Search Results (5,167)

Fractured–vuggy carbonate reservoirs are characterized by highly discrete storage structures, and the number, spatial distribution, and volume of cavities strongly affect well-test responses and reservoir development decisions. This study develops a PEBI-grid finite-volume implementation of a wave–seepage-coupled model for pressure-transient interpretation in reservoirs containing irregular cavities. The objective is not to introduce a new general-purpose finite-volume method but to embed irregular cavities as special control volumes into a locally orthogonal PEBI grid so that the cavity volume, geometry, and well–cavity distance can be represented explicitly in bottom-hole pressure calculations. The model is formulated as a thickness-averaged two-dimensional system in which the fracture–matrix region is treated as an equivalent seepage continuum, and each cavity is assigned a spatially uniform pressure governed by a wave–seepage exchange relation. For the limiting case of zero cavity volume, the numerical bottom-hole pressure agrees closely with the analytical solution and the material-balance estimate. A further cylindrical-cavity benchmark against an analytical wave–seepage solution gives a pressure-drawdown relative L2 error of 4.38%, where the relative L2 error denotes the Euclidean norm of the pressure error vector normalized by that of the reference solution, providing additional validation of the cavity-coupled formulation. Sensitivity analysis shows that increasing the cavity volume delays the characteristic extrema of the pressure derivative and strengthens the contrast between the minimum and maximum, whereas increasing the well–cavity distance mainly shifts the onset of the cavity-dominated response and weakens its amplitude. A field pressure-buildup case from the Fuyuan oilfield is interpreted using the proposed workflow. The matched model indicates a pentagonal cavity with a volume of 169,770 m³, a well–cavity distance of 158.4 m, a permeability of 5.535 md, and an initial reservoir pressure of 86.66 MPa. The results demonstrate that the proposed PEBI-FVM wave–seepage-coupled model can support practical well-test interpretation of irregular cavities, while its reliability depends on the validity of the equivalent-continuum and uniform-cavity-pressure assumptions. Full article

99mTc-pyrophosphate (PYP) scintigraphy is widely used for the noninvasive evaluation of transthyretin cardiac amyloidosis. Although interpretation primarily focuses on myocardial uptake, confirmation of appropriate systemic radiotracer biodistribution is essential. We report a case in which an examination presumed to be 99mTc-PYP scintigraphy demonstrated free 99mTc-pertechnetate-like biodistribution. A 75-year-old woman with chronic kidney disease and conduction disturbance underwent 99mTc-PYP scintigraphy for suspected cardiac amyloidosis. The initial study, recorded as the administration of 740 MBq 99mTc-PYP, was imaged 3 h after injection. Planar imaging showed mild apparent activity over the cardiac region; however, SPECT/CT demonstrated no definite myocardial uptake. Instead, intense uptake was observed in the stomach and thyroid gland, with complete absence of skeletal activity. This distribution was inconsistent with correctly administered 99mTc-PYP and suggested free 99mTc-pertechnetate biodistribution, likely due to radiopharmaceutical preparation or administration error. A repeat 99mTc-PYP scan 1.5 months later showed expected skeletal uptake without gastric or thyroid activity and again demonstrated no myocardial uptake. The study was interpreted as negative for cardiac amyloidosis. Gastric and thyroid uptake with absent skeletal activity on presumed 99mTc-PYP scintigraphy should be considered nondiagnostic rather than negative. Full article

Traditional solid-contact ion-selective electrodes (SC-ISEs) are severely constrained by a long-standing thermodynamic bottleneck, which requires hours of pre-conditioning and stabilization to establish a stable phase-boundary potential. To fundamentally bypass this limitation, we present a paradigm shift in electrochemical ion sensing that exploits dynamic kinetics rather than waiting for thermodynamic equilibrium. In this paper, we report a transient potential profiling method that eliminates the need for equilibration by analyzing the open-circuit voltage decay during the first 60 s of polarization. A discharge step on indicator electrode returns the membrane to a reproducible initial state, allowing for the extraction of a concentration correlated coefficient. Using a calcium ISE with an optimized membrane, the early-stage polarization dynamics were fitted to a single exponential saturation model, predicting the steady state response with an average error of 1.6%. The method achieved high repeatability (intra-day RSD 3.22%), batch to batch reproducibility (4.57%), and recovery rates from 90.7% to 115.0% in real water samples. Validation against ion chromatography showed high agreement (R² = 0.997). This strategy enabled conditioning free, disposable ISEs for point of care and environmental monitoring. Full article

