Optimizing fault tree traversal order for efficient minimal cut set generation: A lightweight graph neural network approach

Smart contracts, pivotal to blockchain technology, automate contract enforcement with precision and reliability but are plagued by security vulnerabilities that have led to significant financial losses. Traditional methods like static analysis and fuzzy testing, though foundational, typically fail to grasp the complex logic of smart contract code, resulting in poor adaptability to the evolving landscape of blockchain applications. To address these challenges, this paper introduces an advanced Graph Neural Network (GNN) approach tailored for smart contract vulnerability detection on the VNT blockchain platform. Our methodology leverages the inherent structural and semantic relationships within contract code, transforming it into a graph representation for deeper learning. The enhanced GNN model effectively captures intricate patterns and dependencies that traditional methods overlook, significantly improving detection accuracy and efficiency. Experimental results demonstrate that our approach outperforms existing techniques, offering a promising avenue for both refining smart contract security analyses and guiding future development practices. We further contribute to the field by providing a robust dataset of smart contracts, fostering further research and application enhancements. Experimental results demonstrate that the proposed method not only advances the security of smart contract in VNT but also establishes a new benchmark for the application of deep learning in blockchain.

Precise Global Navigation Satellite System (GNSS) time series have greatly advanced tectonic studies, particularly in detecting transient deformation signals like slow slip events (SSEs). However, GNSS position data can be noisy, impacting the accuracy of analyses. Traditional denoising methods often struggle with spatially heterogeneous and evolving networks. This study introduces a novel Graph Neural Network (GNN) approach to denoise GNSS time series, effectively managing network heterogeneity and varying station availability. GNNs are robust against temporal gaps, making them suitable for GNSS data. Applied to daily time series for the Cascadia Region processed by the University of Nevada Reno and Central Washington University, our method reduced common-mode noise by more than 70% and 30% on horizontal components, in the two datasets respectively, significantly enhancing surface displacement measurements and slow slip events (SSE) source property estimation. We compared the GNN approach with three simple stack filtering methods, which performed comparably in many situations but are more sensitive to parameter choices. For all methods, substantial noise reduction removes artifacts that could impact geophysical interpretations. Our findings suggest that GNN-based denoising offers a robust, adaptive solution for heterogeneous GNSS networks, enhancing accuracy in tectonic and volcanic process studies, but stack filtering approaches might still outperform the machine learning technique depending on the application.

Since their introduction in form of a canonical representation of logical functions, the Binary Decision Diagrams (BDDs) gained a wide acceptance in numerous industrial applications. This paper summarizes the properties of BDD representation of Minimal Cut Sets (MCS) of Fault Tree (FT) models most typically encountered in nuclear energetics. Cut sets from MCS are defined as paths from the top BDD node to terminal nodes in the BDD, on which a quantitative and qualitative FT analysis (FTA) is performed. The core of the FTA on the BDDs is performed with help of two fundamental algorithms, one for conditional probability evaluation and another for the selection of cut sets. The accuracy of conditional probability evaluation represents an essential feature for an unbiased quantitative analysis, such as the top event probability or the determination of event importance measures. The cut set selection algorithm is shown in a generic version introducing logical predicates for its selection criteria. As it is known, the efficiency of depicted algorithms depends only on the number of BDD nodes used for the FT representation. In order to appraise the compactness of the BDD representation of FT models, their characteristics have herein been evaluated on several real-life models from the Nuclear Power Plant Krško. The extraordinariness of the compactness of the BDD representation reflects in its ability to implement advanced dynamic analysis (i.e. what-if) of FT models. The efficiency of such an approach is recognized by commercial vendors upgrading their FT Tools to new versions by implementing BDD based algorithms.

