Accurate Model Research Articles

Omnidirectional Energetic Electron Fluxes From 150 to 20,000 km: An ELFIN‐Based Model

AbstractThe strong variations of energetic electron fluxes in the Earth's inner magnetosphere are notoriously hard to forecast. Developing accurate empirical models of electron fluxes from low to high altitudes at all latitudes is therefore useful to improve our understanding of flux variations and to assess radiation hazards for spacecraft systems. In the present work, energy‐ and pitch‐angle‐resolved precipitating, trapped, and backscattered electron fluxes measured at low altitude by Electron Loss and Fields Investigation (ELFIN) CubeSats are used to infer omnidirectional fluxes at altitudes below and above the spacecraft, from 150 to 20,000 km, making use of adiabatic transport theory and quasi‐linear diffusion theory. The inferred fluxes are fitted as a function of selected parameters using a stepwise multivariate optimization procedure, providing an analytical model of omnidirectional electron flux along each geomagnetic field line, based on measurements from only one spacecraft in low Earth orbit. The modeled electron fluxes are provided as a function of ‐shell, altitude, energy, and two different indices of past substorm activity, computed over the preceding 4 hr or 3 days, potentially allowing to disentangle impulsive processes (such as rapid injections) from cumulative processes (such as inward radial diffusion and wave‐driven energization). The model is validated through comparisons with equatorial measurements from the Van Allen Probes, demonstrating the broad applicability of the present method. The model indicates that both impulsive and time‐integrated substorm activity partly control electron fluxes in the outer radiation belt and in the plasma sheet.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Research on the Transparency of Civil and Commercial Arbitration from the Perspective of Generative Artificial Intelligence

The emergence of GPT marks a significant progress in natural language processing in the field of generative artificial intelligence. Artificial intelligence has great application potential and value in legal retrieval, document review and production, arbitrator selection, and auxiliary arbitration decision-making. However, there is a contradiction between the development of artificial intelligence and the principle of arbitration confidentiality. Training a more accurate artificial intelligence model requires a large amount of diverse data, while the confidentiality of arbitration limits the acquisition and sharing of data. Therefore, in order to give full play to the potential of artificial intelligence, it is necessary to gradually increase the transparency of arbitration and achieve a balance between arbitration confidentiality and transparency through reasonable institutional design and legal norms.

A data-driven maximum entropy method for probability uncertainty analysis based on the B-spline theory

The probability density function (PDF) is quite important for structural reliability analysis; thus, accurate PDF modeling methods draw increasing attention. This paper proposes a novel metaheuristic data-driven paradigm of the maximum entropy method (MEM) based on the B-spline function theory. Firstly, a B-spline proxy of the MEM PDF is proposed for probability uncertainty analysis. We derive the parameter calculation formulation and calculate the undetermined parameters via the raw data of structural responses. Then, to determine the knots of the B-spline functions, we propose a novel data-driven approach with the aid of a powerful metaheuristic algorithm and the response data information. Different from the traditional MEM, the proposed method is a complete data-driven solution approach and does not involve the statistical moment calculation and the nonlinear equations composed of statistical moments. Combining the advantages of the B-spline theory and the MEM, the proposed method can reconstruct the response PDF with a complex shape, such as the PDF with multiple peaks or heavy tails. For verification, two numerical examples and one engineering example are analyzed, and compared with some classical PDF modeling methods. The results show that the proposed method is superior to the compared methods in terms of computational accuracy, when the same sample data is used.

Assessing Turbulence Model Performance in OpenFOAM for Natural Convection Simulations

Abstract Natural convection processes are pivotal in various engineering applications, necessitating accurate and reliable computational fluid dynamics (CFD) models for their simulation. This study evaluates the efficacy of different turbulence models implemented in OpenFOAM for a natural convection scenario, aiming to identify the most suitable model for capturing complex thermal and fluid dynamic behaviors. We compared several turbulence models, including the k − ε, k − ω, and k − ω SST, using a benchmark thermal convection case. Our methodology involved setting up the simulations to reflect realistic thermal gradients and boundary conditions, followed by a rigorous analysis of temperature distribution, vertical velocity profiles, and computational efficiency. The results indicate that the k − ω SST achieved the lowest averaged root mean square error (RMSE) values for temperature (0.076) and vertical velocity (0.03) while also requiring the fewest convergence iterations (314) compared to k − ε (411) and k − ω (1474). These findings demonstrate that the k − ω SST model is a suitable compromise between accuracy and computational cost for engineering applications. This study underscores the importance of selecting an appropriate turbulence model in OpenFOAM to enhance the accuracy of natural convection simulations, which can significantly influence design and safety considerations in engineering systems.

