This deliverable examines political economy barriers to climate policy through sectoral entry points that can make transitions more just, feasible, and developmentcompatible. It focuses on three areas where governance and politics strongly shape outcomes: coal transition strategies, carbon pricing, and international finance. Across these domains, the report draws on seven peer-reviewed studies and working papers to highlight five strategic entry points: i) decentralized just transition planning, ii) clean industrial development, iii) revenue recycling with social protection, iv) strategic framing and coalition building for carbon taxes, and v) equity-focused international finance reforms. Coal transitions are shown to depend on domestic contexts. Comparative analysis of 12 coal-relevant countries reveals six distinct clusters of political economy dynamics, ranging from civil society-driven transition in South Africa to contested transition pathways in India and Indonesia. Case studies stress the need for regionally tailored approaches. Carbon pricing is politically viable when embedded in broader fiscal or political agendas. Evidence from 46 global policy attempts underlines the role of coalitions, leadership, and framing co-benefits. Microsimulations for 16 Latin American and Caribbean countries show regressive impacts, with many highly affected households lacking social protection. The international finance analysis assesses the G7 pledge and Clean Energy Transition Partnership, tracking shifts in public finance for energy across income groups. While fossil fuel support has declined, clean energy funding has not risen proportionally, remains loan-heavy, and is concentrated in wealthier nations. Low-income countries receive negligible concessional flows, while G7 members continue expanding domestic fossil infrastructure. The study recommends embedding distributive justice into finance governance, scaling grant-based clean energy support for the Global South and aligning domestic actions with international commitments.
Rakan B Aldosari, Farah Kalmey, Abdullah T Alanazi, Ashraf A’aqoulah
Background: Blockchain is a cutting-edge innovation being applied to offer innovative solutions in various fields, including healthcare. The healthcare industry uses blockchain networks to store and distribute patient data across hospitals, physicians, diagnostic labs, and pharmaceutical firms. Blockchain applications are essential in the healthcare industry for identifying crucial fallacies that might be fatal. The effectiveness, security, and transparency in exchanging medical data may thus be improved in the healthcare industry. This technology may also aid medical institutions in procuring information and analysing patient data. Aim: To examine the published papers that discussed the ability of utilization and probable challenges of blockchain technology in KSA's healthcare supply chain management. Methods: Until February 10, 2023, the prime databases: Pub Med, Science Direct, Scopus, Google Scholar, Web of science, Embase and The Cochrane Library were searched. Published studies (except case reports), commentaries, editorials, reviews, and meta-analysis reporting on the use of blockchain technology in healthcare supply chain management were included and a Preferred Reporting Items of systematic Reviews and Meta-analyses (PRISMA) flow diagram was used to present the process. To assess risk of bias and the quality of the included studies, the Joanna Briggs Institute's (JBI) critical evaluation tools were implemented. Results: A total of 22 studies were included and most of them used blockchain in technology for ensuring transparency, security, and storage of electronic health or medical records. Patients benefited from seamless electronic health records provided by a multi-level blockchain eHealth system. It was observed that blockchain technology effectively addresses clinical trial misconduct, and its potential in this area is to boost data productivity for the healthcare sector. The distinctive data storage pattern of blockchain offers a high-security standard that potentially reduces concerns about data tampering. It provides flexibility, accountability, connection, and data access authentication. Blockchain helps the healthcare sector avoid certain risks and offers decentralized data protection. In our study, we observed that the most preferred network for integrating the healthcare authority, manufacturer, wholesaler, retailer, and service was Ethereum (ETH). Conclusion: Healthcare policymakers should implement blockchain in Healthcare Supply Chain Management. Moreover, they need to be aware that the primary issues with blockchain technology in the healthcare industry are the lack of practical applications, the high rate of failed initiatives, and the requirement for collaboration between diverse stakeholders. However, there is a lack of studies on how to evaluate the acceptability and assist healthcare organisations in using blockchain.
