Designing secure electronic voting systems that truly protect voter privacy, ensure vote accuracy, and allow independent verification continues to pose serious difficulties. Many current cryptographic approaches require excessive computational resources and use encryption keys that are too large for practical implementation. This paper proposes modifications to the Chaum, Pedersen and Cramer, Franklin, Schoenmakers, and Yung voting protocols by integrating elliptic curve cryptography (ECC), which offers stronger security per bit and more compact key representations. The use of ECC allows for reduced parameter sizes while maintaining resistance against known attacks, including those targeting the discrete logarithm problem. We present detailed adaptations of these protocols on elliptic curves and demonstrate how they preserve core security properties such as vote secrecy, universal verifiability, and resistance to double voting under a more efficient cryptographic framework. Our findings contribute to the development of scalable, high-assurance e-voting mechanisms suitable for modern digital infrastructures. The presented modifications significantly enhance the scalability and efficiency of e-voting systems without compromising cryptographic strength.
Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin.
Decentralized technologies such as blockchain and federated learning have emerged as promising solutions to improve privacy, transparency, and security in distributed environments. This paper aims to provide updated research directions concerning the unresolved issues of linkability and traceability in decentralized technology transactions. A systematic review was conducted using Scopus and Web of Science databases, covering studies published between 2017 and 2023. A total of 313 papers were initially identified, screened, and filtered based on inclusion and exclusion criteria, resulting in 29 relevant studies. The analysis indicates that most prior works focused on privacy preservation and incentive mechanisms but neglected linkability and traceability concerns. Several approaches, including ring signatures, CryptoNote protocols, and smart contract-based incentives, were identified as potential solutions. While blockchain–federated learning integration enhances privacy, unresolved traceability and linkability issues still pose significant risks in sensitive domains such as healthcare and finance. Future work should prioritize addressing these issues to ensure secure, anonymous, and scalable decentralized transactions.
This paper presents a novel multi-layered hybrid security approach aimed at enhancing lightweight encryption for IoT-Cloud systems. The primary goal is to overcome limitations inherent in conventional solutions such as TPA, Blockchain, ECDSA and ZSS which often fall short in terms of data protection, computational efficiency and scalability. Our proposed method strategically refines and integrates these technologies to address their shortcomings while maximizing their individual strengths. By doing so we create a more reliable and high-performance framework for secure data exchange across heterogeneous environments. The model leverages the combined potential of emerging technologies, particularly Blockchain, IoT and Cloud computing which when effectively coordinated offer significant advancements in security architecture. The proposed framework consists of three core layers: (1) the H.E.EZ Layer which integrates improved versions of Hyperledger Fabric, Enc-Block and a hybrid ECDSA-ZSS scheme to improve encryption speed, scalability and reduce computational cost; (2) the Credential Management Layer independently verifying data integrity and authenticity; and (3) the Time and Auditing Layer designed to reduce traffic overhead and optimize performance across dynamic workloads. Evaluation results highlight that the proposed solution not only strengthens security but also significantly improves execution time, communication efficiency and system responsiveness, offering a robust path forward for next-generation IoT-Cloud infrastructures.
Internet of Things (IoT) devices constantly generate heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated learning and lifelong learning. However, the extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks. This problem is exacerbated by the single point of failure. Furthermore, the single point of trust created by the central server hinders reliable auditing for long-term threats. Blockchain technology provides a tamper-proof foundation for trustworthy FLL. Nevertheless, directly applying blockchain to FLL significantly increases computational and retrieval costs with the expansion of the knowledge base, slowing down the training on resource-constrained IoT devices. To address these challenges, we propose LiFeChain, a lightweight blockchain for secure and efficient federated lifelong learning with minimal on-chain disclosure and bidirectional verification. LiFeChain is the first blockchain tailored for FLL. It incorporates two complementary mechanisms: the Proof-of-Model-Correlation (PoMC) consensus on the server, which couples learning and unlearning mechanisms to mitigate negative transfer; and Segmented Zero-knowledge Arbitration (Seg-ZA) at the client, which detects and arbitrates abnormal committee behavior without compromising privacy. LiFeChain is a plug-and-play component that can be seamlessly integrated into existing FLL algorithms for IoT applications. To demonstrate its practicality and performance, we implement LiFeChain in representative FLL algorithms with Hyperledger Fabric under 6 attacks. Theoretical analysis and extensive evaluations demonstrate that LiFeChain effectively mitigates long-term attacks, and significantly reduces latency and storage overhead compared to state-of-the-art blockchain solutions.
This paper presents a machine learning framework for the early detection of rug pull scams on decentralized exchanges (DEXs) within The Open Network (TON) blockchain. TON's unique architecture, characterized by asynchronous execution and a massive web2 user base from Telegram, presents a novel and critical environment for fraud analysis. We conduct a comprehensive study on the two largest TON DEXs, Ston.Fi and DeDust, fusing data from both platforms to train our models. A key contribution is the implementation and comparative analysis of two distinct rug pull definitions--TVL-based (a catastrophic liquidity withdrawal) and idle-based (a sudden cessation of all trading activity)--within a single, unified study. We demonstrate that Gradient Boosting models can effectively identify rug pulls within the first five minutes of trading, with the TVL-based method achieving superior AUC (up to 0.891) while the idle-based method excels at recall. Our analysis reveals that while feature sets are consistent across exchanges, their underlying distributions differ significantly, challenging straightforward data fusion and highlighting the need for robust, platform-aware models. This work provides a crucial early-warning mechanism for investors and enhances the security infrastructure of the rapidly growing TON DeFi ecosystem.