Study on the parameter identification of lithium-ion battery model under high-rate pulse discharge conditions

With the rapid development of the new energy sector and lithium-ion (Li-ion) battery technologies, using Li-ion batteries with high energy density and high discharge rate to form the primary energy subsystem can further improve the compactness and miniaturization level of pulsed power systems. In traditional electric power systems, Li-ion batteries normally operate under the discharge condition of low current and long time. However, as the main power source of pulsed power systems, it is required that Li-ion batteries should have the capability for high-rate pulse discharge. To ensure the safe and reliable operation of the battery energy storage system, it is necessary to conduct the parameter identification of the Li-ion battery model to establish an accurate pulse discharge model under such working conditions. This paper focuses on the discharge characteristics of a cylindrical high-rate single Li-ion battery with a capacity of 3 Ah. At a 20 C rate, the discharge data were obtained when the pulse repetition frequency was 10 Hz and the duty cycle was 90%. By using the mathematical expressions of the Thevenin equivalent circuit model, the recursive relationship of the circuit parameters was obtained, and the parameter identification of the battery model was conducted based on the experimental data by using the genetic algorithm. The results show that the fitting accuracy of the parameter identification reaches 0.9995, indicating the high precision of the model for the Li-ion battery. This work provides a significant reference for the modeling and parameter identification of Li-ion batteries under high-rate pulse discharge conditions.

Lung nodule classification using radiomics model trained on degraded SDCT images

Background and ObjectiveLow-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challenges, we propose a novel approach using synthetic LDCT images generated from standard-dose CT (SDCT) scans from the LIDC-IDRI dataset. Our objective is to develop and validate an interpretable radiomics-based model for distinguishing likely benign from likely malignant pulmonary nodules. MethodsFrom a total of 1010 CT images (695 SDCTs and 315 LDCTs), we degraded SDCTs in the sinogram domain and obtained 1950 nodules as the training set. The 675 nodules from the LDCTs were stratified into 50%-50% partitions for validation and testing. Radiomic features were extracted from nodules, and three feature sets were assessed using: a) only shape and size (SS) features, b) all features but SS features, and c) all features. A systematic pipeline was developed to optimize the feature set and evaluate multiple machine learning models. Models were trained using degraded SDCT, validated and tested on the LDCT nodules. ResultsTraining a logistic regression model using three SS features yielded the most promising results, achieving on the test set mean balanced accuracy, sensitivity, specificity, and AUC-ROC scores of 0.81, 0.76, 0.85, and 0.87, respectively. ConclusionsOur study demonstrates the feasibility and effectiveness of using synthetic LDCT images for developing a relatively accurate radiomics-based model in lung nodule classification. This approach addresses challenges associated with LDCT screening, offering potential implications for improving lung cancer detection and reducing false positives.

Determination of Submerged Breakwater Efficiency Using Computational Fluid Dynamics

Wind-induced waves can lead to the partial or complete wash-over of beaches, causing erosion that impacts both the landscape and tourist infrastructure. In some regions of the world, e.g., Croatia, this process, which usually occurs during a harsh winter, has a major impact on the environment and the economy, and preventing or reducing this process is highly desirable. One of the simplest methods to reduce or prevent beach erosion is the use of innovative underwater structures designed to decrease wave energy by reducing wave height. In this study, submerged breakwaters are numerically investigated using various topologies, positions, and angles relative to the free surface. Not only is the optimal topology determined, but the most efficient arrangement of multiple breakwaters is also determined. The advantage of newly developed submerged breakwaters over traditional ones (rock-fixed piers) is that they do not require complex construction, massive foundations, or high investment costs. Instead, they comprise simple floating bodies connected to the seabed by mooring lines. This design makes them not only cheap, adaptable, and easy to install but also environmentally friendly, as they have little impact on the seabed and the environment. To evaluate wave damping effectiveness, the incompressible computational fluid dynamics (ICFD) method is used, which enables the use of a turbulence model and the possibility of accurate wave modelling.

