With the rapid proliferation of artificial intelligence generated content (AIGC), non‑fungible tokens (NFTs), and blockchain‑based services, creative works are increasingly born digital and managed as intellectual property (IP) digital assets. However, the assetization of content has outpaced the maturity of the supporting legal, technical, and educational infrastructures. Content creators and learners face fragmented tools for creation, registration, traceability, and infringement detection, which leads to weak evidence chains and high transaction costs in rights protection. This paper proposes an integrated framework for intelligent generation and security protection of IP digital assets that tightly couples AIGC engines with multi‑modal watermarking, blockchain‑based registration, and privacy‑preserving analytics. On this basis, a teaching‑oriented implementation is designed and deployed in a university course on digital media and IP management. The system supports full‑lifecycle management of images, text, code, and multimedia works, enabling students to experience rights creation, proof‑of‑ownership, risk diagnosis, and evidence preservation in realistic project tasks. Experimental results on a mixed benchmark of 4,200 assets show that the proposed scheme improves watermark robustness by 7.5% on average and shortens rights registration latency by 68% compared with traditional workflows, while significantly enhancing students’ IP literacy and compliance intention. The study demonstrates that IP digital‑asset technology can be transformed from a purely legal or technical topic into an operational teaching infrastructure, supporting both innovation and compliance in the AIGC era.
Open access
Blockchain Technology Applications and Security
Digital Rights Management and Security
Physical Unclonable Functions (PUFs) and Hardware Security
This study explores the intersection of cryptocurrency, cybercrime, and global governance. It focuses on identifying criminal techniques, analyzing forensic and regulatory countermeasures, and evaluating the broader governance dilemmas that arise. A qualitative desk-based approach was employed, synthesizing secondary data from peer-reviewed studies, institutional policy papers (FATF, IMF, Europol), and industry reports (Chainalysis, Elliptic, TRM Labs). Thematic content analysis was used to trace patterns in illicit cryptocurrency use, law enforcement responses, and regulatory innovations. The findings indicate that while advances in blockchain forensics and policy coordination have strengthened oversight, criminals increasingly exploit decentralized finance platforms, cross-chain laundering, privacy coins, and mixers to evade detection. Enforcement remains uneven, hindered by fragmented regulations and gaps in cross-border cooperation. Overall, the study concludes that cryptocurrency-enabled cybercrime remains a resilient and evolving threat that challenges the stability of the global financial system and exposes weaknesses in governance frameworks. Without stronger coordination, adaptive regulation, and robust technological capabilities, the risks of illicit finance will continue to outpace control efforts. To mitigate these risks, the study recommends enhancing cross-border collaboration, investing in advanced blockchain forensic tools, and adopting flexible, multi-stakeholder governance models that balance innovation with accountability.
Changhao Wu, Luyu Chen, Kai Wang, Weili Han · 5 authors
Occurring approximately once or twice in each block, sandwich attacks threaten Ethereum’s ecosystem by manipulating prices through strategically placed buy and sell transactions around pending user trades. Existing detection methods primarily rely on rigid heuristic rules, limiting their ability to detect increasingly sophisticated and dynamic attack variants, particularly those residing only in the mempool or spanning multiple blocks. In this paper, we propose SandWatch , a novel Ethereum sandwich attack detection framework that integrates a dual-task graph neural network (Dual-GNN) with heuristic methods. The framework comprises three main components. (1) An order-independent heuristic that captures fundamental token transfer patterns, reducing transaction volume by over 94% for subsequent graph analysis. (2) A Dual-GNN that simultaneously classifies sandwich attack transactions and DEX nodes, dynamically updating an address label pool to enhance accuracy and generalizability. (3) A positive-unlabeled learning strategy to leverage large-scale unlabeled data effectively. We first evaluate Dual-GNN on a benchmark dataset derived from publicly available Ethereum sandwich attack data, achieving an F1-score of 99.78%, outperforming the single-task baseline by 0.93 percentage points. We then deploy SandWatch on Ethereum transactions collected through blockchain interfaces and mempool pre-execution from January to May 2024. SandWatch detects 563,453 sandwich attacks, including 24,404 multi-attack, 4,902 cross-block, and 1,057 pool-failure variants, achieving an overall recall of 98.63% compared to the state-of-the-art benchmark platform. These results demonstrate the robustness of SandWatch in detecting sophisticated sandwich attacks within the real-world Ethereum ecosystem.
NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, temporal non-stationarity, and heterogeneous relational dependencies in a leakage-safe forecasting setting. We propose MM-Temporal-Graph, a cross-modal temporal graph transformer framework for explainable NFT valuation and information-centric risk forecasting. The model encodes image, text, transaction time series, and blockchain behavioral features, constructs a heterogeneous NFT interaction graph (co-transaction, shared creator, wallet relation, and price co-movement), and jointly performs relation-aware graph attention and global temporal–structural transformer reasoning with an adaptive fusion gate. A contrastive multimodal alignment objective improves robustness under market drift, while a risk-aware regularizer and a multi-source risk index enable early warning and interpretable attribution across modalities, time segments, and relational neighborhoods. On MultiNFT-T, MM-Temporal-Graph improves MAE from 0.162 to 0.153 and R2 from 0.823 to 0.841 over the strongest multimodal graph baseline, and achieves 87.4% early risk detection accuracy. These results support accurate, robust, and explainable NFT valuation and proactive risk monitoring in Web3 markets.
As 6G networks evolve, spectrum assets require flexible, dynamic, and efficient utilization, motivating blockchain based spectrum securitization. Existing approaches based on ERC404 style hybrid token models rely on frequent minting and burning during asset transfers, which disrupt token identity continuity and increase on chain overhead. This paper proposes the Semi Fungible Token Lock (SFT Lock) method, a lock/unlock based mechanism that preserves NFT identity and historical traceability while enabling fractional ownership and transferability. By replacing mint/burn operations with deterministic state transitions, SFT Lock ensures consistent lifecycle representation of spectrum assets and significantly reduces on chain operations. Based on this mechanism, a modular smart contract architecture is designed to support spectrum authorization, securitization, and sharing, and a staking mechanism is introduced to enhance asset liquidity. Experimental results on a private Ethereum network demonstrate that, compared with ERC404 style hybrid token models, the proposed method achieves substantial gas savings while maintaining functional correctness and traceability.
Abstract With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, we provide updated empirical evidence on Ethereum’s role in both cryptocurrency and mixed portfolios. The analysis not only evaluates its diversification potential numerically but also sheds light on how the optimal allocations vary across distinct risk preferences and portfolio objectives. Moreover, based on the data of 2017–2024, we estimate that Ethereum futures has a hedging effectiveness on Bitcoin of about 30–40% across different risk preferences. Beyond these findings, the Bayesian mixture copula framework represents a methodological contribution to the modelling of complex dependence structures between financial returns. Taken together, our study delivers new insights that are particularly relevant in light of the evolving cryptocurrency landscape and the increasing integration of digital assets into mainstream investment practice.
Abstract This study empirically assesses the viability of Bitcoin as an alternative investment asset within the Egyptian context from 2011 to 2023. We conduct a comparative analysis of Bitcoin’s risk-return characteristics against traditional Egyptian investment vehicles: the EGX30 stock index, physical Gold, and the USD/EGP exchange rate. Utilizing historical daily data sourced from Coinbase, Bloomberg, Yahoo Finance, and the Central Bank of Egypt, we employ standard financial metrics including annualized returns, volatility (standard deviation), and Sharpe ratios. Correlation analysis is performed to evaluate Bitcoin’s diversification potential. Furthermore, we examine asset performance during significant periods of socio-economic stress: the 2011 Egyptian Revolution, the COVID-19 pandemic (2019-2020), and the EGP devaluation period (2022-2023). Our findings reveal Bitcoin’s exceptionally high volatility ( $$\sigma \approx 3.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>3.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily) and potential for substantial returns, yet yielding a surprisingly negative cumulative return over the entire sample period. Gold demonstrated characteristic stability ( $$\sigma \approx 1.0\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>1.0</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily), while the EGX30 offered moderate growth amidst volatility ( $$\sigma \approx 1.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>σ</mml:mi> <mml:mo>≈</mml:mo> <mml:mn>1.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> daily). Correlation analysis suggests limited diversification benefits between Bitcoin and traditional assets during certain periods. Event analysis highlights varying asset reactions, with Gold often acting as a safe haven, while Bitcoin exhibited mixed behavior. While Bitcoin presents diversification potential, its extreme volatility, negative long-term cumulative return within this sample period, and the prevailing regulatory uncertainty in Egypt necessitate careful consideration for investors seeking alternative assets in a challenging macroeconomic environment characterized by inflation and currency depreciation.