Overhead transmission conductors are flexible cable structures. Their initial equilibrium configuration is affected by self-weight, tension, boundary constraints, and material deformation, and is required for force analysis, sag calculation, and safety assessment. Existing studies use catenary theory or numerical iterative methods. The direct iterative method is used in conductor form-finding. However, its geometric update ratio is assigned in segments based on empirical thresholds. This may cause unsmooth updates, low efficiency, and numerical instability in sensitive cases. This study investigates a single-span conductor within a nonlinear finite element form-finding framework. The direct iterative method is analyzed in terms of control variables, implementation process, and update-ratio control mode. A continuous error-driven adaptive geometric update strategy is proposed and an adaptive direct iterative method is developed. The two methods are compared under the same finite element model, parameters, loads, constraints, convergence threshold, and maximum iterations. Three factors are selected: element number, nonlinear substep number, and initial strain coefficient. A total of 27 full-factorial cases are calculated. Convergence efficiency, final error, and abnormal case distribution are evaluated. The results show that the proposed method reduces iterations, improves computational efficiency, and enhances numerical stability in sensitive cases without changing the finite element solution framework. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

To address the time-consuming and labor-intensive procedures associated with traditional approaches for evaluating the integrated burning rate of wire-embedded propellants in solid rocket motors (SRMs), this study proposes an efficient and reliable prediction method. This new method is based on an improved burning-rate–initial-temperature correlation, achieved through Abaqus-Python secondary development that enables fully automated geometric modeling, transient heat-transfer analysis, and temperature-field extraction for wire-embedded propellants. The relative error between the present method and the experimental results is less than 5%. The accuracy and engineering applicability of the present method are verified. The effects of the material parameters and wire diameters on the integrated burning rate is investigated. The results indicate that wires of different materials exhibit substantial variations in burning-rate enhancement efficiency, with smaller diameters and higher thermal diffusivity producing stronger enhancement effects. When the specific heat capacity and density are held constant, the integrated burning rate increases monotonically with the wire’s thermal conductivity, though the growth trend gradually approaches saturation. In contrast, the influences of the wire’s specific heat capacity and density are comparatively weak. The integrated burning rate prediction framework developed in this study demonstrates strong versatility and scalability. It enables rapid performance evaluation of propellants embedded with wires of various sizes and thermophysical properties, providing valuable theoretical guidance and practical tools for the design and optimization of wire-embedded solid rocket motors. Full article

Discrete diffusion models have become an important class of generative models for categorical data, yet their theoretical understanding remains largely limited to time-homogeneous noise schedules. In this work, we study uniform-rate discrete diffusion models with time-inhomogeneous continuous-time Markov chain forward processes. We establish convergence guarantees for practical reverse-time samplers by directly controlling the total variation distance, avoiding the indirect route of first bounding KL divergence and then applying Pinsker’s inequality. Our analysis decomposes the sampling error into initialization, score-estimation, discretization, and early-stopping errors, and explicitly characterizes how each term depends on the accumulated noise, the local noise rate, and the smoothness of the noise schedule. Under suitable regularity conditions on the noise schedule, we further derive step-complexity guarantees that match the order of existing results for homogeneous samplers. Full article

In precision gear manufacturing, longitudinal crowning on tooth flanks is commonly produced by applying diagonal feed in worm-type generating processes using tools such as variable-tooth-thickness hobs and dressable grinding worms. However, precise twist control remains difficult because the geometric parameters of the generating tool are strongly coupled with the machine feed settings in the underlying generating kinematics. In addition, direct numerical optimization becomes unreliable near the standard tool state, where the sensitivity of the diagonal-feed coefficient degenerates and conventional linearized solvers may lose effectiveness. To address these issues, this study proposes a multi-variable optimization framework for twist-constrained worm-type gear generation. An iterative singular value decomposition (SVD) scheme is developed to construct and update the sensitivity matrix, while a warm-start continuation strategy is introduced to overcome the local singularity and improve numerical robustness. Two closed-form expressions for the diagonal-feed coefficient are also proposed as practically useful initial estimates, corresponding respectively to the minimum SVD topographic residual and the minimum tooth-flank twist. Numerical validation over a 60-case parameter sweep shows maximum relative errors below 1.6% within the tested range. The proposed framework coordinates the tool-geometry design and diagonal-feed selection to generate tooth flanks with prescribed crowning characteristics while satisfying a specified twist requirement and limiting the required diagonal shift. Numerical examples show that the iterative framework reduces the root-mean-square (RMS) topographic error from 1.14 μm to 0.027 μm relative to the analytical setting of Hsu and Fong. These results indicate that the proposed method provides a reliable computational basis for twist control and process-parameter design in advanced CNC gear generation. From a manufacturing standpoint, because the three design criteria are accessed by adjusting only the diagonal-feed ratio on the machine, a single generating-tool design can serve a range of crowning and twist requirements without retooling, reducing setup and tooling efforts in production. Full article

