Field Method Research Articles

Evaluation of the dynamics of nutrients and potentially toxic elements along a major river in Ethiopia using multivariate statistical techniques: Implications of possible co-occurrences

This study utilized established field and laboratory methods and multivariate statistical tools to evaluate the nutrient and potentially toxic elements (PTEs) distribution, identify possible sources, and determine potential co-existence in the Awash River. The levels of soluble reactive phosphorus (SRP), total phosphorus (TP), NO3-N, Mn, Ni, Cr, and Cu generally increased downstream in the upper Awash, with the highest respective values of 0.70, 1.34, 1.19, 1.58, 1.03, 0.85, and 0.11 mg L−1 at the inlet to Lake Koka. Sites found downstream of the inflow of Lake Beseka and Metehara town showed the highest levels of Fe, B, Zn, and dissolved silica. Principal components and correlation analysis findings revealed strong associations (r ≥ 0.80, p < 0.05) between nutrient parameters (SRP, TP, and NO3-N) and PTEs (Cu, Ni, Mn, and Cr), implying common origins and potential co-occurrences. The potential co-occurrences of these parameters may strengthen their potential individual impacts due to complex interactions.

Grain structure control of TC11 alloy in laser direct energy deposition by a static magnetic field

This paper describes a novel in-situ adjustment method of static magnetic field (SMF) for laser additive manufacturing, which realizes the change of grain morphology and weave structure without changing the composition and performing post-processing. The TC11 alloy was prepared by laser direct energy deposition (LDED) under static SMF. The results show that a transverse static SMF of 0.5 T effectively suppresses the tendency of columnar crystals to grow continuously along the build direction and the dominant weaving direction of <0001> texture, and reduces the anisotropy of the TC11 alloy.

A Curriculum-Style Self-Training Approach for Source-Free Semantic Segmentation.

Source-free domain adaptation has developed rapidly in recent years, where the well-trained source model is adapted to the target domain instead of the source data, offering the potential for privacy concerns and intellectual property protection. However, a number of feature alignment techniques in prior domain adaptation methods are not feasible in this challenging problem setting. Thereby, we resort to probing inherent domain-invariant feature learning and propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. In particular, we introduce a curriculum-style entropy minimization method to explore the implicit knowledge from the source model, which fits the trained source model to the target data using certain information from easy-to-hard predictions. We then train the segmentation network by the proposed complementary curriculum-style self-training, which utilizes the negative and positive pseudo labels following the curriculum-learning manner. Although negative pseudo-labels with high uncertainty cannot be identified with the correct labels, they can definitely indicate absent classes. Moreover, we employ an information propagation scheme to further reduce the intra-domain discrepancy within the target domain, which could act as a standard post-processing method for the domain adaptation field. Furthermore, we extend the proposed method to a more challenging black-box source model scenario where only the source model's predictions are available. Extensive experiments validate that our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions datasets.

Oscillatory responses of a spatially random seabed in the vicinity of structures: Validation and application of the integrated CFD-SFEM

A comprehensive examination of the wave-induced oscillatory response of seabeds around structures is of great significance for ensuring the safe operation of marine engineering projects and enhancing the efficiency of marine resource development. Soil properties in nature exhibit spatial variability due to various geological processes, which should be considered in seabed stability analysis. An integrated CFD-SFEM is proposed for spatially heterogeneous seabeds, incorporating multi-physical solvers for nonlinear wave motion and poroelastic seabed response within a unified framework through a one-way coupling procedure. The wave sub-model for nonlinear fluid flow is based on RANS equations, with an internal wave-maker and absorbing layers realized by employing a momentum source function and damping source terms. The spatially heterogeneous seabed sub-model is based on Biot’s poroelastic theory and the random field method. We have implemented the integrated model and automated the iterative algorithm for MCS using MATLAB codes, assisted by the LiveLink platform. The proposed method has been validated from various perspectives and applied to a study of soil response around a partially buried pipeline to demonstrate its practical utility. This study offers a novel framework and perspective for the probabilistic evaluation of oscillatory responses in spatially varied seabeds surrounding structures.

