Blockchain technology and cryptocurrencies have emerged as transformative innovations in the digital age, holding the potential to reshape industries ranging from finance to supply chain management. This paper provides a comprehensive review of the key concepts, mechanisms, applications, challenges, and future trends related to blockchain technology and cryptocurrencies. Blockchain's decentralized, secure, and transparent nature has made it the backbone of cryptocurrencies such as Bitcoin and Ethereum. However, despite its promising potential, the technology faces challenges such as scalability, energy consumption, and regulatory concerns. This review explores these issues, provides insights into the technological evolution of blockchain, and examines how cryptocurrencies are poised to impact various sectors in the coming years.
The object of this paper is blockchain technology in the tracking system of grain supply chains. The current study considers the task of determining the impact of blockchain technology introduction on the tracking system of grain supply chains. The key problems in the system of grain supply chains have been identified and the tasks to solve them have been proposed. The characteristics of key technologies in the implementation of blockchain in the system of grain supply chains have been defined, such as smart contracts, the Internet of Things (IoT), interplanetary file system (IPFS), contactless tags (RFID), Ethereum platform (identified as the best for supply chain tracking). The influence of factors on the introduction of blockchain technologies into the grain supply chain tracking system was determined using a SWOT analysis. Strengths include transparency, increased trust, automation of processes and protection against falsification. Weaknesses include high implementation costs, difficulty scaling, and the need for staff training. Opportunities that open up through the use of blockchain include attracting new partners, increasing competitiveness, and developing new markets. Threats include legal difficulties, technical failures, high energy costs, and resistance from market participants. An assessment of the investment attractiveness of introducing blockchain technology into the grain supply chain tracking system was carried out by calculating such indicators as economic effect; net present value (NPV) of implementing blockchain technologies; payback period of investments. According to the results of analysis, the following data were obtained: NPV (150439 a.u.)>0, the payback period of investments is 2.8 years, which is acceptable for large agricultural holdings. Prospects for development have been determined, in particular, the unification of agricultural holdings for the joint implementation of blockchain technologies in the tracking system of grain supply chains, which would be a strategically beneficial solution for all participants in the supply chain
Sidra Tul Muntaha, Qasim Zeeshan Ahmed, Faheem A. Khan, Zaharias D. Zaharis · 5 authors
We propose a hybrid blockchain-based framework for multi-operator resource sharing and SLA management in 5G Standalone (5GSA) networks. Leveraging Hyperledger Fabric (HLF), we implement secure resource sharing between multiple seller Mobile Network Operators (MNOs) and a single buyer MNO, evaluating HLF’s performance in terms of transaction latency and throughput. The framework incorporates a two-level Multi-Leader Single-Follower (MLSF) Stackelberg game to model pricing and buying strategies. For the upper-layer game, we determine the pricing strategies of seller MNOs using Pattern Search Algorithm (PSA), Genetic Algorithm (GA), and Fmincon, comparing their performances. In the lower-layer game, we design a Lagrange multiplier-based solution for the buyer MNO’s strategy, benchmarking it against PSA, GA, and Fmincon. Our framework also employs smart contracts for SLA automation and enforcement, utilizing Ethereum and IOTA-EVM blockchains. These contracts are implemented with Hardhat and deployed across Goerli, Linea-Goerli, Sepolia, Polygon (Mumbai), and Shimmer EVM Testnets. We measure key metrics, such as transaction latency and throughput, to evaluate the performance of our SLA management system. Results demonstrate the framework’s effectiveness in enhancing resource allocation and SLA enforcement in 5G networks, highlighting the capabilities of different blockchain platforms in managing complex network operations.
Christian Delgado‐von‐Eitzen, Luis Anido, María Ruiz‐Molina, Manuel J. Fernández Iglesias
ABSTRACT Introduction The popularization of blockchain‐based applications made evident a critical challenge, namely the inherent isolation of these decentralized systems, akin to the disconnected and technologically diverse local area networks of the 1970s. This lack of interoperability limits the potential for widespread adoption and innovation in the blockchain space. While various initiatives aim to bridge this gap, many remain nascent. Methods This article addresses this issue by proposing a robust architecture and practical implementation to interconnect two Ethereum‐based blockchains, enabling seamless smart contract interactions across these chains, and facilitating the exchange of complex information beyond mere token transfers. Results Our work explores the emerging landscape of inter‐blockchain communication, highlighting their current maturity and potential, and providing insights on how to overcome the technical hurdles associated with these protocols, particularly in the context of transmitting complex data and executing cross‐chain function calls. Additionally, we illustrate with a case study the challenges posed by linking private blockchains with public ones, ensuring secure and efficient data exchange. Conclusion This article aims to inspire blockchain researchers and practitioners, presenting a foundational framework for enhancing blockchain interoperability, including detailed, practical steps for its implementation. By laying the groundwork for more connected blockchain ecosystems, we intend to support the continued evolution and widespread adoption of blockchain technology.
