Search Results (732)

This study presents a novel data-driven framework for developing airport-specific landing policies and procedures from historical successful-landing data. The proposed process, termed the Airport-Dependent Landing Procedure (ADLP), is motivated by the fact that airports rely on uniquely tailored approach charts reflecting local operational constraints and environmental conditions. While existing approach charts and landing procedures are primarily designed based on expert knowledge, safety margins, and regulatory conventions, the authors argue that data science and data mining techniques offer a complementary and empirically grounded methodology for extracting operationally meaningful structures directly from historical landing data. In this work, we construct a probabilistic three-dimensional environment from real-world aircraft approach trajectories, capturing spatiotemporal relationships under varying atmospheric conditions during approach. The proposed methodology integrates Adversarial Inverse Reinforcement Learning (AIRL) with Recurrent Proximal Policy Optimization (R-PPO) to establish a foundation for automated landing without pilot intervention. AIRL infers reward functions that are consistent with behaviors exhibited in prior successful landings. Subsequently, R-PPO is employed to learn control policies that satisfy safety constraints related to airspeed, sink rate, and runway alignment. Application of the proposed framework to real approach trajectories at Guam International Airport demonstrates the efficiency and effectiveness of the methodology. Full article

Leveraging differential interferometric techniques, ground-based synthetic aperture radar (GB-SAR) delivers highly accurate displacement measurements, typically reaching submillimeter scales. However, in practical engineering, minor platform instability induced by environmental factors gives rise to space-variant baseline errors, which affects the deformation value. In response to this issue, this paper presents a method combining Taylor expansion and singular value decomposition for estimation and compensation of the space-variant baseline error. Initially, the Gaussian Mixture Model (GMM) is employed to adaptively select high-quality Permanent Scatterers (PSs) to facilitate robust data provision for the following error parameter estimation. Subsequently, a three-dimensional multi-parameter model for the space-variant baseline error is established via Taylor expansion, followed by parameter estimation using Singular Value Decomposition (SVD). Experiments indicate that the proposed approach effectively mitigates the error phase arising from platform vibration, thereby enhancing the precision of GB-SAR deformation inversion. Full article

Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss function as a hard constraint during training to ensure physical consistency. To address boundary load uncertainty, two quantification approaches—Bayesian linear regression and surrogate model optimization—are proposed to establish 95% confidence intervals for boundary coefficients. Verification based on simple three-dimensional models and actual geological models of mines shows that PINN inversion achieves a mean absolute relative error as low as 0.0772%, with an error of 15.67% under sparse sampling conditions—significantly lower than the 31.07% error of the traditional Back propagation neural network. This demonstrates excellent robustness and data efficiency. In the practical engineering application of complex geological bodies, the average error of principal stress inversion is 9.35% with a minimum error of 0.137%. All inversion results fall within the permissible accuracy range of engineering, and the stress distribution conforms to basic laws, with an average error of 0.453 in the constitutive relation. Compared with BP neural network and multiple linear regression methods, it shows obvious accuracy advantages. This method provides a new solution for intelligent ground stress prediction with high accuracy, high efficiency, and strong physical interpretability, and also lays the foundation for early identification of geological disasters. Full article

Background: Contemporary three-dimensional morphometric analysis emphasizes quantitative modeling of anatomical interactions. However, the interplay between sagittal and vertical dimensions in determining maxillary transverse morphology remains inadequately characterized. This study introduces the Sagittal Modulation Effect (SME) framework—a model characterizing how sagittal relationships modify the association between vertical pattern and maxillary arch morphology. Methods: A retrospective cross-sectional analysis was conducted on 165 skeletally mature adults (mean age: 25.4 ± 4.8 years), stratified into skeletal Class I, II, and III groups (n = 55 each). Lateral cephalometric analysis and 3D maxillary digital models were obtained. A validated automated algorithm performed arch morphometric analyses. The primary hypothesis was tested using multiple linear regression with interaction terms, validated via bootstrap analysis and cross-validation. Results: A significant SME was identified (p < 0.001). The inverse correlation between SN-MP and maxillary width intensified progressively: minimal in Class I (r = −0.047, p_adj = 0.891), moderate in Class II (r = −0.387, p_adj_ = 0.024), and strong in Class III (r = −0.645, p_adj_ < 0.001). Regression confirmed significant interaction effects (SN-MP × Class III: β = −0.45, p < 0.001; SN-MP × Class II: β = −0.31, p = 0.003). Exploratory analysis identified cohort-specific statistical descriptors associated with narrower arches: SN-MP > 34.2° in Class III (AUC = 0.84) and SN-MP > 36.5° in Class II (AUC = 0.78). These require external validation. Conclusions: This study provides evidence that sagittal classification modulates the vertical–transverse relationship. The SME framework offers class-specific coefficients and exploratory stratification tools for future research pending multi-center validation. Full article

