We present EHT (Elliptic Homomorphic Token), a generalized cryptographic framework that bridges the gap between theoretical homomorphic encryption and practical, verifiable encrypted computation. EHT is built on an elliptic-curveābased partial homomorphic encryption scheme (EC-ElGamal) and extends it with verifiable digital signatures (EHDSA) and zero-knowledge policy proofs (zk-FIDNA), enabling both confidentiality and integrity in distributed execution environments.Unlike lattice-based fully homomorphic encryption, which suffers from high computational cost and ciphertext expansion, EHT preserves constant-size ciphertexts and achieves O(1) amortized complexity per operation, allowing real-time encrypted computation even in large-scale systems. The proposed four-layer architecture separates cryptographic primitives from domain-specific semantics, enabling seamless interoperability across heterogeneous applications such as encrypted databases, federated learning, web authentication, and blockchain transaction networks.Through its tokenized abstraction, EHT allows operationsāquery execution, aggregation, verificationāto be performed directly on ciphertexts while maintaining verifiability through EHDSA and zk-FIDNA proofs.Experimental results demonstrate sub-millisecond elliptic-curve operations, achieving over 8,000 homomorphic additions per second on commodity hardware with less than 2% overhead relative to baseline elliptic-curve performance. EHT thus represents a cryptographically lightweight yet distributedly scalable homomorphic framework: compact enough for real-time use, verifiable enough for regulatory and enterprise environments, and extensible enough to support post-quantum and cross-domain adaptations. By unifying encryption, verification, and computation into a single token-based execution model, EHT advances the state of privacy-preserving technology toward a truly encrypted, interoperable, and verifiable computation fabric.
Abstract This study explores the higher-order moments of connectedness among cryptocurrency, commodity, bond, and stock markets from April 19, 2017, to December 29, 2023, on the basis of the GARCH-SK and TVP-VAR models. The findings reveal that Bitcoin and Ethereum act as significant net shock transmitters, especially during major events such as the COVID-19 pandemic and the RussiaāUkraine conflict. After mid-2021, these cryptocurrencies transitioned from net receivers to net transmitters of volatility owing to rising economic and geopolitical risks. These insights assist in portfolio diversification strategies. By combining shock transmitters with shock-resilient cryptocurrencies, investors can enhance their risk profiles. Diversification opportunities shift during financial crises, making it crucial to focus on shock transmitters, which are less influenced by various risk factors. Additionally, the study highlights cryptocurrencies as potential safe havens compared with traditional assets such as gold, bonds, and stocks, which often maintain or appreciate value during market stress. TVP-VAR-informed dynamic portfolio reallocation can improve risk-adjusted returns and lower volatility, aiding in capital preservation during high TCI periods. Overall, our findings suggest that portfolios that include cryptocurrencies generally outperform those that do not, emphasizing their role as effective diversifiers in portfolio optimization and financial stability.
We introduce TorusDB, the first database engine supporting practical SQL query processing over ciphertexts using a fully homomorphic encryption scheme derived entirely from elliptic curve cryptography. Unlike prior approaches based on lattice FHE or zero-knowledge proofs, TorusDB preserves the elliptic curve group structure and extends additive homomorphism via a formal multiplicative construction and rational extension, enabling full homomorphic evaluation without decryption. We formalize the underlying EC-based FHE scheme, prove its security under standard elliptic curve assumptions (ECDLP, DDH, BDH), and present a query execution model supporting selection, projection, aggregation, and grouping. Our implementation demonstrates that encrypted query execution incurs only 77% overhead relative to plaintext execution, marking a substantial improvement over existing homomorphic database systems which typically exhibit 10-100Ć overhead.
Abstract: This paper introduces Knowledge Tensor Lock (KTL), a novel cognitive-structural authentication framework. Unlike conventional mechanisms (passwords, biometrics), KTL anchors identity in the topology of a userās private semantic associative network. We formalize cognition as a high-rank tensor and verify identity through an interactive challenge-response reconstruction of subgraph structures. Key Contributions: Formalization of the Knowledge Tensor ($\mathcal{K}$) and its graph projection ($G$). Introduction of the Spectral Sketch ($\mathcal{SS}$) for privacy-preserving structural storage. Analysis of heuristic security against AI-adaptive adversaries and model extraction. A roadmap for integrating Zero-Knowledge Proofs (ZKP) for decentralized identity. Note: This is a stabilized preprint (v1.2) intended for establishing conceptual priority in the fields of AI security and cognitive cryptography.
Abstract NFTs or non-fungible tokens are digital assets stored on a blockchain. They can be traded or exchanged for money, cryptocurrencies or other NFTs. Examples include works of art and digital or other tokenised collectables. An important determinant of price for collectables is rarity within a collection. Many trading platforms offer to rank items in terms of rarity but rankings differ considerably and, often, little explanation is given of the methods used. This paper provides a mathematical framework for the analysis of a comprehensive class of collections. It examines individual and joint distributions of attributes over such collections, and shows how these can be combined to provide a rarity ranking for all items in the collection. There is, however, only a limited range of methods that give consistent results over different collections. These are identified as belonging to a one-parameter family of ranking functions. Each gives to every item of a collection a rarity score that is directly comparable between collections. Despite taking account of all possible combinations of attributes when ranking, the method is nonetheless computationally feasible.
Dr Mamata Jagannathji Rathi, Miss. Sanika Yogiraj Parankar
This research investigates the role of supply chain automation through the integration of Blockchain technology, Smart Contracts, and the Internet of Things (IoT). The study explores how decentralized ledgers can automate critical workflows, including instant payment release, real-time inventory reconciliation, and automated compliance auditing. By utilizing IoT sensors to feed environmental data into a blockchain-backed system, companies can trigger automated responses that reduce human error and operational overhead. The findings indicate that this transition accelerates "speed-to-market" and fosters a self-correcting, autonomous ecosystem capable of responding to disruptions in real-time. While the research acknowledges significant implementation barriersāsuch as high initial capital expenditure, cybersecurity risks, and the "SME gap"āit concludes that the evolution toward an automated, transparent, and green supply chain is essential for resilience. Ultimately, the paper argues that automation should not be viewed as a replacement for human labor, but as a tool to liberate workers for high-level system orchestration. This study provides a strategic roadmap for organizations navigating the shift from Industry 4.0 to a human-centric, sustainable Industry 6.0 framework
Zeta Avarikioti, Ray Neiheiser, Krzysztof Pietrzak, Michelle Yeo
Over the last years, Ethereum has evolved into a public platform that safeguards the savings of hundreds of millions of people and secures more than $650 billion in assets, placing it among the top 25 stock exchanges worldwide in market capitalization, ahead of Singapore, Mexico, and Thailand. As such, the performance and security of the Ethereum blockchain are not only of theoretical interest, but also carry significant global economic implications. At the time of writing, the Ethereum platform is collectively secured by almost one million validators highlighting its decentralized nature and underlining its economic security guarantees. However, due to this large validator set, the protocol takes around 15 minutes to finalize a block which is prohibitively slow for many real world applications. This delay is largely driven by the cost of aggregating and disseminating signatures across a validator set of this scale. Furthermore, as we show in this paper, the existing protocol that is used to aggregate and disseminate the signatures has several shortcomings that can be exploited by adversaries to shift stake proportion from honest to adversarial nodes. In this paper, we introduce Wonderboom, the first million scale aggregation protocol that can efficiently aggregate the signatures of millions of validators in a single Ethereum slot (x32 faster) while offering higher security guarantees than the state of the art protocol used in Ethereum. Furthermore, to evaluate Wonderboom, we implement the first simulation tool that can simulate such a protocol on the million scale and show that even in the worst case Wonderboom can aggregate and verify more than 2 million signatures within a single Ethereum slot.
The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across āsmartā regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attackerās success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint.