The inherent complexity and heterogeneity of the Internet of Things (IoT) ecosystem present significant challenges for developing effective intrusion detection systems. While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. This paper proposes a Top-K Similarity Graph Framework (TKSGF) for IoT network intrusion detection. Instead of relying on physical links, the TKSGF constructs graphs based on Top-K attribute similarity, ensuring a more meaningful representation of node relationships. We employ GraphSAGE as the Graph Neural Network (GNN) model to effectively capture node representations while maintaining scalability. Furthermore, we conducted extensive experiments to analyze the impact of graph directionality (directed vs. undirected), different K values, and various GNN architectures and configurations on detection performance. Evaluations on binary and multi-class classification tasks using the NF-ToN IoT and NF-BoT IoT datasets from the Machine-Learning-Based Network Intrusion Detection System (NIDS) benchmark demonstrated that our proposed framework consistently outperformed traditional machine learning methods and existing graph-based approaches, achieving superior classification accuracy and robustness.

The subject of research is to determine the functional safety indicators of a fault-tolerant safety-critical system, namely, the minimal cut sets’ probability for a given duration of the system’s operation, using the state transition diagram (STD). The aim is to create a new method for analyzing the functional safety of a fault-tolerant safety-critical system. This method is based on the methodology of developing models of operational reliability behavior in the form of STD. This methodology provides a detailed representation of inoperable states and their relation with pre-failure (inoperable critical) states. The task is to propose a new classification for inoperable states of the STD to obtain all possible emergencies in the same space of inoperable states. This approach allows consideration the correlations between the failures, that it is impossible to use the fault trees. Since the space of inoperable states can reach hundreds and thousands of states, a method is proposed for their automated determination according to the classification. The state space method was used to conduct the validation of the method of functional safety analysis. The following results were obtained: the system of Chapman-Kolmogorov differential equations is formed in accordance with the STD and it provides the dependence of the functional safety indicator – the minimal cut sets’ probability as a function of the operational duration of the fault-tolerant safety-critical system. This dependence is called the emergency function. The method for determining the emergency function is based on the usage of the emergency mask. Note that the proposed model of operational reliability behavior in the form of STD provides the possibility to conduct both the functional safety and the reliability indicators. The value of the minimal cut sets’ probability for a given duration of operation is determined using the fault tree for the validation of the proposed method of functional safety analysis. The fault tree was built by Reliasoft BlockSim software. The obtained value coincides with the value of the minimal cut sets’ probability, which was defined by the emergency function for the same operational duration. Thus, the designer can comprehensively analyze the feasibility of introducing redundancy (structural, temporal, functional). Conclusions: the scientific novelty of the obtained results is the following: the new method for determining safe, critical and catastrophic states in the set of inoperable states is used in the methodology of the STD developing to obtain the stochastic model of operational reliability behavior of fault-tolerant safety-critical system. This technique ensures an automated defining of emergency function by using an improved structural-automatic model.

Fussell-Vesely Importance (FVI) is one of the most commonly used importance measures. It is based on the concept of Minimal Cut Sets (MCSs), and it quantifies the contribution of a system component to system failure. In paper (Kvassay et al. 2015), the algorithm for identification of all system MCSs based on Direct Partial Logic Derivatives (DPLDs) has been considered. These MCSs are then used to compute the FVI. However, the FVI of a system component depends only on MCSs that contain the considered component, i.e. it is not necessary to identify all MCSs of the system. This implies that it is useful to develop algorithms that will identify not all system MCSs but only those in which a specific component occurs. Such algorithms can be used to compute the FVI without a priori knowledge of MCSs. In this paper, a new algorithm for this task is proposed. The algorithm is based on a relation between DPLDs and MCSs that is also considered in this paper. of algorithms for identification or generation of MCSs of investigated system. These algorithms are developed mainly on the basis of applied fault trees (Sinnamon & Andrews 1997, Vatn 1992) or networks (Emadi & Afrakhte 2014, Rebaiaia & Ait-Kadi 2013). Another algorithm for identification of system MCSs has been considered in paper (Kvassay et al. 2015). This algorithm is based on tools of logical differential calculus, and it can be used for systems of any type (not only for systems presented as networks or for systems described by fault trees). Logical differential calculus has been developed for analysis of dynamic properties of logic functions. Direct Partial Logic Derivatives (DPLDs) are essential part of this tool (Tapia et al. 1991). Their use in reliability analysis has been considered in several papers (Zaitseva 2003, Zaitseva 2012, Zaitseva et al. 2015). These papers have showed that DPLDs are very appropriate for identification of situations in which failure/repair of a system component results in system failure/repair. This fact has been used to develop algorithms for defining system boundary states and for calculation of some measures that can be used to evaluate system reliability. In this paper, we develop ideas presented in paper (Kvassay et al. 2015) to propose an algorithm that identifies not all system MCSs but only MCSs in which a specified component is presented. Such MCSs are useful in computation of FVI of the considered component. As a result new method for calculation of this IM is developed. This method is

BackgroundComprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein–protein interactions for hierarchical clustering.ResultsUsing a large amount of publicly available proteomics data, we created a hierarchical tree for breast cancer protein communities using a novel hierarchical graph neural network, with the supervision of gene ontology terms and assistance of a pre-trained deep contextual language model. Then, a group-lasso algorithm was applied to identify protein communities that are under both mutation burden and survival burden, undergo significant alterations when targeted by specific drug molecules, and show cancer-dependent perturbations. The resulting hierarchical map of protein communities shows how gene-level mutations and survival information converge on protein communities at different scales. Internal validity of the model was established through the convergence on BRCA2 as a breast cancer hotspot. Further overlaps with breast cancer cell dependencies revealed SUPT6H and RAD21, along with their respective protein systems, HOST:37 and HOST:861, as potential biomarkers. Using gene-level perturbation data of the HOST:37 and HOST:861 gene sets, three FDA-approved drugs with high therapeutic value were selected as potential treatments to be further evaluated. These drugs include mercaptopurine, pioglitazone, and colchicine.ConclusionThe proposed graph neural network approach to analyzing breast cancer protein communities in a hierarchical structure provides a novel perspective on breast cancer prognosis and treatment. By targeting entire gene sets, we were able to evaluate the prognostic and therapeutic value of genes (or gene sets) at different levels, from gene-level to system-level biology. Cancer-specific gene dependencies provide additional context for pinpointing cancer-related systems and drug-induced alterations can highlight potential therapeutic targets. These identified protein communities, in conjunction with other protein communities under strong mutation and survival burdens, can potentially be used as clinical biomarkers for breast cancer.

The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature. Our method relies on the use of an ensemble of GNN models to predict the expected stereoselectivity of exemplars for a particular asymmetric reaction. When the majority of these models do not correlate to the reported outcome, the point is labeled as a possible stereochemical misassignment. Such identified cases are few in number and more easily investigated for their cause. We demonstrate the use of this approach to spot potential literature stereochemical misassignments in the ketone products resulting from catalytic asymmetric 1,4-addition of organoboron nucleophiles to Michael acceptors in two different databases, each one using a different family of chiral ligands (bisphosphine and diene ligands). Our results demonstrate that this methodology is useful for curation of medium-sized databases, speeding this process significantly compared to complete manual curation/checking. In the datasets investigated, human expert checking was reduced to 2.2% and 3.5% of the total data exemplars.

In this study, the risk of collision and grounding accidents on bulk carrier and general cargo ships was investigated. A comprehensive risk analysis consisting of past accident reports, fault tree analysis (FTA) and Cut sets importance measure (CS-I) methods was carried out. The failures effective in the occurrence of collision and grounding accidents were determined by examining past accident reports. In the study, the probability for each failure was derived by using real accident data. Using FTA, various potential accident occurrence combinations/scenarios, also known as minimal cut sets (MCSs), for both accident types were obtained. In addition, prioritization was conducted for the MCSs of both types of accidents using CS-I sensitivity analyses. The faults that increase the risk of collision accidents the most were found to be interpretation failure of the Convention on the International Regulations for Preventing Collisions (COLREG) and lack of situational awareness. In addition, the top failures that lead to groundings were determined to be lack of communication in bridge resource management (BRM), fatigue/sleeplessness and inappropriate voyage plan. Recommendations are made for eliminating potential risk factors, preventing the recurrence of such accidents and establishing safer maritime transport.

