Accurate Model Research Articles

Estimation of Understory Fine Dead Fuel Moisture Content in Subtropical Forests of Southern China Based on Landsat Images

The understory fine dead fuel moisture content (DFMC) is an important reference indicator for regional forest fire warnings and risk assessments, and determining it on a large scale is a critical goal. It is difficult to estimate understory fine DFMC directly from satellite images due to canopy shading. To address this issue, we used canopy meteorology estimated by Landsat images in combination with explanatory variables to construct random forest models of in-forest meteorology, and then construct random forest models by combining the meteorological factors and explanatory variables with understory fine DFMC obtained from the monitoring device to (1) investigate the feasibility of Landsat images for estimating in-forest meteorology; (2) explore the feasibility of canopy or in-forest meteorology and explanatory variables for estimating understory fine DFMC; and (3) compare the effects of each factor on model accuracy and its effect on understory fine DFMC. The results showed that random forest models improved in-forest meteorology estimation, enhancing in-forest relative humidity, vapor pressure deficit, and temperature by 50%, 34%, and 2.2%, respectively, after adding a topography factor. For estimating understory fine DFMC, models using vapor pressure deficit improved fit by 10.2% over those using relative humidity. Using in-forest meteorology improved fits by 36.2% compared to canopy meteorology. Including topographic factors improved the average fit of understory fine DFMC models by 123.1%. The most accurate model utilized in-forest vapor pressure deficit, temperature, topographic factors, vegetation index, precipitation data, and seasonal factors. Correlations indicated that slope, in-forest vapor pressure deficit, and slope direction were most closely related to understory fine DFMC. The regional understory fine-grained DFMC distribution mapped according to our method can provide important decision support for forest fire risk early warning and fire management.

Urban drainage efficiency evaluation and flood simulation using integrated SWMM and terrain structural analysis.

A method for evaluating urban drainage system efficiency and simulating pluvial flooding is presented, incorporating rainwater inlet limitations through an integrated SWMM and terrain structural analysis (the corrected model). The corrected model is calibrated using data from two flood events and compared to an overflow model (non-corrected, without considering inlet restrictions) under the same conditions to assess its performance. The results show that the relative error of the flood peak in simulations ranges from 0.27 % to 2.80 %, while the Nash efficiency coefficient ranges from 0.3184 to 0.9504, indicating reasonable accuracy and significant comparability to the overflow model. Drainage efficiency evaluation reveals significant variation among drainage units, decreasing with increasing rainfall until surface depressions fill, after which surface drainage improves notably. This highlights the critical role of surface drainage in overall system efficiency and flood dynamics. Furthermore, two types of urban storm-induced floods are identified: infiltration excess and saturation excess floods. Modeling all floods as saturation excess may lead to substantial misestimations, stressing the need for accurate modeling that accounts for drainage unit characteristics. Understanding urban flood mechanisms is essential for improving modeling accuracy and optimizing drainage system design and flood management strategies.

House Price Pridiction Using Machine Learning

This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This proposed model considers as the most accurate model used for calculating the house price and provides a most accurate prediction. This provides a brief introduction which will be needed to predict the house price. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Predicting the price of a house helps for determine the selling price of the house in a particular region and it help people to find the correct time to buy a home. In this task on House Price Prediction using machine learning, our task is to use data to create a machine learning model to predict house prices in the given region. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to improve the efficiency of the model. for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset

Optimization of global gravitational potential models with detailed gravity anomalies in Southern VietnamOptimization of global gravitational potential models with detailed gravity anomalies in Southern Vietnam

This study evaluates the accuracy of several global gravity field models, specifically EGM 2008, EICGEN-6C4, SGG-UGM-1, SGG-UGM-2, and GECO, using detailed gravity anomaly data from 1968 points in the southern plains of Vietnam. The evaluation process followed a rigorous methodology to ensure precision. Results indicate that these models exhibit similar accuracy levels in the experimental area, with a mean error of approximately 6.2 mGal. Among them, The SGG-UGM-2 model, demonstrated the highest precision, with a fluctuation range of -29.2425 to +74.9010 mGal. These newer generation models, which incorporate additional satellite data, offer enhanced reliability compared to the EGM2008 model. The fluctuation ranges for other models also varied: EGM 2008 from -29.8662 to +82.2140 mGal, EIGEN-6C4 from -28.3626 to +74.2292 mGal, SGG-UGM-1 from -28.9500 to +75.5315 mGal, and GECO from -30.5641 to +73.6775 mGal. This research highlights the critical importance of accurate gravity field models for various applications, including geodesy, geophysics, natural disaster management, resource exploration, and military operations. The methodologies employed for accuracy assessment are adaptable to other regions with similar datasets, underscoring the significance of precise geophysical measurements in Vietnam's diverse geographic landscape. The findings support the essential role of gravity anomaly data in understanding Earth's physical properties and emphasize the need for continued evaluation across different regions in Vietnam to establish comprehensive applicability. This research paves the way for advanced studies and practical applications, contributing to a deeper understanding of Earth's physical properties.

