Accurate Model Research Articles

Modified constitutive models and cutting finite element simulation of Ti-6Al-4V alloy at cryogenic

Mastering the high strain characteristics at cryogenics and constructing an accurate constitutive model are critical for establishing a cryogenic cutting simulation of Ti-6Al-4V alloy. The traditional constitutive model is inadequate for accounting for high and cryogenic conditions. In this paper, cryogenic split Hopkinson pressure bar (SHPB) experiments are conducted, specifically for Ti-6Al-4V alloy, to develop a modified Johnson–Cook (J–C) constitutive model that incorporates the cryogenic effect. A coupled two-dimensional orthogonal finite element simulation involving temperature displacement was conducted for the cryogenic cutting of Ti-6Al-4V alloy. This simulation was performed using ABAQUS, based on a modified J–C constitutive model subroutine. It was observed that the smallest discrepancy between the simulation results and the data from ice-fixed milling experiments was just 2.12%. The modified J–C constitutive model is more adept at simulating the chip morphology under cryogenic conditions. It was also found that cryogenic machining enhances the distribution of temperature and stress within the chip. For every 5°C reduction in processing temperature, the tool temperature diminishes by more than 2%.

An integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools

Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining.

Free Vibration analysis of C/SiC blisk based on modified global mode method

The bladed disk is the core component which is under load in aero-engine, rocket engine and gas turbine. In recent years, the bladed disk has developed towards the direction of integrated bladed disk (blisk) and being applied with ceramic matrix composites. However, there is no accurate semi-analytical dynamic model to describe the dynamic characteristic of ceramic matrix composite blisk. In this paper, a new semi-analytical method, modified global mode method (MGMM) is proposed to model the 2D C/SiC laminated blisk. In proposed method, Chebyshev polynomials series are used to expand the displacements of the blades and disk, constraints between blades and disk is strictly satisfied by multi-modal transformation and integrated into governing equation, and high-order shear deformation theory is combined to establish the dynamic model of the blisk of 2D C/SiC laminated composite material. The proposed method avoids the matrix singular problem appearing in traditional global mode method when modeling of combined structure and makes the system perform dynamic analysis and mode prediction without mode extraction and reconstruction. Then, amplitude frequency response and modal experiment are carried out to verify the correctness and convergence of the proposed method. Finally, under the framework of proposed method, the effects of material parameters and geometric parameters on the modal characteristics of the blisk are analyzed. The results show that compared with the geometric parameters, the material parameters have less influence on the modal characteristics of blisk, additionally, a series of modal steering is observed. The work in this paper can provide theoretical guidance for the dynamic design of composite blisk.

Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey

The coronavirus disease started at the end of 2019 and affected all the countries in the world. In Turkey, the vaccination process started at the beginning of 2021 but performed in slow progress. Thus, the Turkish Government tried to implement precautions to control this virus's spread. In this study, we evaluated and compared five different forecasting models, ARIMA, Prophet, NARNN, Stacked LSTM, and Bidirectional LSTM, in order to show the effect of these precaution strategies on virus spread using a real-world data set. According to the test results, ARIMA and Prophet were found to be the most accurate models for small data sets that are split regarding precautions. Moreover, test results showed that when data size grows, LSTM model performance increases. However, these models' performance decreased when we fed these models by using the entire data set without splitting.

Advances in CMOS Image Sensors and Transistors Leveraging Stochastic Nanomagnets: A Comprehensive Review

Accurate modeling and assessment of complex Complementary Metal-Oxide Semiconductor (CMOS) imaging sensors and transistors, in conjunction with randomized nanomagnets for distributed computation in radiated environments, are crucial for the development of robust electronic systems. The effectiveness and resilience of these intricate parts to radiation exposure is the subject of this study, which is becoming more and more important in applications including space missions, power plants, and high-altitude flying. To take into account the interaction between CMOS technology and the unpredictable behavior of nanomagnets, a comprehensive durability modeling technique is required. The methodology that has been suggested evaluates the susceptibility of image detectors and transistors to malfunctions caused by radiation, such as dislocation damage, single-event upsets (SEUs), and impacts of total ionizing dose (TID). The impact of radiation on the power characteristics and operational dependability of these components is investigated using state-of-the-art modeling tools and experimental data. The findings suggest that random nanomagnets can improve distributed computing devices' resistance to radiation-induced failures. Through the provision of insights into reliability challenges and solutions for enhanced CMOS image sensors and transistors, this work advances the field of radiation-tolerant technologies. Future electronic device developers will find the suggested reliability modeling and evaluation methodology to be a useful resource for creating reliable devices that can resist harsh radioactive environments. Keywords— Reliability Modeling; CMOS Image Sensors; Radiation Environment; Advanced Technology Node; Transistor Structures; Stochastic Nanomagnets; Heterogeneous Computing; Probabilistic Inference