Zero-knowledge Proof (ZKP), is an effective cryptographic primitive that allows one party to verify the correctness of a given statement without disclosing any additional information. It plays a central role in applications such as blockchain transactions and cryptocurrencies. However, implementations of ZKP suffer from the most time-consuming task called Multi-Scalar Multiplication (MSM). Existing works and evaluation criteria primarily emphasize speed enhancement, but overlook optimizations of area overhead. In this paper, a FPGA-based accelerator FusionMSM is designed to reduce the overall latency but also improve area overhead. We attribute the bottleneck of MSM to a three-layer pyramid, including the finite field arithmetic, point operations on elliptic curves and scheduling. For modular arithmetic, we propose an efficient and non-Montgomery modular multiplier by utilizing hybrid multiplication strategy and optimizing multi-bit LUT-based modular reduction. It obtains 1.11 x less area cost and 2.00 x speed-up versus the modular multipliers used in ZKP acceleration works. For point operations, we design a unified and fully pipelined point addition unit, which can run at 500 MHz, the highest frequency in the reported works. On top of that, we present a greedy mechanism to resolve potential collisions, which can reduce the idle cycles of the point addition unit and improve its utilization. As far as we know, FusionMSM achieves the best performance compared to other FPGA-based and ASIC-based works for the input sizes from 218 to 226. For the degree of 220, FusionMSM only needs 12.4% of time in Hardcaml, 24.54% of time in PipeMSM on FPGA, and 36.41% of time in ASIC-based work PipeZK. It also utilizes less resources, resulting in a 90.93% reduction in URAMs, 35.24% reduction in FFs and 47.59% reduction in CARRY8s. Compared to GPU-based implementations, FusionMSM delivers comparable performance but with a lower power of 24.5 W.
LOULID Adil -, GADMI Mariam -, LOTFI Siham, BEN DARKAWI Zakaria -
This paper explores the evolving landscape of SME financing in a context marked by the progressive tightening of traditional bank credit and the emergence of innovative funding alternatives. Small and medium-sized enterprises (SMEs), widely recognized as key drivers of innovation and employment, face increasing difficulties in accessing conventional financial resources due to heightened risk aversion among banks, stricter regulatory requirements, and macroeconomic instability. In response, SMEs are progressively turning to alternative financing solutions, such as crowdfunding, venture capital, peer-to-peer lending, and blockchain-based mechanisms, including smart contracts. The study highlights the dual dynamics shaping the current financing environment: while traditional sources like bank credit and government grants remain essential, they are no longer sufficient on their own. New technologies and decentralized platforms are redefining the financial ecosystem, offering greater flexibility, transparency, and inclusion. However, these alternatives also come with challenges, such as regulatory uncertainty, market saturation, and the need for strategic adaptation. Through a comparative and analytical approach, the paper underscores the importance of fostering a diversified, resilient, and innovation-oriented financial framework. It calls for coordinated efforts between public policy, financial institutions, and technological actors to support the sustainable development and competitiveness of SMEs in an increasingly complex economic environment.
L. Domven, Aliyu Danladi Hina, A. M. Kwami, C. M. Miri · 5 authors
This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elections via mobile devices, the system authenticates voters using registered numbers and records ballots as encrypted points on an elliptic curve. Encrypted votes are published on a public bulletin board alongside zero-knowledge proofs to ensure their validity. To safeguard decryption, Shamir’s secret sharing distributes keys among trusted authorities, enabling collective tallying without exposing individual votes. The system incorporates ECC-based secret sharing, homomorphic encryption, and zero-knowledge proofs, leveraging the hardness of the elliptic curve discrete logarithm problem (ECDLP) for robust security. Both experimental and theoretical evaluations demonstrate that ECC significantly improves computational efficiency and scalability, making the system well-suited for resource-constrained environments. Overall, the integration of ECC and SMPC offers a practical, efficient, and secure framework for mobile elections, effectively balancing privacy, security, and performance.
With the emergence of blockchain technology, many firms have approached cryptocurrency by allowing their customers to use it as a form of payment. However, little research has examined firms’ acceptance of cryptocurrency, the unique strategies that have been undertaken, or the impact on firm-level outcomes. Drawing on signaling theory, the author applies the event study methodology to learn how firm value (i.e., stock price) is impacted by firms’ announcement of cryptocurrency acceptance. The author finds that, on average, firms have lost 2.73% in firm value as a result of announcing cryptocurrency acceptance. However, a moderation analysis reveals that firms that have chosen to accept Bitcoin (as opposed to focusing exclusively on alternative cryptocurrencies) and firms that have approached cryptocurrency acceptance in more recent years experience financial gains. The geographical location (i.e., domestic or international) of cryptocurrency acceptance is not found to have a moderating impact.
