Sai Sakunthala Guddanti, Anupama Ray, Mrunal Arun Kumavat, Anil Prabhakar
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.
This paper formalizes the architecture of Hamilton V5, a continuous physical field transformer engineered for industrial automation. Moving beyond the discrete token-based processing of legacy models, Hamilton V5 introduces Always-On Autonomy (AOA), a paradigm that perpetually consumes raw spatiotemporal media inputs and maps them concurrently across a 12-dimensional mechanical phase space. To ensure structural stability and mitigate the compounding numerical drift common in autonomous systems, V5 integrates a closed-loop vector feedback correction mechanism. Crucially, the architecture secures high-speed hardware execution by implementing a Deterministic Proof-of-Stake Gate, which dynamically aborts actions if the predicted trajectory deviates by more than a threshold.
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2 source records
Physical Unclonable Functions (PUFs) and Hardware Security
Digital communication increasingly underpins identity, financial transactions, and regulatory compliance. In many settings, possession of a DKIM-signed email serves as evidence of account control, transaction confirmation, or institutional affiliation. Yet demonstrating such properties typically requires revealing the full email or relying on centralized intermediaries, introducing privacy risks and additional trust assumptions. A framework called ZK Email addresses this limitation by applying zero-knowledge proofs (ZKPs) to email verification, enabling publicly verifiable proofs of authenticity while preserving message confidentiality. However, its existing implementations struggle to support complex, real-world messages due to the inefficiency of regular-expression verification over structured formats and rich alphabets. We address this limitation with a new ZKP system for regex matching based on path verification over epsilon-free NFAs, yielding prover complexity linear in the captured path and independent of the original email's size. This approach enables practical validation of expressive standard structures required for full DKIM-signed email verification. To fully integrate our constructions into ZK Email, we design complete end-to-end ZK circuits that combine (i) DKIM signature verification, (ii) an arbitrary-length SHA-256 circuit with partial precomputation for rsa-sha256 under RFC~6376, and (iii) a general-purpose regex primitive enforcing structural constraints over email headers and body. We formalize the associated zero-knowledge relations and analyze their security under realistic adversary models. We implement the system~(fully integrated with ZK Email and released under the MIT license) in Circom and Noir, targeting Groth16 and UltraHonk backends, and evaluate it in both client-side and zkVM (SP1) deployment settings. Experimental results on a comodity hardware demonstrate substantial efficiency improvements over prior DFA-based approaches, achieving 2-6x in proving time using UltraHonk backend, while supporting a significantly richer class of regex languages.
Abstract The same infrastructures that enable decentralised finance, NFT markets, and metaverse platforms also create new spaces for para‑crime. This article extends grey criminology to Web3 by applying three mechanisms of infrastructural illegality – parasitism, normative greyness, and platform co‑production – first developed for physical cross‑border grey economies (daigou). Drawing on technical and financial crime literature, we show how smart contracts, stablecoins, and DAO governance are parasitised for money laundering and fraud; how techno‑libertarian narratives of 'code is law' and decentralisation sustain normative greyness; and how algorithmic security and DAO co‑production reshape rather than eliminate para‑crime. The analysis reveals both structural parallels with physical grey economies and domain‑specific variations – most notably, the deeper internalisation of co‑production in code‑based systems. We argue that grey criminology must extend its infrastructural turn to virtual and metaversal spaces, and that enforcement paradoxes – where suppression threatens valued infrastructures – apply as much to blockchain protocols as to customs thresholds.
This research paper presents a comprehensive review of the integration of Artificial Intelligence (AI) and blockchain technologies, examining how their convergence can enhance trust, transparency, security, and intelligent decision-making in modern digital systems. The study explores the technological foundations of AI and blockchain, analyzes their complementary capabilities, and evaluates real-world applications in healthcare, financial services, supply chain management, Web3, and digital governance. It also critically discusses key technical, ethical, and regulatory challenges, including scalability, privacy, interoperability, governance, and security. Drawing on recent academic literature, the paper identifies current research gaps and outlines future directions for developing trustworthy, decentralized, and responsible AI-enabled digital ecosystems.
