Critical Systems Research Articles

Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification

Rapid failure mode identification of oil and gas pipelines can prevent catastrophic consequences, improve fast intervention and enhance the design safety of these critical systems. This paper proposes explainable-based machine learning models using to determine the failure mode of corroded pipelines as a function of geometric configurations, material properties, and corrosion defect details. To determine the best identification model, this study examined eight machine learning models, including Nave Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Category Boosting, based on a comprehensive experimental database for steel pipelines with various corrosion/crack defect configurations. Furthermore, the Shapley additive explanations approach is utilized to rank the input variables for failure mode identification and explains the machine learning model predicting a specific failure mode for a given sample. In identifying the failure mode of corroded pipelines, the proposed Extreme Gradient Boosting model indicated the highest accuracy in term of performance evaluation compared to all other proposed models. In addition, the model-explanation findings show that the important parameter influencing the failure mechanism of corroded pipelines is the depth of corrosion defects followed by the pipeline wall thickness. The proposed framework is adaptable enough to allow further use of experimental results for having new insights.

MCAD: A Machine Learning Based Cyber Attack Detector using SDN for Healthcare Systems

The healthcare industry, increasingly reliant on digital technology, has become a prime target for cyberattacks. While traditional security measures address external threats, they often fail to effectively counter a particularly dangerous foe: insider threats. This project proposes MCAD (Machine Learning-based CyberAttack Detector), a groundbreaking approach that leverages the power of machine learning within Software-Defined Networks (SDNs) to bolster healthcare network security. Healthcare networks are inherently complex ecosystems, housing a diverse range of medical devices alongside traditional IT infrastructure. This intricate web creates a larger attack surface for malicious insiders with access to critical systems. These insiders can be disgruntled employees or attackers exploiting vulnerabilities in poorly designed systems. The COVID-19 pandemic further exacerbated this vulnerability as the surge in telehealth services and remote access points opened new avenues for exploitation. The statistics are alarming, with a staggering 92% of healthcare organizations reporting insider-caused security breaches. These breaches not only compromise sensitive patient data but can also disrupt critical healthcare services, potentially jeopardizing patient safety. MCAD, a Machine Learning-based Cyber Attack Detector, tackles the growing threat of insider attacks in healthcare networks. It employs a multi-pronged approach: collecting both normal and abnormal network traffic to train a real-time machine learning model. This model continuously analyzes network activity, identifying suspicious behavior indicative of insider threats. MCAD seamlessly integrates with SDN controllers for efficient deployment within existing infrastructure, and undergoes rigorous testing with various machine learning algorithms and simulated attacks to ensure optimal protection against evolving cyber threats.

Fatal Software Failures in Spaceflight

Space exploration has become an integral part of modern society, and since its early days in the 1960s, software has grown in importance, becoming indispensable for spaceflight. However, software is boon and bane: while it enables unprecedented functionality and cost reductions and can even save spacecraft, its importance and fragility also make it a potential Achilles heel for critical systems. Throughout the history of spaceflight, numerous accidents with significant detrimental impacts on mission objectives and safety have been attributed to software, although unequivocal attribution is sometimes difficult. In this Entry, we examine over two dozen software-related mishaps in spaceflight from a software engineering perspective, focusing on major incidents and not claiming completeness. This Entry article contextualizes the role of software in space exploration and aims to preserve the lessons learned from these mishaps. Such knowledge is crucial for ensuring future success in space endeavors. Finally, we explore prospects for the increasingly software-dependent future of spaceflight.

A Power availablE Estimation Tool for real‐time estimation in aircraft generators

AbstractA systematic definition of an aircraft generator's “available power” (used to supply loads on its DC bus) is given, which is defined in the context of constraints on transient and steady‐state performance. Using a geometric, data‐driven approach, such a characterisation has been achieved, and a new method, called Power availablE Estimation Tool (PEET), has been developed to determine in real‐time whether a given load can be fulfilled at a given time while maintaining power quality. This is an important problem, especially for safety critical electrical systems such as more electric aircraft, for which it is imperative to know a priori whether an added load will result in voltage variations outside of allowed values. PEET is introduced and its conceptual framework is formalised. Lastly, it provides simulation results that illustrate its performance. The results show that the PEET method produces reliable a priori estimates of power availability, and that this can be achieved within time frames that make it applicable in a real‐time implementation.

Novel Prognostic Methodology of Bootstrap Forest and Hyperbolic Tangent Boosted Neural Network for Aircraft System

Complex aviation systems’ integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95–97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties.

