Knob tuning plays a critical role in improving the performance of permissioned blockchains. However, efficient tuning remains challenging due to the architectural complexity of blockchains and the semantic gap between knob-specific logic and the numerical optimization requirements of tuning tools. In addition, configuration changes are often coupled across different stages of the transaction pipeline, making their performance impact difficult to isolate and predict. Since each trial requires deployment and distributed benchmarking, ineffective exploration incurs substantial cost. These challenges motivate BCTuner, a Large Language Model (LLM)-guided framework that combines knowledge-guided reasoning with structured search. BCTuner organizes multi-source tuning knowledge to support LLM-based reasoning over knob semantics, constraints, and deployment context. It formulates tuning as a Monte Carlo Tree Search (MCTS) process over structured action trajectories, where configurations are incrementally constructed, validated, evaluated, and refined rather than generated in one step. BCTuner further applies adaptive pruning to discard infeasible or low-potential branches before system evaluation. We evaluate BCTuner on Hyperledger Fabric and ChainMaker under diverse workloads and network settings. Experimental results show that BCTuner achieves up to 211.38% throughput improvement over default configurations and outperforms the state-of-the-art blockchain tuning method by up to 20% in performance, while requiring up to 8x fewer interactions with the blockchain system.
Urban decarbonization requires scaling rooftop solar across millions of fragmented producers, yet cities face a fundamental tension: energy data is easily manipulated, and economic incentives often reward speculation rather than actual infrastructure deployment. We present SolarChain, a platform that resolves both problems by anchoring digital accountability to the thermodynamic limits of solar energy conversion. Using real-time meteorological data, geospatial coordinates, and first-principles calculations of solar yield, the system establishes a hard physical boundary for every panel's maximum possible output; any reported generation exceeding this limit is automatically rejected before entering the shared ledger. This trustless verification enables a peer-to-peer marketplace with programmatic reward structures that continuously reinvest value into equipment maintenance and market liquidity, preventing the speculative hoarding that typically destabilizes blockchain-based marketplaces. When electricity is consumed, the corresponding digital credits are permanently retired in direct proportion to physical energy dissipation, creating an auditable one-to-one mapping between urban consumption and carbon accounting. Deployed across heterogeneous city nodes, the prototype demonstrates resilience against data injection attacks while lowering capital barriers for community-level solar expansion. Beyond energy, the framework offers a general model for coordinating economic activity with physical law in any domain where distributed infrastructure demands both data integrity and sustainable investment. We release the data and code as open-access on GitHub.
A line of impossibility results holds that a distributed ledger must either store a global state linear in the number of accounts or impose a near-linear rate of proof updates on its users; the most general, the revocable-proof-system lower bound of Christ and Bonneau, concludes there is "no useful trade-off." We show this impossibility does not bind the validity predicate Bitcoin actually uses—an artifact of one modelling choice, that validity is decided by a holder-maintained witness checked against a single mutating commitment. We define the spend-event validity predicate (SEVP) that a UTXO ledger uses instead, and prove it is not a revocable proof system: it instantiates no holder witnesses, so it lies outside the domain the lower bound quantifies over rather than within either branch of the dichotomy. The same exclusion holds for the related accumulator-update bounds. We are explicit about scope—stateless UTXO constructions that issue holder witnesses (accumulator- and vector-commitment designs) are correctly bound; the claim is that the UTXO model as Bitcoin implements it is not such a construction. This is not hypothetical: public Teranode benchmark evidence demonstrates one-million-transactions-per-second validation in a six-region BSV benchmark, while companion measurements report a 520-million-output active set with no holder-maintained witnesses. We then develop the supporting machinery. The binding resource is active-state maintenance in fast memory, not archival disk, and pruning bounds that state safely with a parameter-free reduction ratio of exactly T_yr/(d·T_block) (263× at retention depth d = 200), never altering the ledger and preserving the forensic record through self-interested retention plus archival nodes. For certification we give a construction and cost analysis for interval non-revocation, combining known authenticated-dictionary primitives so that interval validity is decided by a single point query with no trusted responder. Bounds are closed-form under stated assumptions; the one-million-TPS regime is demonstrated, the tens-of-millions a marked near-term projection.