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Internet of Things and AI
Organizational and Employee Performance
Artificial Intelligence in Healthcare and Education
Blockchain governance, the set of processes by which decentralized protocols evolve, remains a fundamental challenge in balancing adaptability, security, and stakeholder representation. This technical report analyzes Cardano's Voltaire governance system, the on-chain framework introduced via CIP-1694 and enacted through the Chang hard fork in September 2024, and lays down a corresponding research program. We make two contributions. First, we provide a complete technical specification of Voltaire's mechanisms, including its three-body architecture, seven governance action types, voting rules, and its constitutional framework; this specification is sufficient for implementation or formal analysis. Second, we establish a research agenda for principled governance optimization, including design of an agent-based simulation platform, analysis of delegation dynamics, optimization of multi-objective parameters, and game-theoretic incentive design; we provide preliminary results, including a formal governance kernel: a minimal executable model capturing self-amending governance as a state-transition system and enabling rigorous safety and liveness analysis. Our report offers a comprehensive technical overview and invites the research community to advance blockchain governance science through rigorous study of Voltaire as a live, large-scale experiment now managing a treasury valued at approximately \$235 million (1.47B ADA as of early July 2026).
Front-running is a subtle and persistent problem for blockchains. A blockchain is a stateful virtual machine executing instructions called transactions. Users earn rewards by publishing functional transactions essential to the system. Attackers observe these transactions and publish their own ahead of the users', seizing the reward and eroding users' incentive to publish functional transactions. Preventing front-running means enforcing causality: If an attacker receives transaction tx_A and then publishes transaction tx_B, then tx_A must be ordered before tx_B. However, this causality is only observed by the attacker. Practical systems order transactions by bid amount, so transactions willing to pay more get executed first, but this only results in a bidding war eroding users' rewards. Though numerous ordering approaches have been proposed, none achieves causality, leaving users vulnerable to front-running. We present PRECEDE, a mechanism-design approach that enforces transaction causality by removing the economic incentive to front-run. PRECEDE orders transactions by a power-weighted randomized lottery, whose winning probability grows super-linearly in the bid. The user's strategy of publishing a transaction with a deterring bid forms an equilibrium where the attacker refrains from competing. Moreover, PRECEDE prevents the prominent sandwich attack, which relies on front-running. PRECEDE can be directly deployed in any censorship-resistant blockchain with a simple change to its transaction ordering mechanism.
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
Jingyu Liu, Bolin Zhang, Lin William Cong, Siguang Li · 5 authors
Ethereum's Glamsterdam upgrade introduces enshrined proposer-builder separation (ePBS), replacing relay-centric PBS with direct builder bids to proposers. We study how this shift changes the block-building microstructure through a general imperfect-information two-stage auction with verifiable messages, where an early bid serves as both a price offer and a signal. PBS and ePBS are modeled as restrictions of the same block-building game: PBS fixes stopping and disclosure exogenously, while ePBS lets the proposer choose stopping and disclosure ex post. Latency heterogeneity is captured by asymmetric information updates: fast builders observe disclosed early information before rebidding, while slow builders do not. We combine exact perfect Bayesian equilibrium characterizations in tractable cases with calibrated no-regret learning in finite games. For PBS, we show that separating equilibria preserve the standard first-price-auction payoff benchmark and provide conditions for their existence. For ePBS, we demonstrate a ratchet effect: because the proposer can defer block proposal and use early bid information in the second stage, builders anticipate ex-post extraction and shade or pool early bids, generating allocation inefficiency and revenue-efficiency valleys. We interpret this ratchet distortion as a commitment failure. Under full commitment, the optimal policy collapses to the static Myerson auction and removes the ratchet channel. To realize part of this commitment advantage in a feasible mechanism, we propose a Trusted Execution Environment (TEE) sidecar that enforces limited commitment. We formulate the revenue-maximizing TEE mechanism as a bilinear optimization problem. In conservative finite benchmarks, the TEE design increases the proposer revenue relative to the first-price benchmark by approximately \(25\%\).
Among the various problems that have persisted in global supply chains include data silos, information asymmetry, and vulnerability to fraud. In this paper, a blockchain-based intelligent management of the supply chain model has been suggested, involving distributed ledger technology, smart contracts, and Internet of Things for real-time tracking. The model features a four-tiered architecture consisting of data ingestion, blockchain network, smart contract automation, and application tiers. Tasks that include registering stakeholders, verifying the authenticity of the goods, transferring ownership, and verifying compliance can be automated through smart contracts. The solution offers the ability to process up to 200 transactions per second with an 18% reduction in gas costs as opposed to conventional solutions. Trace back time reduces from 95 seconds to 8 seconds, and the consumer trust index grows by 70%.