A study on hot deformation behaviors and microstructures of 10 vol% SiCP/Al-Cu-Mg-Ag composite prepared by spray co-deposition and extrusion

Isothermal compression test is used to investigate the hot deformation behavior of spray co-deposited and extruded 10 vol% SiCP/Al-Cu-Mg-Ag composite. In this study, an accurate strain-compensated model based on Arrhenius constitutive equation is constructed with the corresponding R value of 0.9965 and AARE of 4.05%. The experimental results are in high agreement with the predicted flow stress. Hot deformation activation energy of the SiCP/Al composite (161.685 kJ/mol) is notably higher than that of Al-Cu-Mg-Ag alloy, which is attributed to the dislocations introduced by SiC reinforcement. The microstructure evolution of compressed specimens is characterized using EBSD and TEM. DRX, including CDRX and DDRX, is the main softening mechanism under the hot deformation conditions of high temperature, low strain rate and high strain. The softening mechanism transition from DRV to DRX is due to the subgrains consuming dislocations and then transforming into DRX grains. Comparing the microstructure under different hot deformation parameters, the recrystallization degree is significantly influenced by temperature, followed by strain rate and strain. In addition, the recrystallization process is impeded by the primary Al2Cu phases, T phases and SiC particles in the composite through hindering the migration of dislocations.

Simulation-based analysis of welding process parameters for TC4ELI titanium alloy thick-walled cylinders

Abstract In this paper, ABAQUS is used to numerically simulate the residual stresses during welding of titanium alloy thick-walled cylinders. A detailed simulation analysis of the stress field during the welding process is performed by establishing an accurate finite element model, defining the thermal physical properties, boundary conditions, and heat source model, and using the indirect heat-force coupling method. The results of the study show that the distribution of the weld stress field has an important influence on weld quality, material properties, and structural strength. The movement of the welding heat source results in a dynamic change in the stress field, which gives rise to a significant stress concentration at and in the vicinity of the welding heat source.

DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

A new weight setting method for estimating ODR of sampling oscilloscope time base jitter

The orthogonal distance regression algorithm is used to estimate the sampling oscilloscope timebase jitter. Starting from an accurate variance model, a new method to obtain accurate weight is studied. This method can obtain good timebase jitter calculation results when the traditional weight allocation method of the orthogonal distance regression algorithm fails. The proposed approach was validated using sine signals with different sampling frequencies and jitter conditions, and the results showed that the new weight method is effective.

Early death prediction model for breast cancer with synchronous lung metastases: an analysis of the SEER database.

Breast cancer with lung metastases (BCLM) is a serious condition that often leads to early death. This study aims to screen the risk factors of early death in BCLM patients and establish a simple and accurate nomogram prediction model. Identifying prognostic markers and developing accurate prediction models can help guide clinical decision-making. The Surveillance, Epidemiology, and End Results (SEER) database was used to analyze a sizable sample of data, encompassing 4,238 BCLM patients diagnosed between 2010 and 2015. Stepwise regression was used to manage the collinearity of variables and to construct a prediction model based on the histogram. The results were subjected to internal validation and contrasted with those of related investigations. Of the 4,238 BCLM patients in this study, 3,232 did not die early. Of the 1,006 premature deaths, 891 were cancer specific. Lymph node involvement, tumor size, age, and race were all recognized as prognostic markers for premature mortality. A nomogram was constructed based on these factors to reliably predict cancer-specific death and early all-cause death. This study gives new insights into the prognosis of individuals with BCLM and finds critical prognostic variables for early mortality. The created nomogram might assist physicians in identifying individuals at high risk of early mortality and making treatment options.