Unveiling universal aspects of the cellular anatomy of the brain

Recent cellular-level volumetric brain reconstructions have revealed high levels of anatomic complexity. Determining which structural aspects of the brain to focus on, especially when comparing with computational models and other organisms, remains a major challenge. Here we quantify aspects of this complexity and show evidence that brain anatomy satisfies universal scaling laws, establishing the notion of structural criticality in the cellular structure of the brain. Our framework builds upon understanding of critical systems to provide clear guidance in selecting informative structural properties of cellular brain anatomy. As an illustration, we obtain estimates for critical exponents in the human, mouse and fruit fly brains and show that they are consistent between organisms, to the extent that data limitations allow. Such universal quantities are robust to many of the microscopic details of the cellular structures of individual brains, providing a key step towards generative computational models of the cellular structure of the brain, and also clarifying in which sense one animal may be a suitable anatomic model for another.

Exploring Trade-offs in Explainable AI

Machine Learning (ML) models are increasingly used in systems that involve physical human interaction or decision-making systems that impact human health and safety. Ensuring that these systems are safe and reliable is an important topic of current AI research. For many ML models it is unclear how a prediction (output) is arrived at from the provided features (input). Critical systems cannot blindly trust the predictions of such "black box" models, but instead need additional reassurance via insight into the model's reasoning. A range of methods exist within the field of Explainable AI (XAI) to make the reasoning of black box ML models more understandable and transparent. The explanations provided by XAI methods may be evaluated in a number of (competing) ways. In this paper, we investigate the trade-off between selected metrics for an XAI method called UnRAvEL, which is similar to the popular LIME approach. Our results show that by weighting the terms within the acquisition function used in UnRAvEL, different trade-offs can be achieved.

A novel standalone hybrid renewable energy systems onboard conventional and autonomous tugboats

The recent surge in fuel costs and stringent gas emission regulations mandated by the International Maritime Organization (IMO) has prompted research into integrating renewable energy and energy management strategies (EMS) onboard ships. This study illustrates the computation, simulation, and optimization of a hybrid renewable energy system (HRES) to ensure continuous power supply for auxiliary loads and critical systems on both conventional and fully autonomous tugboats. The primary aim is to design an optimal HRES with minimal annualized cost of system (ACS) and a higher proportion of renewable energy while using an artificial bee colony (ABC) algorithm. Validation of optimization outcomes is conducted using particle swarm optimization (PSO), genetic algorithm (GA), and Hybrid Optimization of Multiple Energy Resources (HOMER) Pro. The HRES incorporates diesel generators (Gensets), photovoltaic (PV) arrays, vertical axis wind turbines (VAWT), and battery banks. The optimal HRES configuration for both conventional and fully autonomous tugboats is found to be Genset/PV/VAWT/Battery. We observe that the ABC algorithm exhibits superior convergence, reliability, cost-effectiveness, renewable energy fraction, and reduced carbon emissions compared to alternative algorithms. Results of robustness tests suggest that the shipload variation, fuel prices, temperature fluctuations, wind speed and solar irradiance along the navigation route have significant impact on the optimal HRES configuration. Ultimately, we conclude that the fully autonomous tugboat demonstrates superior performance in terms of costs, carbon dioxide emissions, and renewable energy fraction compared to its conventional counterpart.

A Real-Time Parallel Programming Approach for Rust

The development of real-time systems is one of the areas with the highest relevance in computer science, and the number of critical systems has increased significantly. These systems considers several applications running concurrently, and inside each of those applications code might be parallelized to improve their performance and control the priority of each parallelizable task. Several efforts have been done in different programming languages to provide real-time systems with parallel programming models, whether by code extensions or annotations, or with specific features in the actual language core.

Spacetime-localized response in quantum critical spin systems: Insights from holography

According to the AdS/CFT correspondence, certain quantum many-body systems in d-dimensions are equivalent to gravitational theories in (d+1)-dimensional asymptotically anti–de Sitter (AdS) spacetimes. When a massless particle is sent from the AdS boundary to the bulk curved spacetime, it reaches another point of the boundary after a time lag. In the dual quantum system, it should appear as if quasiparticles have been transferred between two separated points. We theoretically demonstrate that this phenomenon, which we call “spacetime-localized response”, is actually observed in the dynamics of the one-dimensional transverse-field Ising model near the quantum critical point. This result suggests that, if we can realize a holographic spin system in a laboratory, the experimental probing of the emergent extra dimension is possible by applying a designed stimulus to a quantum many-body system, which is holographically equivalent to sending a massless particle through the higher-dimensional curved bulk geometry. We also discuss possible experimental realizations using Rydberg atoms in an optical tweezers array. Published by the American Physical Society 2024

State of Play of Critical Mineral-Based Catalysts for Electrochemical E-Refinery to Synthetic Fuels.