We develop a systematic framework for incorporating perturbative correction terms into classical finite difference schemes for Allen–Cahn type stochastic partial differential equations. Three distinct correction approaches are introduced, conceptually motivated by perturbative quantum field theory, quantum coherent state evolution, and WKB (Wentzel–Kramers–Brillouin) barrier penetration theory. These quantum-inspired perturbative method (QIPM) corrections function as classical perturbations executing entirely on conventional hardware; quantum-mechanical formalism serves only as a design principle for constructing specific functional forms of correction terms. The primary novelty of this work lies in (i) a generic convergence-preservation theorem establishing sufficient conditions on correction magnitude for any perturbative correction to maintain the base scheme’s accuracy order, and (ii) a systematic translation methodology from quantum-mechanical analogies to explicit, implementable finite difference corrections with rigorous parameter bounds. Through convergence analysis, we demonstrate that appropriately parametrized corrections preserve the accuracy of the underlying numerical scheme, provided the solution possesses sufficient regularity and the parabolic scaling constraint $\Delta t=\mathcal{O}\left(h^2\right)$ holds. Numerical experiments on a spatially discretized domain over a finite time horizon using spatially correlated noise reveal that the anharmonic oscillator correction achieves exceptional accuracy with modest computational overhead, while the amplitude encoding correction provides intermediate accuracy with negligible timing cost. The tunneling-inspired correction exhibits higher error for smooth initial conditions, indicating strong problem-dependence. While these methods enhance accuracy in specific scenarios, genuine speedups on classical hardware are not achieved. The primary value lies in establishing systematic methodologies for perturbative correction design and developing theoretical foundations for future hybrid computational approaches. Full article

Accurate air temperature observation requires minimizing solar radiation-induced deviations, which are strongly influenced by radiation shield performance. However, conventional shields often produce significant errors under strong solar radiation or weak ventilation. In this study, an air temperature observation system integrating a radiation shield and a backpropagation (BP) neural network-based correction method is proposed. Computational fluid dynamics (CFD) simulations were conducted to quantify radiation-induced temperature deviations under representative meteorological conditions, and the simulated dataset was used to train and test the neural network model. Initial field comparison experiments were performed using a 076B forced-ventilation system as a reference, where measured differences were treated as experimental deviations and model outputs as predicted deviations. The results show that, before correction, the proposed system exhibited a maximum deviation of 1.05 °C and a mean deviation of 0.26 °C, while the root mean square error and mean absolute error between experimental and predicted deviations were 0.30 °C and 0.23 °C, respectively. The correction significantly reduced temperature deviations, demonstrating the effectiveness of the proposed system in improving measurement accuracy. Full article

Background/Objective: Low back pain (LBP) is a leading cause of disability worldwide, and patient-reported outcome measures such as the Roland–Morris Disability Questionnaire (RMDQ) are essential for assessing LBP-related disability. While the Modern Standard Arabic version of the RMDQ has demonstrated preliminary reliability, its structural validity has not been thoroughly evaluated. This study aimed to assess the structural validity of the Modern Standard Arabic RMDQ using confirmatory factor analysis (CFA). Methods: A cross-sectional study was conducted for 113 patients with LBP recruited from outpatient physical therapy clinics in Saudi Arabia. Participants completed the Modern Standard Arabic RMDQ, a 24-item instrument scored dichotomously. CFA was performed using the Weighted Least Squares Mean and Variance adjusted estimator to test a unidimensional model. Model fit was assessed using Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Tucker–Lewis Index (TLI), and Comparative Fit Index (CFI). Reliability was evaluated using McDonald’s omega (ω). Results: The initial one-factor CFA model showed close to acceptable fit (RMSEA = 0.044; SRMR = 0.149; TLI = 0.94; CFI = 0.93). After accounting for significant residual correlations between item pairs (items 4 & 21; 13 & 18), model fit improved (Δχ² = 22.33; Δdf = 2; p < 0.001) (RMSEA = 0.038; SRMR = 0.145; TLI = 0.95; CFI = 0.95). Most items had significant loadings on the latent construct, except item 2. McDonald’s ω was 0.91, indicating excellent internal consistency. Conclusions: The findings of this study provide supportive evidence for the structural validity and internal consistency of the Modern Standard Arabic version of the RMDQ and suggest the presence of a dominant unidimensional structure. The Arabic RMDQ may be useful for assessing LBP-related disability in Arabic-speaking patients with LBP, although further validation studies are warranted. Full article

