Search Results (676)

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Magnetic Particle Imaging (MPI) is a cutting-edge noninvasive imaging technique that offers high sensitivity, quantitative accuracy, and operates without the need for ionizing radiation compared to other imaging techniques. Utilizing superparamagnetic iron oxide nanoparticles (SPIONs) as tracers, MPI enables direct and precise visualization of target sites with no limitation on imaging depth. Unlike magnetic resonance imaging (MRI), which relies on uniform magnetic fields to produce anatomical images, MPI enables direct, background-free visualization and quantification of SPIONS within living organisms. This article provides an in-depth overview of MPI’s applications in tracking tumor development and supporting cancer therapy. The distinct physical principles that underpin MPI, including its ability to produce high-contrast images devoid of background tissue interference, facilitating accurate tumor identification and real-time monitoring of treatment outcomes, are outlined. The review outlines MPI’s advantages over conventional imaging techniques in terms of sensitivity and resolution, and examines its capabilities in visualizing tumor vasculature, tracking cellular movement, evaluating inflammation, and conducting magnetic hyperthermia treatments. Recent progress in tracer optimization and magnetic navigation has expanded MPI’s potential for targeted drug delivery, along with deep machine learning procedures for MPI applications. Additionally, considerations around safety and the feasibility of clinical implementation are also discussed in the present review. Overall, MPI is positioned as a promising tool in advancing cancer diagnostics, personalized therapy assessment, and noninvasive treatment strategies. Full article

As the Internet of Things (IoT) expands and spectrum resources become increasingly scarce, Automatic Modulation Classification (AMC) has become critical for enabling dynamic spectrum access, interference mitigation, and spectrum monitoring without coordination or prior signaling. Most deep learning-based AMC methods (e.g., CNNs, LSTMs, Transformers) operate in Euclidean spaces and therefore overlook the non-Euclidean relationships inherent in modulated signals. We propose KGNN, a graph-based AMC architecture that couples a KNN-driven graph representation with GraphSAGE convolutions for neighborhood aggregation. In the KNN stage, each feature vector is connected to its nearest neighbors, transforming temporal signals into structured graphs, while GraphSAGE extracts relational information across nodes and edges for classification. On the RML2016.10b dataset, KGNN attains an overall accuracy of 64.72%, outperforming strong baselines (including MCLDNN) while using only one-eighth the number of parameters used by MCLDNN and preserving fast inference. These results highlight the effectiveness of graph convolutional modeling for AMC under practical resource constraints and motivate further exploration of graph-centric designs for robust wireless intelligence. Full article

IEA Wind Task 43 seeks to “unlock the full value of wind energy through digital transformation”. One mechanism to realize value is through enhanced data-driven decision-making and, while many areas in the wind sector can benefit from improved decision support, this case study focusses on a well-defined wind energy maintenance scenario involving blade inspection and repair. The solution concentrates on the specific damage category of blade leading edge erosion (LEE) and the optimum action to be taken for a given level of damage detected during periodic inspections. The key decision is whether to initiate repairs immediately or continue operating the turbine until the next inspection—and, if so, when that next inspection should take place. Even for such a specific damage type and decision option, the overall solution draws on multiple data types, ranging from damage classifications to cost drivers, and integrates a number of components including damage propagation, performance, and cost models. The core of the solution is a risk-based decision model using heuristic strategies, and Bayesian networks for optimized decision-making. This paper outlines the overall solution, expands on the data and modelling implementations, and discusses the results and conclusions arising from the investigation. Full article