The full-scale war in Ukraine has exposed critical vulnerabilities in centralized energy grids, driving the urgent need for decentralized renewable energy solutions. This study investigates the economic efficiency of state financial and investment support for the advancement of distributed green energy systems in Ukraine, particularly through concessional financing initiatives such as the "5-7-9" program. The decision-making analysis focuses on small and medium-sized enterprises investing in 10-, 20-, and 30-kW hybrid wind-solar photovoltaic systems accompanied by storage facilities. Financial viability was assessed using key indicators, including Levelized Cost of Energy, Net Present Value, Internal Rate of Return, Profitability Index, and Discounted Payback Period. Results indicate that with preferential financing, the considered projects achieved strong economic performance, while traditional commercial loans offered by commercial banks rendered small-scale decentralized renewable energy solutions financially unfeasible. Based on this, it has been demonstrated that strategic public-private collaboration and effective financial policy frameworks are critical for scaling renewable energy adoption and accelerating Ukraine’s green and digital transition. The article presents developed strategies and a roadmap for integrating decentralized power systems into Ukraine’s digital economy, which, during and after the war, will help strengthen energy resilience, reduce operational risks, and foster the country’s sustainable growth. However, limitations include assumptions of stable macroeconomic conditions and a focus solely on internal energy consumption. Future research should investigate tailored financial mechanisms for different business types and explore the broader socio-economic impacts of investments in decentralized green power systems, as well as the sensitivity of projects’ economic indicators for optimal decision-making.
Omar Jarkas, Ryan K. L. Ko, Naipeng Dong, Redowan Mahmud
Integrity verification and attestation are critical in containerized environments, where traditional Linux Integrity Measurement Architecture (IMA) falls short due to its lack of container-specific contextualization. These gaps undermine container autonomy, escalate privacy risks, and impede granular integrity checks. Addressing these challenges, this paper introduces the Virtual IMA (VIMA), a novel framework that refines Linux IMA’s principles to support containerized settings. Using nested Merkle trees, VIMA’s Two-Tree Architecture (2TA) enables detailed integrity assessments across system-wide monolithic trees and individual container trees. Integrating Merkle and zero-knowledge (ZK) proofs establishes VIMA as a secure, privacy-preserving verification and attestation solution. Our comparative analysis and initial prototype testing reveal that VIMA significantly improves upon traditional IMA with minimal performance overhead, offering substantial scope for optimization.
Hao Cheng, Georgios Fotiadis, Johann Großschädl, Daniel Page
Non-degenerate bilinear maps on elliptic curves, commonly referred to as pairings, have many applications including short signature schemes, zero-knowledge proofs and remote attestation protocols. Computing a state-of-the-art pairing at the 128-bit security level, such as the optimal ate pairing over the curve BLS12-381, is very costly due to the high complexity of some of its sub-operations: most notable are the Miller loop and final exponentiation. In the past ten years, a few optimized pairing implementations have been introduced in the literature, but none of those took advantage of the vector (SIMD) extensions of state-of-the-art Intel and AMD CPUs, especially AVX-512; this is surprising, because doing so offers the potential to reach significant speed-ups. Consequently, the questions of 1) how computation of the optimal ate pairing can be effectively vectorized, and 2) what execution time such a vectorized implementation can achieve are still open. This paper addresses said questions by introducing a carefully-optimized AVX-512 implementation of the optimal ate pairing on BLS12-381. A central feature of the implementation is the use of 8-way Integer Fused Multiply-Add (IFMA) instructions, which are capable to execute eight 52 x 52-bit multiplications in a SIMD-parallel fashion. We introduce new vectorization strategies and describe optimizations of existing ones to speed up arithmetic operations in the extension fields Fp4 , Fp6 , and Fp12 as well as certain higher-level functions. Furthermore, we discuss some parallelization bottlenecks and how they impact execution time. We benchmarked our pairing software, which we call avxbls, on an Intel Core i3-1005G1 (“Ice Lake”) CPU and found that it needs 1, 265, 314 clock cycles (resp. 1, 195, 236 clock cycles) for the full pairing, with the Granger-Scott cyclotomic squaring (resp. compressed cyclotomic squaring) being used in the final exponentiation. For comparison, the non-vectorized (i.e., scalar) x64 assembly implementation from the widely-used blst library has an execution time of 2, 351, 615 cycles, which is 1.86 times (resp. 1.97 times) slower. avxbls also outperforms Longa’s implementation (CHES 2023) by almost the same factor. The practical importance of these results is amplified by Intel’s recent announcement to support AVX10, which includes IFMA instructions, in all future CPUs.
