Prediction Of Response Research Articles

Sustainability assessment and mechanical characteristics of high strength concrete blended with marble dust powder and wheat straw ash as cementitious materials by using RSM modelling

Research into environmentally favorable alternatives to conventional concrete, such as reengineering techniques and the recycling of industrial and agricultural waste, has increased dramatically over the past decade. This research intended to examine the potential benefits of utilizing wheat straw ash (WSA) and marble dust powder (MDP) as supplementary cementitious materials (SCM) in high-strength concrete (HSC). The intention is to decrease carbon dioxide emissions and the amount of Portland cement (PC) required. Concrete formulations containing 0%, 15%, 20%, 30%, and 40% by weight of MDP and WSA as SCM, respectively, were evaluated by using response surface methodology (RSM) modelling for multi-objective optimization. This research assessed the environmental ramifications of employing WSA and MDP as SCM in HSC. Specifically, it examined the impacts on embodied carbon and eco-strength efficiency. Cubical specimens underwent a series of tests to attain the desirable compressive strength of approximately 60 MPa after a period of 28 days. The workability of green concrete decreased as the proportion of WSA and MDP used as SCM increased, according to the study. The optimum compressive strength, flexural strength, splitting tensile strength, and modulus of elasticity of HSC was recorded by 68 MPa, 5.98 MPa, 4.25 MPa, and 40 GPa, at 15% of SCM (composed of 10% WSA and 5% MDP) at 28 days respectively. Using ANOVA, response prediction models were validated and developed with a confidence level of 95%. In summary, the increased application of 20% WSA and 20% MDP as SCM in concrete leads to a 31.64% reduction in the carbon footprint of the material.

Towards autonomous inland shipping: a manoeuvring model in confined waterways

ABSTRACT Autonomous inland water vessels are essential for promoting intelligent and sustainable waterborne transport. An accurate ship manoeuvring model ensures reliable control strategies and enhances navigation safety. Although ship manoeuvrability models have existed for decades, few studies address shallow and restricted waters. This study introduces a manoeuvring model for inland water vessels, accounting for confinement effects on ship motion. The Manoeuvring Modelling Group (MMG) model in open water serves as the baseline, incorporating empirical methods for shallow water and bank effects. This approach aims to provide a fast and accurate prediction of vessel motion response. The model was validated with free-running experimental data of a pusher-barge model from turning tests at three water depths. Additional case studies highlight shallow water impact compared to infinite water performance under bank effects. Finally, course-keeping case studies are presented, integrating a Proportional-Derivative controller with combined river current and bank-induced forces..

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment.

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Progress of circulating tumor DNA methylation for gastric cancer screening and management

Circulating tumor DNA (ctDNA) is cell-free DNA released by tumors or circulating tumor cells, containing abundant tumor-specific information that can serve as biomarkers for cancer early screening, monitoring, prognosis, and prediction of treatment response. This is particularly attractive in the field of gastric cancer, where high-quality screening, monitoring, and prediction methods are currently lacking. Gastric cancer exhibits significant tumor heterogeneity, with large differences in genetic and epigenetic characteristics among different subgroups. Methylated ctDNA has high sensitivity and specificity, which can help clarify tumor genotyping and facilitate the formulation of precise diagnostic and therapeutic strategies. Furthermore, numerous studies have confirmed the unique advantages of methylated DNA in predicting treatment response, adjuvant therapy, and drug resistance assessment, which may be used in the future to enhance the efficacy of chemotherapy regimens and improve patient chemotherapeutic response, and even treat multidrug resistance. However, there are several challenges associated with methylated ctDNA, such as low sensitivity and specificity at single-target sites, limited association between some gastric cancer subtypes and ctDNA, off-target risks, and the lack of large-scale and high-quality clinical research evidence. This review mainly summarizes current research on the methylation status of ctDNA in gastric cancer and connects these findings to early screening, recurrence monitoring, and potential treatment opportunities for gastric cancer. With advances in technology and the deepening of interdisciplinary research, ctDNA detection will reveal more disease information and become an essential foundation for gastric cancer research and precision medicine treatment.

