Search Results (2,825)

Structural dynamics analysis is essential for predicting the behavior of engineering systems under dynamic forces. This study presents a hybrid framework that combines analytical modeling, machine learning, and optimization techniques to enhance the accuracy and efficiency of dynamic response predictions for Single-Degree-of-Freedom (SDOF) systems subjected to harmonic excitation. Utilizing a classical spring–mass–damper model, Fourier decomposition is applied to derive transient and steady-state responses, highlighting the effects of damping, resonance, and excitation frequency. To overcome the uncertainties and limitations of traditional models, Extended Kalman Filters (EKFs) and Physics-Informed Neural Networks (PINNs) are incorporated, enabling precise parameter estimation even with sparse and noisy measurements. We use Adam followed by L-BFGS to improve accuracy while limiting runtime. Numerical experiments using 1000 time samples with a 0.01 s sampling interval demonstrate that the proposed PINN model achieves a displacement MSE of 0.0328, while the Eurocode 8 response-spectrum estimation yields 0.047, illustrating improved predictive performance under noisy conditions and biased initial guesses. Although the present study focuses on a linear SDOF system under harmonic excitation, it establishes a conceptual foundation for adaptive dynamic modeling that can be extended to performance-based seismic design and to future calibration of Eurocode 8. The harmonic framework isolates the fundamental mechanisms of amplitude modulation and damping adaptation, providing a controlled environment for validating the proposed PINN–EKF approach before its application to transient seismic inputs. Controlled-variable analyses further demonstrate that key dynamic parameters can be estimated with relative errors below 1%—specifically 0.985% for damping, 0.391% for excitation amplitude, and 0.692% for excitation frequency—highlighting suitability for real-time diagnostics, vibration-sensitive infrastructure, and data-driven design optimization. This research deepens our understanding of vibratory behavior and supports future developments in smart monitoring, adaptive control, resilient design, and structural code modernization. Full article

Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation. Full article

Background: T cell regeneration in the thymus is intrinsically linked to the T cell-biased lineage differentiation of hematopoietic stem and progenitor cells (HSPCs). Although nonhuman primates (NHPs) serve as indispensable models for studying thymic output under physiological and pathological conditions, a non-animal technology facilitating efficient TCR-selected T cell development and evaluating T cell output from NHP-derived HSPCs has been lacking. To address this gap, we established a rhesus macaque-specific artificial thymic organoid (RhATO) modeling primary thymus-tissue-free thymopoiesis. Methods: The RhATO was developed by expressing Rhesus macaque (RM) Delta-like Notch ligand 1 in mouse bone marrow stromal cell line (MS5-RhDLL1). The bone marrow-derived HSPCs were aggregated with MS5-RhDLL1 and cultured forming 3D artificial thymic organoids. These organoids were maintained under defined cytokine conditions to support complete T cell developmental ontogeny. T cell developmental progression was assessed by flow cytometry, and TCR-selected subsets were analyzed for phenotypic and functional properties. Results: RhATOs recapitulated the complete spectrum of thymopoietic events, including emergence of thymus-seeding progenitors, CD4⁺CD3⁻ immature single-positive and CD4⁺CD8⁺ double-positive early thymocytes, and mature CD4⁺ or CD8⁺ single-positive subsets. These subsets expressed CD38, consistent with the recent thymic emigrant phenotype, and closely mirrored canonical T cell ontogeny described in humans. RhATO-derived T cells were TCR-selected and demonstrated cytokine expression upon stimulation. Conclusions: This study provides the first demonstration of an NHP-specific artificial thymic technology that faithfully models thymopoiesis. RhATO represents a versatile ex vivo platform for studying T cell development, immunopathogenesis, and generating TCR selected T cells. Full article