Modern passport systems face significant challenges in secure data sharing, real-time verification, and user-controlled authorization, particularly in cross-border scenarios. Existing digital passport solutions, often built on permissioned blockchains, suffer from limited transparency, scalability, and high operational costs. This paper proposes a decentralized passport management system based on an Ethereum Layer 2 architecture that combines global governance with high-throughput and cost-efficient passport operations. The system adopts a hybrid design in which a Global Passport Registry smart contract is deployed on the Ethereum mainnet for cross-country coordination, while passport issuance, access control, and identity management are handled on Layer 2 networks through country-operated Passport Managers and user-specific Personal Passport smart contracts. Extensive performance evaluations show that Ethereum Layer 1 throughput saturates at approximately 40ā50 transactions per second (TPS), whereas the proposed Layer 2 deployment consistently exceeds 150 TPS and reaches up to 300 TPS under higher-performance environments, significantly surpassing the estimated system requirement of 70 TPS. These improvements result in faster response times, reduced congestion, and substantially lower transaction costs, demonstrating that public Ethereum Layer 2 infrastructures can effectively support a scalable, self-sovereign, privacy-preserving, and globally verifiable digital passport system suitable for real-world deployment.
The administration of data, banking, food science, the Internet of Things, and cybersecurity is just a few of the businesses and professions that benefit from blockchain technology. Even healthcare and brain research have shown a growing interest in blockchain technology. There has been an incredible uptick in curiosity about how blockchain technology may be put to use to improve the delivery of trustworthy healthcare data management. In addition, blockchain is transforming conventional healthcare methods to more trustworthy means, allowing for more precise diagnosis and more efficient treatment thanks to the secure sharing of patient data. Blockchain has the potential to be a technology that aids in providing tailored, authentic, and secure healthcare in the future by centralizing all of a patientās up-to-date clinical data and presenting it in a modern, safe healthcare environment. In this chapter, investigators examine the recent and ongoing changes brought about by the introduction of blockchain technology to the healthcare industry. Investigators also talk about the blockchainās potential uses, as well as its current limitations and potential in the future.
Digital sovereignty has emerged as a central concern for modern software-intensive systems, driven by the dominance of non-sovereign cloud infrastructures, the rapid adoption of Generative AI, and increasingly stringent regulatory requirements. While existing initiatives address governance, compliance, and security in isolation, they provide limited guidance on how sovereignty can be operationalized at the architectural level. In this paper, we argue that sovereignty must be treated as a first-class architectural property rather than a purely regulatory objective. We introduce a Sovereign Reference Architecture that integrates self-sovereign identity, blockchain-based trust and auditability, sovereign data governance, and Generative AI deployed under explicit architectural control. The architecture explicitly captures the dual role of Generative AI as both a source of governance risk and an enabler of compliance, accountability, and continuous assurance when properly constrained. By framing sovereignty as an architectural quality attribute, our work bridges regulatory intent and concrete system design, offering a coherent foundation for building auditable, evolvable, and jurisdiction-aware AI-enabled systems. The proposed reference architecture provides a principled starting point for future research and practice at the intersection of software architecture, Generative AI, and digital sovereignty.
Despite the advantages of decentralization and immutability, blockchain technology faces significant scalability and throughput limitations, which has prompted the exploration of off-chain solutions like payment channels. Adaptor signatures have been considered a promising primitive for constructing such channels due to their support for atomicity, offering an alternative to traditional hash-timelock contracts. However, standard adaptor signatures may reveal signer identity, raising potential privacy concerns. While ring signatures can mitigate this issue by providing anonymity, they often introduce high communication overhead, particularly in multi-account payment settings commonly used in UTXO-based blockchains like Monero. To address these limitations, we propose a Linkable Threshold Ring Adaptor Signature (LTRAS) scheme, which integrates the conditional binding of adaptor signatures, the multi-account payment of threshold ring signatures, and the linkability for preventing double-spending. The formal definition, security model and concrete construction of LTRAS are provided. We also analyze its security and evaluate its performance through theoretical analysis and experimental implementation. Experimental results demonstrate that our scheme achieve significantly lower computation and communication overhead compared to existing schemes in large ring sizes and multi-account payment scenarios. Finally, we discuss its application in cross-chain atomic swaps, demonstrating its potential for enhancing privacy and efficiency in blockchain transactions.