Reliable and efficient detection of persimmons provides the foundation for precise maturity evaluation. Persimmon ripeness detection remains challenging due to small target sizes, frequent occlusion by foliage, and motion- or focus-induced blur that degrades edge information. This study proposes YOLOv11-4ConvNeXtV2, an enhanced detection framework that integrates a ConvNeXtV2 backbone with Fully Convolutional Masked Auto-Encoder (FCMAE) pretraining, Global Response Normalization (GRN), and Single-Head Self-Attention (SHSA) mechanisms. We present a comprehensive persimmon dataset featuring sub-block segmentation that preserves local structural integrity while expanding dataset diversity. The model was trained on 4921 annotated images (original 703 + 6 × 703 augmented) collected under diverse orchard conditions and optimized for 300 epochs using the Adam optimizer with early stopping. Comprehensive experiments demonstrate that YOLOv11-4ConvNeXtV2 achieves 95.9% precision and 83.7% recall, with mAP@0.5 of 88.4% and mAP@0.5:0.95 of 74.8%, outperforming state-of-the-art YOLO variants (YOLOv5n, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n) by 3.8–6.3 percentage points in mAP@0.5:0.95. The model demonstrates superior robustness to blur, occlusion, and varying illumination conditions, making it suitable for deployment in challenging maturity detection environments. Full article

An optical vortex is a typical structured light field characterized by a helical wavefront and a central phase singularity. With its expanding applications in modern information technology, the demand for generating vortex beams with diverse topological charges continues to grow. Existing methods for modulating the topological charges of vortex beams involve complex operations and high costs. This study proposes a novel approach to modulate the topological charges of optical vortices through edge diffraction of a high-order Hermit–Gaussian (HG) mode laser. First, a high-order HG mode laser is built using off-axis pumping configuration. By selectively obscuring specific lobes of the high-order HG beam, various optical vortices are generated using a cylindrical lens mode converter. The topological charge can be continuously tuned by controlling the number of obscured lobes. This method substantially improves the efficiency of topological charge modulation, while also enabling the generation of fractional vortex states. These advancements show potential in mode-division-multiplexed optical communications and encryption. Full article

In the context of global agricultural modernization, the early and accurate detection of sugarcane leaf diseases is critical for ensuring stable sugar production. However, existing deep learning models still face significant challenges in complex field environments, such as blurred lesion edges, scale variation, and limited generalization capability. To address these issues, this study constructs an efficient recognition model for sugarcane disease detection, named CaneFocus-Net, specifically designed for precise identification of sugarcane leaf diseases. Based on a single-stage detection architecture, the model introduces a lightweight cross-stage feature fusion module (CP) to optimize feature transfer efficiency. It also designs a module combining a channel-spatial adaptive calibration mechanism with multi-scale pooling aggregation to enhance the backbone network’s ability to extract multi-scale lesion features. Furthermore, by expanding the high-resolution shallow feature layer to enhance sensitivity toward small-sized targets and adopting a phased adaptive nonlinear optimization strategy, detection and localization accuracy along with convergence efficiency have been further improved. Test results on public datasets demonstrate that this method significantly enhances recognition performance for fuzzy lesions and multi-scale targets while maintaining high inference speed. Compared to the baseline model, precision, recall, and mean average precision (mAP50 and mAP50-95) improved by 1.9%, 4.6%, 1.5%, and 1.4%, respectively, demonstrating strong generalization capabilities and practical application potential. This provides reliable technical support for intelligent monitoring of sugarcane diseases in the field. Full article

