Strength of knowledge (SoK) judgments are a key component in characterising risks from an uncertainty-based risk perspective. A commonly applied assessment scheme for this purpose involves evaluating the SoK based on several criteria: the reasonableness of assumptions, the availability and reliability of data, the degree to which phenomena are understood, the level of expert agreement, and the extent to which the knowledge has been scrutinised. The applicability of this scheme has been widely discussed and debated in the risk research literature. However, less attention has been given to its actual use and to risk practitioners' evaluations. In Norway, many practitioners regularly apply the SoK scheme to address real-world risk problems, as various public and private sectors have incorporated it into their risk assessment standards and guidance documents. In this paper, we examine how 22 risk practitioners use the SoK scheme and assess its utility. The study aims to provide insights into the challenges and benefits of applying the SoK scheme in practical contexts, thereby enhancing its applicability for practitioners and informing its ongoing development within the research community. Key findings relate to the scheme’s practical value in structuring uncertainty assessments and informing risk management, alongside identified needs for improved guidance to support consistent judgment and effective communication.
Tool wear assessment is essential for machining quality, process stability, and maintenance decision making. Existing methods have improved wear classification and wear-value prediction, but many rely on single-modality inputs, shallow fusion, or black-box task-specific models, limiting their ability to explain diagnostic evidence and support practical inspection. To address these limitations, we propose an evidence-guided and stage-aware multimodal large language model framework based on Qwen3-VL-8B. The framework integrates tool images, sensor-derived representations, process context, and structured base-model evidence to jointly diagnose wear type, estimate wear value, and generate engineer-readable reports. These reports provide confidence estimates, evidence interpretation, risk assessment, inspection focus, and maintenance suggestions, thereby transforming multimodal wear prediction into explainable engineering decision support. Experiments on the Multimodal Automatic Tool Wear Inspection dataset show that the proposed method achieves 93.12% accuracy, 77.09% macro-F1 score, 16.64μm mean absolute error, 25.52μm root mean square error, and a coefficient of determination of 0.689. Compared with single-modality, simple fusion, conventional multimodal fusion, and directly prompted MLLM baselines, the proposed method improves wear-type discrimination, maintains competitive regression performance, and generates structured, evidence-grounded reports for practical decision support.
When affected by abrupt severe disruptions such as natural disasters and third-party construction damage, natural gas pipeline networks may suffer serious supply loss and require costly recovery. Existing studies insufficiently integrate supply resilience assessment with economic constraints, making it difficult to identify cost-effective resilience enhancement strategies. This study proposes a cost-resilience trade-off framework for natural gas pipeline networks under stochastic severe disruptions. Demand-fulfillment variation is used to quantify supply resilience, while component disruptions, recovery rules, degraded operation, transmission capacity constraints, and gas supply allocation are integrated into Monte Carlo simulation. Reinforcement costs, recovery costs, and economic losses are further incorporated into a single-objective optimization model that minimizes total cost under a minimum resilience requirement. The case study identifies case-specific turning values within marginal-benefit saturation ranges, namely a reinforcement level of approximately 80% and a recovery rate of approximately 2.664 day-1, beyond which additional reinforcement or recovery acceleration yields diminishing marginal benefits. Scenario-based optimization analyses show that optimal strategies vary with risk, demand, defect, and cost conditions, but the framework consistently reallocates resources between preventive reinforcement and post-disruption recovery to satisfy resilience requirements at minimum cost. The framework supports adaptive resilience investment, critical component protection, and recovery-resource configuration.
The stability of real-world networks—from biological ecosystems to technological infrastructures—depends strongly on the vulnerability of critical components. Identifying such vulnerable nodes remains challenging because simple structural heuristics often neglect complex system dynamics, whereas simulation-based vulnerability evaluations are computationally expensive at scale. We propose a simulation-driven temporal hypergraph learning framework that serves as a neural surrogate for expensive vulnerability simulations. The framework represents evolving networks as temporal hypergraphs to capture higher-order group interactions and temporal evolution, and uses perturbation-aware self-supervised learning to obtain robust node representations under limited labels. A heterogeneous knowledge distillation strategy is then used to transfer the ranking information produced by offline simulation oracles into a lightweight inference model. Experiments on real-world networks show that the proposed method achieves strong performance among learning-based baselines, while substantially reducing online inference cost. The results suggest that simulation-distilled neural surrogates provide a practical route toward scalable vulnerability assessment in large complex networks.