With the rapid development of blockchain technology, encrypted assets have gradually become an important component of the global financial system. This study aims to analyze the concept of Ethereum (ETH) and its applications in finance, online payments, and transactions. This article first reviews the development history and research progress of blockchain, and then explores the current situation of the cryptocurrency market, with a focus on analyzing the basic characteristics and encryption algorithms of Ethereum. In the application section, this article discusses in detail the various applications of Ethereum in decentralized finance (DeFi) and non-fungible tokens (NFT), revealing its important role in promoting financial innovation and digital asset management. At the same time, the article also points out the limitations of Ethereum in terms of technical bottlenecks, compliance, and user education. Nevertheless, the future is still full of hope. Through this study, academia and industry practitioners can gain a deeper understanding of the potential and challenges of Ethereum, providing important references for its sustained development in the global financial ecosystem.
Contemporarily, blockchain techniques have been developed rapidly. This study describes the development of a blockchain-based website designed to display cryptocurrency information securely and transparently. The site utilizes Ethereum and smart contracts to ensure data immutability and transaction transparency. The front end is developed using the React framework to enhance the user experience, and the back end uses Web3.js for real-time blockchain interaction. A detailed case study of the cryptocurrency demo evaluated the performance of the website and the practical application of blockchain technology. The findings demonstrate the effectiveness of blockchain in securely managing and displaying sensitive financial data. In addition, the study highlights blockchain's potential to revolutionize fintech infrastructure by improving security and transparency, which are essential for the dissemination of financial information. These results makes a significant contribution to the use of blockchain in web development, drawing a blueprint for future research directions with a focus on optimizing transaction processes and improving user accessibility. Overall, the study demonstrates the transformative potential of blockchain technology in digital transactions and sets the stage for future innovation in this rapidly evolving field.
Stock price prediction is a challenging research topic because of non-linearity, significant noise and volatility of time series data.Deep learning techniques enable to learn complex and non-linear patterns of sequential time series data.Long Short-Term Memory (LSTM) is a technique which is designed to handle time series data.While LSTM model is used to extract temporal dependencies of stock data, the performance can be limited by noisy data and the challenge of capturing intricate patterns.In this research, LSTM-based framework with residual unit and attention mechanism is proposed to enhance the temporal dependencies and important features of stock price movements.Residual unit with skip connection captures more complex patterns and representations in stock price data and reduces the over-fitting problem to noisy time series data.LSTM with attention focuses on the significant time stamps which enhances the model prediction performance.The proposed system is experimented on five datasets: Apple (AAPL), Bitcoin, Ethereum, Litecoin and GOLD_PRICE.To prove the effectiveness of the model, the proposed system is compared with LSTM and Bidirectional LSTM (Bi-LSTM) models.Experimental results show that the proposed system outperforms baseline models such as LSTM, Bi-LSTM, LSTM+Bi-LSTM and state-of-the-art methods in term of error rates such as mean square error, root mean square error and mean absolute error.
OpenMind, Shaohong Zhong, Adam Zhou, Boyuan Chen · 6 authors
Large Language Models (LLMs) are compact representations of all public knowledge of our physical environment and animal and human behaviors. The application of LLMs to robotics may offer a path to highly capable robots that perform well across most human tasks with limited or even zero tuning. Aside from increasingly sophisticated reasoning and task planning, networks of (suitably designed) LLMs offer ease of upgrading capabilities and allow humans to directly observe the robot's thinking. Here we explore the advantages, limitations, and particularities of using LLMs to control physical robots. The basic system consists of four LLMs communicating via a human language data bus implemented via web sockets and ROS2 message passing. Surprisingly, rich robot behaviors and good performance across different tasks could be achieved despite the robot's data fusion cycle running at only 1Hz and the central data bus running at the extremely limited rates of the human brain, of around 40 bits/s. The use of natural language for inter-LLM communication allowed the robot's reasoning and decision making to be directly observed by humans and made it trivial to bias the system's behavior with sets of rules written in plain English. These rules were immutably written into Ethereum, a global, public, and censorship resistant Turing-complete computer. We suggest that by using natural language as the data bus among interacting AIs, and immutable public ledgers to store behavior constraints, it is possible to build robots that combine unexpectedly rich performance, upgradability, and durable alignment with humans.
