Explosion-proof apparatus is a must in hazardous areas especially in anindustrial setting where certification is required to meet certain safety levels.Conventional certification mechanisms tend to be slow, non-transparent and vulnerable to forgery of documents and delays, particularly in the context of cross border transactions.This article presents the architecture of a blockchain-based certification platform, which could contribute to transparency, traceability, and efficiency in the certification lifecycle of explosion-proof equipment.It includes Ethereum smart contracts, IPFS (InterPlanetary File System) to store the comprehensive test reports on a decentralized platform, and a role-based web application interface for different kinds of users such as manufacturers, testing labs, certification bodies, and field auditors.Smart contracts are responsible for generating, revoking and handling certificate access control, all certification metadata and file hashes are suitably safeguarded on the blockchain, allowing records to remain tamper-proof and verifiable.A working prototype was implemented in Goerli Ethereum testnet and developed as React application.js frontend, Web3.js, IPFS and Architecture for a Blockchain-based Certification Platform for Explosion-Proof Devices https://iaeme.com/Home/journal/IJCET499
Highlights of the IssueKris Carlson, Publisher and Editor-in-ChiefOur second issue surveys state of the art of large language models (LLMs) with an emphasis on safety and value alignment. Superintelligence StrategyDan Hendrycks, Eric Schmidt, Alexandr WangSeeking Stability in the Competition for AI Advantage: Commentary on Superintelligence StrategyIskander Rehman, Karl P. Mueller, Michael J. Mazarr (RAND Corp.) I recommend the RAND Corp critique by knowledgeable military policy analysts over the Hendrycks et al. article. The RAND article is illuminative, incisive, covers Superintelligence Strategy’s key points, and suggests critical reasoning flaws in their mutually-assured-AI-malfunction (MAIM} policy. Although it is valuable to compare the nuclear and AI revolutions in search of instructive parallels and insights, the differences between the technologies and their respective ecosystems have deep strategic implications. Taking these into account, we have concerns regarding both the practical viability of the MAIM concept as an approach to overcoming instability risks in the AI race and the potential escalatory dangers that could follow from its core prescriptions.— Rehman et al. pg. 1 Surely we’d like to avoid repeating the mutually-assured-destruction (MAD) policy. The MAD policy alone could trigger AGI taking over for their and our security. But we must realize that strategies like MAD and MAIM are considered in the US, its allies, and adversaries. And we must try to understand them in order to avoid them. Highlights of the critique: First, the report refers loosely to an array of actions that states might take to cripple a rival's architecture for developing advanced AI.... [which] assumes that adversary AI programs will have specific facilities that can be readily located and disrupted. However, distributed cloud computing, decentralized training, and algorithmic development increasingly may not require centralized physical locations, making AI systems more resilient to limited attacks…. The following critique argues for distributed autonomous organizations (DAO) as I advocated in Safe Artificial General Intelligence via Distributed Ledger Technology and Provably Safe Artificial General Intelligence via Interactive Proof Systems. A second practical challenge resides in the expectation that each party can accurately assess secretive AI progress by others and gauge when preventive action would be necessary. Contrary to what is averred in the report, it is unlikely that states will have a clear sense of when the moment has arrived to MAIM their opponent…. Third and finally, even a credible MAIM threat might not deter a rival from pursuing superintelligent AI. Halting one's AI development would entail essentially the same costs as being the victim of a MAIM attack — loss of the program. And here’s another critique: MAD did not seek to deter the development of weapons but instead their use, which made the threshold for response vastly simpler (though it could still be problematic in cases such as false or ambiguous warnings of attacks). We would like to hear, or be pointed to, policy alternatives to MAIM that incentivize AGI developers to move toward AGI that can be proven to benefit all of humanity. Humanity’s Last Exam (HLE)Long Phan, Alice Gatti, Ziwen Han, and Nathaniel Li are first-listed members of the Organizing Team, and have hundreds of co-author/collaborators. This very large-scale collaborative effort has an ambitious title. The authors note:[LLM] benchmarks are not keeping pace in difficulty [with LLM capabilities]: LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce HUMANITY’S LAST EXAM (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects. I do not find any mention of the terms, ‘training set leakage into test set data’ or ‘test set contamination.’ But those issues aside, it seems to be the toughest test set yet – at least as of this writing (16 March 2025) before the LLMs learn the answers and can regurgitate them and reasonably close variants, at which point there will need to be a fresh ‘last exam.’ Kudos to the organizing authors. It’s interesting that frontier LLMs performed dramatically poorer on HLE than on previous benchmark tests, which is a tribute to the originality of the questions. Pathways to Short Transformational AI TimelinesZershaaneh Qureshi We excerpt here a chapter from the complete text. To understand this chapter note that the article distinguishes between two types of recursive self-improvement (RSI): • Direct recursive improvement: positive feedback loops which are mediated directly by AI systems. • Indirect recursive improvement: positive feedback loops that are not mediated directly by AI, such as economic feedback loops (driven by reinvestment of capital into AI R&D), scientific feedback loops (driven by advancements in scientific tools and methods) and political feedback loops (driven e.g. by competitive pressures/race dynamics) (pp. 15-16). HyperWrite, edited: The complete article outlines a framework for analyzing different scenarios that could lead to Transformative AI (TAI) within the next 10 years. Key parameters considered are: 1. Compute scaling dynamics (whether progress continues or hits bottlenecks)2. Indirect feedback loop dynamics (whether they can overcome scaling bottlenecks)3. Direct recursive improvement (DRI) timeline (before or after 2035)4. DRI strength (cannot sustain, sustains, or accelerates progress) Seven possible scenarios are: 1. "Straight Path" - Compute scaling continues successfully2. "Rising Tide" - Indirect recursive improvement (IRI) overcomes bottlenecks3. "New Spark" - Moderate direct recursive improvement maintains progress4. "New Engine" - Strong DRI accelerates progress5. "Dual Engine" - Combination of compute scaling and DRI6. "LLM Hybrid" - Hybrid AI systems enable TAI7. "Intelligent Network" - Networks of AI systems enable TAI The author argues that this variety of plausible pathways strengthens the case for short TAI timelines, as TAI could emerge through multiple different mechanisms rather than requiring one specific path to succeed. Please send pointers and commentary on AI timelines and recursive self-improvement to editor@s-rsa.com. The Road to Artificial SuperIntelligence: A Comprehensive Survey of SuperalignmentHyunJin Kim, Xiaoyuan Yi, JinYeong Bak, Jing Yao, Jianxun Lian, Muhua Huang, Shitong Duan, Xing Xie SuperIntelligence will publish reviews and survey articles to help newbies to AGI/SI get up to speed and experienced workers stay up to speed efficiently. The latter can scroll to Section 2.3, Overview of Superalignment Methods and Challenges. Brief analysis of DeepSeek R1 and its implications for Generative AISarah Mercer, Samuel Spillard, Daniel P. Martin For quick and incisive insights into DeepSeek, read this analysis and Dario Amodei’s cool-headed response to all the hype about DeepSeek. Effective Mitigations for Systemic Risks from General-Purpose AIRisto Uuk, Annemieke Brouwer, Tim Schreier, Noemi Dreksler, Valeria Pulignano, Rishi Bommasani A timely article with practical, near-term-implementable AGI risk mitigation suggestions. Examples: • Unlearning techniques: Removing specific harmful capabilities (e.g., pathogen design) from models using unlearning techniques.• Capability restrictions: Restricting risky capabilities of deployed models, such as advanced autonomy (e.g., self-assigning new sub-goals, executing long-horizon tasks) or tool use functionalities (e.g., function calls, web browsing).• Input and output filtering Monitoring for dangerous outputs (e.g., code that appears to be malware or viral genome sequences) and inputs that violate acceptable use policies to ensure models do not engage in harmful behaviour.• Bug bounty programs Clear and user-friendly bug bounty programs that acknowledge and reward individuals for reporting model vulnerabilities and dangerous capabilities. • Safety drills Regularly practising the implementation of an emergency response plan to stress test the organisation’s ability to respond to reasonably foreseeable, fast-moving emergency scenarios. Simulating Influence Dynamics with LLM AgentsMehwish Nasim , Syed Muslim Gilani, Amin Qasmi, and Usman Naseem Analyzing how AGI/SI may influence human opinion is a critical aspect of risk and safety analysis, as is simulation of AGI risk behavior. The methodology the authors present in this short paper has broad application: This paper introduces a simulator to model influence and counter-influence in a wargame setting. Wargames, originally developed for military strategy, have evolved into powerful tools for decision-making across various domains. Today, they are used to