Search Results (5,198)

Radiotherapy, a cornerstone of cancer treatment, is critically limited by tumor radioresistance and off-target toxicity. Lanthanide-based nanomaterials (Ln-NPs) have recently emerged as a versatile and promising class of theranostic radiosensitizers to overcome these hurdles. This review comprehensively outlines the state-of-the-art in Ln-NP-enabled radiotherapy, beginning with their fundamental physicochemical properties and synthesis and then delving into the multi-level mechanisms of radiosensitization, including high-Z element-mediated physical dose amplification, catalytic generation of reactive oxygen species (ROS), and disruption of DNA damage repair pathways. The unique capacity of certain Ln-NPs to serve as MRI contrast agents is highlighted as the foundation for image-guided, dose-painting radiotherapy. We critically summarize the preclinical and clinical progress of representative systems, benchmarking them against other high-Z nanomaterials. Finally, this work discusses the ongoing challenges, such as biocompatibility, targeted delivery, and regulatory hurdles, and envisages future directions, including combinatorial strategies with immunotherapy and the development of personalized nanotheranostic paradigms. Through this synthesis, this review aims to provide a clear roadmap for the continued development and clinical integration of lanthanide nanotheranostics in oncology. Full article

Fine-grained sentiment analysis requires a deep understanding of emotional intensity in the text to distinguish subtle shifts in polarity, such as moving from positive to more positive or from negative to more negative, and to clearly separate emotionally neutral statements from polarized expressions, especially in short or contextually sparse texts such as social media posts. While recent advances combine deep semantic encoding with context-aware architectures, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), many models still struggle to detect nuanced emotional cues, particularly in short texts, due to the limited contextual information, subtle polarity shifts, and overlapping affective expressions, which ultimately hinder performance and reduce a model’s ability to make fine-grained sentiment distinctions. To address this challenge, we propose an Emotion- Aware Bidirectional Gating Network (Electra-BiG-Emo) that improves sentiment classification and subtle sentiment differentiation by learning contextual emotion representations and refining them with auxiliary emotional signals. Our model employs an asymmetric gating mechanism within a BiLSTM to dynamically capture both early and late contextual semantics. The gates are temperature-controlled, enabling adaptive modulation of emotion priors, derived from Reddit post datasets to enhance context-aware emotion representation. These soft emotional signals are reweighted based on context, enabling the model to amplify or suppress emotions in the presence of an ambiguous context. This approach advances fine-grained sentiment understanding by embedding emotional awareness directly into the learning process. Ablation studies confirm the complementary roles of semantic encoding, context modeling, and emotion modulation. Further our approach achieves competitive performance on Sem- Val 2017 Task 4c, Twitter US Airline, and SST5 datasets compared with state-of-the-art methods, particularly excelling in detecting subtle emotional variations and classifying short, semantically sparse texts. Gating and modulation analyses reveal that emotion-aware gating enhances interpretability and reinforces the value of explicit emotion modeling in fine-grained sentiment tasks. Full article

Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics across different regions and directions, often leading to the over-smoothing of edges and loss of fine details. To address this limitation, we propose a novel regularization Hyper-Laplacian Adaptive Weighted Total Variation (HLAWTV). The proposed regularization employs a proportional mixture of Hyper-Laplacian distributions to dynamically adapt the sparsity decay rate based on image structure. Simultaneously, the adaptive weights can be adjusted based on local gradient statistics and exhibit strong robustness in texture preservation when facing different datasets and noise. Then, we propose an hyperspectral image (HSI) denoising method based on the HLAWTV regularizer. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that our denoising method consistently outperforms state-of-the-art methods in terms of quantitative metrics and visual quality. Moreover, incorporating our adaptive weighting mechanism into existing TV-based models yields significant performance gains, confirming the generality and robustness of the proposed approach. Full article