Deriving neutron star equation of state from AdS/QCD

Neutron stars are among the main targets for gravitational wave observatories, however, their equation of state is still not well established. Mainly phenomenological models with many parameters are widely used by far, while theoretical models are not so practical. In arXiv:1902.08477, a theoretical equation of state with only one parameter is derived from Witten–Sakai–Sugimoto model, as an application of AdS/QCD, where pointlike instanton case is taken into consideration. When the tidal deformability constraint from gravitational wave event is satisfied, the maximum mass is about 1.7 solar masses. Now we upgrade this model to instanton gas, with one more variable, the instanton width. This is not naively a free parameter, but a function of the chemical potential. Thus we end up with a more complicated and accurate model, but still with only one adjustable parameter. In this case, we find the maximum mass becomes 1.85 solar masses. This is an encouraging result, as a theoretically derived model.

Modelling bridge deterioration using long short-term memory neural networks: a deep learning-based approach

PurposeBridge deterioration is a critical risk to public safety, which mandates regular inspection and maintenance to ensure sustainable transport services. Many models have been developed to aid in understanding deterioration patterns and in planning maintenance actions and fund allocation. This study aims at developing a deep-learning model to predict the deterioration of concrete bridge decks.Design/methodology/approachThree long short-term memory (LSTM) models are formulated to predict the condition rating of bridge decks, namely vanilla LSTM (vLSTM), stacked LSTM (sLSTM), and convolutional neural networks combined with LSTM (CNN-LSTM). The models are developed by utilising the National Bridge Inventory (NBI) datasets spanning from 2001 to 2019 to predict the deck condition ratings in 2021.FindingsResults reveal that all three models have accuracies of 90% and above, with mean squared errors (MSE) between 0.81 and 0.103. Moreover, CNN-LSTM has the best performance, achieving an accuracy of 93%, coefficient of correlation of 0.91, R2 value of 0.83, and MSE of 0.081.Research limitations/implicationsThe study used the NBI bridge inventory databases to develop the bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.Originality/valueThis study provides a detailed and extensive data cleansing process to address the shortcomings in the NBI database. This research presents a framework for implementing artificial intelligence-based models to enhance maintenance planning and a guideline for utilising the NBI or other bridge inventory databases to develop accurate bridge deterioration models. Future studies can extend the model to other bridge databases and other applications in the construction industry.

Spatial modeling for detection of water retention capacity in technosols developed on carboniferous spoil heap after hard coal mining

Post-industrial areas, such as heaps, are an Anthropocene pressure on the environment but show natural potential for new ecosystem services, i.e., water retention, biodiversity, and C-sequestration. All these ecosystem functions of such sites can be developed only in the case of appropriate reclamation treatments, which are especially difficult under the conditions of carboniferous spoil heaps after coal deep mining. The study of reclamation effects has been increasingly supported in recent years by modern remote sensing techniques. This paper presents the possibilities of using selected remote sensing data and topographic indices and their effect on volumetric water content, which is crucial for habitat development. PlanetScope satellite imagery and airborne laser scanning point clouds were used to estimate the volumetric water content (VWC) of soil. This integrated analysis allows for more accurate spatial modeling of soil moisture, a key indicator of ecological restoration in degraded land. The method of generalized additive models was used to develop a predictive model. The developed predictive model was characterized by the following values of model accuracy evaluation parameters: MAE – 4.1%; RMSE – 5.6%; and R2–0.49. As a result of the analysis, topographic and spectral variables with significant influence on VWC were identified, and a map of the retention capacity of the Sosnica heap was developed.

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.

Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' lives-is a major challenge confronting modern medicine. We aimed to establish accurate and informative machine learning (ML) models to identify patients at high risk for PE as they are admitted to the hospital, before their initial clinical checkup, by using only the information in their medical records. We collected demographics, comorbidities, and medications data for 2568 patients with PE and 52,598 control patients. We focused on data available prior to emergency department admission, as these are the most universally accessible data. We trained an ML random forest algorithm to detect PE at the earliest possible time during a patient's hospitalization-at the time of his or her admission. We developed and applied 2 ML-based methods specifically to address the data imbalance between PE and non-PE patients, which causes misdiagnosis of PE. The resulting models predicted PE based on age, sex, BMI, past clinical PE events, chronic lung disease, past thrombotic events, and usage of anticoagulants, obtaining an 80% geometric mean value for the PE and non-PE classification accuracies. Although on hospital admission only 4% (1942/46,639) of the patients had a diagnosis of PE, we identified 2 clustering schemes comprising subgroups with more than 61% (705/1120 in clustering scheme 1; 427/701 and 340/549 in clustering scheme 2) positive patients for PE. One subgroup in the first clustering scheme included 36% (705/1942) of all patients with PE who were characterized by a definite past PE diagnosis, a 6-fold higher prevalence of deep vein thrombosis, and a 3-fold higher prevalence of pneumonia, compared with patients of the other subgroups in this scheme. In the second clustering scheme, 2 subgroups (1 of only men and 1 of only women) included patients who all had a past PE diagnosis and a relatively high prevalence of pneumonia, and a third subgroup included only those patients with a past diagnosis of pneumonia. This study established an ML tool for early diagnosis of PE almost immediately upon hospital admission. Despite the highly imbalanced scenario undermining accurate PE prediction and using information available only from the patient's medical history, our models were both accurate and informative, enabling the identification of patients already at high risk for PE upon hospital admission, even before the initial clinical checkup was performed. The fact that we did not restrict our patients to those at high risk for PE according to previously published scales (eg, Wells or revised Genova scores) enabled us to accurately assess the application of ML on raw medical data and identify new, previously unidentified risk factors for PE, such as previous pulmonary disease, in general populations.

Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China

Abstract. The occurrence of shallow loess landslides induced by prolonged heavy rainfall is prevalent in loess-dominated regions, often leading to property damage, human casualties, and sediment pollution. Developing an accurate prediction model for shallow landslides in loess areas is crucial for effective landslide mitigation. In 2013, prolonged heavy rains from 19–25 July triggered mass sliding-flow loess landslides in Tianshui, China. Landslide data, along with the characteristics of the sliding-flow loess landslides, were obtained through extensive field investigations and remote sensing interpretations. The sliding-flow loess landslide event demonstrated clustering, high density, small areas, and long travel distance. The depth of the sliding surface is correlated with the saturated layer resulting from rainfall infiltration; it is typically less than 2 m deep and negatively correlated with slope steepness. Based on the common characteristics of shallow loess landslides, the mechanisms involved in the sliding-flow landslide are proposed. The Revised Infinite Slope Model (RISM) was introduced using an equal differential unit method to address deficiencies when the safety factor remains constant or increases with increasing slope greater than 40°, as calculated using the Taylor slope infinite model. The relationship between the critical depth and the slope of the shallow loess landslide was determined. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides for different slopes was constructed and combined with the characteristics of rainfall infiltration for use in forecasting regional shallow loess landslides. Additionally, the influence of loess strength on the shallow loess landslide stability was analyzed. The shallow loess landslide stability responds to slope and cohesion but is not sensitive to the internal friction angle.

Toward compound fault diagnosis via EMAGAN and large kernel augmented few-shot learning

Bearings are essential in machinery. Damage to them can cause financial losses and safety risks at industrial sites. Therefore, it is necessary to design an accurate diagnostic model. Although many bearing fault diagnosis methods have been proposed recently, they still cannot meet the requirements of high-accurate prediction of bearing faults. There are several challenges in this: 1) In practical settings, gathering sufficient and balanced sample data for training diagnostic network models proves challenging. 2) The damage to bearings in real industrial production sites is not singular, and compound faults are also a huge challenge for diagnostic networks. To address these issues, this study introduces a novel fault diagnosis model called EMALKNet that integrates DCGAN with Efficient Multi-Scale Attention (EMAGAN) and RepLKNet-XL, enhancing the detection and analysis of bearing faults in industrial machinery. This model employs EMAGAN to explore the underlying distribution of raw data, thereby enlarging the fault sample pool and enhancing the model’s diagnostic capabilities; The large kernel structure of RepLKNet-XL is different from the current mainstream small kernel and has stronger representation extraction ability. The proposed method has been validated on the Paderborn University dataset and the Huazhong University of Science and Technology dataset.