The crystal structures of the cobalt(II) metal–organic frameworks or coordination networks of [Co(pdb)(DMF)] and [Co₂(pdi)(DMF)₃]·2(DMF)·H₂O (H₂pdb = 3,3′-(phenazine-5,10-diyl)dibenzoic acid; H₄pdi = 5,5′-(phenazine-5,10-diyl)diisophthalic acid; DMF = N,N-dimethylformamide) were synthesized solvothermally from cobalt(II) nitrate and the free acid of the linker in DMF. Systematic solvothermal screening demonstrated strong metal- and counterion-dependent framework formation, as crystalline coordination polymers were obtained exclusively from cobalt(II) nitrate, whereas other metal salts and cobalt(II) chloride or sulfate produced no crystalline materials. In catena-[(N,N-dimethylformamide)-μ₄-3,3′-(phenazine-5,10-diyl)dibenzoate-cobalt(II)], [Co(pdb)(DMF)], the Co₂ units, acting as secondary building units, are coordinated by four carboxylate groups from four linkers in a paddle-wheel arrangement, giving a three-dimensional (3D) network with cds (or CdSO₄) topology, in which the wide openings are filled by two symmetry-related nets to form a threefold interpenetrated structure. In catena-[tris(N,N-dimethylformamide)-μ₈-5,5′-(phenazine-5,10-diyl)diisophthalate-dicobalt(II)] bis(N,N-dimethylformamide) hydrate, [Co₂(pdi)(DMF)₃]·2(DMF)·H₂O, there are two different Co atoms, of which only Co₂ is connected to each of the four carboxylate groups of the tetracarboxylate linker and, thus, is responsible for 3D network formation. The network topology in [Co₂(pdi)(DMF)₃] is pts (or platinum(II) sulfide) when taking the Co₂ atom as a tetrahedral node and the linker as a square-planar fourfold node; however, this arrangement is inverse to the common square-planar metal and tetrahedral linker nodes found in PtS and most pts topologies. Full article

Building height is a fundamental parameter for characterizing urban three-dimensional structure and supporting applications such as urban planning, population estimation, and energy assessment. However, traditional shadow-based height inversion methods often suffer from occlusion, shadow overlap, and orientation inconsistencies when applied to heterogeneous urban environments. This study proposes a single-image building height estimation method that explicitly incorporates spatial distribution characteristics to enhance robustness and estimation accuracy. Shadow lengths are first robustly extracted using a fishnet–Pauta strategy, followed by a multi-scenario scaling coefficient model accommodating different sun–sensor geometric configurations. Urban areas are then subdivided into high-rise, mid-to-high-rise mixed, and dense low-rise zones using DBSCAN clustering and a composite indicator system. For each spatial type, tailored optimization strategies—including neighborhood-weighted correction, similarity-constrained local regression, and median smoothing—are applied to suppress systematic biases and local outliers. Experiments on 11,168 buildings across 13 Chinese cities demonstrate strong overall performance, achieving an MAE of 2.07 m, an RMSE of 2.56 m, and an R² of 0.99. The proposed method outperforms existing approaches and remains highly stable across diverse urban morphologies, providing a scalable solution for large-area building height mapping from single high-resolution imagery. Full article

