Aniket P. Kakde, Karan M. Bhoyar, Muhammad Aiman Shad, Prof. Sudesh A. Bachwani
Autonomous agents powered by Large Language Models (LLMs) require reliable and standardized frameworks to connect tools, exchange contextual information, and synchronize tasks across diverse systems. Despite growing interest in such agents, current integration with external tools remains disjointed. Developers often have to manually create interfaces, handle authentication protocols, and navigate incompatible function-calling standards across platforms. To overcome these limitations and promote the evolution of agentic AI, it is critical to establish standardized communication protocols that ensure interoperabilityâenabling agents and systems to seamlessly discover each otherâs capabilities, share data, and coordinate operations. This paper explores a structured overview of emerging communication standards for agents, focusing on the Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP). MCP utilizes a JSON-RPC based client-server architecture to enable secure execution of tools and well-typed data transfer. ACP introduces a REST-compliant message structure with support for asynchronous streaming and multipart formats, facilitating rich, multimodal agent outputs.A2A enables agents to delegate tasks peer-to-peer using capability-rich Agent Cards, enabling scalable and distributed workflows across organizations. ANP facilitates agent discovery and secure collaboration in open networks, leveraging decentralized identifiers (DIDs) and semantic graphs based on JSON-LD.
Svitlana Popereshnyak, Dmytro Chornobryvets, Oleh Bakaiev
The accelerated growth of freelance platforms has brought to light several systemic challenges, such as elevated transaction costs, increased susceptibility to fraud, limited transparency, and inefficiencies in the selection of service providers. This study presents the design and implementation of an AI-powered platform aimed at improving the management and monitoring of freelance services. The platform architecture incorporates a multi-criteria risk assessment framework, which evaluates users based on their ratings, transaction history, account longevity, and digital wallet balance. To address issues of contractor reliability and operational anomalies, the system integrates advanced algorithms for automated selection and anomaly detection. A smart contract mechanism, implemented in Solidity and deployed on the Ethereum blockchain via Web3.js, ensures secure and verifiable transactions. For data storage and retrieval, the platform leverages PostgreSQL and MongoDB, while ECDSA cryptographic techniques are employed to reinforce transaction integrity and user authentication. Empirical evaluation indicates that the platform substantially mitigates fraud risks and enhances the efficiency and transparency of interactions between clients and freelancers. The proposed solution demonstrates the potential to support secure and scalable freelance operations and may be extended for deployment within decentralized finance ecosystems and digital commerce environments.
Purpose This review systematically examines the convergence of Sustainable Digital Finance and Finance 5.0, highlighting their role in advancing financial sustainability, inclusion, and technological innovation. Finance 5.0 represents a transition from profit-driven finance to a human-centric, ethical, and sustainability-aligned financial ecosystem, where Artificial Intelligence (AI), blockchain, Decentralized Finance (DeFi), quantum computing, and RegTech enhance transparency, Environmental, Social, and Governance (ESG) compliance, and financial accessibility. Design/methodology/approach A Systematic Literature Review (SLR) was conducted using the ADO-TCM framework, which organizes research findings into antecedents, decisions, outcomes, theories, contexts, and methodologies. A structured search strategy was conducted across peer-reviewed literature using Scopus and Web of Science databases (2015â2025). Findings The findings indicate the role of Finance 5.0 in advancing sustainable financial ecosystems through AI-driven ESG analytics, blockchain-powered impact investing, and Digital currency-enabled financial inclusion. However, regulatory fragmentation, ethical AI concerns, and financial accessibility disparities remain significant challenges. The findings emphasize the need for standardized ESG metrics, ethical AI governance, and scalable financial policies to bridge sustainability gaps. Additionally, emerging technologies such as quantum computing, DeFi-driven climate finance, and AI ethics in financial decision-making require further exploration to enhance transparency, efficiency, and sustainability in digital financial ecosystems. Originality/value This review presents a novel framework for technological enablers of Sustainable Digital Finance, integrating Finance 5.0 with emerging technologies using the ADO-TCM framework. It addresses gaps in quantum computing, ethical AI, and DeFi-driven climate finance, offering insights for policymakers, financial institutions, and academia in fostering resilient and sustainability-driven financial ecosystems.