Quantum computing stands poised to transform numerous fields of modern technology by offering computational capabilities beyond those of classical systems. This survey offers a detailed analysis of major fields, such as artificial intelligence and machine learning (AI/ML), blockchain, cybersecurity, and digital communication, highlighting how they are significantly transformed through advancements in quantum computing. It presents a comparative analysis of current quantum computing paradigms and architectures, and examines major quantum algorithms such as Shor’s integer factorization algorithm, Grover’s search algorithm, and hybrid quantum–classical approaches like QAOA and VQE, highlighting their implications for real-world problem solving. Significant advancements in quantum hardware are surveyed, from increasing qubit counts and improved coherence to progress in error mitigation and emerging quantum processor technologies, and their impact on near-term and long-term computing capabilities is evaluated. Finally, the current limitations of quantum computing are discussed, and forward-looking insights into future research directions are provided, outlining the path toward fully harnessing quantum power across industries.
This chapter explores the intersection between the deterministic execution of smart contracts and the unpredictable nature of delay, a legal phenomenon historically embedded in human discretion and normative flexibility. While smart contracts promise automated, trustless enforcement, they reveal critical vulnerabilities when confronted with unforeseen disruptions, particularly in the context of technical rigidity and legislative gaps. The discussion navigates through the architectural challenges of code literalism, the oracle dependency problem, and the doctrinal limitations of classical contract law in adjudicating delays devoid of intent or culpability. It also examines emerging hybrid legal-technical frameworks, including regulatory innovations in the EU and UK, and the conceptual development of Lex Cryptographica. Ultimately, the chapter proposes a recalibration of contract theory and practice, advocating for a pluralistic approach that integrates technical resilience with normative safeguards to manage delay in a digitally autonomous age.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
Cryptocurrency is an alternative payment method developed with encryption techniques. To predict Bitcoin values using both weekly and monthly datasets, this study compares four machine learning models: GRU, Weighted LSTM, LSTM, and LSTM with Attention. The models' accuracy and dependability in capturing the dynamics of cryptocurrency prices were assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (RSCORE). While LSTM with Attention did well with an RSCORE of 0.7173, LSTM with Attention had the highest RSCORE of 0.9173 in the weekly dataset, indicating higher ability in modelling short-term sequential patterns. Additionally, weighted LSTM performed well (RSCORE of 0.8002), surpassing GRU (RSCORE of 0.5728), which had trouble keeping up with the volatility of Bitcoin prices. Both LSTM and LSTM with Attention performed best in the monthly dataset, each with the lowest MSE (0.0304) and an RSCORE of 0.8173. With an RSCORE of 0.7002, weighted LSTM came next, using temporal weighting to enhance predictions. Because of its limited capacity to grasp intricate temporal connections, GRU continuously fared poorly in both datasets. According to the analysis, LSTM is the most dependable model for both short-term and long-term forecasts, and for weekly forecasts, LSTM with Attention provides improved interpretability. These results provide a framework for applying machine learning approaches to financial time series forecasting, highlighting the significance of choosing suitable models based on data frequency, volatility, and prediction aims.
Amid growing global urgency for climate action, innovative financial mechanisms are critical for advancing renewable energy transitions in developing economies. This study investigates the role of financial technology (fintech), with a focus on foreign portfolio investment (FPI), in influencing renewable energy investment (REINV) across 54 developing countries in Africa, Asia, and Latin America from 2010 to 2023. Employing a multi-method empirical approach, comprising Spatial Durbin Models (SDM), Quantile Regression (QR), Stochastic Frontier Analysis (SFA), and Spatial Quantile Regression (SQR), the research captures spatial dependencies, distributional heterogeneity, and efficiency dynamics. The SDM results indicate that FPI significantly increases REINV both directly (1.112) and indirectly through spillover effects (0.445), supported by significant spatial autocorrelation (0.334). Economic development and institutional quality also play key roles, with GDP per capita and institutional quality exerting positive and significant direct effects. Quantile regression reveals that FPI has a stronger influence at higher quantiles of REINV, with coefficients rising from 0.745 to 1.445, highlighting distributional inequality in fintech impact. SFA results show that FPI also enhances technical efficiency (0.912), though diminishing marginal returns are evident. Greater financial depth and electricity access reduce inefficiency, while inflation worsens it. Spatial quantile regression further confirms that regional spillovers are more pronounced among high-investment countries, underscoring the role of spatial dynamics in clean energy financing. The findings suggest that fintech can be a catalyst for renewable energy growth, especially in countries with higher institutional and financial capacity. Policy recommendations include strengthening digital infrastructure, enhancing regulatory coordination, and ensuring macroeconomic stability to fully leverage fintech's potential. Future research should explore emerging fintech tools such as decentralized finance and blockchain-based green bonds.
A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de análise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisão de detecção (de 41,7% para 24,3%), levantando dúvidas sobre o real progresso das ferramentas.
Prizadevanje za vzpostavitev evropskega okvira za digitalno identiteto je leta 2024 doprineslo do pomembnega koraka naprej, saj je 20. maja 2024 začela veljati novela EU uredbe št. 910/2014 za e-identifikacijo in storitve zaupanja, ki jo poznamo tudi kot Uredba eIDAS 2.0. Ta vzpostavlja pravno podlago za uvedbo evropske denarnice za digitalno identiteto po vsej EU. Z denarnico bodo uporabniki lahko tudi varno pridobili, shranili in delili svoje pomembne dokumente, npr. o izobrazbi in licencah, pooblastila za zastopanje pravnih oseb, finančne podatke in podatke o družbah, ter elektronsko podpisovali oz. v primeru denarnic za podjetja elektronsko žigosali dokumente. Da bi dosegli interoperabilnost med denarnicami, izdanimi s strani držav članic, so v izvedbenih aktih k Uredbi eIDAS 2.0 določeni standardi za evropsko denarnico, ki jih morajo upoštevati vse implementacije denarnic po državah, pravila za certificiranje denarnic in sporočanje Evropski komisiji. Skupne zahteve za denarnico se pripravljajo v okviru Arhitekturnega in referenčnega okvirja (ARF), poleg tega pa Evropska komisija pripravlja tudi referenčno implementacijo denarnice. V prispevku so podrobneje predstavljene nekatere visokonivojske zahteve ARF, ki se nanašajo na področje zasebnosti, še posebej uporaba metod ničelno spoznalnih dokazov (angl. Zero knowledge Proof) za zagotavljanje zasebnosti v ekosistemu denarnic.
