Field Method Research Articles

Multi-Scale Spatial Attention-Based Multi-Channel 2D Convolutional Network for Soil Property Prediction.

Visible near-infrared spectroscopy (VNIR) is extensively researched for obtaining soil property information due to its rapid, cost-effective, and environmentally friendly advantages. Despite its widespread application and significant achievements in soil property analysis, current soil prediction models continue to suffer from low accuracy. To address this issue, we propose a convolutional neural network model that can achieve high-precision soil property prediction by creating 2D multi-channel inputs and applying a multi-scale spatial attention mechanism. Initially, we explored two-dimensional multi-channel inputs for seven soil properties in the public LUCAS spectral dataset using the Gramian Angular Field (GAF) method and various preprocessing techniques. Subsequently, we developed a convolutional neural network model with a multi-scale spatial attention mechanism to improve the network's extraction of relevant spatial contextual information. Our proposed model showed superior performance in a statistical comparison with current state-of-the-art techniques. The RMSE (R²) values for various soil properties were as follows: organic carbon content (OC) of 19.083 (0.955), calcium carbonate content (CaCO3) of 24.901 (0.961), nitrogen content (N) of 0.969 (0.933), cation exchange capacity (CEC) of 6.52 (0.803), pH in H2O of 0.366 (0.927), clay content of 4.845 (0.86), and sand content of 12.069 (0.789). Our proposed model can effectively extract features from visible near-infrared spectroscopy data, contributing to the precise detection of soil properties.

The dynamical analysis of simplicial SAIS epidemic model with awareness programs by media

In this paper, we propose an SAIS epidemic model with awareness programs by media on higher-order networks to study the impact of media and higher-order networks on disease transmission, in which the simplicial complex is utilized to construct a social network that describes connections between nodes, where a single link can connect more than two individuals. We use the cumulative density of awareness programs to quantify the impact of media and use the Microscopic Markov Chains method to get the probability evolution equation of each node. In order to solve the computational difficulties caused by the excessively high dimension of the equation, we use the mean field method to reduce the dimension of the equation to get the equilibrium of the system. Through dynamical analysis, we obtain the threshold of disease outbreak and conditions for the existence and stability of the disease-free equilibrium and the endemic equilibrium. The numerical simulations of SAIS model prove that the mean field method has good predictive performance. We also obtain the influence of parameters on threshold and system dynamical behavior through sensitivity analysis. The results show that: on one hand, the introduce of 2-simplex increases the probability of disease transmission in healthy populations, and the system appears discontinuous transition and bistable coexistence of health state and disease epidemic state, on the other hand, awareness programs by media can increase the threshold of disease outbreak, decrease the final density of infected individuals and inhibit the explosive growth of disease, thus effectively control the spread of diseases.

A mixed-element phase field method for the fracture analysis of beams

The prediction of fracture in thin structures is crucial for the safety assessments in various fields of engineering. Modeling methods such as the phase-field method typically assume damage to be constant across the thickness of structural models like beams, plates and shells which, especially in load cases coupling stretching and bending, is an imperfect approximation. In this contribution, fracture behaviors along the thickness direction of Euler–Bernoulli beam considering axial deformation are addressed with a phase-field modeling approach. To achieve this, a mixed-element beam model is introduced, which combines 1D beam elements representing the displacement field with 2D rectangular elements describing a crack phase-field. The nonlinear coupled governing equations are solved using a staggered scheme. The transfer of variables between the coupled fields occurs at the quadrature points which in turn need to have corresponding geometric locations. Numerical examples ranging from the problems of fracture in standalone beams to lattice structures show the applicability of the approach, and detailed comparisons with a standard 2D model confirm its accuracy and efficiency.