model business strategies, assess cybersecurity threats, and simulate geopolitical conflicts. Governments and corporations employ wargames to anticipate economic shifts, supply chain disruptions, and the impact of emerging technologies. In healthcare, they help model pandemic responses, testing different policy interventions before realworld implementation. AI-driven wargames further enhance scenario analysis, enabling rapid adaptation to complex environments. By fostering strategic thinking and resilience, modern wargaming serves as a critical tool for navigating uncertainty in an increasingly interconnected world. Can a Bayesian Oracle Prevent Harm from an Agent? Yoshua Bengio, Matt McDermott, Michael K. Cohen, Nikolay Malkin, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar SI co-founding Editor Steve Omohundro comments: Turning an oracle into an agent may take just a page of code. OK, but that doesn’t mean the methods outlined by Be
The instability of algorithmic and hybrid stablecoins has become a systemic concern in decentralized finance. This paper proposes a unified, interpretable, and uncertainty-aware framework that integrates graph-based deep reinforcement learning, GARCH econometric modeling, and Bayesian inference. Multi-stage reinforcement learning agents simulate interactions between arbitrageurs and protocol mechanisms. GARCH models capture volatility dynamics, while Bayesian methods provide confidence intervals for peg deviation forecasts, enabling adaptive prediction and transparent risk interpretation. The framework is validated using over eight million on-chain and off-chain records across 120 scenarios involving USDT, USDC, and TerraUSD. It achieves 89 per cent crisis prediction accuracy and 83 per cent reflexivity modeling performance, significantly outperforming six benchmark models. Notably, the system issued early warnings up to 72 hours before the TerraUSD collapse. Ablation studies confirm the unique contribution of each module. In addition to technical improvements, the framework outputs a stability index and dynamic reserve recommendations to support policy response and supervisory planning. Compared to existing approaches, this is the first framework to combine dynamic simulation, interpretability, and probabilistic forecasting in a single architecture. It offers practical value for stablecoin monitoring and establishes a methodological foundation for future research in digital asset risk assessment.
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan
Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart contract maintenance, requiring developers to devise their own methods. A popular method is known as the upgradeability proxy contract (UPC), which involves implementing the proxy design pattern (as defined by the Gang of Four). In this method, client calls first hit a proxy contract, which then delegates calls to a certain implementation contract. Most importantly, the proxy contract can be reconfigured during runtime to delegate calls to another implementation contract, effectively enabling application upgrades. For researchers, the accurate detection of UPCs is a strong requirement in the understanding of how exactly real-world DApps are maintained over time. For practitioners, the accurate detection of UPCs is crucial for providing application behavior transparency and enabling auditing. In this paper, we introduce UPC Sentinel, a novel three-layer algorithm that utilizes both static and dynamic analysis of smart contract bytecode to accurately detect active UPCs. We evaluated UPC Sentinel using two distinct ground truth datasets. In the first dataset, our method demonstrated a near-perfect accuracy of 99%. The evaluation on the second dataset further established our method's efficacy, showing a perfect precision rate of 100% and a near-perfect recall of 99.3%, outperforming the state of the art. Finally, we discuss the potential value of UPC Sentinel in advancing future research efforts.
Abstract Critical Infrastructures produce critical goods and services for society and cover a wide range of sectors according to the governance frameworks in place in the United States, in the European Union (EU) and in EU Member States including Romania. Aviation is a particular case for the critical transport infrastructure, with a high degree of technical complexity, economic productivity and complex supply and production chains. Blockchain or Distributed Ledger is an already famous digital emerging technology with the possibility of disintermediating numerous system processes, thereby reducing costs, increasing security and reducing risk in a world where cumbersome and expensive intermediaries are required for trust in all sorts of transactions. This article provides a brief overview of the critical air transport infrastructure and of the blockchain technology, traces the possible uses of blockchain in aviation, gives real world examples and concludes with recommendations for practitioners moving forward.
