Search Results (199)

Diffusion probabilistic models are powerful generative tools but are purely data-driven, limiting their ability to incorporate domain knowledge—such as physical laws, degradation trends, or engineering priors—in scientific and engineering applications. We introduce Prior-Guided Diffusion Processes (PGDPs), a unified mathematical framework that integrates arbitrary differentiable prior knowledge into the reverse diffusion dynamics by augmenting the score function with a guidance term derived from a prior potential V(x,t) and weighted by a time-dependent strength γ_t. This formulation subsumes existing mechanisms (classifier guidance, model-based diffusion, physics-informed corrections) as special cases. We analyze the guided path measures, providing an upper bound on the Kullback–Leibler divergence between guided and unguided marginals (Theorem 1), quantifying the inherent trade-off between data fidelity and prior satisfaction. Experiments on synthetic data confirm the predicted dependence on γ_t. On the NASA C-MAPSS turbofan benchmark, we enforce compressor-oriented physical constraints (e.g., speed–pressure consistency, monotonicity) within PGDP; remaining useful life scores are reported only as reference metrics under transparent protocols. A cross-domain study on the NASA IGBT accelerated aging dataset, using the same backbone with a replaced physics module, achieves a 99.98% reduction in monotonicity loss, demonstrating generality across distinct degradation mechanisms. PGDP provides a principled, extensible template for knowledge-informed generative modeling with theoretical guarantees and verifiable physical consistency. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

This work presents an FPGA-based edge-event timing front-end for time-resolved sensing and event-driven measurement scenarios. The proposed design is intended as a detector-independent timing subsystem whose architectural choices are motivated by constraints that are common in single-photon avalanche diode (SPAD)-based and other asynchronous time-resolved sensing workflows, including event trustworthiness, dead-time sensitivity, and constrained downstream readout. Rather than treating the implementation as an isolated interpolation macro, this work evaluates it as an experimentally observable timing subsystem that combines carry-chain-based fine interpolation, coarse–fine timestamp formation, explicit event-quality assessment, dead-time-aware handling, and lightweight host-visible export. The experimental validation is organized around two complementary modes. An internal ILA-based mode is used to verify coherent front-end behavior under MHz-range short-pulse excitation, while a UART-based campaign identifies practical host-visible operating regions through baseline, repeatability, pulse-width, safe-versus-aggressive, and intermediate frequency-sweep experiments. The results identify a safe export-compatible operating point, a more exploratory high-rate regime, and an experimentally interpretable transition between them that, while not strictly monotonic in all metrics, does not exhibit catastrophic degradation across the explored frequency range. Taken together, the measurements indicate that the proposed architecture is best understood not as a best-case standalone time-to-digital (TDC) benchmark but as an experimentally characterized timing front-end whose practical behavior can be interpreted across complementary internal and export-visible operating regimes. Full article

The systematic influence of signal electrode width on electro-optic bandwidth and insertion loss in L-type traveling-wave lithium niobate modulators has not yet been comprehensively quantified, limiting the parametric engineering design of this device configuration. This study presents a full-band systematic simulation sweep of signal electrode width and three auxiliary geometric parameters in an L-type traveling-wave lithium niobate Mach–Zehnder modulator, combined with optical mode simulation to establish joint microwave–optical optimization constraints. The study reveals the coupled modulating effect of signal electrode width on characteristic impedance, velocity mismatch, and transmission loss; it elucidates the competition mechanism underlying non-monotonic high-frequency loss behavior; and it identifies the complete impedance-neutral characteristic of the electrode–waveguide contact width as an independent loss-tuning degree of freedom decoupled from the impedance constraint. Full-system validation confirms that the final design simultaneously satisfies broadband impedance matching, low insertion loss, and high electro-optic bandwidth. The results are distilled into four quantitative design rules that provide simulation-driven guidance directly applicable to the engineering design of L-type thin-film lithium niobate modulators, advancing the systematic establishment of a parametric design methodology for this device configuration. Full article