Rad analizira digitalnu transformaciju u industriji osiguranja s posebnim naglaskom na primjenu blockchain tehnologije i pametnih ugovora. Istražuje kako telemetrija i oracle tehnologija omogućuju prikupljanje i korištenje podataka iz stvarnog svijeta za dinamično oblikovanje ugovora o osiguranju, što vodi razvoju novih modela poput mikroosiguranja, peer-to-peer osiguranja i osiguranja temeljenog na stvarnoj uporabi. Rad također razmatra pravne aspekte pametnih ugovora, njihovu pravnu valjanost, ograničenja u interpretaciji, te izazove u zaštiti privatnosti i regulatorne izazove koje donosi njihova primjena unutar EU i Republike Hrvatske. Poseban naglasak stavlja se na važnost stvaranja jasnih i prilagodljivih pravnih rješenja koja će omogućiti odgovornu i učinkovitu integraciju novih tehnologija u osigurateljnu praksu.
In today's rapidly evolving landscape of smart city applications, particularly in sensitive areas like the healthcare sector, safeguarding the security, integrity, and privacy of data has become a significant and challenging concern. Specifically in the healthcare sector, the sharing and access of patient records across various stages of care by doctors, nurses, pharmacies, and diagnostic centers introduce new complexities and potential vulnerabilities. However, these challenges intensify more in the case of distributed healthcare networks where data is fragmented across institutions. This work addresses issues such as data vulnerability and misuse in distributed healthcare environments by proposing a Blockchain-enabled Distributed Healthcare System (BeDHS). The model is designed to facilitate secure, transparent, and privacy-preserving collaboration among healthcare entities. It adopts a hybrid approach, integrating a quantum key-based image encryption technique to enhance the security of health records. The encrypted images are securely stored in the InterPlanetary File System (IPFS) to ensure data integrity and availability. Additionally, a Federated Learning (FL) framework is employed to enable collaborative training of AI models across institutions without exposing sensitive patient data. The proposed BeDHS model is implemented using Solidity-based smart contracts on the Ethereum blockchain, ensuring decentralized and tamper-resistant operations. Simulation results demonstrate that the proposed model outperforms existing healthcare data management systems in terms of efficiency and security. • A blockchain-enabled distributed healthcare system is proposed, where the number of healthcare institutions of a smart city are integrated to form a collaborative and transparent model for sharing health records while maintaining security, privacy, and immutability. • A Quantum-Chaos-Encryption cryptographic technique integrated with blockchain for protecting digital documents and medical images from unauthorized access. • To build a privacy-preserved distributed-collaborative healthcare system, a federated learning approach is incorporated that trains the AI models directly at the data source of multiple healthcare institutions while eliminating the need to transfer between the institutions.
The use of Enterprise Data Warehouse (EDWs) has been experienced as the analytical backbone of risk management, financial reporting and regulatory reporting of the data in very regulated sectors like banking, insurance, and capital markets. They were based on batch-oriented Extract Transform Load (ETL) paradigms, tight coupled schema and monolithic governance models that are better suited to stability than agility. Nevertheless, the increasing regulatory complexity, impacts of the near-real time risk visibility requirements, and increasing cost of infrastructure have emanated inherent weaknesses of the legacy EDW architectures. At the same time, the emergence of hybrid cloud platforms, scalable object storage, distributed query engines, and workflow orchestration system has made it possible to make the paradigm shift toward Extract–Load–Transform (ELT), domain-driven data products, and decentralized ownership models. In spite of these developments, in numerous organizations, the pressure to modernize reporting pipes based on strong backward compatibility criteria, audit limitations and the operational risks of massive data migrations makes this a challenge. This paper gives a detailed blueprint of modernization in the process of moving the old EDW centric ETL architectures to the hybrid cloud ELT platforms to suit the risk, finance, and regulatory reporting. Its proposed solution integrates domain-driven data products and ELT pushdown transformations orchestrating control planes and explicit data contracts that is applied in an incremental fashion with a strangler pattern. The framework focuses on retrogressively compatible schemas, reconcilability determinacy, the rollback safety nets, and regulated cutover plans to provide continuous regulatory compliance. Using a well-organized migration roadmap, cost and performance metrics and an official risk register, the paper will show how organizations can shorten report delivery cycles, enhance service-level agreement (SLA) compliance and minimize the overall cost of ownership without sacrificing auditability and strict governance. The findings have shown that hybrid cloud ELT systems may cut the latency in report by more than 40%, cut compute expenditure by up to 35, and become much more responsive to regulatory cases without infection of information integrity or resilience.
Muhammad Ali Nawaz, Wajid Alim, Sammar Abbas, Shahid Manzoor Shah · 5 authors
The study investigates the co-movement relationships between cryptocurrencies and South Asian stock markets, focusing on five leading cryptocurrencies: Bitcoin, Ethereum, Tether, Binance Coin, and Ripple, and five South Asian stock indices: BSE, PSX 100, DSE 30, NEPSE, and Sri Lanka's All Share Index, and also used five major global indices for the accuracy of analysis. The study aims to understand their integration and causal dynamics. The analysis uses 357 weekly observations of historical prices from November 6, 2017, to September 2, 2024, applying econometric tools such as the Augmented Dickey-Fuller and Phillips-Perron tests, Johansen's Cointegration Test, Vector Auto-Regression, Vector Error Correction Model, and Granger causality to examine statistical properties, integration, and causality among the variables. Results show significant cointegration and causality between cryptocurrencies and South Asian stock indices, with cryptocurrency prices exhibiting higher volatility and faster adjustments than stock indices. These findings provide actionable insights for investors, policy-makers, and researchers regarding regulation and cross-market investment strategies. This study uniquely explores the interplay between emerging digital assets and traditional finance in a South Asian context, offering novel evidence on volatility dynamics and causal relationships that inform coupled regulatory frameworks and cross-market investment planning.