Progress and application of lung-on-a-chip for lung cancer

Lung cancer is a malignant tumour with the highest incidence and mortality worldwide. Clinically effective therapy strategies are underutilized owing to the lack of efficient models for evaluating drug response. One of the main reasons for failure of anticancer drug therapy is development of drug resistance. Anticancer drugs face severe challenges such as poor biodistribution, restricted solubility, inadequate absorption, and drug accumulation. In recent years, “organ-on-a-chip” platforms, which can directly regulate the microenvironment of biomechanics, biochemistry and pathophysiology, have been developed rapidly and have shown great potential in clinical drug research. Lung-on-a-chip (LOC) is a new 3D model of bionic lungs with physiological functions created by micromachining technology on microfluidic chips. This approach may be able to partially replace animal and 2D cell culture models. To overcome drug resistance, LOC realizes personalized prediction of drug response by simulating the lung-related microenvironment in vitro, significantly enhancing therapeutic effectiveness, bioavailability, and pharmacokinetics while minimizing side effects. In this review, we present an overview of recent advances in the preparation of LOC and contrast it with earlier in vitro models. Finally, we describe recent advances in LOC. The combination of this technology with nanomedicine will provide an accurate and reliable treatment for preclinical evaluation.

QRS length/left ventricular mass index: a novel predictive factor of response to cardiac resynchronization therapy

Abstract Introduction The length of QRS interval is an extensively studied predictor of response to CRT. There is little evidence of the probability of response in patients with QRS between 130 and 150 ms and its relationship with LV mass. (1,2) Objective Evaluation of the QRS length/LV mass index (QRS/LVMi) as a "relative dyssynchrony" marker and its correlation to response after CRT implantation. Methods Retrospective study that included patients over 18 years of age with HFrEF (LVEF ≤ 35%) and CRT implantation between years 2018 – 2022 (n = 92). QRS length (ms) was measured with CardioLab 3.0 Software and divided by LV mass (g), according to Deveraux equation using echocardiography measurements. Primary outcome was response to CRT defined by: Improvement of at least 1-point in NYHA functional class + (i) increase of at least 5% in LVEF or (ii) decrease of at least 15% in LVESD. Results QRS/LVMi value ≥ 0.6 was associated with a higher frequency of response to CRT than values < 0.6 (83% responders vs 17% non responders; OR 5.1; CI 2.7–12.1, p < 0.05). There was no difference in response to CRT between QRS > 150 ms and 130 – 150 ms in patients with QRS/LVMi ≥ 0.6 (80% vs 82% of responders, p = 0.8). A multivariate analysis showed a significantly higher probability of response to CRT in patients with QRS/LVMi ≥ 0.6 compared to other known predictive factors (OR 4.1; CI 1.2–12, p < 0.05). Conclusions The use of QRS/LVMi was associated with a better prediction of response to CRT than other traditional factors. There was no difference in response between QRS > 150 ms and 130 - 150 ms in patients with QRS/LVMi ≥ 0.6, suggesting the efficacy of CRT in patients with shorter QRS interval length and less LV mass, highlighting the importance of the concept of relative dyssynchrony.Total Response according QRS/LMViMultivariate analysis

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Multiscale simulation and experimental studies on modal damping of ultra‐thick CFRP laminates

AbstractThe vibration characterization of ultra‐thick laminates widely used in aerospace and marine industries was different from that of thin laminates. However, modal testing and simulation methods were generally limited to relatively thin laminates. To explore the vibration behavior of ultra‐thick laminates, a novel multiscale simulation technology based on the representative volume modal strain energy (RVMSE) method is presented for predicting modal damping in this paper. The adaptive Greedy‐Based Generation (GBG) algorithm is used to create a 3D random distribution of fibers for composites. Modal shape information of the structure is obtained according to the Lanczos algorithm, which is used as an input parameter for the microscopic model. This is followed by homogenization based on the volumetric analysis performed on representative volume elements (RVEs) to determine the strain energy in six directions. The calculation of modal damping is performed by using the modal strain energy (MSE) method and the damping properties of the six directions. The fast rate of convergence and high accuracy of the method are demonstrated through different examples. The vibration response predictions produced by novel RVMSE methodology are shown to closely match experimental measurements, providing scope to expand the application of this approach to more complex, ultra‐thick laminate components.Highlights The RVMSE method uses a higher level of homogenization to overcome the limitations of the traditional MSE method for calculating the damping of ultra‐thick CFRP laminates. The modal parameter error is controlled to less than 10%, and the experimental results fill the gap of the damping data of ultra‐thick CFRP structure. The proposed RVMSE model improves the accuracy of dynamic response prediction for ultra‐thick laminates.

Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning

The implementation of a machine learning approach to predict vibration suppression, as derived from nanocomposite laminates with piezoelectric shunted systems, is studied in this article. Datasets providing the vibration response and vibration attenuation are developed using parametric finite element simulations. A graphene/fibre-reinforced laminate cantilever beam is used in those simulations. Parameters, including the graphene and fibre reinforcements content, as well as the fibre angles, are among the inputs. Output is the vibration suppression achieved by the piezoelectric shunted system. Artificial Neural Networks are trained and tested using the derived datasets. The proposed methodology can be used for a fast and accurate prediction of the vibration response of nanocomposite laminates.

Diagnostic Accuracy of DNA-Methylation in Detection of Cervical Dysplasia: Findings from a Population-Based Screening Program

Background: Epigenetic biomarkers in cancer have emerged as promising tools for early detection, prognosis, and treatment response prediction. In cervical cells, hypermethylation of the host and viral HPV-genome increases with the severity of lesions, providing a useful biomarker in the triage of hr-HPV-positive women and during treatment. The present study focuses on evaluating the clinical performance of the FAM19A4/miR124-2 methylation test in a population-based cervical screening program. Methods: Previously collected cervical samples, after bisulfite-converted DNA, were analyzed by PreCursor-M+ kit (distributed by Fujirebio Europe), for DNA methylation. The sensitivity, specificity, and negative/positive predictive values of DNA methylation were compared to histology, colposcopy, the HPV-DNA test, and cytology results. Results: Among the 61-sample set, the specificity of methylation vs. positive histology (≥CIN2) and colposcopy (≥G2) were 87% and 90%, whereas the sensitivity was 50% and 33.3%, respectively. The combination of methylation analysis with standard methods increases diagnostic accuracy. Conclusions: Overall, we found a good specificity of DNA methylation in comparison to currently used techniques. Further larger studies could support the use of FAM19A4/miR124-2 as reliable biomarkers in the prevention of cervical cancer as triage in the screening protocol.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype.

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open. An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype. Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ). The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

RedCDR: Dual Relation Distillation for Cancer Drug Response Prediction.

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction. The source code is available at https://github.com/mhxu1998/RedCDR.

Understanding mesopredator responses to changes in apex predator populations in Europe: implications for the mesopredator release hypothesis

Abstract Conservation successes in Europe have sparked the recovery of apex predators, which may affect the populations of mesopredators. The mesopredator release hypothesis (MRH) predicts that a decline in apex predators triggers a ‘release’ of mesopredators from suppression. We expanded the MRH to include predictions of inverse responses following increases in apex predator abundance or distribution. We examined European mesopredator responses to changes in apex predator populations and evaluated the extended MRH's explanatory power. We examined the results of 47 studies on European predators for supporting evidence, mixed evidence, no evidence, or contrasting evidence for the extended MRH. Out of 38 apex predator – mesopredator pairings, 10 predominantly supported the extended MRH, while others provided limited or no support. In 23 instances across 17 pairings, mesopredator responses contradicted predictions. Initial findings suggest the extended MRH better predicts mesopredator responses in pairings with intermediate to large body size differences and in pairings where mesopredators primarily hunt. Our results reveal non‐uniform mesopredator responses to changing apex predator populations. This emphasises the complexity of predator interactions, which extends beyond suppression. We suggest the MRH's scope is more restricted than previously thought, cautioning against overreliance on the MRH as a default expectation for European mesopredator responses to apex predator population changes.

