Peter Ince, Jiangshan Yu, Joseph K. Liu, Xiaoning Du
Recent years have seen an explosion of activity in Generative AI, specifically Large Language Models (LLMs), revolutionising applications across various fields. Smart contract vulnerability detection is no exception; as smart contracts exist on public chains and can have billions of dollars transacted daily, continuous improvement in vulnerability detection is crucial. This has led to many researchers investigating the usage of generative large language models (LLMs) to aid in detecting vulnerabilities in smart contracts. This paper presents a systematic review of the current LLM-based smart contract vulnerability detection tools, comparing them against traditional static and dynamic analysis tools Slither and Mythril. Our analysis highlights key areas where each performs better and shows that while these tools show promise, the LLM-based tools available for testing are not ready to replace more traditional tools. We conclude with recommendations on how LLMs are best used in the vulnerability detection process and offer insights for improving on the state-of-the-art via hybrid approaches and targeted pre-training of much smaller models.
Smart contracts deployed on blockchain platforms are vulnerable to various security vulnerabilities. However, only a small number of Ethereum contracts have released their source code, so vulnerability detection at the bytecode level is crucial. This paper introduces SmartBugBert, a novel approach that combines BERT-based deep learning with control flow graph (CFG) analysis to detect vulnerabilities directly from bytecode. Our method first decompiles smart contract bytecode into optimized opcode sequences, extracts semantic features using TF-IDF, constructs control flow graphs to capture execution logic, and isolates vulnerable CFG fragments for targeted analysis. By integrating both semantic and structural information through a fine-tuned BERT model and LightGBM classifier, our approach effectively identifies four critical vulnerability types: transaction-ordering, access control, self-destruct, and timestamp dependency vulnerabilities. Experimental evaluation on 6,157 Ethereum smart contracts demonstrates that SmartBugBert achieves 90.62% precision, 91.76% recall, and 91.19% F1-score, significantly outperforming existing detection methods. Ablation studies confirm that the combination of semantic features with CFG information substantially enhances detection performance. Furthermore, our approach maintains efficient detection speed (0.14 seconds per contract), making it practical for large-scale vulnerability assessment.
Nishant Jagannath, Christopher Kevin Wong, Braden Mcgrath, Md. Faruque Hossain · 7 authors
The rapid advancement of artificial intelligence (AI) has brought about sophisticated models capable of various tasks ranging from image recognition to natural language processing. As these models continue to grow in complexity, ensuring their trustworthiness and transparency becomes critical, particularly in decentralized environments where traditional trust mechanisms are absent. This paper addresses the challenge of verifying personalized AI models in such environments, focusing on their integrity and privacy. We propose a novel framework that integrates zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) with Chainlink decentralized oracles to verify AI model performance claims on blockchain platforms. Our key contribution lies in integrating zk-SNARKs with Chainlink oracles to securely fetch and verify external data to enable trustless verification of AI models on a blockchain. Our approach addresses the limitations of using unverified external data for AI verification on the blockchain while preserving sensitive information of AI models and enhancing transparency. We demonstrate our methodology with a linear regression model predicting Bitcoin prices using on-chain data verified on the Sepolia testnet. Our results indicate the framework's efficacy, with key metrics including proof generation taking an average of 233.63 seconds and verification time of 61.50 seconds. This research paves the way for transparent and trustless verification processes in blockchain-enabled AI ecosystems, addressing key challenges such as model integrity and model privacy protection. The proposed framework, while exemplified with linear regression, is designed for broader applicability across more complex AI models, setting the stage for future advancements in transparent AI verification.