Periodic supercell lattice structures with elements of random polydispersity disorder were created to simulate the effect of randomization on photonic crystals using finite-difference time domain (FDTD) methods. As a key exemplar system, a three-dimensional “inverse opal” structure of a face-centered cubic lattice with air spheres in a silicon dielectric was simulated, with sphere radii within supercells following a randomized Gaussian distribution, with characteristic standard deviation and mean. A corresponding ordered lattice with a bandgap with magnitude 3.5% of the normalized frequency range was used as a direct control, with sphere radius 0.34 times the lattice constant a. For a range of standard deviations, up to 5.9% of the 0.34a mean, a Monte Carlo-style approach was adopted, with photonic band properties analyzed over a large number of repeat simulations to ensure statistical significance. The corresponding Gaussian distribution in the resultant photonic bandgap magnitudes is broadened with increasing polydispersity such that an evolving fraction of simulations no longer exhibits a non-zero bandgap. A characteristic pseudo-transition occurs at a standard deviation of approximately 4.1% of the 0.34a mean, above where the frequency of simulations still returning a finite bandgap rapidly diminishes. Some isolated configurations, with a high degree of uniqueness, can exhibit enhanced bandgap properties (greater than the 3.5% benchmark) despite considerable polydisperse disordering; we envisage that these findings point towards the use of engineered randomness in supercell systems to create desired photonic crystal properties and functionality, such as localization and photonic bandgaps. Full article

Primary open angle glaucoma (POAG) is one of the leading causes of irreversible blindness and is associated with dysfunction of the trabecular meshwork (TM), a three-dimensional (3D) structure that regulates aqueous humor outflow and, consequently, intraocular pressure (IOP). IOP is the only modifiable factor for glaucoma. Outflow facility is the inverse of aqueous humor outflow resistance caused by the presence of the TM and adjacent tissues, and reflects the TM’s central role in IOP control, representing the most physiologically relevant measure of human trabecular meshwork (HTM) function. Therefore, development of ex vivo systems to study outflow facility and IOP regulation is critical for advancing glaucoma research. We present a comprehensive review of bioengineering approaches to generation of 3D HTM models using synthetic, natural, and hybrid hydrogels, micro- and nanofabricated synthetic substrates or porous scaffolds, and microfluidic devices. These 3D HTM systems have been designed to capture key features such as topography, stiffness, and fluid flow in the conventional outflow pathway. In particular, we highlight HTM models that recapitulate IOP regulation and allow measurement of outflow facility, which directly reflect pressure-dependent outflow resistance in dynamic HTM physiology and glaucoma pathophysiology. By integrating these bioengineering approaches with emerging stem cell technologies, this review offers an evidence-based landscape overview and framework for designing next-generation 3D human-relevant TM models for outflow physiological studies and IOP-modulating drug discovery. Full article

The advancement of large-scale marine infrastructure demands increasingly accurate prediction of settlement in deep-water foundations. The caisson is an important type of deep-water foundation whose additional settlement induced by superstructure construction directly impacts the overall safety of the project. This study focuses on the main tower foundation of the Changtai Yangtze River Bridge, recognized as the world’s largest deep-water caisson foundation. A three-dimensional finite element model was developed using the hardening soil model with small-strain stiffness (HSS) constitutive model to simulate the settlement response of the caisson foundation throughout the entire superstructure construction process. The model’s reliability was verified through systematic comparison with field monitoring data. Furthermore, an inversion analysis was conducted on the initial shear modulus ($G_0^{\mathrm{ref}}$), the most sensitive parameter of the HSS model, based on the measured data. The results reveal that its optimal value exhibits significant load dependency, varying according to the construction stage. Accordingly, practical strategies for parameter determination are proposed: a fixed-value method ($G_0^{\mathrm{ref}}$ = 2$E_{\mathrm{ur}}^{\mathrm{ref}}$) suitable for conventional design and a more precise stage-specific value method. Both approaches markedly enhance the settlement prediction accuracy, particularly under high-load conditions. The findings offer valuable insights for the refined design and safety assessment of similar deep-water mega-foundation projects. Full article

This study presents a reliable methodology for analyzing reinforced ultra-high-performance fiber-reinforced concrete (UHPFRC) elements by linking material behavior to structural performance. A non-linear finite element model (NLFEM) is proposed to simulate the tensile response of reinforced UHPFRC elements, with particular emphasis on shrinkage effects. The model operates in two phases: the first simulates shrinkage during specimen storage and the second simulates the mechanical tensile test, using the internal stresses from the first phase as initial conditions. The model was validated through an experimental program involving reinforced UHPFRC ties. The NLFEM accurately reproduced the load–displacement response using average UHPFRC tensile parameters obtained from a simplified Four-Point bending test Inverse Analysis method (4P-IA). It reliably predicted the shrinkage strain range and its impact on stiffness loss during microcrack initiation and stabilization, where tension-stiffening behavior is critical. Additionally, the simulation from the model captured the transition from microcracking to macrocrack formation and the role of fiber bridging in maintaining stiffness. The predicted shrinkage strain aligns with values reported in the literature and represents a conservative upper bound, neglecting the potential effects of creep and relaxation. Overall, the NLFEM effectively simulates the full tension-stiffening behavior of reinforced UHPFRC, including three-dimensional effects, and provides a reliable tool for structural analysis and design. Full article

Land subsidence induced by excessive groundwater abstraction has emerged as a major geo-environmental hazard in arid oasis regions, calling for reproducible methods to quantitatively assess the abstraction-reduction–subsidence response and to support zoned management. This study integrates Sentinel-1A PS-InSAR deformation data with groundwater-level measurements to develop and calibrate a MODFLOW-SUB model that couples three-dimensional groundwater flow and one-dimensional skeletal compaction. The InSAR deformation field is used to constrain the conceptual model and key parameters. Four abstraction-reduction scenarios (20%, 40%, 60%, and 80%) are designed to characterize response curves using indicators such as maximum cumulative subsidence, annual subsidence rate, and the area exceeding specified thresholds. In addition, a multi-criteria composite index integrating driving forces, geological susceptibility, and exposure is applied for hazard zoning and scenario comparison. The results show that PS-InSAR constraints improve the spatial agreement of the simulations. The time-series RMSE between simulated and InSAR-derived deformation is approximately 20 mm, and the end-of-period cumulative subsidence error is within 10 mm. From 2019 to 2020, the maximum cumulative subsidence reached 166 mm, and the peak subsidence rate reached 101 mm/a. A clear lag between groundwater-level fluctuations and subsidence is observed, with the maximum correlation occurring at ~35 days for ACJ-1 and ~59–83 days for ACJ-2. This spatial variability is associated with the thickness and permeability of clay layers. Forecasts for 2021–2028 indicate that, under business-as-usual abstraction, maximum subsidence may reach 695 mm. Across scenarios, subsidence mitigation exhibits diminishing marginal returns with increasing abstraction reduction. Under the adopted model settings, a 20% reduction in abstraction yields substantial decreases in maximum subsidence and high-hazard area, representing a practical trade-off between mitigation benefits and water-use costs. Overall, the integrated workflow of monitoring, inversion, coupled modeling, scenario analysis, and zoning, together with the resulting zoned management recommendations, provides decision support for land-subsidence mitigation and water-allocation planning in arid oasis regions. Full article

We develop a function theory on a three-dimensional reduced quaternionic model endowed with a projected (and, therefore, non-associative) product, together with its natural dual extension generated by a nilpotent infinitesimal unit. After introducing the associated first-order Dirac-type system, we construct explicit Cauchy kernels and prove a Cauchy–Pompeiu representation for sufficiently smooth functions with values in the dual algebra. We derive a Teodorescu-type right inverse, Liouville- and uniqueness-type principles, and residue formulas for isolated singularities. For smooth hypersurfaces, we establish Plemelj–Sokhotski boundary limits for the Cauchy transform and its dual lift. Worked examples illustrate how the reduced product interacts with boundary geometry and provide a practical route to computation. Full article

In order to solve the problems of insufficient medical image feature extraction, high classification accuracy, and computational complexity in automatic diagnosis of skin lesions in the edge computing environment, this paper proposes a real-time pseudo-multimodal low-delay diagnosis framework, SCGViT, based on a vision transformer. The framework is constructed around three functional objectives: mitigating data imbalance through generative modeling, capturing diverse representations via multi-dimensional perception, and optimizing feature fusion through adaptive refinement. Firstly, using Class-Conditioned Generative Adversarial Networks (CGANs) simulates manifolds of minority class samples in latent space, achieving preliminary balance of data distribution. Secondly, a branch feature-extraction path is constructed to simulate inversion (INV) and infrared (IR) modes in the original visual primary color mode (RGB), in order to achieve multi-dimensional perception. Finally, a cross-attention mechanism is combined for cross-branch feature aggregation, and a channel-attention mechanism (squeeze and excitation) is embedded for secondary refinement of the mixed global local features to enhance the representation ability of key pathological regions by integrating complementary structural and contrast information. The experimental results on the HAM10000 dataset showed that the F1 score reached 0.973, the inference speed reached 304.439 FPS, the parameter count was only 0.524 M, and the computational complexity was only 0.866 G FLOPs, achieving a balance between high accuracy and light weight. Full article

