The rapid digitalization of financial ecosystems has transformed online payments, transaction processing, and investment management into highly interconnected, data-intensive infrastructures. This transformation has simultaneously expanded exposure to cyber fraud, money laundering, identity theft, and market volatility, necessitating intelligent and adaptive security mechanisms. Advanced artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and graph-based analytics, have emerged as critical enablers of secure payment processing, real-time fraud detection, and predictive financial forecasting. Intelligent architectures embedded within online payment systems facilitate dynamic risk scoring, anomaly detection, behavioral profiling, and automated decision-making under strict latency constraints. This chapter presents a comprehensive examination of intelligent system frameworks for digital finance, integrating scalable cloud-based deployment, blockchain-enabled transaction integrity, explainable AI for regulatory compliance, and synthetic data generation for fraud simulation. Reinforcement learning approaches for portfolio optimization and risk-aware forecasting are analyzed to highlight adaptive investment strategies in volatile markets. Emphasis is placed on addressing class imbalance, adversarial threats, model interpretability, privacy preservation, and governance challenges within automated financial infrastructures. Emerging research directions such as federated learning, decentralized finance intelligence, and AI-driven anti-money laundering systems are also discussed to outline future technological trajectories. The presented synthesis establishes a structured foundation for developing secure, transparent, and scalable intelligent financial ecosystems aligned with regulatory and operational requirements of modern digital economies.
The rapid expansion of digital payment ecosystems has transformed global financial transactions through the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Smart point-of-sale terminals, wearable payment devices, biometric authentication systems, and cloud-integrated banking platforms generate massive volumes of real-time transactional data, demanding intelligent, scalable, and secure processing frameworks. Conventional security architectures struggle to address evolving cyber-financial threats, including adaptive fraud schemes, adversarial attacks, identity compromise, and decentralized finance exploits. An integrated AI–IoT security paradigm offers a resilient solution by enabling real-time anomaly detection, adaptive risk scoring, device-level authentication, and continuous behavioral monitoring across distributed financial infrastructures. This book chapter presents a comprehensive exploration of AI-driven analytics, reinforcement learning–based adaptive decision models, federated learning for privacy-preserving intelligence, and lightweight deployment strategies tailored for resource-constrained IoT financial devices. A unified Zero-Trust architecture combined with blockchain-assisted auditability strengthens transaction integrity while ensuring regulatory compliance and data governance alignment. Emphasis is placed on explainable AI mechanisms to enhance transparency in automated financial decision-making and to support accountability within high-stakes payment environments. Emerging research challenges, including adversarial robustness, energy-efficient model optimization, and cross-platform interoperability, are critically examined to establish a forward-looking framework for secure digital finance. The proposed perspective advances a scalable and privacy-aware AI–IoT integrated security architecture designed to mitigate financial risk, reduce false positives, and enhance trust in decentralized and intelligent payment systems. This contribution aims to support researchers, financial technologists, and policy architects in developing next-generation digital payment infrastructures capable of sustaining security, efficiency, and transparency in an increasingly connected global economy.
This study examines the determinants of transparency and accountability in village financial management, focusing on the roles of village facilitator competence, village government commitment, and oversight by the Village Consultative Body (BPD). Grounded in good governance and principal–agent theory, the study addresses persistent governance challenges at the village level, where substantial public funds are managed amid limited administrative capacity and uneven institutional oversight. Using a quantitative design, primary data were collected through a structured survey administered to 510 respondents from 85 villages in Donggala and Sigi Regencies, Indonesia. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS), enabling simultaneous testing of direct and mediating relationships among governance variables. The results show that village facilitator competence, village government commitment, and BPD oversight each have significant positive effects on financial transparency and accountability. Importantly, transparency plays a central mediating role, indicating that the effects of competence, commitment, and oversight on accountability are largely transmitted through improved information disclosure. These findings confirm that accountability in village financial management is difficult to achieve without adequate transparency mechanisms that reduce information asymmetry between village governments and the community. Theoretically, this study extends the application of good governance and principal–agent theory to the village governance context by empirically validating transparency as a key governance mechanism linking institutional factors to accountability outcomes. Practically, the findings highlight the importance of strengthening facilitator capacity, fostering integrity-driven leadership commitment, and enhancing the effectiveness of BPD supervision as integrated strategies to improve village financial governance. By providing evidence from a large-sample empirical study, this research offers insights for policymakers and practitioners seeking to promote transparent and accountable village finance management in decentralized governance systems.