Data-driven model assessment: A comparative study for ship response determination

Several machine learning approaches to determine ship responses via data-driven models have been applied. Input features and parameters used relied on time-series analyses obtained from computational-fluid-dynamics approach. As inputs into the data-driven model, the heave and pitch motions of a ship advancing in irregular, i.e., natural seaway were considered. By using different models in the framework of machine learning, required computation for the associated ship motions may be avoided, thus reducing the computational effort to forecast ship motions. Comparative predictions with numerical simulations revealed that the deep-neural-network method for training in auto-machine-learning instructions yielded the highest accuracy in heave motion, resulting in a non-normalized mean-absolute-error of 0.74, against the corresponding error of 1.07 from numerical computations, whereas the method trained with the tree-based models (the extreme gradient boosting and the Hist gradient boosting regressor) predicted less accurate motions for the tested ship. The model trained with the random forest regressor exhibited an error of 1.10. Numerical simulation based on a field method proved to be the most suitable choice for pitch motion. Despite the few samples available to train the regressors, results demonstrated that the measured data was sufficient to assess the developed data-driven model for ship response determination.

A nonlocal macro-meso-scale damage model based modeling for crack propagation in ferroelectric materials

The accurate simulation of crack growth for ferroelectric materials plays a crucial role in the application of ferroelectric materials in electronic devices. In recent years, a nonlocal macro-meso-scale consistent damage (NMMD) model has been proposed for simulating the crack propagation in brittle materials, which offers the advantage of higher efficiency compared to the phase-field method. Different from the phase field method, this model does not need to solve the phase field equation at the same time. Therefore, the degree of freedom in solving can be reduced, thereby reducing the computational workload. In this paper, the authors proposed a new integration strategy for microscopic damage and devised a simple and efficient implementation of the NMMD model for the modelling of quasi-static fracture in the general purpose commercial software developer, COMSOL Multiphysics based on the finite element method (FEM) and studied the crack propagation for ferroelectric materials under the applied stress and electric fields. Different from the original NMMD model, only the tensile stress induced geometric damage is accounted for crack propagation by using the decomposition of elastic strain energy. The effects of different crack-face conditions, electrical boundary conditions and the electromechanical loading for crack growth of ferroelectric materials have been considered in our FEM simulation are discussed in this work.

Application of the River Habitat Survey Method in the Assessment of the Human Pressure Within the Lowland River Catchment: The Mollusc Biodiversity Versus Habitat Features

The objectives of our study were to analyse the degree of human pressure within the lowland river catchment in relation to the mollusc communities and to assess the usefulness of the River Habitat Survey as a field method in determining the human pressure in the mollusc biodiversity context. The River Habitat Survey (RHS), an essential method for hydromorphological studies of rivers under the requirements of the European Union Water Framework Directive, was applied. This study showed that the diversity of molluscs was impacted by several environmental factors acting simultaneously, including pH, concentration of ammonium nitrogen in water, and the habitat features depending on the degree of human pressure on the river. The result of the RHS method confirmed that the occurrence of molluscs including Unio crassus and Pseudanodonta complanata, the endangered species on a global scale, was associated with the extensive presence of several natural habitat features in the river channel. The RHS method proved to be an indispensable tool for assessing the relationships between the diversity of aquatic organisms and the degree of habitat anthropogenic modification of river environments. It seems innovative and necessary, especially in restoring the natural character of rivers.

Optimal control of the gradient systems

In this paper we consider the optimal control problems governed by the gradient systems for the Ginzburg-Landau free energy where denotes a potential function and ε is the diffusivity. One example of gradient systems are the Schlögl equation arising in chemical waves with a quartic potential function F(y). Gradient systems are characterized by energy decreasing property . Numerical integrators that preserve the energy decreasing property in the discrete setting are called energy or gradient stable. It is known that the implicit Euler method is first order unconditionally energy stable method. The only second order unconditionally energy stable method is the average vector field (AVF) method. We discretize the gradients systems by discontinuous Galerkin method in space and by AVF integrator in time. We solve optimal control problems for the Schlögl equation with traveling and spiraling waves using sparse and regularized controls .

Underpricing in Indonesia: A Systematic Literature Review

This study employs the charting field method to empirically examine the evolution of underpricing research in Indonesia over the past decade (2014-2023). The research is categorized into four main areas: the development of underpricing studies in Indonesia, theoretical mapping, measurement mapping of underpricing, and causal relationship mapping. The findings reveal that underpricing research in Indonesia has advanced over the last ten years, albeit with fluctuations in the number of publications each year. The three most commonly applied theories in this field are signaling theory, information asymmetry theory, and agency theory. Initial return is the most frequently used measure of underpricing, accounting for 79% of the studies. Additionally, the causal relationship mapping highlights that many variables produce inconsistent results, indicating a research gap that future scholars can address. This study lays the groundwork for further exploration of the variables that influence underpricing, particularly by investigating underwriter reputation as a moderating factor. The research has practical implications for various stakeholders: Initial Public Offering (IPO) companies can use the findings to design more effective strategies, investors can rely on them to guide their investment decisions, and regulators can leverage the insights to develop policies that enhance transparency in the IPO process.

A flamelet-based Eulerian transported PDF method for the modeling and simulation of supersonic combustion

This paper presents a high-efficiency and high-fidelity approach to model supersonic combustion using the extended flamelet-generated manifold (FGM) and the Eulerian transported probability density function (PDF), also known as the Eulerian stochastic fields (ESF) method. The efficiency benefits from the FGM, where the compressibility effects induced by shock waves are considered using two extra control variables, i.e., the pressure (p) and the absolute internal energy of the oxidizer (Eox), in addition to the mixture fraction (Z) and progress variable (Yc) in traditional flamelet tables. The joint PDF for these control variables is modeled using transported PDF based on the ESF method. The ESF method enhances accuracy in the prediction of turbulence-chemistry interactions, avoiding complexity induced by the presumed PDF in the flamelet table and ad-hoc presumed and independent joint PDF assumptions, e.g., the typical presumed β-PDF for Z and δ-PDF for Yc. This FGM-ESF method is tested in large eddy simulations of two canonical hydrogen supersonic flames: a strut-stabilized hydrogen supersonic flame (DLR case) and a transverse hydrogen jet flame in a high-enthalpy incoming flow (Stanford case). For both cases, results show that including the compressibility effects in the FGM table is essential for properly describing the flame behaviors near shock waves. The sub-grid PDF of control variables significantly influences near-wall and shear-layer combustion, and the ESF method demonstrates superior performance in predicting the near-wall reaction zone and the shear-layer reaction zone compared to the perfectly micro-mixed sub-grid model (δ-PDF) for the Stanford case. This study marks the first application of the FGM-ESF approach with a Z-Yc-Eox-p FGM table to simulate supersonic flames, offering a novel perspective for future modeling efforts in this domain.Novelty and Significance StatementThe novelty of this research lies in the development and application of an efficient computational approach that for the first time couples the extended flamelet-generated manifold (FGM) method with the Eulerian stochastic fields (ESF) to simulate supersonic flames. This combined FGM-ESF method incorporates pressure and oxidizer energy as additional control variables in the FGM tabulation to account for compressibility effects. The ESF is used to describe the sub-grid scale joint probability density function without relying on complex presumed functions. Application to a canonical strut-stabilized hydrogen supersonic flame and transverse hydrogen jet flames in a high-enthalpy incoming flow demonstrate the superior predictive capabilities of the FGM-ESF method in capturing flame dynamics. The comparison between four-dimensional and two-dimensional FGM tables provides new insights into the importance of compressibility effects for these configurations.

Investigation of oxygen transport in porous transport layer with different porosity gradient configurations using phase field method

Minimizing oxygen accumulation in the porous transport layer (PTL) is crucial for reducing mass transfer losses in proton exchange membrane (PEM) electrolyzer. This study develops a two-dimensional transient model of gas-liquid two-phase flow at the anode of PEM electrolyzer using the phase field method. The model investigates the mechanisms of oxygen transport and the interactions among various oxygen paths in PEM electrolyzer. We explore the impact of porosity gradient configurations in the PTL and the presence of a surface microporous layer (MPL) on oxygen transport. The findings indicate that for PTL with an average porosity of 60%, forward gradient configuration—where porosity increases from the catalyst layer (CL) towards the channel (CH)—promotes the merging of bubble sites and path contraction, thereby reducing oxygen saturation. The optimal gradient configuration, with porosities of 50% at the CL and 70% at the CH, achieves a 29.5% reduction in oxygen saturation. Conversely, reverse gradient configuration, with decreasing porosity from CL to CH, results in increased oxygen saturation. The addition of surface MPL further lowers oxygen saturation and shortens oxygen breakthrough time; smaller MPL particle sizes correspond to lower oxygen saturation and shorter breakthrough times. This study provides valuable insights for the optimal design of PTL structures in PEM electrolyzers.

Analysis of CO2 bubble growth detachment kinetics in direct methanol fuel cell flow channels

CO2 bubbles flow in the anode flow channel is an important issue in the commercialization process of direct methanol fuel cells (DMFC). A T-channel model is built in COMSOL using the phase field method to investigate the CO2 bubbles flow at the anode side of the DMFC. The factors and mechanisms of single bubble detachment are discussed by analyzing the methanol inlet velocity (Ul), CO2 inlet velocity (Ug), contact angle of the diffusion layer, and the Weber number during bubble detachment. The research findings indicate that increasing the liquid flow rate leads to the generation of smaller bubbles that detach more rapidly due to the increases of drag force (FD) and the shear-life force (FSL) to overcome the surface tension on the bubble. The CO2 inlet velocity can promote the bubble detachment due to the increase in FSL, but also leads to a larger detachment diameter. Compared to hydrophobic surfaces, hydrophilic surfaces are more conducive to bubble detachment and removal. In all case We (Weber number) is significantly less than 0.6, indicating that liquid momentum dominated the bubble detachment process. Once the ratio of the gas momentum to the liquid one is greater than 1, the bubble is hard to detach. The contour map of bubble flow patterns and the bubble detachment diameters distribute with the ratio of Ug/Ul can further indicate that the bubble detachment is connected with the ratio of Ug/Ul closely, which will have guiding significance for the selection of inlet velocity of DMFC.

Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia

This study developed neural network potentials (NNPs) specifically tailored for pure ethylene and ethylene-ammonia blended systems for the first time. The NNPs were trained on a dataset generated from density functional theory (DFT) calculations, combining the computational accuracy of DFT with a calculation speed comparable to reactive force field methods. The NNPs are employed in reactive molecular dynamics simulations to explore the thermal decomposition reaction mechanisms of ethylene and ammonia. The simulation results revealed that adding ammonia reduces the activation energy for ethylene decomposition, thereby accelerating ethylene consumption. Furthermore, the addition of ammonia uncovers a new reaction pathway for hydrogen radical consumption, which reduces the occurrence of H-abstraction reactions from ethylene by hydrogen radicals. The inhibition effect of ammonia addition on soot formation mainly acts in two aspects: on the one hand, ammonia decomposition products react with carbon-containing species, ultimately producing C1N products, thereby decreasing the carbon numbers involved in soot formation. This significantly reduces the concentrations of C5C9 molecules and key polycyclic aromatic hydrocarbons (PAHs) precursors like C2H2 and C3H3. On the other hand, ammonia promotes the ring-opening reactions of six-membered carbon rings at high-temperature conditions, thereby reducing the formation of PAHs precursors. The results show that with the addition of ammonia, six-membered carbon rings tend to convert into seven-membered carbon rings at lower temperatures, while at higher temperatures, they are more likely to transform into three- and five-membered carbon rings. These variations in the transformation of six-membered carbon rings may also affect soot formation. The insights gained from understanding these fundamental chemical reaction mechanisms can guide the development of ethylene-ammonia co-firing systems.

Interband and intraband transitions, as well as charge mobility in driven two-band model with electron-phonon coupling.

Interband and intraband transitions are fundamental concepts in the study of electronic properties of materials, particularly semiconductors and nanomaterials. These transitions involve the movement of electrons between distinct energy states or bands within a material. In addition, charge mobility is also a critical parameter in materials science and electronics. A thorough understanding of these transitions and mobility is critical for the development and optimization of advanced electronic and optoelectronic devices. In this study, we investigate the influence of external periodic drivings on interband and intraband transitions, as well as charge mobility, within a driven two-band model that includes electron-phonon coupling. These external periodic drivings can include a periodic laser field, a time-varying magnetic or electric field, or an alternating current voltage source. We have developed the Floquet surface hopping and Floquet mean field methods to simulate electronic dynamics under various drivings in both real and reciprocal spaces. Our findings demonstrate that periodic drivings can enhance interband transitions while suppressing intraband transitions. In addition, charge mobility is restrained by these external periodic drivings in the driven two-band model.

The Effects of Parliamentary Debate as a Pedagogy for Argumentation in L1 and L2 Contexts

AbstractDebates have long been an effective educational method in various fields, including argumentation education. In debates in which participants are divided into affirmative and negative sides, engagement in argumentation is heightened. Numerous studies have attempted to demonstrate this, but the question of whether participation in debates leads to increased argumentativeness, or whether individuals who engage in debates inherently have a higher orientation towards argumentation, remains unresolved. In the present study, debates were conducted for 15 weeks, and argumentativeness was measured using pre-and post-tests. The results confirmed that debate participation increases argumentativeness. Furthermore, while previous research has mostly focused on argumentativeness in the first language, the present study deepens the relationship between argumentativeness in both first and second languages. Specifically, focusing on Japan, where the first language (Japanese) is high-context and the second language (English) is low-context, this research clarifies the relationship between argumentativeness in the first and second languages, as well as the impact of debate.

Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization.

Generalizing real-world data has been one of the most difficult challenges for application of machine learning (ML) in practice. Most ML works focused on improvements in algorithms and feature representations. However, the data quality, as the foundation of ML, has been largely overlooked, also leading to the absence of data evaluation and processing methods in ML fields. Motivated by the challenge and need, we selected an important but difficult reorganization energy (RE) prediction task as a test platform, which is an important parameter for the charge mobility of organic semiconductors (OSCs), to propose a data-quality-navigated strategy with chemical intuition. We developed a data diversity evaluation based on structure characteristics of OSC molecules, a reliability evaluation method based on prediction accuracy, a data filtering method based on the uncertainty of K-fold division, and a data split technique by clustering and stratified sampling based on four molecular descriptor-associated REs. Consequently, a representative RE data set (15,989 molecules) with high reliability and diversity can be obtained. For the feature representation, a complementary strategy is proposed by considering the chemical nature of REs and the structure characteristics of OCS molecules as well as the model algorithm. In addition, an ensemble framework consisting of two deep learning models is constructed to avoid the risk of local optimization of the single model. The robustness and generalization of our model are strongly validated against different OSC-like molecules with diverse structures and a wide range of REs and real OSC molecules, greatly outperforming eight adversarial controls. Collectively, our work not only provides a quick and reliable tool to screen efficient OSCs but also offers methodological guidelines for improving the generalization of ML.

A multi-physics dual-phase field model for chloride-induced localized corrosion process and cracking in reinforced concrete

Corrosion-induced deterioration poses a significant threat to the serviceability of reinforced concrete (RC) structures. This study develops a comprehensive mesoscale model utilizing dual-phase field methods to capture the entire time-dependent chloride-induced corrosion and cracking mechanisms, incorporating interactive multi-physics. The model accurately delineates the evolution of corrosion morphology and cracking patterns, focusing on their interdependent interactions within localized areas through detailed electro-chemo-mechanical processes, from steel dissolution to precipitation. The robustness of the proposed numerical method is confirmed through alignment with previously reported experimental results. Extensive parametric studies investigate the impacts of various geometric and material characteristics on corrosion processes. Furthermore, the model’s applicability to RC configurations with multiple reinforcements is also demonstrated. These findings offer pivotal insights into the substantial correlations between rebar corrosion and concrete cracking across multi-physics fields, with their coupling driven by the variable corrosion current density.

WPS-Dataset: A Benchmark for Wood Plate Segmentation in Bark Removal Processing

Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of deep learning methods in the application field of wood plate bark removal equipment, a benchmark for wood plate segmentation in bark removal processing is proposed in this study. Firstly, a costumed image acquisition device is designed and assembled on bark removal equipment to capture wood plate images in real industrial settings. After data filtering, enhancement, annotation, recording, and partitioning, a benchmark dataset named the WPS-dataset containing 4863 images was constructed. The WPS-dataset is evaluated by training six typical semantic segmentation models. The experimental results show that the models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. The WPS-dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field.

Stochastic analysis of dynamic fracture of concrete using CT-image based mesoscale models with a rate-dependent phase field method

Concrete structures are commonly exposed to dynamic loads spanning a wide range of strain rates, and the inherent mesoscale heterogeneities complicate stochastic dynamic fracture mechanisms even more. This work develops a numerical framework using mesoscale concrete models based on micro computed tomography (CT) images to investigate such mechanisms with meaningful stochastic analyses. A rate-dependent phase field model is proposed to characterise the dynamic initiation and propagation of cracks by incorporating both micro-viscosity and macroscopic viscoelasticity, which is described by two standard Maxwell elements with different relaxation times to consider a wide range of strain rates. Moreover, the viscoelastic constitutive relation is formulated in the full strain space, which allows for a spectral decomposition of the strain tensor to determine the effective damage driving force, thus effectively addressing the issue of compressive fracture. A numerical implementation scheme is developed by combining user-defined element and material subroutines in ABAQUS/Explicit solver. Extensive Monte Carlo simulations of dynamic tension up to a strain rate of 200 s−1 are performed with statistical analyses. This work reveals the intricate dynamics associated with mesoscale heterogeneities and identifies the critical transition state at 20 s−1. The transition is characterised by changing modes of fracture patterns, stress wave propagation, and load-carrying capacities. A new TDIF–strain rate–standard deviation relation is also proposed and aligns well with the increasing dispersion of experimental data. The relationship between void content and tensile strength reflects the formation characteristics of crack networks, with the void content exhibiting a positive correlation with the TDIF from 20 s−1 to 100 s−1.

An adaptive mesh refinement algorithm for crack propagation with an enhanced thermal–mechanical local damage model

This paper presents a computationally effective approach for crack propagation under mechanical and thermal loads based on an adaptive mesh refinement (AMR) approach tailored for our recently developed enhanced local damage model. The mesh-dependent issue encountered in the classical local theories is effectively mitigated by incorporation of fracture energy and element characteristic length into the damage evolution function. Our previous research has demonstrated that being equipped by a novel equivalent strain derived from the bi-energy norm concept and a new damage criterion recently proposed by Mazars et al., the model provides results comparable to the reference experimental data as well as other numerical models based on non-local/gradient damage and phase field method. In the framework of computational efficiency using finite elements, we significantly enhance the performance of our enhanced local model by considering adaptive mesh refinement (AMR). The finite element mesh is locally refined in the damaged zone, and the mesh refinement is conducted on-the-fly during the analysis. For that purpose, the damage parameter whose information is stored at integration points is selected as an indicator to mark whether an element should be refined or not after every loading step. For quadrilateral element mesh, a quad-tree technique is utilized, meaning that each marked element is further divided into four smaller quadrilateral elements. The so-called hanging nodes appear during the process, and the elements are thus treated as n-gons and are constructed by the Laplace shape functions, instead of the usual Lagranges shape functions. To show the accuracy and effectiveness of the proposed scheme, several numerical examples involving homogeneous and heterogeneous materials are studied. In these examples, the damage is induced either by only mechanical loads or by both mechanical and thermal loads.