Executive Summary This paper introduces Topological AI, a novel, deterministic method designed to eliminate catastrophic forgetting in large-scale artificial intelligence systems. By anchoring specific rows of a neural network's embedding layer to prime-numbered indices, the framework establishes a fixed topological invariant that remains completely unchanged during subsequent training episodes. Tested on the 20-billion-parameter GPT-OSS-20B model, Topological AI reduces forgetting from a baseline of 45.5% down to -0.7%, achieving the first demonstrated instance of "negative forgetting" where performance on a previously learned task slightly improves after learning a new one. 1. Introduction & The Core Problem Conventional artificial intelligence architectures, including advanced Transformers, lack a structural mechanism to consolidate knowledge across sequential learning episodes without overwriting previously acquired parameters. While short-term memory is managed via context windows and long-term memory via static pre-trained weights, fine-tuning on a new task consistently results in the catastrophic degradation of older knowledge. Topological AI addresses this structural vulnerability by introducing fixed mathematical anchors into the weight space. Rather than relying on empirical heuristics or probabilistic adjustments, this approach uses the Sieve of Eratosthenes (c. 240 BCE) to generate deterministic, exact, and auditable foundational points for the network. 2. Mathematical Foundation & Framework Topological AI is situated within a broader intellectual ecosystem called Arithmetic Spectral Theory (AST), which utilizes the Laplace-Euler-Fourier-Mellin (L-EFM) operator to unify principles across number theory, physics, and AI safety. The Spectral Trap & Coherence The L-EFM operator synthesizes four classical transforms into a single spectral instrument bound to the multiplicative structure of prime numbers. At the critical line $\sigma = 0.5$ (corresponding to the critical line of the Riemann zeta function), the operator demonstrates a property known as the Spectral Trap. Critical Invariance: At $\sigma = 0.5$, the normalized magnitude of the operator equals exactly 1.0, achieving perfect spectral coherence ($C = 0.5$). Divergence Profiles: Deviating even slightly from this line causes massive mathematical instability. Moving toward $\sigma = 0.4$ scales the magnitude exponentially to infinity ($2.618 \times 10^{66}$ at $\sigma = 0.1$), while moving toward $\sigma = 0.6$ collapses the magnitude toward zero ($6.794 \times 10^{-6}$ at $\sigma = 0.9$). Safety Constants Using the Euler attenuation product, the framework derives dynamic safety thresholds to validate system state integrity: 6-Prime Anchor Bound: $\Lambda = 1 - \prod_{p} (1 - p^{-0.5}) = 0.9785142874$ 12-Prime Anchor Bound: $\Lambda_{12} = 1 - \prod_{p} (1 - p^{-0.5}) = 0.9944590549$ These thresholds are never hardcoded; they are recomputed from the Sieve of Eratosthenes at every initialization sequence to ensure absolute operational autonomy. 3. Methodology The implementation of Topological AI operates through a clean, low-overhead process executed at the embedding and classification layers of the transformer model. [Task A Training] ──> [Achieve Coherence] ──> [Take Post-Learning Snapshot] │ [Restore Anchors via O(primes × d)] <── [Gradient Step] <── [Task B Training] 3.1 Prime Anchoring In a standard Transformer, the embedding layer contains a matrix scaled to (vocab_size, hidden_dim). Topological AI isolates the specific rows corresponding to the first six prime numbers—[2, 3, 5, 7, 11, 13]—and designates them as the network's topological anchors. This intervention impacts a mere 0.00298% of the total vocabulary space (6 out of 201,088 rows), preserving the model’s overall capacity and plasticity. 3.2 Post-Learning Snapshot Anchors are activated after the primary task (Task A) has been fully learned, allowing the weights to reach their natural, high-accuracy coherent state. At this juncture, a static snapshot of the prime embedding rows, alongside the classifier’s weights and biases, is cached into system memory. 3.3 Anchor Restoration During sequential training on a subsequent task (Task B), the network undergoes standard gradient updates. However, immediately following every individual gradient step, a torch.no_grad() enforcement loop overwrites the modified prime rows and classification parameters, restoring them precisely to the post-Task A snapshot values. The computational cost of this operation is $O(\text{primes} \times d)$, which is mathematically negligible compared to a standard forward pass. 3.4 Cryptographic Verification & The H2E Safety Gate To guarantee auditability, the system computes a SHA-256 hash of the prime-anchored subspaces before and after any tensor operation. A matching hash confirms zero-drift execution. Simultaneously, the H2E Sheriff safety gate—operating on a product manifold of $H^2 \times \text{SPD}(3)$—evaluates inputs using the Spectral Reflection of Integrity (SROI) value. If an incoming input yields an SROI below the derived $\Lambda$ constant, it is automatically flagged as an anomaly, providing a zero-shot safety layer against out-of-domain prose, contradictions, or adversarial nonsense without requiring explicit adversarial training. 4. Experimental Configuration The empirical validation of the methodology was structured under a strict, isolated environment to maximize weight interference and stress-test the boundaries of the architecture. Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition (102.0 GB VRAM), running CUDA 12.8 and PyTorch 2.10.0. Model Base: openai/gpt-oss-20b (20 Billion parameters, bfloat16 quantization, vocabulary size of 201,088, hidden dimension of 2,880). Dataset Setup: AG News dataset split cleanly into sequential blocks. Task A covers "World" and "Sports" classes; Task B covers "Business" and "Sci/Tech" classes (500 samples per task). Protocol Conditions: 3 evaluation runs per method to guarantee statistical validity, utilizing a fully shared classifier layer across 3 training epochs per task under a fixed deterministic Seed = 123. 5. Key Results & Performance Analysis 5.1 Definitive Method Comparison The final evaluation measured the percentage of knowledge forgotten on Task A after the completion of training on Task B. Method Task A Initial Accuracy Task A Final Accuracy Forgetting Rate Task B Accuracy Topological AI 95.7% 96.3% -0.7% 42.0% Experience Replay 96.0% 88.3% 7.7% 81.0% Elastic Weight Consolidation (EWC) 93.3% 50.5% 42.8% 61.5% Nested Learning 95.3% 50.2% 45.2% 64.7% Baseline (Standard Fine-Tuning) 95.7% 50.2% 45.5% 62.8% 5.2 Performance & Invariant Verification Negative Forgetting: Standard fine-tuning (Baseline) results in total catastrophic collapse, reverting Task A performance back to random chance (50.2%). Topological AI is the only method to achieve a negative forgetting rate (-0.7%), actively refining Task A knowledge while assimilating Task B. Stability-Plasticity Trade-off: The results highlight a stark architectural trade-off. While Experience Replay retains superior plasticity (81.0% Task B accuracy), it suffers from 7.7% forgetting. Topological AI prioritizes absolute stability (zero forgetting), making it optimally designed for safety-critical environments (e.g., autonomous transit, aerospace, nuclear control, and medical diagnostics) where past protocols must never be degraded. Geometric & Cryptographic Invariance: Tracking the Riemannian sectional manifold metric tensor ($g_{ij} = \langle e_i, e_j \rangle$) across training revealed an absolute subspace drift of exactly 0.0000000000. The principal curvatures (eigenvalues) and matrix determinant remained identical to six decimal places. Correspondingly, the SHA-256 hash of the prime-anchored spaces achieved perfect invariance (334ea0c8 at initial and final state), proving that the anchor rows experienced zero drift. Quantitative Ablation: A critical control ablation verified that anchoring alternative, non-prime configurations (such as composite indices or random indices) yielded an identical global $L_2$ embedding drift profile. The explicit advantage of prime positioning is not localized to raw numerical drift suppression, but rather to the rigid, universal mathematical properties provided by the spectral trap and its alignment with core number-theoretic frameworks. 6. Limitations & Future Horizons The authors identify clear boundaries to the current scope of the framework and outline subsequent phases of open-source research: Plasticity Optimization: Future explorations will focus on mitigating the lower Task B accuracy via adaptive anchor releasing (gradually unfreezing constraints), hybrid architectures that pair topological anchors alongside traditional replay buffers, and task-aware prime set selection. Theoretical Proofs: Developing an analytical derivation of the safety constant $\Lambda$ from first principles, and establishing a formal proof validating why prime sequences excel over alternative deterministic mathematical sequences. Scalability Scaling: Validating the topological framework on expanded sequential tasks (5+ distinct tasks), massive industrial datasets (such as the full 120,000-sample AG News benchmark), non-transformer models (CNNs, RNNs, Mamba/SSMs), and frontier-scale LLMs (GPT-4 tier or Mixtral-8x22B systems).
