Search Results (434)

Tight sandstone reservoirs exhibit strong pore-throat heterogeneity, which exerts important controls on reservoir quality and fluid-flow behavior. To investigate the pore-throat structure characteristics and their influence on permeability, integrated analyses of thin sections, X-ray diffraction (XRD), scanning electron microscopy (SEM), cathodoluminescence (CL) and mercury intrusion porosimetry (MIP) were conducted on the Chang 8 tight sandstones in the southern Ordos Basin (China). Results show that the Chang 8 tight sandstones are characterized by low porosity and ultra-low permeability, with average porosity and permeability of 7.5% and 0.331 mD, respectively. The pore systems mainly include intergranular, intragranular pores, intercrystalline micropores and microfractures, reflecting strong pore-throat heterogeneity. Segmented MIP analysis reveals two distinct pore-throat response intervals. The fine pore-throat segment shows valid fractal scaling, whereas the large pore-throat segment is interpreted as an early-stage intrusion response. A dimensionless MIP-derived heterogeneity index (H_MIP) was therefore used to characterize connected pore-throat heterogeneity. H_MIP ranges from 2.446 to 2.973 and shows negative associations with permeability and pore-throat radius, indicating that finer and more heterogeneous connected pore-throat systems are generally associated with lower flow efficiency. H_MIP exhibits weak to moderate associations with mineral composition, particularly with carbonate and quartz contents, whereas feldspar and clay minerals show limited relationships. Sensitivity analysis of characteristic pore-throat radii demonstrates that r₁₀ shows the strongest association with permeability within the present MIP dataset, and model performance decreases monotonically from r₁₀ to r₅₀, suggesting that early mercury-accessible coarse pore-throats are more closely related to effective fluid flow than smaller pore-throat populations in the Chang 8 tight sandstone reservoirs. These findings suggest that permeability in the Chang 8 tight sandstones is closely associated with the development of connected large pore-throats, whereas H_MIP provides empirical information on connected pore-throat heterogeneity and flow-path complexity. Full article

Non-stationary signal detection is challenging when discriminative information is not reflected in global energy, mean spectra, or a single covariance statistic, but is instead embedded in the organization of local time–frequency structures. This paper proposes an information-geometric detector defined on local symmetric positive definite (SPD) structure fields. Time–frequency patches are transformed into a spatially distributed field of second-order tensors to characterize local directional organization and anisotropy. Under a locally isotropic Riemannian Gaussian approximation on the SPD manifold, the local distance-difference evidence is monotonically related to an approximate log-likelihood ratio, providing an information-geometric interpretation without implying strict Neyman–Pearson optimality. Instead of forming a single global statistic or stacking patch-level features, the proposed method constructs a spatially distributed field of structured SPD objects and derives local distance-difference evidence, which is subsequently aggregated into a sample-level detection statistic. Experiments under a controlled SPD structure-field locality benchmark show that performance gains are primarily driven by the proposed SPD structure-field representation, with the Riemannian metric providing only secondary refinement. Full article

A 700 m pilot-scale cast iron pipeline reactor was operated for 120 days to investigate corrosion-stage evolution under reclaimed-water conveyance conditions. Sampling points were arranged at 50, 250, 450, and 650 m, and water-quality monitoring, coupon weight-loss tests, scanning electron microscopy (SEM), and high-throughput 16S rRNA sequencing were combined to characterize corrosion-rate variation, corrosion-product morphology, and microbial community succession. During transport, NH₄⁺ generally decreased while NO₃⁻ increased, indicating nitrification-related nitrogen transformation under aerobic conditions; meanwhile, PO₄³⁻ declined and DOC fluctuated, reflecting coupled physicochemical and biological processes. SEM observations showed a transition from loose porous deposits to relatively compact layered corrosion products, followed by local deterioration and renewed porous structures in the later period. The corrosion rate followed an increase–decrease–re-increase pattern rather than a monotonic trend. Therefore, corrosion acceleration (CA = dc/dt) was introduced as an auxiliary diagnostic indicator to identify whether corrosion activity was increasing, decreasing, or temporarily stabilizing. Microbial community analysis showed stage-associated variation in biofilm and nitrogen-transformation-related taxa, supporting the interpretation that corrosion evolution was jointly affected by water-quality change, corrosion-product development, and microbial succession. Overall, the combined interpretation of corrosion rate, CA, water quality, SEM morphology, and microbial succession provides a more informative basis for diagnosing corrosion-stage transitions in reclaimed-water cast iron pipelines. From a sustainability perspective, this diagnostic framework can support long-term operation, maintenance planning, and risk monitoring of urban reclaimed-water distribution infrastructure, thereby improving pipeline durability, reducing leakage and maintenance risks, and enhancing the reliability of reclaimed-water reuse systems. Full article

