Search Results (694)

This article introduces the Evidence Propagation Calculus (EPiC), an operational framework for first-order reasoning built on a simple but productive observation: familiar inference patterns such as Modus Ponens and Modus Tollens behave like the movement of evidential markers across a structured graph. Positive evidence at an antecedent propagates forward to the consequent; negative evidence at a consequent propagates backward. When both markers coexist at a node, the system is locally inconsistent but not operationally broken. To make this observation precise, EPiC grounds reasoning in a four-valued evidential domain $\mathbb{V}=\{N,T,F,B\}$, where N denotes absence of evidence, T positive evidence, F negative evidence, and B their coexistence. Each logical connective is assigned a local evidential table, and inference is treated uniformly as the progressive restriction of admissible configurations under an evidential order: inadmissible values are eliminated, minimal surviving values are selected as the next effective evidential states, and the resulting restrictions propagate across shared variables. Compound formulas are decomposed into families of local unary and binary constraints through auxiliary variables, making the propagation process explicit and structurally uniform. Within this setting, Modus Ponens, Modus Tollens, and polarity-switching negation are not postulated as primitive rules. They emerge as derived consequences of the same local table calculus. The framework distinguishes different operational routes of justification. In some cases, positive support reaches the target formula directly through successive local restrictions. In others, propagation first stabilizes the relevant components and the target occurrence is then fixed by the corresponding connective table. Consistency is not a second basic notion of justification but a distinguished property of certain justified outcomes. The article establishes local and global soundness, conservativity over the classical fragment, and a conditional adequacy result. It further develops a translation between decomposed formulas and informational graphs, with a reverse reconstruction theorem for well-formed graphs. The result is a unified operational account of first-order reasoning situated between model-theoretic and proof-theoretic approaches, in which semantics, propagation, and graphical structure are mutually supporting rather than independently layered. Full article

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases. Full article

Agricultural Global Value Chains (GVCs) link input suppliers, primary production, processing, and consumption across borders but are increasingly exposed to upstream disruptions. This study develops a network-based Leontief framework to analyze international trade in agricultural GVCs, explicitly modeling fixed-proportions technologies, intermediate input dependence, trade costs, and capacity constraints. It traces how final demand and supply-side shocks propagate through multi-country input–output networks, affecting both quantities and prices. A stylized numerical illustration motivated by war-related disruptions in Ukraine demonstrates how export constraints, trade frictions, and fertilizer shortages can be represented within the proposed framework. The illustrative exercise shows how nonlinear downstream effects may arise mechanically within a fixed-coefficient production network when upstream constraints bind. Fertilizer availability is treated as a potential amplification channel rather than as an empirically estimated determinant of output losses. Full article

Firewall rule ordering aims to improve packet filtering efficiency while preserving the dependency constraints that guarantee the intended security behavior of the policy. Existing approaches often rely either on local criteria, such as rule frequency, or on iterative optimization procedures whose behavior depends on initialization, parameter settings and search budget. In this paper, we propose PPCO, a deterministic dependency-aware rule ordering method based on propagated probability combined with descendant-based centrality. The proposed score reflects both the traffic relevance of a rule and its structural influence in the dependency graph. The structural component is essential, especially when some rules are inactive or have zero activation probability, since it prevents probability-based ties from violating dependency constraints. The final policy is obtained directly by sorting rules in a decreasing score order. Experiments were conducted on synthetic rule sets ranging from 50 to 2000 rules and on ClassBench-ng benchmark instances, showing that PPCO consistently achieves a competitive ordering quality among the compared deterministic methods under the considered experimental settings. The method remains stable as the policy size and dependency rate increase, produces zero dependency violations in all valid configurations, achieves the lowest score-coherence values, and maintains competitive execution times at large scales. These results suggest that PPCO provides an effective, robust, and computationally efficient solution for dependency-aware firewall rule ordering within the scope of the evaluated configurations. Full article

Long-delay multipath pushes underwater acoustic mixing beyond the instantaneous model assumed by many classical algorithms; spectral overlap among mechanically and biologically generated sources compounds the difficulty, and low signal-to-noise ratios erode the higher-order statistical cues used by methods such as FastICA and JADE. This work adapts frequency-domain second-order decorrelation (FSD) to convolutive underwater mixtures by using multi-block joint diagonalization of cross-power spectral density matrices in the short-time Fourier transform domain together with compact time-domain regularization of the demixing filters. To provide a controlled and traceable evaluation, we introduce ShipsEarBSS, a simulated benchmark that combines single-source ShipsEar recordings with deep-water BELLHOP arrival responses to form virtual multichannel mixtures with known reference sources. Under a five-trial, eight-SNR protocol spanning $-5$ to 30 dB, an optimized compact FSD configuration is evaluated against the frozen reference FSD, PCA-SVD, and AuxIVA, and its main design choices are further examined through filter-length, multi-block CPSD, and output-ordering ablations. The results support a cautious conclusion: under the tested ShipsEarBSS protocol, compact time-domain regularization improves the FSD operating point, while the choices of filter support, CPSD block count, and output ordering remain empirical configuration decisions rather than universal optima. Full article

