This study introduces the ordered weighted average budget (OWAB) operator, a novel aggregation framework that integrates objective financial data with hierarchical attitudinal preferences in budgeting. Building on classical ordered weighted averaging (OWA) operators and advances in fuzzy logic and generalized aggregation, OWAB provides a flexible mechanism for synthesizing budget components across multiple organizational levels and stakeholder perspectives. The framework encompasses traditional budget aggregation methods as special cases and extends them through generalized and quasi-arithmetic variants, enabling nuanced modeling of optimistic, pessimistic, and neutral attitudes in budget evaluation. Applications to multi-person and multi-country budget aggregation demonstrate the operator’s ability to handle complex, multitiered decision environments. An empirical case study of Australian regional and international research and development (R D) budgets illustrates the practical usefulness of OWAB in supporting transparent, consensus-oriented resource allocation. Taken together, the proposed framework advances both the theoretical foundations and practical methodologies of budget analysis in diverse economic and policy settings.
This paper addresses the pulse-load disturbances introduced by the downstream inverter-side H-bridge. The front-end LLC converter typically suffers from large output-voltage drop, long recovery time, and significant steady-state ripple under such operating conditions. Conventional linear control cannot achieve a satisfactory trade-off between dynamic response and steady-state accuracy. To solve this problem, a switching control strategy based on H-bridge mode identification is proposed. First, a state-plane model of the LLC converter under pulse-load conditions is established to analyze the optimal trajectory radius and switching instants during the transition from no-load to full-load operation. Then, the switching states of the H-bridge are used to construct a load-mode criterion, according to which the controller switches among three operating modes: no-load, full-load, and load-transition modes. Fast regulation is achieved through state-trajectory transition at the instant of load variation. The results show that the proposed strategy effectively suppresses voltage overshoot and drop without requiring additional hardware. The dynamic settling time is reduced by 50
Fuzzy multi-criteria decision-making (MCDM) methods such as AHP and TOPSIS are widely used to evaluate complex alternatives under uncertainty. The introduction of Z-numbers, incorporating both fuzzy restriction and reliability information, has further extended the expressive capability of these models. However, despite their growing application, computational realisations of Z-number fuzzy AHP–TOPSIS are often presented as procedural workflows in which modelling assumptions remain implicit and difficult to isolate. This paper presents an R-based computational implementation and framework prototype that formalises established triangular Z-number AHP–TOPSIS procedures within an explicit, script-controlled architecture. The framework preserves the conventional mathematical structure while exposing key modelling components - linguistic scale specification, reduction of Z-numbers, weight defuzzification, and ranking configuration - as selectable parameters. The approach is demonstrated through the computational replication of a published Z-number fuzzy AHP–TOPSIS study, with all data and code provided in an online repository to enable independent execution of the complete decision pipeline. By separating representation, transformation, aggregation, and ranking stages, the framework improves transparency and analytical traceability without altering the theoretical foundations of Z-number fuzzy decision-making.
Vision-Language Models (VLMs) show great potential in autonomous driving. However, they often operate as black boxes and fail in safety-critical scenarios. Physical hallucinations and causal confusion cause these failures. To address these Explainable AI challenges, we propose PG-Agent (Physics-Grounded Agent). This framework enhances robust decision making under uncertainty. It uses a neuro-symbolic approach to align neural perception with symbolic causality. Traditional methods rely on implicit feature matching. Our approach explicitly grounds the VLM through a dual-stream mechanism. First, we employ WideGazeNet. This lightweight network generates semantic-aware visual prompts to simulate human visual attention and filter environmental noise. Second, we align this visual stream with a Structural Causal Model derived from simulator ground truth. This step translates expert physical laws into Chain-of-Thought supervision. We also introduce a visual counterfactual training strategy. It physically removes risk objects from the scene. This intervention forces the agent to learn the true causal link between object existence and actions. Experiments on the DATAD benchmark show that PG-Agent achieves top performance in lateral control. It demonstrates highly interpretable and real-time decision making in complex scenarios.
Addressing the critical challenge of detecting incipient bearing faults in low signal-to-noise ratio (SNR) environments, a Health Indicator (HI) framework is formulated utilizing Crest Factor-driven chaotic synchronization. By exploiting the inherent sensitivity of chaotic systems to infinitesimal perturbations, the methodology nonlinearly amplifies weak, fault-related impulses typically submerged in heavy background noise. The Crest Factor (CF) is integrated as a dynamic driver for a Lorenz chaotic system, where the resulting synchronization error energy serves as a high-sensitivity HI for degradation assessment. Empirical validation on the XJTU-SY dataset yields an Early Detection Percentage (EDP) of 18.6