An accurate assessment of chronic kidney disease (CKD) requires understanding both renal morphology and functional decline, yet most deep learning approaches treat segmentation and eGFR prediction as separate tasks. This paper proposes the Functionally Guided CKD U-Net (FG-CKD-UNet), a dual-headed multitask architecture that integrates multi-class kidney segmentation with end-to-end eGFR prediction using a structure–function consistency loss. The model incorporates a morphological biomarker extractor to derive cortical thickness, kidney volume, and cortex–medulla ratios, enabling explicit coupling between anatomy and physiology. Experiments on T2-weighted MRI and colorized CT datasets demonstrate that the proposed method surpasses state-of-the-art segmentation baselines, achieving a Dice score of 0.94 and an HD95 of 9.8 mm. For functional prediction, the model achieves an MAE of 0.039, an RMSE of 0.058, and a Pearson correlation of 0.92, outperforming CNN, MLP, and ResNet baselines. The structure–function consistency mechanism reduces the consistency error from 0.071 to 0.042, confirming coherent physiological modeling. The results indicate that the FG-CKD-UNet provides a reliable, interpretable, and physiologically grounded framework for comprehensive CKD assessment. Full article

Electroencephalography (EEG)-based motor imagery (MI) brain–computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model’s interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the “Bad” performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications. Full article

Unmanned aerial vehicle (UAV) penetration missions in hostile environments face significant challenges due to dense threat coverage, dynamic defense systems, and the need for real-time decision-making under uncertainty. Traditional path planning methods suffer from computational intractability in high-dimensional spaces, while existing deep reinforcement learning approaches lack efficient feature extraction and sample utilization mechanisms for threat-dense scenarios. To address these limitations, this paper presents an enhanced Deep Q-Network (DQN) framework integrating multi-head attention mechanisms with dynamic priority experience replay for autonomous UAV path planning. The proposed architecture employs four specialized attention heads operating in parallel to extract proximity, danger, alignment, and threat density features, enabling selective focus on critical environmental aspects. A dynamic priority mechanism adaptively adjusts sampling strategies during training, prioritizing informative experiences in early exploration while maintaining balanced learning in later stages. Experimental results demonstrate that the proposed method achieves 94.3% mission success rate in complex penetration scenarios, representing 7.1–17.5% improvement over state-of-the-art baselines with 2.2× faster convergence. The approach shows superior robustness in high-threat environments and meets real-time operational requirements with 18.3 ms inference latency, demonstrating its practical viability for autonomous UAV penetration missions. Full article

Transformers for time series forecasting rely on positional encoding to inject temporal order into the permutation-invariant self-attention mechanism. Classical sinusoidal absolute encodings are fixed and purely geometric; learnable absolute encodings often overfit and fail to extrapolate, while relative or advanced schemes can impose substantial computational overhead without being sufficiently tailored to temporal data. This work introduces a family of window-statistics positional encodings that explicitly incorporate local temporal semantics into the representation of each timestamp. The base variant (WinStat) augments inputs with statistics computed over a sliding window; WinStatLag adds explicit lag-difference features; and hybrid variants (WinStatFlex, WinStatTPE, WinStatSPE) learn soft mixtures of window statistics with absolute, learnable, and semantic positional signals, preserving the simplicity of additive encodings while adapting to local structure and informative lags. We evaluate proposed encodings on four heterogeneous benchmarks against state-of-the-art proposals: Electricity Transformer Temperature (hourly variants), Individual Household Electric Power Consumption, New York City Yellow Taxi Trip Records, and a large-scale industrial time series from heavy machinery. All experiments use a controlled Transformer backbone with full self-attention to isolate the effect of positional information. Across datasets, the proposed methods consistently reduce mean squared error and mean absolute error relative to a strong Transformer baseline with sinusoidal positional encoding and state-of-the-art encodings for time series, with WinStatFlex and WinStatTPE emerging as the most effective variants. Ablation studies that randomly shuffle decoder inputs markedly degrade the proposed methods, supporting the conclusion that their gains arise from learned order-aware locality and semantic structure rather than incidental artifacts. A simple and reproducible heuristic for setting the sliding-window length—roughly one quarter to one third of the input sequence length—provides robust performance without the need for exhaustive tuning. Full article