The Recursive Edge: A Synthesis of Adaptive Spline Architectures and Agentic Paradigms in 2026 1. Introduction: The Structural Turn in Deep Learning The trajectory of artificial intelligence research in the mid-2020s has been characterized by a decisive pivot away from the "Depth Hypothesis"—the long-standing conviction that stacking layers of fixed, node-centric non-linearities (such as Rectified Linear Units or GeLUs) is the singular path to increasing representational power. For nearly a decade, the Multi-Layer Perceptron (MLP) served as the atomic unit of deep learning, embedding a fundamental assumption: that the complexity of the world is best approximated by global linear transformations followed by static point-wise activations. However, the years 2025 and 2026 have witnessed the emergence of a "Structural Turn," a paradigm shift where the focus has moved from the depth of the network to the mathematical quality of the connections themselves. At the forefront of this shift is the Kolmogorov-Arnold Network (KAN), an architecture that relocates learnable non-linearities from the neurons to the edges, parameterizing weights not as scalar values but as univariate B-spline functions. This architectural reorientation is not merely a cosmetic change; it represents a fundamental rethinking of how neural networks approximate continuous functions, grounded in the rigorous mathematical framework of the Kolmogorov-Arnold Representation Theorem of 1957.1 Simultaneously, in the domain of Natural Language Processing (NLP), the limitations of fixed context windows have necessitated a similar structural revolution, giving rise to Recursive Language Models (RLMs) that replace monolithic attention mechanisms with agentic, recursive control flows.3 This report presents an exhaustive technical analysis of these advancements. Unlike standard survey papers, this document prioritizes a "recurse the data" methodology: we do not merely summarize findings but verify the underlying mathematical formulations, cross-reference empirical contradictions, and synthesize second-order insights regarding the causal mechanisms of catastrophic forgetting and context retention. We scrutinize the "Nexus Mirror"—a conceptual framework suggesting that the modular additivity of KANs and the recursive nature of RLMs mirror the causal and physical structures of reality more faithfully than the entangled representations of traditional MLPs.1 By rigorously checking the math of B-spline recursions, least-squares grid extensions, and intrinsic dimensionality bounds, we aim to provide a definitive account of the state of neural architecture in 2026. 2. Theoretical Foundations: The Kolmogorov-Arnold Paradigm To understand the operational mechanics and the theoretical legitimacy of KANs, one must first dissect the mathematical divergence between the original representation theorem proposed in the mid-20th century and its practical realization in modern computational frameworks. 2.1 The Kolmogorov-Arnold Representation Theorem (1957) In 1957, answering David Hilbert’s thirteenth problem, mathematicians Andrey Kolmogorov and Vladimir Arnold established a representation theorem that fundamentally challenged the understanding of multivariate functions. The theorem posits that any continuous multivariate function $f: ^n \to \mathbb{R}$ can be represented as a superposition of continuous univariate functions and addition. The canonical form of this representation is given by: $$f(x_1, \dots, x_n) = \sum_{q=0}^{2n} \Phi_q \left( \sum_{p=1}^{n} \psi_{p,q}(x_p) \right)$$ In this formulation, the inner summation $\sum_{p=1}^{n} \psi_{p,q}(x_p)$ maps the $n$-dimensional input vector to a scalar value, which is then processed by the outer function $\Phi_q$. Crucially, the theorem asserts that the inner functions $\psi_{p,q}$ are continuous and monotonic, and remarkably, they are independent of the target function $f$.2 All information specific to $f$ is encoded in the outer functions $\Phi_q$. Mathematical Verification and Historical Critique: While theoretically profound, the direct application of this theorem to neural networks was stalled for decades by a critical practical limitation. As highlighted by Girosi and Poggio (1989), the inner functions $\psi_{p,q}$ constructed in the original proofs are "pathological"—they are highly non-smooth, often exhibiting fractal characteristics that make them indistinguishable from noise in a practical setting.8 Because these functions are non-differentiable (or have derivatives that are singular almost everywhere), they are fundamentally incompatible with gradient descent-based learning algorithms like backpropagation. Thus, for nearly seventy years, the Kolmogorov-Arnold theorem was regarded as a mathematical curiosity—an existence proof with no constructive utility for machine learning. 