Thibauld Feneuil, Matthieu Rivain, Auguste Warmé-Janville
Side-channel attacks pose significant threats to cryptographic implementations, which require the inclusion of countermeasures to mitigate these attacks. In this work, we study the masking of state-of-the-art post-quantum signatures based on the MPC-in-the-head paradigm. More precisely, we focus on the recent threshold-computation-in-the-head (TCitH) framework that applies to some NIST candidates of the post-quantum standardization process. We first provide an analysis of side-channel attack paths in the signature algorithms based on the TCitH framework. We then explain how to apply standard masking to achieve a d-probing secure implementation of such schemes, with performance scaling in O(d2), for d the masking order.Our main contribution is to introduce different ways to tweak those signature schemes towards their masking friendliness. While the TCitH framework comes in two variants, the GGM variant and the Merkle tree variant, we introduce a specific tweak for each of these variants. These tweaks allow us to achieve complexities of O(d) and O(d log d) at the cost of non-constant signature size, caused by the inclusion of additional seeds in the signature. We also propose a third tweak that takes advantage of the threshold secret sharing used in TCitH. With the right choice of parameters, we show how, by design, some parts of the TCitH algorithms satisfy probing security without additional countermeasures. While this approach can substantially reduce the cost of masking in some part of the signature algorithm, it degrades the soundness of the core zero-knowledge proof, hence slightly increasing the size of the signature.We analyze the complexity of the masked implementations of our tweaked TCitH signatures and provide benchmarks on a RISC-V platform with built-in hash accelerator. We use a modular benchmarking approach, allowing to estimate the performance of diverse signature instances with different tweaks and parameters. Our results illustrate how the different variants scale for an increasing masking order. For instance, for a masking order d = 3, we obtain signatures of around 14 kB that run in 0.67 second on a the target RISC-V CPU with a 250MHz frequency. This is to be compared with the 4.7 seconds required by the original signature scheme masked at the same order on the same platform. For a masking order d = 7, we obtain a signature of 17.5 kB running in 1.75 second, to be compared with 16 seconds for the stardard masked signature.Finally, we discuss the extension of our techniques to signature schemes based on the VOLE-in-the-Head framework, which shares similarities with the GGM variant of TCitH. One key takeaway of our work is that the Merkle tree variant of TCitH is inherently more amenable to efficient masking than frameworks based on GGM trees, such as TCitH-GGM or VOLE-in-the-Head.
Florian Hirner, Florian Krieger, Constantin Piber, Sujoy Sinha Roy
Zero-knowledge proofs (ZKPs) are cryptographic protocols that enable one party to prove the validity of a statement without revealing any information beyond its truth. Central building blocks in many ZKPs are polynomial commitment schemes (PCS) where constructions with linear-time provers are especially attractive. Two such examples are Brakedown and its extension Orion, which enable linear-time and quantum-resistant proving by leveraging linear-time encodable Spielman codes. However, these PCS operate over large datasets, creating significant computational bottlenecks. For example, committing to and proving a degree 228 polynomial requires around 1.1 GB of data while taking 463 seconds on a high-end server CPU.This work addresses the performance bottleneck in Orion-like PCS by optimizing their most critical operations: Spielman encoding and Merkle commitments. These operations involve Gigabytes of data and suffer from random off-chip memory access patterns that drastically reduce off-chip bandwidth. We resolve this issue and introduce inverted expander graphs to eliminate random writes and reduce off-chip memory accesses by over 50%. Additionally, we propose an on-the-fly graph sampling method that avoids streaming large auxiliary data by generating expander graphs dynamically on-chip. We also provide a formal security proof for our proposed graph transformation. Beyond encoding, we accelerate Merkle Tree construction over large data sets through a scalable multi-pass SHA3 pipeline. Finally, we reutilize existing hardware components used in commitment to accelerate the so-called proximity and consistency checks during proof generation.Building upon these concepts, we present the first hardware architecture for PCS – with linear prover time – on an Xilinx Alveo U280 FPGA. In addition, we discuss the practical challenges of manually partitioning, placing, and routing our large-scale architecture to efficiently map it to the multi-SLR and HBM-equipped FPGA. The final implementation achieves a speedup of two orders of magnitude for full proof generation, covering commitment and proving steps. When combined with Virgo as an outer CP-SNARK protocol, our accelerator reduces end-to-end latency by up to 3.85x – close to the theoretical maximum of 3.9x.