Revolutionizing engineered cementitious composite materials (ECC): the impact of XGBoost-SHAP analysis on polyvinyl alcohol (PVA) based ECC predictions

This study integrates previous experimental data and employs machine learning (ML) methods, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtreme Gradient Boosting (XGBoost), to predict the compressive strength (CS) and tensile strength (TS) of engineered cementitious composites (ECC). XGBoost emerged as the superior model among the four ML models, providing an interpretable and highly accurate predictive framework. To optimize the model performance, hyperparameter tuning using a fivefold cross-validation approach with the data divided into 80% training and 20% testing subsets. The Shapley Additive Explanations (SHAP) algorithm was also employed to reveal the impact of important features, such as the water/binder ratio, fly ash content, and water reducer dosage, on the model’s predictions and their interrelationships. The XGBoost demonstrates the most exemplary performance, as reflected in the R2 values of 0.92 and 0.97 for CS and TS testing, respectively. The SHAP analysis provided insights into the impact of individual features on CS and TS, shedding light on how specific characteristics influence the predictive accuracy of these properties. This highly accurate prediction model uncovers insights into correlated features, aids in creating new mix designs of ECC, and supports global efforts toward a low-carbon future in the construction industry by reducing carbon emissions.

A Novel Approach to Heart Disease Prediction Using Artificial Intelligence Techniques

INTRODUCTION: Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient prediction models OBJECTIVES: To research new models for heart disease prediction METHODS: This paper presents a novel approach for predicting heart disease using advanced artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms RESULTS By leveraging patient data and integrating various AI models, this approach aims to enhance prediction accuracy and support early diagnosis and intervention CONCLUSION: This study presents a novel AI-based approach for heart disease prediction, demonstrating the efficacy of ML and DL models in improving diagnostic accuracy

Learning three-dimensional aortic root assessment based on sparse annotations.

Analyzing the anatomy of the aorta and left ventricular outflow tract (LVOT) is crucial for risk assessment and planning of transcatheter aortic valve implantation (TAVI). A comprehensive analysis of the aortic root and LVOT requires the extraction of the patient-individual anatomy via segmentation. Deep learning has shown good performance on various segmentation tasks. If this is formulated as a supervised problem, large amounts of annotated data are required for training. Therefore, minimizing the annotation complexity is desirable. We propose two-dimensional (2D) cross-sectional annotation and point cloud-based surface reconstruction to train a fully automatic 3D segmentation network for the aortic root and the LVOT. Our sparse annotation scheme enables easy and fast training data generation for tubular structures such as the aortic root. From the segmentation results, we derive clinically relevant parameters for TAVI planning. The proposed 2D cross-sectional annotation results in high inter-observer agreement [Dice similarity coefficient (DSC): 0.94]. The segmentation model achieves a DSC of 0.90 and an average surface distance of 0.96mm. Our approach achieves an aortic annulus maximum diameter difference between prediction and annotation of 0.45mm (inter-observer variance: 0.25mm). The presented approach facilitates reproducible annotations. The annotations allow for training accurate segmentation models of the aortic root and LVOT. The segmentation results facilitate reproducible and quantifiable measurements for TAVI planning.

Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling

This research aims to enhance the customer experience in e-commerce furniture portals through the integration of augmented reality (AR), a dynamic pricing tool, and AI-driven 3D modeling. Utilizing machine learning algorithms, specifically SIFT and FLAAN, we develop highly accurate 3D models of furniture. The portal features various filters, including price range and categories, to streamline the shopping experience. The AI-enabled pricing tool, implemented through web scraping techniques on platforms like Amazon using BeautifulSoup, ensures sellers can set competitive and accursate prices, enhancing customer value. The platform allows sellers to request the addition of 3D models for their products. Admins then select the most similar 3D .obj models from the Shapenet dataset and embed AR links into the portal, enabling buyers to visualize furniture in their own space through augmented reality. This capability significantly improves the shopping experience by allowing customers to see how furniture fits in their home environment before purchase. The portal is built using Django for the backend and PostgreSQL for database management, ensuring a fast, secure, and reliable user experience. By combining AR, advanced 3D modeling, AI-driven pricing, and robust backend technologies, this research contributes to the development of a seamless and engaging e-commerce platform for furniture retail.

A method for predicting methane production from anaerobic digestion of kitchen waste under small sample conditions.