Offshore high-temperature steam flooding suffers severe steam channeling, uneven steam intake and low thermal efficiency, while conventional profile control agents fail to adapt to coupled harsh environments of 200–350 °C high temperature, ultra-high salinity and continuous steam shear. Existing reviews mainly focus on onshore thermal reservoirs or single foam/gel materials, lacking a targeted, gel-oriented systematic review matching unique offshore platform constraints. Guided by the integrated framework of “flow control–diversion–enhanced sweep efficiency”, this work establishes a six-dimensional quantitative screening standard and unified performance comparison database to systematically review foam, gel, particle, thermo-responsive and composite profile control systems. Differing from petroleum engineering-oriented summaries, this paper subdivides high-temperature gels into six categories from a polymer material perspective, elaborating their crosslinking mechanisms, thermal rheology and cyclic steam degradation rules; the inherent advantages, limitations and offshore applicable boundaries of each medium are quantitatively compared, with special emphasis on the unique “deep migration followed by in situ thermal activation” mechanism of thermo-responsive materials. Composite systems relieve single-material defects via multi-mechanism synergy yet face complicated on-site deployment barriers. Three core bottlenecks restricting field application are identified: the irreconcilable trade-off between deep propagation and stable plugging, large deviation between static aging results and dynamic anti-scouring performance, and exclusive engineering limitations of offshore platforms. A dedicated standardized dynamic laboratory evaluation scheme for cyclic steam flooding is proposed to narrow lab-field performance gaps. Future research priorities include salt-resistant thermally responsive composite gel modification, low-cost multi-component compound formula optimization, unified dynamic evaluation criteria and staged material matching guidelines to realize balanced performance of high-temperature tolerance, deep delivery and offshore operability. Full article

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. Full article

Background/Objectives: Automated cephalometric landmark detection using deep learning has the potential to streamline routine orthodontic diagnosis. However, the clinical relevance of artificial intelligence (AI) localisation accuracy depends on how detection errors propagate into derived angular measurements and skeletal classifications. We retrospectively evaluated 14 YOLO-based model configurations and quantified the agreement between AI-derived and expert-derived ANB-based skeletal classifications. Methods: Twelve working YOLO-based models (YOLOv5xu, YOLOv11 nano/small/medium/large variants) were trained on a single-centre dataset of 120 lateral cephalograms and evaluated on an independent test set of 11 cephalograms (stratified across skeletal Classes I, II, III). The four ANB-defining landmarks (Sella, Nasion, A-point, B-point) were the focus of the analysis. Each test cephalogram had been annotated by four orthodontists (44 measurements per image), yielding the expert reference. We assessed the effects of architecture, bounding-box size (40/100/150 px), training dataset scale (235–4255 images) and training epochs on localisation accuracy (mean radial error, MRE; Successful Detection Rate, SDR) and on the downstream ANB-based skeletal classification. Diagnostic concordance was quantified by classification agreement, Cohen’s $\kappa$ with bootstrap 95% confidence intervals (10,000 iterations), an exact one-sided binomial test for discordance, and Wilson exact CIs per class. Results: The best-performing model (Model 2; YOLOv11l, 40 × 40 px bounding box, 1175 training images) achieved an MRE of $3.10\pm 1.00$ mm and a SDR@4 mm of 87.2% for S, N, A, and B. ANB-based skeletal classification demonstrated 96.9% concordance with expert assessments (95% bootstrap CI: 93.8–99.2%; Cohen’s $\kappa$ = 0.946 [95% CI 0.89–0.99]; exact binomial test against a 90% concordance threshold $p=0.003$). Per-class concordance was Class I 95.8% (23/24), Class II 94.9% (56/59), and Class III 100% (47/47). Three of four discordant cases clustered near the Class I/II diagnostic threshold (expert ANB $\approx 4.5°$). Bounding-box size dominated localisation accuracy, with a 3.5-fold increase in MRE from 40 × 40 to 150 × 150 px configurations and SDR@4 mm collapsing from 82.8% to 0%. Conclusions: Within the constraints of a retrospective single-centre design with a small (n = 11) independent test set, YOLO-based AI landmark detection demonstrated promising diagnostic concordance with expert consensus for ANB-based skeletal classification. These findings warrant prospective, multi-centre external validation before clinical deployment and support a confidence-aware workflow in which AI predictions for borderline ANB values undergo mandatory clinician verification. Bounding-box calibration emerged as the single most impactful preprocessing decision. Full article