Models suggest social anxiety is characterized by negative processing biases. Negative biases also arise from negative mood, i.e., state affect. We examined how social anxiety influences emotional processing and whether state affect, or mood, modified the relationship between social anxiety and perceptual bias. We quantified bias by determining the point of subjective equality, PSE, the face judged equally often as happy and as angry. We found perceptual bias depended on social anxiety and state affect. PSE was greater in individuals high (mean PSE: 8.69) versus low (mean PSE: 3.04) in social anxiety. The higher PSE indicated a stronger negative bias in high social anxiety. State affect modified this relationship, with high social anxiety associated with stronger negative biases, but only for individuals with greater negative affect. State affect and trait anxiety interacted such that social anxiety status alone was insufficient to fully characterize perceptual biases. This raises several issues such as the need to consider what constitutes an appropriate control group and the need to consider state affect in social anxiety. Importantly, our results suggest compensatory effects may counteract the influences of negative mood in individuals low in social anxiety. Full article

Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrupt data integrity. These variations create systematic biases between supposedly identical measurement units, which undermine scientific reproducibility and yield. The current site-to-site variation detection methods require extensive sampling or make rigid distributional assumptions, making them impractical for many applications. We introduce a novel framework that transforms measurement data into density-based feature vectors using Kernel Density Estimation, followed by anomaly detection with Isolation Forest. To automate the final classification, we then apply a novel probabilistic thresholding method using Gaussian Mixture Models, which removes the need for user-defined anomaly proportions. This approach identifies problematic measurement sites without predefined anomaly proportions or distributional constraints. Unlike traditional methods, our method works efficiently with limited samples and adapts to diverse measurement contexts. We demonstrate its effectiveness using semiconductor multisite testing as a case study, where our approach consistently outperforms state-of-the-art methods in detection accuracy and sample efficiency when validated against industrial testing environments. Full article

Despite the widespread success of convolutional deep learning frameworks in computer vision, significant limitations persist in medical image analysis. These include low image quality caused by noise and artifacts, limited data availability compromising robustness on unseen data, class imbalance leading to biased predictions, and insufficient feature representation, as conventional CNNs often fail to capture subtle patterns and complex dependencies. To address these challenges, we propose DAME (Diffusion-Augmented Meta-Learning Ensemble), a unified architecture that integrates hybrid modeling with generative learning using the Denoising Diffusion Probabilistic Model (DDPM). The DDPM component improves resolution, augments scarce data, and mitigates class imbalance. A hybrid backbone combining CNN, Vision Transformer (ViT), and CBAM captures both local dependencies and long-range spatial relationships, while CBAM further enhances feature representation by adaptively emphasizing informative regions. Predictions from multiple hybrids are aggregated, and a logistic regression meta classifier learns from these outputs to produce robust decisions. The framework is evaluated on the HAM10000 dataset, a benchmark for multi-class skin cancer classification. Explainable AI is incorporated through Grad CAM, providing visual insights into the decision-making process. This synergy mitigates CNN limitations and demonstrates superior generalizability, achieving 98.6% accuracy, 0.986 precision, 0.986 recall, and a 0.986 F1-score, significantly outperforming existing approaches. Overall, the proposed framework enables accurate, interpretable, and reliable medical image diagnosis through the joint optimization of contextual modeling, feature discrimination, and data generation. Full article