Finite element model updating and response prediction of a frame structure based on optimal sensor placement

This paper proposes an approach for finite element (FE) model updating and response prediction of frame structures based on optimal sensor placement (OSP), which integrates sensor placement optimization, mode expansion, and model updating techniques. Firstly, sensor optimization layout and modal testing analysis are conducted on a bolted laboratory frame structure. The covariance-driven stochastic subspace identification (SSI-COV) method identifies the real-world structure’s natural frequencies, modal damping, and mode shapes. Secondly, the complete mode shapes are expanded using the measured incomplete modal data from the limited number of sensors. Thirdly, a multi-objective function based on frequency and mode shapes is established to adjust the parameters of the FE model. This ensures that the updated model accurately represents the dynamic properties of the actual structure within a specific frequency range. Finally, the Rayleigh damping of the frame structure is estimated, and the damping matrix is assembled to enhance the accuracy of dynamic response prediction in the updated model. By comparing the response prediction results of the updated FE model with and without considering the updated damping effects to the measurement data of the real-world structure, it is demonstrated that the proposed method considering updated damping effects can more effectively predict the structural response.

Deep learning-based prediction of structural responses of RC slabs subjected to blast loading

Considering the risk of explosion, studying the dynamic structural behavior of reinforced concrete (RC) slabs subjected to blast loads is crucial for enhancing their resistance. Conventional approaches, including costly experimental tests, highly hypothetical analytical methods, and time-consuming numerical simulations, have their limitations. This study presents two deep learning-based models for rapid and accurate prediction of explosion-induced responses in RC slabs. Available literature data and supplemented numerical simulation data are used for training and testing. First, a multi-layer perceptron (MLP) model is established to predict the maximum displacement of RC slabs subjected to explosions. The input features include 10 parameters. The training results show that the MLP model exhibits good prediction performance. Second, a one-dimensional convolutional neural network (1D-CNN) model is constructed to predict the failure modes of RC slabs under explosive loads. The input features of the model are consistent with those of the MLP model. The 1D-CNN model outperforms the other five conventional machine learning models in classification. Furthermore, a permutation feature importance analysis is conducted to determine the effects of the 10 input features on the predicted outcomes. This analysis makes the predictions interpretable, thereby bolstering the credibility of the models. Results show that the proposed deep learning models offer an efficient and reliable method to predict the structural response of RC slabs under explosion loads.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

BackgroundThe emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.MethodsRelevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.ResultsA total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629–0.828) for PFS in the training set, 0.760 (95%CI: 0.728–0.792) and 0.819 (95%CI: 0.757–0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721–0.771) and 0.700 (95%CI: 0.677–0.724) for OS in the training and validation sets, respectively.ConclusionMachine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Prediction of TKI response in EGFR-mutant lung cancer patients-derived organoids using malignant pleural effusion

Patient-derived organoids (PDOs) are valuable in predicting response to cancer therapy. PDOs are ideal models for precision oncologists. However, their practical application in guiding timely clinical decisions remains challenging. This study focused on patients with advanced EGFR-mutated non-small cell lung cancer and employed a cancer organoid-based diagnosis reactivity prediction (CODRP)-based precision oncology platform to assess the efficacy of EGFR inhibitor treatments. CODRP was employed to evaluate EGFR-tyrosine kinase inhibitors (TKI) drug sensitivity. The results were compared to those obtained using area under the curve index. This study validated this index by testing lung cancer-derived organoids in 14 patients with lung cancer. The CODRP index-based drug sensitivity test reliably classified patient responses to EGFR-TKI treatment within a clinically suitable 10-day timeline, which aligned with clinical drug treatment responses. This approach is promising for predicting and analyzing the efficacy of anticancer, ultimately contributing to the development of a precision medicine platform.