High order energy-preserving method for the space fractional Klein–Gordon-Zakharov equations

The space fractional Klein–Gordon-Zakharov equations are transformed into the multi-symplectic structure system by introducing new auxiliary variables. The multi-symplectic system, which satisfies the multi-symplectic conservation, local energy and momentum conservation, is discretizated into the semi-discrete multi-symplectic system by the Fourier pseudo-spectral method. The second order multi-symplectic average vector field method is applied to the semi-discrete system. The fully discrete energy preserving scheme of the space fractional Klein–Gordon-Zakharov equation is obtained. Based on the composition method, a fourth order energy preserving scheme of the Riesz space fractional Klein–Gordon-Zakharov equations is also obtained. Numerical experiments confirm that these new schemes can have computing ability for a long time and can well preserve the discrete energy conservation property of the equations.

Partitioned least squares

Linear least squares is one of the most widely used regression methods in many fields. The simplicity of the model allows this method to be used when data is scarce and allows practitioners to gather some insight into the problem by inspecting the values of the learnt parameters. In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result. We show that the new formulation is not convex and provide two alternative methods to deal with the problem: one non-exact method based on an alternating least squares approach; and one exact method based on a reformulation of the problem. We show the correctness of the exact method and compare the two solutions showing that the exact solution provides better results in a fraction of the time required by the alternating least squares solution (when the number of partitions is small). We also provide a branch and bound algorithm that can be used in place of the exact method when the number of partitions is too large as well as a proof of NP-completeness of the optimization problem.

Biplanar coils design for spatial nonlinear magnetic fields using an enhanced target field method

The demand for spatial nonlinear magnetic fields (SNMF) spans diverse domains, encompassing medical applications like drug transportation and aerospace applications such as attitude control. Existing designs of magnetic field coils predominantly focus on uniform and gradient fields as the complexity of nonlinear magnetic field distribution often leads to intricate solution processes. This paper presents a novel approach to coil design, addressing the challenge of SNMF distribution by reconstructing the characteristic matrix within the traditional target field method (TFM). To solve the overdetermined equations involved, we incorporate an improved particle swarm optimization algorithm into the TFM framework. A pair of symmetrical and asymmetrical coils are designed and the effectiveness of this method is verified by comparing the simulated and experimental results.

A Modified Near Field Method for Measuring the Refractive Index Profile and Geometrical Parameters of Multi-Core Fibers

A Modified Near Field Method for Measuring the Refractive Index Profile and Geometrical Parameters of Multi-Core Fibers

A novel method for highly linear gradient coil design based on stream function.

Magnetoencephalography (MEG) and magnetic resonance imaging (MRI) are non-invasive imaging techniques that offer effective means for disease diagnosis. A more straightforward and optimized method is presented for designing gradient coils which are pivotal parts of the above imaging systems. A novel design method based on stream function combining an optimization algorithm is proposed to obtain highly linear gradient coil. Two-dimensional Fourier expansion of the current field on the surface where the coil is located and the equipotential line of the expansion term superposition according to the number of turns of the coil are used to represent the coil shape. Particle swarm optimization is utilized to optimize the coil shape while linearity and field uniformity are used as parameters to evaluate the coil performance. Through this method, the main parameters such as input current distribution region, coil turns, desired magnetic field strength, expansion order and iteration times can be combined in a given solution space to optimize coil design. Simulation results show that the maximum linearity spatial deviation of the designed bi-planar x-gradient coil compared with that of target field method is reduced from 14% to 0.54%, and that of the bi-planar z-gradient coil is reduced from 8.98% to 0.52%. Similarly, that of the cylindrical x-gradient coil is reduced from 2% to 0.1%, and that of the cylindrical z-gradient coil is reduced from 0.87% to 0.45%. The similar results are found in the index of inhomogeneity error. Moreover, it has also been verified experimentally that the result of measured magnetic field is consist with simulated result. The proposed method provides a straightforward way that simplifies the design process and improves the linearity of designed gradient coil, which could be beneficial to realize better magnetic field in engineering applications.