A new biomechanical approach to cranial suture function: the role of contact elements in linear and nonlinear models

Abstract Understanding cranial sutures and how they relieve and dissipate stress is essential to assess their role in cranial biomechanics and to develop highly accurate predictive models. This involves examining how ontogeny affects cranial sutures, as well as their morphology and function, and how these changes through time may impact essential biomechanical loadings such as chewing or direct biting. In this work, we study the cranial sutures of Crocodylus niloticus in detail using contact elements under finite element analysis. Contact elements permit the creation of a physical relationship between two bones that are in contact and even the configuration of these relationships, for example, in terms of movement or flexibility. The definition of bone contacts may require linear and/or nonlinear computational solutions to attain higher accuracy. Herein, skull geometry is tested to determine how bones may be altered by different types of contacts under various conditions. As predicted, the absence of sutures or cranial kinesis leads to a reduction in stress distribution across the skull, whereas sutures and cranial kinesis help the skull relieve stress and prevent certain bones from sustaining high stress levels. The type of contact used in individual sutures has a significant effect on model outcomes. Additionally, feeding behaviors significantly impact cranial biomechanics, reflecting the influence of other variables that may be applied to the models. As highlighted by the results, in order to obtain accurate results when analyzing fossil taxa, the nature of the cranial sutures should be taken into account. Therefore, developing predictive models based on living taxa is invaluable, because it facilitates the study of extinct taxa for which there is a lack of information on the fibrous joints due to poor or no preservation in the fossil record.

Development of an FEM for the Combined Electromagnetic and Hydraulic Forming Process Based on Experimental Data

Electrohydraulic forming (EHF) which demonstrates reduced bouncing effect, formation in narrow areas, and no effect on the electrical conductivity of the blank can overcome the shortcomings of deep drawing and electromagnetic forming. However, considerable time is involved in evaluating the possibility of forming a specific part through experiments. Developing an accurate finite element model can reduce the opportunity costs of an experiment by reducing unnecessary trial and error in forming a specific part. In this study, the chamber, die, and blank components of the EHF experimental equipment in our laboratory were reverse-modeled using CATIA V5R18. Subsequently, the IGES format of the components was imported into LS-DYNA R12, and an FEM model to simulate the EHF experiment was constructed. The experimental and simulation results of nine cases, based on the SUS430 material, input voltage, and blank thickness, were compared for model verification. The forming results for all cases in the constructed finite element analysis model nearly matched the experimental results. Moreover, the linear increase in the blank thickness with input voltage and thickness was simultaneously confirmed. In a computing environment using a 4.3 GHz, 24-Core CPU and 64 GB memory, the time required for one finite element analysis was approximately 1 h.

Abstract 4142793: Development of an Angiography-Based Multimodal AI Model for Predicting the risk of In-Stent Restenosis

Background: Despite advances in Second-generation drug-eluting stents (DES), 5-10% of patients still experience in-stent restenosis (ISR) after percutaneous coronary intervention (PCI), which generates significant financial burden and elevates the risk of acute coronary syndrome (ACS) and rehospitalization. Thus, early identification of patients at high risk for ISR is crucial for guiding clinical stratification and early intervention. Aims: To develop and validate a multimodal artificial intelligence (AI) model based on coronary angiography images for predicting ISR risk in patients post-DES implantation. Methods: To establish an accurate predictive model, our approach begins with the pre-training on 100,000 angiographic images to enhance the model’s capability in recognizing image features. Subsequently, we employ the DenseNet architecture as the primary deep learning model, incorporating angiographic images from 2,000 cases of DES-treated de novo lesions—1,000 from patients who did not experience ISR within two years and 1,000 from those who did. A multivariate logistic regression analysis, including radiomic features, clinical baselines, and functional information, constructs the predictive model. Additionally, a separate prospective cohort of 300 cases was assembled for validation to simulate real-world application and to verify the model's reliability and accuracy. Results: Our study successfully developed an AI prediction model for ISR, utilizing a large cohort of coronary angiography images, which effectively predicts ISR with high accuracy. Leveraging the DenseNet architecture and finely tuned machine learning algorithms, the model achieved a sensitivity and specificity of 90% in the validation cohort. The ROC curve from the test phase demonstrated an AUC above 0.90, underscoring the model’s exceptional diagnostic capabilities. Furthermore, the implementation of this model in a prospective cohort confirmed its reliability and practical utility in real-world clinical settings. Conclusions: This study introduces the first multimodal AI model using angiographic imaging to predict ISR. By demonstrating high diagnostic accuracy and reliability in real-world settings, this model serves as an essential tool for early ISR detection and intervention, ultimately helping to reduce the incidence of major adverse cardiac events (MACEs) and mortality.

