Muhammad Ali Hassan Ahmad, Muhammad Hashim Ali, Muhammad Ali Amer, Muhammad Naiman Jalil · 6 authors
A blockchain is a decentralized, secure ledger system that enables transparent and immutable record-keeping, essential for trust and security in digital transactions. Smart contracts are self-executing agreements encoded on a blockchain, enabling different parties to fulfill the terms of the agreement automatically. These contracts trigger corresponding actions when conditions are met, ensuring decentralized and transparent transactions. Writing reliable smart contracts is challenging due to the lack of standardization. To find security vulnerabilities, tools based on various approaches, including symbolic execution, are used. However, these tools often report a large number of false positives, raising concerns about their reliability. The time and effort spent investigating false positives diverts resources from addressing actual vulnerabilities. Therefore, such tools must also be evaluated according to the rate of false positives they exhibit. More importantly, the algorithms and heuristics used by the tools must be enhanced to distinguish between true vulnerabilities and false alarms. In this paper, we first demonstrate the prevalence of false positives in vulnerability reports generated by Mythril, a symbolic execution-based analysis tool for Ethereum smart contracts. We analyze the root causes of these inaccuracies and devise a rule-based approach based on the gained insight to reduce false positives. We implement our rules for the most impactful vulnerabilities in Mythril and assess the effectiveness of our approach. Our results show a significant reduction in false positives without compromising the detection of true vulnerabilities, thus enhancing the tool's reliability.
Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.
Stablecoins have rapidly emerged as an important class of digital assets and a component of the digital financial ecosystem. Despite their growing importance, the statistical properties of stablecoin transaction activity remain largely unexplored. To the best of our knowledge, this is the first study to investigate scaling behavior in stablecoin transaction data, focusing on USDT and USDC. We analyze approximately 370 million USDT and USDC transactions recorded on the Ethereum blockchain across six periods spanning June 2024 to February 2026. Based on interactions between Externally Owned Accounts (EOAs) and Smart Contracts (SCs), we classify transactions into four categories: EOA-EOA, EOA-SC, SC-EOA, and SC-SC. Using maximum-likelihood estimation of power-law exponents, we find that transaction value distributions exhibit heavy-tailed scaling for both stablecoins across all periods and interaction categories. We identify two distinct scaling regimes: EOA-involved categories cluster around 1.45-1.60, whereas SC-SC transactions exhibit higher exponents of approximately 1.72-1.73. Sensitivity analysis confirms that this separation is robust across periods, stablecoins, and fitting sample sizes. Counterfactual analysis shows that changes in category weights alone cannot explain the observed variation in the overall exponent. Across different sample sizes, the counterfactual path accounts for only about 10%-35% of the total temporal range observed in the actual data. Overall, our results indicate two broadly differentiated scaling regimes in the tail of stablecoin transaction values. Power-law tail behavior is observed throughout stablecoin transaction activity, but the exponent depends on whether transactions are driven by EOAs or SCs. These findings provide a basis for further research on scaling behavior and transaction heterogeneity in blockchain-based financial systems.
Autonomous multi-agent systems powered by Large Language Models (LLMs) are increasingly deployed in high-frequency algorithmic trading, decentralized finance (DeFi), and complex financial decision-making workflows. However, existing multi-agent interaction architectures rely heavily on implicit semantic trust: context passing between upstream and downstream agents occurs via unauthenticated, unstructured natural language or JSON payloads. This design creates critical vulnerabilities, exposing systems to indirect prompt injection, context tampering, system prompt spoofing, and multi-turn cascade poisoning. When an upstream agent ingests malicious external data, adversarial payloads can propagate through the inter-agent execution graph, bypassing single-agent perimeter guardrails and hijacking downstream financial execution logic. To resolve these vulnerabilities, we introduce AgentShield-Crypto, a zero-trust cryptographic framework for multi-agent LLM trading pipelines. AgentShield-Crypto enforces the Know-Your-Agent (KYA) protocol, replacing probabilistic natural language filters with deterministic cryptographic state boundaries. Under KYA, every agent's identity, static system prompt hash H(S_i), temporal liveness timestamp t_i, and output payload M_i are encapsulated into cryptographically signed state envelopes (E_i) using HMAC-SHA256. Inter-agent communication channels are guarded by Inline Cascading Anomaly Firewalls (ICAF), which evaluate verification predicates before allowing state transitions into downstream context windows. We construct and release AgentInject-Bench v1.0, an empirical benchmark comprising 7,000 test vectors spanning direct prompt injections, indirect context hijacking, multi-turn cascade poisoning, system prompt spoofing, and clean financial market baselines across GPT-4o, Claude 3.5 Sonnet, and Llama-3-70B-Instruct. Empirical evaluation demonstrates that AgentShield-Crypto achieves a 100.00% Defense Mitigation Rate (DMR) with a 0.00% False Positive Rate (FPR), completely eliminating multi-hop context hijacking while incurring sub-millisecond per-message execution latency (0.382 ms).
