Accuracy Time Research Articles

Comparison of Short Fast Fourier Transform and Continuous Wavelet Transform in Study of Stride Interval

Neurodegenerative diseases (NDD) are a heterogeneous group of complex diseases characterized by neuronal loss and progressive degeneration of different areas of the nervous system. Gait analysis presents an early recognition system for NDD which is important to increase the patient’s awareness of their health conditions. However, it is very difficult to identify and formulate suitable digital biomarkers from the data collected from gait experiments such as stride interval and swing. The objective of this paper is to compare the result of Short - Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) on the collected stride interval of healthy young people and healthy old people. In this paper, STFT and CWT are performed on the collected stride interval and from the result of the STFT and CWT, further features are extracted like instantaneous RMS and maximum RMS value. STFT is performed on the collected stride interval from a window length of 64 to 512 while CWT is performed on the collected stride interval from the scale of 128 to 2048. The processing time of the STFT and CWT with varied window lengths and scales respectively are collected. Besides, the actual maximum time from the time - frequency plot derived from STFT and CWT is also collected. Both STFT and CWT show that the young group has a higher maximum RMS, an indication of higher stride interval than the old group and higher variance, an indication of higher gait complexity. The suitable window lengths for STFT in analyzing the stride interval are 64 and 128 while the scale for CWT should be set to the lowest scale. Overall, STFT with a window length of 64 and 128 is better in analyzing the stride interval due to low processing time at the expense of slightly less accurate time - frequency representation.

Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning

Objectives:This study aims to develop and evaluate hourly water level prediction models for agricultural reservoirs using an automated machine learning (AutoML) approach, specifically, employing TPOT.Methods:The study focuses on the Baekrok and Baekryeon reservoirs using rainfall forecast data from the Korea Meteorological Administration, along with observed rainfall and reservoir water level data. TPOT, which utilizes genetic algorithms to automate the generation of optimal machine learning pipelines, was used to build models. Additionally, Random Forest (RF) was implemented as a benchmark to evaluate TPOT’s applicability. Predictions were generated with lead times of 1, 3, 6, and 12 hours, and model accuracy was evaluated using the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), and root-mean-square error (RMSE).Results and Discussion:The predictions from both TPOT and RF showed high accuracy for shorter lead times, with NSE values exceeding 0.99 for the 1-hour predictions. However, predictive accuracy decreased as lead time increased, likely due to greater uncertainty in rainfall forecast data, particularly for the 12-hour predictions. Despite this trend, TPOT-derived models maintained more stable and accurate performance compared to RF models across all lead times.Conclusion:This study demonstrates the applicability of TPOT-based AutoML techniques in predicting hourly water levels in agricultural reservoirs. Future work should explore strategies to mitigate the decline in accuracy for longer lead times.

Artificial intelligence-enhanced electrocardiography predicts 10-year risk of atherosclerotic cardiovascular disease