Medical image segmentation has been a central research focus in deep learning, but methods based on convolutions have limitations in modeling the long-range validity of images. To overcome this issue, hybrid CNN-Transformer architectures have been explored, with SwinUNet being a classic approach. However, SwinUNet still faces challenges such as insufficient modeling of relative position information, limited feature fusion capabilities in skip connections, and the loss of translational invariance caused by Patch Merging. To overcome these limitations, the architecture RE-XswinUnet is presented as a novel solution for medical image segmentation. In our design, relative position biases are replaced with rotary position embedding to enhance the model’s ability to extract detailed information. During the decoding stage, XskipNet is designed to improve cross-scale feature fusion and learning capabilities. Additionally, an SCAR Block downsampling module is incorporated to preserve translational invariance more effectively. The experimental results demonstrate that RE-XswinUnet achieves improvements of 2.65% and 0.95% in Dice coefficients on the Synapse multi-organ and ACDC datasets, respectively, validating its superiority in medical image segmentation tasks. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) exhibits marked resistance to immunotherapy. Beyond its characteristically low tumor mutational burden, post-translational modifications (PTMs) remodel the immunopeptidome and promote immune escape through reversible, enzyme-driven programs. Subject Matter: We synthesize evidence that aberrant glycosylation, O-GlcNAcylation, phosphorylation, and citrullination constitute core determinants of antigen visibility operating within spatially discrete tumor niches and a desmoplastic stroma. In hypoxic regions, HIF-linked hexosamine metabolism and OGT activity stabilize immune checkpoints and attenuate antigen processing; at tumor margins, sialylated mucins engage inhibitory Siglec receptors on innate and adaptive lymphocytes; within the stroma, PAD4-dependent NET formation enforces T cell exclusion. We also delineate technical barriers to discovering PTM antigens labile chemistry, low stoichiometry, and method-embedded biases and outline practical solutions: ETD/EThcD/AI-ETD fragmentation, PTM-aware database searching and machine-learning models, and autologous validation in patient-derived organoid–T cell co-cultures. Finally, we highlight therapeutic strategies that either immunize against PTM neoepitopes or inhibit PTM machinery (e.g., PAD4, OGT, ST6GAL1), with stromal remodeling as an enabling adjunct. Conclusions: PTM biology, spatial omics, and patient sample models can uncover targetable niches and speed up PDAC vaccination, TCR, and enzyme-directed treatment development. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Artificial Intelligence (AI) has achieved immense progress in recent years across a wide array of application domains, with biomedical imaging and sensing emerging as particularly impactful areas. However, the integration of AI in safety-critical fields, particularly biomedical domains, continues to face a major challenge of explainability arising from the opacity of complex prediction models. Overcoming this obstacle falls within the realm of eXplainable Artificial Intelligence (XAI), which is widely acknowledged as an essential aspect for successfully implementing and accepting AI techniques in practical applications to ensure transparency, fairness, and accountability in the decision-making processes and mitigate potential biases. This article provides a systematic cross-domain review of XAI techniques applied to quantitative prediction tasks, with a focus on their methodological relevance and potential adaptation to biomedical imaging and sensing. To achieve this, following PRISMA guidelines, we conducted an analysis of 44 Q1 journal articles that utilised XAI techniques for prediction applications across different fields where quantitative databases were used, and their contributions to explaining the predictions were studied. As a result, 13 XAI techniques were identified for prediction tasks. Shapley Additive eXPlanations (SHAP) was identified in 35 out of 44 articles, reflecting its frequent computational use for feature-importance ranking and model interpretation. Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDPs), and Permutation Feature Index (PFI) ranked second, third, and fourth in popularity, respectively. The study also recognises theoretical limitations of SHAP and related model-agnostic methods, such as their additive and causal assumptions, which are particularly critical in heterogeneous biomedical data. Furthermore, a synthesis of the reviewed studies reveals that while many provide computational evaluation of explanations, none include structured human–subject usability validation, underscoring an important research gap for clinical translation. Overall, this study offers an integrated understanding of quantitative XAI techniques, identifies methodological and usability gaps for biomedical adaptation, and provides guidance for future research aimed at safe and interpretable AI deployment in biomedical imaging and sensing. Full article

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation. Full article

With ongoing climate transformations, reliable Arctic sea ice forecasts are essential for understanding impacts on shipping, ecosystems, and climate teleconnections. This research examines physics-free neural architectures versus physics-informed statistical models for long-term Arctic projections by implementing Fourier Neural Operator (FNO) and Convolutional Neural Network (CNN) alongside a seasonal SARIMAX time series model incorporating physical predictors including temperature anomalies and ice thickness. We test whether neural models trained on historical ice data can match physics-informed SARIMAX reliability, and whether approaches exhibit systematic biases toward specific emission pathways. Using data from January 1979 to December 2024, we conducted forecasts through 2100, with SARIMAX driven by CMIP6 sea ice thickness under SSP2-4.5 and SSP5-8.5 scenarios. Results decisively reject the first hypothesis: both neural models projected ice free Arctic summer by September 2089 regardless of emission scenario, while SARIMAX maintained physically plausible seasonal coverage throughout the century under both pathways. Neural approaches demonstrated systematic bias toward extreme warming exceeding even high-emission projections, revealing fundamental limitations in physics-free deep learning for climate forecasting where physical constraints are paramount. Full article