Accurate crack severity classification in rotating shafts remains a challenging task due to the strong spectral overlap between adjacent damage levels and the absence of distinct fault-specific frequency components. In such conditions, conventional vibration-based approaches relying on global spectral descriptors often fail to provide sufficient discriminatory information. This work proposes a stability-driven multi-resolution framework for crack severity classification based on the Wavelet Packet Transform (WPT). The approach aims to identify frequency bands that exhibit consistent diagnostic relevance across multiple decomposition levels while maintaining a monotonic relationship with crack severity. To this end, an interpretability-driven analysis based on Random Forest feature importance is combined with a frequency stability criterion and a monotonicity constraint, enabling the selection of physically meaningful and consistent spectral regions. The proposed framework has been evaluated on vibration data acquired from a rotating shaft test bench under multiple operating speeds and damage conditions. The results have shown that crack progression is characterised by distributed energy variations across specific frequency regions rather than by the emergence of isolated spectral peaks. It can be concluded that the proposed stability-driven band selection approach enables the identification of these regions in a consistent manner across spectral resolutions and operating conditions. Furthermore, the integration of WPT-based features with conventional time- and frequency-domain descriptors leads to a hybrid multi-scale representation that improves classification performance, particularly in intermediate severity regimes where spectral overlap is most pronounced. Overall, the proposed methodology provides a physically interpretable and consistent framework for vibration-based crack severity classification, with potential applicability to a wide range of rotating machinery diagnostics problems. Full article

Motivated by the requirements for wide field-of-view (FOV) reception in underwater wireless optical communication (UWOC) systems, this study investigates the performance of multi-faceted receivers based on various regular polyhedral geometries. A truncated Gumbel minimum distribution model with geometric boundary constraints is proposed in order to characterize the statistical properties of the minimum incidence deflection angle associated with the selected receiving facet. Numerical simulations demonstrate that the proposed model effectively captures the angular response characteristics of multi-faceted receivers, with the root mean square error (RMSE) of the fitted cumulative distribution function (CDF) below $2.2\times {10}^{-2}$ for all regular polyhedral structures. Furthermore, this paper evaluates the effects of different polyhedral structures and receiver FOVs on the bit error rate (BER) and outage probability. The results further show that system performance does not vary monotonically with the number of receiving facets. Under the constraints of the same total effective detection area and unified system parameters, the dodecahedral structure achieves the best performance in terms of average BER and outage probability, followed by the cube, whereas the icosahedral structure exhibits the worst performance. Taking typical link distances of 35–40 m as an example, the average BER of the dodecahedral structure is approximately one order of magnitude lower than that of the icosahedral structure. These findings provide design guidance for the structural design and parameter optimization of multi-faceted receivers in UWOC systems. Full article

Addressing the challenges of fluctuating renewable energy integration and stable green ammonia production, this study develops and optimizes a deeply integrated system comprising Solid Oxide Electrolysis Cells (SOEC), Liquid Air Energy Storage (LAES), Air Separation Units (ASU), and Haber–Bosch (HB) synthesis. We constructed a simulation model in Aspen Plus incorporating Ru/C catalyst kinetic parameters to analyze key subsystem parameters and optimize operating conditions based on maximized economy and efficiency. At the integrated system level, a parametric analysis of ammonia condensation temperature was further conducted to investigate the coupling characteristics. Using real power output data from Inner Mongolia, we formulated a dynamic energy scheduling strategy satisfying 24-h self-balancing constraints. Results indicate that a system producing 1415 tons of ammonia per day achieves a maximum hourly integrated profit of 69,838 CNY under optimal conditions: a hydrogen-to-nitrogen ratio of 2.98:1, operating pressure of 169 bar, reactor inlet temperature of 380 °C, and ammonia condensation temperature of −9 °C. Increasing the LAES throttle valve outlet pressure from 1 bar to 9 bar improved round-trip efficiency from 52.65% to 72.18%. The integrated-level parametric analysis reveals that the specific electricity consumption per unit mass of ammonia exhibits a non-monotonic trend with a minimum of 8.67 kWh/kg at −10 °C, reflecting the trade-off between refrigeration power consumption and cold energy recovery. In dynamic scheduling scenarios, the system maintains a maximum constant load of 45.78 MW with a steady-state liquid ammonia output of 6543 kg/h. This work optimizes both economic performance and system stability, providing a significant reference for the large-scale development of green ammonia systems. Full article

Accurate prediction of superheated steam temperature (SST) is critical for the safe and efficient operation of large-scale thermal power units, particularly under large load variations and high thermal inertia. This study proposes an iTransformer-based SST prediction framework (iTransformer-SST) to address limitations of conventional proportional–integral–derivative (PID) control and existing data-driven models in capturing multivariable coupling, time-delay effects, and physical consistency. Using the A-side subsystem of a 1000 MW thermal power unit, 19-dimensional process data were collected continuously over two months with a sampling interval of 2.4 s. After data preprocessing, time-lagged cross-correlation (TLCC) analysis combined with expert knowledge was employed for feature screening, resulting in ten highly relevant input variables. To enhance predictive robustness, the baseline iTransformer was augmented with a Local Temporal Convolution (LTC) module for local disturbance modeling and a physics-guided regularization term to enforce delayed monotonicity and smoothness constraints. In 240 min rolling forecasts of the final-stage superheater outlet temperature, the proposed model achieved a mean squared error (MSE) of 0.0887, a mean absolute error (MAE) of 0.2312, and a coefficient of determination ($R^2$) of 0.9650, significantly outperforming long short-term memory (LSTM), Informer, and the baseline iTransformer. The combined LTC and physics-guided design reduced MSE by 13.5%, demonstrating strong potential for feedforward-assisted SST control in industrial thermal power applications. Full article