International Maritime Organization (IMO) implements the principles of risk management through a systematic process called Formal Safety Assessment (FSA). FSA is a structured and systematic methodology, aimed at enhancing maritime safety, including protection of life, health, the marine environment and property, by using risk analysis and cost benefit assessment. One of the most widely used hazard identification and risk analysis techniques in FSA is Fault Tree Analysis (FTA) method. However, only basic conventional methodology of the FTA technique is used in FSA. When dealing with large fault trees, the limitations of FTA in terms of accuracy and the efficiency become apparent. It is necessary for IMO to improve FTA method in FSA. In recent years, the Binary Decision Diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. In this paper, the BDD method is introduced for the application in FSA, as it has never been used in FSA studies. A commonly used construction method and some basic event ordering methods of BDD are described. By applying BDD to FTA, two main issues concerned in FSA, minimal cut set and top event probability, can be obtained efficiently and accurately comparing to the analysis using a conventional FTA method.

A railway substation transforms the electric power transmitted from electric power plants and supplies it to the railway power system for the appropriate operation of traction system.For the appropriate operation of the traction system, it is very important to maintain the security of electric power system.This paper proposes the case when an AC railway substation does not operate right, and Fault Tree(FT), Event Tree(ET) and Minimal Cut Sets(MCS) algorithm to calculate the reliability indices of railway substation.Event tree is created and gate results of fault tree analysis are used as the source of event tree probabilities.Fault tree represents the interaction of failures and basic events within a system.Minimal Cut Sets analysis is used to minimize system errors, Event Tree and Fault Tree analysis result is helpful to assessing the reliability to interpreted.The reliability indices can be used to determine the equipment to be replaced for the entire system reliability improvement.

A new method is developed for tracing the unreliability contributions of system components and recognizing the system weak parts using the fault tree analysis (FTA). The method is based on the minimum cut set (MCS) algorithm for evaluating the system reliability using the FTA and the proportional sharing principle (PSP). The fault tree can be expressed as MCSs and the system unreliability can be mathematically expressed by a certain terms of probability of occurrence of MCSs. A unreliability tracing (UT) principle is proposed for allocating the probability of each term to the basic events fairly and reasonably, and then the direct contribution relationship between the system unreliability and basic events can be established. The system UT sharing factors (UTSFs) are derived to easily identify the weakness parts in a system. The applicability of the proposed methods is illustrated by case studies of a simple system and a multiple-output power distribution system.

In this paper, we introduce a novel Markov chain (MC) representation aided minimum bit error rate (MBER) detection method that is applicable to an M-QAM modulated SDM/SDMA uplink system. Compared to the conventional MBER scheme, the proposed MC-MBER scheme is capable of reducing the complexity imposed with the aid of its efficient detection candidate set generation assisted by the Markov chain process. Our performance results demonstrate that the MC-MBER multi-user detection (MUD) is capable of reducing the computational complexity by a factor of eight in comparison to the conventional MBER MUD in a rank-deficient system transmitting four 4-QAM uplink substream with the aid of two receive antennas at the base station (BS), while achieving a BER performance comparable to that of the MBER MUD.

BackgroundA large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development.ObjectiveThis study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings.MethodsThe search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer.ResultsFrom ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies.ConclusionsThis is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user’s point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

<div class="htmlview paragraph">A 24 kW, 30% efficient gas turbine-driven generator set is being developed for hybrid electric vehicles. With the generator mounted on the same shaft as the compressor and the turbine, the rotor group is the only moving part other than the fuel pump. There are no engine-driven accessories. There is no oil as the rotor is mounted on air bearings. There is no water or antifreeze as the generator set is air cooled. Orientation can be vertical or horizontal.</div> <div class="htmlview paragraph">Emission levels on unleaded gasoline, methanol, M85, ethanol or natural gas are much less than ULEV standards using a lean premix combustor. They are one or two orders of magnitude less than ULEV standards using a catalytic combustor. This paper addresses some of the design and application factors considered in developing the hardware.</div>