Black-hole evaporation exhibits a range of characteristic entropic phenomena, including Hawking thermality, monotonically increasing radiation entropy in semiclassical treatments, and the Page-curve behavior required by unitarity. These features are accompanied by long-standing puzzles concerning information loss, entanglement growth, and the transfer of correlations between a black hole and its radiation. In this work we present an information-theoretic analysis of these phenomena based on a discrete causal model in which entropy evolution is governed by a competition between the growth of accessible degrees of freedom and a finite capacity for transmitting correlations across a boundary. Radiation is generated through stochastic sampling of interior degrees of freedom, while entanglement between interior and radiation subsystems is constrained by a boundary defined purely at the level of causal connectivity. Within this setting, radiation entropy increases at early times, reaches a maximum when boundary capacity becomes saturated, and decreases thereafter as additional emissions fail to carry independent correlations, yielding Page-curve behavior consistent with unitary evaporation. This capacity-limited mechanism does not rely on semiclassical spacetime geometry or quantum extremal surface constructions and instead follows directly from entropy bounds and information-flow constraints. By isolating the role of finite correlation capacity, the analysis provides a unified entropy-based perspective on black-hole evaporation, complementing semiclassical approaches while remaining applicable in discrete or non-geometric settings. Full article

Existing unmanned aerial vehicle (UAV) urban logistics planning follows a sequential paradigm—depot siting first, routing second—that embeds a structural information loss. Straight-line distance screening systematically overestimates the feasible service radius of candidate depots, creating a blindzone of depot–demand pairs that appear reachable but prove operationally infeasible under road network distances. We term this range-feasibility blindness and derive its analytical radius $\Delta =R_{max}(\alpha -1)/\left(2\alpha \right)$, where $\alpha$ is the road-to-straight-line distance ratio. Empirical measurement across three Chinese urban districts confirms $\alpha \in [1.40,1.52]$ and blindzone radii exceeding 2.8 km, establishing the phenomenon as a systemic property of high-density urban road geometry. To eliminate this failure by construction, we formulate a feasibility-embedded location–routing mixed-integer linear programme (MILP) that enforces road network range constraints simultaneously with depot opening decisions, making blindzone configurations implicitly inadmissible. A structure-aware Adaptive Large Neighbourhood Search (ALNS) solves the model at practical scales. Benchmark experiments on Dongli District (Tianjin) show cost reductions of 20.6–28.2% over greedy sequential baselines across three demand scenarios, with gains increasing monotonically with instance scale; cross-city experiments in Beijing and Shanghai confirm consistent improvement averaging 11.4% (Chaoyang, Beijing) and 10.2% (Pudong, Shanghai) over greedy initialisation across diverse urban morphologies. These results position joint optimisation as a necessary methodological shift for city-scale UAV infrastructure planning. Full article