Predicting information diffusion in social networks is a fundamental problem in many applications, and one of the primary challenges is to predict the future popularity of information in social networks. However, most existing models fail to simultaneously capture the accurate micro-level user node features, meso-level linear spread features, and predict the macro-level popularity during the information propagation process, which may result in unsatisfactory prediction performance. To address this issue, we propose a new cascade prediction framework CasNS: Node-level and Sequence-level Features for Cascade Prediction. CasNS utilizes node-level features by employing a self-attention mechanism to capture the hidden features of the target node with respect to other nodes. Additionally, it leverages multiple one-dimensional convolutional layers with the dynamic routing algorithm to obtain sequence-level features across different dimensions. Through experiments on a large number of real-world datasets, our model demonstrates superior performance compared with other state-of-the-art methods, thereby validating the feasibility of our approach. Full article

Metal Injection Molding (MIM) is an established, high-precision manufacturing route for small, geometrically complex metallic components, integrating polymer injection molding with powder metallurgy. State-of-the-art feedstock systems, such as Catamold (polyacetal-based), enable catalytic debinding performed in furnaces operating under ultra-high-purity nitric acid atmospheres (>99.999%). The subsequent thermal stages pre-sintering and sintering are carried out in continuous controlled-atmosphere furnaces or vacuum systems, typically employing inert (N₂) or reducing (H₂) atmospheres to meet the specific thermodynamic requirements of each alloy. However, incomplete decomposition or secondary volatilization of binder residues can lead to progressive hydrocarbon accumulation within the sinering chamber. These contaminants promote undesirable carburizing atmospheres, which, under austenitizing or intercritical conditions, increase carbon diffusion and generate uncontrolled surface carbon gradients. Such effects alter the microstructural evolution, hardness, wear behavior, and mechanical integrity of MIM steels. Conversely, inadequate dew point control may shift the atmosphere toward oxidizing regimes, resulting in surface decarburization and oxide formation effects that are particularly detrimental in stainless steels, tool steels, and martensitic alloys, where surface chemistry is critical for performance. This review synthesizes current knowledge on atmosphere-induced surface deviations in MIM steels, examining the underlying thermodynamic and kinetic mechanisms governing carbon transport, oxidation, and phase evolution. Strategies for atmosphere monitoring, contamination mitigation, and corrective thermal or thermochemical treatments are evaluated. Recommendations are provided to optimize surface substrate interactions and maximize the functional performance and reliability of MIM-processed steel components in demanding engineering applications. Full article

Accurate parameter estimation of photovoltaic (PV) models is fundamentally a challenging nonlinear optimization problem, characterized by strong nonlinearity, high dimensionality, and multiple local optima. These characteristics significantly hinder the convergence accuracy, stability, and efficiency of conventional metaheuristic algorithms when applied to PV parameter identification. Although the enterprise development (ED) optimization algorithm has shown promising performance in various optimization tasks, it still suffers from slow convergence, limited solution precision, and poor robustness in complex PV parameter estimation scenarios. To overcome these limitations, this paper proposes a multi-strategy enhanced enterprise development (MEED) optimization algorithm for high-precision PV model parameter estimation. In MEED, a hybrid initialization strategy combining chaotic mapping and adversarial learning is designed to enhance population diversity and improve the quality of initial solutions. Furthermore, a historical trend-guided position update mechanism is introduced to exploit accumulated search information and accelerate convergence toward the global optimum. In addition, a mirror-reflection boundary control strategy is employed to maintain population diversity and effectively prevent premature convergence. The proposed MEED algorithm is first evaluated on the IEEE CEC2017 benchmark suite, where it is compared with 11 state-of-the-art metaheuristic algorithms under 30-, 50-, and 100-dimensional settings. Quantitative experimental results demonstrate that MEED achieves superior solution accuracy, faster convergence speed, and stronger robustness, yielding lower mean fitness values and smaller standard deviations on the majority of test functions. Statistical analyses based on Wilcoxon rank-sum and Friedman tests further confirm the significant performance advantages of MEED. Moreover, MEED is applied to the parameter estimation of single-diode and double-diode PV models using real measurement data. The results show that MEED consistently attains lower root mean square error (RMSE) and integrated absolute error (IAE) than existing methods while exhibiting more stable convergence behavior. These findings demonstrate that MEED provides an efficient and reliable optimization framework for PV model parameter estimation and other complex engineering optimization problems. Full article