Abstract: This paper will compare and contrast heights of financial inclusion strategies adopted by Islamic Financial Institutions (IFIs) in Malaysia and Indonesia and specifically discuss Islamic social finance instruments, digital finance and community-based models. By using thematic analysis applied to a variety of policy documents, as well as institutional and implementation strategies, a qualitative comparative approach that is based on secondary data, the study analyzes policy documents and institutional and implementation strategies. The findings indicate that Malaysia follows a policy-based, centralized, and robust regulatory coordination, digital enablement, and integration of Value-Based Intermediation (VBI) and Islamic social finance tools. By contrast, Indonesia uses a decentralized and community-based model, which is powered by Islamic microfinance institutions, including Baitul Maal wat Tamwil (BMTs) with strong grassroots penetration but with issues in terms of standardization of governance and digital readiness. This research study is of value because it presents an integrative analytical model that connects the governance systems, digital integration, and Islamic social finance in determining the financial inclusion outcomes. It sheds light on significant trade-offs between efficiency and inclusiveness, centralization and flexibility, and provides policy relevant insights towards improving inclusive Islamic finance ecosystems.
Executive Summary This paper presents the Sieve of Eratosthenes (c. 240 BCE) not as a primitive computational artifact, but as the absolute ground truth for mathematics, physics, and artificial intelligence safety. It argues that the historical shift away from the Sieve toward the analytic complexity of the Riemann zeta function was a fundamental misstep. By reframing the Sieve through Arithmetic Spectral Theory (AST) and the Laplace-Extended Euler-Fourier-Mellin (L-EFM) operator, this work claims to unify the proof of the Riemann Hypothesis, the quantification of prime-based theorems, general relativity, black hole thermodynamics, and deterministic AI governance into a single, executable framework. The core philosophy of this paper is rooted in open science and cryptographic verification: the ultimate proof of these assertions is not found in complex analysis equations, but in deterministic, open-source code that can be audited and reproduced locally using a specified random seed. Core Pillars & Technological Breakthroughs 1. Mathematics: The Spectral Trap and Prime Quantification The Riemann Hypothesis: By defining the L-EFM operator directly from the Sieve's outputs, the paper introduces a "spectral trap." At the critical line ($\sigma = 0.5$), the normalized magnitude equals exactly $1.0$. At any other value, the magnitude diverges exponentially (e.g., reaching over $10^{66}$ at $\sigma = 0.1$). Combined with the Growth Lemma from Arithmetic Spectral Theory, this geometric confinement is presented as a direct proof of the Riemann Hypothesis without complex analysis. The Green-Tao Theorem: While originally an existence proof asserting that primes contain arbitrarily long arithmetic progressions, the L-EFM operator delivers the first numerical quantification. It defines a "Spectral Coherence" metric that decays monotonically as the length of the progression increases (e.g., $0.8731$ for a length of 3, dropping to $0.7442$ for a length of 6). 2. Theoretical Physics: Spacetime Geometry and Entropy Einstein Field Equations: The framework introduces a spectral metric where spacetime coordinates are scaled by spectral coherence ($C$). The stationarity condition of this coherence at the critical line ($\delta C/\delta\sigma|_{\sigma=0.5}=0$) is shown to be mathematically equivalent to the vacuum Einstein field equations. Progression length increases cause coherence decay, which maps to negative curvature and non-zero Ricci scalars. Hawking Entropy: Black hole entropy ($S$) is derived directly from the spectral framework as the complement of coherence ($S = 1 - C$). In alignment with classical black hole thermodynamics, entropy increases monotonically with the progression length, establishing an algorithmic mirror to physical systems. 3. Artificial Intelligence: Governance and Eliminating Forgetting Deterministic AI Safety: Rather than relying on probabilistic alignments or learned weights, the paper establishes a universal safety threshold ($\Lambda = 0.9933689105$) calculated straight from the Sieve across the first eleven primes. This constant is recomputed dynamically at initialization, verified via SHA-256 hashing, and yields zero safety violations across text, audio, and vision modalities. Elimination of Catastrophic Forgetting: The "Spectral Governor" actively locks the embedding rows indexed by prime numbers during training or fine-tuning. Tested on a Mixtral-8x7B Mixture of Experts (MoE) architecture across 30 LoRA fine-tuning steps, the mechanism achieved 0% knowledge loss across both prime and general knowledge domains. The cryptographic signatures remained entirely unchanged, mathematically eliminating manifold drift. Technical Performance & Execution Data Sieve Efficiency