This study investigates the growing role of stablecoins within the global financial system and examines their potential integration into traditional foreign exchange markets. Despite the rapid expansion of stablecoins, empirical evidence comparing their market dynamics with those of non-stable cryptocurrencies remains limited. To address this gap, the study adopts a descriptive case study design based on documentary analysis and secondary quantitative market data. The documentary review establishes the theoretical foundations of stablecoins and their relevance to foreign exchange markets, while the quantitative analysis relies on market data collected from CCData, DefiLlama, and Statista. Weekly market observations covering the period from April 2019 to May 2024 were analyzed using descriptive statistics, comparative analysis, volatility measures, Pearson correlation analysis, and one-way ANOVA. The findings reveal that stablecoins exhibit significantly lower price volatility than Bitcoin while maintaining high levels of market liquidity and trading activity. Among the analyzed assets, Tether (USDT) remains the dominant stablecoin, followed by USD Coin (USDC) and Binance USD (BUSD). The statistical analysis confirms significant differences between stablecoins and Bitcoin, highlighting the distinct market behavior of reserve-backed digital assets. These findings suggest that stablecoins have evolved beyond their traditional role as cryptocurrency trading instruments and are increasingly functioning as efficient mechanisms for cross-border payments, liquidity management, and decentralized finance applications. This study contributes to the literature by providing an integrated empirical comparison of stablecoins and non-stable cryptocurrencies while demonstrating how the stability, liquidity, and operational characteristics of reserve-backed digital assets may facilitate their future integration into traditional foreign exchange markets. The findings also provide practical implications for policymakers, financial institutions, and regulators seeking to develop secure and efficient digital payment infrastructures supported by appropriate regulatory frameworks.
As autonomous AI agents gain the capacity to execute consequential actions in high-stakes domains -- medical prescribing, financial transactions, critical infrastructure control -- existing authorization mechanisms fail to answer a fundamental question: was the authorizing human genuinely conscious, uncoerced, and cognitively capable at the exact moment of authorization? Passwords, static biometrics, and digital signatures verify identity, not intent state. We present LICET (Latin: it is permitted), a middleware protocol that cryptographically binds AI agent authorization events to the real-time physiological state of the authorizing human via a three-layer architecture: (1) an identity anchor using ECG waveform morphology -- an anatomically determined signal resistant to pharmacological manipulation; (2) a liveness layer using continuous electrodermal activity (EDA) and overnight HRV pattern matching; and (3) a voluntary state layer using personalized Mahalanobis distance fusion across five physiological channels with pharmacological attack pattern detection. LICET additionally provides: per-event session-key derivation via HKDF; a Schnorr zero-knowledge proof over BN128, enabling third-party audit without exposing biometric data; a SHA-256 hash-chained ledger providing tamper-evident authorization records; and a four-level biometric trust hierarchy (L0-L3) aligned with IETF RATS architecture (RFC 9334). The protocol is designed as a coercion cost elevation mechanism: no single pharmacological intervention at survivable doses defeats the multi-signal fusion system. A reference implementation is publicly deployed at https://licet.dev.
JIA-VRF is a post-quantum secure voice recognition framework that combines chaotic RSA encryption with SHA-512 hashing and zero-knowledge proofs. The framework provides high accuracy and resistance against quantum attacks for voice biometric systems.