Systematic bias due to mismodeling precessing binary black hole ringdown

Accurate waveform modeling is crucial for parameter estimation in gravitational wave astronomy, impacting our understanding of source properties and the testing of general relativity. The precession of orbital and spin angular momenta in binary black hole (BBH) systems with misaligned spins presents a complex challenge for gravitational waveform modeling. Current precessing BBH waveform models employ a coprecessing frame, which precesses along with the binary. In this paper, we investigate a source of bias stemming from the mismodeling of ringdown frequency in the coprecessing frame. We find that this mismodeling of the coprecessing frame ringdown frequency introduces systematic biases in parameter estimation, for high-mass systems in particular, and in the inspiral-merger-ringdown (IMR)-consistency test of general relativity. Employing the waveform model henom, we conduct an IMR-consistency test using a Fisher matrix analysis across parameter space, as well as full injected signal parameter estimation studies. Our results show that this mismodeling particularly affects BBH systems with high-mass ratios, high-spin magnitudes, and highly inclined spins. These findings suggest inconsistencies for all waveform models which do not address this issue. Published by the American Physical Society 2024

Impact of Polymorphic Microfibers for Establishment of Neuronal Model

ABSTRACTThe biological and mechanical environment of cells is better mimicked in 3D compared to 2D cell cultures. However, creating accurate 3D cell culture models particularly for ultra‐soft tissues like brain or spinal cord is challenging since the hydrogels that match these properties are mechanically fragile. Therefore, implementing reinforcing structures, such as microfibers, is essential to provide the necessary support. Particularly, fibrous systems are of interest since they offer natural fibrillar structures similar to the extracellular matrix. This study focuses on exploring the interactions between a motor neuron‐like cell line and multiple microfiber‐morphologies and mechanics. Monitoring cell‐microfiber interactions over time we unveiled various dynamic undetected behaviors and interactions happening upon contact depending on the used microfiber properties. These highly defined microfiber fragments were fabricated using multiple processes—electrospinning, Melt Electrowriting, and microfluidic spinning—with properties differing in size, mechanics, and surface chemistry. The excellent control over our microfiber systems enabled the investigation of single parameters in an isolated manner. In addition, we quantified the observed varying movement modes of the monitored cell‐microfiber tandems. The study demonstrates the significance of microfiber design for biological applications and establishes methodological foundations for the implementation of customized microfiber systems in the field of biofabrication.

Neural Network Methods in the Development of MEMS Sensors

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

High-Frequency Modeling and Analysis of Single-Layer NiZn Ferrite Inductors for EMI Filtering in Power Electronics Applications

In the high-frequency (HF) region, specifically within the 150 kHz to 30 MHz range for conducting electromagnetic interference (EMI) modeling, NiZn inductors exhibit enhanced efficiency due to their low core losses and stable permeability. Consequently, the accurate modeling of NiZn ferrite toroidal inductors is essential, given their widespread applications in the HF domain, with the aim of addressing existing knowledge gaps. Previous inductor models often relied on the perfect electric conductor (PEC) assumption, which simplifies the analysis but does not fully represent the electromagnetic behavior of the cores to which the PEC assumption cannot be applied. This study investigates the actual electromagnetic behavior of NiZn cores, treating them as dielectrics, which diverges from the traditional PEC-based models. Furthermore, this research fully considers the actual geometry of the inductors, proposing a comprehensive and precise analytical model for NiZn ferrite toroidal inductors. The impact of various winding methods on core capacitance is also explored. The paper provides a detailed explanation of the physical significance underlying the proposed model. A comparative analysis of both modeling methods is presented, and the efficacy of the suggested approach is validated through simulations and experimental results in several distinct scenarios.

Line-parameter identification of medium-voltage distribution systems based on deep deterministic policy gradients

Accurate line-parameter identification is an important foundation for refined the regulation, protection, and control of distribution systems. Traditional identification models provide accurate modeling, while conventional identification approaches are hindered by the high complexity and low observability of power systems. In this article, a parameter identification method based on the deep deterministic policy gradient is proposed for medium voltage distribution systems. The proposed method starts with objective function constructing, followed by power flow analysis and parameter identification modeling, where the L2 normalization theory is introduced to improve the computation efficiency. On this basis, the parameter identification framework is constructed through designing the Markov decision process of a parameter and using a training mechanism. An adaptive parameter correction method is proposed to improve the accuracy and efficiency of a deep-reinforcement-learning-based agent. The performance of the proposed modal is tested on IEEE 14-node and IEEE 33-node medium-voltage distribution systems. Case simulation results demonstrate that the proposed modal exhibits superior computational capability, while achieving fewer errors compared to traditional methods.