2.2 The Modern KAN Architecture (2024-2026) The breakthrough that enabled the KAN architectures of 2025/2026 did not come from solving the fractal nature of the original $\psi$ functions, but rather from relaxing the theorem's strict conditions. The modern KAN specification, introduced by Liu et al. (2024) and expanded upon in 2025, generalizes the theorem to arbitrary network depths and widths, and most importantly, replaces the fixed, fractal inner functions with learnable, smooth splines.1 A KAN layer in this modern paradigm is defined not by a weight matrix $W$, but by a function matrix $\mathbf{\Phi}$. If a layer has $n_{in}$ inputs and $n_{out}$ outputs, the layer is parameterized by a grid of $n_{in} \times n_{out}$ univariate functions: $$\mathbf{\Phi} = \{ \phi_{q,p} \}, \quad p=1\dots n_{in}, \quad q=1\dots n_{out}$$ The pre-activation of the $q$-th neuron in the subsequent layer is the sum of these function outputs: $$x_{q}^{(l+1)} = \sum_{p=1}^{n_{l}} \phi_{q,p}^{(l)} \left( x_{p}^{(l)} \right)$$ This structure fundamentally differs from the MLP. In an MLP, the linear combination happens before the non-linearity ($ \sigma(\sum w x) $). In a KAN, the non-linearity is applied to each input individually *before* the summation ($\sum \phi(x)$). This "pre-summation non-linearity" allows the network to model complex multiplicative interactions (like $x \times y$) through the identity $xy = \frac{1}{4}[(x+y)^2 - (x-y)^2]$, using only sums and univariate squares—a capacity that MLPs struggle to achieve without significant depth.1 2.3 Mathematical Verification of B-Splines and Recursion The choice of basis function for $\phi(x)$ is the critical engineering decision in KANs. To enable local plasticity—the ability to update knowledge in one region of the input space without corrupting knowledge in distant regions—KANs utilize B-splines. A B-spline curve is constructed from a linear combination of B-spline basis functions $N_{i,k}(x)$ of order $k$: $$\phi(x) = \sum_{i} c_i N_{i,k}(x)$$ The basis functions are defined recursively via the Cox-de Boor formula. We explicitly verify the recursive structure here to confirm the local support property claimed in the literature.13 Base Case ($k=0$): The zeroth-order basis function is a step function (indicator function) over the $i$-th knot interval $$. This mathematical fact is the engine of KANs' continual learning capability: updating a coefficient $c_i$ affects the function $\phi(x)$ only within the compact support of $N_{i,k}(x)$. If a new task provides data outside this interval, the coefficient $c_i$ receives a zero gradient and remains unchanged, thereby preserving the "memory" of the previous task.15 Correction on Notation: Snippets 13 and 14 utilize slightly different indexing conventions ($B_{i,n}$ vs $N_{i,k}$). However, the underlying recurrence relation is identical. It is crucial to note that efficient implementations (like EfficientKAN) assume a uniform grid where $t_{i+1} - t_i = h$ (constant), which simplifies the denominator terms to constants (e.g., $k \cdot h$), replacing division operations with simpler multiplications to accelerate GPU throughput.17 3. Computational Implementation: From PyKAN to MatrixKAN The transition from theoretical construct to practical tool involved significant algorithmic optimization. The initial implementation, referred to as PyKAN, prioritized mathematical clarity over computational efficiency, leading to severe bottlenecks that hindered scaling. 3.1 The Memory Bottleneck in PyKAN In the naive PyKAN implementation 18, the evaluation of spline bases was performed by expanding the input tensor. For a batch size $B$, input dimension $N_{in}$, and grid size $G$, PyKAN would expand the input $x$ to a tensor of shape $(B, N_{in}, G)$. Memory Complexity: $O(B \cdot N_{in} \cdot G)$. Issue: For high-dimensional data (e.g., an image with flattened dimension 1024) and fine grids (e.g., $G=100$), this intermediate tensor becomes prohibitively large, exhausting GPU VRAM even for small batches. 3.2 EfficientKAN: The Matrix Reformulation To address this, the community developed EfficientKAN.17 This implementation reformulates the B-spline computation. instead of expanding the input, it exploits the fact that the spline output is a linear combination of basis functions. Algorithmic Verification: Instead of computing the full expansion, EfficientKAN likely calculates the basis activations $N_{i,k}(x)$ and performs the linear combination with coefficients $c_i$ as a matrix multiplication. Optimization: The memory complexity is reduced to $O(B \cdot N_{in} + N_{in} \cdot N_{out} \cdot G)$ because the batch dimension is decoupled from the grid expansion in memory. Result: Snippet 17 notes that this "simplifies the computation to a basic matrix multiplication." This reformulation was essential for enabling KANs to be used in deeper architectures like Vision Transformers. 3.3 MatrixKAN: Parallelizing the Recursion A further refinement, MatrixKAN, optimizes the Cox-de Boor recursion itself.20 Since t