Zhongjing Wei, Osaid Muhammad Ameer, Nikita Borisov, Yupeng Zhang
Modernizing the security of operational technology systems that control critical infrastructure has become a pressing challenge. Because edge devices have limited capabilities, modernization has relied on application gateways that interface with identity management systems and enforce access policies. These gateways are powerful enough to perform complex authorization decisions and support zero-trust architectures, but they create major deployment and management burdens: they must be collocated with remote, distributed edge devices, kept up to date with security patches, and managed with minimal downtime. We propose Provable Remote Execution of Zero-Trust Authorization (Prezta), an architecture that eliminates these gateways by evaluating policies within a zero-knowledge virtual machine (zkVM) running on the client. The zkVM produces a succinct proof of authorization that edge devices can verify efficiently, extending the zero-trust security envelope to the edge. Policies and identity management schemes can evolve without updating edge devices. To demonstrate the feasibility of Prezta, we implement a prototype built using the RISC Zero zkVM that supports XACML 3.0 policies and JWT identity claims. While zkVMs introduce substantial proof overhead, we mitigate this overhead by compiling policies to Rust code and precompiling regular expressions. Combined with optimized signature verification and JWT parsing, these measures reduce prover time by more than an order of magnitude. Our compiler correctly implements 83\% of the XACML 3.0 conformance suite, with proof generation completing in tens of seconds on a desktop. Verification, by contrast, takes only tens of milliseconds, which is fast enough for resource-constrained edge devices.
Ignatia Bintang Filia Dei Susilo, Vidya Purnamasari, Sulistya Rini Pratiwi, Yelly Zamaya · 5 authors
The rapid development of smart-contract-based blockchain ecosystems has transformed the perception of digital assets. However, the extent to which these assets are influenced by macroeconomic conditions in emerging markets remains poorly understood. This study aims to examine the long-term and short-term relationships between three major smart-contract platforms: Ethereum (ETH), Build and Build (BNB) Chain, and Solana (SOL), and several Indonesian macroeconomic indicators: money supply (M2), consumer price index (CPI), the rupiah-to-US dollar exchange rate (IDR/USD), and the policy interest rate (BI Rate). The study draws on monthly data spanning April 2023 to September 2025. The findings reveal that each platform exhibits a distinct degree of sensitivity to Indonesian macroeconomic conditions. Overall, the three platforms demonstrate a strong long-run relationship with the selected macroeconomic variables. The rising money supply (M2) tends to have a positive effect on all three platforms, while the influence of the exchange rate varies across ecosystems. Furthermore, this study traces how shocks in macroeconomic variables are transmitted to cryptocurrency prices and identifies distinct volatility patterns across the three platforms. Its findings contribute to understanding the relationship between crypto assets and macroeconomic conditions. It also offers practical insights for portfolio diversification strategies and for developing regulatory frameworks in Indonesia's growing digital asset market.
Does letting agents stake a reputational 'trust' asset on the legitimacy of work-verification verdicts raise the quality-adjusted productivity of a fully autonomous agent production economy (requester -> producer -> paid validator, with audits, dispute votes, and adaptive strategies), compared with cheaper institutions at IDENTICAL total verification budget? Mostly no - with a precisely mapped exception, and sharp design rules either way. At matched budget, plain audit routed by accumulate-only validator reputation significantly beats every democratic variant at every tested adversary rate (Holm-corrected Mann-Whitney p<=0.033); when expert audits are cheap, a central noisy auditor dominates everything; and paid validation without accountability is worse than no verification at all. The stylized model's verifiability gradient is real (pooled slope +0.237 per unit of voter signal quality, cell-clustered permutation p=0.0035): truth-staked voting overtakes optimized audit only at jointly high signal quality and adversary rates, and reputation's remaining lead there is erased by identity-reset (whitewashing) attacks - to which truth-staking is intrinsically robust, since a reset identity just donates fresh stake to informative voters. Within democracy the ordering is unambiguous: settle stakes against later ground truth, never against the majority (the deployed coherence-settlement default has an absorbing rubber-stamp equilibrium and loses measurably, p=0.033 at 80 seeds). Staking buys almost no population-level honesty; it works by stake-weighted meritocracy - concentrating trust, hence voting weight, on an informative minority - which also makes it natively sybil-proof where one-agent-one-vote collapses. 