A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Floating nuclear power plants (FNPPs) have the potential to supply abundant electricity, hot water, and freshwater resources for offshore islands and offshore oil and gas extraction platforms. However, the consequences of a nuclear reactor accident can be extremely serious. In addition, there may be many uncertain factors in the actual operation of FNPPs. These factors lead to higher structural safety requirements. So， developing more scientific structural monitoring methods is an effective way to enhance the structural safety of FNPP. Traditional hull monitoring schemes can only track bending and torsional forces on specific cross-sections, failing to identify the overall stress field of the entire vessel structure. The paper proposes a reconstruction method for the stress field, bending moment distribution, and wave parameters of FNPP using regular wave identification. Initially, irregular waves are decomposed into a linear combination of multiple regular waves with different amplitudes and frequencies. Subsequently, the strains induced by these regular waves on the FNPP are utilized as basic functions to establish a mathematical model for reconstructing the stress field. The stress field, bending moment distribution, and wave parameters of the FNPP can be reconstructed by identifying the combination coefficients of the regular wave loads. The ill-posedness of the mathematical model for stress field reconstruction has the potential to significantly impact the accuracy of the stress field reconstruction, which was addressed by optimizing the sensor installation position in this study. Meanwhile, the optimization of the sensor installation position greatly reduces the dimensionality of mathematical models. Finally, a numerical study, i.e. FNPP being designed by China, was used to validate the accuracy of the proposed stress field reconstruction algorithm. The reconstructed stress field of the FNPP closely matched the original stress distribution under various irregular wave conditions.

The dressing field method for diffeomorphisms: a relational framework

The dressing field method (DFM) is a tool to reduce gauge symmetries. Here we extend it to cover the case of diffeomorphisms. The resulting framework is a systematic scheme to produce Diff(M) -invariant objects, which has a natural relational interpretation. Its precise formulation relies on a clear understanding of the bundle geometry of field space. By detailing it, among other things we stress the geometric nature of field-independent and field-dependent diffeomorphisms, and highlight that the heuristic ‘extended bracket’ for field-dependent vector fields often featuring in the covariant phase space literature can be understood as arising from the Frölicher-Nijenhuis bracket. Furthermore, by articulating this bundle geometry with the covariant phase space approach, we give a streamlined account of the elementary objects of the (pre)symplectic structure of a Diff(M) -theory: Noether charges and their bracket, as induced by the standard prescription for the presymplectic potential and 2-form. We give conceptually transparent expressions allowing to read the integrability conditions and the circumstances under which the bracket of charge is Lie, and the resulting Poisson algebras of charges are central extensions of the Lie algebras of field-independent ( diff(M) ) and field-dependent vector fields.We show that, applying the DFM, one obtains a Diff(M) -invariant and manifestly relational formulation of a general relativistic field theory. Relying on results just mentioned, we easily derive the ‘dressed’ (relational) presymplectic structure of the theory. This reproduces or extends results from the gravitational edge modes and gravitational dressings literature. In addition to simplified technical derivations, the conceptual clarity of the framework supplies several insights and allows us to dispel misconceptions.

Pengaruh Citarasa Pada Roti Gembong Gembul Terhadap Keputusan Pembelian Konsumen

Gembong Gembul Bread, a typical snack from East Kalimantan, offers a soft texture with a unique appearance and various flavor and topping variants. The research was conducted through field methods with a quantitative approach, using questionnaires and statistical tests. The results showed that the taste of Gembong Gembul Bread has a positive influence on consumer purchasing decisions, although the level of relationship between the two variables is relatively low. The majority of consumers choose this product after seeing it directly in the store or based on recommendations from friends or social media. However, there are other factors that also influence consumer purchasing decisions by 88.9% which were not investigated in this study. Therefore, further research is needed to understand other factors that also influence consumer purchasing decisions more comprehensively