Jinghan Liu, Hui Zhao, Chenyang Lin, Dan Wang · 5 authors
The current security problem of smart contracts is becoming a common concern for researchers and developers. Existing smart contract vulnerability detection methods rely heavily on fixed expert rules, resulting in low detection accuracy. In order to cope with complex and changing smart contract application scenarios, we chose to use graph neural networks to detect vulnerabilities. In this paper, we proposed a vulnerability detection model called ESA based on the enhanced sequential algorithm. During the coding process, the contract function source code is described as a contract graph, which increases the model&rsquo;s global insight into node features during the learning process and reduces the number of noise nodes unrelated to vulnerabilities while retaining sufficient contextual semantic features. Compared to the cutting-edge methods, our model has significantly improved the accuracy of reentrant and timestamp dependency vulnerabilities, with detection accuracies of 89.09% and 88.49%, respectively.
Ahmed Albeltagi, Tiia Tyystälä, Mikko Nelo, Heli Jantunen · 7 authors
ABSTRACT Insulating and conductive self‐healing elastomers represent a high‐potential paradigm shift in the development of soft radio‐frequency (RF) electronics applications, such as coplanar waveguide (CWP) RF transmission lines. In this article, we present a novel stretchable, self‐healing CPW RF transmission line that uses self‐healing materials for both the substrate and the conductor. The used self‐healing liquid metal elastomer composite achieves a conductivity of approximately 2000 S cm −1 at zero strain. S‐parameter measurements of reflection ( S 11 ) and transmission ( S 21 ) were performed for the coplanar waveguide as the electrical length was uniaxially stretched up to 100%. The stretchable and self‐healing CPW RF transmission lines maintain remarkable consistency in transmission response at 1–6 GHz when mechanically stretched at 0%–50% for 1000 stretch‐release cycles. To the best of our knowledge, this is the first proof‐of‐concept demonstration of a fully self‐healing CPW transmission line, paving the way for durable and reconfigurable soft RF devices.
We examine the association between cryptocurrency environmental attention and cryptocurrency bubbles. Our results indicate that environmental attention is positively associated with the probability of a cryptocurrency bubble and ranks as the second most important explanatory factor. The positive association is more pronounced for smaller, less-mature, and proof-of-work (PoW) cryptocurrencies, indicating that cryptocurrency characteristics are important determining factors of bubble formation.
Edmund Kofi Yeboah, Daniel Yaw Addai Duah, Joseph Kobi, Benjamin Yaw Kokroko
Multinational companies have been struggling with unprecedented difficulties in treasury activities in different jurisdictions, such as liquidity management, cross-border payment, and regulatory compliance, and financial transparency. Conventional treasury management systems are usually characterized by fragmentation, manual handling, and the inability to have real time visibility of cash positions and financial flows. The current paper examines how blockchain technology is being employed in the corporate treasury management systems of multi-nationals. We discuss the application of the distributed ledger technology to revolutionize the treasury processes via real-time settlement and automated compliance checks, improved transparency, and minimized organizational expenses through in-depth review of the available literature and industry experiences. The study examines blockchain-based treasury systems technical architecture, implementation issues, regulatory aspects, and multinational strategic advantages. Our suggestion to the blockchain implementation in treasury management is a system covering interoperability needs, integration of smart contracts, security measures, and governance. Based on the findings, the blockchain technology has high potentials of enhancing the efficiency of the treasury and mitigating the counterparty risk, as well as making the cash management in the global operation more effective. Nevertheless, the implementation should be done with specific attention to the maturity of technologies, governmental alignment, organizational preparedness, and collaboration in the ecosystem. The study can be an addition to the literature on the use of blockchain in corporate finance and can offer effective advice to treasury practitioners who might be considering an adoption of distributed ledger technology.