Abstract Background The ACC/AHA pooled cohort equations (PCE) calculates the 10-year primary risk of atherosclerotic cardiovascular disease (ASCVD), an indication to initiate primary prevention statin therapy in international guidelines. The validity of PCE is limited by its overestimation of risk in high-risk cohorts and an ethnic heterogeneity. Electrocardiography (ECG) has not been incorporated in current ASCVD risk estimation tools. Artificial Intelligence (AI)-based models are capable to capture patterns that are informative for cardiovascular prognosis. Purpose We explored the predictive value of a discrete-time survival model in 10-year ASCVD risk in comparison to PCE. Methods We developed an AI-ECG model to predict the 10-year ASCVD risk in an unselected secondary care population from the USA comprised of 1,163,401 ECGs from 189,539 patients. Data was split into training/validation and hold-out test sets by patient ID. Our AI-ECG model uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves, accounting for both time to event and censorship (i.e., loss to follow up). We compared the performance of out AI-ECG model using a subset of outpatient data (n=4590) of the BIDMC dataset to PCE and ASCVD risk factors including systolic blood pressure, total cholesterol, HDL cholesterol, hypertension, smoking history, diabetes mellitus, and ethnicity. Performances were evaluated using cox survival analysis. Our AI-ECG model was externally validated in the UKB Biobank (UKB), where we compared the performance of our AI-ECG model to predictions of existing AI-ECG models such as the Stanford Estimator of Electrocardiogram Risk (SEER) model. Results Our AI-ECG successfully predicted future ASCVD events in individuals (C-index 0.721 (0.719-0.723), 5-year AUC 0.761 (0.758-0.763), and differentiated between high and low risk group with an adjusted HR of 2.33 (2.26-2.41). Upon internal validation our AI-ECG model showed a superior performance (C-index 0.679 (0.651-0.708)) than PCE (0.605 (0.577-0.634)) and all risk factors combined (C index 0.642 (0.613-0.672)). When combining AI-ECG predictions with all risk factors, the model showed additional predictive value (0.686 (0.657-0.715)) (Figure 1). Our AI-ECG model showed modest predictive value within the UKB dataset (C-index 0.655 (0.637-0.673)), but significantly outperformed the SEER model (C-index 0.547 (0.527-0.567), p <0.0001 for comparison with AIRE). Conclusion We have shown the potential of AI-ECG models in predicting future ASCVD risks. AI-ECG models demonstrated accurate discrete time survival prediction and good classification performance superior to that of the SEER model, as well as PCE and ASCVD risk factors. . Accurate risk assessment of ASCVD may help prevent adverse events in high risk subjects and save unnecessary pharmacological interventios in low risk subjects.Figure 1

Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models

AbstractFloods are among the most common natural disasters, causing countless losses every year worldwide and demanding urgent measures to mitigate their impacts. This study proposes a novel combination of artificial intelligence and process-based models to construct a flood early warning system (FEWS) for estuarine regions. Using streamflow and rainfall data, a deep learning model with long short-term memory layers was used to forecast the river discharge at the fluvial boundary of an estuary. Afterwards, a hydrodynamic process-based model was used to simulate water levels in the estuary. The river discharge predictors were trained using different forecasting windows varying from 3 h to 36 h to assess the relationship between the time window and accuracy. The insertion of attention layers into the network architecture was evaluated to enhance forecasting capacity. The FEWS was implemented in the Douro River Estuary, a densely urbanised flood-prone area in northern Portugal. The results demonstrated that the Douro Estuary FEWS is reliable for discharges up to 5000 m3/s, with predictions made 36 h in advance. For values higher than this, the uncertainties in the model predictions increased; however, they were still capable of detecting flood occurrences.

On the Numerical Investigation of Natural-Convection Heat Sinks Across a Wide Range of Flow and Operating Conditions

Many designs for natural-convection heat sinks and semi-empirical correlations have been proposed in the recent years, but they are only valid in a limited range of Elenbaas numbers El and were mostly tested for laminar flows. To alleviate those limits, parametric studies with 2D and quasi-3D models were carried out, in ranges of Grashof numbers up to 1.55×1011 and Elenbaas numbers up to 3.42×107. Ansys Fluent’s laminar, transition-SST, SST k-ω and k-ϵ models were applied. In addition, when used in this valid range, i.e., mean Elenbaas numbers, with the simplified quasi-3D model, the transition-SST model could predict better results, overestimating the heat flux by 10 to 15% compared to semi-empirical correlations. The 2D model was not deemed satisfying, regarding turbulence models. Consequently, a quasi-3D model was developed: it appeared to be an efficient trade-off between computational time and prediction accuracy, in particular for turbulence models. New grouping factors were also found, to ensure proper dimensioning of natural-convection heat sinks. They corresponded to non-dimensional parameters that dictated the physical behaviour of the heat sink with respect to the semi-empirical correlations. Typically, the ratio of the spacing to the optimal spacing predicted by Bar-Cohen’s correlation turned out to be an appropriate grouping factor with a threshold of 1, above which the fins could safely be considered as isolated, thus greatly simplifying all further calculations.