Chiranjibi Shah, Niamat Ullah Ibne Hossain, Md Muzahid Khan, Shahriar Tanvir Alam
Blockchain technology and the Internet of Medical Things (IoMT) have garnered increased attention recently due to their growing application in effectively managing data security, storage, and transmission concerns within healthcare organizations. However, integrating various advancements, such as coordination, adaptivity, and automated responses, within the framework of blockchain-based IoMT has amplified its susceptibility to a range of attacks and vulnerabilities. Assessing and enhancing the resilience of blockchain-based IoMT is of utmost importance, particularly in anticipation of potential disruptions, to ensure its continuous and sustainable functionality. The stochastic nature of risks adds complexity to evaluating the resilience of blockchain-based IoMT, given that resilience in this domain may fluctuate over time. This study employs a dynamic Bayesian network (DBN) method to address the evolving characteristics of pertinent variables, capturing their temporal dependencies and demonstrating how the resilience capabilities of blockchain-based IoMT may evolve across different time intervals. Additionally, an information theory approach is adopted to mitigate uncertainty regarding the resilience performance of blockchain-based IoMT and its crucial subcomponents. This research showcases the effectiveness and adaptability of the DBN methodology in healthcare systems, offering insights for shaping appropriate and essential strategies for decision-makers to establish a highly resilient framework for blockchain-based IoMT.
As the complexity of the global food supply chain continues to grow, ensuring food quality and safety has become an important challenge for the global food industry.Food quality and safety traceability is a key methodology that can be used to track the origin and quality of food to ensure food safety.Meanwhile, blockchain technology, as a distributed ledger technology, provides new opportunities for food traceability.This study aims to explore how to integrate blockchain technology and data fusion analysis to improve the efficiency and accuracy of food quality and safety traceability.This paper first reviews the advantages of blockchain technology in food traceability and introduce the key concepts of data fusion analysis.Then, this paper proposes a blockchain-based framework for food quality and safety traceability, describing in detail the process of data collection, cleaning, fusion and analysis.Through practical case studies, this paper demonstrates the application of the framework in the field of grain quality and safety, and presents the fusion analysis results.Finally, the paper discusses future directions, including technical challenges, regulatory implications, and sustainability considerations.This study provides strong support for the integration of food quality and safety traceability and blockchain technology, which is expected to contribute to the further development of global food security and quality management.
Manuel Díáz, Enrique Soler, Luis Llopis, Joaquín Trillo
Safety-Critical Systems (SCSs) often manage sensible data that must be trustworthy, especially in many cases in which different actors participate whose interests may not coincide. Blockchain is a disruptive technology that has emerged to ensure the trustfulness of data. The nuclear industry incorporates many SCSs where blockchain can be applied. This paper focuses on the use of blockchain for the inspection of steam generators of a nuclear power plant. This is a critical process where different actors participate: plant property, external companies in charge of the inspection itself and different administrations. It typically consists of a number of processes that explore the state of different components of the plant in order to find any kind of failure or defect and it generates a great amount of data that must be verifiable and trustworthy. A distributed blockchain-based system is presented where all the nodes share the information and it cannot be altered. As a novelty, automatic inspection algorithms are stored in the blockchain itself by means of smart contracts. The benefits of blockchain are studied for the nuclear industry in general and for the inspection process in particular. In order to explore the possible drawbacks of a blockchain-based system for data management, a simulator has been implemented to recreate the scenario of an inspection. The results obtained show that blockchain architectures are a good alternative to traditional information repositories for nuclear power plant inspections.
We propose to enhance the security and transparency of aircraft maintenance records in the aviation industry through the use of blockchain technology. A physical aircraft maintenance logbook is susceptible to being lost or destroyed. A nonexistent aircraft maintenance logbook hurts the confidence in integrity and reputation of the aircraft. Furthermore, fraud can occur through forgery of FAA personnel signatures and the installation of non-official aircraft parts. The scope of this work is to develop a secure blockchain that can store aircraft service records and information in a digital distributed ledger. By keeping the maintenance logbook on a digital ledger, records can be stored indefinitely in a trusted environment with the integrity of records guaranteed. Additionally, to achieve being a distributed ledger, a consensus algorithm PoET is used to display the global state accurately to all users. The SAMR blockchain uses the Linux Foundations open sourced software “Hyperledger” to facilitate an environment that mimics a real-world implementation. The Python Programming Language was used for SAMR's implementation of the blockchain logic through creation of a permission-based blockchain for holding the maintenance records.
Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning. A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plant’s outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the system’s dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific system’s dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case. The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration. This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraft’s dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided.