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It is known that field emission from random nano-structured surfaces cannot be well characterized with the original field emission model known as the Fowler–Nordheim (FN) law or the Murphy–Good (MG) law. To account for the roughness effects, the traditional approach was to introduce the arbitrary field enhancement factor β in the FN and MG laws. In this paper, by treating the random roughness of field emitters as a fractional dimension object with 0 < α < 1 , fractional models of the FN and MG law, which were formulated recently may serve as alternate characterization tools. At α = 1 , the models will recover the traditional FN and MG laws valid for a perfect flat planar surface. These generalized fractional FN law ( FN α) and the fractional MG law ( MG α) will be used to characterize various field emission experiments by finding the best 0 < α < 1 values to account for the degree of the random roughness. The findings will be compared among the traditional and fractional emission models, where the best-fitting results will be shown for each case. It is found that the fractional field emission models provide more physical and appropriate field emission parameters, i.e., field enhancement factor and field emission area, in comparison with traditional field emission models. [ABSTRACT FROM AUTHOR]

We present comprehensive numerical results from a study of model H, which describes phase separation kinetics in binary fluid mixtures. We study the pattern dynamics of both density and velocity fields in d = 2, 3. The density length scales show three distinct regimes, in accordance with analytical arguments. The velocity length scale shows a diffusive behavior. We also study the scaling behavior of the morphologies for density and velocity fields and observe dynamical scaling in the relevant correlation functions and structure factors. Finally, we study the effect of quenched random field disorder on spinodal decomposition in model H. [ABSTRACT FROM AUTHOR]

We investigate the nucleation dynamics of the three-dimensional random field Ising model under an external field. We use umbrella sampling to compute the free-energy cost of a critical nucleus and use forward flux sampling for the direct estimation of nucleation rates. For moderate to strong disorder, our results indicate that the size of the nucleating cluster is not a good reaction coordinate, contrary to the pure Ising model. We rectify this problem by introducing a coordinate that also accounts for the location of the nucleus. Using the free energy barrier to predict the nucleation rate, we find reasonable agreement, although deviations become stronger as disorder increases. We attribute this effect to cluster shape fluctuations. We also discuss finite-size effects on the nucleation rate. [ABSTRACT FROM AUTHOR]

In infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures. [ABSTRACT FROM AUTHOR]

Some natural phenomena with areal/lattice data structures include extreme values or outliers. In such situations, the assumption of Gaussianity for the random field may not be reasonable, and no Gaussian transformation can be found because they exhibit heavy tails. A non-Gaussian stable random field, which is heavy-tailed, could be a more appropriate choice in these cases. This article introduces a sub-Gaussian α-stable (SGαS) random field for spatial analysis of multivariate areal data using a multivariate conditional autoregressive model. We, specifically, focus on the spatial clustering problem of such areal data. To address it, we develop methods that work based on adjacency constraints. To group the data, we offer an adjacency-constrained hierarchical agglomerative clustering (HAC) technique that considers both spatial and non-spatial attributes existing in the multivariate areal data. The proposed clustering algorithm is developed based on spatial adjacency constraint criteria incorporated into the HAC technique. We employed the developed algorithm to group Luxembourg communes based on simulated areal data from the SGαS distributions and French cities along the Gironde estuary based on the estuary areal dataset. We compare the results of the proposed method with the mentioned adjacency criteria, various dissimilarity, and linkages measures in clustering these two datasets. [ABSTRACT FROM AUTHOR]

We introduce a notion of algorithmic randomness for algebraic fields. We prove the existence of a continuum of algebraic extensions of Q${\mathbb {Q}}$ that are random according to our definition. We show that there are noncomputable algebraic fields which are not random. We also partially characterize the index set, relative to an oracle, of the set of random algebraic fields computable relative to that oracle. In order to carry out this investigation of randomness for fields, we develop computability in the context of the infinite Galois theory (where the relevant Galois groups are uncountable), including definitions of computable and computably enumerable Galois groups and computability of Haar measure on the Galois groups. [ABSTRACT FROM AUTHOR]

In the fused filament fabrication (FFF) additive manufacturing process, a part is manufactured by layer-by-layer deposition of thermoplastic filaments extruded from a nozzle. This process results in microscopic voids that are stochastic in size and randomly distributed in space. Also, the elastic properties of the extruded filaments show variation. These uncertainties in the FFF process and the resulting microstructure cause a variation in the structural response (load–displacement curve) of the final printed part. A multiscale uncertainty propagation framework was developed in this study to examine the effect of microscopic inhomogeneity on the variations in the macroscopic structural behavior. Experimental data was used to quantify uncertainty at the micro-scale, while Monte Carlo simulation was used to quantify uncertainty at the meso- and macroscales. A hook model was considered as a case study for uncertainty propagation in the FFF process. [ABSTRACT FROM AUTHOR]

Magnetic field lines in magnetohydrodynamic turbulence are stochastic curves and, therefore, understanding their structure is a difficult problem of theoretical physics. One way to study the properties of such field lines is to perform simulations in which field line equations are solved numerically. In most previous articles studies have been performed in the context of the two-component turbulence model where one assumes a superposition of slab and two-dimensional modes. In the current paper we perform simulations for so-called noisy slab turbulence which is an example for three-dimensional turbulence with small Kubo numbers. We discuss in detail the challenges of the numerical magnetic field creation for this type of turbulence, perform the simulations, and compare our findings with analytical theory. It is demonstrated that a large number of wave modes is needed in order to obtain accurate results. Furthermore, we also show that analytical theories are very accurate for this type of turbulence model. The obtained results will also be useful for the analytical description of energetic particle transport in the considered turbulence model. [ABSTRACT FROM AUTHOR]

Effective distress detection and quantitative analysis play a crucial role in road maintenance and driving safety. The Pavement distress segmentation network (PDSNet) is designed to combine the pyramid scene parsing network (PSPNet) and U-Net, providing both prior global information and local features that can overcome the common detection issues on the pavement data set faced by a single network. This paper proposes an efficient and improved architecture of PDSNet called PDSNet II for enhanced global modeling and retrieving fine details capacities. The proposed PDSNet II represents two major modifications on the original PDSNet. Firstly, a shifted window based on fully connected conditional random fields (FC-CRFs) layer is purposefully introduced to provide connections among consecutive self-attention layers that significantly enhance modeling power. Secondly, PDSNet II adopts multiple-head attention mechanisms to capture diverse interaction information across multiple projection spaces. Consequently, the output maps from the pyramid pooling module (PPM) head and the U-Net tail are fed into a neural window FC-CRFs layer. PDSNet II was trained using a data set consisting of 12,648 two-dimensional (2D) intensity and three-dimensional (3D) range images depicting various pavement conditions. The experimental results demonstrate that PDSNet II outperforms the original PDSNet in terms of F1-score and intersection over union (IoU). Compared with state-of-the-art networks, PDSNet II exhibits superior performance in detecting complex distress patterns, while effectively reducing noise and maintaining robustness. Overall, the proposed PDSNet II framework shows promising results in pavement distress segmentation, highlighting its potential for practical applications. [ABSTRACT FROM AUTHOR]

Plate anchors have been recognized as an attractive option for floating offshore structures due to their low cost and high effectiveness. The prediction of anchor pullout capacity often assumes a homogeneous sand with uniform properties over the entire soil mass. However, natural soils typically exhibit significant spatial variability due to their geological history of soil formation. To account for this inherent spatial variability, this paper has proposed an analytical approach to probabilistically estimate the pullout capacity of strip plate anchors in sand. The soil friction angle was represented by a random field, and the first two moments and the probability density function of the anchor breakout factor were determined analytically using a local average theory. The proposed analytical approach was validated by the random finite-element method (RFEM) over a wide range of soil and anchor parameters. The findings suggest that the anchor embedment depth ratio has a limited effect on the estimated anchor failure probability, whereas the coefficient of variation and correlation length of the soil friction angle have a significant influence. This confirms the importance of sufficient site investigation for cost-effective and reliable anchor design. Overall, the proposed analytical framework can be regarded as a reliable and practical alternative to the computationally intensive RFEM. [ABSTRACT FROM AUTHOR]

Natural soils often exhibit significant spatial variability due to their geological history of soil formation. As an attractive anchoring solution for floating offshore structures, this paper has investigated the pullout capacity of strip plate anchors in clay considering the inherent soil spatial variability. In this study, the soil properties were represented by random fields, and an analytical framework was developed to estimate the first two moments and the probability density function of the pullout capacity factor for shallowly and deeply embedded anchors. The analytical approach was validated by the random finite element method (RFEM) over a wide range of soil and anchor parameters. The results show that the coefficient of variation and correlation length of the soil significantly affect the prediction of anchor pullout capacity, providing evidence that sufficient site investigation is of great importance for cost-effective and reliable anchor design. Probabilistic charts were also developed to aid in the probabilistic analysis of anchor pullout capacity. Overall, the developed analytical framework can be used as a good approximation to the computationally intensive RFEM. [ABSTRACT FROM AUTHOR]

The roughness of a joint has a significant influence on the physical and mechanical properties of rock mass, and it is a hot and challenging research topic to determine the joint roughness quickly and accurately. In this paper, based on the random field theory and the traditional roughness evaluation index Z2s, a novel three-dimensional (3D) joint roughness evaluation index named Zσ,θd is proposed. The novel index involves three parameters: the standard deviation (SD) and the scales of fluctuation (SOF) of elevation distribution, which are related to the joint shape, and the sampling interval (SI), which is determined by the sampling method. The 3D joint is generated by the random field method, and the variation of Zσ,θd with SD, SOF, and SI is investigated. The results show that the roughness of the 3D joint generated by the random field method has good robustness, with a maximum fluctuation error of 3.87%. The roughness increases linearly with the increase of SD and decreases exponentially with the increase of SOF and SI, and the fitting formula is obtained. On this basis, the normalization method of Zσ,θd is put forward to eliminate the influence of SI on roughness, and the validity of Zσ,θd is verified by the actual joint in the public database. Finally, the application prospect of this method in the preparation of joint specimens is discussed, and the corresponding 3D surfaces are given according to the 10 typical joint profiles provided by Barton. This paper contributes a quantitative roughness evaluation index and a method for generating the 3D joint, which can serve as a basis for model experiments and numerical calculations. [ABSTRACT FROM AUTHOR]

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses. [ABSTRACT FROM AUTHOR]

Introduction: Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. Methods: In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Results: Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Discussion: Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability. [ABSTRACT FROM AUTHOR]

AbstractThe aim of this contribution is to propose new flexible spatial models based on a class of generalized sinh transformations. Our proposal greatly extends the Gaussian random field mainly in terms of asymmetry, heavy tail behavior, and bimodality. We demonstrate that we obtain a valid random field. We ensure that it is mean square continuous. We derive explicit expressions for the moments, the covariance function, the skewness, and the kurtosis of the random fields. In comparison with the available skew-Gaussian models, the proposed models could capture a greater amount of skewness and also accommodate clustered spatial outliers. The proposed models are computationally very tractable within the Bayesian framework here adopted. They are compared with the Gaussian and skew-Gaussian spatial models through simulation studies and an application to the spatial prediction of weekly rainfall near Darwin, Australia. The result suggests that the predictive performance in terms of different criteria under our method tends to be smaller than those obtained from the competing ones. [ABSTRACT FROM AUTHOR]

The proximity of residential areas to traffic corridors has raised concerns among researchers regarding train-induced vibrations. Experimental studies have consistently shown variations in train-induced vibrations, which can be attributed to uncertainties and randomness in both the vibration source and propagation path. This study aims to investigate the influence factors and quantify their contributions to train-induced vibration variations through a comprehensive field measurement campaign and numerical simulations using random finite element models. During the field measurements, vibrations were recorded at the ground surface and the column base for a total of 53 passbys of the same type of metro train on parallel rail lines. Variations in train-induced vibrations caused by the source-to-receiver distance and spatially varied soil properties were analyzed and compared. Specifically, the investigation focused on the spatial variation of soil properties including elastic modulus, density and damping ratio in the topsoil and the second layer of clay, while simultaneously considering the influence of spatial variations of all three soil properties on the transmission of train-induced vibrations from the surface soil to the structural columns. Understanding these variations is crucial for the probabilistic assessment of building serviceability during train passbys. The findings of this research provide valuable insights for probabilistic prediction and evaluation of building vibration comfort. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes. [ABSTRACT FROM AUTHOR]

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement. [ABSTRACT FROM AUTHOR]

Site investigation provides essential geotechnical parameter information for analysis and design. However, three conflicting objectives, namely exploration effort, robustness and parameter uncertainty, pose a challenge to the development of an optimal site investigation program. In this study, a three objective optimization framework for the site investigation program is proposed based on the Bayesian approach and the non‐dominated sorting genetic algorithm (NSGA‐II). The only inputs required by the proposed framework are prior distribution of geotechnical parameters and error information. The prior distribution of geotechnical parameters is derived from integrating engineering experience and measurements from basic exploration boreholes. The error information is obtained based on literature and expert judgment related to the specific project. Firstly, a design pool of candidate investigation programs is generated using Bayesian approach to determine the locations and number of exploration boreholes. The NSGA‐II is then applied to identify the optimal program that balances lower cost, higher robustness, and lower uncertainty. The proposed multiobjective optimization framework is illustrated and validated through a real site investigation case in Chongqing, China, aimed at determining the ultimate bearing capacity of the rock foundation. The spatial correlation of parameters within the study area is also considered. The optimal program is represented by the location and number of exploration boreholes. By comparing measurements with predictions from different site investigation programs, the efficiency of the proposed multiobjective framework is demonstrated. Additionally, the influence of engineering experience and random field modeling on the investigation program is discussed. [ABSTRACT FROM AUTHOR]

Stirring a fluid through a Gaussian forcing at a vanishingly small Reynolds number produces a Gaussian random field, while flows at higher Reynolds numbers exhibit non-Gaussianity, cascades, anomalous scaling and preferential alignments. Recent works (Yakhot & Donzis, Phys. Rev. Lett. , vol. 119, 2017, 044501; Khurshid et al. , Phys. Rev. E , vol. 107, 2023, 045102) investigated the onset of these turbulent hallmarks in low-Reynolds-number flows by focusing on the scaling of the velocity gradients and velocity increments. They showed that the onset of power-law scalings in the velocity gradient statistics occurs at low Reynolds numbers, with the scaling exponents being surprisingly similar to those in the inertial range of fully developed turbulence. In this work we address the onset of turbulent signatures in low-Reynolds-number flows from the viewpoint of the velocity gradient dynamics, giving insights into its rich statistical geometry. We combine a perturbation theory of the full Navier–Stokes equations with velocity gradient modelling. This procedure results in a stochastic model for the velocity gradient in which the model coefficients follow directly from the Navier–Stokes equations and statistical homogeneity constraints. The Fokker–Planck equation associated with our stochastic model admits an analytic solution that shows the onset of turbulent hallmarks at low Reynolds numbers: skewness, intermittency and preferential alignments arise in the velocity gradient statistics through a smooth transition as the Reynolds number increases. The model predictions are in excellent agreement with direct numerical simulations of low-Reynolds-number flows. [ABSTRACT FROM AUTHOR]

To address the issues of sparse training data, complex entity structures, and uneven entity distribution in the industrial equipment failure domain, this study constructs an industrial equipment failure named entity recognition corpus. Due to the difficulty of character level named entity recognition models in representing the professional vocabulary information in the field of industrial equipment failure, this study proposes a character enhanced industrial equipment failure named entity recognition model to address this problem. In the embedding layer, professional vocabulary information is directly fused between the Transformer layers of ROBERT WWM (Robustly Optimized BERT Pretraining Approach with Whole Word Masking) to allocate word information to each of its constituent characters for enhanced semantics. The global semantic information is obtained through a BiLSTM (Bidirectional Long Short Term Memory), and the CRF (Conditional Random Field) is used to learn the dependency relationship between adjacent labels to obtain the optimal sentence level label sequence. Experimental results demonstrate that the proposed model has good performance on industrial equipment fault named entity recognition tasks, with an average F1 score of 92.403%. [ABSTRACT FROM AUTHOR]

In this paper, three-dimensional nonlinear dynamic finite-element analyses are conducted to examine the effect of soil property variability on the lateral displacement (D) of liquefiable ground reinforced by granular columns. A suite of 20 ground motions is selected from the NGA-West2 database as input. A soil-granular column ground system consisting of an intermediate liquefiable layer is modeled in OpenSees. Both the random variable (RV) and random filed (RF) methods are adopted to model the variability of soil property parameters. Dynamic analyses are then conducted to estimate the earthquake-induced deformation of the soil-granular column system. It is found that modeling the variability of soil parameters based on the RV method generally increases the geometric mean and standard deviation (σlnD) of D for the soil-granular column system. Enlarging the spatial correlation of soil parameters in the RF model brings in a slight increase of the mean D and comparable σlnD values, respectively. Hence, incorporating the spatially correlated soil property parameters may not be necessarily increase the variation of D for the soil-granular column system. Specifically, the statistical distribution of D is more sensitive to the vertical scale of fluctuation rather than the horizontal one. The results presented could aid in addressing the variability issue for performance-based design of granular column-reinforced liquefiable ground in engineering applications. [ABSTRACT FROM AUTHOR]

This study proposes a stochastic method to analyse the propagation of hydraulic fractures affected by layered heterogeneity in rocks in a toughness-dominated regime. The study utilises the phase-field method in the context of two-dimensional finite element analysis to model the hydraulic fracture (HF) propagation in rock materials in laboratory scale. Field data on hydrogeologic properties of some rocks reveal that material heterogeneity may appear in the form of leptokurtic marginal distributions. Generalised sub-Gaussian (GSG) model is capable of capturing physical characteristics of such rocks, and it is employed to stochastically model rocks with layered lithologic heterogeneity by generating a large number of auto- and cross-correlated random fields for hydro-geomechanical properties. To investigate the sensitivity of the cracking response to the inherent characteristics of material heterogeneity, various GSG distribution forms are considered in Monte Carlo (MC) analyses. The HF's deviation from the theoretically predicted direction, which is perpendicular to the direction of the minimum in situ stress, is correlated with the distribution of hydro-geomechanical properties, showing a Gaussian-type distribution. This study concludes that the differential stress and the bedding orientation are the main factors affecting the HF deviation and the required breakdown pressure for initiating the HF propagation from a borehole. In the application of directional hydraulic fracturing (DHF), the effect of bedding layers becomes dominant when the bedding orientation is aligned with the direction of perforations in the boreholes. Highlights: The phase-field theory in the context of finite element method is employed for stochastic analysis of toughness-dominated hydraulic fracture propagation in layered heterogeneous rocks. Generalised sub-Gaussian random fields are generated to model the natural hydromechanical features in heterogeneous rocks. By order of importance, differential stress, i.e. the difference between maximum and minimum in situ stresses, and the bedding orientation are found to be the main factors in specifying the hydraulic fracture propagation path in layered heterogeneous rocks. Hydraulic fracture trajectories from perforated boreholes are mainly influenced by the orientation of the heterogeneous bedding layers with respect to the direction of the maximum in situ stress. [ABSTRACT FROM AUTHOR]

The submarine massive mud transports of several seas around the world, including the Shenhu Sea, are thought to relate with hydrate decomposition, which occur even at small gradients. This article investigates the stability and reliability of marine slopes considering three factors related to hydrate decomposition and earthquake, namely decrease in sediment strength, increase in excess pore pressure, and activation by external seismic force. Taking the SH2 hole, implemented by the first Chinese offshore hydrate drilling mission (GMGS-1), as an example, the impacts of three factors mentioned above on marine slopes stability are analyzed with infinite slope theory. In addition, the space distribution of physical and mechanical parameters in hydrate deposits is under consideration. The cohesion, friction angle and buoyant unit weight in a non-stationary random field are treated as spatial random variables, where the means of the first two increase linearly with depth. Variation characteristics of the failure probability for various slope angles are analyzed under different scenarios. The outcomes reveal that the safety factor of the slopes at 30% potential for damage is around 1.25 within a given variant characterization. The proposed integrated stability and reliability analysis can effectively provide early warning for collapse of marine hydrate-bearing slopes. [ABSTRACT FROM AUTHOR]

To improve slope reliability calculations and address high-nonlinearity in random fields, an AI algorithm, namely Convolutional Neural Network (CNN) with asymmetric convolution is introduced. The method accounts for the interdependence and auto-correlation of soil material and uses Python-based secondary development in ABAQUS Version 6.14 to improve computational efficiency and user-friendliness in finite element simulations. A Cholesky decomposition-based centroid point method is used for random fields to simplify computation. Additionally, an asymmetric convolution-based CNN surrogate model replaces finite element simulations to address challenges such as parameter correlations and random field discretization for improved analysis efficiency. The methodology uses random field samples and safety factors as inputs and outputs for training, which improves predictability and addressing high-dimensional issues. Its effectiveness is demonstrated through case studies involving single-layer undrained saturated clay slopes and double-layer cohesive soil slopes. The results demonstrate the effectiveness of the CNN approach that utilizes asymmetric convolution, with outcomes closely resembling those obtained through finite element simulation. This method demonstrates a 95.8% improvement in time efficiency compared to software-based calculations and a 93.5% enhancement over batch calculations using ABAQUS. These results confirm the effectiveness of the introduced reliability analysis method and the ability to provide accurate results while significantly boosting computational efficiency. [ABSTRACT FROM AUTHOR]

Proper consideration of variations in soil properties and their effects is necessary to enhance the seismic safety of structures. In this study, the effect of spatial variations in the cyclic resistance ratio on seismic ground behavior was investigated. Initially, dynamic centrifuge model tests were conducted on sandy ground featuring a 20% mixture of weak zones with low relative density and on homogeneous sandy ground with no mixture of weak zones. Subsequently, an effective stress analysis was performed by modeling the distribution of weak zones in the centrifuge model tests. Finally, after confirming the validity of the parameter settings, several analytical models with different weak-zone distributions were generated and numerically analyzed using random field theory. The results indicate that a local mixing of approximately 20% weak zones has only a limited effect on overall ground behavior. However, differences were observed in the rate of increase and dissipation of the excess pore water pressure ratio and in the residual horizontal displacement. [ABSTRACT FROM AUTHOR]

For d ≥ 3 , we study the Ising model on Z d with random field given by { ϵ h v : v ∈ Z d } where h v 's are independent normal variables with mean 0 and variance 1. We show that for any T < T c (here T c is the critical temperature without disorder), long range order exists as long as ϵ is sufficiently small depending on T . Our work extends previous results of Imbrie (1985) and Bricmont–Kupiainen (1988) from the very low temperature regime to the entire low temperature regime. [ABSTRACT FROM AUTHOR]

The Global Industry Standard on Tailings Management (GISTM) promotes performance-based approaches in geotechnical assessments. Hence, characterizing the spatial variability of deposited tailings is expected to be a key input for some tailings storage facilities (TSFs); however, it has seldom been investigated. In this study, we assess the spatial variability of thickened and conventional tailings, which have been deposited into the same TSF, providing a unique opportunity to investigate two tailings technologies. A dense array of 15 cone penetration tests (CPTus) with an average offset of 1.5 m has been conducted to collect data. In addition to evaluating the spatial variability, the collected information is also used to assess the potential of machine learning (ML) for detrending when deriving random fields. Using a new proposed stationarity score, we find that an ML-based detrending outperforms traditional procedures for most scenarios. In terms of correlation lengths, we find similar ranges for thickened and conventional tailings (vertical: δwv=0.2–0.6 m , horizontal δwh=1.5–4.5 m) and similar distributions, likely influenced by the depositional processes. In contrast, the variance in the conventional tailings is higher, which we attribute to its segregating nature. Finally, by inspecting previous studies on natural soils, we find that the variability of mine tailings (δwh/δwv=2–21) resembles that observed in alluvial deposits, which we attribute to the parallels in the depositional processes. [ABSTRACT FROM AUTHOR]

Recent studies in earthquake engineering have outlined the difficulty of ground response analyses (GRAs) to replicate the observed ground motion and related variability at borehole array sites. Improvement of the seismic site response estimation requires accounting for and propagating the uncertainties in local soil conditions into surface ground motion. Uncertainties in site conditions arise from a number of factors, which include the uncertainties in the shear-wave velocity (VS) that are mainly caused by the natural spatial variability of soils and rocks. In this paper, a novel VS randomization approach is proposed to propagate the small-scale spatial VS heterogeneities into samples of VS profiles within a nonstationary probabilistic framework, to be further used in one-dimensional (1D) GRAs. The nonstationary approach is based on partitioning a borehole base-case VS profile into several locally stationary layers. The proposed approach was applied at three European sites exhibiting different subsurface soil conditions. Compared with both the classical stationary and an approach from the literature for VS randomization, the proposed approach provides a set of VS profiles fully consistent with the pseudoexperimental site signatures in terms of surface-wave dispersion curves, fundamental and higher-mode resonance frequencies, and site amplification. This paper also outlines the importance of the method used to measure VS profile in both the estimation of depth-dependent variability of VS at a given site and the prediction of site response variability. [ABSTRACT FROM AUTHOR]

Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tackling complex problems for which classical methods might fail. In this respect, deep autoencoders play a fundamental role, as they provide an extremely flexible tool for reducing the dimensionality of a given problem by leveraging on the nonlinear capabilities of neural networks. Indeed, starting from this paradigm, several successful approaches have already been developed, which are here referred to as Deep Learning-based ROMs (DL-ROMs). Nevertheless, when it comes to stochastic problems parameterized by random fields, the current understanding of DL-ROMs is mostly based on empirical evidence: in fact, their theoretical analysis is currently limited to the case of PDEs depending on a finite number of (deterministic) parameters. The purpose of this work is to extend the existing literature by providing some theoretical insights about the use of DL-ROMs in the presence of stochasticity generated by random fields. In particular, we derive explicit error bounds that can guide domain practitioners when choosing the latent dimension of deep autoencoders. We evaluate the practical usefulness of our theory by means of numerical experiments, showing how our analysis can significantly impact the performance of DL-ROMs. [ABSTRACT FROM AUTHOR]

Precise classification of Light Detection and Ranging (LiDAR) point cloud is a fundamental process in various applications, such as land cover mapping, forestry management, and autonomous driving. Due to the lack of spectral information, the existing research on single wavelength LiDAR classification is limited. Spectral information from images could address this limitation, but data fusion suffers from varying illumination conditions and the registration problem. A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type, namely, multispectral point cloud, thereby improving classification performance. However, spatial and spectral information of multispectral LiDAR has been processed separately in previous studies, thereby possibly limiting the classification performance of multispectral LiDAR. To explore the potential of this new data type, the current spatial–spectral classification framework for multispectral LiDAR that includes four steps: (1) neighborhood selection, (2) feature extraction and selection, (3) classification, and (4) label smoothing. Three novel highlights were proposed in this spatial – spectral classification framework. (1) We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood. (2) We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy. (3) We conducted spatial label smoothing by a conditional random field, accounting for the spatial and spectral information of the neighborhood points. The proposed method demonstrated by a multispectral LiDAR with three channels: 466 nm (blue), 527 nm (green), and 628 nm (red). Experimental results demonstrate the effectiveness of the proposed spatial – spectral classification framework. Moreover, this research takes advantages of the complementation of spatial and spectral information, which could benefit more precise neighborhood selection, more effective features, and satisfactory refinement of classification result. Finally, this study could serve as an inspiration for future efficient spatial–spectral process for multispectral point cloud. [ABSTRACT FROM AUTHOR]

We consider the numerical approximation of Gaussian random fields on closed surfaces defined as the solution to a fractional stochastic partial differential equation (SPDE) with additive white noise. The SPDE involves two parameters controlling the smoothness and the correlation length of the Gaussian random field. The proposed numerical method relies on the Balakrishnan integral representation of the solution and does not require the approximation of eigenpairs. Rather, it consists of a sinc quadrature coupled with a standard surface finite element method. We provide a complete error analysis of the method and illustrate its performances in several numerical experiments. [ABSTRACT FROM AUTHOR]

This paper presents a framework for assessing the reliability of fiber reinforced polymer (FRP)–strengthened reinforced concrete (RC) beams in flexure using stochastic nonlinear finite-element (SNFE) analysis and k-w-means clustering, based on active-learning kriging Monte Carlo simulation (AK-MCS), in which spatial variations in the concrete and bond material properties are considered. A computer algorithm was developed to augment commercially available nonlinear finite-element (FE) analysis software and automate the process for conducting the SNFE clustering-based AK-MCS analysis. The k-w-means clustering was based on the U learning function to provide multipoint enrichment to improve convergence of the stopping criteria by allowing parallel computation of the SNFE models. Parametric analysis indicated the accuracy of the reliability prediction of the examined member and proved the efficiency of the proposed analysis in reducing the number of calls to SNFE models compared with data in the existing literature, when using probability-based stopping criteria. Practical Applications: The quality of the FRP-to-concrete bond is affected by the integrity of the concrete at the interface, which varies across the dimensions of the strengthened member, causing added uncertainty in predicting the structural response, and hence the reliability of the FRP-strengthened member. This study proposes a computationally efficient approach to assess the reliability of FRP-strengthened concrete members by considering the spatial variation in the concrete properties (compressive strength, tensile strength, bulk modulus) and the quality of the FRP-to-concrete bond (shear and normal bond strength) by using an adaptive machine-learning technique. The proposed framework may be utilized by engineers to design FRP-strengthening systems for concrete members experiencing variation in the concrete properties due to poor quality control or active deterioration. [ABSTRACT FROM AUTHOR]

We measure the three-dimensional power spectrum (P3D) of the transmitted flux in the Lyman- |$\alpha$| (Ly  |$\alpha$|⁠) forest using the complete extended Baryon Oscillation Spectroscopic Survey data release 16 (eBOSS DR16). This sample consists of |$\sim$| 205 000 quasar spectra in the redshift range |$2\le z \le 4$| at an effective redshift |$z=2.334$|⁠. We propose a pair-count spectral estimator in configuration space, weighting each pair by |$\exp (i\mathbf {k}\cdot \mathbf {r})$|⁠ , for wave vector |$\mathbf {k}$| and pixel pair separation |$\mathbf {r}$|⁠ , effectively measuring the anisotropic power spectrum without the need for fast Fourier transforms. This accounts for the window matrix in a tractable way, avoiding artefacts found in Fourier-transform based power spectrum estimators due to the sparse sampling transverse to the line of sight of Ly  |$\alpha$| skewers. We extensively test our pipeline on two sets of mocks: (i) idealized Gaussian random fields with a sparse sampling of Ly  |$\alpha$| skewers, and (ii) log-normal LyaCoLoRe mocks including realistic noise levels, the eBOSS survey geometry and contaminants. On eBOSS DR16 data, the Kaiser formula with a non-linear correction term obtained from hydrodynamic simulations yields a good fit to the power spectrum data in the range |$(0.02 \le k \le 0.35)\, h\, {\rm Mpc}^{-1}\,$| at the 1–2σ level with a covariance matrix derived from LyaCoLoRe mocks. We demonstrate a promising new approach for full-shape cosmological analyses of Ly  |$\alpha$| forest data from cosmological surveys such as eBOSS, the currently observing Dark Energy Spectroscopic Instrument and future surveys such as the Prime Focus Spectrograph, WEAVE-QSO, and 4MOST. [ABSTRACT FROM AUTHOR]

Context. Fast radio bursts (FRBs) are poised to become important cosmological tools in the near future, as the number of observed FRBs is increasing rapidly with multiple surveys underway. A large sample of FRBs will soon have available dispersion measures (DMs) and rotation measures (RMs), which can be used to study the cosmic baryon density and the intergalactic magnetic field. However, the observed DM and RM of FRBs consists of multiple contributions that must be quantified to estimate the DM and RM of the intergalactic medium (IGM). Aims. In this paper, we estimate one such contribution to DM and RM, namely, of FRB host galaxies. We show how this contribution changes with redshift, galaxy type, and the stellar mass of the galaxies. We also investigate its dependence on galaxy inclination and on an FRB's offset from the center of the galaxy. Methods. Using the TNG50 simulation of the IllustrisTNG project, we selected 16 500 galaxies at redshifts of 0≤ ɀ ≤2, with stellar masses in the range of 9 ≤ log(M*/M⊙) ≤ 12. In each galaxy, we calculated the DM and RM contributions of 1000 sightlines; from these, we constructed the DM and RM probability density functions (PDFs). Results. We find that the rest frame DM distributions of all galaxies at a given redshift can be fitted by a log normal function and its median and width increase as a function of redshift. The rest-frame RM distribution is symmetric, with a median RMhost,rf=0 rad m–2 and it can be fitted by a combination of a Lorentzian and two Gaussian functions. The redshift evolution of the distribution width can be fitted by a curved power law. The parameters of these functions change for different subsets of galaxies with different stellar mass, inclination, and FRB offset. These changes are due to an increasing ne with redshift, SFR, and stellar mass. We do find a more ordered B field at lower ɀ compared to higher ɀ, as suggested by the presence of more galaxies with B field reversals and B fields dominated by random B field at higher ɀ. Conclusions. We estimated the FRB host DM and RM contributions, which can be used in the future to isolate the IGM contribution from the observed DM and RM of FRBs. We predict that to constrain a σRM,IGM of 2 rad m–2 to the 95% confidence level, we would need to observe 95 000 FRBs at ɀ = 0.5, but only 9 500 FRBs at ɀ = 2. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel framework for high-resolution and robust microwave correlation imaging. In order to generate a more diverse random radiation field distribution, the unified random radiation field (URRF) model is proposed. The URRF model can accurately characterize the joint random modulation in the signals' phase, amplitude, and frequency. Furthermore, we build a parametric imaging model based on URRF which clearly describes the relationship between the image to be reconstructed and the signals by the URRF model. By using this imaging model, the reconstruction of an image is converted into solving a multi-parameter optimization problem with multiple constraints. To solve this optimization problem with high efficiency and accuracy, the model-constrained adaptive alternating multiple parameter estimation (MC-AAMPE) algorithm is proposed. This algorithm decomposes the high-dimensional multi-parameter optimization problem into several sub-optimization problems. The renewing solutions to these sub-optimization problems make the multi-parameter optimization converge to the image of the target and the parameters of clutter and noise, which are all unknown before the solution. In comparison with the existing methods, the proposed scheme generates images with higher resolution and is more robust under noise conditions. Extensive simulation experiments confirmed the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Motivated by the papers of Mladenović and Piterbarg (2006), Krajka (2011), and Pereira and Tan (2017), we study the limit properties for the maxima from non stationary random fields subject to missing observations and obtain the weakly convergence and almost sure convergence results for these maxima. Some examples such as Gaussian random fields, chi-random fields, and Gaussian order statistics fields are given to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

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Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical literature retrieval, and intelligent medical question–answering systems, enabling doctors and researchers to obtain the required medical information more quickly and thereby helping to improve the accuracy of diagnosis and treatment decisions. The current methods have certain limitations in dealing with contextual dependencies and entity memory and fail to fully consider the contextual relevance and interactivity between entities. To address these issues, this paper proposes a Chinese medical named entity recognition model that combines contextual dependency perception and a new memory unit. The model combines the BERT pre-trained model with a new memory unit (GLMU) and a recall network (RMN). The GLMU can efficiently capture long-distance dependencies, while the RMN enhances multi-level semantic information processing. The model also incorporates fully connected layers (FC) and conditional random fields (CRF) to further optimize the performance of entity classification and sequence labeling. The experimental results show that the model achieved F1 values of 91.53% and 64.92% on the Chinese medical datasets MCSCSet and CMeEE, respectively, surpassing other related models and demonstrating significant advantages in the field of medical entity recognition. [ABSTRACT FROM AUTHOR]

Geological parameters of soil exhibit spatial variability. Inverse analysis allows the acquisition of accurate spatial distributions of key geological parameters, which is crucial for structural safety assessment. In this study, an ensemble Kalman filter (EnKF) is employed in the context of data assimilation. Random fields are used as the initial input ensembles for the algorithm. The present study effectively integrates the ensemble Kalman filter with the numerical simulation software ABAQUS, enabling the inversion of parameter fields under various operating conditions. An in-house Python code script is developed to control ABAQUS for finite element computations and to obtain observations at target points. During the stepwise computation process, the algorithm can utilize newly acquired observations to accelerate the convergence of the parameter field to the true field. The effectiveness of the algorithm is validated, and the method is applied to a case study of double-tunnel excavation and a stepwise excavation analysis of a three-layered slope. The impact of the number of ensemble members and the ratio of the horizontal correlation scale to the vertical correlation scale of random fields on the effectiveness of updating the parameter field have also been investigated. [ABSTRACT FROM AUTHOR]

Owing to the architected void-filled low-density configurations, metamaterials are prone to defects during the complex manufacturing process, or damages under operational conditions. Recently mechanical cloaking has been proposed to shield the effect of such disorders in terms of homogenized mechanical responses. The major drawback in these studies are that the damage location should be known a priori, and the cloak is designed around that damaged zone before manufacturing. Such postulation does not allow unsupervised damage resilience during the manufacturing and service life of metamaterials by active reconfiguration of the stress field depending on the random and unpredictable evolution of damage. Here, we propose a radically different approach by introducing piezoelectric lattices where the effect of random appearance of any single or multiple disorders and damages with complex shapes, sizes and distributions can be shielded through active multi-physically controlled cloaks by voltage-dependent modulation of the stress fields within the cloaking region. Notably, this can be achieved without breaking periodicity and any additional material in the cloaking region unlike earlier studies concerning mechanical cloaks. The proposed active class of elastic metamaterials will bring a step-change in the on-demand mechanical performance of critically important structural components and unsupervised damage resilience for enhanced durability and sustainability. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 1)'. [ABSTRACT FROM AUTHOR]

In this study, the effects of soil spatial variability on the behaviour of the embankment supported with a combined retaining structure (CRS) were investigated. The numerical model of the CRS embankment was established and validated with the field data. An application programming interface (API) was developed to deal with the data exchanging issue between the numerical model and the spatial variability characterization model. Based on the verified numerical model and the API, the probabilistic analysis with 500 Monte Carlo simulations was automatically computed. Three influencing factors of the retained soil (the mean of the friction angle, the variation of the friction angle and the vertical correlation length of the random field) are analysed by parametric analysis. The results show that the vertical correlation length of the random field is most important in the earth pressure calculation process, while the mean of the friction angle is the factor with least impact. On the whole, the spatial variability of soil properties has minimal impact on the distribution and magnitude of earth pressure behind the retaining structure. [ABSTRACT FROM AUTHOR]

Any pure state |Ψ⟩$|\Psi \rangle$ of a compound system S=(S1,S2)$S=(S_1,S_2)$ with the state space H=H1⊗H2${\cal H}= {\cal H}_1\otimes {\cal H}_2$ determines a kind of covariance operator D̂Ψ$\hat{D}_\Psi$ acting in the Cartesian product H=H1×H2${\bf H}= {\cal H}_1\times {\cal H}_2$. If this operator is positively defined, then it determines a random field valued in H${\bf H}$. In this case compound quantum system S$S$ can be treated as a classical random field system whose configuration space is not tensor, but Cartesian product space. It happens that D̂Ψ≥0$\hat{D}_\Psi \ge 0$ and a subquantum process exists if and only if quantum state |Ψ⟩$|\Psi \rangle$ is not entangled. The technical framework used in this note is already presented by von Neumann. [ABSTRACT FROM AUTHOR]

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks. [ABSTRACT FROM AUTHOR]

This paper presents a recent advancement that transforms the problem of decaying turbulence in the Navier–Stokes equations in 3 + 1 dimensions into a Number Theory challenge: finding the statistical limit of the Euler ensemble. We redefine this ensemble as a Markov chain, establishing its equivalence to the quantum statistical theory of N Fermions on a ring, interacting with an external field associated with random fractions of π. Analyzing this theory in the turbulent limit, where N → ∞ and ν → 0 , we discover the solution as a complex trajectory (instanton) that acts as a saddle point in the path integral over the density of these Fermions. By computing the contribution of this instanton to the vorticity correlation function, we obtain an analytic formula for the observable energy spectrum—a complete solution of decaying turbulence derived entirely from first principles without the need for approximations or fitted dimensionless parameters. Our analysis reveals the full spectrum of critical indices in the velocity correlation function in coordinate space, determined by the poles of the Mellin transform, which we prove to be a meromorphic function. Real and complex poles are identified, with the complex poles reflecting dissipation and uniquely determined by the famous complex zeros of the Riemann zeta function. Universal functions of the scaling variables supersede the traditional turbulent scaling laws (K41, Heisenberg, and multifractal). These functions for the energy spectrum, energy decay rate, and velocity correlation significantly deviate from power laws but closely match the results from grid turbulence experiments [Comte-Bellot and Corrsin, J. Fluid Mech. 48(2), 273–337 (1971); Comte-Bellot and Corrsin, J. Fluid Mech. 25(4), 657–682 (1966)] and recent direct numerical simulation data [Panickacheril John, Donzis, and Sreenivasan, Philos. Trans. A Math. Phys. Eng. Sci. 380(2218), 20210089 (2022)] within experimental error margins. [ABSTRACT FROM AUTHOR]

There has been a long-standing challenge in developing locally stationary Gaussian process models concerning how to obtain flexible partitions and make predictions near boundaries. In this work, we develop a new class of locally stationary stochastic processes, where local partitions are modeled by a soft partition process via predictive random spanning trees that leads to highly flexible spatially contiguous subregion shapes. This valid nonstationary process model knits together local models such that both parameter estimation and prediction can be performed under a unified and coherent framework, and it captures both discontinuities/abrupt changes and local smoothness in a spatial random field. We propose a theoretical framework to study the Bayesian posterior concentration concerning the behavior of this Bayesian nonstationary process model. The performance of the proposed model is illustrated with simulation studies and real data analysis of precipitation rates over the contiguous United States. for this article are available online. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the impact of spatial variability in the subgrade layer on the critical response of pavements and the effectiveness of geogrid reinforcement, employing the random field finite difference analysis (RFFDA). A comprehensive parametric study was conducted to examine the influence of two crucial factors: the coefficient of variation ( COV E ) and scale of fluctuation (SOF) of the subgrade modulus. Further investigation was conducted to uncover the statistical and mechanical mechanisms underlying the impact of subgrade spatial variability with emphasis on the critical strain distributions and their correlation with both the overall modulus and the local spatial variability of the key influence zone. Furthermore, this study explored the influence of subgrade spatial variability on the effectiveness of geogrid in reducing critical strains, considering various placement positions and geogrid moduli. The following main conclusions are drawn: (a) subgrade spatial variability has a substantial amplifying effect on critical pavement strains due to low modulus dominating effect, (b) there exists a worst value of SOF that results in the most unfavorable statistics of critical subgrade strain, (c) the effect of subgrade spatial variability on critical subgrade strain is more pronounced compared to its effect on critical asphalt strain, (d) the mean value of critical subgrade strain in RFFDA can be significantly underestimated when assuming fixed location for the strain, and (e) the effectiveness of geogrid in reducing critical strains is impacted by subgrade spatial variability, with the impact varying with the type of critical strain and geogrid location. Specifically, when placed at the base course–subgrade interface, the ability of geogrid to reduce critical subgrade strain is significantly compromised due to the subgrade spatial variability. [ABSTRACT FROM AUTHOR]

Semantic labeling of very high-resolution remote sensing images (VHRRSI) has emerged as a crucial research area in remote sensing image interpretation. However, challenges arise due to significant variations in target orientation and scale, particularly for small targets that are more prone to obscuration and misidentification. The high interclass similarity and low intraclass similarity further exacerbate difficulties in distinguishing objects with similar color and geographic location. To address this concern, we introduce a self-cascading multiscale network (ScasMNet) based on a fully convolutional network, aimed at enhancing the segmentation precision for each category in remote sensing images (RSIs). In ScasMNet, cropped Digital Surface Model (DSM) data and corresponding RGB data are fed into the network via two distinct paths. In the encoder stage, one branch utilizes convolution to extract height information from DSM images layer by layer, enabling better differentiation of trees and low vegetation with similar color and geographic location. A parallel branch extracts spatial, color, and texture information from the RGB data. By cascading the features of different layers, the heterogeneous data are fused to generate complementary discriminative characteristics. Lastly, to refine segmented edges, fully conditional random fields (DenseCRFs) are employed for postprocessing presegmented images. Experimental findings showcase that ScasMNet achieves an overall accuracy (OA) of 92.74% on two challenging benchmarks, demonstrating its outstanding performance, particularly for small-scale objects. This demonstrates that ScasMNet ranks among the state-of-the-art methods in addressing challenges related to semantic segmentation in RSIs. [ABSTRACT FROM AUTHOR]

In recent years, deep learning technology has developed significantly in the judicial field. More and more scholars are introducing deep learning to solve problems in the judicial field and improve the efficiency of judicial agencies in handling cases. An important task of judicial work is to identify the key elements of complex cases, establish the relationship between entities in the document, accurately grasp the development of the case, provide a basis for the understanding, analysis, and ruling of the case, and increase the interpretability of the case results. However, judging from the actual application situation, the application of deep learning technology in the judicial field is not yet mature and still faces huge challenges. On the one hand, the existing legal text element identification methods only consider the identification of core elements and ignore the basic elements. However, the basic elements contain necessary legal-related information, which is basic case information unique to the legal field and has important reference value for judicial staff to analyze cases. On the other hand, the traditional method of identifying key elements does not consider the contextual semantic relationship and ignores the element semantic information and global semantic information is lost, resulting in poor recognition of key elements of legal text. Therefore, in the identification of key elements of legal texts, not only the core elements and basic elements must be considered, but also the semantic features of sentences and the semantic features of elements must be comprehensively considered, thereby improving the effect of identifying key elements of legal texts and promoting the efficiency of judicial organs in handling cases. Aiming at the problem of identifying the basic elements of legal texts, this paper proposes a model for identifying the basic elements of legal texts based on dynamic representation. This algorithm uses the dynamic representation capability of the BERT model to vectorize text and considers global domain semantic information during the pre-training process to achieve a more comprehensive vector representation. Secondly, the memory component of the bidirectional long short-term memory network is used to integrate all the feature information between long-distance words in the legal text, effectively express the meaning of the word in the context, and realize the recognition of the basic elements of the legal text. In addition, the conditional random field model is used to learn the transfer rules of labels between entities and output the label sequence that best conforms to the actual rules. Finally, the basic element identification model proposed in this article has been significantly improved by comparing it with the current better methods. [ABSTRACT FROM AUTHOR]

This study examines how the variability of the Manning coefficient (n) affects the position of hydraulic jumps downstream of hydraulic structures. Using a robust finite volume method and random field theory, the study investigates the impact of spatial variations in n on hydraulic jump characteristics. Two scenarios are considered: one with a stilling basin and one without. Both one-dimensional and two-dimensional spatial distributions of n are analyzed. The results show that without a stilling basin, there are significant variations in the location of hydraulic jumps in the riverbed. The uncertainty in the location of the hydraulic jump is much higher than the uncertainty in the values of conjugate depths. Additionally, one-dimensional spatial distribution of n leads to higher standard deviations in the estimated location compared to two-dimensional distribution. In scenarios with a stilling basin, increasing riprap length causes the hydraulic jump to move upstream, while standard deviation remains constant. [ABSTRACT FROM AUTHOR]