Shale gas “sweet spot” prediction serves as a pivotal technical link in shale gas exploration and development, directly governing the efficiency of exploration deployment and the economic viability of development projects. To address the research gap in sweet spot prediction for complex synclinal structures, this study establishes an integrated geology–engineering–economics evaluation framework, incorporating artificial intelligence (AI)-assisted parameter optimization and dynamic weight adjustment. This innovative approach overcomes the inherent limitations of single-parameter and static evaluation methods commonly employed in new exploration areas. Focusing on the Upper Ordovician Wufeng Formation to Lower Silurian Longmaxi Formation shale sequences within the Anchang Syncline of northern Guizhou, a comprehensive geological characterization of shale reservoirs was accomplished through the fine processing of 3D seismic data (dominant frequency: 30 Hz; signal-to-noise ratio: 8.5) and statistical analysis of logging data. Prestack elastic parameter inversion technology was utilized to quantitatively predict key geological sweet spot parameters, including the total organic carbon (TOC) content and total gas content, with model validation conducted using core test data. Coupled with prestack and poststack seismic attribute analysis, engineering sweet spot evaluation indicators—encompassing fracture development, in situ stress, the pressure coefficient, and the brittleness index—were established with well-defined quantitative criteria. By integrating multi-source data from geology, geophysics, and engineering dynamics, a three-dimensional evaluation system encompassing “preservation conditions–reservoir quality–engineering feasibility” was constructed, with the random forest algorithm employed for sensitive parameter screening. Research findings indicate that high-quality shale in the study area exhibits a thickness ranging from 17 to 22 m, characterized by a TOC content ≥ 4%, gas content of 4.3–4.8 m³/t, effective porosity of 3.5–5.25%, and brittleness index of 55–75. These properties collectively manifest the “high organic matter enrichment, high gas content, and high brittleness” characteristics. Through multi-parameter weighted comprehensive evaluation using the Analytic Hierarchy Process (AHP), complemented by sensitivity testing, sweet spots were classified into three grades: Class I (63 km²), Class II (31 km²), and Class III (27 km²). An optimized well placement scheme for the southern region was proposed, taking into account long-term production dynamics and economic assessment. This study establishes a multi-parameter, multi-technology integrated sweet spot evaluation system with strong transferability, providing a robust scientific basis for the large-scale exploration and development of shale gas in northern Guizhou and analogous complex structural regions worldwide. Full article

Wheat canopy chlorophyll content (CSPAD) is an important physiological parameter characterizing the photosynthetic capacity and nutritional status of crops. Precision agricultural technologies are widely used for non-destructive monitoring of wheat SPAD, but the SPAD inversion models have limitations due to the incorporation of many principal components besides spectral parameters. In the current study, combined with the SPAD values measured by a handheld instrument, an effective approach for estimating CSPAD from unmanned aerial vehicle (UAV) hyperspectral data is proposed. A fusion modeling scheme based on spectral parameters was constructed by extracting (1) the traditional vegetation index (VI), (2) the sensitive-band index (2D-COSI) screened based on two-dimensional correlation spectroscopy (2D-COS), and (3) the geometric-angle index (SPADSI) constructed by combining the SPA and the PROSAIL model. Finally, the CSPAD estimation model was developed by using Gaussian Process Regression (GPR) and Support Vector Machine Regression (SVM), and their accuracy comparison and feature importance analysis were conducted at different growth stages. We found that the model integrating three types of spectral parameters performed better as compared to the model with a single type of parameter. Further, the GPR model had the highest estimation efficiency at 20 days after the anthesis stage (R² = 0.90, RMSE = 5.95, MAE = 4.47) as compared to the SVM model and other growth stages. This study provides innovative insights and technical support based on a CSPAD estimation framework integrating multiple types of spectral characteristics for the rapid and non-destructive monitoring of wheat CSPAD and for overall sustainability in farmland management. Full article