We investigate the contagion effects of rapid memecoin growth, a phenomenon often characterised by irrational exuberance and illicit behaviour. Using an EGARCH methodology, the results indicate that while memecoin growth generates revenue for host platforms like Ethereum and Solana, it broadly increases market-wide risk and is detrimental to established cryptocurrencies, such as Bitcoin. Furthermore, we find that PolitiFi memecoins are uniquely susceptible, characterised by positive responses to broad memecoin growth, exhibiting statistical properties deeming them attractive due to the cloaking provided by wider memecoin market growth, without evidence for tangible purposes. • We investigate memecoin contagion effects on the cryptocurrency market using EGARCH and on-chain data. • Memecoin growth adds systemic risk towards major cryptocurrencies such as Bitcoin. • We find strong evidence of sentiment contagion from launchpads to the entire memecoin sub-class. • PolitiFi memecoins are highly susceptible to contagion, suggesting use for opaque financing. • We demonstrate that the memecoin sector is a source of systemic risk from irrationality and illicit activity.
European cybersecurity is rapidly evolving to address complex and emerging threats fueled by advancements in technology. AI-powered threat analysis has become a cornerstone, enabling faster detection of anomalies, predictive threat modeling, and real-time incident response. As Europe enters the quantum age, cybersecurity strategies are increasingly focused on quantum-resistant encryption to protect critical infrastructure and sensitive data from future quantum attacks. Simultaneously, the rise of blockchain technologies and cryptocurrencies introduces new vulnerabilities, such as smart contract exploits and decentralized finance (DeFi) fraud, requiring targeted regulatory oversight. In response, the EU is strengthening its regulatory frameworks, such as the NIS2 Directive and the Digital Operational Resilience Act (DORA), to ensure a harmonized, proactive approach to cybersecurity governance, resilience, and accountability across sectors. This multifaceted strategy reflects Europe’s commitment to safeguarding digital sovereignty and fostering trust in its digital ecosystem. The study deals with the transformation of the European cyber security ecosystem within the framework of artificial intelligence (AI) supported threat analysis. The paper discusses the security risks that arise in the quantum and post-quantum era, the possibility of blockchain/crypto systems being broken by quantum computers, the limitations of the existing data set, and the need for human-like thinking skills. In addition, the European Union's (EU) cybersecurity policies, data privacy principles, ethical standards, transparency, accountability, and human-centered AI design approaches are examined within the scope of the EU's global norm-setting role. This article also aims to shed light on the strategic steps that will shape the future of AI-powered cyber defense. Study shows that Europe should develop artificial intelligence (AI)-powered cybersecurity solutions in its preparations for the post-quantum era, it also should invest in AI models that transcend current data set limits and have humanoid thinking capacities.
As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes-including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers-based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5-24.3%, achieves a task success rate of 84.2-88.5%, improves average resource utilization to 88.9-92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs.