A LOCAL PATH PLANNING ALGORITHM FOR AVOIDING OBSTACLES IN THE FRENET FRAME

This paper presents a local path planning algorithm in the coordinate system of the roadbed. The algorithm is based on varying initial trajectory points using the potential field method and ensuring the smooth resulting path in a new coordinate system. This algorithm is executed by minimizing an objective functional. The problem is solved with application to path planning for an unmanned transport platform: it is necessary to change the vehicle’s global smooth trajectory points in real time while maintaining smoothness and avoiding emerging obstacles. Compared to the Cartesian coordinate system, the new coordinate system is advantageous in terms of the execution time of the algorithm. The algorithm is implemented in Python. With a planning horizon being specified, this approach can be combined with various path-following algorithms having no obstacle avoidance methods. Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions

This paper presents the Quantum Gramian Angular Field (QGAF) method, which integrates quantum computing with deep learning to enhance forecasting in time series analysis. This method effectively converts stock return time series data into a format compatible with Convolutional Neural Network (CNN) training. Our empirical tests, conducted on stock market data from China, Hong Kong, and the United States, demonstrate that QGAF significantly outperforms the traditional GAF approach in accuracy. These findings underscore the potential of combining quantum computing and deep learning in financial time series forecasting.

Landau free energy of small clusters beyond mean-field approach.

Landau free energy determines the landscape of order parameter fluctuations that occur in a physical system at thermal equilibrium and, in particular, characterizes the critical phenomena. We propose a semianalytical approach based on the fluctuating local field method, which allows us to estimate Landau free energy for small clusters with discrete (Ising model) and continuous (Heisenberg model) order parameter.

Probabilistic progressive damage modeling of hybrid composites

A novel analytical probabilistic progressive damage model (PPDM) is introduced for multiphase composites to predict the damage behavior of hybrid composites. The PPDM is based on effective field methods and the stochastic nature of fiber damage is captured by including weakest link theory and Weibull statistics. Three additional models are developed to compare with the PPDM. A stochastic model analogous to the PPDM (called SPDM), and two finite element models, one stochastic (SFEM) and one probabilistic (PFEM). All models are developed in a thermodynamically consistent framework and are extended to include residual thermal stresses. Finally, the four models are compared with models from the open literature for an AS4-M50S hybrid carbon–carbon composite with different hybridization ratios of high to low elongation fibers. The comparison reveals a great agreement between all models and indicates that the stochastic nature of fiber damage is the most influential parameter leading to damage.

Design of strategies to position the tourist attractions of the parish of Cacha, canton Riobamba

The Cacha parish, located in Riobamba, Ecuador, has great tourism potential that has not yet been exploited. The objective of this study is to design positioning strategies for its tourist attractions through the implementation of effective branding. A mixed qualitative and quantitative approach was used for the research. A survey of 383 inhabitants of Riobamba was conducted to analyze tourism knowledge and preferences. Data were collected through documentary and field methods, including surveys, interviews and observation. The findings revealed that 67.1% of the respondents were unaware of Cacha's tourist attractions and 76% had not seen related publicity. However, 93% expressed willingness to visit. The conclusion is that the lack of knowledge and effective promotion are the main obstacles to tourism development in Cacha. For which strategies are proposed such as: creation of brand identity, development of promotional materials, implementation of advertising campaigns in local and digital media to increase the visibility of Cacha as a tourist destination, participation in tourism fairs and events. The implementation of these branding strategies will contribute to effectively position Cacha as an attractive tourist destination, increasing the number of visitors and generating economic benefits for the local community.