ABSTRACT This study provides a comprehensive systematic review and bibliometric analysis of 125 peer‐reviewed articles on bid‐ask spread estimators published between 1987 and 2025. Using the PRISMA framework, we map the intellectual evolution of the field, identifying a significant shift from foundational parametric models to data‐driven approaches. While early research focused on simple covariance‐based metrics, the field has recently been transformed by significant technical advances. Our network analysis identifies five major thematic clusters ranging from market dynamics and liquidity definitions to microstructure in high‐frequency and volatile environments. We highlight a critical research priority: utilizing high‐frequency data to validate low‐frequency models for reliable application in unobserved contexts, such as emerging markets and decentralized finance (DeFi). The findings underscore the enduring relevance of estimators in construction of long‐span historical series and noise‐adjusted liquidity measures. Future research must bridge existing methodological silos by integrating behavioral finance perspectives and advancing real‐time analytics for fragmented, high volatile global markets.
This study examines how blockchain-based smart contracts can support environmental law enforcement by enhancing transparency, compliance monitoring, and regulatory coordination within legally pluralistic governance systems. despite the rapid expansion of blockchain applications in sustainability governance, existing research has largely examined smart contracts from technical or economic perspectives, with limited attention to their integration within formal environmental legal systems. this study addresses this gap by positioning blockchain-enabled smart contracts as legally embedded compliance-support instruments rather than purely technological solutions. A qualitative comparative case-study approach was employed, combining doctrinal environmental law analysis with examination of blockchain governance frameworks, statutory instruments, judicial rulings, and relevant policy documents. the study contributes novel empirical and conceptual insight by integrating sustainability-index modeling with legal analysis of smart contract–based environmental governance, a dimension insufficiently addressed in prior blockchain scholarship. a combination of stakeholder interviews and quantitative modeling also played key roles in assessing the effectiveness of integrated legal frameworks at reducing conflicts and driving sustainable outcomes. quantitative analysis was conducted using sustainability indices and governance-efficiency metrics derived from blockchain-based assessment models, enabling comparative evaluation of regulatory performance, compliance reliability, and cost-efficiency outcomes across jurisdictions. Findings indicate that regions implementing co-management agreements, along with culturally responsive policies, experienced marked declines in both, legal challenges and environmental harm. the percentage improvements reflect modeled regulatory-performance scenarios derived from comparative sustainability indices rather than experimental intervention outcomes. the sustainability indices were improved by 25-45% with cost-efficiency gains in the range of 18-25%. the findings further demonstrate that smart contracts, when embedded within existing statutory oversight mechanisms, can strengthen environmental enforcement through automated verification, immutable recordkeeping, and standardized sustainability reporting, without displacing judicial authority. stakeholder assessments indicated the highest acceptance levels when blockchain-supported regulatory frameworks aligned automated enforcement mechanisms with existing institutional and community governance structures. references to family and customary legal systems are incorporated only insofar as they affect the institutional implementation of environmental regulation and do not constitute the primary analytical focus of the study. the study emphasizes the necessity of adjusted, integrative legal frameworks that adhere to cultural standards, enhance legal institutions, and include local communities.
Aditya Rathore, Kratika Mishra, Vidhi Chandrayan, Pareek Ch. S.
Blockchain technology has evolved into one of the most influential digital innovations of the 21st century, enabling decentralized, trustless, and tamper‑resistant data management across global networks. Its rapid rise can be attributed to groundbreaking applications across cryptocurrencies, decentralized finance (DeFi), healthcare, supply chain, and identity management systems. Despite this explosive growth, blockchain technology still faces major challenges—most critically, scalability. This extended study explores blockchain’s historical development, factors driving adoption, technical architecture, and the limitations restricting mass deployment. The paper includes an in‑depth analysis of publicly available blockchain datasets that support research in security, analytics, and scalability modeling. Furthermore, the study reviews emerging scalability frameworks such as sharding, off‑chain computation, Layer‑2 rollups, DAG-based systems, and consensus optimization. The goal is to provide a comprehensive foundation for understanding blockchain’s evolution while outlining future paths toward global-scale adoption.