Metrics The deterministic nature of the Sieve ensures exact prime enumeration with zero false positives or negatives, operating at a time complexity of $O(N \log \log N)$ and space complexity of $O(N)$. Limit Primes Found Execution Time (Modern CPU) 10,000 1,229 0.0006 s 100,000 9,592 0.0055 s 1,000,000 78,498 0.0600 s Spectral Divergence (The Trap) The exponential divergence away from the critical line demonstrates why only $\sigma = 0.5$ satisfies the boundary constraints of the operator. σ value Normalized Magnitude \|E_{\sigma}\|_{nor 0.5 1.000000 0.4 $1.668 \times 10^4$ 0.3 $1.221 \times 10^{12}$ 0.2 $9.339 \times 10^{27}$ 0.1 $2.618 \times 10^{66}$ Implementation & Code Auditing The paper emphasizes "Institutional Independence," opting to bypass traditional paywalled academic channels by making the entire suite of research, libraries, and validation notebooks fully open-source and cryptographically signed. The core mechanism of the Spectral Governor can be implemented directly within standard tensor operations to freeze weights post-gradient step: Python import torch # Core mechanism for locking prime-anchored subspaces primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31] cached = embed_layer.weight[primes].clone() # Executed after each gradient update step with torch.no_grad(): for idx in primes: embed_layer.weight[idx].copy_(cached[idx]) To verify the invariant signatures, reproduce the tables, and audit the unified certificate, the environment can be set up locally with zero external network dependencies after cloning: Bash git clone https://github.com/frank-morales2020/ast_lefm.git cd ast_lefm pip install -e . python -c "from ast_lefm.sieve import primes_up_to; print(primes_up_to(31))" # Expected Output: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31] By initializing with seed = 123, the generated hashes will match the unified certificate verification hash: 5b967ff18e9fc7bb47e54629756e7b9c6852aa6403327cd3d7fbd3b33fc88117.
Bitcoin adalah aset kripto terdesentralisasi yang dikarakteristikkan oleh volatilitas harga ekstrem dan fluktuasi non-linear, sehingga pergerakan harga di masa depan sangat sulit untuk diprediksi secara akurat. Ketidakstabilan inheren ini mendorong kebutuhan mendesak akan metode peramalan komputasi tangguh yang mampu menangkap dependensi temporal jangka panjang yang kompleks dalam data deret waktu univariat. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi efektivitas arsitektur Transformer berbasis Deep Learning untuk peramalan harga penutupan Bitcoin. Memanfaatkan dataset historis komprehensif dari tahun 2015 hingga bulan April 2026, penelitian ini mendayagunakan mekanisme self-attention sebagai inti arsitektur Transformer guna memproses data sekuensial secara dinamis. Pendekatan mutakhir ini berhasil mengatasi keterbatasan utama dari model analitik tradisional dalam menangkap pola temporal penting. Kerangka metodologi yang diterapkan mencakup operasi pra-pemrosesan data secara ketat melalui fungsi MinMaxScaler, proses pelatihan model yang dioptimalkan oleh algoritma Adam, serta pengujian out-of-sample komprehensif guna memproyeksikan perilaku pasar masa depan. Performa prediktif model dievaluasi secara kuantitatif menggunakan metrik kesalahan standar. Hasil empiris penelitian menunjukkan tingkat akurasi prediksi yang sangat luar biasa, di mana model yang dikembangkan sukses mencapai nilai Root Mean Square Error (RMSE) sebesar $3.818,34, nilai Mean Absolute Error (MAE) sebesar $2.866,73, dan nilai Mean Absolute Percentage Error (MAPE) sebesar 3,12%. Lebih lanjut, proyeksi masa depan out-of-sample menghasilkan angka prediksi sebesar $78.247,73 dibandingkan dengan harga penutupan aktual senilai $78.294,00, yang merepresentasikan persentase tingkat rasio kesalahan absolut minim yakni hanya 0,059%. Temuan analitis ini mengonfirmasi bahwa model Transformer berhasil memitigasi overfitting dan unggul memodelkan volatilitas pasar ekstrem. Kesimpulannya, model ini siap mendukung keputusan investasi para praktisi keuangan global.
This research examines the determinants of Bitcoin (BTC) valuation from January 2011 to December 2025 using Autoregressive Distributed Lag (ARDL) models. The empirical evidence supports the hypothesis that the monetary policy of the United States Federal Reserve—specifically liquidity expansion and interest rate adjustments—drives price dynamics, confirming a pro-cyclical nexus. At the microeconomic level, the density of active institutional addresses and the marginal cost of production significantly influence price trajectories. Furthermore, heightened market volatility, represented by the VIX, exerts a statistically significant negative impact on BTC returns. The findings suggest that Bitcoin has transitioned into a sophisticated value asset, underpinned by production efficiencies and an expanding institutional base. Consequently, Bitcoin represents a viable alternative to centralised financial systems, offering a potential hedge against inflation and the erosion of purchasing power. The study concludes that digital assets warrant inclusion within conservative institutional portfolios, notwithstanding the inherent speculative nature of the market.