This study aims to provide a comprehensive analysis of tools and methods for ensuring smart contract security.The research employs a systematic review of static analysis, dynamic testing, and formal verification approaches.Static analysis tools, including Oyente, Mythril, and Slither, are systematically evaluated regarding their effectiveness in identifying vulnerabilities at early development stages, highlighting strengths in detecting known vulnerability patterns as well as limitations such as false positives.Dynamic analysis methodologies, such as fuzz testing (e.g., Echidna, Harvey) and symbolic execution (e.g., MAIAN, teEther), are assessed for their capability to identify complex logical vulnerabilities that are typically missed by static methods, examining their accuracy, scalability, and real-world applicability.Formal verification approaches employing K-framework, Why3, and Coq are thoroughly examined for their ability to deliver rigorous mathematical guarantees of smart contract correctness, along with their practical applicability, complexity, and integration into typical smart contract development workflows.The study reveals that an integrated security strategy, combining static analysis, dynamic testing, and formal verification methods, is essential for comprehensive and robust smart contract protection, effectively mitigating diverse vulnerabilities across the entire contract lifecycle.The research contributes to the field by offering a comparative analysis of current tools, identifying their strengths and limitations, and proposing future research directions, including automated specification generation and AI-driven vulnerability prediction.
Traditional portfolio optimization models, rooted in the mean–variance framework of Markowitz, rely heavily on variance as a risk measure. Although theoretically elegant, this approach becomes fragile in volatile and structurally unstable markets such as cryptocurrencies, where return distributions deviate significantly from normality, cor-relations are unstable, and concentration risk emerges. These limitations have motivated the search for alternative frameworks capable of capturing uncertainty in a more flexible and distribution-free manner. Entropy, originally introduced by Shannon as a measure of information, has gradually been recognized in the financial literature as a suitable proxy for diversification and systemic uncertainty. To address the shortcomings of variance-based models, this paper introduces the Weighted Shannon Entropy (WSE) model as a diversification-oriented alternative. By extending the classical Shannon entropy with asset-specific informational weights, the WSE framework provides additional flexibility for modeling heterogeneous asset char-acteristics, such as liquidity, informational value, or perceived reliability. Using the principle of maximum entropy and the method of Lagrange multipliers, we derive ex-ponential-form solutions for portfolio weights that naturally discourage concentration, ensure balanced allocations, and remain analytically tractable. The methodology is validated empirically on a portfolio of four leading cryptocurren-cies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—using market data from January to March 2025. The results demonstrate that the entropy-based optimization framework produces well-diversified portfolios, robust to volatility and structural instability, and provides a distribution-free alternative to the classical mean–variance model. Beyond its empirical performance, the WSE formulation highlights the conceptual advantage of entropy in integrating return, risk, and diversification into a single unified framework. The paper contributes both theoretically and practically: it strengthens the mathematical foundation of entropy-based portfolio selection, extends its applicability to digital asset markets, and illustrates how weighting schemes can enrich the classical Shannon measure. Future research may extend this approach to multi-period optimization, gen-eralized entropies such as Tsallis and Kaniadakis, or integration with machine learning models for dynamic portfolio management.
Metamaterials are artificially engineered systems in which the geometry and arrangement of designed unit cells give rise to effective properties that are not available in natural materials. Intelligent metamaterials extend this concept by integrating stimulus-responsive materials with programmable architectures, thereby creating functional matter that blurs the conventional boundary between materials and structures and enables dynamic, adaptive, and reconfigurable functionalities. These systems can respond to diverse stimuli such as thermal, electrical, optical, magnetic, and mechanical inputs, and convert them into tunable shape change, adaptive mechanical/optical responses, and other reconfigurable functionalities [1-5]. Through this synergy, they acquire lifelike and emergent behaviors, making them attractive platforms for next-generation applications in soft robotics, bioengineering, information encryption, and mechanical computation. Yet, without this integration, both components face intrinsic limitations. Standalone smart materials are typically constrained by specific modes, directionalities, and spatial complexities, restricting their use in multifunctional devices. Many promising material behaviors remain underutilized due to challenges in harnessing and controlling their properties at the system level. Likewise, mechanical structures alone are limited by their static configuration, which severely curtails their functional versatility. To overcome these challenges, a promising approach lies in the synergistic integration of smart materials with structural designs. Coupling programmable geometries with responsive materials not only surmounts the intrinsic limitations of each component but also unlocks emergent functionalities unattainable by either alone. Examples of these novel capabilities include programmable shape morphing, adaptive mechanical properties, and multimodal responses, all arising naturally from this codesign paradigm. This perspective elucidates this transformative paradigm by focusing on the integration of smart materials and structural architectures as a platform for intelligent metamaterials. We systematically review representative classes of smart materials and structural design and then highlight the fundamental principles underpinning material–structure coupling and discuss how structural design facilitates the full realization of material functionalities. Finally, we examine emerging applications and identify key challenges and future directions essential for developing the next generation of architected intelligent metamaterials. Figure 1 illustrates the core concept: the nexus of material properties, structural design, and emergent functionalities, where reconfigurability and dynamic operation arise from seamless integration, opening avenues to intelligent, reprogrammable metamaterials with profound technological impact. Synergistic integration of smart materials and structural design, highlighting how their coupling provides the foundation for intelligent metamaterials. One of the defining features of smart materials is their ability to actively respond to environmental stimuli. These responses originate from intrinsic molecular architectures, phase transitions, or energy conversion mechanisms [6], as illustrated in Figure 2A. Thermal responsiveness represents the most fundamental and widely applied category. Phase transitions provide the driving force: shape-memory polymers (SMPs) [7] and shape-memory alloys (SMAs) [8] recover their programmed configuration upon heating