Discrete‐time full‐order sensorless control of high‐speed interior permanent magnet synchronous motor in electric vehicle traction system

AbstractA discrete‐time full‐order sensorless control strategy for high‐speed interior permanent magnet synchronous motor (IPMSM) used in electric vehicle (EV) traction system is designed in this paper. While most speed and position observer (SPO) methods are developed in the continuous‐time domain, the presence of a unit‐time‐delay block existed in feedback loop and discretisation of the algorithm can lead to performance degradation during digital implementation. In this work, speed and angle are observed using the discrete‐time four‐order IPMSM model. A quadrature phase‐locked loop model with compensation is introduced, and its stability condition is established for the first time. Furthermore, to address the potential for sudden speed changes in vehicle applications, the parameter range is further refined to keep the estimation error within a specified range. The sliding mode observer is discretised and its stability is analysed using discrete Lyapunov theory. Additionally, the time sequence of interactive signals between SPO and field‐oriented control is thoroughly examined to ensure accurate time cycle. Finally, the proposed control strategy is validated and demonstrated through bench tests and real EV (LS6 of IM Motors) tests with a 190 kW IPMSM, which can improve the accuracy by 0.3 radians at 8000 rpm compared to traditional methods, and achieve stable control at 15,000 rpm on the test bench and at 120 kph with the EV.

Accuracy of warm ischemia time measurement using a surgical intelligence software in partial nephrectomies: A validation study

AbstractObjectivesThe objectives of this study are to compare the accuracy of warm ischemia times (WITs) derived by a surgical artificial intelligence (AI) software to those documented in surgeon operative reports during partial nephrectomy procedures and to assess the potential of this technology in evaluating postoperative renal function.Patients and methodsA surgical AI software (Theator Inc., Palo Alto, CA) was used to capture and analyse videos of partial nephrectomies performed between October 2023 and April 2024. The platform utilized computer vision algorithms to detect clamp placement and removal, enabling precise WIT measurement. Expert‐reviewed surgical videos served as the ground truth. Platform‐derived WITs were compared to those in surgeon operative reports using paired‐sample t‐tests. Additionally, we analysed the correlation between platform‐derived WITs and postoperative creatinine levels extracted from electronic health records (EHRs) integrated via health level seven (HL7) messaging protocols.ResultsOf 64 eligible cases, 61 were included in the final analysis. Platform‐derived WITs were within 1 min of the ground truth in all procedures, within 30 s in 97%, and within 10 s in over 80%. The mean difference between platform‐derived WITs and ground truth was 8.3 s, significantly lower than the 2.45 min difference for operative reports (p < 0.001). No significant correlation was found between platform‐derived WIT and postoperative creatinine changes, aligning with the view that WIT may not independently determine postoperative renal function. Although not the primary goal of this study, significant correlations were observed between WIT, tumour size and RENAL score.ConclusionThis study demonstrates the high accuracy of a surgical intelligence platform in measuring WIT during partial nephrectomies. The findings support the use of AI‐based surgical time measurement for precise intraoperative documentation and highlight the potential of integrating these data with EHRs to advance research on surgical outcomes.

Ruído visual dinâmico afeta a tarefa de imagem mental com base no paradigma de retro-dica

The retro-cue effect (RCE) is known in memory tasks to produce an increase in accuracy and decrease in response time. The retro-cue brings the information to the focus of attention in an active state, but it is not known whether it is retrieved as a conscious mental image. In this study, we investigated whether the retro-cue brings the cued item to an active state, allowing its phenomenological consciousness. In each trial, the participants rated the vividness of the retro-cued item and then recognized it. Perceptual visual interference was used to certify the visual nature of the retrieved items. Our results showed a significant RCE in memory recognition, but not in the vividness rating. The mental image vividness and the recognition tasks are based on different cognitive processes and are subject to different interferences, but despite the different cognitive processes, there is a correlation between them.

How do design factors of stacked directional signs affect their visual cognition?

Stacked directional signs are widely used on Chinese roads, but research on them is relatively scarce. This study aimed to investigate the effects of the Language used, Layout format, Amount of information, and use of Separating lines on the visual cognition of stacked directional signs. A visual cognition experiment involving thirty-two drivers was conducted, and the reaction time and response accuracy were recorded. The results revealed that a greater Amount of information was correlated with increased visual cognition reaction times. Bilingual signs exhibited significantly greater reaction times compared to monolingual counterparts, while a simpler Layout format resulted in shorter reaction times. The Language used, Amount of information, Layout format, and use of Separating lines were in decreasing order of importance for visual cognition reaction time, with a high Amount of information having the greatest effect. The implications of these findings can be applied to the design and optimisation of stacked directional signs.