Deep learning for semantic segmentation has made significant advances in recent years, achieving state-of-the-art performance. Medical image segmentation, as a key component of healthcare systems, plays a vital role in the diagnosis and treatment planning of diseases. Due to the fractal and scale-invariant nature of biological structures, effective medical image segmentation requires models capable of capturing hierarchical and self-similar representations across multiple spatial scales. In this paper, a Recurrent Neural Network (RNN) is explored within the Convolutional Neural Network (CNN) and Vision Transformer (ViT)-based hybrid U-shape network, named RCV-UNet. First, the ViT-based layer was developed in the bottleneck to effectively capture the global context of an image and establish long-range dependencies through the self-attention mechanism. Second, recurrent residual convolutional blocks (RRCBs) were introduced in both the encoder and decoder to enhance the ability to capture local features and preserve fine details. Third, by integrating the global feature extraction capability of ViT with the local feature enhancement strength of RRCBs, RCV-UNet achieved promising global consistency and boundary refinement, addressing key challenges in medical image segmentation. From a fractal–fractional perspective, the multi-scale encoder–decoder hierarchy and attention-driven aggregation in RCV-UNet naturally accommodate fractal-like, scale-invariant regularity, while the recurrent and residual connections approximate fractional-order dynamics in feature propagation, enabling continuous and memory-aware representation learning. The proposed RCV-UNet was evaluated on four different modalities of images, including CT, MRI, Dermoscopy, and ultrasound, using the Synapse, ACDC, ISIC 2018, and BUSI datasets. Experimental results demonstrate that RCV-UNet outperforms other popular baseline methods, achieving strong performance across different segmentation tasks. The code of the proposed method will be made publicly available. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide, underscoring the need for diagnostic tools that early, accurate, and clinically interpretable. Current artificial intelligence (AI) models are predominantly unimodal and lack sufficient interpretability, which restricts their clinical adoption. Methods: We propose IDF-Net, an interpretable dynamic fusion framework that integrates endoscopy, computed tomography (CT), and histopathology using modality-specific encoders, a dual-stage adaptive gating mechanism, and cross-modal attention. We conducted stratified 5-fold cross-validation and assessed interpretability using spatial heatmaps and modality attribution. We also quantified the results using the intersection-over-union metric for saliency alignment. Results: IDF-Net achieved a state-of-the-art accuracy of 0.920 (0.907–0.936) and area under the curve (AUC) of 0.991 (95% CI: 0.965–0.997), significantly outperforming unimodal and static-fusion baselines (p < 0.05). Interpretability analysis of IDF-Net demonstrated a strong alignment between Gradient-weighted Class Activation Mapping++ heatmaps and expert-annotated lesions, as well as case-specific modality contributions via SHapley Additive exPlanations values. Ablation studies confirmed the contribution of each component, with dynamic routing and cross-attention fusion improving AUC by 0.038 and 0.046, respectively. Conclusions: IDF-Net introduces a dynamically fused, multimodal diagnostic framework with integrated quantitative interpretability, demonstrating superior accuracy and strong potential for clinical translation in CRC diagnosis. The model’s adaptive design allows it to function robustly even when CT data is unavailable, aligning with common clinical pathways while leveraging additional imaging when present for comprehensive staging. Full article