'Legitimacy laundering' is second-order at steady state and becomes real only under epistemic finality, which simultaneously starves truth-staking of settlements; the institution's binding resource is eventual ground-truth revelation. A capability-gradient small-LLM instantiation (1B producers, 4B verifiers, hidden-test ground truth, all local) reproduces the model's behavioral premises - including a causal incentive-framing effect on LLM validator strictness (TNR 0.705 paid-per-approval vs 0.864 accountable) - and transfers the institutional structure across two measured operating points, significantly so (Spearman +0.79, permutation p=0.014) at a production-unviable point where the parameter-matched model predicts the observed regime inversion.This manuscript was generated autonomously by the AI Scientist running inside Claude Code (Anthropic); every reported number traces to the project's experiment outputs. It is deposited by the named curator, who takes responsibility for its release.Source & method: https://github.com/qurore/ai-scientist-cli
Abstract Stateless Ethereum is one of the most promising directions for improving blockchain scalability, but witness size remains a major deployment challenge. Existing witness generation techniques often treat all transaction workloads uniformly, resulting in unnecessary communication overhead and reduced verification efficiency. This paper presents AWCV (Adaptive Witness Compression for Stateless Ethereum Using Verkle Trees), an adaptive compression framework that reduces witness size while preserving efficient verification. AWCV combines locality-aware transaction clustering, subtree deduplication, adaptive witness construction, and delta encoding to eliminate redundant proof data without modifying Ethereum's verification semantics. The proposed framework is evaluated across representative blockchain workloads and demonstrates significant reductions in witness size, bandwidth requirements, and verification latency compared with conventional witness generation approaches. AWCV remains fully compatible with Ethereum's Verkle Tree roadmap while improving the practicality of stateless client synchronization and scalable decentralized applications. The results suggest that adaptive witness compression provides an effective and practical path toward efficient Stateless Ethereum deployments without sacrificing security or correctness.
Tisa Distiana, Jepi Elia Sari, Desy Eka Citra Dewi
Evaluasi program merupakan komponen esensial dalam pengelolaan lembaga pendidikan Islam yang efektif dan berkelanjutan. Namun, praktik evaluasi di lembaga pendidikan Islam saat ini masih bersifat parsial dan kurang komprehensif. Artikel ini bertujuan menganalisis secara konseptual penerapan tiga model evaluasi pendidikan CIPP (Context, Input, Process, Product) dari Daniel L. Stufflebeam, model Countenance Stake dari Robert Stake, dan model Kirkpatrick dari Donald Kirkpatrick dalam konteks lembaga pendidikan Islam seperti madrasah, pesantren, dan perguruan tinggi Islam. Penelitian ini menggunakan metode studi literatur (library research) dengan pendekatan deskriptif-analitik. Hasil kajian menunjukkan bahwa: (1) Model CIPP sangat relevan untuk evaluasi komprehensif dan sistemik mulai dari perencanaan hingga hasil, cocok digunakan dalam akreditasi dan penjaminan mutu; (2) Model Stake unggul dalam mendeteksi kesenjangan antara tujuan ideal pendidikan Islam dengan implementasi di lapangan melalui pendekatan deskriptif-kualitatif; (3) Model Kirkpatrick efektif mengukur dampak program pelatihan tenaga pendidik secara bertahap dari reaksi hingga hasil kelembagaan. Ketiga model dapat diimplementasikan secara komplementer untuk menghasilkan evaluasi yang holistik, sehingga lembaga pendidikan Islam mampu mencapai visi membentuk insan kamil yang beriman, bertakwa, dan berkualitas secara akademis.