through reversible thermal transitions (glass transition or melting of crystalline domains) and reversible martensite–austenite transformation, which release the stored elastic strain energy and drive macroscopic shape recovery accordingly, whereas liquid-crystalline elastomers (LCEs) [9] actuate through the reorientation of mesogenic units, which directly drives macroscopic deformation. Electrical responsiveness can be classified into direct and indirect mechanisms. Direct response arises from electrochemical reactions, or piezoelectric conversion, where electrical input is translated into mechanical deformation or sensing output [10, 11]. Indirect responses are mediated by joule heating: composites, for example, incorporating carbon nanotubes, silver nanowires, or conductive polymers generate localized heating that triggers thermal deformation [12]. Optical responsiveness mostly originates from either photothermal conversion or photochemical reactions. In the former, absorbed light is transformed into heat that drives thermal actuation, whereas in the latter, molecular transformations, such as azobenzene cis-trans isomerization, induce reversible deformation or stiffness modulation [13, 14]. Representative (A) smart materials with diverse active mechanisms and (B) typical structural designs with various deformation modes. Reproduced with permission from Ref. [6]. Copyright 2024, Science China Press, and Oxford University Press. Besides these common actuation mechanisms, magnetic responsiveness offers an additional pathway for remote, wireless, and rapid control. Polymers embedded with magnetic particles or nanomaterials can undergo orientation, deformation, or stiffness modulation under external magnetic fields, enabling noncontact actuation and programmability [15]. Meanwhile, fluidic and chemical responsiveness arises from interactions with liquid environments: swelling or contracting hydrogels [16], ionic polymers, or pH-sensitive systems undergo reversible volume expansion, contraction, or surface reconstruction, which are particularly valuable in biomedical and soft-robotic applications. Finally, the integration of multiple responsive units—thermal, electrical, optical, magnetic, and fluidic—offers a pathway toward higher-order intelligence. Modular coupling of these mechanisms enables synergistic functions such as self-sensing, adaptive morphing, and multifunctional actuation, thereby greatly expanding the design space of smart material systems. Meanwhile, structural design serves as the cornerstone in the development of metamaterials. Over the years, numerous fundamental strategies have emerged: kirigami structures that exploit rotational motion of patterned cuts [17]; origami configurations in which crease-induced stiffness reduction enables programmable 3D folding [18]; post-buckling 3D architectures assembled through mechanically guided deformation [19-21]; interlocking or Lego-like assemblies formed by geometric fitting; torsional configurations generated under twisting loads; and horseshoe-shaped unit cells derived from cantilever bending (Figure 2B). Based on these strategies, metamaterials with unique mechanical behaviors, such as auxetic response [22], zero stiffness [23], J-shaped stress-strain profile [24], and high specific stiffness [25], can further be enhanced by lattice arrangements and hierarchical combinations of unit cells. Beyond these, more complex systems have been created, such as compression-torsion couplings [26], multistable [27] and snap-through architectures [28], and path-dependent design [29]. To fully exploit the potential of structural design, researchers increasingly pursue two complementary design dimensions. The first focuses on geometric nonlinearity amplification, where origami, kirigami, and bimetallic structures can achieve large deformations under minimal actuation, enabling programmable morphing and multistability for deployable devices and bioinspired actuators. The second emphasizes coupled optimization of topology and deformation mechanisms. For example, auxetic systems can reversibly switch between positive and negative Poisson's ratios through localized rotations or tensile mechanisms, and when integrated with responsive materials, they combine high compliance with enhanced energy absorption. Unlike conventional functional materials, which are constrained by intrinsic composition, metamaterials derive their properties from structural freedom, offering virtually unlimited opportunities to tailor deformation modes and mechanical responses. The integration of smart materials with architected structures opens broad opportunities for advancing intelligent metamaterials toward revolutionary functionalities. However, a fundamental challenge lies in the mismatch between the microscale actuation mechanisms of smart materials and the macroscale deformations required by structural architecture. Bridging this disparity to fully leverage the strengths of both components and unlock unprecedented performance remains highly attractive but nontrivial. In this section, we review two representative approaches and discuss key considerations spanning material fabrication, structural design, and coupling strategies. These insights lay the foundation for the development of the next generation of intelligent metamaterials. A widely used strategy for fabricating intelligent metamaterials from smart materials relies on direct incorporation via additive manufacturing (i.e., 3D printing), molding, or subtractive manufacturing [30], offering important pathways to achieve material-structure synergy. When coupled with architected deformation modes, the inherent responsiveness of smart materials to external cues such as temperature, magnetic fields, light, pH, or ion concentration enables direct actuation for programmable structural reconfigurations and motions at the system level [31]. For example, LCEs provide a representative example, where actuation strain and elastic modulus can be tuned by the transition temperature across different thermal states [32] (Figure 3A). Triangular lattice metamaterials composed of LCEs with distinct transition temperatures and moduli allow complex patterns that switch at programmed temperatures. By encoding different LCE types in each lattice strut, spatially differentiated actuation can be achieved, enabling local thermal reconfigurations that generate reversible global shape transformations. Besides, embedding magnetic components introduces an additional degree of actuation freedom. For instance, magnetic sheets folded into origami-based configurations form programmable magnetic origami metamaterials. Under applied magnetic fields, these architectures exhibit multimodal behaviors, such as directed deformation, rolling, contraction, and crawling, highlighting the versatility of integrating magnetic actuation with origami mechanics [33] (Figure 3B). In addition, integrating smart materials responsive to solvents, pH, or ion concentration further broadens the design space [40]. A notable case involves lattice structures composed of microscale liquid-crystalline polymer (LCP) plates [34] (Figure 3C). Exposure to acetone softens the LCP, lowering its modulus so that capillary forces dominate: plates are pulled together, eliminating original nodes and generating new ones, thus reconfiguring the lattice. Upon solvent evaporation, the LCP plates stiffen, locking in the new geometries. Re-exposure to dichloromethane (DCM) induces swelling, and ethanol enables gradual and controllable recovery, thereby restoring the original configuration. This system demonstrates how solvent–structure interactions, coupled with the tunable stiffness, can enable reversible