The impact of climate change on watershed hydrological processes has become increasingly significant, with the frequent occurrence of extreme flood events posing a severe challenge to the water resource security of the upper Yangtze River and the Three Gorges Reservoir. To enhance the understanding of runoff evolution under future climate scenarios, this study focuses on the upper Yangtze River Basin, integrating CMIP6 climate model data with the WRF-Hydro model to systematically assess the effects of climate change on runoff projections. Firstly, using CMFD data as a benchmark, the systematic biases in CMIP6 simulation results were evaluated and corrected. Precipitation and temperature data accuracy were improved through Local Intensity Correction (LOCI) and Daily Bias Correction (DBC). Secondly, a large-scale WRF-Hydro model suitable for the upper Yangtze River was developed and calibrated. Finally, based on the corrected CMIP6 data, the climate and runoff changes under the SSP2-4.5 and SSP5-8.5 scenarios for the period 2021–2050 were projected. The results show that: (1) the corrected CMIP6 data significantly improved issues of overestimated precipitation and underestimated temperature, providing a more realistic reflection of regional climate characteristics; (2) the sub-basin calibration strategy outperformed the overall calibration strategy at key control sites, with high runoff simulation accuracy during the validation period; (3) future temperatures exhibit a continuous rising trend, while precipitation changes are not significant—however, the magnitude and uncertainty of extreme events increase—and (4) under the SSP5-8.5 scenario, the inflow to the Three Gorges Reservoir during the wet season significantly increases, raising flood risk. The findings provide a scientific basis for understanding the hydrological response mechanisms in the upper Yangtze River Basin under climate change and offer decision-making support for flood control scheduling and water resource management at the Three Gorges Reservoir. Full article

Arid-region water resource management is hindered by severely inadequate river discharge monitoring, with effective observations of hydrological processes particularly lacking in narrow river channels. To overcome this bottleneck, this study proposes an integrated multi-model remote sensing retrieval framework and systematically evaluates the applicability of Manning’s equation, the At-Many-Stations Hydraulic Geometry (AHG) model, and the AHG’s relaxed form (AMHG) in typical arid-region rivers on the northern slope of the Kunlun Mountains. Runoff was estimated by integrating multi-source remote sensing imagery (Sentinel-2, Landsat-8, and Gaofen-1) on the Google Earth Engine platform and combining it with genetic algorithms for parameter optimization. The results indicate that Manning’s equation performed the best overall (RMSE = 21.78 m³/s, NSE = 0.94) and was highly robust to river width extraction errors, with Manning’s roughness coefficient having a significantly greater impact than the hydraulic slope. The AHG model can construct long-term discharge series based on limited measured data but is sensitive to the accuracy of river width extraction. Although the AMHG model improved the retrieval performance, its effectiveness was constrained by systematic biases in proxy variables. The study also found that the AHG exponent b in the rivers of this region exhibits high stability (coefficient of variation < 0.09), providing a theoretical basis for constructing a sustainable discharge monitoring system. The integrated method developed in this study offers a reliable technical pathway for dynamic hydrological monitoring and quantitative water resource management in data-scarce arid regions. Full article

Different viewing or shooting situations can affect color authenticity and generally lead to visual inconsistencies for the same images. At present, deep learning has gained popularity and opened up new avenues for image processing and optimization. In this paper, we propose a novel regression model named TococoNet (Tongue Color Correction Network) that extends from CNN (convolutional neural network) to eliminate the color bias in tongue images. The TococoNet model consists of symmetric encoder-–decoder U-Blocks which are connected by M-Block through concatenation layers for feature fusion at different levels. Initially, we train our model by simulatively introducing five common biased colors. The various image quality indicators holistically demonstrate that our model achieves accurate color correction for tongue images, and simultaneously surpasses conventional algorithms and shallow networks. Furthermore, we conduct correction experiments by introducing random degrees of color bias, and the model continues to perform well for achieving excellent correction effects. The model achieves up to 84% correction effectiveness in terms of color distance ΔE for tongue images with varying degrees of random color cast. Finally, we obtain excellent color correction for actual captured images for tongue diagnosis application. Among these, the maximum ΔE can be reduced from 30.38 to 6.05. Overall, the TococoNet model possesses excellent color correction capabilities, which opens promising opportunities for clinical assistance and automatic diagnosis. Full article