Contrasting carbon cycle responses to dry (2015 El Niño) and wet (2008 La Niña) extreme events at an Amazon tropical forest

Land surface models diverge in their predictions of the Amazon forest's response to climate change-induced droughts, with some showing a catastrophic collapse of forests, while others simulating resilience. Therefore, observations of tropical ecosystem responses to real-world droughts and other extreme events are needed. We report long-term seasonal dynamics of photosynthesis, respiration, net carbon exchange, phenology, and tree demography and characterize the effect of dry and wet events on ecosystem form and function at the Tapajós National Forest, Brazil, using over two decades of eddy covariance observations that include the 2015–2016 El Niño drought and La Niña 2008–2009 wet periods. We found strong forest responses to both ENSO events: La Niña saw forest net carbon loss from reduced photosynthesis (due to lower incoming radiation from increased cloudiness) even as ecosystem respiration (Reco) was maintained at mean seasonal levels. El Niño induced the opposite short-term effect, net carbon gains, despite significant reductions in photosynthesis (from a drought-induced halving of canopy conductance to CO2 and significant losses of leaf area), because drought suppression of Reco losses was even greater. However, long-term responses to the two climate perturbations were very different: transient during La Niña –the forest returned to its “normal” state as soon as the climate did, and long-lasting during El Niño –leaf area loss and associated declines in photosynthetic capacity (Pc) and canopy conductance were exacerbated and extended by feedbacks from higher temperatures and atmospheric evaporative demand and persisted for ∼3+ years after normal rainfall resumed. These findings indicate that these forests are more vulnerable to drought than to excess rain, because drought drives significant changes in forest structure (e.g., leaf-abscission and mortality) and ecosystem function (e.g. reduced stomatal conductance). As future Amazonian climate change increases frequencies of hydrological extremes, these mechanisms will determine the long-term fate of tropical forests.

Response prediction and probabilistic analysis of the vehicle-ballasted track system considering track irregularity based on long-short term memory neural network

It is necessary to efficiently and accurately predict the dynamic behavior of the train-track system subjected to massive random track geometric excitations for system evaluation and maintenance. This paper proposed an engineering practical approach for the train-track system behavior prediction by integrating the deep learning surrogate model-based long-short term memory (LSTM) and physical model of the train-ballasted track interaction (TBTI) system, and then the generalized probability density evolution method (GPDEM) is introduced to achieve the probabilistic assessment of the TBTI system under limited track irregularities, where the track irregularity probability model (TIPM) coupled with generalized-F discrepancy points sets is employed to compress the track irregularity sets to reduce the number of system responses (as training target) but without loss of the responses completeness, therefore, it can promote the efficiency and accuracy of training model. The prediction performance of the surrogate model is illustrated by comparing the time-frequency domain information, mean and standard deviation of the responses obtained by the physical model and surrogate model. The results indicated the proposed method can accurately predict the system response, especially the low and medium frequency response below 50 Hz, and improve the efficiency of stochastic analysis for the TBTI system by approximately 20 times. Furthermore, the applicability of the proposed method is further examined by investigating the influence of probability levels of track irregularity and train speeds on train performance. Its extended model is also implemented to predict the response of systems induced by any measured track irregularity in real-time, which promotes the generalization capability of the proposed surrogate model.

Advanced Computational Analysis of Cobalt-Based Superalloys through Crystal Plasticity.

This study introduces an advanced computational method aimed at accelerating continuum-scale processes using crystal plasticity approaches to predict mechanical responses in cobalt-based superalloys. The framework integrates two levels, namely, sub-grain and homogenized, at the meso-scale through crystal plasticity finite element (CPFE) platforms. The model is applicable across a temperature range from room temperature up to 900 °C, accommodating various dislocation mechanisms in the microstructure. The sub-grain level explicitly incorporates precipitates and employs a dislocation density-based constitutive model that is size-dependent. In contrast, the homogenized level utilizes an activation energy-based constitutive model, implicitly representing the γ' phase for efficiency in computations. This level considers the effects of composition and morphology on mechanical properties, demonstrating the potential for cobalt-based superalloys to rival nickel-based superalloys. The study aims to investigate the impacts of elements including tungsten, tantalum, titanium, and chromium through the homogenized constitutive model. The model accounts for the locking mechanism to address the cross-slip of screw dislocations at lower temperatures as well as the glide and climb mechanism to simulate diffusions at higher temperatures. The model's validity is established across diverse compositions and morphologies, as well as various temperatures, through comparison with experimental data. This advanced computational framework not only enables accurate predictions of mechanical responses in cobalt-based superalloys across a wide temperature range, but also provides valuable insights into the design and optimization of these materials for high-temperature applications.