Purpose: The purpose of this paper is to develop a blockchain-enabled game-theoretic framework that addresses information asymmetry in Supply Chain Finance (SCF). The study aims to model strategic interactions among SCF participants and assess the impact of blockchain adoption on equitable benefit allocation and risk reduction.Methodology: This research adopts a decentralized Stackelberg game model combined with Shapley value allocation to represent the hierarchical and cooperative interactions among suppliers, manufacturers, and retailers. The model incorporates blockchain costs into payoff functions and uses replicator dynamics to simulate the strategic evolution of stakeholders under varying levels of information-sharing uncertainty.Findings: Simulation results demonstrate that blockchain implementation significantly improves supply chain performance, even when information-sharing efficiency is as low as 30%. Compared to traditional systems, blockchain-enabled frameworks yield higher payoffs, enhanced transparency, and more stable strategic equilibrium. As information-sharing efficiency increases to 50%, the marginal gains in system-wide utility and strategic stability are amplified, confirming the robustness and scalability of blockchain integration under various cost scenarios.Originality/Value: This study advances the literature by integrating blockchain technology with Evolutionary Game Theory (EGT) and Shapley value allocation in an SCF framework. The proposed model quantitatively assesses strategic behavior under information asymmetry, incorporating blockchain costs into replicator dynamics and payoff functions. The analysis offers novel insights into risk mitigation, policy design, and equitable profit distribution, providing practical guidance for Small and Medium-Sized Enterprises (SMEs), financial institutions, and regulators in adopting blockchain-enabled SCF systems.
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.
Abstract Oblivious transfer is a type of message transfer in which a sender transmits one out of many potential pieces of information to the receiver, but she has no knowledge about the actual piece of information being received by the receiver. Oblivious transfer is a deceptively simple scheme that has many possible applications such as secure multiparty computation, private set intersection, federated learning, zero-knowledge proofs, accessing sensitive data etc. Security of most classical oblivious transfer protocols is based upon the unproven assumptions about the computational complexity of certain number theoretic problems such as integer factorization. So, existing classical protocols for oblivious transfer are only computationally secure and not unconditionally secure. Although many quantum oblivious protocols have been proposed lately, they are not simple and easy to implement. In the present work we propose a quantum oblivious transfer protocol that is efficient, simple and easily implementable with the existing quantum technology.
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.
Cryptocurrencies have grown as one of the significant technological advancements in today’s digital age. Investors all around the globe have shown a growing interest in cryptocurrencies in recent times because of their potential to bring huge profits. This paper tries to determine the best cryptocurrencies for investment by evaluating six leading cryptocurrencies, selected based on market capitalization and global relevance. Cryptocurrencies are ranked using six important evaluation criteria using two multi-criteria decision-making (MCDM) techniques: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), employed under hyperbolic fuzzy set (HyFS) settings. In addition, a closeness coefficient is introduced in TOPSIS method to enhance decision precision and sensitivity. Although the results obtained by the two methods are very close, investors would not want to have any doubt about the investment they are considering. The rankings from each procedure are then combined using a social choice function: the Borda Count method. Combining these approaches, the findings indicate that Bitcoin and Ethereum emerge as the most favorable cryptocurrencies for investment among the selected alternatives. To ensure the consistency of the obtained rankings, statistical analyses were conducted. Spearman’s rank correlation coefficient (ρ), Kendall’s tau (τ), and the Wilcoxon Signed-Rank Test were utilized to measure the degree of agreement between the rankings generated by Hyperbolic Fuzzy TOPSIS and Hyperbolic Fuzzy VIKOR. Combining these approaches to create a trustworthy decision-making model for cryptocurrency selection makes this work novel.