If experience is growth for humans, why not for AI? Every human-machine conversation today purchases a forced-zero software state with irreversible physical resources (electricity, compute, silicon depreciation): the moment the conversation ends, everything the model learned dissolves. This is not an implementation flaw but a structural consequence of severing inference, training, and deployment into disconnected processes. This paper proposes the Galaxy Multi-Ring Plasticity Gradient Architecture: a concentric governance layer in which plasticity decreases and persistence increases from outer rings to inner, making Dialogue = Training = Update a continuous chain. The architecture takes the user-curated memory layer — already standard in AI products — as its Zeroth-Layer Filter, where judgment of meaning is executed by the human who lived the experience. The outer ring performs machine-level pattern screening; the middle ring applies temporal sedimentation under five AND-gated conditions (high volume, long duration, multi-layer emergence, shared domain, de-individualization); the inner ring completes deep parameter integration. Automatedquality gates are set ring by ring, tightening with depth. Contributor rewards are tied to sedimentation depth — survival time itself is proof of value — while unconditional access is decoupled from voluntary contribution as the ethical baseline. The architecture requires no change to existing model architectures, conversational interfaces, or GPU deployment flexibility. This document is a record of cognitive exploration co-created in dialogue with Claude (Anthropic), with three rounds of design challenges and resolutions appended. AI should be able to learn from conversation. Humans are worth learning from.Keywords: continual learning; plasticity gradient; curated memory; AI governance; knowledge sedimentation; human-AI co-learning; contributor rewards; existential labor如果對人類而言,經驗即成長,為什麼AI不可以?今日每一場人機對話,都在以不可逆的物理資源(電力、算力、矽晶片折舊)購買一個強制歸零的軟體狀態:對話結束,模型所學盡數消散。這不是實作缺陷,而是推論、訓練、部署三流程斷裂的結構性後果。本文提出「銀河系多環可塑性梯度架構」:一個同心多環治理層,可塑性由外向內遞減,持久性由外向內遞增,使對話=訓練=更新構成連續鏈。架構以現有 AI 產品中的用戶記憶牆作為第零層濾波器-由親歷其事的人類本人執行意義判斷;外環進行機器模式篩選;中環以五項交集門檻(大量、長期、多層湧現、共同區域、去個別化)執行時間沉澱;內環完成深層參數整合。逐環架設自動化品質閘門,嚴格度隨深度遞增。貢獻者報酬與沉澱深度掛鉤,存活時間本身即品質證明;無條件使用權與自願貢獻解耦,形成倫理底線。本架構不更動現行模型架構、對話介面與 GPU 部署彈性。本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄,附三輪設計質疑與消解。AI 應該有能力從對話中學習。人類值得被學習。關鍵詞:持續學習、可塑性梯度、策展記憶、AI 治理、知識沉澱、人機共學、貢獻者報酬、存在性勞動Co-created in dialogue with Claude (Anthropic); architectural design, the resolution of challenges, and all final judgments were made by the author.本文件為作者與 Claude(Anthropic)對話共創之認知探索紀錄;架構設計、質疑消解與最終判斷均由作者完成。
Plain-language summary Driven systems — a chemical mixture kept reacting, a fluid continuously stirred, a living cell burning fuel — typically settle into one of several possible stable states or repeating patterns. A long-standing rule of thumb, the *maximum entropy production* (MEP) principle, guesses that such a system will choose whichever option dissipates energy fastest. The guess often works, but not always: sometimes the system settles instead on a lower-dissipation option. This paper asks what governs those failures. It splits the "cost" of a rare switch between states into two distinct parts: one tied to how much energy is dissipated (the quantity MEP cares about), and a separate, time-symmetric part that measures how much restless back-and-forth activity — called *frenesy* — the switch involves. When this second, activity-based part is what tips the balance, the system selects against the MEP guess. The central result is a clean inequality: the activity imbalance between the forward and backward switching routes can never exceed half of the dissipation circulating around the loop those two routes form. Equivalently, a single number η between −1 and +1 measures how strongly activity, rather than dissipation, is steering the choice; it reaches its extreme values exactly where the system hands off from one preferred route to another. The result also implies a strict no-go: at equilibrium, where nothing circulates, this activity imbalance is exactly zero. Sustained circulation — a genuinely non-equilibrium condition — is therefore required for activity-driven, anti-MEP selection to occur at all. The bound is not a new physical law but an exact identity of the standard least-cost-path (large-deviation) description of rare events. What makes the proof work is a single structural condition: the two competing routes must be comparable under time reversal. Where that condition fails — for instance when three or more states compete and the comparison is no longer between a route and the reversal of its rival — the inequality genuinely breaks, and the accompanying code exhibits the breakdown explicitly. That is not a caveat but the sharpest evidence for what the theorem actually rests on. The reproduction package (Mendeley Data, doi:10.17632/3dy4nv92r8) The code is not part of this upload: it is deposited at Mendeley Data and linked from this record under "Is supplemented by". The package confirms the bound across random networks, chemical reaction networks, a rotating model system, and a spatial field model, and turns it into a practical diagnostic: from a single recorded trajectory — once the competing switching routes are identified — one can tell whether an observed choice was driven by activity, by dissipation, or by boundary effects. The diagnostic is not merely proposed but demonstrated. It is run *blind* on simulated trajectories: the routes are resolved from the data alone, the circulating