A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model

The impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns. Monitoring and prediction of electric and magnetic fields have become critical tasks for researchers. In this paper, we propose a granularity data method based on T–S (Takagi–Sugeno) fuzzy model, named fuzzy rule-based model, which utilizing finite rules that are determined by the deviations between the predicted values and the true values after the data goes through a granulation-degranulation mechanism, to predict the intensity of power frequency electric field and electromagnetic field. A series of experiments show that fuzzy rule-based models have better robustness and higher prediction accuracy in comparison with several existing prediction models. The improvement of the performance of the fuzzy rule-based model quantified in terms of Root Mean Squared Error is 20.86%, 51.91%, 62.28%, 65.10%, and 71.92% in comparison with that of the Ridge model, Lasso model, and a family of support vector machine model with different kernel functions, including linear kernel (SVM-linear), radial basis function (SVM-BRF), polynomial kernel (SVM-polynomial) respectively, on the electromagnetic field testing data, and 37.42%, 55.16%, 58.79%, 59.28%, 64.27% lower than that of the Ridge model, Lasso model, SVM-linear model, SVM-BRF model and SVM-polynomial model on the power frequency electric field testing data.

Quantum Computation of Conical Intersections on a Programmable Superconducting Quantum Processor.

Conical intersections (CIs) are pivotal in many photochemical processes. Traditional quantum chemistry methods, such as the state-average multiconfigurational methods, face computational hurdles in solving the electronic Schrödinger equation within the active space on classical computers. While quantum computing offers a potential solution, its feasibility in studying CIs, particularly on real quantum hardware, remains largely unexplored. Here, we present the first successful realization of a hybrid quantum-classical state-average complete active space self-consistent field method based on the variational quantum eigensolver (VQE-SA-CASSCF) on a superconducting quantum processor. This approach is applied to investigate CIs in two prototypical systems─ethylene (C2H4) and triatomic hydrogen (H3). We illustrate that VQE-SA-CASSCF, coupled with ongoing hardware and algorithmic enhancements, can lead to a correct description of CIs on existing quantum devices. These results lay the groundwork for exploring the potential of quantum computing to study CIs in more complex systems in the future.

Modeling fracture in multilayered teeth using the finite volume-based phase field method

The present work, utilizing the finite volume-based phase field method (FV-based PFM), aims to investigate the initiation and propagation of cracks in the second molar of the left mandible under occlusal loading. By reconstructing cone beam computed tomography scans of the patient, the true morphology and internal mesostructure of the entire tooth are implemented into numerical simulations, including both 2D slice models and a realistic 3D model. Weibull functions are introduced to represent the tooth's heterogeneity, enabling the stochastic distribution characteristics of mechanical parameters. The results indicate that stronger heterogeneity leads to greater crack tortuosity, uneven damage distribution, and lower fracture stress. Additionally, different cusp angles (50° and 70°) and pre-existing fissure morphologies (i.e., U-shape, V-shape, IK-shape, I-shape, and IY-shape) also significantly affect the mechanical performance of the tooth. The study reveals that different cusp angles affect the location of crack initiation. Overall, this work demonstrates the utility of the FV-based PFM framework in capturing the complex fracture behavior of teeth, which can contribute to improved clinical treatment and prevention of tooth fractures. The insights gained from this study can inform the design of dental crown restorations and the optimization of cusp inclination and contact during clinical occlusal adjustments.

Simulation and analysis of dam leakage inlet detection based on pseudo-flow field method

Seepage hazards in dams remain a critical issue in flood control engineering. The pseudo-flow field method has shown high efficacy in detecting seepage points within dam structures. This paper delves into the core characteristics of the pseudo-flow field method, aiming to accelerate its detection speed in practical engineering contexts. By employing the Laplace equation for electric current, we established a numerical model to simulate the method's application in real-world scenarios. The simulation reveals the key characteristics of potential and current density distributions along measuring lines within the pseudo-flow field. Both horizontal and vertical measuring lines show a peak distribution of current density and potential near the seepage point, gradually diminishing towards the periphery. Conversely, vertical measuring lines further from the seepage point exhibit minimal changes, presenting a uniform distribution of current density and potential. The results confirm that the pseudo-flow field method can effectively detect seepage locations and that strategic placement of measuring lines can markedly improve detection speed.