Introduction. Digital assets, such as cryptocurrency, tokens and NFTs (non-fungible tokens), other digital objects are rapidly changing the economic landscape, creating new opportunities for businesses and investors. However, unresolved issues regarding their legal regulation and accounting pose serious challenges for governments, businesses and financial institutions. This problem is of particular importance as digital assets become increasingly important in the global economy. Purpose of the study. The purpose of the study is to analyze the content of digital assets in a global context, to consider their specifics and place in modern economic realities. Special attention is paid to the challenges that arise in connection with unresolved issues regarding the regulation of digital assets. Research methods. In the process of research and writing the article, the following methods were used: dialectical, systems analysis, generalization, comparison, logical. Results. The article explores the essence of digital assets as an innovative object, it is determined that despite the similarity, the terms «digital assets» and «virtual assets» have different emphases in application, understanding their differences allows us to more accurately determine the legal status of the asset, the scope of its use and the features of accounting or regulation; digital assets cover a wider range of objects, while virtual assets are a subcategory of digital assets focused on the financial sphere. The main challenges associated with the use of digital assets in the financial and economic spheres are also highlighted. Among them are regulatory uncertainty, volatility risks, security issues and the lack of uniform accounting and financial reporting standards. The author emphasizes that the development of digital assets requires a clear legal framework and the implementation of international standards. The author predicts the growing role of digital assets in financial transactions, investments and tokenization of traditional assets. The conclusions emphasize that digital assets are not only a technological but also an economic phenomenon that transforms traditional approaches to storing and managing values. Prospects. Further study of the study of digital assets will not only allow for a deeper understanding of their nature, but will also contribute to the development of effective approaches to their regulation, integration into traditional economic processes and maximization of their potential in the modern world. The need to create unified approaches to the classification, assessment and reflection of digital assets in financial reporting is urgent. Research may include the development of new accounting standards that take into account the specifics of cryptocurrencies, tokens and NFTs.
The increasing adoption of blockchain technology has presented significant challenges in maintaining the security and reliability of smart contracts. This study addresses the problem of identifying security flaws in smart contracts, which may result in monetary damages and diminished confidence in blockchain systems. A Hybrid Quantum–Classical Deep Neural Network (HQCDNN) approach was proposed, combining quantum computing principles with classical deep learning methods to identify various vulnerability types, including access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The SmartBugs Wild Dataset was used for training, with TF-IDF employed as a preprocessing technique optimized for hybrid architectures. Experiments were conducted using hybrid architectures with 2-qubit and 4-qubit quantum layers, alongside a classical deep neural network (DNN) model for comparative analysis. The HQCDNN model attained accuracy levels ranging from 96.4% to 78.2% and F1-scores between 96.6% and 80.2%, showcasing enhanced performance compared to the classical and deep learning models referenced in the literature. These results highlight the capability of HQCDNNs to improve the identification of security flaws in smart contracts. Future work could focus on evaluating the model on actual quantum devices and expanding its application to larger datasets for further validation.
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa · 5 authors
Abstract Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator , a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.
Dalia Elbanna, Ema Izati Zull Kepili, Nik Hadiyan Nik Azman
Ethereum's anonymity and uncontrolled cryptocurrency attraction have attracted investors.Ethereum's price dynamic inspired this study's prediction analyses.Previous study has focused on either technical analysis or on-chain analysis, leaving investors without the synergistic effects of integrating the two.This study addresses missed insights and lack of cross-comparisons by identifying variable relationships and dependencies and comparing a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost) in Ethereum price prediction.The dependent variable is Ethereum price and the independent variables are opening, high, low, closing, adjusted closing, volume traded, market capitalization, cumulative return, transactions, blocks, and gas utilized.Prices and market capitalization, traded volume, and volume are strongly correlated, and the LSTM model is the most promising due to its greater prediction accuracy and generality.The analysis reveals the bitcoin market's complexity, affecting investing and risk management.
Kamel Touhami, Ilyes Abidi, Mariem Nsaibi, Maissa Mejri
This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO2 emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers.