An automated framework for the parametric FE model generation and sizing of composite aircraft wings suitable for early-stage studies is presented. Implemented in Python and HyperMesh TCL, the tool controls both outer-geometry parameters, such as span, taper ratio, and twist, and internal-structural layout parameters, such as spar locations, rib spacing, and stringer layouts, and generates analysis-ready 2D composite GFEM models with material assignment and layups for size optimization. To demonstrate the workflow, a Design of Experiments (DoE) is performed on a representative transport wing internal structural layout, while keeping the outer geometry fixed. For each DoE point, OptiStruct performs gradient-based composite-size optimization to minimize structural mass, subject to composite strength (max strain), buckling, and metallic no-yielding constraints. A staged multi-run strategy is implemented to mitigate the effects of local minima. DoE results show a strong correlation and a non-monotonic effect of stringer number, an increase in mass as the front spar moves aft, and a comparatively weaker effect of the number of aluminum ribs. As a preliminary baseline, a Random Forest surrogate trained on the DoE predicts the wing structural mass with reasonable accuracy (RMSE $=0.081$), motivating the future implementation of Gaussian process models with uncertainty modeling. The framework accelerates early-stage structural design exploration and is amenable to surrogate-based optimization. Full article

We propose a novel subspace derivative-free conjugate gradient method for solving large-scale nonlinear monotone equations with convex constraints. At each iteration, the search direction is constructed by minimizing a quadratic model within a subspace spanned by the current negative function value vector and the two most recent search directions. The algorithm incorporates a hyperplane projection technique to generate feasible iterative points. Under reasonable assumptions, we establish the global convergence and R-linear convergence rate of the proposed method. Extensive numerical experiments on benchmark problems demonstrate that the new algorithm significantly outperforms state-of-the-art derivative-free methods in terms of number of iterations, function evaluations, and CPU time. The results confirm the efficiency and robustness of the proposed approach for solving large-scale monotone systems. Full article

Femtosecond laser ablation (FLA) is efficient for the machining of micro-groove arrays on the surface of ultrahard cutting tools. The depth of the groove determines the precision and efficiency of ablation. In this study, an “Attention-based Monotonic Physics-Guided Neural Network” (AM-PGNN) algorithm is proposed to accurately predict groove depth in the FLA of tungsten carbide (WC). The new algorithm incorporates machining parameters directly governing the energy deposition and thermal accumulation, thereby determining the prediction of the micro-groove depth generation. By embedding the physics-guided monotonic relationships of parameter depth into the learning process, a dedicated physical loss coupled with an attention mechanism to enable adaptive feature weighting is constructed, which strengthens the representation of causal dependencies. Experimental data for training and testing are obtained from the FLA of WC with different machining parameters. Comparison between AM-PGNN and typical algorithms, including a Support Vector Machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Gradient Boosting Decision Tree (GBDT), and a conventional PGNN, demonstrates that the proposed AM-PGNN achieves superior prediction accuracy. Moreover, AM-PGNN attains a physical consistency degree (PCD) of 100%, indicating strict adherence to monotonicity consistent with the actual situation. AM-PGNN also exhibits enhanced robustness to input perturbations, as reflected by reduced standard deviation (Std) and normalized absolute deviation (NAD). Finally, AM-PGNN is shown to be applicable in the FLA of different materials through additional experiments on Cu and SiC, achieving R² values above 0.93 while maintaining a PCD of 100%. Full article

Modern enterprise breaches are no longer isolated events but coordinated, multi-stage campaigns whose success depends on the defender’s inability to translate detection into timely containment. While existing frameworks—such as attack-lifecycle models, Zero Trust architectures, and detection-driven systems—provide valuable capabilities, they lack a formal mechanism for coupling inferred adversarial state with coordinated, cross-layer enforcement. This paper presents AMNDA, an Adaptive Multi-layer, stage-aware Network Defense Architecture that operationalizes lifecycle-aware defense through explicit state-to-control mapping and executable orchestration. Adversarial progression is modeled as a probabilistic state-transition process, and inferred states are systematically mapped to synchronized controls across edge protection, identity governance, internal segmentation, and behavioral detection. A formally defined orchestration function transforms detection outputs into stage-conditioned policy updates, enforcing monotonic tightening of containment as adversarial capability escalates. AMNDA is implemented and validated in a reproducible Microsoft Azure environment. Empirical results show that stage-aligned enforcement actions execute within 1.0–3.1 s, while detection latency remains the dominant constraint, with a median of 1034 s across the validation corpus. This separation reveals a critical operational insight: in modern cloud environments, the limiting factor in lifecycle defense is not enforcement capability but detection timing. The contribution of AMNDA is therefore not a new detection technique but a formal, deployable architecture that converts attack-stage inference into coordinated, low-latency containment. By bridging lifecycle modeling, Zero Trust principles, and automated orchestration, the proposed approach establishes a practical foundation for state-aware, adaptive cyber defense. Full article