Anaerobic digestion (AD) has the great potential to treat organic waste and achieve remarkable results effectively. However, it is very tough to establish an accurate mechanistic model for this process. Data-driven modeling technology has opened a new door to solving this problem. While when the sample set is small, traditional data-driven modeling methods are often powerless. In this paper, an effective method is proposed for data-driven high-precision modeling in small sample scenarios. A time series generative adversarial network (TimeGAN) is first utilized to augment the original high-quality small-sample data collected during the AD methane production. A novel hybrid kernel extreme learning machine (HKELM) is then designed to form a better structure of the data-driven model, whose regularization coefficient C0 is optimized by the sparrow search algorithm (SSA). Finally, this semi-finished model (SSA-HKELM) is trained by the augmented data to form the final mathematical model (TimeGAN-SSA-HKELM) for the AD methane generation process. Comparative experiments of the methane daily production prediction error haveverified the effectiveness of the method, which can be extended to other similar small sample data-driven modeling scenarios.

Multiscale insights on compression cast sea sand and seawater concrete

Reinforced concrete (RC) structures in coastal and marine regions face two primary challenges, the shortage of raw materials and severe service environments. Concrete made from sea sand and seawater (SS-SW) can address the shortage of natural sand by utilizing locally available resources. Meanwhile, the newly developed compression cast technology (CCT) significantly improves the micro and macro properties of concrete, enhancing its durability in harsh environments. Therefore, the combination of SS-SW concrete and CCT to form a new compression-cast SS-SW concrete can simultaneously solve the problems of materials shortage and durability issues for coastal RC structures. However, till now, research on this type of concrete is still an open issue. This paper aims to carry out multilevel investigations on the macro and micro performance of compressed SS-SW concrete. First, 69 concrete columns were tested to understand the stress-strain behavior and associated mechanical indices. The compressive strength of compressed concrete was found to be up to 1.86 times greater than that of uncompressed concrete. Meanwhile, the porosities in both mortar and the interfacial transition zone (ITZ) of concrete under a casting pressure of 15 MPa were approximately 50 % of those in uncompressed counterpart. Existing models are inadequate for predicting the behavior of compressed SS-SW concrete, leading to the development of new and accurate models to predict its compressive strength, elastic modulus, peak strain and stress-strain curve. These findings pave the way for the design and application of this kind of concrete.

Fast finite difference solver for optical microscopy in deep biological tissue

Optical scattering poses a significant challenge to high-resolution microscopy within deep tissue. To accurately predict the performance of various microscopy techniques in thick samples, we present a computational model that efficiently solves Maxwell’s equation in highly scattering media. This toolkit simulates the deterioration of the laser beam point spread function (PSF) without making a paraxial approximation, enabling accurate modeling of high-numerical-aperture (NA) objective lenses commonly employed in experiments. Moreover, this framework is applicable to a broad range of scanning microscopy techniques including confocal microscopy, stimulated emission depletion (STED) microscopy, and ground-state depletion microscopy. Notably, the proposed method requires only readily obtainable macroscopic tissue parameters. As a practical demonstration, we investigate the performance of Laguerre–Gaussian (LG) versus Hermite–Gaussian (HG) depletion beams in STED microscopy.

Thermophysical Characterization of Materials for Energy-Efficient Double Diaphragm Preforming

The Double Diaphragm Preforming (DDPF) process uses vacuum pressure and heat to pre-shape dry carbon fabric reinforcements between flexible diaphragms in liquid composite molding (LCM). Accurate modeling of heat transfer within DDPF requires knowledge of the thermophysical properties of the constituent materials under processing conditions. This study investigates the thermal conductivity of silicone diaphragms and carbon fabrics with embedded thermoplastic binder webs, utilizing transient plane source (TPS) and modified transient plane source (MTPS) methods, respectively. Additionally, the specific heat of silicone, carbon fibers (both chopped and powdered), and binder were measured using differential scanning calorimetry (DSC). Mixed samples comprising chopped fibers with 1.8 wt% binder and powdered fibers with 3.6 wt% binder was also analyzed with DSC. This study also examined the influence of reheating cycles on the specific heat of carbon fiber—3.6 wt% binder samples—and the effect of compaction and vacuum on the thermal conductivity of carbon fabrics with an embedded binder. While silicone exhibited linear-specific heat behavior, the thermoplastic binder showed non-linearity due to phase change. The combined carbon fiber-binder samples demonstrated slight non-linear specific heat variations depending on reheating cycles. The thermal conductivity of the fabric preforms decreased with the addition of thermoplastic binder and under vacuum-compaction conditions. The established temperature-dependent specific heat relationships and thermal conductivity provide valuable data for optimizing DDPF preforming parameters and enhancing energy efficiency in composite manufacturing.

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.