The pursuit of decarbonization involves leveraging waste CO2 for the production of valuable fuels and chemicals (e.g., ethanol, ethylene, and urea) through the electrochemical CO2 reduction reactions (CO2RR). The efficacy of this process heavily depends on electrocatalyst performance, which is generally reliant on high loading of critical minerals. However, the supply of these minerals is susceptible to shortage and disruption, prompting concerns regarding their usage, particularly in electrocatalysis, requiring swift innovations to mitigate the supply risks. The reliance on critical minerals in catalyst fabrication can be reduced by implementing design strategies that improve the available active sites, thereby increasing the mass activity. This review seeks to discuss and analyze potential strategies, challenges, and opportunities for improving catalyst activity in CO2RR with a special attention to addressing the risks associated with critical mineral scarcity. By shedding light onto these aspects of critical mineral-based catalyst systems, this review aims to inspire the development of high-performance catalysts and facilitates the practical application of CO2RR technology, whilst mitigating adverse economic, environmental, and community impacts.

RESEARCH AND ANALYSIS OF EFFICIENCY INDICATORS OF CRITICAL INFRASTRUCTURES IN THE COMMUNICATION SYSTEM

The efficiency indicators of the functioning critical information infrastructures in the communication system are analyzed based on the architectural concept of future networks. The object of the study is hardware and software complexes critical information infrastructures for special purposes. Critical information infrastructure represents information and telecommunication communication systems, the maintenance, reliability and security which are necessary for the safe operation special-purpose enterprises. In order to avoid the occurrence of various security and reliability incidents, the studied critical infrastructures communication systems require constant analysis and updating operating rules. The subject of the research is a method for calculating quality indicators of the functioning of critical information infrastructures in communication systems. In this work, using the example of a communication system based on modern technologies, the sequence of actions for analyzing threats to the security of a critical information infrastructure facility is considered. The purpose of the study is to develop a new approach for creating methods for calculating indicators of efficiency, reliability and information security systems. Based on the analysis of the work, a method for calculating efficiency indicators critical information infrastructures of communication systems is proposed and important analytical expressions for further research are obtained. As a result of the study, the main conclusions of the study were obtained, which can be implemented and used in critical infrastructures of communication systems to calculate the quality of functioning public computer and telecommunication systems.

Cyber Security Incident Response

In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the related literature focuses on the use of commercial DLL injection tools. In contrast, little attention has been paid to the effectiveness of using open-source DLL injection detection tools. Thus, this research project aims to evaluate the effectiveness of three widely used open-source tools, VirusTotal, Sysinternals, and Yara, in detecting DLL injection incidents. This study's findings highlight each tool's strengths and limitations, which in turn enables cybersecurity professionals to make informed decisions when selecting the most suitable tool for DLL injection detection. Furthermore, the study emphasizes the importance of continuous tool development and updates to keep pace with evolving malware techniques and emerging threats. By highlighting the effectiveness of the tools, this research enhances the overall security posture of organizations and individuals, empowering them to mitigate the risks associated with DLL injection attacks proactively. The outcomes of this research project also underscore the significance of leveraging advanced tools to fortify cybersecurity defenses and safeguard critical systems and data.

On a class of Hardy-Sobolev critical quasilinear elliptic systems on compact Riemannian manifolds

In this work, we investigate the existence and regularity of solutions to the critical system{−Δp,gu+a(x)|u|p−2u+b(x)[(p−1)|u|p−2+|v|p−2]pv=αp⋆(s)f(x)u|u|α−2|v|βdg(x,x0)sinM,−Δp,gv+b(x)[(p−1)|v|p−2+|u|p−2]pu+c(x)|v|p−2v=βp⋆(s)f(x)v|v|β−2|u|αdg(x,x0)sinM, where (M,g) is a smooth closed Riemannian manifold of dimension n≥2, dg is the Riemannian distance, Δp,g=divg(|∇gu|p−2∇gu) is the p-Laplace-Beltrami operator on (M,g), p∈(1,n), a,b,c∈C0,ϱ(M) for some ϱ∈(0,1), with b≡0 when 1<p<2, x0∈M, s∈[0,p), f is a smooth function in M with f(x0)=maxM⁡f>0, and α>1, β>1 are two real numbers such that α+β=p⋆(s), where p⋆(s)=p(n−s)/(n−p) denotes the critical Hardy-Sobolev exponent.

Research on cascading effect analysis of civil aircraft electrical power system failures

Abstract Cascade effect analysis is a qualitative analytical method recommended by SAE ARP 4761A [1] for evaluating complex integrated civil aircraft systems. The Electrical Power System (EPS) is one of the critical onboard systems. It can provide electrical power to the avionics system, flight control system, and other critical systems on the aircraft, and it has a wide range of failure effects. The consequences of EPS failure effects are difficult to determine. This paper applies the cascading effect analysis method to carry out the qualitative safety assessment of the EPS on the aircraft and finally confirms that the architectural design of the EPS met the safety design requirements. It also gives the engineering method (such as test, MBSA) suggestion to further check and verify the correctness and completeness of the theoretical analysis of CEA.