Background: The rising prevalence of gastric, biliary, and pancreatic surgeries has led to an increasing population of patients with surgically altered anatomy (SAA). In this setting, conventional endoscopic retrograde cholangiopancreatography (ERCP) is often limited by anatomical barriers, resulting in high rates of technical failure and complications. While device-assisted enteroscopy (DAE) has expanded therapeutic possibilities, its efficacy remains modest in complex reconstructions. Methods: This review analyzed recent literature from PubMed, Embase, and Scopus up to April 2025, focusing on diagnostic and therapeutic roles of endoscopic ultrasound (EUS) in SAA. Particular attention was given to cases where standard endoscopic, percutaneous, or surgical techniques failed and to studies comparing EUS-guided approaches with alternative modalities. Results: EUS has transitioned from a primarily diagnostic modality to a versatile therapeutic platform in SAA. Techniques such as EUS-guided rendezvous, antegrade drainage, and hepaticogastrostomy have shown technical and clinical success rates exceeding 80–90%, often comparable or superior to interventional radiology, while reducing the need for external drains. Innovative procedures, including EUS-directed transgastric ERCP (EDGE) and EUS-directed enteroenteric bypass (EDEE), have transformed the management of Roux-en-Y gastric bypass and bilioenteric anastomoses, providing durable and reusable access for repeated interventions. Despite these advances, EUS-guided interventions remain technically demanding, requiring advanced endoscopic and radiologic skills, specialized devices, and are best performed in tertiary referral centers. Conclusions: EUS has redefined the treatment paradigm of biliopancreatic diseases in patients with SAA, increasingly emerging as the preferred minimally invasive approach when conventional techniques fail. Future developments will focus on dedicated devices, standardized guidelines, and structured training programs to optimize outcomes. Multidisciplinary collaboration and centralization in high-volume centers remain essential to ensure safety, efficacy, and reproducibility. Full article

Current research on heritage corridors predominantly focuses on linear heritage in Europe and America, while studies in Asia urgently need to be expanded. This study investigates China’s linear heritage. Based on the minimum cumulative resistance (MCR) model, it conducts heritage corridor construction for the Henan section of the Sui–Tang Grand Canal, and reveals the following: (1) A total of 252 heritage sites were classified into three categories: canal hydraulic heritage (13.5%), canal settlement heritage (21.4%) and related heritage (65.1%), exhibiting a “local clustering under global dispersion” pattern with a core–secondary–edge structure. (2) The influence of natural–social resistance factors was ranked as follows: elevation > roads > land use > slope. Interwoven corridors were simulated by GIS and optimized to four primary corridors with multiple secondary corridors. (3) The transverse zone of the primary corridors was stratified into core area (0–10 km from the centerline), buffer area (10–25 km), and influence area (>25 km) with a total width of 25–30 km. The longitudinal section was partitioned into four subsections based on hydrological continuity and heritage density. Then, a tripartite conservation framework characterized by “heritage clusters–holistic corridor–transverse stratification and longitudinal section” was proposed. It aimed to provide insights into methodologies and content structuring for transnational linear heritage (e.g., the Silk Road and the Inca Trail). Full article

Deep neural network-based approaches have obtained remarkable progress in monaural speech enhancement. Nevertheless, current cutting-edge approaches remain vulnerable to complex acoustic scenarios. We propose a Symmetric Combined Convolution Network with ConvLSTM (SCCN) for monaural speech enhancement. Specifically, the Combined Convolution Block utilizes parallel convolution branches, including standard convolution and two different depthwise separable convolutions, to reinforce feature extraction in depthwise and channelwise. Similarly, Combined Deconvolution Blocks are stacked to construct the convolutional decoder. Moreover, we introduce the exponentially increasing dilation between convolutional kernel elements in the encoder and decoder, which expands receptive fields. Meanwhile, the grouped ConvLSTM layers are exploited to extract the interdependency of spatial and temporal information. The experimental results demonstrate that the proposed SCCN method obtains on average 86.00% in STOI and 2.43 in PESQ, which outperforms the state-of-the-art baseline methods, confirming the effectiveness in enhancing speech quality. Full article