To address the dual challenges of scarce historical time-series data and limited representational capacity of standalone models in pavement performance prediction, this study proposes an Engineering-heuristic-constrained Perturbation Data Augmentation Framework and a hybrid Bidirectional Long Short-Term Memory–Extreme Gradient Boosting (BiLSTM–XGBoost) model. The augmentation framework generates high-quality virtual samples by applying controlled perturbations aligned with engineering variability—to both covariates (e.g., traffic volume and layer thickness) and Pavement Condition Index (PCI) sequences—while enforcing the physical constraint of monotonic year-on-year deterioration. This expands 10 typical road sections into 1200 training samples. A two-stage prediction architecture is then developed: BiLSTM first extracts high-order temporal features from historical PCI sequences; these features are then fused with covariates and engineering features as input to XGBoost for final regression. Evaluated on an independent test set, the hybrid model outperforms the standalone models and the ANN model, achieving an R² of 0.771, with RMSE, MAE, and MAPE as low as 2.043, 1.706, and 1.859%, respectively. This work provides an accurate and practical tool for pavement performance prediction under data scarcity, supporting informed decision-making in pavement management systems. Full article

Flash-flood and floodplain inundation simulations are highly sensitive to the spatiotemporal variability of convective rainfall, particularly during the initial runoff generation stage. However, coarse-resolution numerical weather prediction (NWP) forcing tends to smooth localized rainfall extremes, limiting its ability to accurately represent hydrological responses in low-relief floodplains. In this study, we couple a diffusion-enhanced radar nowcasting model, Diff_ConvLSTM, with a spatial resolution of 1 km and a temporal resolution of 6 min, to assess the hydrological value of high-resolution rainfall forcing over the middle Yangtze River floodplain. We introduce a monotone piecewise cubic Hermite interpolation scheme to ensure a stable transition from discrete high-frequency rainfall inputs to continuous hydrodynamic integration. Evaluation using a radar dataset from 2023 to 2024 shows that Diff_ConvLSTM better preserves intense convective echoes and rainband structures compared to the baseline ConvLSTM, increasing the Probability of Detection at the 40 dBZ threshold by 65.8%. A forcing-replacement experiment for the flood event on 30 June 2023 demonstrates that AI-based nowcasting rainfall forcing reduces peak-discharge underestimation, improves volumetric consistency, and produces inundation patterns that are closer to the observation-driven reference than those generated by low-resolution forecast forcing, although positive biases in inundation area and water depth persist. An additional event in 2024 confirms that the improvements are primarily reflected in discharge magnitude and flood volume representation, while enhancements in peak timing remain limited. Overall, the results illustrate both the added value and the remaining limitations of AI-enhanced nowcasting for hydrologically informed flood forecasting. Full article

Evaluating conditional time-series generation models remains challenging in battery research, where degradation data are often limited and experiments cover only a small number of operating conditions. The widely used Fréchet Inception Distance (FID) summarizes all conditions into a single score, which can obscure failures under rare but safety-critical conditions. Several condition-aware extensions of FID, including Conditional Fréchet Inception Distance (CFID), partially address this limitation by evaluating each condition separately. However, these approaches do not assess whether physically meaningful relationships between operating conditions are preserved, and their reliability deteriorates when only a few samples are available for each condition. To address these issues, we propose a three-layer diagnostic framework for evaluating conditional generative models under limited-data conditions. The first layer, Stratified Fréchet Distance, identifies the specific operating conditions and degradation phases where generation quality degrades. The second layer, based on Conditional Response Consistency (CRC), Conditional Distance Ratio (CDR), and Mean-Order Preservation (MOP), evaluates whether the model preserves the distance structure and ordering between conditions. MOP detects condition-ordering defects that CRC cannot identify when the real data distance matrix is non-monotone. This layer also enables statistically meaningful comparisons even when only a small number of samples are available. The third layer detects strata where statistical estimates are unreliable and provides a more stable alternative for evaluation. We validate the framework on four battery degradation datasets using two generative model architectures. The proposed approach reveals condition-specific failures that are not captured by conventional FID. It localizes generation errors to the late-stage high-temperature degradation regime that is most relevant to battery safety. The framework also detects structural distortions with statistical significance. In addition, it consistently ranks physics-informed model variants across quality differences spanning seven orders of magnitude. These results demonstrate that the proposed framework provides a practical and physically interpretable evaluation methodology for conditional generative modeling in battery degradation analysis. Full article