We introduce a modified Schnorr signature scheme to allow for time-bound signatures for transaction fee auction bidding and smart contract purposes in a blockchain context, ensuring an honest producer can only validate a signature before a given block height. The immutable blockchain is used as a source of universal time for the signature scheme. We show the use of such a signature scheme leads to lower MEV revenue for builders. We then apply our time-bound signatures to Ethereum's EIP-1559 and show how it can be used to mitigate the effect of MEV on predicted equilibrium strategies.
Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-tier vaccine supply chain within a continuous-time differential game framework. The model incorporates dynamic system equations that account for both vaccine quality and manufacturer goodwill. The research explores the effectiveness and characteristics of different government subsidy strategies, considering factors such as price sensitivity, and provides actionable managerial insights. Key findings from the analysis and numerical simulations include the following: First, from a long-term perspective, proportional subsidies for technological investments emerge as a more strategic approach, in contrast to the short-term focus of volume-based subsidies. Second, when the public is highly sensitive to vaccine prices and individual vaccination benefits closely align with government objectives, a volume-based subsidy policy becomes preferable. Finally, the integration of blockchain technology positively impacts the vaccine supply chain, particularly by improving vaccine quality and enhancing the profitability of manufacturers in the later stages of production.
Lamia SEBAI, Jahmane Abderrahman, K. M. Rezaul Karim
This paper analyses the relationships between the volatilities of five major stock markets (S&P 500, CAC 40, DAX, FTSE 100, and Nikkei 225) and five cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, and Ripple), (WTI), and gold. The GARCH model, which describes the volatility of financial assets and cryptocurrencies, was used. A significant and higher volatility spillover was observed across these market pairs. The conditional correlation between Bitcoin and other cryptocurrencies is time-varying, but the conditional correlations between crypto-currencies and gold and all assets are negative during the period (2017-2018) and positive. At the beginning of the COVID-19 crisis, the conditional correlation between cryptocurrencies, stock indices, and WTI increased, which confirms the impact of COVID-19 related contagion between them.Our findings show that cryptocurencies and gold are considered hedges for the international investors during the period 2017-2018.
Cryptocurrencies have revolutionized the financial landscape, introducing decentralized digital assets like Bitcoin and Ethereum. Their growth has spurred interest in statistical methods for monitoring and analyzing transactions, especially in the context of traditional financial systems like SWIFT (Society for Worldwide Interbank Financial Telecommunication). Statistical methods play a crucial role in identifying patterns, anomalies, and potential risks associated with cryptocurrency transactions. These methods involve data analysis, clustering, and machine learning algorithms to detect fraudulent activities, money laundering, and market trends. The integration of blockchain technology ensures transparency and immutability, enhancing statistical analysis accuracy.On the other hand, SWIFT transactions, widely used for cross-border payments, rely on statistical techniques to track and validate international fund transfers. These methods aid in fraud detection, regulatory compliance, and transaction efficiency. Combining the statistical prowess of cryptocurrencies and SWIFT transactions offers a comprehensive approach to secure and efficient global finance.In conclusion, cryptocurrencies have emerged as a disruptive force in the world of finance, offering decentralized, secure, and borderless transactions. Their popularity has grown exponentially, attracting both enthusiasts and skeptics. They have disrupted traditional finance, offering decentralized digital assets like Bitcoin and Ethereum. Statistical methods are crucial for monitoring and securing transactions on the SWIFT network, the backbone of global financial messaging. One of the recommendations was that advanced data analytics to detect anomalies, trend analysis for fraud prevention, and machine learning algorithms for predictive modeling.
Samsudin Samsudin, Muhammad Dedi Irawan, Muhammad Irwan Padli Nasution, Raissa Amanda Putri
Bitcoinâs extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.