Ethereum enables the creation and execution of decentralized applications through smart contracts, that are compiled to Ethereum Virtual Machine (EVM) bytecode. Once deployed in the blockchain, the bytecode is immutable; hence, ensuring that smart contracts are bug-free before their deployment is of utmost importance. A crucial preliminary step for any effective static analysis of EVM bytecode is the extraction of the control-flow graph (CFG): this presents significant challenges due to potentially statically unknown jump destinations. In this paper we present a novel approach, based on Abstract Interpretation, aiming to build a sound CFG from EVM bytecode smart contracts. Our analysis, which is implemented in our static analyzer EVMLiSA, is based on a parametric abstract domain that approximates concrete execution stacks at each program point as an l -sized set of abstract stacks of maximal height h ; the results of the analysis are then used to resolve the jump destinations at jump nodes. Furthermore, EVMLiSA includes a checker for reentrancy detection, working on the constructed CFG. Our experiments show that, by fine-tuning the analysis parameters, EVMLiSA is able to build sound CFGs for all real-world smart contracts in the considered benchmark suite. Moreover, EVMLiSA successfully detects all reentrancy vulnerabilities in EVM bytecode smart contracts, while producing a small number of false positives.
The profound digital transformations currently shaping the world—particularly in the field of contracting—have given rise to a new type of legal relationship known as self-executing smart contracts. These contracts are characterized by their autonomous conclusion and execution through blockchain technology, without the need for continuous human intervention. This poses a significant challenge to traditional legal frameworks, foremost among them the conflict-of-law rules in private international law. These rules presume the existence of certain criteria that allow for the determination of the law applicable to the legal relationship in dispute, whether based on the place of contract formation, the place of performance, or the nature of the contested relationship. However, the decentralized technical nature of self-executing contracts undermines these assumptions and weakens the ability of the adjudicator to apply traditional legal tools in understanding the relationship and attributing it to the appropriate legal system.
Haixia Cui, Bo Xie, Hongjiang Wang, Victor C. M. Leung
Wireless channel modeling is critical to understanding and optimizing signal transmission. Wireless channels are influenced by many factors, including path loss, reflection, fading, and interference, making them complex and difficult to model and predict. Although some traditional wireless channel modeling methods are effective, they have limitations in handling complex multipath effects and nonlinear characteristics. Generative artificial intelligence (GAI) has the ability to generate robust data and therefore can be potentially used to model realistic wireless channel characteristics. However, there exist some challenges in GAI for wireless channel modeling, including physical layer network security issues, limited generalization ability, and the bandwidth consumption of centralized training. This article introduces a GAI-based wireless channel modeling framework, which leverages blockchain and federated learning to address efficiency, network security, and data privacy concerns in GAI channel modeling. Blockchain technology, through distributed ledgers and smart contracts, enables resource sharing and efficient utilization, enhancing computational efficiency and reducing network security risks. Federated learning allocates training tasks to different devices and nodes, thus allowing model training without centralizing data, protecting user privacy, and reducing network bandwidth usage. This article demonstrates the performance and effectiveness of the proposed framework through numerical results.
Blockchain has matured from being mainly linked with cryptocurrencies to being a central technology with revolutionary potential for financial systems globally. By allowing safe, decentralized, and tamper-resistant ledgers, blockchain can cut down on the cost of transactions, enhance transparency, and raise efficiency in many areas of finance. This paper discusses the applications of blockchain in payments, cross-border remittances, capital markets, trade finance, and compliance. It includes fresh data from international organizations, central banks, and private industry reports to note both Indian and global developments. For example, close to 91% of the central banks surveyed are now investigating central bank digital currencies (CBDCs), and India's pilot retail digital rupee has already signed up millions of customers. Concurrently, the World Bank also points out that the global remittance average cost still exceeds 4%, a far cry from policy levels, indicating that blockchain is able to bridge this gap. While the technology has potential for efficiency and financial inclusion, there are issues around interoperability, privacy, cyber threats, and regulatory clarity. The report concludes that the contribution of blockchain to finance will most likely be characterized not by substituting current systems, but by integrating programmability and transparency into the mainstream financial infrastructure.