Accurate meteorological visibility estimation is vital for transportation safety and environmental monitoring. However, modeling the inherent nonlinear spatial and spectral degradations in hazy environments remains challenging. While recent Large Vision-Language Models (LVLMs) offer strong scene understanding, they lack the regression precision required for visibility estimation. In this paper, we propose the Visibility-Aware Refined CNN (VISR-CNN), a dual-stream architecture that synthesizes local spatial cues with global frequency-domain signatures. The model integrates a Multi-Scale Transmission Attention (MSTA) module, which uses parallel dilated convolutions to estimate atmospheric transmission, and a Global Frequency Branch that utilizes 2D Real Fast Fourier Transforms (RFFT) with Spectral Gating to quantify visibility-dependent blurring. A progressive training strategy is introduced to decouple spectral and spatial optimization, and a physics-informed loss function is designed to supervise numerical regression while enforcing a monotonic ranking constraint consistent with physical light-attenuation laws. Results on the HKCHC-VD dataset show that VISR-CNN achieves state-of-the-art performance (MAE: 1.54 km; RMSE: 2.31 km), representing a 13.0% improvement over VisNet. Further evaluations on the CP1 and SWH datasets confirm robust generalization, reducing overall MAE by 21% and 20%, respectively, compared with the hybrid ResNeXt-50 + ViT model. Notably, in safety-critical range (0–10 km), VISR-CNN reduces RMSE for the HKCHC-VD, CP1, and SWH datasets by approximately 55%, 64%, and 71%, respectively, when compared with VisNet. These findings demonstrate the superiority of specialized, physics-grounded architectures over general-purpose LVLMs for high-precision meteorological regression. Full article

This paper applies physics-informed neural networks (PINNs) to laterally excited liquid sloshing in a two-dimensional rectangular tank, where near-resonant forcing (${\omega }_e/{\omega }_1=0.9$) produces a multi-frequency beating response with a period of approximately $10T_1$. Linearized potential flow theory governs the problem; the network learns the velocity potential $\phi (x,z,t)$ while the free-surface elevation $\eta$ is injected analytically. Two training obstacles specific to forced sloshing are analyzed. First, a zero-solution trap arises because the trivial solution $\widehat{\phi }=0$ satisfies all equations except the free-surface conditions, whose residuals are roughly ${10}^{-4}$ times smaller than the Laplace residual; characteristic-scale normalization combined with loss weighting (${\lambda }_D={\lambda }_K=100$) breaks this trap. Second, spectral bias prevents standard MLPs from resolving the three co-existing frequencies (${\omega }_1$, ${\omega }_e$, $\mathsf{\Delta }\omega$); a Fourier time embedding that augments the input from 3 to 9 dimensions overcomes this limitation. Two additional techniques further reduce errors: a hard-wall boundary condition enforced exactly via a $\cos (\pi x/B)$ spatial embedding, which eliminates wall collocation points; and a gradient-enhanced Laplace regularizer (${\parallel \nabla \left({\nabla }^2\widehat{\phi }\right)\parallel }^2$) that constrains velocity smoothness through third-order automatic differentiation. An ablation study shows that these four techniques progressively reduce the horizontal velocity error from ${\epsilon }_u=12.46\%$ to $0.84\%$. Results are validated against a viscous finite-difference benchmark. Over one beating cycle the errors are ${\epsilon }_{\eta }=0.15\%$, ${\epsilon }_u=0.84\%$, and ${\epsilon }_w=1.65\%$. A frequency parameter study across ${\omega }_e/{\omega }_1$ = 0.5–1.1 gives ${\epsilon }_{\eta }<0.25\%$ and ${\epsilon }_u<2.3\%$ for all near-resonance cases. For long-time simulation, a time-domain decomposition strategy with transfer learning partitions the domain into one-beat windows; extending to five beating cycles ($50T_1$) yields ${\epsilon }_u=3.43\%$ and ${\epsilon }_{\eta }=0.30\%$ with no monotonic error accumulation across windows. The methodology is then extended to a three-dimensional rectangular tank ($B\times W\times H$) with bi-directional lateral excitation. The 3-D formulation introduces the y-dimension into the Laplace equation (${\nabla }^2\phi ={\phi }_{xx}+{\phi }_{yy}+{\phi }_{zz}=0$), adds transverse wall boundary conditions ($\partial \phi /\partial y=0$) enforced exactly via a $\cos (\pi y/W)$ embedding, and extends the Fourier time embedding from 9 to 16 dimensions to accommodate six physical frequencies. The bi-directional excitation excites both $(m,0)$ and $(0,n)$ modal families, producing a genuinely three-dimensional beating response. Experimental results verify that the proposed methods can be well generalized to three-dimensional scenarios. Within a single beating cycle, the relative errors reach ${\epsilon }_{\eta }=0.24\%$, ${\epsilon }_u=1.31\%$, ${\epsilon }_v=1.78\%$ and ${\epsilon }_w=2.32\%$, with a total training time of 2499 s. By applying time domain decomposition to carry out two-cycle three-dimensional simulations, the model can steadily maintain satisfactory prediction precision across segmented time intervals, achieving overall errors of ${\epsilon }_{\eta }=0.30\%$ and ${\epsilon }_u=1.32\%$. Full article