The Accuracy of the Titan 1+ 10 Hz Global Positioning System for Measures of Time, Distance, and Top Speed

Introduction: Global Positioning System (GPS) technology provide quantifiable data concerning the number and types of movements athletes complete across training session and match player. The Titan 1+ is a novel GPS device that has not been investigated to establish measures of accuracy or reliability. The purpose of this study was to establish device accuracy for metrics including distance, top speed, and time between the Titan 1+ 10 Hz GPS device and criterion measures. Criterion measures include tape measure, stopwatch, and radar gun for measures of distance, time, and top speed, respectively. Methods and Materials: 16 male NCAA collegiate soccer players completed running protocols of varying distances and speeds, including long and short duration straight-line running (100m Run and SLR), tight and gradual change of direction running (COD T and COD G), and a team-sport simulated circuit. Results: The Titan 1+ was not significantly different from tape measured distance during the SLR protocol for 10m jog and all movements speed across 20m and 40m (p < .05). The Titan 1+ was significantly correlated to radar gun top speed measures for all distances and movement speeds during the SLR (p < .01). The Titan 1+ was significantly different from the stopwatch for measures of time accuracy (p < .0001), however significant correlations were identified for all jogging distances (p < .01), the 20m and 40m strides (p < .01), and the 10m sprint (p < .01). COD G and COD T distances were accurately measured between the Titan 1+ and tape measure (p < .05). The Titan 1+ revealed correlations for time measures during jogging and striding during the COD G (p = .001 and p < .0001) and during the COD T (p < .0001 and p< .0001). Device accuracy was established for measure of time during the TSSC (p < .05), with time measures significantly correlated between the Titan 1+ and stopwatch (p < .01). Top speed for the TSSC was significantly different between the radar gun and Titan 1+ device (p = .001), but both devices were significantly correlated (p < .01). Conclusions: The results of the present study indicate that the Titan 1+ demonstrates accurate measures of time, distance, and top speed when compared to criterion measures.

Improving PPP timing and time transfer based on signal distortion biases calibration

Abstract Precise point positioning (PPP) technology has been widely used for more than a decade in timing and time transfer. Pseudorange bias caused by signal distortion will reach up ±3 ns, which cannot be neglected in sub-nanosecond level PPP timing and time transfer. We propose to improve the performance of PPP timing and time transfer by considering signal distortion bias (SDB). In principle, SDB will affect PPP timing and time transfer by influencing the initial clock offset. Then, it is verified by theoretical and experimental analyses. The theoretical results show that the impact of SDB on PPP timing can reach up 1.8 ns in different regions. Also, the impact of SDB on time transfer can reach up 0.8 ns in different regions even if the baseline length and receiver types are the same. For experimental results, the accuracy of PPP timing and time transfer improves with SDB correction. For time transfer, the RMSs are reduced by 54.8%, 61.1% and 17.6% for GPS, BDS3 and Galileo in all links, respectively. The clock frequency consistency obtained from different systems has been improved. And the frequency stability of time link with Hydrogen clocks is improved by 53.0% at 10000s interval with SDB correction.

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event can influence treatment plans, such as percutaneous coronary intervention (PCI). PCI is most beneficial within 3 hours of AMI onset. MicroRNAs (miRNAs) are promising biomarkers, with potential of early AMI diagnosis, since they are released before cell death and subsequent release of larger molecules [e.g., cardiac troponins (cTn)], and have greater sensitivity and stability in plasma versus cTn regardless of timing of AMI onset. However, miRNA-based AMI diagnosis can result in false positives due to miRNA content overlap between AMI and stable coronary artery disease (CAD). Accordingly, we explored the possibility of using a miRNA profile, rather than a single miRNA, to distinguish between CAD and AMI, as well as different stages following AMI onset. First we screened a library of 800 miRNA using plasma samples from 4 patient cohorts; no known CAD, CAD, ST-segment elevation myocardial infarction (STEMI) and STEMI followed by PCI, using Nanostring miRNA profiling technology. From this screening, based on machine learning SCAD and Lasso algorithms, we identified 9 biomarkers (miR-200b, miR-543, miR-331, miR-3605, miR-301a, miR-18a, miR-423, miR-142, and miR-132) that were differentially expressed in CAD, STEMI and STEMI-PCI and explored them to identify a miRNA profile for rapid and accurate AMI diagnosis. These 9 miRNAs were selected as the most frequently identified targets by SCAD and Lasso, as indicated in the "drum-plot" model in the machine learning approach. We used age-matched patient samples to validate selected 9 miRNA biomarkers using a multiplexed ion-exchange membrane-based miRNA sensor platform, which measures specific miRNAs, and cTn as a control, simultaneously as a point-of-care device. Findings from this study will inform timely and accurate diagnosis of AMI and its stages, which are essential for effective management and optimal patient outcomes.