Historically, in the initial stages of solid rocket motor (SRM) development, performance parameters, such as specific impulse, total impulse, mass, and thrust, have been prioritized, with cost considerations often treated as secondary. Consequently, SRM performance optimization under cost constraints has emerged as a central objective in aerospace propulsion. To address this gap, this study establishes a cost–performance evaluation model for SRMs. A Kriging surrogate model, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are leveraged to minimize the manufacturing cost and maximize the terminal velocity of SRM engines, subject to constraints associated with the maximum operating pressure of the combustion chamber and burn time. First, a cost–performance calculation model for an SRM is developed and validated. Subsequently, Pearson correlation analysis and Sobol-based global sensitivity analysis are combined to reduce the dimensionality of the design parameters, and optimal Latin hypercube sampling is used to generate the training samples. Building on this foundation, a Kriging surrogate model is constructed. The cost–performance model of the SRM is subjected to multi-objective optimization using NSGA-II and TOPSIS to support decision-making. The results indicate that the proposed cost–performance calculation model achieves an error below 5%, demonstrating high accuracy. Among the design parameters, the combustion chamber length, nozzle outlet area, and expansion ratio significantly influence the cost and performance of SRMs. The surrogate models exhibit strong predictive accuracy, with coefficients of determination exceeding 0.9. The optimized TOPSIS scheme yields a performance improvement of 10.94% with a cost increase of 4.15% compared with the reference scheme. In summary, the cost–performance evaluation and optimization framework established in this work provides quantitative decision support for SRM design under cost constraints, and the integrated methodology can be extended to other aerospace propulsion systems or complex engineering equipment. This contributes to achieving synergistic optimization of performance and cost under resource limitations, and offers practical guidance for advancing affordability-driven design in propulsion engineering. Full article

This paper investigates the problem of adaptive event-triggered control with prescribed performance for a class of strictly feedback-controlled nonlinear systems with unknown initial tracking conditions. To overcome the dependence of traditional prescribed performance control on the system’s initial tracking conditions, this paper introduces a novel algebraic saturation function that first maps tracking error initial values of arbitrary magnitude to a bounded interval and then imposes predefined performance constraints on this bounded interval. This strategy ensures that, even when the system’s initial state is unknown, the tracking error still converges to a small neighborhood near the equilibrium point in accordance with the prescribed performance. Furthermore, the strategy employs a fixed-threshold event-triggered mechanism, which effectively reduces the system’s update frequency and alleviates the communication load. Furthermore, by combining a logarithmic barrier Lyapunov function with neural network-based unknown function approximation techniques, this strategy proposes an adaptive prescribed performance event-triggered controller that is independent of the system’s initial state, in other words, independent of the initial tracking conditions. Simulation results validate the effectiveness and superiority of the proposed controller. Full article

This paper is concerned with cooperative multi-UAV navigation in a planar obstacle environment. A hierarchical embodied swarm framework with leader, subleader, and follower roles is proposed. At the high level, a passable-corridor-driven decision layer is developed to perform split–merge reconfiguration and navigate/encircle mode switching. At the low level, a multi-term force synthesis controller is constructed for formation maintenance, inter-agent collision avoidance, obstacle avoidance, and sub-swarm cohesion. To accommodate both rule-based and local large language model (LLM) decisions, a feasibility projection operator is introduced so that only kinematically admissible structural actions are executed. In addition, a LiDAR-based obstacle-repulsion term and an occlusion-attenuated attraction mechanism are incorporated to improve navigation safety in cluttered environments. A Lyapunov analysis of the smooth controller core further certifies that, for a known (possibly time-varying) cruise velocity compensated by feedforward, the formation tracking error is uniformly bounded by the initial energy. Finally, multi-seed numerical simulations verify the proposed framework in standard, ablated, and complex scenarios. In the hardest alternating-gate scenario, the LLM-assisted variant raises mission success from $0.000$ to $0.100$, increases the goal-reaching ratio from $0.025$ to $0.125$, and reduces the mean terminal error from $44.738\mathrm{m}$ to $39.851\mathrm{m}$, showing the value of semantic high-level reconfiguration under tight passage constraints. Full article