This analysis focuses on password-free Electronic IDentity (eID) solutions for eGovernment services under the federated identity management framework Electronic IDentification, Authentication and trust Services (eIDAS). The scope of eID systems is centred on their alignment of the associated technical, legal, and procedural challenges. Through an analysis of five password-free eID solutionsâFast IDentity Online 2 (FIDO2) tokens, Secure Identity Across Borders Linked (STORK), distributed ledgers, mobile authenticators, and eID cardsâthe study evaluates their compliance with eIDAS standards and identifies key gaps in their design and implementation. While certain solutions, such as FIDO2 tokens and mobile authenticators, demonstrate full compliance, others, including STORK and distributed ledger-based systems, face challenges in achieving interoperability, privacy, and regulatory alignment. This research contributes to the discourse on digital identity management by offering insights into current limitations and recommending pathways for advancing the design, standardization, and deployment of eID systems. These results support the larger objectives of the European digital single market by highlighting the significance of regulations, innovations, and user-oriented design in creating password-free eID systems.
Predicting Bitcoin prices has always been challenging due to its high volatility and lack of linearity, and this challenge becomes even more pronounced in the case of market disruptions. In this context, this paper investigates the appropriateness of four machine learning models in forecasting, namely XGBoost, Long Short-Term Memory, Bagging Ensemble, and Stacking Ensemble, during two recent Black Swan periods, such as the COVID-19 pandemic and the RussiaâUkraine war. For this purpose, a dataset of 1240 Bitcoin daily closing prices from February 23, 2020, to August 8, 2023, was considered for the prediction purpose. The next day, Bitcoin prices were forecasted, and the prediction accuracy was measured against root mean square error and mean absolute percentage error. The results revealed that the Bagging and Stacking Ensemble was the most precise model during both Black Swan events. In contrast, Long Short-Term Memory (LSTM) and XGBoost were the most and least accurate, respectively. The finding indicates the robustness of ensemble-based approaches to coping with financial uncertainty. The study is instrumental because it allows comparing multiple models during extreme conditions, which provides vital insights for traders, analysts, and policymakers striving to identify the most accurate sources. Finally, this study contributes to the limited body of AI-driven financial forecasting literature focusing on models during actual Black Swan events instead of hypothetical situations.
Amy Thomas, Maria-Jose Schmidt-Kessen, Simon Karlin
This chapter explores the role of intellectual property (IP) in the commercialisation and regulation of sports and eSports, focussing on copyright, trade marks, and image rights. It outlines how these rights enable key stakeholders - such as sports organisers, players and fans - to assert control over various aspects of sporting content and performances. Though comparative analysis of legal frameworks in Germany, the EU, and the UK, the chapter highlights significant jurisdictional differences in the protection and interpretation of these rights, particularly in relation to the use of player likenesses and ownership of performance outputs. The chapter also investigates how new technologies, including generative artificial intelligence (AI) and Non-Fungible Token (NFTs), might complicate rights-based relationships in both fields. A central theme is the imbalance of rights and bargaining power among stakeholders, especially players, whose creative contributions are often excluded from IP protection. In doing so, the chapter raises normative questions and critical reflections on fairness, enforcement, and contractual practices in the regulation of sports and eSports content.
Puguh Hiskiawan, Jovan William, Louis Feliepe Tio Jansel
Bitcoin, a highly volatile and decentralized digital asset, presents considerable challenges for accurate price forecasting. This study proposes an applied data science framework that compares traditional statistical approaches with modern Artificial Intelligence (AI)-based models to predict Bitcoinâs daily closing price. Using BTC-USD historical data from January 2020 to December 2024, we converted prices into Indonesian Rupiah (IDR) to increase local relevance. Our forecasting horizon is 30 days, based on a 60-day lookback window. We evaluate six models: Linear Regression, ARIMA, and Prophet as traditional techniques, alongside Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks as AI approaches. All models were trained using lag-based or sequence-based time series features and evaluated using MAE, RMSE, R², MAPE, and SMAPE. Results show that AI models, particularly LSTM and XGBoost, offer better performance in capturing short-term non-linear dynamics compared to traditional models. LSTM provides high accuracy, though with greater computational demand, while XGBoost strikes a balance between speed and precision. Prophet and ARIMA remain effective for quick and interpretable forecasts but struggle with abrupt trend shift common in cryptocurrency markets. In addition to performance metrics, we include a robustness analysis based on median absolute error and outlier detection to assess model stability under extreme variations. Visual analyticsâincluding forecast curves, error distributions, and uncertainty boundsâhelp interpret and communicate model behavior. This comprehensive evaluation offers practical insights for investors, analysts, and fintech practitioners, and the pipeline can be extended to other volatile assets.