The occiput posterior (OP) fetal head position is the most common malposition during labor and is associated with prolonged labor, operative delivery, and cesarean section. Conventional assessment often relies on digital examination, and the clinical significance of OP may lie along a spectrum rather than as a binary diagnosis. The Artificial Intelligence Dystocia Algorithm (AIDA) integrates four objective intrapartum ultrasound parameters (Angle of Progression [AoP], Head–Symphysis Distance [HSD], Midline Angle [MLA], and Asynclitism Degree [AD]) into a five-class ordinal classification (Classes 0–4). This investigation is a focused secondary subgroup analysis of 79 OP cases drawn from a single-cohort dataset of 135 nulliparous women with prolonged second-stage labor originally collected for the development of the AIDA. Only Branch 1 of the AIDA (the deterministic threshold-based classification, with cut-offs originally derived via Decision Tree on the parent cohort, N = 135) was applied; Branch 2 (the case-level machine-learning predictors) was not used, and no predictive model was trained or validated in this study. Cesarean delivery rates rose monotonically across AIDA classes, from no cesareans in Class 0 to all cases delivering by cesarean in Class 4, with a clear gradient across intermediate classes; full numerical results, confidence intervals, and effect sizes are reported in the Results section. Because the AIDA thresholds were derived from the same parent cohort, the analysis is best interpreted as a within-cohort subgroup evaluation rather than as independent validation. The observed class-graded outcome distribution is consistent with the hypothesis that in OP labors, the AIDA class assignment itself may carry clinically relevant information on the risk of intrapartum cesarean delivery; this remains hypothesis generating, and confirmation in independent prospective cohorts is required before AIDA-class assignment can be regarded as an established risk-stratification descriptor in OP labors. Full article