MResGat: Multi-head Residual Dilated Convolution Assisted Gated Unit Framework for Crop Yield Prediction

The importance of predicting crop yields has increased due to growing concerns of surrounding food security. Early forecasting of crop yields holds a pivotal role in avoiding starvations by estimating the food supply available for the expanding global population. Several Deep Learning (DL) and Machine Learning (ML) techniques are involved to develop effective and accurate crop yield prediction model. Nevertheless, existing models faces some limitations such as less accuracy, high error rate because of noisy data, high training time and extracted less effective features for prediction. To overcome these issues, the novel DL methodology is introduced for attaining high accurate crop yield prediction. Initially, the soil, weather and other resources big data are collected from the various agriculture field. In data collection phase, the input data of larger size are stored in the Hadoop platform for the purpose of storing as well as processing the entire data in a distributed manner. The data are pre-processed through the utilization of Missing value imputation and z-score based data normalization. From the pre-processed data, the optimal features are considered using Integrated Correlation Random recursive elimination (InCorRe) approach. Based on the previous soil and weather information, the suitable yield of crops can be predicted using Multi head Residual dilated convolution assisted gated unit (MResGat) model. Finally, the losses of the network model can be optimized using African vulture optimization algorithm (AVO). The proposed method is evaluated using the several performance metrics, which achieved 0.023% of MSE value and 0.036% of MAE values.

Koopman Modeling for Optimal Control of the Perimeter of Multi-region Urban Traffic Networks

The purpose of perimeter control is to regulate the transfer flow between the perimeters of the urban traffic network, so that the vehicle aggregation in each region is maintained at a desired level. It is regarded as one of the most important methods to solve traffic congestion in urban road networks. Accurate mathematical modeling of perimeter controlled traffic dynamics remains a challenge due to its high nonlinearity and uncertainty. Machine learning methods have been used to learn traffic dynamic models for perimeter control. However, these existing techniques lack generalization and interpretability. In this paper, we propose a Koopman modeling approach (i.e., a two-stage method) that uses a new eigenfunction of the Koopman operator based on a novel L0 norm approximation to construct an interpretable finite-dimensional approximation of the Koopman operator, which is a linear operator that describes how eigenfunctions evolve along the trajectory of a specific nonlinear dynamical system. The main advantage of utilizing the Koopman operator is that it can characterize the nonlinear system in a global linear lifted feature space. Furthermore, an algorithm based on the Koopman modeling method and model predictive control is proposed for real-time perimeter control of urban road networks. Some experiments are carried out to demonstrate the effectiveness of the proposed algorithm.

A voxel-based 3D indoor model to support 3D pedestrian evacuation simulations

Three-dimensional (3D) indoor models enable realistic simulation of pedestrian evacuations in 3D space. However, existing indoor models do not fully identify navigable spaces located above/below physical components (e.g., chairs, stairs, desks), and the connections between these spaces and their navigable surfaces remain underexplored. These limitations hinder the accurate modelling of 3D pedestrian motions during evacuations. To address the shortcomings, we present novel automatic approaches to create a voxel-based 3D indoor model. First, based on different types of 3D motions, indoor spaces are automatically identified as freely navigable spaces, navigable spaces under conditions and non-navigable spaces. Then, navigable surfaces are identified to simulate moving above/below in 3D space. Vertical links are identified to connect the spaces and surfaces at different heights. A 3D pathfinding approach is subsequently developed to validate the model’s effectiveness. Finally, we evaluate the created model’s accuracy, robustness and efficiency across different voxel sizes. The primary findings are: 1) as voxel size increases, accuracy decreases, robustness changes and efficiency improves; 2) with very small voxel sizes, identification becomes challenging, while larger voxel sizes prevent some spaces and vertical links from being identified; and 3) indoor spaces with complex geometries require more time for identification. Our key contributions are: 1) an approach for automatically identifying indoor spaces for 3D motions; 2) two approaches for automatically identifying navigable surfaces and vertical links; and 3) a 3D pathfinding approach considering distinct 3D motion costs. This work advances 3D modelling to facilitate more realistic evacuation simulations.