Comparison of vibration values of rotating discs with variable parameters obtained by finite element analysis modeling with different machine learning algorithms

PurposeRotating functionally graded (FG) disks of variable thickness generates vibration. This study aims to analyze the vibration generated by the rotating disks using a finite element program and compare the results obtained with the regression methods.Design/methodology/approachTransverse vibration values of rotating FG disks with variable thickness were modeled using different regression methods. The accuracies of the obtained models are compared. In the context of comparing regression methods, multiple linear regression (MLR), extreme learning machine (ELM), artificial neural networks (ANNs) and radial basis function (RBF) were used in this study. The error graph between the observed value and the predicted value of each regression method was obtained. The error values of the regression methods used with scientific error measures were calculated.FindingsThe analysis of the transverse vibration of rotating FG disks with the finite element program is consistent with the studies in the literature. When the variables and vibration value determined on the disk are modeled with ELM, MLR, ANN and RBF regression methods, it is concluded that the most accurate model order is RBF, ANN, MLR and ELM.Originality/valueThere are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling the vibration value of rotating discs.

A general subpopulation model for the Animal Landscape and Man Simulation System (ALMaSS)

ALMaSS (the Animal Landscape and Man Simulation System) is a well-established modelling system known for its detailed agent-based animal models. However, for certain species, such as aphids, extremely high population sizes and limited detailed individual behaviour information make agent-based approaches either computationally expensive or unfeasible. Additionally, when these species are modelled as a prey base for other species, such as aphids for ladybirds, an efficient and accurate modelling approach is required. To address these challenges, we introduce a subpopulation-based model framework that effectively handles high population densities, while reducing computational complexity. This framework divides the landscape into a non-overlapping grid and population dynamics are managed within each grid using a flexible stage-structured population model. Grids are interconnected through short- and long-distance dispersal mechanisms, allowing for realistic movement and interactions. Parallel programming is employed to enhance performance, enabling faster simulation runs. We demonstrate the application of this model framework with a hypothetical species (not a species in the real world) and showcase its use in modelling aphid populations. This foundational subpopulation model could be used to model species with large population sizes within the ALMaSS framework and can be integrated into other simulation systems.

Detection of low-level fentanyl concentrations in mixtures of cocaine, MDMA, methamphetamine, and caffeine via surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0-339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm-1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0-0.4 mM, medium = 0.4-14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.

Research progress on the development of myopia prediction models and their predictive performance

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.

Prediction of SO2 concentration at desulfurization outlet of thermal power units based on ReliefF-SC and ISAO-ARELM

Abstract The flue gas desulfurization (FGD) system of thermal power units operates under complex conditions and exhibits significant nonlinearity. Establishing an accurate prediction model for outlet SO2 concentration is crucial for optimizing the control of the FGD system. The study constructs an autoregressive limit learning machine (ARELM) prediction model for SO2 concentration at the desulfurization outlet of thermal power units, leveraging the improved feature selection algorithm ReliefF-SC and the improved snow ablation optimizer (ISAO). Initially, the time delays of the input variables are compensated and the ReliefF-SC algorithm, which incorporates the Spearman correlation coefficient and cosine similarity, is designed to obtain the optimal feature set for predicting SO2 concentration at the outlet. To enhance the ELM's capacity to process time-series data, the autoregressive (AR) concept is integrated into the ELM framework. Furthermore, to mitigate the impact of random initialization of ARELM network parameters on model stability, the ISAO algorithm is proposed by introducing the sine-cosine position update strategy and adaptive adjustment of subpopulation size. Finally, experimental validation is conducted using actual plant operation data. The results indicate that the established SO2 concentration prediction model for desulfurization outlets of thermal power units is highly accurate and offers valuable theoretical guidance and technical support for optimizing the control of the FGD systems.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential.

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

Scene Representations for Robotic Spatial Perception

The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.

Data-driven pressure prediction for self-pressurization liquid hydrogen tank using transfer learning method

Prediction of pressure evolution in liquid hydrogen (LH2) tanks utilizing data-driven technology has gradually garnered significant attention. However, training an accurate data-driven model for pressure prediction in LH2 tanks is a challenge due to inadequate experimental data. To end this, this paper presents a Backpropagation Neural Network model for pressure prediction in LH2 tanks based on transfer learning on pre-trained models, using experimental data and results from thermodynamics models. Pre-trained models are firstly optimized by Bayesian optimization algorithms with large sample datasets from the thermal multi-zone model program and then performed the fine-tuning method with small sample datasets from experiments. The effectiveness of the proposed approach is validated by experimental data and numerical results. Compared with baseline models trained only with experimental data, the results demonstrate better model performance, with a maximum of 10.51% accuracy improvement. The proposed approach demonstrates considerable potential in facilitating the application of advanced machine learning algorithms for LH2 tank pressure prediction, significantly reducing the dependence on experimental data collection.