Kripto paralar 21. yüzyılın ilk çeyreğine damgasını vuran finansal varlıklardır. Finansal piyasalarda işlem görmeye başlamalarının ardından kısa süre içerisinde işlem hacimlerinin artması ile çok sayıda yeni kripto para birimi üretilerek piyasada işlem görmeye başlamıştır. Kripto paraların üretim süreçleri, fiziksel varlığa sahip olmamaları, merkeziyetsiz yapıları gibi geleneksel finansal varlıklardan ayrılan özellikleri dikkat çekmiştir. Dikkat çeken bir diğer önemli özellikleri ise şüphesiz kripto para birimlerinde yaşanan ciddi fiyat dalgalanmaları olmuştur. Kripto para birimlerinin yaşamış oldukları bu fiyat dalgalanmaları piyasanın volatil yapısını ön plana çıkarmıştır. Bu nedenle kripto varlıklar arasındaki volatilite yayılımın analiz edilmesi gerek yatırımcılar gerekse araştırmacılar açısından önem kazanmıştır. Bu çalışmada kripto para piyasasında en yüksek piyasa değerine sahip 4 kripto para birimi arasındaki volatilite yayılımı analiz edilmiştir. Analizlerde BTC (Bitcoin), ETH (Ethereum), BNB (Binance Coin) ve SOL (Solano) için 13.07.2020 ile 05.09.2024 tarihleri arasına ait günlük getiriler kullanılmış ve volatilite yayılımının analizi için TVP-VAR modeli oluşturularak kripto para birimleri arasındaki dinamik bağlantı incelenmiştir. Analiz bulgularından, kripto para birimlerinin volatilitelerindeki toplam dinamik bağlantının Covid-19 Pandemisi ve Bitcoin ETF’lerinin onaylanmasına ilişkin gelişmelerden etkilendiği ve bu dönemlerde artış gösterdiği tespit edilmiştir. Ayrıca, kripto para birimleri arasındaki toplam volatilite yayılımının gücünün yüksek olmadığı, kripto para birimlerinden BNB ve BTC’nin analiz dönemi içerisinde volatilite yayıcısı, ETH ve SOL’un ise volatilite alıcısı özellik gösterdiği bulgusu elde edilmiştir. Kripto para birimleri arasında volatilite yayıcısı olan değişkenler etki güçleri açısından sıralandığında en güçlü volatilite yayıcısı olan para biriminin BNB olduğu ve bunu BTC’nin takip ettiği belirlenmiştir. Diğer yandan SOL, volatilite alıcısı olan kripto para birimleri arasında volatiliteyi en çok alan kripto para birimi olurken, ETH ise ikinci sıradadır. Kripto para birimlerinin volatilitelerindeki değişimin açıklanmasında öncelikle ilgili kripto para biriminin kendi geçmiş fiyat şoklarının etkili olduğu belirlenmiştir. Analizlerde dikkat çeken bir diğer husus ise özellikle BNB ve BTC’nin SOL’a güçlü şekilde volatilite yaymasıdır. Analize dahil edilen 4 kripto para biriminin volatilite yayılım ilişkisinin çok yüksek olmaması, aynı portföyde bulundurulabilecekleri ve birbirlerine risk bulaştırıcı etkilerinin sınırlı olabileceği şeklinde değerlendirilebilir. Bunun yanı sıra BNB’nin en yüksek volatilite yayıcısı olma özelliği dikkate alınarak portföylerin oluşturulması ve takip edilmesi, yatırım verimliliği açısından önem taşıyacaktır. Benzer şekilde SOL’un da diğer kripto para birimlerinden güçlü şekilde volatilite alması, yatırım süreçlerinde dikkat edilmesi gereken bir diğer husus olarak değerlendirilebilir.
Nasir Nadeem, Ahmad Ramli Saad, Zeeha Aslam, Zohaa Naveed
The purpose of this study is to assess this form of technocrimeand identify the misinformation gaps to restrict area suggestions, educational offer frameworks, and legislative proposals aimed at advancing the digital financial literacy of prospective young investors. The research aims to highlight how these scams affect multilateral financial inclusion, economic empowerment, and a reliable digital financial ecosystem. These schemes are targeted at university students who possess low financial literacy and are lured by the prospects of easy money, which endanger their lives in the long run. This descriptive research is based on an online survey conducted among students of Multan, using simple random sampling, collected through an online survey. The study will analyze the relationship between financial literacy and victimization in order to test the hypothesis that those with lower literacy are more susceptible. The research will also look into the disinformation marketing and recruitment strategies on social media and other Internet platforms regarding cryptocurrency. The study aids in accomplishing SDG 8: Decent Work and Economic Growth within the context of Pakistan’s digital economy. This research helps to understand the contribution of technocrime to the obstacles of financial inclusion and helps to develop a strongerdigital economic infrastructure proposal.