Tunnel boring machine (TBM) excavation generates continuous mechanical vibration that can obscure weak, short-duration propagating responses related to structural-plane disturbance. This study develops a Signal-Constrained Activation Detection and Localization (SCADL) framework using continuous three-component geophone-array records. SCADL first constructs an adaptive multi-station consistency trigger from synthesized three-component envelopes and rejects non-propagating mechanical disturbances using coherence and polarization constraints. First arrivals are picked by fusing statistical abrupt-change, local onset-gradient, and polarization-variation evidence, and event locations are estimated using an ahead-of-face layered velocity model and relative correction of similar event pairs. A multi-evidence activation index then integrates spatial clustering, coupling with the face-disturbance zone, shear/compression energy ratio, temporal evolution, and event quality to identify high-confidence candidate structural-plane activation events. The workflow was evaluated using one 16 h continuous field monitoring record acquired from a single TBM monitoring section and manually reviewed reference sets comprising 286 propagating events, 136 high-confidence events for arrival-time evaluation, and 96 events for activation-assessment review. SCADL identified 263 valid propagating events, achieved an event-level F1-score of 0.88, reduced the median arrival-time picking error to 2.4 ms, constrained the localization residual to 2.9 ms, and compressed the corrected cluster thickness to 0.82 m. Among the detected events, 86 high-confidence candidate activation events formed two clusters spatially consistent with the F04 and F02 structural zones confirmed by post-excavation geological validation. These results support the feasibility of SCADL for single-section TBM monitoring. Full article

To improve emergency response efficiency in passenger ship fire accidents and ensure the life safety of passengers and crew, this paper proposes a dynamic route planning model for passenger ship fire evacuation that accounts for fire spread. Firstly, a passenger ship fire spread model is established based on the field simulation theory, and an emergency evacuation network model is constructed by determining the evacuation network topology from the fire propagation process. Secondly, the factors affecting emergency evacuation route planning during fire spread are analyzed, and a multi-objective optimization model for dynamic evacuation routes is developed. Thirdly, an improved ant colony optimization algorithm is designed to solve the problem. Finally, using the RP1 vessel from the publicly available ship evacuation dataset of the EU Seventh Framework Program SAFEGUARD project as a case study, simulation and comparative experiments are conducted. The results show that, in medium- and large-scale evacuation problems, the proposed method consistently maintains higher computational efficiency compared with the DABD algorithm and the FOA. In terms of objective optimization, it demonstrates that the proposed method has better effectiveness and feasibility than the shortest-path evacuation strategy, as it minimizes evacuation cost while satisfying assembly station capacity constraints and achieves a more balanced utilization of all emergency exits. Full article

Accurate attitude estimation for highly dynamic spacecraft relies on robust fusion of star-tracker and inertial measurements. However, asynchronous sensing, motion blur in star images, and delayed star-tracker outputs can significantly degrade estimation accuracy and temporal consistency. To address these challenges, this paper proposes a dual-layer factor graph optimization framework for asynchronous star-tracker/IMU fusion under highly dynamic conditions. At the lower layer, high-rate IMU measurements are combined with motion-blurred star streak observations to construct a local factor graph over the exposure interval. The proposed local fusion process reconstructs discrete star-trail points, estimates angular velocity, and selects IMU-aligned representative observations for temporally consistent association of blurred star measurements. At the upper layer, delayed attitude constraints, propagated star-vector information, and inertial rotational constraints are jointly incorporated to refine the attitude trajectory. Simulation and semi-physical experimental results demonstrate that the proposed framework achieves higher estimation accuracy, stronger robustness, and better tolerance to delayed or intermittent star-tracker observations than the comparison methods, while maintaining practical computational efficiency for near-real-time onboard implementation. Full article

3D reconstruction of construction scenes is an important enabling technology for digital and intelligent construction project management. Recurring foreground occluders and dynamic disturbances in tower-crane imagery can destabilize image registration and introduce spurious depth responses. This paper proposes an occluder-mask-constrained 3D reconstruction framework driven by multi-view geometric anomalies. Adjacent-view geometric outliers are spatially aggregated to generate foreground prompt points, which are converted into occluder masks using Segment Anything Model 2 (SAM2). The masks are propagated as unified pixel-validity constraints through sparse feature filtering, Adaptive Patch Deformation Multi-View Stereo (APD-MVS) matching-cost evaluation, support-region selection, and depth-map fusion. Experiments on three real construction-site datasets show increased sparse-registration completeness in the tested sequences and fewer visually identifiable occluder-induced artifacts in dense point clouds. A representative 308-image sequence was further evaluated against no-mask reconstruction, You Only Look Once version 8 (YOLOv8) bounding-box removal, manually prompted Segment Anything Model 2.1 (SAM2.1), a Segment Anything Model 3 (SAM3) text-prompt baseline, and Visibility-Aware Multi-View Stereo Network (Vis-MVSNet). The evaluation combines sparse-reconstruction metrics, pixel-level mask-quality metrics from a manually annotated validation subset, module-wise runtime accounting, controlled ablations, and aligned dense-point-cloud visualization. These results show improved sparse-stage registration completeness and visible artifact suppression. Because high-precision 3D reference point clouds are unavailable, the dense results are interpreted as visual evidence of artifact suppression rather than as proof of improved absolute dense-reconstruction accuracy. Full article