Operations research has long contributed to addressing energy and environmental challenges through mathematical modeling and decision-support methods. In particular, numerous studies have examined investment planning, capacity expansion, and policy design for renewable energy systems under uncertainty. As efforts to achieve carbon neutrality intensify worldwide, the expansion of renewable energy has become a critical policy and investment priority. However, the inherent variability of renewable power generation and the substantial upfront investment costs continue to hinder investment decisions and limit the adoption of renewable energy. To address the economic challenges associated with renewable energy penetration, recent studies have explored the use of cryptocurrency mining as a means of monetizing surplus renewable electricity. This study contributes to this emerging research stream by developing a real options model that captures the interaction between renewable energy investment and cryptocurrency mining under uncertainty. The numerical results show that cryptocurrency mining increases the value of renewable energy investment and accelerates investment by lowering the investment threshold. Moreover, the equilibrium determination of mining capacity reduces the renewable energy investment threshold by approximately 40.5% compared with the benchmark in which mining capacity is specified exogenously.
Prejudice and discrimination operate at individual and systemic levels, yet much remains unknown about how bias transcends from the individual to shape the behaviors and cognitions of social systems. Distributed cognition theory offers a needed perspective in this gap, positing that cognition is not confined to individual minds, but rather is distributed across individuals and environments throughout time. As such, system-level outcomes are not directly predictable from the cognitive properties of individuals but instead emerge from interactions of individuals with one another and with their environments. Here, a distributed bias approach is proposed, such that individuals within institutions and broader social systems interact with each other and the complex array of tools in their environments, transforming the cognitive processes involved in group-based discrimination, consequently propagating systemic institutional bias. This approach offers a new mechanistic foundation for the emergence of institutional bias and proposes several avenues for future research and application.
Due to the rapid development of new generation information technology worldwide, universities’ cultivation of digital and intelligent talents has become a necessity of the times. To address the common challenges in traditional practice-oriented teaching, including outdated course design, insufficient authenticity of experimental environments, and cross-institutional trust deficits in student evaluation, this study takes the ‘Digital-Intelligence Workshop’ as the implementation context. Guided by the principles of emerging engineering education, a student-centered philosophy, and an OBE- and project-driven teaching framework, the study systematically improves the curriculum system. By introducing a blockchain-enabled data governance layer, this platform ensures the tamper-resistance of learning records, enables secure data sharing between universities and enterprises, and facilitates transparent smart-contract-based assessments. This article shows that by carrying out interdisciplinary collaboration and real enterprise case projects on the trusted training platform, students’ digital thinking and algorithm practice abilities have significantly improved, and teachers have also made progress in achievement transformation. Relevant teaching practices indicate that the blockchain-enabled ‘Digital Intelligence Workspace’ effectively promotes the improvement of talent cultivation quality in universities, providing a feasible new approach and practical reference for undergraduate education reform in the era of intelligence.
Samuel A. Olaosebikan, Bosede T. Ojo, Wahab Grace, Akesire A. Abdurrahmon
Abstract Being the most populous country in Africa and the leading oil producer in the continent, Nigeria is at a vital crossroads. The nation has the pressing need to reduce energy poverty, which still plagues over 85 million of its citizens (IEA, 2023), and at the same time meet its goal of realizing the global mandate of the net-zero by 2060. This essay goes further to propose a broad-based energy transition model that is specifically geared towards coping with this twofold challenge. The main aim of this study is to find the reconciliation between deep carbonization and the fact that the country heavily depends on fossil fuel resources as the present source of its foreign exchange, and it contributes over 80% of the revenues of the country (NESG, 2022). The suggested methodology is organized in the form of three pillars of strategic approach. These are, Transitional Gas Strategy that unites Carbon Capture, Utilization and Storage (CCUS) and powerful methane abatement; Fiscal Liquidity due to the sustained Oil Production; and an expedited Renewable Energy Deployment program based on the deployment of decentralized solar photovoltaic systems and battery storage infrastructure. The model aims to create the fiscal space required to finance widespread grid modernization, access to electricity by all by 2030, a 50 percent reduction in the intensity of emissions by 2060, and long-term macroeconomic stability by targeting a sustained level of oil production (2.1-2.7 million barrels per day). That would provide a potential blueprint to other resource-dependent economies facing similar transitions.