Cloud computing has become a critical component of modern IT infrastructure, offering businesses scalability, flexibility, and cost efficiency. Unoptimized cloud migration strategies can lead to significant financial waste due to inefficient resource allocation, redundant workloads, and unpredictable cloud expenses. Traditional methods often rely on static provisioning and manual decision-making, leading to suboptimal cloud resource utilization. This research introduces an AI-driven framework for intelligent cloud planning and migration aimed at reducing cloud costs while maintaining high performance and compliance standards. The proposed framework leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques to automate workload distribution, real-time scaling, and dynamic cost optimization. It integrates Predictive Analytics Engine: Uses AI models (Long Short-Term Memory LSTMs, CNNs, and Transformers) to analyze historical workload data and forecast future resource demands. Optimization Algorithm: Implements AI-driven cost minimization functions, optimizing resource allocation while maintaining Quality of Service (QoS). Automated Migration Engine: Reduces manual intervention by executing AI-based cloud workload transfers efficiently. Security and Compliance Module: Uses explainable AI (XAI) and federated learning to maintain cloud security, privacy, and regulatory compliance. A proof of concept (PoC) is developed and evaluated across multiple cloud platforms (AWS, Azure, Google Cloud) with real-world datasets. Experimental results indicate that the AI-driven framework achieves: Cost savings of up to 42% compared to traditional cloud migration strategies. Resource utilization improvement by 53%, ensuring minimal wastage. Reduction in system downtime by 75%, leading to higher reliability. Reduction in manual intervention by 85%, automating resource scaling and load balancing. The research paper also presents real-world case studies across finance, healthcare, e-commerce, and manufacturing sectors, demonstrating the tangible impact of AI-based cloud optimization. This research explores future advancements in cloud computing, including Quantum AI for cloud workload acceleration, Blockchain for transparent cloud cost auditing, and Decentralized AI governance for multi-cloud management. This study contributes to the growing field of AI-driven cloud cost optimization, providing a roadmap for enterprises, cloud architects, and AI researchers to achieve cost-efficient, high-performance, and automated cloud management.
We investigate the long-range cross-correlation and cross-multifractality between the “dirty” and “clean” cryptocurrencies and the major financial assets: the Dow Jones Index (DJI), the Euro–Dollar exchange rate (EURUSD), and Gold. The analysis shows a high long-range correlation between most pairs with some exceptions, including the DJI–Ripple and Gold–Polygon. When the DJI is paired with clean cryptocurrencies such as Polygon and Cardano, they exhibit multifractal properties. As for the EURUSD–BTC and Gold–BTC, these two pairs demonstrated the highest level of multifractality in their corresponding pairs. All pairs of cryptocurrencies and main financial indices are persistent, with the exceptions of EURUSD–POLYGON (H = 0 . 4970 ± 0 . 0048 for q =2), GOLD–BTC (H = 0 . 5039 ± 0 . 0058 for q =2) and GOLD–LTC (H = 0 . 5044 ± 0 . 0057 for q =2) that are Brownian, and GOLD–POLYGON (H = 0 . 4917 ± 0 . 0055 for q =2) which is anti-persistent. For q =5, all are anti-persistent, except DJI-Eth, XRP, and ADA are Brownian, and EURUSD-XRP is persistent. We also assessed the asymmetric persistence behavior when the market is upward or downward and found that for the pairs involving dirty cryptocurrencies with DJI and EURUSD, there is a higher level of persistence during the downward market. On the other hand, Gold-related pairs were almost symmetric. Thus, we identified the complexity and variability of the cryptocurrency pairs with the traditional financial instruments, which shows their various reactions to the changes in the market and types of assets.
Debugging and auditing zero-knowledge-compatible smart contracts remains a significant challenge due to the lack of source mapping in compilers such as zkSolc. In this work, we present a preliminary source mapping framework that establishes traceability between Solidity source code, LLVM IR, and zkEVM bytecode within the zkSolc compilation pipeline. Our approach addresses the traceability challenges introduced by non-linear transformations and proof-friendly optimizations in zero-knowledge compilation. To improve the reliability of mappings, we incorporate lightweight consistency checks based on static analysis and structural validation. We evaluate the framework on a dataset of 50 benchmark contracts and 500 real-world zkSync contracts, observing a mapping accuracy of approximately 97.2% for standard Solidity constructs. Expected limitations arise in complex scenarios such as inline assembly and deep inheritance hierarchies. The measured compilation overhead remains modest, at approximately 8.6%. Our initial results suggest that source mapping support in zero-knowledge compilation pipelines is feasible and can benefit debugging, auditing, and development workflows. We hope that this work serves as a foundation for further research and tool development aimed at improving developer experience in zk-Rollup environments.
Riadh Benammar, Anas Elmelki, Nadia Arfaoui, Adel Boubaker
ABSTRACT This paper investigates how the geopolitical risk (GPRD), economic policy uncertainty (EPU) index, and Twitter economic uncertainty (TEU) related to the Russo‐Ukrainian conflict can affect cryptocurrency returns (Bitcoin [BTC], Ethereum [ETH], Ripple [XRP], Dogecoin [DOGE], Litecoin [LTC], Cardano [ADA], BNB, and TRON [TRX]) over the period ranging from January 1, 2020, to April 24, 2023. Using the Spectral Breitung Candelon causality and wavelet coherence methods, interesting findings are reported. This study reports noteworthy findings. First, we observe that during the armed battle, ADA, BNB, DOGE, LTC, TRX, and XRP appear as hedges against GPRD. However, we found a negative impact on BTC and ETH. Second, the results show that EPU and TEU have no effect on cryptocurrency, respectively. These findings provide a comprehensive overview of cryptocurrency fluctuations during the ongoing conflicts in Ukraine. Finally, findings show that only ADA, BNB, DOGE, LTC, TRX, and XRP could be used as hedging tools during times of uncertainty. These results have practical implications for cryptocurrency investors and elements influencing its returns, especially during uncertain times.