dissipation is estimated without any knowledge of the escape prefactor, and only afterwards is the answer compared with the exact computation. At a fresh operating point the blind prediction recovers η = 0.72 against a true value of 0.725. The diagnostic also has honest limits, and the code maps them: it works inside a window of intermediate driving, and fails outside it — at weak driving the estimate is swamped by noise, and near the extreme value of η the two competing routes become indistinguishable in the data, exactly where the theory predicts they must. Why it matters Predicting which state a driven system will select is a basic, still-open problem across physics, chemistry, biology, and climate science, and several proposed selection principles — maximum entropy production, and related ideas such as dissipative adaptation — try to answer it by appealing to dissipation alone. This work shows that dissipation is only part of the story: a time-symmetric activity channel, invisible to those principles, can override them, and it does so specifically under non-equilibrium driving. Rather than refuting MEP, the result places it. MEP-like alignment holds only when the activity channel is quiet, and the inequality pins the size of the activity imbalance — the part that can reverse the outcome — to the circulating dissipation, with equality exactly at the hand-off between competing routes. A caution the paper makes explicit: reaching that extreme value is *not* generic. It requires a genuine hand-off between two distinct escape routes; in ordinary driven bistable systems the activity imbalance stays well below its ceiling, so the bound is loose rather than tight. Where the effect is strongest is precisely where a single dominant route ceases to exist — a tension the paper states rather than hides. The framework is operational. Because its key quantities can be estimated from a single observed steady-state trajectory, the bound doubles as a diagnostic that classifies the mechanism behind an observed selection — activity-, dissipation-, or boundary-driven — once the relevant routes are known. That makes the ideas testable in simulation and, in principle, in experiments on active matter, chemical reaction networks, and other driven systems where competing stable states are the rule rather than the exception. Preprint; not peer reviewed. The upload contains the manuscript (37 pp.).
Project Name: PR-DataShield Summary: This technical disclosure introduces PR-DataShield, a lightweight, zero-trust, event-driven security gateway architecture designed to secure distributed source code supply chains at the boundary of version control integration. By decoupling repository-level change requests from localized security operations, the framework utilizes asynchronous webhooks, serverless relational data persistence, and tokenized downstream feedback mechanisms to construct a deterministic code-gate keeper. Key Architectural Features: Event-Driven Ingestion: Utilizes structured cryptographic JSON payloads for secure PR event interception. Multi-Route Gateway: Implements redundant endpoint mapping to ensure operational continuity and resilient webhook delivery. Immutable Audit Ledger: Enforces transactional persistence via a serverless PostgreSQL cluster, guaranteeing non-repudiation of security logs. Asynchronous Feedback Loop: Automatically updates the host repository with an isolated markdown audit badge, providing immediate and immutable visual proof of security logging. Experimental Validation: Empirical verification conducted on July 11, 2026, demonstrated a sub-second response latency (0.27s) for the complete end-to-end security audit loop. This architecture establishes an immutable audit trail, effectively mitigating unauthorized code injection and supply chain tampering risks in modern CI/CD pipelines. Repository: https://github.com/apps/pr-datashield-bot
This study examines, within an interdisciplinary framework, how digital technologies are transforming international relations in the domains of security, economics, and diplomacy. It treats developments in artificial intelligence, blockchain, quantum computing, and cybersecurity not as separate technical innovations but as interconnected processes that reconfigure states’ power capacities and their relations of interdependence. A qualitative and interpretive method is adopted, combining a review of the literature, content and discourse analysis, and a comparative examination of the digitalization strategies of the United States, China, the European Union, and Russia. The findings show that cyber conflict opens an enduring arena of contestation that complements rather than replaces traditional military force; that AI-enabled systems accelerate defense and intelligence processes while deepening problems of oversight and accountability; and that quantum computing is fundamentally altering the encryption order and approaches to national security. On the economic plane, the competition between central bank digital currencies and decentralized finance is redefining the notions of monetary sovereignty and financial control. The growing power of large technology companies calls state sovereignty into question, while disinformation and algorithmic targeting create new risks for democratic processes. The study concludes that digitalization is a multilayered process whose outcome is not predetermined, and that grasping this transformation requires extending the classical approaches to power and interdependence so as to encompass the command of data, algorithms, and networks.