Abstract Flaring gas is a major problem in the Niger Delta, Nigeria, especially in the marginal oil fields, where the volume of produced associated gas is normally insufficient to sustain traditional gas utilisation schemes. This associated gas is often flared due to the high cost of infrastructure and the unavailability of gas processing plants, thus leading to loss of resources and pollution of the environment. This paper examines the concept of digital flare mitigation as a feasible alternative for utilising flared associated gas, specifically focusing on servicing telecommunication base stations and mining Bitcoin in the Niger Delta. The research paper takes a conceptual techno-economic viewpoint anchored on a literature analysis, publicly accessible gas flaring data, and standard marginal field production parameters. On-site gas engines or micro-turbines convert the associated petroleum gas from the wells into electricity. The power generated is enough to serve the telecommunication base stations around it, hence cutting down on the use of high CO2-emitting diesel generators and maximizing on the operating costs. Bitcoin mining can also be supported by using the same gas-to-power system, which is flexible and location-independent to monetise stranded gas in remote locations. The associated gas is usually be used in the firing of gas engines, but simple gas treatment like dehumidification and condensable hydrocarbon removal is necessary. Higher hydrocarbons may necessitate engine derating, and desulphurization is needed in the presence of large amounts of hydrogen sulphide (H₂S). The analysis results shows that digital flare mitigation is a feasible solution to flare gas sites and capable of working at small scales in marginal fields. The paper shows how this will be effective in gas flaring reduction, economics of marginal fields and offer cost-effective and environmentally friendly solutions in gas utilisation in the Niger Delta.
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi-scale volatility analysis with technical indicators. A quantitative descriptive design was applied to approximately one year of historical hourly price data for Bitcoin and Ethereum, covering open, high, low, close, volume, and percentage change attributes. The data were chronologically sorted, numerically cleaned and normalized, transformed into log returns, and analyzed through rolling standard deviation. Volatility was estimated across three explicitly defined horizons: short-term 7-period, medium-term 30-period, and long-term 90-period rolling windows. Moving Average (MA), Relative Strength Index (RSI), and Average True Range (ATR) were then incorporated to contextualize trend direction, momentum, and fluctuation intensity. The results show that volatility is strongly horizon-dependent: short-term movements responded more sharply to market shocks, whereas longer horizons produced smoother risk patterns. Across the analyzed Bitcoin and Ethereum hourly series, the reported 42.3% short-term and 21.7% medium-term increases were calculated as relative differences against long-term rolling volatility during identified high-uncertainty intervals, not as predictive accuracy measures. These findings indicate that combining rolling volatility with MA, RSI, and ATR can improve the transparency of descriptive cryptocurrency risk assessment. The framework may support preliminary interpretation for novice or risk-averse investors, although it does not empirically test investor comprehension or subsequent decision quality.
Mohammad Quthbul Widad, Rizky Parlika, Firza Prima Aditiawan
Although Bitcoin is acknowledged as the largest cryptocurrency by market capitalization and trading volume in the world's financial market, investors face a great deal of risk and uncertainty due to its exceptionally high volatility and non-linear price changes. To provide a data-driven foundation for risk reduction and forecasting support, accurate modeling techniques are crucial. This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset spanning from January 1, 2020, to January 1, 2026. The research methodology follows a structured data science pipeline, beginning with data acquisition from Yahoo Finance, followed by preprocessing using Min-Max Scaling fitted strictly on the training partition to eliminate data leakage. Model development involves an experimental approach where both LSTM and GRU neural controllers are tuned to extract optimal structural weights. The predictive precision of these models is rigorously evaluated using three standard metrics: MAE, RMSE, and MAPE, while processing throughput is measured via hardware execution times. The research findings indicate that the optimized LSTM model achieved superior one-step-ahead predictive precision with a MAPE of 2.32%, whereas the GRU model recorded a higher error rate of 3.94%. However, the GRU model demonstrated a significant advantage in computational efficiency, completing the training process 8.45 times faster than LSTM. In conclusion, while LSTM is recommended as a forecasting support tool for high-precision financial analysis, GRU remains a viable, parameter-efficient alternative for real-time monitoring on resource-constrained systems before real-world financial deployment.