Kemal Ihsan Kilic, Samir Maity, Inkyung Sung, Peter Nielsen
Maritime Search and Rescue (MSAR) operations face significant challenges due to high uncertainty, dynamic conditions, and resource constraints. Additionally, rigid organizational structures and hierarchical human-centered communication frameworks, fail to adapt to the challenging conditions of maritime environments. This paper provides a comprehensive review of the integration of Artificial Intelligence (AI) into MSAR operations, highlighting how AI can transform these systems through enhanced decision-making, real-time adaptability, decentralized autonomy, and resource optimization. Through analysis and synthesis, we identified and categorized key challenges in traditional SAR frameworks, such as inherent environmental and structural challenges. We discussed AI-driven solutions that offer efficient, autonomous, resilient, and decentralized coordination. Our thematic and statistical analysis of existing literature reveals significant research gaps, particularly regarding the holistic integration of AI across all SAR stages toward a decentralized fully autonomous paradigm shift. The paper also considers the technological challenges for the integration and adaptation of AI in SAR. By envisioning fully autonomous, AI-driven MSAR operations, this study sets the stage for future research and practical innovations, aiming to improve effectiveness and efficiency in maritime rescue efforts. • Comprehensive Literature Review of AI over MSAR. • Identification of Gaps and Key Challenges in MSAR. • Proposed AI-Driven Solutions Strategies for MSAR. • Trends and Future Directions through AI in MSAR.
Meselu Tegenie Mellaku, Yibeltal T. Wassie, Pernille Seljom, Muyiwa S. Adaramola
The economy of East Africa (EA) is striving for a structural transformation with a strong focus on expanding the manufacturing sector. However, challenges related to modern and reliable energy supply have hindered the sector's growth performance across the region. This systematic review explores the potential, opportunities, and challenges to integrating decentralized renewable energy solutions to bridge the energy supply-demand gap in the EA's manufacturing sector. It also provides up-to-date insights into the extent of integration of decentralized renewable energy technologies in the EA manufacturing sector. Relevant data and information for the review were retrieved from 46 references, including databases and web-based sources. The findings highlight that the EA region possesses abundant untapped solar, wind, and bioenergy resources that can close the sector's energy supply-demand gap. The review also reveals that renewable energy solutions are becoming increasingly techno-economically competitive with conventional energy sources for hybrid and stand-alone applications in the manufacturing sector. However, several challenges impede the integration of decentralized renewable energy technologies in the sector, including a lack of enabling regulatory frameworks, limited financing options, limited access to renewable technologies , and a lack of skilled labor. Nonetheless, international initiatives aimed at supporting developing countries in combating climate change can help overcome the region's financial and technological constraints by facilitating technology transfer, capacity building, and offering affordable financing options. Furthermore, the ambition of East African nations to expand their manufacturing sectors presents a stimulating opportunity to accelerate the integration of decentralized renewable energy technologies into the sector.
Mustaph KHALFOUNI, Rafia FRIJ, Mohammed Lamartı Sefian, Noaman LAKCHOUCH
This paper examines the interdependencies between Bitcoin and gold within the context of Bitcoin halving cycles. Using a comprehensive econometric approach, including cointegration tests, VAR and VECM models, DCC-GARCH modeling, and wavelet coherence analysis, we investigate short- and long-term dynamics linking these two assets.Our findings indicate that, over the long term, Bitcoin exhibits characteristics similar to gold as a safe haven despite its high volatility and sensitivity to short-term shocks. Moreover, the incorporation of macroeconomic variables, such as stock market indices and oil prices, highlights the significant influence of broader economic conditions on this relationship. These results suggest that while Bitcoin may serve as a complementary asset to gold in diversified portfolios, prudent management is essential to mitigate the risks associated with its speculative nature.