Advanced Modeling and Interpretation for Accurate Intersection Traffic Time Prediction

Advanced Modeling and Interpretation for Accurate Intersection Traffic Time Prediction

Bolus tracking from pulsed x-ray projections: A feasibility study using a five-dimensional cardiac CT contrast dynamics model.

Cardiac computed tomography (CT) exams are some of the most complex CT exams due to the need to carefully time the scan when the heart chambers are near the peak contrast concentration. With current "bolus tracking" and "timing bolus" techniques, after contrast medium is injected, a target vessel or chamber is scanned periodically, and images are reconstructed to monitor the opacification. Both techniques have opportunities for improvement, such as reducing the contrast medium volume, the exam time, the number of manual steps, and improving the robustness of correctly timing the peak opacification. The objective of our study is to (1) develop a novel autonomous cardiac CT clinical workflow to track contrast bolus dynamics directly from pulsed x-ray projections, (2) develop a new five-dimensional virtual cardiac CT data generation tool with programmable cardiac profiles and bolus dynamics, and (3) demonstrate the feasibility of projection-domain prospective bolus tracking using a neural network trained and tested with the virtual data to find the contrast peak. In our proposed workflow, pulsed mode projections (PMPs) are acquired with a wide-open collimator under sparse view conditions (monitoring phase). Each time a new PMP is acquired, the neural network is used to estimate the contrast enhancement inside the target chambers. To train such a network, we introduce a new approach to generate clinically realistic virtual scan data based on a five-dimensional cardiac model, by synthesizing user-defined contrast bolus dynamics and patient electrocardiogram profiles. In this study, we investigated a scenario with one single PMP per rotation. To find the optimal PMP view angle, 20 angles were explored. For each angle, 300 virtual exams were generated from 115 human subject datasets and divided into training, validation, and testing groups. Twenty neural networks were trained and evaluated in total to find the optimal network. Finally, a simple bolus peak time estimation algorithm was developed and evaluated by comparing to the ground truth bolus peak time. To evaluate the accuracy of a bolus time-intensity curve estimated by the network, the cosine similarity between the estimation and the ground truth was computed. The cosine similarity was larger than 0.97 for all projection angles. A view angle corresponding to the x-ray tube at 30 degrees from vertical (left-anterior of subject) showed the lowest errors. The amplitude of the estimated bolus curves (in Hounsfield Units) was not always correctly predicted, but the shape was accurately predicted. This resulted in an RMSE of 1.23s for the left chambers and 0.78s for the right chambers in the contrast peak time estimation. In this study, we proposed an innovative real-time way to predict the contrast bolus peak in cardiac CT as well as an innovative approach to train a neural network using virtual but clinically realistic data. Our trained network successfully estimated the shape of the time-intensity curve for the target chambers, which led to accurate bolus peak time estimation. This technique could be used for autonomous diagnostic cardiac CT to trigger a diagnostic scan for optimal contrast enhancement.