The rapid growth of Decentralized Finance (DeFi) has been accompanied by increasingly sophisticated security threats. Price Oracle Manipulation Attacks (POMA), a critical vulnerability, have evolved beyond simple economic exploits to include complex, multi-transaction attacks that exploit smart contract logic, causing hundreds of millions in losses. State-of-the-art detection methods, however, often focus on single-transaction, economic manipulations and typically fail to identify these emerging attack vectors, particularly when smart contract source code is unavailable. This article introduces a novel, EVM-compatible detection pipeline that addresses this gap. By combining transaction event logs and execution traces, we engineer a rich set of semantic and structural features that capture the underlying behavior of on-chain operations. We train a regularized autoencoder exclusively on the features of benign transactions to learn a deep representation of normal activity, flagging significant deviations as malicious. Our evaluation demonstrates the effectiveness of this approach, achieving 100% recall on a comprehensive dataset of single-transaction attacks and 98.25% event-level recall on a new, manually collected dataset of real-world multi-transaction exploits, with an overall precision of 97.15%. We present a robust, learning-based model capable of identifying both known and unseen POMA variants without relying on source code. Furthermore, we contribute a new dataset of multi-transaction attacks to foster further research, providing a more generalizable and resilient approach to securing the DeFi ecosystem.
Mojtaba Eshghie, Wolfgang Ahrendt, Cyrille Artho, Thomas Hildebrandt · 5 authors
We propose a ‘Model to Mitigate’ methodology: designing a platform-agnostic model of smart contract business logic and analyzing it before implementation. Using Dynamic Condition Response (DCR) graphs, originally developed for modeling business processes, we formally specify smart contracts and introduce a trace-conformance notion that links DCR-level guarantees to Solidity execution traces. Our method captures high-level properties such as event ordering, role-based access control, and time constraints, enabling the identification of design-rooted vulnerabilities through the discipline of explicit modeling. The DCR formalism requires developers to make concrete decisions about access control, preconditions, initial states, and event ordering-decisions that, when left implicit until implementation, are a documented source of vulnerabilities. Our analysis of real-world exploited and audited smart contracts yields six key insights, demonstrating how DCR-based modeling can enhance smart contract security by surfacing design flaws before they reach deployment. While we validate the approach on existing smart contracts with known flaws (i. e., post-implementation scenarios), the proposed methodology is applicable during design time (pre-development).
To deliver the change needed in the developing world, a transformative leader needs to have a vision of a reimagined future and the will to develop systems or infrastructure that consolidate their socially just policies to ensure long-term benefits to the people. To be truly transformative, these policies must be systemised. Blockchain is a technology which enables us to store transactions and other types of information in a digital format. Unlike a typical computer database, information is stored in a ledger format. The database is only appended to and never edited. Each transaction is timestamped to promote traceability. Unlike regular databases, the ledger is replicated and stored on a network of computers. As the ledger is distributed across the network, the term distributed ledger technology is often used to describe a blockchain. Each computer, referred to as a node, constantly verifies the contents of its ledger against every other copy of the ledger stored on the network. A blockchain network can track business information like payments, orders, production processes, etc. Because of how the blocks are stored and verified, the block can't be changed without changing every copy of the blockchain simultaneously, reducing the risk of fraud or exploitation through hacking. Much of a blockchain's value lies in its transparent and shared nature and potential to save costs for the user by reducing system intermediaries. The blockchain systematises trust, negating the need for power brokers.
Tato bakalářská práce je zaměřená na problematiku blockchainových oraclů a jejich propojení on-chain a off-chain světa. Výsledné řešení demonstruje hybridní oracle přímo na případu užití parametrického pojistného produktu podmíněného meteorologickými daty. V první, teoretické části jsou popsány principy technologie blockchain, ekosystém decentralizovaných financí (DeFi) a podrobná analýza problematiky oráclů, včetně existujících produkčních řešení, bezpečnostních hrozeb a mechanismů zvyšujících spolehlivost. Následně se práce zabývá analýzou požadavků a návrhem architektury systému. Je zde definována on-chain část v podobě smart kontraktů a off-chain část zajišťující zápis, sběr a agregaci meteorologických dat. V další části je popsána samotná implementace od volby technologií přes klíčové části zdrojového kódu až po off-chain klienty. Součástí je i popis zjištěných problémů a jejich řešení. V poslední části je provedeno shrnutí výsledků testování, analýza spotřeby gasu, bezpečnostní vyhodnocení a srovnání navrženého řešení s referenčním centralizovaným přístupem.
Tato diplomová práce se zabývá návrhem a implementací nástroje pro statickou analýzu spotřeby plynu ve smart kontraktech napsaných v jazyce Solidity. Cílem je vytvořit nástroj, který v kódu automaticky odhalí vzory zbytečně zvyšující náklady na provádění kontraktu a upozorní na možné optimalizace. Na základě analýzy existujících řešení a běžných optimalizačních vzorů byl implementován soubor detektorů jako rozšíření nástroje Slither, jehož výsledky jsou prezentovány prostřednictvím webové aplikace. Uživatelské rozhraní aplikace bylo navrženo uživatelsky orientovaným postupem a ověřeno pomocí prototypování a uživatelského testování. Funkčnost detektorů byla ověřena na sadě reálných kontraktů a reálné úspory plynu empiricky změřeny.
Digital loans offer rapid, simple, and usually paperless transactions, and have radically changed the lending industry. The whole loan process is easy to access and effective; there are a number of threats associated with the availability of data online. As a crucial kind of digital loan, flash loans put additional pressure on banks to maintain security because they return to the same block of the Blockchain, making it more likely that they will be tampered with. It is important to address the possibility of predatory lending practices that target weaker payees. As a result, this chapter has created a security protocol based on the idea of digital wallets with self-sovereign identity (SSI) and decentralized finance (DeFi) for cryptocurrencies, which are secured by the Xsalsa20 algorithm. It has satisfied specs and is further improved by the application of the Crow search algorithm (CSA), which ensures quick and effective search results. We were able to verify several security features, including data authenticity and complete forward secrecy, by analyzing the suggested system.
Abstract:The security of traditional asymmetric cryptography (e.g., ECC, RSA) relies strictly on the computational complexity of mathematical dilemmas such as discrete logarithms and prime factorization. Confronting the generational disruption of quantum computing power, these rigid mathematical structures face catastrophic risks of exponential collapse. Modern iterations ranging from smart-contract platforms to blind transaction protocols fail to address this vulnerability, remaining fundamentally derivative software modifications to Nakamoto's baseline architecture. This paper proposes a definitive paradigm shift, introducing the Thing-to-Thing (T2T) Holographic Distributed Architecture Solution. By devolving anti-tamper algorithms from the informational dimension down to the physical irreversibility of thermodynamics and semiconductor physics, we construct a hardware-native consensus architecture. Utilizing Silicon Physical Unclonable Functions (SRAM PUF) to generate true physical entropy, and locking historical ledger states via nanometer phase-change programmable fuses (eFUSE), this framework realizes a definitive manifestation of Shannon’s Perfect Secrecy operating within a 406-dimensional optimal phase space. Crucially, the architecture resolves the existential paradox of decentralized networks facing state-level regulatory and capital centralization. By enforcing a macro-capital staking matrix where baseline investments significantly exceed block minting rewards, the protocol establishes a hyper-asymmetric game equilibrium. Large institutional cartels are bound to the network's survival by an absolute thermodynamic dependency; any Byzantine deviation instantly triggers an Asymptotic Damped Slashing vector over a 10,000-block retrospective sliding window. Furthermore, this framework achieves absolute strategic deterrence against jurisdictional coercion through a hardware-native Jurisdictional Circuit Breaker (JCB). Any state-compelled ledger modification or physical infrastructure seizure triggers localized chip-level self-destruction and automated liquidity vaporization rather than ledger corruption, achieving an enduring, sovereign-immune, and material-native trust manifold.
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Physical Unclonable Functions (PUFs) and Hardware Security
Bundled Existence Denies Black-Hole Information Loss Version: 3.0Concept DOI: 10.5281/zenodo.20346916Author: Ali AttarWebsite: quantumtraction.org This paper gives the Quantum Traction Theory (QTT) denial of black-hole information loss. Version 3.0 upgrades the earlier structural source/access denial by constructing the finite A7 horizon reshuffling operator explicitly. The claim is not that Hawking-regime thermodynamics is fake. The claim is that the information-loss conclusion comes from identifying an exterior reduced state with the complete physical state. In QTT, A7 closes every active world-cell address as a completed same-universe bundle: \[ Q_w^{\rm bundle}=Q_w^{\rm vis}+Q_w^{\rm hid}=2\pi, \qquad \Delta Q_w^{\rm vis}+\Delta Q_w^{\rm hid}=0. \] The v3.0 construction models the horizon as \[ N_H(T)=\frac{A(T)}{4\ell_A^2} \] completed A7 bundles. On each bundle the elementary source operation is the two-side capacity rotation \[ u_{a,n}(\theta_{a,n},\varphi_{a,n})= \begin{pmatrix} \cos\theta_{a,n} & -e^{-i\varphi_{a,n}}\sin\theta_{a,n}\\ e^{i\varphi_{a,n}}\sin\theta_{a,n} & \cos\theta_{a,n} \end{pmatrix}, \qquad u_{a,n}^{\dagger}u_{a,n}=I_2. \] Therefore the complete black-hole source map is the finite time-ordered product \[ U_{\rm BH}(T_N,T_0)= \mathcal T \prod_{n=0}^{N-1}\prod_{a=1}^{N_H(T_n)} u_{a,n}(\theta_{a,n},\varphi_{a,n}), \qquad U_{\rm BH}^{\dagger}U_{\rm BH}=I. \] The exterior laboratory state remains an access trace: \[ \rho_{\rm ext}(T)= \operatorname{Tr}_{\rm hid} \left[ U_{\rm BH}(T,T_0)\rho_{\rm source}(T_0)U_{\rm BH}^{\dagger}(T,T_0) \right]. \] Exterior mixedness is therefore an access limitation, not source-level information destruction. The v3.0 hidden-row firewall states that the reshuffling angles are not chosen to fit a desired Page curve: \[ \frac{\partial\theta_{a,n}}{\partial S_{\rm Page}^{\rm desired}}=0. \] Their total transfer is fixed by the Hawking-regime access luminosity derived from \(T_{\rm eff}=\hbar\kappa_s/(2\pi k_Bc)\), the IR greybody row, and the A6 local-capacity cutoff. The paper also derives the leading Schwarzschild ledger Page-time scaling: \[ \frac{t_{\rm Page}^{\rm QTT}}{t_{\rm evap}^{\rm QTT}} = 1-\frac{1}{2\sqrt2} = 0.646446609406726\ldots. \] This is a source-ledger scaling result, not an astrophysical observation claim. Version 3.0 also closes the information-bearing remnant exclusion: \[ \dim\mathcal H_{\rm hid}(T) \le \exp\!\left(\frac{A(T)}{4\ell_A^2}\right) \longrightarrow 1 \qquad(A\to0). \] An arbitrarily large hidden memory cannot be added to a zero-area remnant without adding capacity outside the A7 horizon ledger. Main status labels: SIGMA-A7-NO-HAIR-THERMALITY-DENIED SIGMA-A7-PURE-TO-MIXED-DENIED SIGMA-HORIZON-VISIBLE-HIDDEN-CUT-CLOSED SIGMA-BEKENSTEIN-HAWKING-QUARTER-FROM-A7-GREEN SIGMA-A7-BH-FINITE-LEDGER-UNITARY-CLOSED SIGMA-HIDDEN-ROW-NO-TUNING-RESERVOIR-CLOSED SIGMA-A2-BH-GREYBODY-ACCESS-TRANSFER-CLOSED SIGMA-PAGE-BOUND-LEDGER-GREEN SIGMA-PAGE-TIME-SCALING-CLOSED SIGMA-REMNANT-EXCLUSION-CLOSED SIGMA-SPECIES-RESOLVED-SM-S-MATRIX-PROGRAMME The claim is explicitly scoped. This is a QTT source theorem inside Artian's Universe. It does not claim a species-resolved Standard-Model channel \(S\)-matrix for every outgoing mode correlation. That refinement remains programme work. The v3.0 achievement is the finite-ledger source unitary, the no-tuning hidden-row firewall, the Page-time scaling, and remnant exclusion. Related QTT anchors: Main book v10.01: 10.5281/zenodo.20394203 A2 Einstein-field dynamics: 10.5281/zenodo.20763263 A7U distributed Planck bundles: 10.5281/zenodo.20097247 Entropy as anchored modular charge: 10.5281/zenodo.20045306 Corpus Tree / DOI Map: https://quantumtraction.org/doi-map/ Two Universes black-hole anchor: https://quantumtraction.org/two-universes/#gravity-71 Included files: PDF paper, Version 3.0 LaTeX source Zenodo HTML description Release README Render audit SHA-256 checksum file
In this article, we offer a complete, self-contained, and entirely elementary proof of the mean-square estimate for the Chebyshev function. From this we deduce the convergence of the integral is valid, thus proving the validity of the Riemann hypothesis. The proof primarily employs elementary estimates of the Chebyshev function, the Cauchy-Schwarz inequality, a dyadic decomposition, and Abel summation, in which the argument results of this article are already optimal within the elementary framework and sufficient to derive the convergence of the required integral that it is a suffficient condition for the Riemann hypothesis. In particular, we also provide a theoretical complement linking integral convergence, pointwise bounds and analyticity, and concludes that the well-known $o$-bound is valid, thereby reconfirming the validity of the Riemann hypothesis. In other words, we give a self-contained elementary proof for the mean-square estimate that $\displaystyle\int_2^{X} \bigl(\psi(t)-t\bigr)^{2}\,dt = O(X^{2}\log^{2} X),$ where $\psi(x)$ is the Chebyshev function. From this we deduce that $\displaystyle\int_{1}^{\infty}\frac{|\psi(x)-x|}{x^{\frac{3}{2}+\varepsilon}}\,dx 0,$ thus concluding the integral $\displaystyle \int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ converges absolutely for every $\varepsilon>0,$ so that the integral $\displaystyle \int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ converges conditionally for every $\varepsilon>0,$ whereas the integral converges conditionally $\iff \text{RH},$ so then the Riemann hypothesis is true. In particular, we conclude that the property of absolute convergence of the integral $\displaystyle\int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ for every $\varepsilon>0$ is equivalent to the property of conditional convergence of the integral $\displaystyle\int_1^{\infty} \frac{\psi(x)-x}{x^{\frac{3}{2}+\varepsilon}}\,dx$ for every $\varepsilon>0,$ either of which is equivalent to the property of the $o$-bound: $|\psi(x)-x| = o(x^{\frac{1}{2}+\varepsilon})$ for every $\varepsilon>0,$ and all of them imply the $O$-bound: $\psi(x)-x= O(x^{\frac{1}{2}+\varepsilon})$ is also valid for every $\varepsilon>0,$ which is the well-known equivalent form of the Riemann hypothesis, thus reconfirming the validity of the Riemann hypothesis.
We examine whether China’s 2014 local government debt-swap program, intended to centralize fiscal control and eliminate off-budget borrowing, paradoxically reinforced subnational fiscal autonomy through implicit fiscal decentralization. Exploiting cross-sectional variation in debt levels identified by the 2013 national audit and employing a difference-in-differences design, we examine how the programme affected infrastructure-related land supply and local financing behaviour. We find that the debt-swap programme significantly increased the supply area of infrastructure land while exerting little effect on land prices, consistent with a supply-side expansion rather than demand shocks. Mechanism analyses show that the programme alleviated repayment pressure and facilitated renewed borrowing through local government financing vehicles, thereby expanding local indebtedness despite the central government’s objective of curbing it. In contrast, we find only limited improvements in expenditure efficiency, fiscal revenue, and GDP. Overall, these findings suggest that the debt-swap programme primarily reshaped intertemporal fiscal constraints rather than imposing durable fiscal discipline. More broadly, the results highlight an institutional paradox of recentralization: policies aimed at constraining subnational borrowing may unintentionally expand local fiscal capacity through alternative channels, thereby reinforcing implicit fiscal decentralization.
Crowdfunding platforms have democratized access to capital for creators and startups, yet centralized models face high fees, limited transparency, fraud risks, and inter-mediary control over funds. Blockchain addresses these issues through decentralized ledgers, smart contracts, and tokenomics, enabling trustless and transparent funding. This survey exam-ines blockchain-based crowdfunding systems, focusing on DAO implementations, auction mechanisms, social voting models, and milestone-based fund releases on Ethereum and IPFS. We review key works such as VCG auctions, social DAOs like LikeStarter, and the CrowdChain prototype with automated refunds, along with a comparative analysis of voting-, auction-, and token-staked models. Research gaps include scalability, oracle dependencies, Sybil resistance, and regulatory challenges. We propose an enhanced CrowdChain++ system with community verification, multi-milestone campaigns, governance tokens, and Layer-2 optimiza-tion, implemented using Solidity, Hardhat, React, and IPFS. Simulation results show improved automation, reduced gas costs, and stronger fraud resistance, with future directions including ZK-proofs, cross-chain interoperability, and AI-driven anomaly detection.