Moses Arthur Baidoo, Wang ZhiCheng, Liu Qi, Zhou ShuMin · 6 authors
Access to reliable electricity remains a pressing challenge in Sub-Saharan Africa, particularly in rural areas where over 600 million people live without power. This paper explores the potential of decentralized solar energy systems; such as solar home systems, mini-grids, and solar-powered appliances in addressing energy access challenges across rural Sub-Saharan Africa. While these systems offer clean, reliable, and scalable alternatives to conventional grid expansion, their adoption is constrained by regulatory uncertainty, limited financing options, and local capacity gaps. Drawing on case studies from five countries, the paper examines how recent innovations -including mobile-based Pay-as-you-go (PAYG) financing, hybrid renewable systems, and improved energy storage technologies are reshaping energy access models. It also outlines policy recommendations aimed at strengthening regulatory coherence, promoting regional cooperation, and enhancing sustainability. Ultimately, the study highlights how decentralized solar solutions can contribute to long-term environmental, financial, and social resilience, with direct implications for poverty alleviation and inclusive rural development in the region.
Smart contracts, serving as self-executing programs on blockchain platforms, have emerged as a key innovation for enhancing data security. Despite advancements in both blockchain technology and smart contracts (SCs), Ethereum-based SCs remain vulnerable to security breaches. Exploitation of these vulnerabilities can result in substantial financial losses for both service providers and users. Consequently, the detection and mitigation of security vulnerabilities in smart contracts are critical to ensuring the security and reliability of blockchain platforms. Machine learning approaches are emerging as effective alternatives to traditional vulnerability detection methods, though many rely heavily on expert knowledge and primarily target familiar vulnerabilities. This chapter explores the creation of an AI-driven framework for detecting vulnerabilities in smart contracts, aimed at reducing risks and improving the reliability of blockchain systems. By incorporating advanced Machine Learning (ML) and Deep Learning (DL) techniques, the framework seeks to improve the accuracy and efficiency of vulnerability detection, addressing the shortcomings of traditional static and dynamic analysis methods. The proposed approach not only strengthens the security of smart contracts but also contributes to the broader goal of building more resilient and reliable blockchain ecosystems. Through an in-depth analysis of methodologies and case studies, this chapter highlights the essential role of AI in advancing the secure development and deployment of smart contracts.
Purpose The primary purpose of this research is to empirically analyze the co-movement, nonlinear dynamics, and spillover effects among non-fungible tokens (NFTs) and decentralized finance (DeFi) tokens, carbon exchange-traded funds (ETFs). The study aims to quantify these interactions, especially during major global crises, to derive practical implications for constructing sustainable and diversified investment portfolios. It seeks to provide a quantitative foundation for environmentally conscious investors to navigate the risks and opportunities at the intersection of digital finance and sustainability, addressing a significant gap in the existing literature. Design/methodology/approach This study employs a quantitative approach using advanced econometric models to analyze the daily returns of NFTs, DeFi tokens and Carbon ETFs. The methodology is centered on time-frequency analysis to capture dynamic relationships. Key methods include wavelet coherence (WTC) to identify co-movements across different time scales, partial wavelet coherence (PWC) to isolate direct linkages by controlling for systemic factors and wavelet correlation to examine how these relationships evolve over various investment horizons. This robust framework moves beyond traditional linear models to analyze complex, nonlinear market dynamics. Findings The relationship between digital assets and carbon ETFs is profoundly dynamic, event-driven and frequency-dependent. Co-movements, weak in the short term, intensify dramatically during global crises like the COVID-19 pandemic and geopolitical conflicts. The correlation strengthens progressively as the investment horizon lengthens, indicating carbon ETFs serve as a strong proxy for long-term systemic factors. PWC analysis confirms these are genuine, direct linkages, not merely spurious correlations, highlighting the true interconnectedness of these markets during periods of global instability. Research limitations/implications This study is limited by its focus on a specific set of assets and a defined time period (2020–2024); therefore, findings may not be generalizable to all market conditions or digital assets. The use of CRBN and SMOG as proxies for the carbon market may not capture all nuances of environmental finance. Future research could expand this framework by incorporating other financial markets, such as bonds and commodities, or by applying regime-switching models like SETAR to further explore nonlinear dynamics and enhance the robustness of