The power sector is responsible for 32 percent of global greenhouse gas emissions. Data centers and cryptocurrencies use significant amounts of electricity and contribute to these emissions. Demand-side flexibility of data centers is one possible approach for reducing greenhouse gas emissions from these industries. To explore this, we use novel data collected from the Bitcoin mining industry to investigate the impact of load flexibility on power system decarbonization. Employing engineered metrics to explore curtailment dynamics and emissions alignment, we provide the first empirical analysis of cryptocurrency data centers' capability for reducing greenhouse gas emissions in response to real-time grid signals. Our results highlight the importance of strategically aligning operational behaviors with emissions signals to maximize avoided emissions. These findings offer insights for policymakers and industry stakeholders to enhance load flexibility and meet climate goals in these otherwise energy intensive data centers.
The construction industry faces significant challenges regarding material waste and sustainable practices, necessitating innovative solutions that integrate automation, traceability, and decentralised decision-making to enable efficient material reuse. This paper presents a blockchain-enabled digital marketplace for sustainable construction material reuse, ensuring transparency and traceability using InterPlanetary File System (IPFS). The proposed framework enhances trust and accountability in material exchange, addressing key challenges in industrial automation and circular supply chains. A framework has been developed to demonstrate the operational processes of the marketplace, illustrating its practical application and effectiveness. Our contributions show how the marketplace can facilitate the efficient and trustworthy exchange of reusable materials, representing a substantial step towards more sustainable construction practices.
Philippe Bergault, Sébastien Bieber, Olivier Guéant, Wenkai Zhang
In traditional financial markets, yield curves are widely available for countries (and, by extension, currencies), financial institutions, and large corporates. These curves are used to calibrate stochastic interest rate models, discount future cash flows, and price financial products. Yield curves, however, can be readily computed only because of the current size and structure of bond markets. In cryptocurrency markets, where fixed-rate lending and bonds are almost nonexistent as of early 2025, the yield curve associated with each currency must be estimated by other means. In this paper, we show how mathematical tools can be used to construct yield curves for cryptocurrencies by leveraging data from the highly developed markets for cryptocurrency derivatives.
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method outperforms the best baseline by over 10% in F1-score on two of the datasets.
Yifan Jia, Ye Tian, Liguo Zhang, Yanbin Wang · 6 authors
Ethereum's rapid ecosystem expansion and transaction anonymity have triggered a surge in malicious activity. Detection mechanisms currently bifurcate into three technical strands: expert-defined features, graph embeddings, and sequential transaction patterns, collectively spanning the complete feature sets of Ethereum's native data layer. Yet the absence of cross-paradigm integration mechanisms forces practitioners to choose between sacrificing sequential context awareness, structured fund-flow patterns, or human-curated feature insights in their solutions. To bridge this gap, we propose KGBERT4Eth, a feature-complete pre-training encoder that synergistically combines two key components: (1) a Transaction Semantic Extractor, where we train an enhanced Transaction Language Model (TLM) to learn contextual semantic representations from conceptualized transaction records, and (2) a Transaction Knowledge Graph (TKG) that incorporates expert-curated domain knowledge into graph node embeddings to capture fund flow patterns and human-curated feature insights. We jointly optimize pre-training objectives for both components to fuse these complementary features, generating feature-complete embeddings. To emphasize rare anomalous transactions, we design a biased masking prediction task for TLM to focus on statistical outliers, while the Transaction TKG employs link prediction to learn latent transaction relationships and aggregate knowledge. Furthermore, we propose a mask-invariant attention coordination module to ensure stable dynamic information exchange between TLM and TKG during pre-training. KGBERT4Eth significantly outperforms state-of-the-art baselines in both phishing account detection and de-anonymization tasks, achieving absolute F1-score improvements of 8-16% on three phishing detection benchmarks and 6-26% on four de-anonymization datasets.
Purpose The paper aims to identify suitable conditional variance models for the estimation and forecasting of cryptocurrency returns volatility. Design/methodology/approach The methodology comprises the use of GARCH-family models estimated by maximum likelihood considering different scedastic functions, number of parameters and error distributions. A cross-validation approach is conducted under different market dynamics to provide robust results. Findings Results indicated that the best GARCH methods for digital coins volatility modeling and forecasting are those associated with a small number of parameters, allowing for asymmetric volatility behavior and considering normal/student distributions. Research limitations/implications The findings indicated that volatility behaves differently for each evaluated cryptocurrency, and the selection of the best scedastic function depends on the corresponding digital coin more than the period under evaluation. Practical implications Investors should prefer parsimonious GARCH structures when modeling and forecasting cryptocurrency volatility, and must consider the current state of the market as the methods lose accuracy in high-volatile periods. Social implications The work provides a better understanding of the volatility dynamics of cryptocurrencies, providing evidence of more accurate tools for risk management in this volatile market. Further, better-informed investors on the risks associated with this market are less susceptible to high price variations. Originality/value The research presents an extensive experimental study to identify the optimal GARCH structure for modeling and forecasting return volatility in digital currencies, considering various market conditions and digital coins, which yields more robust results.