Minghui Zheng, Shicheng Huang, Deju Kong, Xing Fu · 6 authors
Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members' public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size.
Purpose: Automated scripts and workflows have been implemented in clinics to streamline the planning process, improving efficiency and consistency. However, standardized scripts often lack adaptability for patient-specific scenarios, requiring considerable effort to modify for non-standard cases. To address this, we present an interactive large language model (LLM)–driven approach for flexible workflow automation across radiation oncology tasks. This work presents a proof-of-concept agentic LLM integration that enables flexible, natural-language automation across a broad set of radiotherapy (RT) workflow operations. Methods: An LLM-based assistant system was integrated into the MIM software platform. It includes a recursive MIM workflow, an agentic orchestrator, and coordinated agents: an LLM Consultant for selecting relevant functions, a code generator that compiles executable Java extensions, a Quality Checker for independent verification, and a Knowledge Accumulator that captures and stores valuable insights such as coding patterns, errors, and user preferences. The system uses a prompt-based approach with continuous learning from both successful executions and error corrections to enhance accuracy and adaptability. Its generalizability was validated using 57 realistic simple queries, robustness through repeatability and failure-rate testing, and overall performance through four complex examples addressing advanced clinical tasks across various stages of the adaptive RT workflow. Results: The system effectively replicated standard clinical workflows with high adaptability and flexibility. Early queries required extensive function library accumulation, while later ones mainly reused existing functions. Its multi-agent architecture enabled robust error recovery, with automatic correction loops reducing failure rates from 1% to near zero. Average execution time per query was 13–14 s. All complex examples were successfully implemented in MIM, supporting interactive use, dynamic workflow customization, and straightforward execution. Conclusion: By integrating an interactive AI assistant, the novel LLM-powered tool provides crucial workflow flexibility alongside automation—reducing workflow rigidity, enhancing efficiency, and promising a paradigm shift toward dynamic, patient-specific treatment planning and data management.
Open access
Advanced Radiotherapy Techniques
Advances in Oncology and Radiotherapy
Artificial Intelligence in Healthcare and Education
(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support.
Distributed by Grasshopper Film, 12 East 32nd St., 4th Floor, New York, NY 10016Produced by Nicholas Bruckman, Shawn Hazelett, and Rahilla ZafarDirected by Nicholas Bruckman2024, Streaming, 77 mins Minted: The Rise (And Fall?) of the NFT, directed by Nicholas Bruckman, focuses on the explosive growth of the NFT (non-fungible token) digital art market in the late 2010s and early 2020s. Bruckman introduces how NFTs are situated within broader conversations about creativity, ownership, and value in digital environments. Using interviews with artists, collectors, technologists, and cultural critics, the viewer is asked to consider how NFTs are not simply a speculative trend, but rather a disruption, a way to represent artwork as a token for transactions that bypass traditional intermediaries in the art world (like galleries and museums). The strength of the film is Bruckman’s highlighting of the experiences of artists with new opportunities for visibility and economic independence through their NFTs. Notably, the documentary amplifies the perspectives of some female and BIPOC creators, framing NFTs as a space that seems to offer alternatives to the exclusionary and gatekeeping structures of the traditional art market. While the film highlights these voices, it stops short of fully examining whether the NFT ecosystem dismantled or merely reproduced the existing inequities within the art world. Minted is a timely film for conversations about digital literacy, information ethics, and the economics of creative labor. While the documentary does not offer a comprehensive critique of blockchain systems, it is successful as a snapshot of a significant cultural moment and a useful prompt for viewers to question the intersections of art, technology, and society. Awards:SXSW, Audience Award; Next Generation Indie Film Awards, Best Documentary Feature; Cordillera International Film Festival, Grand Jury Award for Best Documentary
Kwestan Ahmed Ismael, Heshu Othman Faqe, Mohammed Hussein Abdalla, Hindreen A. Taher
In this work we use historical market data from Bitget to predict weekly open prices of Ethereum (ETH) for a 96-week period with the Prophet forecast model trained by using Particle Swarm Optimization (PSO) algorithm. Because of this, the research delves into automated hyperparameter tuning for Prophet in order to improve forecast performance on cryptocurrency markets where volatility, structural breaks and irregular trading patterns pose a significant challenge to time series prediction. The PSO algorithm is a good method to explore the high dimensional parameter space in which it can strike between the global analysis and local exploitation for detecting minimal forecast errors. Based on evaluating model performance for which we used accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in training, test holdout & full-fit situations. PSO-optimized Prophet: The results show a great in-sample fitting and fast convergence behaviour, as the best CV RMSE is slightly higher than the lowest one should have obtained if used only 10 iterations. Although forecasts exhibit stability and track long-term trends well, the model does not predict short-term fluctuations in the holdout set with high accuracy (wider forecast uncertainty intervals). Our results shed light on the utility of PSO to improve Prophet-based price prediction in cryptocurrencies, reinforce the relevance of uncertainty quantification in asset markets and inform risk-aware decisions of financial agents dealing with unstable assets.
