicating that future development should prioritize real-world datasets over simplistic toy contracts.
The global oil and gas markets are characterized by extreme price volatility driven by geopolitical events, supply-demand imbalances, and macroeconomic factors. Traditional trading strategies often struggle to maintain profitability while mitigating risks in such unpredictable environments. This study explores the development and implementation of innovative trading strategies that optimize profitability and reduce risk in global oil and gas markets. By leveraging advanced analytics, algorithmic trading, and real-time market intelligence, traders can improve decision-making, enhance risk-adjusted returns, and achieve greater market resilience. The research examines key components of effective trading strategies, including price forecasting models, quantitative risk management techniques, and adaptive trading algorithms. Machine learning and artificial intelligence (AI) are integrated to analyze historical data, detect emerging trends, and generate predictive insights for market positioning. Additionally, the study explores the role of hedging instruments such as futures, options, and swaps in reducing exposure to market fluctuations. A comprehensive framework is proposed that incorporates sentiment analysis, technical indicators, and fundamental analysis to optimize trading margins and maximize profitability. Furthermore, the study highlights the significance of real-time data analytics and high-frequency trading (HFT) in capitalizing on short-term market inefficiencies. Scenario-based simulations and stress testing are employed to evaluate strategy performance under different market conditions, ensuring robustness and adaptability. The research also discusses the importance of regulatory compliance, liquidity management, and risk mitigation techniques in sustaining long-term profitability. Findings suggest that integrating AI-driven forecasting models and quantitative trading strategies significantly improves accuracy in market predictions, leading to enhanced profitability and reduced risk exposure. The proposed strategies offer actionable insights for energy traders, financial analysts, and policymakers seeking to navigate the complexities of the oil and gas markets. By adopting a data-driven, technology-enhanced approach, traders can gain a competitive advantage and improve market efficiency. Future research should explore blockchain-based trading platforms and decentralized finance (DeFi) solutions for further optimizing oil and gas trading strategies.
Decentralized Finance (DeFi) is a new financial industry built on blockchain technologies. Decentralized financial services have consequently increased the ability to lend, borrow, and invest in decentralized investment vehicles, allowing investors to bypass third party intermediaries. DeFi's promise is to reduce the cost of transaction and management fees whilst increasing trust between agents of the Financial Industry 3.0. This paper provides an overview of the different components of DeFi, as well as the risks involved in investing through these new vehicles. We will also propose an allocation methodology which will integrate and quantify these risks.
With the increasing of using workflow management systems workflow improvement becomes a new emerging problem. Many issues must be considered to handle all aspects of the workflow improvement. Workflows might become quite complex, especially when we move to Web3 (ubiquitous computing web). Workflows from different domains (e.g., scientific or business) have similarities and, more important, differences between themselves. Some concepts and solutions developed in one domain may be readily applicable to the other. In ubiquitous computing, multi-domain workflow data analysis might cause Big Data challenge. This paper investigates the problem of workflow improvement having an observed behavior (i.e., event logs). It proposes a cross-domain concept extraction by similarity assessment to solve some aspects of workflow improvement problem, and it has a new research effort at the intersection of workflow domains. Besides, the proposed technique is evaluated with the benefit of using Deep learning and Transfer learning. One of the greatest assets to use these both learning methods is analyzing a massive amount of data. Our results show that our proposed technique is effectively applicable for analyzing real-life huge data in workflow improvement.