Blockchain technology is revolutionizing various industries through decentralized architecture and secure transaction mechanisms, yet its core application—smart contracts—faces increasingly sophisticated security threats. Recognizing the critical need for enhanced protection in this emerging domain, this paper introduces TPH-Fuzz, a two-phase hybrid fuzzing framework designed to overcome current limitations in vulnerability detection. TPH-Fuzz combines global exploration with local vulnerability targeting. It utilizes dynamic symbolic execution for semantics-aware path analysis and employs data-dependency-based state modeling to generate effective transaction sequences. These methods improve both path exploration and vulnerability detection precision significantly. Experiments on a coverage dataset of 9309 contracts demonstrate an 85% branch coverage on complex contracts, outperforming conventional methods; meanwhile, tests on a vulnerability dataset of 1086 labeled contracts show a detection precision of 89.24% across eight vulnerability categories. The promising results underscore the framework’s potential to transform security auditing practices in the blockchain industry, paving the way for more reliable smart contract development and deployment.
Cryptocurrencies have attracted significant attention from investors, regulators and the media since their emergence. In a world where digital advancements are increasingly included in everyday relations, studying the behaviour of cryptocurrencies and their impact on financial markets becomes a necessity. This paper introduces a comparative analysis towards a hybrid model combining classical and modern methods for predicting cryptocurrency prices. This study deals with everyday recordings of 10 cryptocurrencies that represent different technological innovations and use cases. Studying these cryptocurrencies can help understand volatility, volumes and price movements. We aim to develop a time series statistical model and to study the effectiveness of deep learning (DL) models, specifically long short-term memory (LSTM) model and the autoregressive integrated moving average (ARIMA) model, for predicting cryptocurrency prices accurately and forecasting stationary data. Combining ARIMA and LSTM, we managed to obtain a high value of R² for Binance Coin (BNB) cryptocurrency (0.936) with an average R² for all evaluated cryptocurrencies of 0.6555.
This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future.
Suhyeon Lee, Euisin Gee, Najmeh Soroush, Muhammed Ali Bingol · 5 authors
Simple commit-reveal beacons are vulnerable to last-revealer strategies, and existing descriptions often leave accountability and recovery mechanisms unspecified for practical deployments. We present Commit-Reveal$^2$, a layered design for blockchain deployments that cryptographically randomizes the final reveal order, together with a concrete accountability and fallback mechanism that we implement as smart-contract logic. The protocol is architected as a hybrid system, where routine coordination runs off chain for efficiency and the blockchain acts as the trust anchor for commitments and the final arbiter for disputes. Our implementation covers leader coordination, on-chain verification, slashing for non-cooperation, and an explicit on-chain recovery path that maintains progress when off-chain coordination fails. We formally define two security goals for distributed randomness beacons, unpredictability and bit-wise bias resistance, and we show that Commit-Reveal$^2$ meets these notions under standard hash assumptions in the random-oracle model. In measurements with small to moderate operator sets, the hybrid design reduces on-chain gas by more than 80% compared to a fully on-chain baseline. We release a publicly verifiable prototype and evaluation artifacts to support replication and adoption in blockchain applications.
Censorship resistance with short-term inclusion guarantees is an important feature of decentralized systems, missing from many state-of-the-art and even deployed consensus protocols. In leader-based protocols the leader arbitrarily selects the transactions to be included in the new block, and so does a block builder in protocols such as Bitcoin and Ethereum. In a different line of work, since the redundancy of consensus for implementing distributed payments was formally proven, consensusless protocols have been described in theory and deployed in the real world. This has resulted in blockchains and payment systems that are more efficient, and at the same time avoid the centralized role of a leader or block builder. In this report we review existing consensus and consensusless protocols with regard to their censorship-resistance, efficiency, and other properties. Moreover, we present an approach for new constructions with these properties in mind, building on existing leader-based protocols.
Blockchain technology presents a promising solution for healthcare, addressing key challenges like data breaches, patient control, and interoperability. This paper analyzes blockchain applications in three areas: electronic health records, pharmaceutical supply chain traceability, and clinical trials. The authors explore security concerns, regulatory compliance, and smart contract vulnerabilities, proposing solutions like advanced cryptography and improved consensus mechanisms. Real-world examples, such as Medicalchain and Chronicled's MediLedger, demonstrate enhanced transparency and security. However, adoption faces barriers like scalability, computational costs, and regulatory complexities. The study also highlights ethical issues around data ownership and suggests future research into improving interoperability and integrating technologies like artificial intelligence and internet of medical things for better healthcare outcomes.