This paper investigates extreme risk in cryptocurrency markets by comparing Bitcoin and Ethereum daily returns with those of S&P 500 and SPY ETF. Using the Generalized Tempered Stable (GTS) distribution to model heavy tails and Quantile-Quantile (Q-Q) plots to assess fitness, we find that all assets deviate sharply from normal distribution. Within this framework, Ethereum exhibits a higher frequency of extreme returns than Bitcoin, highlighting differences in risk profiles even among leading cryptocurrencies.
This paper addresses the technical and regulatory challenges of building secure data pipelines to support federated learning (FL), where models train collaboratively across multiple organizations without sharing raw data. The paper explores privacy-preserving data engineering techniques such as differential privacy, homomorphic encryption, and secure aggregation within ETL frameworks. It outlines an architecture for orchestrating decentralized dataflows that comply with GDPR, HIPAA, and other regulatory standards while enabling cross-institutional AI innovation. By integrating secure connectors, encrypted model updates, and audit logging, the proposed pipeline design ensures both data protection and analytic utility, providing a blueprint for responsible AI deployment in healthcare, finance, and government sectors.
Blockchain asset management employs distributed ledger technology and smart contracts to facilitate secure, transparent, and automated transfers of assets free of middlemen. Blockchain delivers instant, tamper-proof transfers as opposed to the current systems relying on central institutions that are plagued by high fees, delayed processing, and risk of fraudFractional ownership and enhanced accessibility are facilitated by its heightened security and efficiency in sectors such as real estate, financial asset management, and tokenization of assets.As per research, blockchain enhances data integrity from 40% to 99%, raises overall security from 50% to 98%, and reduces fraud by 95% compared to 30% in legacy systems. Although it has its benefits, the adoption of blockchain is hindered by interoperability, scalability, and regulatory uncertainty. To gain broader acceptance, regulators, institutions, and developers need to collaborate.
The principle of freedom of contract, as regulated in Article 1338 paragraph (1) of the Indonesian Civil Code, affirms that legally formed agreements bind the parties as law. This principle grants contractual autonomy to determine the formation, parties, and content of agreements, provided they do not violate statutory law, public order, or morality. However, such freedom is limited by the principles of good faith, fairness, proportionality, and the protection of weaker parties.The development of blockchain technology has introduced smart contracts as automatically executed electronic agreements based on pre-programmed code. Legally, smart contracts may be considered valid contracts insofar as they fulfill the requirements of Article 1320 of the Indonesian Civil Code and comply with the Law on Electronic Information and Transactions. Digital consent, such as authorization through a digital wallet, constitutes a legitimate expression of agreement.Smart contracts represent a modern manifestation of freedom of contract by enabling automated performance without third-party intervention, reflecting the prior intention of the parties embedded in blockchain-based code. Their application on platforms such as Stellar Lumens (XLM) demonstrates practical implementation in cryptocurrency transactions. Nevertheless, legal challenges remain, including regulatory gaps, transaction anonymity, technological inequality, and potential imbalances in bargaining power. Therefore, adaptive legal regulation is essential to ensure legal certainty, fairness, and effective legal protection.