High-resolution seismic reconstruction is a challenging inverse problem because field seismic traces are inherently band-limited and their high-frequency components are further degraded by source bandwidth limitations, acquisition conditions, random noise, and attenuation during wave propagation. Classical resolution enhancement methods can partially sharpen seismic events, but they usually rely on restrictive assumptions about stationarity, minimum-phase wavelets, or accurate attenuation models. In this study, we propose a structure-preserving bidirectional bandwidth translation network for seismic resolution enhancement. Instead of formulating the task as a one-way paired regression problem, the proposed approach interprets resolution enhancement as unpaired translation between low-bandwidth and high-bandwidth seismic domains. A cycle-consistent adversarial objective is combined with an SSIM-based structural constraint so that the model simultaneously improves spectral recovery, waveform fidelity, and reflector continuity. To reduce the domain gap between synthetic and field data, we further construct a hybrid training corpus by combining field-extracted wavelets with synthetic reflectivity sequences and train a lightweight one-dimensional residual generator–discriminator architecture tailored to oscillatory seismic traces. Comprehensive experiments are conducted on synthetic data, a field seismic profile, and the public SEG Open Data benchmark. In addition to comparisons with conventional deconvolution and time-varying frequency deconvolution, the manuscript reports quantitative comparisons with representative learning-based baselines, together with ablation studies, parameter sensitivity analysis, robustness evaluation under different noise levels and bandwidth settings, and computational cost analysis. The results show that the proposed method consistently achieves a favorable balance between spectral extension and structural preservation, demonstrating its potential as a practical data-driven solution for seismic resolution enhancement. Full article

This study introduces an open-source hybrid shape optimization workflow for acoustic wave problems that integrates acoustic wave propagation analysis with a two-stage optimization strategy. A coarse Parameter Sweep (PS) is first used for feasibility screening and global exploration, followed by derivative-free local refinement using the Nelder–Mead (NM) method. The framework is demonstrated on three benchmark problems: (i) an acoustic horn optimized for improved impedance matching and reduced reflections, (ii) a noise barrier reshaped to minimize acoustic pressure in the shadow zone, and (iii) a crescent-shaped scatterer designed to attenuate downstream pressure amplitude. Across all cases, the PS–NM strategy achieved lower objective values than baseline-initialized local optimization, at the expense of increased computational cost. All analyses were performed in the open-source FEniCS environment within Jupyter Notebooks. Comparisons with published results support the accuracy and consistency of the implementation. By combining accessibility with flexibility, the framework provides a reproducible methodology for acoustic shape optimization. Potential extensions include multi-objective formulations, frequency-adaptive designs, improved constraint-handling strategies, and integration with metamaterial concepts. Full article

Accurate characterization of subsurface stratigraphy is essential for reliable landslide stability assessment. However, stratigraphic models constructed solely from sparse borehole data are often constrained by incomplete spatial coverage and substantial interpretive uncertainty. To address this issue, this study developed an integrated probabilistic stratigraphic modeling framework that combines borehole data with electrical resistivity tomography (ERT) data. In the proposed framework, borehole logs provide direct lithological labels and spatial prior information, while the inverted ERT resistivity profile is introduced as a continuous geophysical constraint. Specifically, logarithmic resistivity and the borehole-derived expected stratigraphic configuration were combined into a support vector machine classifier to establish a nonlinear mapping between geophysical responses and stratigraphic categories. A bootstrapping strategy was also used to quantify the stratigraphic uncertainty. The proposed method was then applied to the Panzhuangzu Landslide in Henan Province, China. Based on the probabilistic stratigraphic models, multiple plausible stratigraphic realizations were generated, and their corresponding stability responses are evaluated through numerical analysis. Monte Carlo simulations were further performed to examine how stratigraphic uncertainty propagates into landslide stability predictions. The results show that incorporating ERT data improves the geological plausibility of the inferred stratigraphy. Compared with the borehole-only case, the results obtained from the integrated framework exhibited reduced uncertainty in both the inferred stratigraphic model and the corresponding landslide stability assessment. These findings indicate that the proposed borehole–geophysical data fusion method can provide a more reliable geological basis for landslide stability analysis. Full article