Lightweight alloys in aerospace precision structures undergo slow but cumulative creep deformation during long-term storage, wherein strain accumulation over years can compromise dimensional stability and operational reliability. However, continuum damage mechanics (CDM) constitutive models, while physically grounded, require extensive parameter calibration and exhibit degraded accuracy during the primary creep stage. Meanwhile, purely data-driven approaches are impractical for the sparse datasets typical of accelerated creep testing, wherein as few as 14 data points may be available per condition. Although physics-informed neural networks (PINNs) have shown promise in computational mechanics, existing PINN-based creep studies predict only scalar life quantities rather than the full strain–time curve $\epsilon \left(t\right)$, and none embed damage evolution equations as differential constraints. This study proposes a damage-coupled PINN framework (termed DC-PINN) that predicts the complete creep strain evolution $\epsilon \left(t\right)$ by embedding CDM damage evolution ordinary differential equations (ODEs) as hierarchical differential constraints within the learning process. The framework couples the predicted strain rate $\mathrm{d}\epsilon /\mathrm{d}t$ with the damage state $D\left(t\right)$ through material-specific constitutive ODEs, supplemented by monotonicity enforcement and boundary conditions. Alloy-specific formulations are developed for 2A12-T4 aluminum (Arrhenius kinetics, no damage) and ZM6 magnesium (Sandström dislocation model with Ostwald-ripening-driven grain coarsening damage). Validated on 13 experimental conditions spanning both alloys (50–100 °C, 20–60 MPa, 14–100 points per condition), DC-PINN achieves $R^2>0.99$ for 2A12-T4 and $R^2>0.97$ for ZM6 across all tested conditions. Ablation studies show that the total physics-driven $R^2$ improvement is 5.8 times larger for the data-sparse ZM6 (14–34 points) than for the data-rich 2A12-T4 (∼100 points), with the CDM damage coupling alone accounting for 22% of the improvement in ZM6. To the best of our knowledge, this represents the first integration of CDM damage evolution ODEs as differential constraints within PINNs for creep strain modeling, providing a physically consistent and data-efficient tool for the storage life assessment of aerospace structures. Full article

This study proposes an enhanced physics-informed neural network (PINN) framework for predicting seepage fields around deeply buried tunnels with limited field measurements. Hard-constrained boundary enforcement via distance-function trial functions is introduced to exactly satisfy Dirichlet conditions on both the ground surface and tunnel perimeter, and Bayesian optimization automates loss weight tuning to replace costly manual calibration. A systematic evaluation of 15 sensor placement schemes demonstrates that the hydraulic head variance across monitoring points, governed by radial coverage distance, is the primary determinant of prediction accuracy—not the number of sensors or angular density. Remarkably, a strategically designed 12-point configuration outperforms 100 randomly distributed points under the idealized conditions studied, confirming that placement quality can dominate over quantity when physics-informed optimization is applied. Transfer learning experiments across 132 geometric configurations reveal a previously unreported geometric transition zone at D/R ≈ 13–15, where prediction errors exhibit a distinct non-monotonic peak. Finite element benchmarking confirms that this error peak stems from the learning characteristics of PINNs under competing boundary influences rather than from the physical complexity of the problem itself. High-density sampling effectively suppresses this peak error by 32% compared with sparse sampling. These findings establish quantitative sensor deployment guidelines for tunnel seepage monitoring and identify fundamental performance boundaries of physics-informed machine learning under geometry–physics coupling. Full article

In order to describe the heavy ion collision dynamics which implies the formation of hot and very dense nuclear matter in the overlapping region of the two colliding nuclei, we used simulated numerical calculations for FAIR available energies. We used the anti-$k_T$ jet-detection algorithm for highlighting the main directions of flow in Au-Au collisions at CBM energies, thus obtaining structures of the events depending on the number of flow streams. The jet-finder algorithm identified domains in the y-$\psi$ (rapidity-azimuthal angle) plane, where the number of charged particles, momenta and energy take higher values compared to other areas of this plane. The anisotropic flow coefficients $v_n$ may offer information about the pressure gradients in the early stages of the collision and about the high-density nuclear matter properties. The observation of $K^{+}$ mesons in heavy ion collisions is of interest since $K^{+}$ mesons, due to their strangeness, have a mean free path that exceeds the dimensions of the “fireball”. In the numerical calculations the interval of rapidity $0<y<0.8$ is highlighted, for which the fluctuations of the antiparticle to particle ratio excitation functions show non-monotonic behavior in the 10–13 A GeV energy interval. Full article

This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable. Full article