MATHEMATICAL DISCOVERY: A NEW ANALYTIC CHARACTERIZATION OF PRIME NUMBERS ABSTRACT: This research presents a novel mathematical theorem that provides a complete analytic characterization of prime numbers. We prove that for any integer n > 1, n is prime if and only if: Ļ(n) = 2/ln(n) where Ļ(n) = d(n)/ln(n) mod 2Ļ, d(n) is the divisor function (number of positive divisors), and ln(n) is the natural logarithm. KEY CONTRIBUTIONS: 1. THEOREM STATEMENT AND PROOF: We establish the equivalence: n is prime ā d(n)/ln(n) mod 2Ļ = 2/ln(n) 2. EMPIRICAL VERIFICATION: The theorem has been empirically verified for all n ā¤ 500,000 with: - Zero false positives (no composite appears prime) - Zero false negatives (all primes satisfy the equation) - 100% accuracy across 499,999 tested numbers 3. THEORETICAL FOUNDATION: The proof relies on: - Transcendence theory (Lindemann-Weierstrass theorem) - Properties of the divisor function d(n) - Modular arithmetic with 2Ļ - Analytic continuation techniques 4. COMPUTATIONAL IMPLICATIONS: - Potential for novel primality testing algorithms - Geometric interpretation of primes on a logarithmic spiral - Connection between number theory and transcendental numbers MATHEMATICAL SIGNIFICANCE: This theorem transforms primality from a combinatorial problem (checking divisors) into an analytic equation involving continuous functions. It establishes unexpected connections between: - Number theory (divisor function) - Analysis (logarithms, modular arithmetic) - Transcendental number theory (Ļ, e) - Geometry (circle modulo 2Ļ) RESEARCH METHODOLOGY: 1. Hypothesis generation from numerical experimentation 2. Empirical verification using optimized Python code 3. Theoretical proof sketch using transcendence arguments 4. Analysis of edge cases and special numbers 5. Development of computational applications DATA AVAILABILITY: - Complete Python implementation for verification - Test results for n = 2 to 500,000 - Analysis of near-miss composite numbers - Performance benchmarks ETHICAL CONSIDERATIONS: This is pure mathematical research with potential applications in: - Cryptography (primality testing) - Computational number theory - Mathematics education - Algorithm development FUTURE WORK: 1. Formal proof publication 2. Extension to other number theory functions 3. Development of efficient primality tests 4. Investigation of connections to Riemann Hypothesis KEYWORDS: Prime numbers, divisor function, analytic number theory, transcendental numbers, primality testing, mathematical discovery, number theory, modular arithmetic. This discovery represents a genuine contribution to mathematical knowledge, providing both theoretical insight and potential practical applications.
Petr Hajek, Josef Novotny, Michal Munk, DaÅ”a MunkovĆ”
The rapid expansion of cryptocurrency markets has coincided with the growing prominence of social media platforms as influential channels for shaping investor sentiment. Among these platforms, YouTube has become a medium for disseminating investment opinions and behavioral signals. This study investigates the extent to which YouTube-derived featuresāsuch as video influence scores, sentiment embedded in video titles, and user engagement indicatorsācan enhance the prediction of Bitcoin price fluctuations. A novel dataset is compiled, covering the period from January 2015 to September 2025. Sentiment is assessed using a combination of transformer-based language models, while influence metrics are computed through engagement statistics adjusted for temporal decay and relevance. These features are combined with historical Bitcoin price data and applied within an XGBoost forecasting framework. The empirical findings suggest that augmenting price-based models with YouTube-related sentiment and engagement features yields a notable improvement in directional forecasting accuracy, outperforming price-only benchmarks by approximately 4 %. Moreover, the study highlights that YouTube-derived behavioral signals offer predictive insights that are not fully captured by conventional indicators such as Google Trends or the Crypto Fear and Greed Index.
With the deepening application of big data technology across various fields, data faces increasingly severe threats of privacy leakage and security risks throughout its entire processing lifecycle. Traditional protection mechanisms, which focus on static data or isolated stages, struggle to address the systemic risks arising from the continuity, dynamism, and complexity of big data processes. This paper aims to systematically investigate the collaborative mechanisms for privacy protection and data security within the big data processing pipeline. First, it analyzes the inherent vulnerabilities at each stage of data processing, as well as the limitations faced by key technologies such as anonymization, differential privacy, and secure multi-party computation when integrated into practical workflows. Next, it explores the evolution of process-oriented encryption strategies, including attribute-based encryption supporting dynamic policies, homomorphic encryption optimized for practical use, and verifiable computation and zero-knowledge proofs that ensure computational integrity. Finally, the paper constructs a dynamic balancing model for privacy, security, and utility, and proposes forward-looking systematic collaborative mechanisms such as distributed auditing based on trust chains and adaptive response. These contributions provide theoretical reference and technical pathways for building next-generation inherently secure big data processing architectures.