and reprogrammable lattice transformation. Two strategies for synergizing smart materials with designed structures. (A–D) Intelligent metamaterials directly composed of response materials. (A) Lattice metastructure composed of printable LCE exhibiting tunable actuation strain and elastic modulus. Reproduced with permission from Ref. [32]. Copyright 2024, Wiley. (B) Origami metamaterials actuated by magnetic fields, enabling multimodal motion. Reproduced with permission from Ref. [33]. Copyright 2022, Springer Nature Ltd. (C) Micro-lattice with tunable cell topology induced by capillary force. Reproduced with permission from Ref. [34]. Copyright 2021, Springer Nature Ltd. (D) 3D concatenated metamaterials demonstrating reversible and precise deformation driven by electrostatic force. Reproduced with permission from Ref. [35]. Copyright 2025, AAAS. (E–H) Smart-substrate enabled intelligent metamaterials. (E) Kirigami-designed structures exhibiting reconfigurable deformation when stretched by an LCE substrate. Reproduced with permission from Ref. [36]. Copyright 2021, Wiley. (F) Micro-metamaterials embedded in hydrogel, enabling intelligent information decryption via thermally induced deformation. Reproduced with permission from Ref. [37]. Copyright 2023, Springer Nature Ltd. (G) Electrochemical-driven, microscopically configurable origami metamaterial with crease designs for morphing. Reproduced with permission from Ref. [38]. Copyright 2025, Springer Nature Ltd. (H) Nanomagnetic encoding of morphing 3D architected structure. Reproduced with permission from Ref. [39]. Copyright 2019, Springer Nature Ltd. Notably, interfacial forces such as electrostatics can dominate as structural dimensions shrink. Acrylic polymers fabricated into 3D annular concatenated metamaterials via two-photon lithography and coated with copper [35] (Figure 3D) expand upon electrostatic charging in a Van de Graaff generator. As electrostatic repulsion between interlocked rings overcomes gravity, the initially collapsed structure deploys outward; once discharged, it rapidly returns to its original state. This reversible transition illustrates how electrostatic interactions can be harnessed for microscale structural reconfiguration. Together, these examples demonstrate that integration of smart material responsiveness with architected deformation modes—whether thermal, magnetic, chemical, or electrostatic—enables sophisticated, reversible, and multimodal transformations. Moreover, when the characteristic dimensions are at the microscale, forces such as capillarity and electrostatics become increasingly influential, and when combined with smart materials that mitigate stiffness or gravity constraints, they provide powerful mechanisms for reversible and precise structural reprogramming. A complementary strategy employs smart materials as active platforms to drive otherwise passive, architected structures, enabling them to morph into specific shapes on demand [42]. In this scheme, the intrinsic responsiveness of the material couples with predesigned structures, allowing both global and local control of deformation [43]. Global control relies on uniform actuation of the smart material—often serving as an active substrate—where tailored structural patterns translate large-scale deformations into functional morphologies [44]. For instance, uniaxially aligned LCEs deform upon heating, stretching microscale kirigami structures to achieve reconfigurable patterns that switch between distinct configurations, enabling information display and encryption [36] (Figure 3E). Similarly, microscale metastructures embedded into thermal-responsive hydrogels can yield broad configuration programmability: different sinusoidal morphologies can be generated as a result of the site-specific variations in the induced structural deformation, which encode and decode complex images upon heating and cooling, such as high-resolution paintings [37] (Figure 3F). Besides, localized manipulation provides more precise control by selectively actuating specific points or regions of a structure [42]. In monostable systems, introducing pneumatic actuation creates competition between pneumatic forces and elastic restoring forces, giving rise to tunable dynamic behaviors. For example, the inflation of soft pneumatic actuators induces bending in a monostable structure, where stored elastic energy is rapidly released through snap-through behavior, followed by snap-back upon the application of negative pressure [45]. Beyond pneumatics, diverse localized actuation methods have been demonstrated, including electronically driven actuation [38] (Figure 3G), magnetic encoding [39, 46, 47, 41] (Figure 3H), and electric heating [48, These strategies enable site-specific of smart materials, including LCEs and thereby offering tunable responses otherwise structures. By coupling the responsiveness of smart materials with engineered structures, devices can exhibit and emergent behaviors that materials structures achieve alone. One is the of and where geometric design material responses into Figure illustrates a energy that the to motion each the from to under then as and to generate electrical Similarly, designs such as or geometries can to motion. Figure demonstrates a on a that thermal generating that and drives of an embedded Besides, strategies provide toward behaviors. For example, a fabricated into a structure with (Figure can into by shape as swelling of the in the the to the into Representative intelligent devices enabled by the of architectures and tailored smart materials. (A) Reproduced with permission from Ref. Copyright Wiley. (B) Reproduced with permission from Ref. Copyright Springer Nature Ltd. (C) Reproduced with permission from Ref. Copyright 2023, Springer Nature Ltd. (D) intelligence. Reproduced with permission from Ref. Copyright 2024, AAAS. can also arise from and reconfigurable systems, where interactions such as and allow and adaptive multiple For instance, Figure illustrates with unit a of multistable interlocking features that form between units, enabling Upon thermal the on the reconfigurable and a which the and a of the metamaterials. these to to materials and architected structures from smart materials, smart to actuate otherwise architectures, and driven by smart materials. Direct integration and deformation but the codesign of material and are more and with conventional materials, but they challenges in between materials, and the to deform making it to achieve spatial In direct integration is most for to systems actuation and fabrication, whereas approaches are for reconfigurable devices and By coupling the of smart materials with the unique mechanisms of architected structures, intelligent metamaterials greatly expand the application space of conventional smart materials and otherwise static metamaterials with and transformative functionalities. diverse such as soft robotics, bioengineering, information encryption, and it This representative applications and how intelligent metamaterials are design strategies the principles that their exploit the responsive properties of smart materials through embedding structural where and actuation under external stimuli. Representative examples include soft actuators (Figure and soft which diverse modes but remain due to on or programmed external in of intelligent metamaterials across representative (A) soft actuators. Reproduced with permission from Ref. Copyright 2025, AAAS. (B) motion of or structures under Reproduced with permission from Ref. Copyright 2024, Springer Nature Ltd. (C) LCE lattice for Reproduced with permission from Ref. Copyright 2021, Wiley. (D) metamaterial composed of materials and a