This paper explores the integration of Blockchain technology into Structural Health Monitoring (SHM) to enhance data traceability, integrity, and automation in infrastructure asset management. Traditional SHM approaches, including Digital Twin-based systems, often face limitations related to data tampering, sensor unreliability, and the lack of transparent and verifiable data workflows. To address these challenges, the SHERPA framework is proposed. SHERPA leverages decentralized storage via the InterPlanetary File System and three Smart Contracts dedicated to data validation, anomaly flagging, and automated workflow execution. Rather than focusing on the structural interpretation of data, SHERPA establishes a secure and auditable backbone for SHM data governance. A prototype implementation on the Canalone Viaduct in Italy demonstrated the feasibility of the system, showcasing automated response to threshold violations and immutable data registration. The framework proved effective in enhancing transparency, traceability, and stakeholder confidence, positioning SHERPA as a promising enabler of more trustworthy and accountable SHM systems.
Proof-of-stake blockchains require consensus protocols that support Dynamic Availability and Reconfiguration (so-called DAR setting), where the former means that the consensus protocol should remain live even if a large number of nodes temporarily crash, and the latter means it should be possible to change the set of operating nodes over time. State-of-the-art protocols for the DAR setting, such as Ethereum, Cardano's Ouroboros, or Snow White, require unrealistic additional assumptions, such as social consensus, or that key evolution is performed even while nodes are not participating. In this paper, we identify the necessary and sufficient adversarial condition under which consensus can be achieved in the DAR setting without additional assumptions. We then introduce a new and realistic additional assumption: honest nodes dispose of their cryptographic keys the moment they express intent to exit from the set of operating nodes. To add reconfiguration to any dynamically available consensus protocol, we provide a bootstrapping gadget that is particularly simple and efficient in the common optimistic case of few reconfigurations and no double-spending attempts.
The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting in significant financial losses for investors. Currently, there is a lack of effective methods for identifying and analyzing such new types of fraudulent activities. This paper categorizes these scams into four structural types and explores the intrinsic characteristics of Ponzi scheme contract source code from a program analysis perspective. The Mythril tool is employed to conduct static and dynamic analyses of representative cases, thereby revealing their vulnerabilities and operational mechanisms. Furthermore, this paper employs shell scripts and command patterns to conduct batch detection of open-source smart contract code, thereby unveiling the common characteristics of Ponzi scheme smart contracts.
Blockchain technology has become one of the most disruptive innovations of the 21st century, reshaping industries such as finance, supply chain management, healthcare, and governance. However, the conventional blockchain ecosystemâparticularly models based on Proof of Work (PoW)âhas been widely criticized for its excessive energy consumption and ecological footprint. As societies move toward sustainability and carbon-neutral goals, the exploration of energy-efficient blockchain models becomes not just an academic pursuit but also an ethical imperative. This manuscript investigates the evolution of energy-efficient consensus mechanisms and their integration into âgreen smart contracts,â which enable automated, verifiable, and sustainable digital agreements. It highlights consensus algorithms such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), Proof of Space-Time (PoST), Practical Byzantine Fault Tolerance (PBFT), and emerging hybrid mechanisms. The manuscript offers a comprehensive literature review, outlines statistical insights comparing energy and performance trade-offs, and proposes methodologies for integrating eco-friendly smart contract architectures. The results emphasize that while PoW-based systems consume up to 99% more energy than PoS-based models, hybrid approaches demonstrate a promising balance between security, decentralization, and efficiency. The study concludes that energy-efficient blockchain models, when strategically aligned with sustainability frameworks, can redefine smart contract ecosystems to meet global climate commitments while maintaining reliability, transparency, and scalability.