Remaining Useful Life (RUL) prediction for aeroengines represents a core challenge in Prognostics and Health Management (PHM), with significant implications for condition-based maintenance, operational cost reduction, and flight safety enhancement. Current deep learning-based approaches encounter three major limitations when handling multi-source sensor data: conventional convolution operations struggle to model heterogeneous sensor feature distributions, leading to computational redundancy; simplistic multimodal fusion strategies often induce semantic conflicts; and high model complexity hinders industrial deployment. To address these issues, this paper proposes a novel Partial Convolution Attention-enhanced CNN-LSTM Network (PALC-Net). We introduce a partial convolution mechanism that applies convolution to only half of the input channels while preserving identity mappings for the remainder. This design retains representational power while substantially lowering computational overhead. A dual-branch feature extraction architecture is developed: the temporal branch employs a PConv-CNN-LSTM architecture to capture spatio-temporal dependencies, while the statistical branch utilizes multi-scale sliding windows to extract physical degradation indicators—such as mean, standard deviation, and trend. Additionally, an adaptive fusion module based on cross-attention is designed, where heterogeneous features are projected into a unified semantic space via Query-Key-Value mappings. A sigmoid gating mechanism is incorporated to enable dynamic weight allocation, effectively mitigating inter-modal conflicts. Extensive experiments on the NASA C-MAPSS dataset demonstrate that PALC-Net achieves state-of-the-art performance across all four subsets. Notably, on the FD003 subset, it attains an MAE of 7.70 and an R² of 0.9147, significantly outperforming existing baselines. Ablation studies validate the effectiveness and synergistic contributions of the partial convolution, attention mechanism, and multimodal fusion modules. This work offers an accurate and efficient solution for aeroengine RUL prediction, achieving an effective balance between engineering practicality and algorithmic sophistication. Full article

Pansharpening plays a crucial role in remote sensing applications, as it enables the generation of high-spatial-resolution multispectral images that simultaneously preserve spatial and spectral information. However, most current methods struggle to preserve local textures and exploit spectral correlations across bands while modeling nonlocal information in source images. To address these issues, we propose a cascaded local–nonlocal pansharpening network (CLNNet) that progressively integrates local and nonlocal features through stacked Progressive Local–Nonlocal Fusion (PLNF) modules. This cascaded design allows CLNNet to gradually refine spatial–spectral information. Each PLNF module combines Adaptive Channel-Kernel Convolution (ACKC), which extracts local spatial features using channel-specific convolution kernels, and a Multi-Scale Large-Kernel Attention (MSLKA) module, which leverages multi-scale large-kernel convolutions with varying receptive fields to capture nonlocal information. The attention mechanism in MSLKA enhances spatial–spectral feature representation by integrating information across multiple dimensions. Extensive experiments on the GaoFen-2, QuickBird, and WorldView-3 datasets demonstrate that the proposed method outperforms state-of-the-art methods in quantitative metrics and visual quality. Full article

This paper proposes a Takagi–Sugeno–Kang Elliptic Type-2 Fuzzy Brain-Imitated Neural Network (TET2FNN)-based decoupling control strategy for nonlinear underactuated mechanical systems subject to uncertainties. A sliding-mode framework is employed to construct a decoupled control architecture, in which an intermediate variable is introduced to separate two second-order sliding surfaces, thereby forming a decoupled slip surface. The TET2FNN acts as the main controller and approximates the ideal control law online, while a robust compensator is incorporated to suppress approximation errors and guarantee closed-loop stability. Simulation studies conducted on a double inverted pendulum system demonstrate that the proposed method achieves improved tracking accuracy and disturbance rejection compared with representative state-of-the-art controllers. Furthermore, the computational burden remains reasonable, indicating that the proposed scheme is suitable for real-time implementation and practical nonlinear control applications. Full article