Shaoyu Li, Hexuan Yu, Md Mohaimin Al Barat, Yang Xiao · 6 authors
With the rise of decentralized finance, fiat-to-cryptocurrency exchange platforms have become popular entry points into the cryptocurrency ecosystem. However, these platforms frequently fail to ensure adequate privacy protection, as evidenced by real-world breaches that exposed personally identifiable information (PII) and crypto addresses. Such leaks enable adversaries to link real-world identities to cryptocurrency transactions, undermining the presumed anonymity of cryptocurrency use. We propose FC-GUARD, a privacy-preserving exchange system designed to preserve user anonymity without compromising regulatory compliance in the exchange of fiat currency for cryptocurrencies. Leveraging verifiable credentials and zero-knowledge proof techniques, FC-GUARD enables fiat-to-cryptocurrency exchanges without revealing users' PII or fiat account details. This breaks the linkage between users' real-world identities and their cryptocurrency addresses, thereby upholding anonymity, a fundamental expectation in the cryptocurrency ecosystem. In addition, FC-GUARD complies with key regulations over cryptocurrency usage, such as know-your-customer requirements and auditability for tax reporting obligations by integrating a lawful de-anonymization mechanism that allows the auditing authority to identify misbehaving users. This ensures regulatory compliance while defaulting to privacy protection. We implement our system on both desktop and mobile platforms, and our evaluation shows its feasibility for practical deployment.
The integration of IoT technology in smart grids has revolutionized the energy sector by enabling decentralized energy production, real-time monitoring, and peer-to-peer energy trading. However, these advancements introduce challenges such as ensuring security, scalability, and data privacy, which are critical for the reliable operation of IoT-enabled smart grids. Blockchain technology has emerged as a promising solution to address these challenges by providing decentralized, secure, and transparent frameworks for managing energy transactions. This study aims to explore the application of blockchain in enhancing the security and scalability of IoT-enabled smart grids while addressing challenges related to resource limitations and privacy concerns. Simulation and experimental analyses were employed to evaluate blockchain performance in a decentralized energy network. The study focused on key metrics: latency, transaction throughput, energy consumption, and data integrity. The study shows Proof of Authority (PoA) excels in IoT smart grids with < 200 ms latency, 190 Tx/s throughput, and 0.5–0.9 J/Tx energy use—outperforming PoW (450-780 ms, 5.2–10.3 J/Tx). While Proof of Stake (PoS) offers competitive 0.3–0.7 J/Tx efficiency and higher 210 Tx/s scalability, its latency (150–300 ms) remains slightly higher than PoA. These results position PoA as ideal for resource-constrained IoT nodes, while PoS better suits more extensive networks needing higher throughput. The findings highlight how consensus mechanisms can be tailored to different smart grid requirements, with PoA providing the best balance for most decentralized energy applications. Additionally, blockchain's immutable ledger ensured zero unauthorized data modifications, enhancing data security and transparency. The practical implementation of these results highlights blockchain's potential to transform IoT-enabled smart grids. By reducing security vulnerabilities and operational inefficiencies, blockchain enables secure and efficient peer-to-peer energy trading and enhances the resilience of decentralized energy systems. Future work should optimize scalability beyond 500 nodes and integrate advanced privacy-preserving mechanisms to ensure the widespread adoption of blockchain in innovative grid applications.