Electron-only Reconnection and Ion Heating in 3D3V Hybrid-Vlasov Plasma Turbulence

We perform 3D3V hybrid-Vlasov simulations of turbulence with quasi-isotropic, compressible injection near ion scales to mimic the Earth’s magnetosheath plasma, and investigate the novel electron-only reconnection, recently observed by NASA’s Magnetospheric Multiscale mission, and its impact on ion heating. Retaining electron inertia in the generalized Ohm's law enables collisionless magnetic reconnection. Spectral analysis shows a shift from kinetic Alfvén waves to inertial kinetic Alfvén and inertial whistler waves near electron scales. To distinguish the roles of inertial scale and gyroradius (d i and ρ i), three ion beta (β i = 0.25, 1, 4) values are studied. Ion-electron decoupling increases with β i, as ions become less mobile when the injection scale is closer to ρ i than d i, highlighting the role of ρ i in achieving an electron magnetohydrodynamic regime at sub-ion scales. This regime promotes electron-only reconnection in turbulence with small-scale injection at β i ≳ 1. We observe significant ion heating even at large β i, with Q i/ϵ ≈ 69%, 91%, and 96% at β i = 0.25, 1, and 4, respectively. While ion heating is anisotropic at β i ≤ 1 (T i,⊥ > T i,∥), it is marginally anisotropic at β i > 1 (T i,⊥ ≳ T i,∥). Our results show ion turbulent heating in collisionless plasmas is sensitive to the separation between injection scales (λ inj) and ρ i, β i, and finite-k ∥ effects, necessitating further investigation for accurate modeling. These findings have implications for other collisionless astrophysical environments, like high-β plasmas in intracluster medium, where processes such as microinstabilities or shocks may inject energy near ion-kinetic scales.

Automatic modeling of the current state components of ancient buildings based on 3D deformation algorithm

AbstractAncient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model.

Measuring Lens Focal Length in Lens Characteristics Experiments through Science Process Skills

Abstract This research aims to measure the focal distance of a lens using practical lens properties. The specifications of this practical tool are made from durable acrylic material, light and easy to use. The lens nature practice equipment consists of a light source in the form of an incandescent lamp, lens path, image capture screen, arrow diaphragm, power supply, strong positive lens (+100), weak positive lens (+50) and negative lens (-50). In this practical, the light source, object, lens and screen are placed parallel. Measurements are carried out through scientific process skills including observation, classification, measurement, prediction, communication and drawing conclusions which begin with observing the image produced by the lens using three different object-to-screen distances, namely 40 cm, 60 cm and 80 cm. In addition to determining the focal distance, experiments with positive lenses produce real, inverted and magnified images. Meanwhile, in a negative lens, the image is virtual and upright. In the positive strong lens experiment, the focal distances measured at distances L = 80 cm, 60 cm, and 40 cm respectively produced average focus values of 10.53 cm, 10.49 cm, and 9.95 cm with values The standard error is 0.123 and the R square value is 0.928, indicating a fairly accurate model. In the weak positive lens experiment, the focal distances measured at distances L = 80 cm, 60 cm, and 40 cm respectively produced average values of 4.42 cm, 4.58 cm, and 4.34 cm with standard values The error is 0.0018 and the R square value is 0.9423, indicating a very precise model. In the negative lens experiment, the focal distances measured at distances L = 80 cm, 60 cm, and 40 cm respectively produced average values of 9.57 cm, 9.47 cm, and 8.95 cm with standard error values of 0.08 and an R square value of 0.967, indicating a very strong model in explaining data variations. This is in accordance with the theory of lens properties, namely that converging lenses generally have the ability to form real images, while diverging lenses often produce virtual images

Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning

Accurate modeling of methane (CH4) and sulfide (H2S) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML) framework (Micro-ML), which integrates microbial process representations from mechanistic models (microbial information) with ML models. Results indicate that Micro-ML model enhanced predictions of CH4 and H2S production, where microbial information provides more information for model optimization. The feature importance of microbial information performed comparable weightings for 58.12 % and 55.16 %, respectively, but their relative significance in influencing Micro-ML model performance varies considerably. The application of Micro-ML performed great potential in reducing CH4 and H2S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH4 and H2S predictions but also offers a valuable tool for effective management strategies for sewer systems.