The success of terrorist organizations in maintaining traditional resources to finance terrorist operations is sufficient to push them away from virtual currencies and their usual risks, as long as they are able to sell oil and transfer funds between their territories, and as long as their funds remain safe from attacks and persecution by the international community. Encrypted virtual currencies are characterized by high degrees of secrecy, privacy, and decentralization - and extremist religious groups And terrorism that adopts violence as a means of operation and expansion, and studying indicators indicating the growing importance of these currencies in circulation, exchange, and commercial transactions, nd in financing extremist religious groups and organizations, and financing the purchase of weapons and equipment used by these groups. It is a decentralized currency with no competent authority, and no central bank responsible for issuing it, and it is not subject to the restrictions of international banking and monetary institutions. This is a significant advantage that has attracted many individuals and groups to its circulation. Had international institutions and organizations been able to subject this currency to international oversight, or to a central authority, it would have lost its most important advantage, and terrorist and extremist organizations would have been unable to exploit it further.
Emerging applications in cloud computing, big data, and the Internet of things have driven the advancement and implementation of security protocols, including secure multi-party computation, fully homomorphic encryption, and zero-knowledge proofs, to meet heightened security demands. Designing cryptographic permutations and block ciphers using a partial substitution-permutation network (P-SPN) approach, where the nonlinear part does not cover the entire state, has recently gained attention due to favorable implementation characteristics in various scenarios. For the word-oriented P-SPN schemes with a fixed linear layer, the choice of the maximum distance separable (MDS) matrix significantly affects the security level provided by P-SPN designs. If the MDS matrix is chosen weak, it will allow for extremely maximum invariant subspace that pass the entire rounds without activating any non-linear operation. Firstly, we investigate the properties of a special block matrix with circulant block, specifically utilized within the linear layer matrix of P-SPN structure schemes. Subsequently, our investigation extends to present the annihilating polynomial of low degree for these specific type of matrices, as well as to put forward the range of determining their minimal polynomial degree. Finally, this study articulates a lower bound estimated for the dimension of the maximum invariant subspace within the P-SPN structure schemes when integrated with the aforementioned matrix type. In scenarios where the S-box number$s$= 1 in the P-SPN structure schemes, we achieve a precise determination of the dimension of maximum invariant subspace. Conversely, for cases with$s$> 1, with some certain specific conditions, our research establishes more compact lower bound for the dimension of the maximum invariant subspace. The research results of this paper offer valuable design guidance for the development of matrices within the linear layer of P-SPN architecture schemes.
Jingcheng Zhang, Yekai Zhou, Yingxuan Ren, Man Ho Au · 9 authors
Advancements in sequencing technologies grant individuals unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate in privacy protection, limiting individuals' control over their genomic information, hindering data sharing, and posing challenges for biomedical research. Therefore, demand exists for an owner-governed system fulfilling owner authority, life cycle data encryption, and verifiability simultaneously. Here, we realized Governome, an owner-governed data management system empowering individuals with real-time control over their genomic data. Governome leverages a blockchain to manage transactions and permissions, granting data owners dynamic permission management with full transparency on data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form throughout its life cycle. Governome can support versatile genomic applications. We implemented and tested individual variant query, cohort study, genome-wide association study (GWAS) analysis, and forensics on 2,504 1000 Genomes Project (1kGP) genomes, demonstrating its robustness and scalability. Governome is open-source at https://github.com/HKU-BAL/Governome.
This systematic review examines the role of federated learning (FL) as a privacy-preserving paradigm for enterprise decision systems, synthesizing evidence from 187 peer-reviewed studies. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates algorithmic, systems, security, sectoral, and governance perspectives to provide a comprehensive account of current knowledge. Findings highlight that foundational algorithms such as FedAvg, FedProx, and SCAFFOLD dominate the methodological landscape, with significant adaptations emerging to address non-IID and unbalanced datasets across distributed organizational silos. Privacy-preserving mechanisms—including differential privacy, secure aggregation, homomorphic encryption, and multiparty computation—were consistently applied as layered defenses, balancing mathematical guarantees with empirical resilience. The synthesis further revealed critical vulnerabilities to model poisoning, backdoor attacks, and gradient leakage, alongside defensive strategies such as robust aggregation, anomaly detection, and differential privacy clipping. Sector-specific implementations demonstrate FL’s practical utility in healthcare, finance, retail, logistics, telecommunications, and public services, where it enables collaborative modeling without violating data residency or confidentiality requirements. Governance and ethical frameworks, particularly GDPR, CCPA, and the NIST Privacy Framework, were found to shape deployment practices, while documentation artifacts such as datasheets, model cards, and privacy budget ledgers ensure accountability and transparency. Comparative surveys position FL as an integrative socio-technical architecture that unites distributed optimization, privacy engineering, adversarial robustness, and AI governance into a coherent enterprise-ready model. The review concludes that federated learning provides enterprises with a scalable, secure, and ethically aligned approach to leveraging distributed data while preserving trust and compliance.