the findings. Practical implications For environmentally conscious investors, this study provides a quantitative foundation for building climate-aligned portfolios. The findings demonstrate that integrating carbon ETFs into a digital asset portfolio is a sound risk management strategy that enhances diversification and hedges against both market volatility and potential regulatory risks tied to blockchain’s carbon footprint. The results suggest a strategic allocation approach: utilizing stablecoins as portfolio anchors, carefully managing exposure to central shock transmitters and incorporating carbon ETFs for long-term stability and hedging. Social implications This research provides a data-driven roadmap for aligning the burgeoning field of digital finance with pressing sustainability goals. By demonstrating how to construct portfolios that are both financially robust and environmentally responsible, it addresses the significant environmental concerns surrounding blockchain technology. This contributes to a more sustainable financial ecosystem, offering a pathway for investors to participate in innovative digital asset markets while actively managing and hedging against their carbon footprint, thereby promoting greater corporate and social responsibility in finance. Originality/value This paper’s originality lies in its comprehensive empirical analysis of the co-movement and nonlinear dynamics among the specific triad of NFTs, DeFi tokens and carbon ETFs – an intersection that remains largely unexplored. By applying advanced wavelet-based methodologies, the study provides novel, actionable insights into the event-driven and frequency-dependent nature of their interconnectedness. It successfully bridges the gap between digital finance and sustainability, offering a unique, data-driven framework for constructing resilient, next-generation portfolios that are both financially sound and environmentally conscious.
The global digital identity landscape is undergoing an unprecedented crisis. Approximately 1.1 billion individuals worldwide lack any verifiable form of digital identity, while existing identity systems face existential threats from the industrialization of deepfake technology with injection attacks targeting biometric verification surging 900% since 2022 and occurring at a rate of once every five minutes in 2024. Simultaneously, conventional blockchain-based identity proposals that store biometric templates on-chain introduce critical privacy vulnerabilities incompatible with emerging regulatory frameworks including the EU AI Act (2024) and GDPR. This paper presents ZKP-GDIS (Zero-Knowledge Proof Global Decentralized Identity System), a novel, privacy-by-design identity architecture that fundamentally departs from prior work in three key dimensions. First, ZKP-GDIS never stores raw biometric data on-chain; instead, it employs zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) cryptographic commitments that allow identity verification without any disclosure of underlying biometric features. Second, we introduce a Hybrid Deepfake-Resistant Liveness Pipeline (HDRLP) — a multi-modal anti-spoofing layer that fuses passive CNN-based texture analysis, photoplethysmography (PPG) heart-rate detection, and hardware-attested device fingerprinting to defeat both presentation and injection attack vectors. Third, the system adopts W3C Decentralized Identifier (DID) standards and implements a federated governance model, enabling cross-jurisdictional interoperability while respecting national digital sovereignty. We provide formal security proofs under the computational Diffie-Hellman hardness assumption, evaluate the system against the ISO/IEC 30107-3 Presentation Attack Detection benchmark, and report experimental results demonstrating 99.87% genuine acceptance rate, 0.004% false acceptance rate under deepfake attack, and 94% reduction in on-chain gas costs versus Ethereum mainnet through zkEVM Polygon deployment. ZKP-GDIS establishes a reproducible, standards- compliant, and audit-ready framework for the next generation of global digital identity infrastructure.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and Toda–Yamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
The rapid expansion of algorithmic decision systems, blockchain infrastructures, and autonomous operational technologies has begun to transform the structural organization of corporations. Increasingly, firms rely on automated governance mechanisms, decentralized ledgers, and smart contract frameworks that allow economic transactions and corporate decisions to occur with minimal direct human intervention. This transformation raises serious ethical and professional questions for accounting, a discipline historically grounded in human judgment, fiduciary responsibility, and professional oversight. Ethical accounting within autonomous corporate structures therefore emerges as a critical field of inquiry. The study examines how accounting ethics must evolve when financial reporting, asset transfers, contractual obligations, and performance measurement are executed through autonomous computational systems rather than traditional managerial decision chains. Particular attention is given to the implications for accountability, transparency, auditability, and stakeholder trust when algorithmic processes replace or supplement human managerial authority. Drawing upon ethical theories, accounting governance principles, and recent technological developments such as blockchain based corporate systems and algorithmic management, the study explores how ethical safeguards may be preserved in environments where corporate operations become partially or fully self executing. The analysis also considers the responsibilities of accountants, auditors, regulators, and system designers in ensuring that autonomous corporate structures remain aligned with principles of fairness, transparency, and societal responsibility. Ultimately, the discussion highlights the need for expanded ethical frameworks capable of addressing emerging technological realities within modern corporate governance systems.