The growth of freelancing exposed several drawbacks in the platforms that are now in use, including high service costs, fraud risks, and late payments [1]. A well-designed system architecture is necessary to develop a freelance platform to guarantee scalability, security, and effectiveness. A modular architecture was chosen to give the required flexibility, scalability, and ease of maintenance to overcome these issues. This paper presents the design of a microservices-based architecture for a crypto freelance exchange platform, which uses Domain-Driven Design principles [2]. The architecture is built to support decentralized transactions, smart contract integration, and secure user authentication. It also ensures high availability and fault tolerance. The system uses a multi-layered architecture incorporating PostgreSQL, MongoDB, Redis, and Web3.js. The main components are a web application, KrakenD API Gateway, auth microservice, files microservice, and main microservice for managing transactions, orders, and payments. They are designed to meet critical non-functional requirements such as scalability, security, and maintainability. Each service can be independently deployed, updated, and scaled according to transaction volumes. With an emphasis on security, the platform uses JWT token techniques and multi-factor authentication to authenticate users. Also, the integration of blockchain technology enhances transparency, enabling freelancers and clients to have a trusted record of all transactions and reducing the risk of fraud. The architecture is visualized through UML and C4 model diagrams showing component interactions and service orchestration. This paper also discusses the rationale behind the chosen technologies, security mechanisms, and the benefits of using a modular microservices approach for building a crypto freelance platform.
This work focuses on the study of distributed ledger applications, presenting proposals of fair and decentralised applications to counter scenarios in which centralisation of wealth and power are the norm. The first contribution is a novel architecture for a decentralised data market, in which participants crowd-source data and receive a fair share of the reward. The market is shown to be resilient against a number of adversarial behaviours. Subsequently, an algorithm to prove one's location is presented. This algorithm is a key component necessary to the functioning of the data market. In contrast to prior approaches, the design does not require assumptions of honest participation, nor dependence on an external ground truth to identify malicious actors. It is fully peer-to-peer, robust in highly adversarial settings, and compatible with privacy-preserving techniques. The security and reliability of the algorithm are evaluated empirically and characterised mathematically. The protocol is then generalised into a consensus mechanism applicable beyond location verification. An extended mathematical model is developed for this case, and its performance under varying operational conditions is systematically characterised. Finally, a study of governance vulnerabilities in Distributed Ledger Technologies is presented. This work provides a taxonomy of formalised properties necessary for good governance, solutions to implement them and an evaluation of how the absence of these cause severe vulnerabilities. The analysis is then extended to realm of Decentralised Autonomous Organisations (DAOs), which are a class of applications implemented on Distributed Ledger Technologies. The findings anticipated several governance exploits that later materialised, incurring losses in the scale of millions for multiple DAOs. Overall, this thesis aims to contribute to the technological development of distributed ledger applications with the goal of furthering social good, presenting architectures, algorithms, and governance properties that prioritise fairness, decentralisation, and resilience.
In recent years, significant research efforts have focused on improving blockchain throughput and confirmation speeds without compromising security. While decreasing the time it takes for a transaction to be included in the blockchain ledger enhances user experience, a fundamental delay still remains between when a transaction is issued by a user and when its inclusion is confirmed in the blockchain ledger. This delay limits user experience gains through the confirmation uncertainty it brings for users. This inherent delay in conventional blockchain protocols has led to the emergence of preconfirmation protocols -- protocols that provide users with early guarantees of eventual transaction confirmation. This article presents a Systematization of Knowledge (SoK) on preconfirmations. We present the core terms and definitions needed to understand preconfirmations, outline a general framework for preconfirmation protocols, and explore the economics and risks of preconfirmations. Finally, we survey and apply our framework to several implementations of real-world preconfirmation protocols, bridging the gap between theory and practice.