This article presents a literature review of various solutions and analyses concerning the use of blockchains and/or smart contracts to manage aspects of intellectual property assets. These include proper registration to establish prior art, ownership traceability, copy control, payment automation, contract execution, and related functions. The analyses focus on the application of these technologies to copyright, industrial property, sui generis protection, and technology transfer agreements. The methodology comprised a keyword search in scientific databases, followed by a qualitative content analysis to extract the most relevant points from each document. Overall, the findings indicate that most proposed applications address copyright-related issues, followed by patent-related uses. In the majority of proposed solutions, blockchain registration is restricted to information about the asset, without necessarily storing the asset itself on the blockchain.
This article presents a bibliographic review about different solutions and analyses due to the use of blockchains and/or smart contracts to the management of some aspects regarding intellectual property assets, such as the proper register to proof of existence, tracking of ownership, copy control, payment automatization, enforcement of contracts etc. The analyses have been made considering these technologies when applied to copyright, industrial property, sui generis protection and technology transfer contracts. The methodology consists of the search for keywords on scientific bases, with further qualitative analysis about the content for extracting the most important points on each document. In general, one can observe that most of the proposed applications refers to the aspects regarding copyright, followed by applications for patents, whereas in most solutions, the registration on blockchains is limited on information about the asset, without necessarily including it on the blockchain.
Whether fiscal transfers can simultaneously achieve the dual goals of equity and growth has been a key topic of public finance research. This paper examines China's fiscal decentralization system and its intergovernmental transfer practices, proposing two conditions under which equity-oriented transfer systems may promote economic growth: The effectively motivate local officials' enthusiasm for economic development and the receiving regions' high marginal capital returns. We employ unique fiscal data from China's county-level economies for the period 2016–2021 to conduct regression analyses. The results show that provinces with more equitable distribution of transfer payments exhibit better economic growth at the county level. However, at the provincial level, there is a non-significant but noteworthy economic loss. This is attributed to the reverse incentives created by the equalization of fiscal transfers, which encourage growth in smaller counties but hinder growth in larger ones. The main mechanisms driving these reverse incentives include insufficient growth potential, distorted fiscal spending preferences, and an over-reliance on transfer payments. Our study demonstrates that, even within China's unique fiscal system and local development incentives, the allocation of fiscal transfer funds still faces a trade-off between equity and growth. This deepens our understanding of the effectiveness of fiscal transfer systems and the logic of local fiscal operations under a multi-level fiscal governance framework.
Given Vietnam's current anticorruption campaign and its distinctive context of decentralized governance and public sector dominance, this paper investigates how anticorruption efforts affect corporate investment behaviour during 2006 and 2019. Using a novel text-based measure of anticorruption and comprehensive firm-level datasets, we uncover a consistent pattern that firms tend to delay investments in response to heightened uncertainty triggered by anticorruption activities. This strategic hesitation reflects a rational response to avoid potential regulatory and political uncertainty, and holds across a wide range of robustness checks, including alternative model specifications, variable definitions, and advanced estimation techniques such as system GMM and entropy balancing. Our findings also reveal that anticorruption campaigns significantly reduce informal business costs—particularly bribery, thus highlighting institutional improvements and a more transparent business environment. Notably, while public sector investment efficiency improves under the campaign, private firms show no significant efficiency gains, underscoring the asymmetry in how reforms affect different ownership structures. By bridging institutional reform with corporate finance, the study offers new insights into the channels through which anticorruption influences firm decision-making, governance, and political strategy. This research fills a critical gap in the literature, demonstrating that anticorruption is not merely a legal or ethical issue, but a transformative force in corporate investment dynamics.