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Classification of Infant Crying Sounds Using SE-ResNet-Transformer

Recently, emotion analysis has played an important role in the field of artificial intelligence, particularly in the study of speech emotion analysis, which can help understand one of the most direct ways of human emotional communication—speech. This study focuses on the emotion analysis of infant crying. Within cries lies a variety of information, including hunger, pain, and discomfort. This paper proposes an improved classification model using ResNet and transformer. It utilizes modified Mel-frequency cepstral coefficient Mel-frequency cepstral coefficient (MFCC) features obtained through feature engineering from infant cries and integrates SE attention mechanism modules into residual blocks to enhance the model’s ability to adjust channel weights. The proposed method achieved 93% accuracy rate in experiments, offering advantages of shorter training time and higher accuracy compared to other traditional models. It provides an efficient and stable solution for infant cry classification.

Prediction of Threonine-Tyrosine Kinase Receptor-Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics.

Accurately describing protein-ligand binding and unbinding kinetics remains challenging. Computational calculations are difficult and costly, while experimental measurements often lack molecular detail and can be unobtainable. Here, we extend our multiscale milestoning method, Simulation-Enabled Estimation of Kinetics Rates (SEEKR), with metadynamics molecular dynamics simulations to yield accurate small molecule drug residence times. Using the pharmaceutically relevant threonine-tyrosine kinase (TTK) and eight long-residence-time (tens of seconds to hours) inhibitors, we demonstrate accurate prediction of absolute and rank-ordered ligand residence times and free energies of binding.

Analysis of a Crank–Nicolson fast element-free Galerkin method for the nonlinear complex Ginzburg–Landau equation

A fast element-free Galerkin (EFG) method is proposed in this paper for solving the nonlinear complex Ginzburg–Landau equation. A second-order accurate time semi-discrete system is presented by using the Crank–Nicolson scheme for the temporal discretization, and then a meshless fully discrete system is established by using the EFG method for the spatial discretization. In the proposed EFG method, Nitsche’s technique is used to impose the essential boundary conditions in a weak sense, and the reproducing kernel gradient smoothing integration is used to accelerate the calculation. Theoretical errors for the time semi-discrete system and the fully discrete EFG system are analyzed in detail. Optimal error estimates of the fully discrete Crank–Nicolson EFG method are obtained in both L2 and H1 norms. Numerical results validate the theoretical results and the effectiveness of the method.

High-Accuracy Positivity-Preserving Finite Difference Approximations of the Chemotaxis Model for Tumor Invasion.

Numerical simulation of the complex evolution process for tumor invasion plays an extremely important role in-depth exploring the bio-taxis phenomena of tumor growth and metastasis. In view of the fact that low-accuracy numerical methods often have large errors and low resolution, very refined grids have to be used if we want to get high-resolution simulating results, which leads to a great deal of computational cost. In this paper, we are committed to developing a class of high-accuracy positivity-preserving finite difference methods to solve the chemotaxis model for tumor invasion. First, two unconditionally stable implicit compact difference schemes for solving the model are proposed; second, the local truncation errors of the new schemes are analyzed, which show that they have second-order accuracy in time and fourth-order accuracy in space; third, based on the proposed schemes, the high-accuracy numerical integration idea of binary functions is employed to structure a linear compact weighting formula that guarantees fourth-order accuracy and nonnegative, and then a positivity-preserving and time-marching algorithm is established; and finally, the accuracy, stability, and positivity-preserving of the proposed methods are verified by several numerical experiments, and the evolution phenomena of tumor invasion over time are numerically simulated and analyzed.

Clarifying early period hydration process of ultra‐low W/B cementitious composite subjected to underwater circumstance

AbstractAn innovative method is proposed in this study that combines long sequence precision prediction model with experiments to study how early hydration affects ultra‐low water‐binder ratio cementitious composite (ULWBRCC) properties. To be specific, by using heat release and degree of hydration as variables and introducing various temporal models to analyze the early hydration process, the micro‐ and macroscopic properties of specimens at different ages are assessed in detail. The obtained results can be summarized as follows: The long‐sequence accurate time series model can predict the earliest hydration process of underwater ULWBRCC, and the predicted late‐stage hydration pattern matches the experimental results. The underwater environment optimizes ULWBRCC's hydration process and increases its early compressive strength by 19.6%. The hydration of ULWBRCC is predicted using a time series model in the paper, filling the gap of uncertainty in the earliest performance of underwater rapid repair cement‐based composite materials, and the MSE of the Informer is improved by 92.88% compared with other models.