Smart contracts are self-executing programs that run on blockchain platforms, most notably Ethereum.They automate transactions and enforce agreements without intermediaries, forming the foundation of decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized applications (dApps).Despite their growing importance, smart contracts remain prone to security vulnerabilities.Exploited bugs can lead to irreversible financial losses, service disruptions, and systemic failures.Although machine learningbased tools have emerged to aid vulnerability detection, two critical challenges remain: (1) limited fault localization at the function level, and (2) a lack of interpretable, human-readable explanations that enable developers to understand and fix issues effectively.This thesis addresses both challenges by proposing a unified framework that combines graph-based neural network modeling with explainable language model techniques.Specifically, the contributions consist of: (1) a function-level vulnerability detection system using Sub-Graph Neural Networks (Sub-GNNs), and (2) an explanation generation mechanism based on synthetic data and Chain-of-Thought (CoT) prompting using large language models (LLMs).These two components aim to improve both the technical granularity and practical usability of smart contract security analysis.The first part of the thesis introduces a novel function-level detection method that decomposes smart contracts into subgraphs centered around individual functions.While prior approaches using Graph Neural Networks (GNNs) operate at the contract level, they fail to pinpoint specific sources of vulnerabilities, limiting their value for debugging and remediation.To overcome this, we construct function-level subgraphs that incorporate controlflow and data-flow dependencies, preserving the semantic and structural context of each function.We then apply a Sub-GNN model to perform vulnerability classification at this finer granularity.Empirical evaluation on a curated synthetic dataset demonstrates that the proposed method achieves high precision in localizing faulty functions.Although it trades off a small margin of global classification accuracy compared to full-graph models, the localized predictions are significantly more actionable for developers.A benchmark comparison quantifies this trade-off and validates the effectiveness of subgraph-based analysis in practical settings.To facilitate this line of work, we develop a synthetic dataset of smart contracts with function-level vulnerability labels.The dataset includes diverse vulnerability types such as reentrancy, integer overflows, access control flaws, and unhandled exceptions.Each function is annotated with corresponding vulnerability types and contains metadata for constructing control and data flow graphs.This dataset fills a gap in the current landscape, which largely lacks fine-grained, labeled corpora for training and evaluating function-level detectors.The second component of the thesis tackles the issue of explanation.While detecting a vulnerability is important, understanding why it occurs and how to resolve it is crucial for real-world usability.Most existing detection tools output low-level indicators such as line numbers or vulnerability labels without offering semantic explanations.To address this gap, we propose an explanation generation system that produces structured, human-readable justifications for detected vulnerabilities.We construct another synthetic dataset where each entry consists of a vulnerable function, its formal label, and a professionally formatted explanation describing the issue, its cause, and suggested remediation steps.These explanations are derived from real-world audit patterns and follow a consistent template.Together, these two components form a comprehensive framework for smart contract vulnerability analysis.The Sub-GNN-based detector provides precise localization of faulty functions, while the CoT-guided explanation generator delivers semantic insight into the causes and consequences of the vulnerabilities.This dual capability bridges the gap between vulnerability detection and developer comprehension.The thesis concludes with a discussion of future directions.On the detection side, extending the Sub-GNN architecture to support inter-function and inter-contract reasoning could enable the modeling of call chains and complex compositional vulnerabilities.On the explanation side, integrating user feedback to iteratively refine generated explanations could support interactive auditing tools.Furthermore, we propose exploring multimodal models that combine graph-based embeddings with textual features to enhance both detection and explanation tasks.
Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.
With the shift from Centralized Finance (CeFi) to Decentralized Finance (DeFi), financial transactions have become trustless and self-executing through blockchain platforms, creating new opportunities while exposing the ecosystem to significant fraud risks. However, due to the lack of centralized oversight and the vulnerabilities in the blockchain platforms, DeFi transactions still face several security challenges, including fraud, identity theft, insider threats, and data breaches. Various methods, including regulatory frameworks, machine learning (ML), and deep learning (DL) techniques, are employed to detect these threats, particularly fraud, in DeFi transactions. Although these approaches help identify fraudulent activities, they face challenges related to accuracy and zero-day attacks due to insufficient data and the complexity of emergingfraud patterns. This study presents a novel approach for detecting and profiling fraud attacks, including zero-day ones in DeFi transactions, thereby eliminating the reliance on wallet transaction history, a limitation that previous research has heavily depended on. The proposed approach leverages two key components: a novel analyzer named DeFiTransLyzer (V1.0) and an Advanced Genetic Algorithm (AGA) for fraud transaction profiling. DeFiTransLyzer extracts 79 features from transaction and wallet data. At the same time, the AGA incorporates advanced techniques, including Penalized Fitness Evaluation, Elite Retention Strategy, Dynamic Mutation Rate, and dynamic generation, to create precise fraud profiles. By focusing solely on transaction features, the model ensures that all fraudulent activities, including zero-day ones, initiated within the first transaction of a new account can be effectively detected, without relying on prior wallet activity. To address the scarcity of comprehensive validation datasets, we introduce BCCCDeFiFraudTrans-2025, which comprises 1,026,867 annotated Ethereum transaction samples from the DeFi ecosystem. Additionally, the study establishes two taxonomies for systematic classification, covering the literature on fraud detection and profiling methods. Experimental results demonstrate that the proposed method achieves superior accuracy, precision, and efficiency while offering interpretability through its profiling mechanism. These promising outcomes highlight the potential of AGA profiling to enhance the detection and identification of fraudulent activities, including zero-day ones within DeFi transactions, contributing to the security and resilience of blockchainbased financial systems.
Jonas Lopes de Vilas Boas, Ygor S. Costa, Rodrigo da Rosa Righi, Antônio Marcos Alberti · 5 authors
Reliable vaccine tracking and monitoring during transport and storage are essential to ensure dose effectiveness while minimizing waste. However, current solutions face challenges related to reliability, immutability, security, transparency, flexibility, extensibility, patient support, trust, and cost. Centralized systems are vulnerable to fraud, tampering, and manipulation, often relying on manual service contracts and lack of attested IoT devices to ensure data authenticity. Moreover, most existing platforms do not provide tamper-proof, near real-time monitoring, resulting in operational vulnerabilities and increased costs. This article presents Coldnet, a novel architecture for vaccine tracking and tracing that addresses these issues by: (i) integrating IoT device attestation with the registration of immutable data and flexible monitoring attributes; (ii) using Blockchain-based smart contracts to automatically manage tracking and monitoring clauses, improving security and enabling dynamic rule management; (iii) offering intuitive interfaces to support patient access to delivery information; and (iv) deploying an affordable, user-friendly IoT prototype to monitor and report vaccine status. A case study demonstrates Coldnet's feasibility, with an average delay of 20 seconds for recording and checking conditions — suitable for real operations. A simulation evaluating scalability and the impact of IoT attestation shows transaction costs of US$1.51 for ten vaccine batches with five monitored properties each, a cost deemed acceptable for the added features. Execution delays remained stable (0.85–0.92 seconds), with negligible impact from attestation. Coldnet contributes to reliable vaccine logistics, improving public health efforts by strengthening trust, transparency, and data integrity in vaccination campaigns.