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholders’ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
Severin Bonnet, Jan-Gero Alexander Hannemann, Frank Teuteberg
Abstract In this paper, we report on the initial stage of a design science research (“DSR”) project aimed at establishing design principles for DeFAI (the intersection of DeFi and AI) generative AI-based chatbot assistants tailored to decentralized finance (“DeFi”). Addressing challenges such as user trust, data privacy and security, and regulatory compliance, we conducted a targeted literature review, expert interviews, as well as iterative prototype ideation and evaluation to derive three design principles: (1) Human-Centered Design, (2) Resilience and Interoperability, and (3) DeFi-Native User Experience Together, these principles operationalize general chatbot design guidance for DeFi contexts characterized by self-custody, irreversible transactions, and adversarial risk environments. We demonstrate these principles through DeFAIGuide, a mockup that illustrates how technical barriers in DeFi can be abstracted to enhance accessibility for novice users while also offering advanced features for expert users. Our study contributes actionable design knowledge to support the future development of DeFAI solutions that advance inclusivity, security, privacy, and self-sovereignty, paving the way for a more transparent and participatory financial future.
У статті досліджується проблематика надмірного енергоспоживання класичних блокчейн-мереж та розробка екологічно стійких архітектур для промислової Web3-інфраструктури. На тлі глобальних кліматичних ініціатив (таких як Європейський зелений курс) та жорстких нормативних вимог (регламент MiCA) обґрунтовано необхідність системного підходу до технологічної оптимізації децентралізованих систем. Проаналізовано еволюцію протоколів консенсусу з акцентом на застосуванні оптимізованих модифікацій алгоритму PBFT (зокрема ієрархічних, репутаційних та багатолідерних моделей) як найефективнішого стандарту для корпоративних консорціумних мереж. Розглянуто переваги диверсифікації мікроархітектур, зокрема стратегічний перехід від традиційних процесорів x86 до спеціалізованих енергоефективних ARM-рішень, що здатні знизити споживання енергії вузлами на 60%. Окрему увагу приділено подоланню термодинамічних обмежень центрів обробки даних завдяки впровадженню технології двофазного занурювального охолодження (2-PIC), яка дозволяє досягти безпрецедентного показника енергоефективності PUE на рівні 1.02 у прохолодному кліматі. Визначено критичну роль рішень другого рівня (Layer 2, зокрема ZK-Rollups) та горизонтального масштабування через шардинг у радикальному розвантаженні базового обладнання та зниженні сукупного енергоспоживання. Доведено, що інтеграція алгоритмів глибокого навчання з підкріпленням (DRL) для динамічного та автономного розподілу ресурсів дозволяє підвищити пропускну здатність мереж і зменшити споживання обчислювальних потужностей на 30%. Робиться висновок, що комплексне поєднання наведених технологій гарантує оптимізацію сукупної вартості володіння (TCO) та відповідність індустріальних блокчейн-рішень сучасним міжнародним ESG-стандартам екологічної стійкості.
Web3 has gained increasing attention in recent years as a paradigm that aims to establish a new phase of the Internet by enabling a decentralized web infrastructure without reliance on centralized authorities or intermediaries. A fundamental difference between Web3 decentralized applications (dApps) and traditional Web2 applications lies in their underlying architecture and trust model. Web3 dApps rely on blockchain networks and smart contracts to execute application logic and manage shared state. This paper presents a systematic literature review that examines the architectural components, architectural patterns, and development challenges of Web3 decentralized applications. A set of peer-reviewed research articles was analyzed to provide a structured overview of current research and practice. The results identify the core building blocks of Web3 dApps, as well as highlighting the most adopted architectural styles, with a particular emphasis on hybrid on-chain/off-chain and fully decentralized architectures. Furthermore, this study synthesizes the most frequently reported challenges in Web3 dApp development. By systematizing existing knowledge, this work contributes to a clearer understanding of Web3 dApp architectures and provides a foundation for future research and improved engineering practices in decentralized application development.