Economic and Financial Elements of Critical Infrastructure Augmentation

Abstract This article will explore the intersections between economics and finance that underpin the strengthening of critical infrastructure systems. Exploring this area, I can realise that improving critical infrastructures is a necessity that every country depends on. Given the technological advances and threats that are constantly developing, we will be able to conclude that the need to understand the importance of these infrastructures and how to develop them depends on the economic implications that are part of a country’s investment plan. Through a comprehensive review of case studies and economic theories, this article will highlight various factors that influence critical infrastructure development decisions, ranging from fiscal and budgetary constraints to development or upgrading decisions. In addition, this paper will analyse, from a theoretical point of view, the importance of public-private partnerships, government funding and risk management strategies that underpin the resilience and sustainability of critical infrastructure. Finally, this paper will provide a nuanced perspective on economic and financial management, the growth of critical systems that are vital to the optimal and effective functioning of national security.

Unlearning regularization for Boltzmann machines

Boltzmann machines (BMs) are graphical models with interconnected binary units, employed for the unsupervised modeling of data distributions. When trained on real data, BMs show the tendency to behave like critical systems, displaying a high susceptibility of the model under a small rescaling of the inferred parameters. This behavior is not convenient for the purpose of generating data, because it slows down the sampling process, and induces the model to overfit the training-data. In this study, we introduce a regularization method for BMs to improve the robustness of the model under rescaling of the parameters. The new technique shares formal similarities with the unlearning algorithm, an iterative procedure used to improve memory associativity in Hopfield-like neural networks. We test our unlearning regularization on synthetic data generated by two simple models, the Curie–Weiss ferromagnetic model and the Sherrington–Kirkpatrick spin glass model. We show that it outperforms Lp -norm schemes and discuss the role of parameter initialization. Eventually, the method is applied to learn the activity of real neuronal cells, confirming its efficacy at shifting the inferred model away from criticality and coming out as a powerful candidate for actual scientific implementations.

Bayesian network-based resilience assessment of interdependent infrastructure systems under optimal resource allocation strategies

Critical infrastructure systems (CISs) play a key role in the socio-economic activity of a society, but are exposed to an array of disruptive events that can greatly impact their function and performance. Therefore, understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for, and mitigate the impact of, future disruptions. Resilience is one characteristic of CISs that influences the extent and severity of the impact induced by extreme events. Resilience is often dissected into four dimensions: robustness, redundancy, resourcefulness, and rapidity, known as the “4Rs”. This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs, with resilience considered as a stochastic variable. The proposed framework combines an agent-based infrastructure interdependency model, advanced optimization algorithms, Bayesian network techniques, and Monte Carlo simulation to assess the resilience of an infrastructure network. The applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin, Texas, where the resilience of the network is assessed and a “what-if” analysis is performed.

Towards Precision Navigation: Curved lane Detection System in Vehicles

Abstract: Curved lane detection is a critical componentof advanced driver assistance systems (ADAS) and autonomous vehicles, playing a pivotal role in ensuring safeand efficient navigation on complex roadways. In the pursuitof enhancing accuracy and expediting processing speed in road lane detection, researchers have proposed numerous methodologies. Despite these efforts, several challenges persist, including variations in lane markings, fluctuations in lighting conditions, and the emergence of shadows. Addressing these obstacles is crucial for establishing dependable lane detection systems. The curve lane detection algorithm exhibits a robust performance, as evidenced by the experimental results, which consistently reveal a notably high success rate in accurately identifying and delineating curved lanes

Empirical loss weight optimization for PINN modeling laser bio-effects on human skin for the 1D heat equation

The application of deep neural networks towards solving problems in science and engineering has demonstrated encouraging results with the recent formulation of physics-informed neural networks (PINNs). Through the development of refined machine learning techniques, the high computational cost of obtaining numerical solutions for partial differential equations governing complicated physical systems can be mitigated. However, solutions are not guaranteed to be unique, and are subject to uncertainty caused by the choice of network model parameters. For critical systems with significant consequences for errors, assessing and quantifying this model uncertainty is essential. In this paper, an application of PINN for laser bio-effects with limited training data is provided for uncertainty quantification analysis. Additionally, an efficacy study is performed to investigate the impact of the relative weights of the loss components of the PINN and how the uncertainty in the predictions depends on these weights. Network ensembles are constructed to empirically investigate the diversity of solutions across an extensive sweep of hyper-parameters to determine the model that consistently reproduces a high-fidelity numerical simulation.