ABSTRACT With the retirement of 21GW of legacy coal fleet looming, the task to replace Australia's thermal capacity with alternative firmed renewable ‘bulk energy’ solutions is non‐trivial. Indeed, calibrating necessary capital investment in new plant stock with efficient economic outcomes, vis‐à‐vis cost and reliability, represents a fundamental challenge to the ‘dynamic efficiency’ ideal for energy‐only markets. Recently, a major review of Australia's National Electricity Market (NEM) has proposed that underwriting of new energy generation utilise fungible derivative contracts instead of the canonical run‐of‐plant (RoP) revenue contract design. The proposition is based upon re‐linking medium‐ and long‐term energy markets, while mitigating some of the worst externalities attributed to off‐market underwriting policies when implemented at‐scale. However, sound economic theory can collide with applied corporate finance in practice. The very basis of the RoP design has been to address the needs of capital markets vis‐à‐vis revenue quality for new‐entrant plant, making bankability a threshold issue for contract viability. This article examines a set of proposed fungible contract designs, leveraging 100 years of synthetic, stochastic market data to draw conclusions on the compatibility of contract fungibility and bankability. Findings indicate that eliminating incentive incompatibilities between plant dispatch and market signals (which are inherent in the RoP design) appears adequate to counteract ‘shape’ risk—both idiosyncratic and systematic—within the confines of a typical project finance structure. In other words, bankability and fungibility do not appear to be mutually exclusive.
The rapid development of digital technology has increased the use of web applications, making security a crucial aspect of their development. One often overlooked threat is Cross-Site Request Forgery (CSRF), which enables attackers to execute commands on behalf of users without their knowledge. This study aims to implement and evaluate CSRF token mechanisms in the CodeIgniter 4 framework as a preventive measure. The testing method used is penetration testing with various attack scenarios, both on applications without CSRF protection and with protection using the Secret Validation Token and Random Validation Token. The results show that the implementation of CSRF tokens significantly improves web application security. Compared to the Secret Validation Token, the Random Validation Token proved more effective in minimizing the success rate of attacks. Without CSRF protection, applications are highly vulnerable to unauthorized command execution through valid authentication sessions. In conclusion, consistent implementation of CSRF tokens, especially with token regeneration after validation, is an essential step to strengthening modern web application security. Keywords — web security, CSRF, codeigniter 4, penetration testing, CSRF token
Large language model (LLM) inference services convert request arrivals into coupled input-token prefill and output-token decoding workloads. Capacity planning therefore depends on token volume, temporal bursts, service mix, queueing behavior, and reliability signals rather than request counts alone. This study evaluates an integrated planning pipeline on BurstGPT v1.1, comprising 5,288,173 raw requests over 121 trace days and 5,188,507 completed requests. Strictly chronological experiments aggregate demand, forecast hourly completed tokens, detect minute-level burst pressure, estimate zero-response risk, simulate capacity policies, and replay representative test hours in Vidur. Random forest, selected on the validation interval, achieved 64.73% weighted absolute percentage error (WAPE) on the locked test interval; XGBoost achieved the lowest test WAPE (64.57%), while the last-hour baseline reached 67.42%, indicating limited forecastability under a pronounced level shift. A seasonal-residual burst detector achieved F1 = 0.653, although burst prevalence was sensitive to rolling-horizon and quantile settings. For minute-level zero-response risk, raw XGBoost achieved ROC-AUC = 0.813 and average precision = 0.116; isotonic calibration improved the Brier score (0.0230–0.0204) and 10-bin expected calibration error (0.0262–0.0122), despite low F1. Static P90/P95 capacity eliminated under-provisioned test hours at cost indices of 13.10 and 18.09. More economical dynamic baselines achieved 12.65% under-provisioned hours at a cost index of 2.37 and 13.63% at 2.43. The validation-selected random-forest policy was cheaper but less reliable (34.31% at 1.23). Vidur replay linked normalized demand to A100/H100 GPU counts, latency, batching, and memory pressure. The results support conservative interpretation of point forecasts and validation of reserve rules under distribution shift, rare-event calibration, and serving-stack constraints.
Abstract This reply addresses a recent comment concerning the proof of the Nernst theorem. I clarify how a Carnot engine can consistently operate at $$T=0$$ T = 0 through a continuous deformation of a cycle operating at $$T>0$$ T > 0 . By examining the limit where heat exchange with the cold reservoir vanishes, I show that the Nernst theorem ensures that the concept of temperature remains physically consistent at the absolute zero limit.
PARI is a recent SNARK based on equifficient polynomial commitments, giving an exceptionally compact proof of just 1280 bits over the BLS12-381 curve, which is the smallest among all the known SNARKs in the literature. However, PARI does not achieve the zero-knowledge property; despite being very efficient, it is therefore less suitable for applications requiring witness privacy. In this work, we propose a zero-knowledge extension of PARI making it ideal for privacy-centric applications yet keeping the proof size compact. We prove perfect completeness, perfect zero-knowledge in the random-oracle model with challenge space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>𝔽</mml:mi> <mml:mi>⧵</mml:mi> <mml:mi>K</mml:mi> </mml:mrow> </mml:math> , and knowledge soundness in the algebraic group model with random oracles under the SDH assumption.
Blockchain technology, initially developed as a backbone for cryptocurrencies, has rapidly expanded into broader domains of finance and business. Its unique attributes – transparency, decentralization, immutability, and enhanced security – offer solutions to persistent challenges in financial services. This chapter examines blockchain applications beyond cryptocurrency, focusing on its role in transforming financial services such as Know Your Customer (KYC), cross-border payments, and compliance. A case-based exploration of blockchain-enabled KYC demonstrates how distributed ledgers can streamline identity verification while ensuring trust and efficiency. The chapter also outlines benefits, limitations, and potential applications, contributing to a holistic understanding for policymakers, financial institutions, researchers, and practitioners.
This study investigates short-term predictive relationships between major cryptocurrencies—specifically Ethereum (ETH) and Solana (SOL)—and their respective sub-tokens (DYDX, UNI, GRT, JUP, RAY, PYTH) by employing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. Using high-frequency data across different time intervals (15 minutes, 1 hour, and 4 hours), the study examines whether price movements in major tokens are associated with enhanced short- and medium-term predictability of ecosystem-based sub-tokens. The empirical results indicate that the hybrid CNN-LSTM model achieves strong forecasting performance at shorter time horizons, while prediction accuracy declines as the time interval increases, reflecting the limiting role of market volatility. The results indicate statistically significant short-term predictive relationships and pronounced co-movement patterns between major token movements and sub-token pricing behavior, particularly at higher-frequency intervals, suggesting that Ethereum and Solana provide informative signals related to ecosystem-wide liquidity and trading activity.
Zero-knowledge proofs (ZKPs) are a fundamental building block in cryptography, enabling powerful privacy-preserving and verifiable computations. In the post-quantum era, hash-based ZKPs have emerged as a promising direction due to their conjectured resistance to quantum attacks, along with their simplicity and efficiency. In this work, we introduce SmallWood, a hash-based polynomial commitment scheme (PCS) and zero-knowledge argument system optimized for relatively small instances. Building on the recent degree-enforcing commitment scheme (DECS) from the Threshold-Computation-in-the-Head (TCitH) framework, we refine its formalization and combine it with techniques from Brakedown. This results in a new hash-based PCS that is particularly efficient for polynomials of relatively small degree –typically up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> – outperforming existing approaches in this range. Leveraging this new PCS, we design a hash-based zero-knowledge argument system that outperforms the state-of-the-art in terms of proof sizes for witness sizes ranging from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>6</mml:mn> </mml:msup> </mml:mrow> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> . Additionally, we present exact zero-knowledge arguments for lattice-based problems using SmallWood, demonstrating highly competitive performance: our scheme yields proof sizes under 25 KB across a wide range of lattice parameters, including Kyber and Dilithium instances.
Ryan Anthony, Jechenthia Maria Taso, Stephen Yohanes Christopher, Ridhwan Ardiyansyah
This study aims to analyze and compare the performance of three algorithms, namely Support Vector Regression (SVR) with a linear kernel, XGBoost, and LightGBM, in predicting the Price of Ethereum cryptocurrency based on daily historical data. The study uses Ethereum Price data in USD for the last five years obtained from the investing.com website. The variables used are Close, Open, High, and Low Prices. The study uses two data splitting scenarios: 80% training data and 20% testing data, and 70% training data and 30% testing data. This study also uses time step variations to test the effect of time dependency on algorithm performance. The results indicate that the LightGBM algorithm has the best performance compared to the other two algorithms with an average MAE value for High Price of 75.486, SVR has a value of 115.590, and XGBoost has a value of 77.314 in the 80% training data and 20% testing data split. In the 70% training data and 30% testing data split, the LightGBM algorithm still excels with an average MAE value for High Price of 78.228, SVR of 104.356, and XGBoost of 83.573. Other evaluations such as RMSE and R2 also show the superiority of the LightGBM algorithm. For the required computation time, the SVR algorithm outperforms the other two algorithms.
The efficiency and performance of logistics, and thereby business operations, increasingly depend on decentralized and tamper-proof records for managing transactions and data flows. For these reasons, the use and professional implementation of blockchain technology (BCT) has become essential to preserving the competitiveness of the digital and globalized economy. My aim was to examine the collaboration networks of articles on blockchain in logistics published in recent years, and to identify the key studies that may prove fundamental. Data was collected from the Web of Science scientific database in accordance with the PRISMA guidelines. The results were visualized using the VOSviewer bibliometric analysis software. As a result of the screening process, I analyzed 32 relevant studies in detail. I found that the United Kingdom and China play leading roles in this field, with several research groups contributing simultaneously to the scientific outcomes. Based on keyword analysis, a total of five clusters were identified, each representing distinct research areas.
Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.
Supplementary appendix to the article "Digital Cash and the Governance of Payment Finality." It contains material displaced from the main text for length, referenced at the corresponding points in the article, and forming no part of the manuscript word count. The appendix documents in full the evidence that claims for digital cash treat technical irreversibility as legal finality, with the passages and page references from Böhme, Christin, Edelman and Moore (2015), Cong and He (2018), De Filippi and Hassan (2016), Kiviat (2015), Atzori (2017) and Politou, Casino, Alepis and Patsakis (2019), together with the passages in which several of those authors qualify or abandon the claim. It further sets out the argument that append-only recording does not entail unrecoverable entitlement: the regulatory history of write-once, read-many electronic recordkeeping under SEC Rule 17a-4 and its 2022 amendment; the accounting mechanics by which a chargeback operates as a contra entry rather than an erasure; the equivalence between that mechanism and reversal by subsequent transaction on a distributed ledger; and two limits on the argument, being value irretrievable because no person holds the key, and records whose own existence is the wrong, as with personal data subject to an erasure right.