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.
Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers' valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28\% at moderate affiliation ($ρ=0.5$) and up to 30\% for small bidder counts. Applied to observed bribe totals, this gap corresponds to \$10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as entity detection and transaction classification. Large-scale datasets like Elliptic have allowed for a rise in the analysis of these systems and in tasks such as fraud detection. In these settings, the amount of transactional context available to each node is determined by the neighborhood aggregation and sampling strategies, yet the interaction between these receptive fields and embedding geometry has received limited attention. In this work, we conduct a controlled comparison of Euclidean and tangent-space hyperbolic GNNs for node classification on a large Bitcoin transaction graph. By explicitly varying the neighborhood while keeping the model architecture and dimensionality fixed, we analyze the differences in two embedding spaces. We further examine optimization behavior and observe that joint selection of learning rate and curvature plays a critical role in stabilizing high-dimensional hyperbolic embeddings. Overall, our findings provide practical insights into the role of embedding geometry and neighborhood depth when modeling large-scale transaction networks, informing the deployment of hyperbolic GNNs for computational social systems.
This paper presents a software engineering framework for designing and implementing Distributed Ledger Technology (DLT)-based traceability systems in agri-food and agrotouristic ecosystems. The proposed approach integrates goaloriented requirements modeling (GOReM) with a permissioned blockchain infrastructure, specifically Hyperledger Fabric, to ensure transparent, verifiable, and sustainable supply chain processes. The methodology supports the conceptualization of stakeholder goals, business processes, and data flows through UML-driven modeling, enabling traceability from agricultural production to final consumption. A case study conducted in Calabria, a region in Southern Italy - developed in collaboration with the Regional Agency for Agricultural Development (ARSAC) - demonstrates the applicability of the framework across key sectors such as viticulture, olive oil production, and citrus farming. The resulting prototype enhances product certification, process transparency, and trust while aligning with the principles of Blockchain-Oriented Software Engineering (BOSE). Experimental evaluation confirms the system's scalability, low-latency performance, and suitability for real-world deployment, paving the way toward interoperable, model-driven DLT architectures for sustainable agri-food systems.
Francesco Salzano, Marco Guglielmi, Simone Scalabrino, Rocco Oliveto · 5 authors
This study examines the combined effect of fine-tuning and semantic prompt enrichment on Large Language Model-based vulnerability detection in Solidity smart contracts. We fine-tune ChatGPT-4o through a two-phase process aligned with the DASP Top 10 taxonomy—first to internalize theoretical vulnerability knowledge, then to specialize on labeled Solidity functions. We further enhance the fine-tuned model with automatically generated and human-validated code summaries as semantic enrichments to its prompts. The resulting model achieves an average F1-score of 0.58, a 66% improvement over the baseline (0.35), with the largest gains in Access Control ($+146 \%$), Denial of Service ($+353 \%$), and Reentrancy ($+35 \%$) detection. These results show that domain-aligned fine-tuning and semantic prompt enrichment jointly improve the precision and recall of LLM-based smart-contract auditing, offering a practical path toward AI-assisted security analysis.
This paper explores how social influence and peer networks shape the adoption of cryptocurrencies and decentralized finance (DeFi) platforms. Drawing from qualitative interviews and social theory, the study examines how interpersonal communication, social media influence, and online communities impact user behavior. Findings reveal that peer endorsement and communal learning are strong drivers of trust and experimentation in the crypto space, especially in regions with limited institutional trust. Peer networks act as informal but powerful educational structures, providing newcomers with advice, emotional support, and real-time market insights. In many cases, peer encouragement is what propels hesitant individuals to take the first step toward using crypto wallets or engaging in DeFi protocols. However, the influence of peers can also perpetuate hype-driven narratives, misinformation, and herd behavior, leading to poor financial decisions or susceptibility to scams. The paper concludes with recommendations for leveraging peer networks in designing effective crypto awareness and onboarding strategies. These include integrating community leaders into education campaigns, offering platform incentives for verified peer mentorship, and collaborating with trusted influencers to communicate risks and best practices. Understanding the dynamics of social influence can help policymakers, educators, and platforms foster more ethical, inclusive, and informed crypto adoption pathways globally.