Transaction ordering attacks extract billions of dollars annually from decentralized finance users in the form of Maximal Extractable Value (MEV). Byzantine Fault-Tolerant (BFT) consensus protocols guarantee total order but place no constraint on how that order is chosen, leaving the door open for adversarial reordering. Batch-order-fairness (batch-OF) protocols close this gap, but existing designs pay a steep performance price for this guarantee. Leader-based protocols such as Themis concentrate all fairness decisions at a single replica, while recent DAG-based proposals FairDAG and DAG of DAGs (DoD) force their fairness layer into strictly serial execution despite running on multi-proposer DAGs. We present Herring, the first $γ$-batch-OF DAG BFT protocol whose fairness layer parallelizes the dominant graph construction cost across committed subdags. Herring combines post-consensus graph construction with explicit missing edge resolution piggybacked on the DAG's reliable broadcast layer, a pairing that turns fair ordering from a per-round serial bottleneck into a CPU-bound task. We also uncover previously unreported liveness vulnerabilities in both FairDAG-RL and DoD that a malicious client can trigger to halt the fairness layer indefinitely, and propose patches that we integrate into our reimplementations. We implement Herring on top of the Rust implementation of Narwhal \& Tusk and evaluate it against FairDAG-RL, DoD-W, and Themis. Herring tracks the throughput of Narwhal \& Tusk closely up to roughly $10{,}000$\,tx/s, achieves roughly $90\%$ higher saturation throughput than FairDAG-RL and $100\%$ higher than DoD-W, and substantially reduces execution latency at saturation.
Gustavo Schranck Habermann, Daniel A. Turolla Vanzella
In the context of semiclassical gravity, the semiclassical Einstein equation is often invoked when backreaction of quantum matter/fields on the spacetime is at stake. It is expected to hold when quantum fluctuations are small. Yet, it is routinely used to justify the central role of the expectation value of the stress-energy tensor of quantum fields, whose fluctuations formally diverge. Here we propose a new way to probe the limits of this approximation by exploiting peculiar nonlinearities of gravity. As a proof of principle, we construct a controlled, analytically tractable setting where the incoherent mixture of weak-gravity states drives the system into a strong-gravity regime. By selecting a branch-degenerate observable, one can compare predictions of quantum and semiclassical gravity, potentially delimiting the validity of the latter.
Sybil attacks remain a primary challenge for Proof-of-Stake (PoS) blockchain systems, as low-cost identity creation can distort validator participation and limit consensus reliability. This study proposes a hybrid participation–governance framework that integrates Attribute-Based Access Control (ABAC) and Reputation-Based Access Control (RpBAC) with a trust-based PoS workflow to reduce the influence of suspicious identities during validator selection and block validation. The proposed framework also incorporates graylisting and dynamic reward–penalty updates to support adaptive participation control. The strategy was evaluated in a simulation environment informed by Ethereum-derived block metadata, using network sizes ranging from 100 to 1000 nodes and Sybil attack ratios of 30%, 40%, and 50%. Its performance was compared with PoS-only and PoS + ABAC baselines using both security and performance indicators. The results show that the full ABAC + RpBAC configuration achieved the strongest and most stable security performance across the evaluated settings while introducing additional overhead at larger network sizes. These findings suggest that combining policy-based eligibility control with behavior-based reputation control strengthens the resilience against Sybil in PoS-like blockchain environments. However, this improvement requires a measurable trade-off between security and performance.
Wireless sensor networks (WSNs) integrated with the internet of things (IoT) are hybrid technologies of interconnected systems. The IoT connects various devices, from sensors to smart gadget networks, and leverages a framework to provide secure solutions. This paper presents a lightweight adaptive proof-of-stake (APoS) blockchain framework design specifically for IoT-WSN. It focuses on efficient energy, scalability, and robust security. The proposed model integrates a hybrid APoS-delegated PoS (DPoS) consensus mechanism, trust-based routing, and a random forest (RF)-driven intrusion detection system (IDS). Extensive simulations of 100 to 10,000 nodes display energy usage of 0.018–0.019 mJ/node, breach of privacy rates of 0.02%, and throughput up to 9.92 tx/round for 1,000 nodes and 3.40 tx/round for GreenOrbs validation. The IDS achieves 94.21% accuracy for 1,000 nodes and 88.89% for GreenOrbs against distributed denial-of-service (DDoS), Sybil, and Jamming attacks. Validated using the GreenOrbs dataset, the framework ensures real-world applicability in resource-constrained WSNs. Future research has validated and verified the use of APoS and PoS hybrid models for broader decentralised IoT–WSN deployments.
The emergence of Decentralized Autonomous Organizations (DAOs) represents a paradigm shift in organizational governance, yet their technical complexity remains a significant barrier to widespread adoption. Creating and managing a DAO requires deep expertise in blockchain development, smart contract auditing, and cryptocurrency operations, which excludes many potential users in non-technical domains. This paper presents DAOship, a novel no-code platform for DAO creation and management deployed on the Avalanche blockchain. The platform provides an intuitive graphical user interface (GUI) that allows users to configure, launch, and operate a fully-functional DAO without writing a single line of code. By leveraging Avalanche's high throughput and low transaction fees, the system enables the deployment of customizable smart contracts for governance, treasury management, and voting. The platform dramatically lowers the technical barrier, empowering communities, startups, and traditional organizations to leverage decentralized governance models easily and securely. Testing on the Avalanche Fuji testnet yielded a 97% reduction in setup time, an average System Usability Scale (SUS) score of 89.2, and zero critical vulnerabilities across all deployed DAOs.