Muhammad Diaz Syahmi Oktavian, Rizky Parlika, Firza Prima Aditiawan
The extreme price volatility of Bitcoin frequently prevents its widespread adoption. The persistent "Digital Gold" narrative often dominates its price analysis, largely ignoring the predictive value of strategic industrial commodities like Platinum Group Metals. This study aims to investigate whether integrating industrial metals specifically platinum and rhodium enhances the short-term forecasting accuracy of Bitcoin prices. Utilizing high-frequency 5-minute interval data over 729 days, this research applies a comparative quantitative approach using univariate and multivariate Long Short-Term Memory (LSTM) deep learning architectures. Results demonstrate the multivariate LSTM model achieves highly accurate forecasting, recording a Mean Absolute Percentage Error (MAPE) of 3.95% and a Root Mean Squared Error (RMSE) of 0.0598. Compared to the univariate baseline model (MAPE of 5.14%, RMSE of 0.0725), the multivariate approach demonstrates a notable decrease in error rates. This improvement suggests platinum and rhodium price movements contain useful informational value for Bitcoin forecasting, rather than mere random noise. Specifically, rhodium demonstrates strong predictive relevance for Bitcoin market movements. In conclusion, while not strictly proving causal structural integration, these findings highlight Bitcoin's sensitivity to the global real-sector economic cycle. Practically, these findings suggest investors can refine short-horizon forecasting and mitigate risk by monitoring industrial commodity prices. Given persistent nominal offset deviations, future research should prioritize explicit connectedness testing (e.g., lead-lag analysis) and develop a hybrid model incorporating Natural Language Processing (NLP) for news sentiment analysis.
Abstract This paper presents a provenance-preserving deep-learning framework for computational design screening and full-wave validation of high-isolation millimeter-wave cylindrical dielectric resonator antennas in a two-port MIMO configuration. The framework integrates Ansys HFSS, Python-based sampling, deep-learning prediction, and blockchain- and IPFS-based provenance within a reproducible computational workflow. An HFSS-Python pipeline generates 300 parametric CDRA geometries, each represented through binary image encoding and associated electromagnetic response data, including the reflection coefficient ( $$S_{11}$$ ) and the HFSS-exported total radiated electric-field response ( $$r_E$$ ). CNN, LSTM, CNN–LSTM, and multimodal Image–CNN–LSTM models are trained to predict radiation behaviour and rank candidate designs through pseudo-ECC-based screening. The selected CDRA is subsequently assessed through full-wave HFSS simulation in an orthogonal MIMO arrangement. At 27.96 GHz, the validated design achieves an $$S_{11}$$ of $$-20.79$$ dB, an $$S_{21}$$ of $$-50.13$$ dB, an ECC of $$7.78\times 10^{-8}$$ , diversity gain close to 10 dB, TARC of $$-21.09$$ dB, and CCL of 0.02185 bits s $$^{-1}$$ Hz $$^{-1}$$ . The orthogonal layout also improves isolation by about 20.31 dB compared with the parallel arrangement. The generated simulation and learning artefacts are further registered through a local Ethereum development network integrated with IPFS to support authenticity, traceability, and tamper-evident record keeping. The study demonstrates a practical computational route for automated and verifiable antenna-design screening, linking data-driven optimization with provenance-preserved management of electromagnetic design artefacts.