structure. Reproduced with permission from Ref. Copyright 2022, Wiley. (E) display and encryption enabled by materials combined with structures. Reproduced with permission from Ref. Copyright 2023, Wiley. (F) information and encryption of structures driven by Reproduced with permission from Ref. Copyright 2023, Springer Nature Ltd. (G) magnetic metamaterial with Reproduced with permission from Ref. Copyright 2021, Springer Nature Ltd. (H) metamaterials composed of soft conductive materials and kirigami structures. Reproduced with permission from Ref. Copyright 2022, Springer Nature Ltd. demonstrate that coupling structural designs with zero elastic energy modes and induced strain enables under A between structural and strain by surface the LCE to across or fluidic under the (Figure Similarly, achieve motion by and between and external under designs on energy inputs, the potential of soft toward more and systems. Besides, engineered smart actuators as the energy and environmental exhibit diverse complex structural hierarchical stiffness, and unique deformation or challenges for biomedical The integration of soft smart materials with architected metamaterial designs provides a promising pathway to these challenges by material responsiveness with structural For instance, a lattice of designed to exhibit high elastic under fully to its (Figure This the mechanical for where is essential for LCEs into such the temperature of When integrated into these LCE as and for that and In embedded with magnetic particles can be actuated via magnetic heating to generate forces for the with metamaterials further enabling transition between states for and states for through the (Figure approaches demonstrate how smart material–structure integration can of the biomedical The reconfigurability of metamaterials opens a novel pathway for information display and By the of smart materials, information can be selectively or under specific environmental through structural or architectures provide a mechanical for encoding information and each is by energy that external stimuli to where smart materials can the by external materials with moduli provide a thermal approach between For instance, into can be with specific when all remain in their the functions as an when to the upon heating patterns serving as an information display (Figure To achieve information such as two-photon provide powerful By in of with and enabling information Upon heating, an of the into a new of (Figure a system dynamic encryption and reversible information display at high devices can face in such as high temperature, or which to complementary systems To this intelligent metamaterials provide a promising platform by embedding and into responsive and reconfigurable structures, thereby opening new avenues toward and systems. For instance, origami metamaterials have been to fundamental such as and When combined with these systems acquire the ability to directly and environmental offering a form of this designs coupling structural multistability with functional materials yield metamaterials with In such systems, the two states of a unit and whereas responsive components allow reprogrammable energy between states in reprogrammable metamaterials with or (Figure soft conductive materials with kirigami mechanically integrated of fundamental These systems have also been to and directly through (Figure the of smart materials and architected structures both and the for adaptive, and lifelike intelligent systems The development of intelligent metamaterials to face (Figure A lies in the optimization of material properties and structural architectures, as the coupling between thermal, and chemical is highly complex and remains to or with such as or and the to emergent interactions across challenges and future opportunities in the synergistic integration of smart materials and structural designs. methods additional constraints, whereas systems, where 3D and have rapid limited integration and interfacial between materials the realization of complex designs. and are also on such as two-photon which are and for large-scale or programmability reversible actuation high without or is particularly in soft systems. due to their on systems and of input and output metamaterials at the level are to directly information with through or other conventional systems such as systems remains an to be between intelligent metamaterials and control of local global actuation is and systems for Finally, under remains a polymers from or and responsive hydrogels can or For biomedical and strategies be these challenges, the offers numerous opportunities for (Figure design insights from the that result from systems provide a design where hierarchical swelling, and local interactions achieve and from or future metamaterials or in design, particularly topology design, and to the of architectures with tuned responses. In these design such as can be on large or and generate new designs to optimization in a space to and coupling designs of structures and materials These design rapidly functionalities with material–structure combinations and In the of material–structure coupling for engineered and designs that compliance and Moreover, with the of the coupling between materials and structures, such as active deformation, fields, and strain can be by This external combined with intelligent a new intelligent and optimization which the between intelligent metamaterials and are additive and such as or lithography are expanding design and integration enabling of complex devices and dynamic programmability with in precise and including in interfacial architectures, and or strategies that mitigate environmental further and system also in functional and into smart metamaterials enable control and Coupling these systems with or yield more intelligent In of the potential is For example, in robotics, smart metamaterials soft with multimodal In they enable adaptive dynamic or smart systems. In adaptive and multifunctional energy are In information smart metamaterials mechanical encryption, and strategies. the of materials and be key to challenges and the full potential of intelligent metamaterials. original original review and This by the University of The of can be from the
Bruno M. F. Ricardo, Lucas C. Cardoso, Leonardo T. Kimura, Marcos A. Simplício · 5 authors
In 2023, Barreto and Zanon proposed a three-round Schnorr-like blind signature scheme, leveraging zero-knowledge proofs to produce one-time signatures as an intermediate step of the protocol. The resulting scheme, called BZ, is proven secure in the discrete-logarithm setting under the one-more discrete logarithm assumption with (allegedly) resistance to the Random inhomogeneities in a Overdetermined Solvable system of linear equations modulo a prime number p attack, commonly referred to as ROS attack. The authors argue that the scheme is resistant against a ROS-based attack by building an adversary whose success depends on extracting the discrete logarithm of the intermediate signing key. In this paper, however, we describe a distinct ROS attack on the BZ scheme, in which a probabilistic polynomial-time attacker can bypass the zero-knowledge proof step to break the one-more unforgeability of the scheme. We also built a BZ variant that, by using one secure hash function instead of two, can prevent this particular attack. Unfortunately, though, we show yet another ROS attack that leverages the BZ scheme’s structure to break the one-more unforgeability principle again, thus revealing that this variant is also vulnerable. These results indicate that, like other Schnorr-based strategies, it is hard to build a secure blind signature scheme using BZ’s underlying structure.