2008 yılında Bitcoin’in ortaya çıkmasından sonra kripto paralar kısa zamanda önemli bir varlık sınıfı haline gelmiştir. Kripto paralar; uzlaşma prensibine dayalı, birimler arası doğrudan işlem yapma imkânı sunan, işlemlere ait kayıtlara tüm birimlerin erişebildiği, merkeziyetsiz bir yapı olan blockchain teknolojisi ile işletilirler. Bu çalışmanın amacı, Forbes tarafından 2024 yılı için, blockchain endüstrisinde faaliyet gösteren firmalara ait sermaye varlıkları yatırımlarında uzmanlaşan en iyi borsa yatırım fonlarının 2021 Ekim ile 2024 Haziran dönemindeki haftalık getirileri ile aynı dönemdeki Bitcoin ve Ethereum haftalık getirilerinin zaman serileri Vektör Oto Regresyon Analizi ile incelenmesidir. Çalışmada Varyans Ayrıştırması ve Etki-Tepki Testleri yapılarak serilerin birbirlerine karşı etki düzeyleri incelenmiştir. Ayrıca seriler arasındaki nedensellik ilişkileri Granger Nedensellik Testi yöntemiyle araştırılmıştır. Çalışmanın sonucunda; seçili blockchain yatırım fonlarından First Trust SkyBridge Crypto Industry and Digital Economy (CRPT) haftalık getirilerinin, Bitcoin ve Ethereum haftalık getirileri ile %5 anlamlılık seviyesinde tek yönlü, sadece Bitcoin haftalık getirileri ile %10 anlamlılık düzeyinde çift yönlü Granger Nedensellik ilişkisine sahip olduğu belirlenmiştir.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production on the Ethereum blockchain has adopted an auction-based mechanism known as Proposer--Builder Separation (PBS), where validators outsource block creation to builders competing in MEV--Boost auctions for Maximal Extractable Value (MEV) rewards. We employ empirical game-theoretic analysis based on simulations to examine how advantages in latency and MEV access shape builder strategic bidding and auction outcomes. We find that a small set of dominant builders leverage these advantages, consolidating power, reducing auction efficiency, and heightening centralization. Our results underscore the need for fair MEV distribution and sustained efforts to promote decentralization in Ethereum's block building market.
Originally developed to power cryptocurrencies such as Bitcoin and Ethereum, Distributed Ledger Technology or DLT is rapidly expanding its influence across all kinds of industries. Its attributes-decentralization, transparency, security, and immutability-are being applied in finance, healthcare, supply chain management, governance, among other sectors, offering solutions that streamline the conduct of operations, cost cuts, and increase trust.
With the rapid development of blockchain technology and the popularity of cryptocurrency, phishing scams pose an increasingly severe threat to the security of cryptocurrency transactions. Existing fraud detection methods have not accurately identified phishing behaviors, especially failing to capture key neighbor information and its impact effectively. To address this problem, we proposed a phishing detection framework based on FAAN-GBM (Feature and Attention Augmented Network with Gradient Boosting Machine), which aims to improve phishing fraud detection effectiveness on the Ethereum platform by further refining the extraction of phishing account features. This framework integrates basic features, transaction features, and interaction features of nodes, optimizes feature aggregation through importance analysis and attention mechanism of neighbor node, and uses autoencoders to deepen the nonlinear expression of node features. Through extensive testing on real Ethereum datasets, FAAN-GBM has demonstrated superior performance over existing methods, effectively improving the identification accuracy of phishing fraud nodes.
Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Mushfiquddin Rafid, Tunazzina Binta Mostafa, Rejwanul Haque Wasi, Rupok Islam Avi
The Readymade Garments Industry of Bangladesh contributes about 80% of the export earnings and is currently positioned 2nd worldwide in exporting clothes and fabric industry. The competition within this industry is intensifying rapidly. In addition to fierce global competition labor union movements and higher-quality market demands are making it more challenging for brands to maintain their foothold. This study introduces strategies that may improve the resiliency of the supply chain in Bangladesh's Garments using AI and Big Data tools. About 3-4 case study designs are used in this study to compare the technological developments in Bangladesh's RMG industry with benchmark practices in China. Secondary sources such as case studies (HLA group, Alibaba, H&M, Zara), scholarly works, industry reports, and regulatory documents were used to gather data. The study shows China's adoption of advanced AI tools such as AI-based quality control cameras, Big Query to data analyze, blockchain platforms like Hyperledger Fabric, AI-generated digital patterns and Ethereum-based solutions, and physics-based simulators for garment modeling, which is contributing at an streamline operations, enhance transparency. On the other hand, Bangladesh has applied innovations like computer vision for quality inspections (Aamra vision by Amara technologies limited) and predictive analytics for supply chain planning and many . Development is still hampered by inadequate investment in digital technologies, a lack of qualified personnel, and inadequate infrastructure. Bangladesh's apparel and garments industry can improve supply chain resilience, lower costs, and improve compliance with international standards by giving targeted investments in AI and big data and making them a top priority. To ensure sustainability and long-term competitiveness, this study offers policymakers, governments and business executives practical suggestions for unlocking the full potential of AI and big data.
The development of information technology has had a significant impact on various sectors, including the health sector. This research aims to implement blockchain technology with smart contracts in health data management to create a secure, transparent, and efficient system. The methods used include needs analysis, smart contract development, and system simulation using the Ethereum platform and IPFS-based off-chain storage. Data was collected through literature studies, interviews, observations, and system simulations. The results show that the smart contract successfully automates the process of granting and revoking data access permissions, with an average transaction time of under 2 seconds. The system ensures data security through encryption, private key-based authentication, and immutable transaction records. Off-chain storage overcomes blockchain's capacity limitations for big data such as medical imaging results, without compromising privacy. However, this research faces the challenges of high transaction fees on public blockchains and scalability of the system at large user scales. This research contributes to the development of innovative solutions for health data management with full control by patients, while improving the transparency and efficiency of the system.
With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anony... | Find, read and cite all the research you need on Tech Science Press
Non-fungible tokens (NFTs) are a technological innovation that has been widely used to provide a form of digital reward. However, the application of NFTs in the social domain, especially in blood donation programs, has not been widely explored. This research aims to develop an NFT-based reward system using blockchain technology as an appreciation for blood donors. The system is designed and developed using the Ethereum test network due to its stability in decentralized applications. This research uses the research and development (R&D) method with the 4D model approach, which consists of the Define, Design, Develop, and Disseminate stages. In the Define stage, a needs analysis was conducted to determine the system specifications. The Design stage involves the design of a web-based system3 to support NFT management. In the Develop stage, the system was developed using the Ethereum testing network. The Disseminate stage includes system testing using the black box method to ensure that all key features, such as NFT claims and data transparency, function properly. The result of the research is an NFT-based blockchain application that allows blood donors to easily claim their NFTs as a form of digital recognition. The evaluation showed an acceptability score of 61.34%, indicating that this application is acceptable to the community and has the potential to increase blood donor motivation. The implementation of this system is expected to have a sustainable positive impact on increasing blood donor participation in the future.
Hina Binte Haq, Syed Taha Ali, Asad Salman, Patrick McCorry · 5 authors
The transaction pool plays a critical role in processing and disseminating transactions in cryptocurrency networks. However, increasing transaction loads strain the resources of full node deployments. We present Neonpool, an innovative transaction pool optimization using bloom filter variants, which reduces the memory footprint of the transaction pool to a fraction. Implemented in C++ and benchmarked using a unique Bitcoin and Ethereum dataset, our solution verifies and forwards transactions with over 99.99\% accuracy and does not necessitate a hard fork. Neonpool is ideally suited for lightweight cryptocurrency clients and for resource-constrained devices such as browsers, systems-on-a-chip, mobile or IoT devices.