Abstract Indigenous Nigerian oil and gas companies face growing pressure to report environmental social and governance performance as funding for new oil and gas projects dwindles. Present day manual reporting systems are subject to errors and do not provide the transparency needed by global lenders. Therefore, this research explores a transparent automated software tool that will enable indigenous Nigerian oil and gas companies to track scope 1 and scope 2 greenhouse gases using blockchain technology to build credibility with lenders and regulatory bodies. The proposed solution uses permissioned hyper ledger fabric blockchain ledger to create an immutable and timestamped log of all data collected from the facilities’ digital flow meter readings and fuel consumption records. Additionally, the proposed solution will include an automated reporting module for the company's ESG performance compliant with the IFRS S2 Climate-Related Disclosure Standard. The results show that the automated system was able to reduce the time required to manually report emissions by 75%, eliminated errors in manual spreadsheet-based reports and provided verifiable and tamper-proof data on carbon intensity. The proposed solution enables indigenous Nigerian oil and gas companies to obtain green transition finance and sustainability linked loans; thus enabling them to demonstrate that true decarbonization in the upstream oil and gas sector requires true data transparency.
Abstract Nigeria, a leading natural gas producer in Africa, faces significant challenges in optimising its gas infrastructure to support efficient commercialisation, reduce resource wastage, and foster transparency in transactions. Current systems for custody transfer, contract enforcement, and payment reconciliation often suffer from inefficiencies, manual interventions, and lack of trust among stakeholders. This paper proposes a novel technical framework that leverages Blockchain and the Internet of Things (IoT) to address these challenges by digitalising the country's gas pipeline network. The proposed framework deploys IoT-enabled smart sensors at key custody transfer points across the gas supply chain to monitor critical parameters such as gas flow rate, volume, temperature, and pressure in real time. These parameters are securely stored on a blockchain network, ensuring data immutability, transparency, and auditability. Smart contracts integrated into the blockchain enable automated execution of contractual terms, including the initiation of payment processes via Enterprise Resource Planning (ERP) systems once predefined conditions—such as the transfer of an agreed gas volume—are met. This system minimises delays, mitigates risks of data tampering, and eliminates disputes between producers, suppliers, off-takers, and consumers by providing a single source of truth. Furthermore, the framework supports smaller producers by enabling fair participation in the market and providing a mechanism to monetise flared gas. The paper outlines the technical architecture of the proposed solution, including blockchain consensus mechanisms, IoT device integration protocols, and cybersecurity safeguards tailored to the Nigerian context. To validate the feasibility of this approach, the study examines key implementation considerations, including regulatory compliance, scalability, and integration with Nigeria's existing gas infrastructure. A comparative analysis with similar initiatives globally was conducted to highlight best practices and lessons learned. By demonstrating the potential for enhanced efficiency, accountability, and economic growth, this paper provides a robust pathway for transforming Nigeria's gas sector through advanced digital technologies.
Abstract Indigenous Nigerian oil and gas companies face growing pressure to report environmental social and governance performance as funding for new oil and gas projects dwindles. Present day manual reporting systems are subject to errors and do not provide the transparency needed by global lenders. Therefore, this research explores a transparent automated software tool that will enable indigenous Nigerian oil and gas companies to track scope 1 and scope 2 greenhouse gases using blockchain technology to build credibility with lenders and regulatory bodies. The proposed solution uses permissioned hyper ledger fabric blockchain ledger to create an immutable and timestamped log of all data collected from the facilities’ digital flow meter readings and fuel consumption records. Additionally, the proposed solution will include an automated reporting module for the company's ESG performance compliant with the IFRS S2 Climate-Related Disclosure Standard. The results show that the automated system was able to reduce the time required to manually report emissions by 75%, eliminated errors in manual spreadsheet-based reports and provided verifiable and tamper-proof data on carbon intensity. The proposed solution enables indigenous Nigerian oil and gas companies to obtain green transition finance and sustainability linked loans; thus enabling them to demonstrate that true decarbonization in the upstream oil and gas sector requires true data transparency.
Novan Ilham Ramadhan, Rizky Parlika, Ardhon Rakhmadi
Electronic voting (e-voting) systems continue to face challenges related to transparency, result validation, and duplicate voting prevention. Blockchain technology offers characteristics such as decentralization, transparency, and immutability that can support more auditable voting processes. This study presents a prototype implementation of a blockchain-based e-voting system using Ethereum smart contracts to support transparent vote recording, public auditability, and wallet-level double voting prevention. The system was implemented using Solidity-based smart contracts deployed on the Ethereum Sepolia Testnet and integrated with MetaMask for transaction authentication. Smart contracts manage election creation, candidate registration, voter registration, vote recording, duplicate vote prevention, and voting result finalization. An audit trail mechanism was implemented to allow voting activities and transaction records to be publicly verified through the Etherscan blockchain explorer. All predefined functional testing scenarios were executed successfully. The evaluation results indicate an average gas usage of 75,109 gas, an estimated transaction cost of 0.000113 ETH, and a transaction latency of approximately 4–5 seconds. The implemented wallet validation mechanism and hasVoted mapping effectively prevent duplicate voting attempts associated with the same wallet address. However, the proposed system represents a prototype-level evaluation conducted on the Ethereum Sepolia Testnet and does not provide voter identity verification, ballot anonymity, or real-world election readiness. The findings demonstrate the feasibility of Ethereum smart contracts for transparent auditability and wallet-level double voting prevention in blockchain-based voting environments.