The book&s;s emphasis is shifted to the future in Chapter 8 , “Emerging Trends and Advanced Topics,” which offers a survey of the cutting-edge ideas and revolutionary technologies that are set to completely alter the logistics industry. The chapter makes the case that logistics is developing into a hyperconnected, intelligent, and autonomous ecosystem rather than a collection of distinct tasks. The convergence of multiple important technological domains, each supported by complex mathematical and engineering principles, is what is driving this evolution. In order to enable strong predictive analytics, demand forecasting, and real-time optimization, artificial intelligence and machine learning are positioned as the brains of logistics operations in the future. This is where the story starts. The idea of “digital twins,” which produce virtual representations of entire supply chain networks in real time, expands on this theme. These virtual models, mathematically grounded in dynamical systems and Markov Decision Processes, allow companies to simulate complex scenarios, assess risks, and test optimization strategies in a virtual environment before physical implementation. From optimization, the chapter moves to the critical issues of trust and security, highlighting Blockchain for Supply Chain Transparency. It explains that blockchain&s;s function will transcend simple record-keeping, creating a decentralized and tamper-proof ledger for all transactions. The text delves into the mathematical foundations securing this trust, including cryptographic hash functions, Zero-Knowledge Proofs (ZKP), and secure consensus algorithms. The physical implementation of these trends in warehouse automation and robotics is finally examined in this chapter. Multi-agent reinforcement learning and graph-based optimization models are used to coordinate swarms of autonomous mobile robots and cooperative “cobots” in hyper-automated warehouses of the future, achieving previously unheard-of levels of efficiency and adaptability. This idea is expanded to the scale of advanced manufacturing and smart factories, where material flows are coordinated in real-time to satisfy changing manufacturing demands and logistics becomes a deeply integrated, cyber-physical part of the production system itself. According to the chapter&s;s conclusion, the combination of these technologies will result in intelligent, self-adjusting, and extremely resilient logistics networks, offering businesses that adopt this technological change a major competitive edge.
Millions of women face exclusion from banking services which acts as a fundamental obstacle to their economic development because of institutional barriers. Blockchain technology represents an efficient approach to providing secure decentralized financial solutions that increase access to financial services. The analysis will explore blockchain capabilities to expand financial access for women while promoting economic opportunity as per SDG 5 (Gender Equality). Traditional banking creates barriers for women in financial services because they lack money funds and poor credit recording along with restricted bank access. This paper examines how blockchain resolves such problems by implementing decentralized finance (DeFi), smart contracts blockchain-based microfinance, and digital identity verification. Using blockchain technology leads to safe financial payments while eliminating middle agents and creating open transaction documentation which gives users better economic management abilities. The application of blockchain technologies in real-world situations generates positive effects on female entrepreneurship and the business performance of small business owners and workers within the informal economy. The widespread implementation of blockchain faces barriers because of regulatory restrictions and technological limitations together with digital skill level disparities. This research adopts strategic guidelines and policy recommendations that enhance blockchain advantages for female financial empowerment. The implementation of blockchain technology delivers financial independence worldwide market entry and sustainable economic stability to women. The achievement of SDG 5 depends on stakeholders who join forces to build financial structures that support gender equality.
Ermanno Francesco Sannini, Lucia Simeone, Corrado Aaron Visaggio, Andrea Di Sorbo
In Ethereum's immutable environment, high-quality documentation is essential for users and auditors to fully understand smart contract behavior and build trust. However, an empirical, manually conducted, and comprehensive investigation that analyzes and identifies undocumented implementation details, implicit assumptions, and comment-code inconsistencies in operational smart contracts is still missing. To address this gap, this paper examines the commenting practices occurring in the source code of smart contracts through a systematic manual review of 100 up-to-date Solidity smart contract projects mined from Etherscan, divided into high-usage and low-usage groups based on the number of transactions they received. By combining quantitative analysis, validated with Fisher's exact test, and a multidimensional qualitative checklist, we identify a systemic deficiency in documentation, especially concerning smart contract-specific facets, such as critical security patterns and gas optimization strategies. Our findings show that documentation quality is generally insufficient regardless of a contract's popularity. Smart contract developers tend to prioritize functionality over verifiability, highlighting an urgent need for domain-specific documentation standards and best practices that better support the entire development lifecycle of blockchain applications.