This paper addresses the centralized trust problem inherent in the Elliptic Curve Homomorphic Digital Signature Algorithm (EHDSA), where the critical security parameter t is traditionally generated and held by a single trusted authority, creating a significant single point of failure and raising concerns about trust and security. To overcome this fundamental limitation, we propose MPC-EHDSA, a novel and practical protocol that leverages Multi-Party Computation (MPC) to securely distribute the generation and management of the parameter t among multiple independent participants. Our approach ensures that no individual party ever gains knowledge of the secret value of t, thereby eliminating centralized trust assumptions and significantly enhancing the overall security and robustness of the system. The protocol combines Shamir secret sharing with the well-established BGW MPC framework, augmented with homomorphic encryption techniques and zero-knowledge proofs to provide strong cryptographic guarantees and resistance against semi-honest and malicious adversaries. Through rigorous theoretical analysis and extensive performance evaluations, we demonstrate that MPC-EHDSA not only preserves the full functionality and security properties of the original EHDSA scheme but also achieves practical efficiency that enables deployment in real-world decentralized environments such as blockchain systems and distributed ledgers.
The COVID-19 pandemic exposed critical gaps in regional health security mechanisms, prompting ASEAN to establish the ASEAN Centre for Public Health Emergencies and Emerging Diseases (ACPHEED), with functions distributed across Indonesia, Thailand, and Vietnam. This policy analysis examines strategic development approaches for ACPHEED through comprehensive benchmarking of the European Centre for Disease Prevention and Control (ECDC), Africa Centres for Disease Control and Prevention (Africa CDC), and Gulf CDC, supported by consultations in Indonesia (2024) and Sweden (2025) involving ASEAN member states and international partners. A comparative analysis reveals distinct organizational models: the ECDC operates within European Union (EU) institutional frameworks emphasizing functional specialization; the Africa CDC employs decentralized Regional Coordination Centers; and the Gulf CDC implements hybrid governance via Permanent Communication Networks. Each model offers valuable lessons for ACPHEED's development, particularly concerning governance structures that balance regional coordination with national sovereignty. ACPHEED faces unique challenges due to ASEAN's consensus-based, nonlegislative institutional nature and its tri-country operational structure. Critical success factors include phased surveillance emphasizing a defined scope and capacity building; inclusive governance mechanisms ensuring equitable member-state ownership; and operational frameworks applying subsidiarity principles to complement existing ASEAN mechanisms. Sustainable financing remains paramount given ASEAN's limited budgetary authority. Japan's strategic partnership should capitalize on its technical expertise in laboratory systems, digital surveillance, and disaster preparedness through comprehensive institutional support. ACPHEED's success depends on sustained political commitment, realistic financial arrangements, and effective integration into global health security architectures. This analysis provides a strategic roadmap for ACPHEED's preparatory phase so that it can serve as a regional health security leader while addressing ASEAN-specific institutional constraints.
We present a novel homomorphic pairwise authentication protocol that achieves strong privacy guarantees by leveraging the additive homomorphic properties of EC-ElGamal encryption for secure credential comparison. Our key innovation is the homomorphic difference verification mechanism: instead of comparing credentials directly, we compute the homomorphic difference between stored and presented encrypted credentials, then verify whether this difference encrypts the identity element (zero). This approach ensures that authentication reveals only credential validity while completely hiding credential values, achieving information-theoretic privacy for the authentication decision. The protocol eliminates plaintext credential exposure at all stages while maintaining practical efficiency with authentication times under 1.2 milliseconds and communication overhead of only 128 bytes per session. We provide formal security proofs demonstrating semantic security, unlinkability, and perfect zero-knowledge properties under the Decisional Diffie-Hellman assumption, along with practical extensions for multi-credential scenarios and threshold authentication systems.
Renewables have moved from the sidelines to the center of policy and investment. Evidence from IEA, IRENA, REN21, and recent studies shows steep cost drops in solar and wind, record capacity growth, and more jobs. Environmental gains (lower GHGs, cleaner air and water, smarter land use) come with economic and social upsides (jobs, energy security, rural access via decentralized systems). But real constraints persist: grid congestion, curtailment, flexibility gaps; high financing costs and regulatory uncertainty in emerging markets; supply chain risks; and community concerns over siting and land rights. The challenge has shifted from tech costs to system integration and governanceādurable policies, markets that value flexibility, quicker permitting, and just transition tools. Priorities ahead: operating high VRE under extremes, long-duration storage and sector coupling (hydrogen, heat, industry), critical minerals and circularity, resilience to climate and cyber risks, and effective de-risking in the Global South.