Tapasi Bhattacharjee, Amalendu Singha Mahapatra, Dipika Pramanik
Educational crowdfunding has emerged as a promising approach to provide educational resources to underprivileged communities. Conventional systems often suffer from a lack of transparency, weak accountability, inefficient allocation of funds, and inadequate traceability of resource use. To address these issues, the present study proposes an intelligent and efficient educational supply chain management system, “EduDonateBlock.” It uses a blockchain-based crowdfunding framework to ensure transparency, accountability, and efficiency. Decentralization, immutability, and verifiable transactions are supported in educational campaigns. The entire workflow is decomposed into modular smart contracts. These are the identity and access contract (IAC), campaign and donation contract (CDC), verification and allocation contract (VAC), and supply chain and tracking contract (SCTC). These contracts are designed to ensure traceability, accountability, and efficient resource allocation among donors, educational institutions, and administrators. The mathematical framework of EduDonateBlock determines the optimal level of blockchain transparency. This minimizes the Total Expected Cost (TEC) of smart-contract operations. Numerical analysis identifies an optimal transparency level of 87.16% on-chain integration. This finding underscores the economic trade-off between transaction costs and the benefits of automation, operational efficiency, and reduced fraud risk. The proposed framework achieves a campaign success probability of 89.45% and an institutional payoff of Rs. 11,335.99. Furthermore, executing smart contracts requires 0.0044 ETH, and the average latency remains at 6.25 s. The simulation results show that EduDonateBlock offers a more efficient, reliable, and transparent solution for decentralized educational crowdfunding and socially impactful digital supply chains.
ViTs have achieved excellent performance in image recognition tasks, but their large parameter counts and high computational complexity limit their deployment on resource-constrained devices. Most existing ViT pruning methods adopt class-agnostic pruning strategies, which fail to distinguish the diverse structural requirements of different target classes. As a result, they are prone to removing critical features, leading to class-wise accuracy imbalance in practical deployment. To address this issue, this paper proposes a class-aware joint pruning framework for ViTs, which collaboratively compresses the model from two orthogonal dimensions: MLP neurons and visual tokens. Specifically, (1) based on first-order Taylor expansion, we quantify the contribution of each MLP neuron to the target classes and adaptively prune redundant neurons to achieve structured compression, followed by lightweight fine-tuning on the target class subset; (2) we propose a Class-Guided Token Selection (CGTS) method, which constructs class prototype vectors using a few support samples of the target classes and then dynamically selects patch tokens that are semantically highly relevant to the target classes during inference in a zero-shot manner, requiring no additional training or fine-tuning. The two modules complement each other, achieving dual compression from the parameter dimension and the inference data dimension. Experiments on CIFAR-100 and TinyImageNet datasets using DeiT-Tiny/Small models demonstrate that, compared with state-of-the-art pruning methods, our method reduces GMACs on target class subsets by up to 48%, improves inference speed by nearly 50%, and requires only 0.8 KB of additional storage overhead per subset, ultimately achieving a superior trade-off among accuracy, computational efficiency, and storage overhead.
Abstract This paper presents a concise framework for proving the structure of Fermat's Last Theorem (a^n + b^n = c^n) where there are no solutions for a, b, c, n ∈ ℕ⁺ when n > 2, using variable transformation and additive parameter reduction. By including integer Markdown displacements (u, v) and residual gap coefficients (k, g) in a hypothetical target exponential basis, the framework structures the exponential components to isolate structural inequalities. The resulting equilibrium identities show that changes in cumulative expansion prevent direct integer power matching, thus establishing a direct limit to the inequality.