Generative AI systems trained on synthetic data exhibit progressive degradation known as model collapse. This paper provides a theoretical explanation of this phenomenon using Shannon’s Data Processing Inequality (DPI), modeling iterative synthetic-data training as a Markov chain of lossy transformations. We show that mutual information with respect to the original data distribution must decrease monotonically, yielding qualitative predictions for exponential decay tendencies and indicating that information loss arises from general finite-precision and capacity constraints rather than from any specific architectural mechanism. Building on this analysis, we introduce the AI conceptual theorem, a generalized stability limit for computable systems. The theorem states that any purely computational system that generates outputs iteratively under finite precision, bounded capacity, and without external low-entropy input must experience cumulative information degradation after a finite number of steps. DPI-based collapse emerges as a special case of this broader principle. The framework is intended as a conceptual information-theoretic perspective rather than a fully formalized theory, with several assumptions intentionally simplified to highlight the underlying entropic mechanism. The results should therefore be interpreted as principled limits that motivate further empirical and mathematical investigation rather than as definitive closed-form predictions. Together, DPI and the AI Theorem provide a unified information-theoretic framework for understanding degradation in synthetic training, long-horizon inference, and other iterative computational processes. The resulting predictions are quantitatively falsifiable and offer guidance for designing more stable and information-preserving AI systems. Full article

This paper investigates the solution theory of a class of prescribed positive Q-curvature equations with power-law singularity at the origin and polynomial growth at infinity in the four-dimensional Euclidean space. We focus on the equation involving the biharmonic operator and an exponential nonlinearity, with the prescribed curvature function combining a singular term and a growth term, where a parameter characterizes the strength of the conical singularity at the origin and another parameter describes the growth rate at infinity. Under the finite total curvature constraint, we systematically analyze the asymptotic behavior of normal solutions, establish the necessary condition for existence, prove the existence and uniqueness of radially symmetric normal solutions, and give a complete characterization of the optimal admissible range of the total curvature. Our main results are as follows: (i) We derive the sharp asymptotic behavior of normal solutions both near the singular origin and at infinity, and establish the Pohozaev identity for the singular Q-curvature equation, which yields a universal necessary condition for the existence of normal solutions. (ii) We prove the existence of radially symmetric normal solutions via the Leray–Schauder fixed point theorem combined with a regularization technique, and establish the uniqueness of radial solutions with respect to the initial value at the origin by the strong maximum principle and monotonicity analysis. (iii) We prove the continuity of the total curvature with respect to the initial value via blow-up analysis and energy quantization, and determine the optimal range of the total curvature: for small growth rates, the necessary and sufficient condition for existence is that the total curvature lies between two critical values; for large growth rates, we give a sharp necessary condition and an explicit sufficient condition for the existence of radial solutions. Full article

Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics-regularized neural network framework for data-efficient PV power modeling using only 45 real experimental measurements of irradiance and temperature. To address data sparsity while preserving physical realism, a physics-guided synthetic augmentation strategy is introduced to generate additional training samples strictly within experimentally validated operating bounds. The proposed Physics-Informed Neural Network (PINN) incorporates two complementary physical constraints directly into the training objective: (i) enforcement of the Shockley–Queisser thermodynamic efficiency limit to maintain compliance with theoretical conversion bounds and (ii) monotonicity regularization to ensure non-negative power gradients with respect to irradiance. Unlike conventional post-processing correction methods, these physical constraints are embedded during model training, enabling simultaneous improvement in predictive accuracy and physical consistency. When benchmarked against a structurally identical unconstrained Artificial Neural Network (ANN), the proposed framework achieves strong predictive performance (R² = 0.9947, RMSE = 5.21 W) while reducing monotonicity violations by approximately 82%. Robustness evaluations under extrapolated irradiance conditions and elevated temperature scenarios further demonstrate stable and physically admissible behavior beyond the training domain. Overall, the results demonstrate that integrating limited experimental measurements with embedded physical priors enables reliable and physically consistent PV power prediction in sparse-data environments, highlighting the potential of physics-regularized learning for renewable energy modeling applications. Full article