Introduction: Due to financial technology advances, cryptocurrency is a new asset with great potential for academics. Price volatility and dynamism make Cryptocurrencies prediction difficult. Objectives: This paper presents three Recurrent Neural Network (RNN) methods for predict Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) prices to predict the price of Cryptocurrency. Methods: This study uses three RNN algorithms LSTM, Bi-LSTM, and GRU to predict the prices of Bitcoin (BTC), Binance Coin (BNB), and Ethereum. (ETH) Results: BTC, ETH, and BNB prices are predicted using three machine learning algorithms. The model's accuracy was assessed using performance metrics. We followed by comparing actual and predicted prices of models. The GRU algorithm surpassed its competitors, attaining MAPE values of 2.562795642 for BNB, 2.921155091 for ETH, and 3.363400599 for BTC. Conclusion: The value of cryptocurrency swings regularly. Since the cryptocurrency market is nonlinear, time series data is challenging to evaluate when making price predictions. Machine learning algorithms are used in many financial and economic activities. Investors, researchers, and professionals need cryptocurrency value forecasts. In this study, GRU outperformed than LSTM and Bi-LSTM algorithms in predicting prices of BTC, ETH and BNB.
With the widespread adoption and increasing application of blockchain technology, cryptocurrency wallets used in Bitcoin and Ethereum play a crucial role in facilitating decentralized asset management and secure transactions. However, wallet security relies heavily on private keys, with insufficient attention to the risks of theft and exposure. To address this issue, Chaum et al. (ACNS’21) proposed a “proof of ownership” method using a “backup key” to prove ownership of private keys even when exposed. However, their interactive proof approach is inefficient in large-scale systems and vulnerable to side-channel attacks due to the long key generation time. Other related schemes also suffer from low efficiency and complex key management, increasing the difficulty of securely storing backup keys. In this paper, we present an efficient, non-interactive proof generation approach for ownership of secret keys using a single backup key. Our approach leverages non-interactive zero-knowledge proofs and symmetric encryption, allowing users to generate multiple proofs with one fixed backup key, simplifying key management. Additionally, our scheme resists quantum attacks and provides a fallback signature. Our new scheme can be proved to capture unforgeability under the computational indistinguishability from the Uniformly Random Distribution property of a proper hash function and soundness in the quantum random oracle model. Experimental results indicate that our approach achieves a short key generation time and enables an efficient proof generation scheme in large-scale decentralized systems. Compared with state-of-the-art schemes, our approach is applicable to a broader range of scenarios due to its non-interactive nature, short key generation time, high efficiency, and simplified key management system.
Hamra Afzaal, Nazir Ahmad Zafar, Aqsa Tehseen, Shaheen Kousar
Ethereum 2.0 Beacon Chain has adopted the Proof of Stake (PoS) consensus mechanism to significantly advance the security, scalability, and decentralization of blockchain technology. It has played an integral role in organizing validator operations and addressing scalability issues. The Beacon Chain’s rewards and penalty system incentivizes honest behavior and penalizes dishonest actors inside the network. Ensuring the correctness of this mechanism is paramount for guaranteeing the system’s integrity. Therefore, this work formally verifies the rewards and penalty mechanism in the Beacon Chain using model checking, a formal methods-based technique. The formal specification of the rewards and penalties processes and several other epoch operations are described using the Communicating Sequential Programs (CSP#) language. The properties for rewards and penalties procedures are specified with Linear Temporal Logic (LTL). The Process Analysis Toolkit (PAT) model checker is utilized for verifying the formal model against the properties. The PAT model checker takes the specified formal model as input and determines whether the properties are satisfied or violated. The properties are examined in terms of verification time, visited states, total transitions, and memory utilization.
Mohammad Pishdar, Mahdi Bahaghighat, Rajeev Kumar, Qin Xin
The general public is becoming increasingly familiar with blockchain technology. Numerous new applications are made possible by this technology's unique features, which include transparency, strong security via cryptography, and distribution. These applications need certain programming tools and interfaces to be implemented. This is made feasible by smart contracts. If the prerequisites are satisfied, smart contracts are carried out automatically. Any mistake in smart contract coding, particularly security-related ones, might have an impact on the project as a whole, available funds, and important data. The current paper discusses the flaws of the Ethereum smart contract in this respect. By examining publically accessible scientific sources, this work aims to present thorough information about vulnerabilities, examples, and current security solutions. Additionally, a substantial collection of current Ethereum (ETH) smart contracts has undergone a static code examination to conduct the vulnerability-finding procedure. The output has undergone assessments and statistical analysis. The study's conclusions demonstrate that smart contracts have several distinct flaws, including arithmetic flaws, that developers should be more aware of. These vulnerabilities and the solutions that can be used to address them are also included.