Blockchain technology has rapidly emerged as a transformative force across sectors such as healthcare, supply chains, energy, and voting systems. Its decentralized, transparent, and secure architecture improves efficiency, enhances trust, and reduces costs. Among these domains, finance has experienced the greatest disruption, with blockchain reshaping banking by fostering transparency, security, and efficiency. This study presents a bibliometric analysis of blockchain in finance, mapping trends, patterns, and intellectual trajectories. The analysis explores publication growth, document types, and leading contributors, while identifying the most cited works shaping the field. Using VOSviewer, keyword co-occurrence and bibliographic coupling visualize thematic clusters and intellectual linkages. By synthesizing these findings, the study highlights blockchain’s current research landscape, identifies gaps, and proposes future directions.
IntroductionResearch on government decentralization is well established both globally and domestically. However, a notable gap persists in the literature: few studies have systematically examined the relationship between decentralization and sustainable development. This gap is especially pronounced in the Iranian context, where—despite some early efforts (e.g., Obedeh & Mousavi, 2009)—empirical investigations linking decentralization to sustainability remain scarce. Moreover, much of the existing sustainability discourse has narrowly focused on environmental, economic, and social pillars, often overlooking the critical role of governance structures, particularly decentralization, in enabling sustainable outcomes. Addressing this lacuna, the present study aims to identify and prioritize key dimensions of government decentralization that contribute to sustainable development. MethodologyThis study adopts an applied, non-experimental descriptive design. The research population comprises 25 experts in public administration, including 16 university faculty members and 9 senior officials from government organizations. Participants were selected through purposive sampling using the snowball technique. Data were collected via a structured expert questionnaire grounded in a nine-dimensional conceptual framework. Reliability was assessed using the inconsistency index, while validity was evaluated through the Lausche coefficient. Factor prioritization was conducted using the Analytic Hierarchy Process (AHP), a multi-criteria decision-making method widely employed in policy and governance research. FindingsBased on the results, it is clear that the index of monitoring local conditions with a weight of 0.192 is the first priority. The index of attracting local funds with a weight of 0.084 is in the second priority. The index of financing by the government with a weight of 0.075 is in the third priority. The index of determining goals based on the principles of sustainability with a weight of 0.075 is in the fourth priority. The index of alignment of sustainability goals with the needs of the local community with a weight of 0.073 is in the fifth priority. The index of trust in local managers with a weight of 0.065 is in the sixth priority. The budgeting index based on sustainability goals with a weight of 0.063 is ranked seventh. The index of commitment to accountability with a weight of 0.054 is in the eighth priority. The index of increasing the authority of local institutions with a weight of 0.049 is in the ninth priority. The education index of local managers with a weight of 0.049 is in the tenth priority. Discussion and ConclusionThe criterion of contextual targeting—defined as aligning policies and governance decisions with local conditions—emerged as the highest priority, with a normalized weight of 0.339. This underscores a fundamental principle of effective decentralization: one-size-fits-all mandates are ill-suited to diverse regional contexts. Leading decentralized systems worldwide calibrate the scope of authority and resource allocation to subnational governments (e.g., provinces, municipalities) based on continuous monitoring of local socioeconomic, environmental, and institutional conditions. This finding resonates with Ishrodoost (2021), who identifies the absence of region-specific targeting as a key barrier to decentralization in Iran, and with Fua’s (2022) analysis of Russia’s decentralization reforms, which emphasizes the necessity of tailoring governance structures to regional realities.The second-highest priority was financial considerations (weight: 0.222). Globally, the fiscal relationship between central and local governments constitutes a critical determinant of local autonomy and service delivery capacity. In Iran, where local authorities suffer from chronic revenue shortages and limited fiscal autonomy, strengthening intergovernmental financial mechanisms is essential. Embedding decentralization within a good governance framework—characterized by fiscal transparency, equitable resource distribution, and performance-based budgeting—can help address systemic challenges such as bureaucratic inefficiency, rising administrative costs, and the central government’s limited responsiveness to local needs. Only through such reforms can municipalities establish sustainable economic foundations and stable revenue streams. These insights align with prior studies by Zarkhani et al. (2018) and Mohammadi (2008).Third in priority is local sustainability capacity building (weight: 0.207). This entails a systematic, multi-level approach to strengthening the capabilities of local institutions, communities, and leaders to plan, implement, and sustain development initiatives. Effective capacity building integrates leadership development, community engagement, organizational learning, and institutional adaptation to foster resilience and well-being at the local level. As DeCorby et al. (2018) argue, local capacity is a prerequisite for meaningful devolution of power; without it, decentralization risks becoming symbolic rather than substantive. Similarly, Choi et al. (2019) highlight the centrality of local capacity in UN-supported decentralization efforts in developing countries.Transparency and reporting ranked fourth (weight: 0.134). Robust accountability mechanisms—including systematic data collection, performance monitoring, and public reporting—are vital for ensuring that decentralized authorities remain aligned with policy objectives, learn from implementation outcomes, and adapt decision-making accordingly. This finding is consistent with Zuidervik et al. (2021) and Lin et al. (2018), who emphasize transparency as a cornerstone of effective local governance.Finally, human resource performance received the lowest weight (0.097), though it remains strategically significant. Skilled, motivated, and ethically grounded personnel are essential for translating decentralization policies into practice. Human capital constitutes a core organizational asset, particularly in public institutions, where competent staff can drive innovation and sustainable performance. Chigbo (2021) identifies human resource competencies as a key enabler of decentralization, a view echoed by Rashidi et al. (2021) in their analysis of administrative decentralization in Iran.