Historic urban alleys encapsulate cultural identity and collective memory but are increasingly threatened by commercialization and context-insensitive redevelopment. Preserving their authenticity while enhancing environmental resilience requires design strategies that integrate both heritage and ecological values. This study explores the potential of generative artificial intelligence (AI) to support biophilic design in historic alleys, focusing on Daegu, South Korea. Four alley typologies—path, stairs, edge, and node—were identified through fieldwork and analyzed across cognitive, emotional, and physical dimensions of place identity. A Flux-based diffusion model was fine-tuned using low-rank adaptation (LoRA) with site-specific images, while a structured biophilic design prompt (BDP) framework was developed to embed ecological attributes into generative simulations. The outputs were evaluated through perceptual and statistical similarity indices and expert reviews (n = 8). Results showed that LoRA training significantly improved alignment with ground-truth images compared to prompt-only generation, capturing both material realism and symbolic cues. Expert evaluations confirmed the contextual authenticity and biophilic effectiveness of AI-generated designs, revealing typology-specific strengths: the path enhanced spatial legibility and continuity; the stairs supported immersive sequential experiences; the edge transformed rigid boundaries into ecological transitions; and the node reinforced communal symbolism. Emotional identity was more difficult to reproduce, highlighting the need for multimodal and interactive approaches. This study demonstrates that generative AI can serve not only as a visualization tool but also as a methodological platform for participatory design and heritage-sensitive urban regeneration. Future research will expand the dataset and adopt multimodal and dynamic simulation approaches to further generalize and validate the framework across diverse urban contexts. Full article

Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics coupling framework has been established, characterization of dynamic evolution over complex terrain and coupled physical mechanisms remains inadequate. Detection technology is undergoing a paradigm shift from traditional contact measurements to non-contact intelligent perception. Visual systems based on UAVs and fixed platforms have achieved breakthroughs in ice recognition and thickness retrieval, yet their performance remains constrained by image quality, data scale, and edge computing capabilities. Anti-/de-icing technologies have evolved into an integrated system combining active intervention and passive defense: DC de-icing (particularly MMC-based topologies) has become the mainstream active solution for high-voltage lines due to its high efficiency and low energy consumption; superhydrophobic coatings, photothermal functional coatings, and expanded-diameter conductors show promising potential but face challenges in durability, environmental adaptability, and costs. Future development relies on the deep integration of mechanistic research, intelligent perception, and active prevention technologies. Through multidisciplinary innovation, key technologies such as digital twins, photo-electro-thermal collaborative response systems, and intelligent self-healing materials will be advanced, with the ultimate goal of comprehensively enhancing power grid resilience under extreme climate conditions. Full article

Mitral valve repair has evolved significantly with the advent of advanced surgical and transcatheter techniques. Innovations such as 3D visualization, robotic surgery, and transcatheter edge-to-edge repair have improved procedural precision and expanded treatment options for high-risk patients. Emerging technologies, including transcatheter mitral valve repair, annuloplasty, and chordal systems, offer tailored solutions for complex mitral pathology. Personalized treatment strategies, guided by multimodality imaging and artificially intelligence-driven planning, are reshaping clinical decision-making. Ongoing trials and next-generation devices are poised to enhance long-term outcomes, marking a shift toward minimally invasive, precision-guided mitral valve therapy. This review aims to provide a comprehensive overview of recent technological advances, clinical applications, and future directions in mitral valve repair across surgical and interventional domains. Full article

Accurate automatic sleep stage classification from single-channel EEG remains challenging due to the need for effective extraction of multiscale neurophysiological features and modeling of long-range temporal dependencies. This study aims to address these limitations by developing an efficient and compact deep learning architecture tailored for wearable and edge device applications. We propose MultiScaleSleepNet, a hybrid convolutional neural network–bidirectional long short-term memory–transformer architecture that extracts multiscale temporal and spectral features through parallel convolutional branches, followed by sequential modeling using a BiLSTM memory network and transformer-based attention mechanisms. The model obtained an accuracy, macro-averaged F1 score, and kappa coefficient of 88.6%, 0.833, and 0.84 on the Sleep-EDF dataset; 85.6%, 0.811, and 0.80 on the Sleep-EDF Expanded dataset; and 84.6%, 0.745, and 0.79 on the SHHS dataset. Ablation studies indicate that attention mechanisms and spectral fusion consistently improve performance, with the most notable gains observed for stages N1, N3, and rapid eye movement. MultiScaleSleepNet demonstrates competitive performance across multiple benchmark datasets while maintaining a compact size of 1.9 million parameters, suggesting robustness to variations in dataset size and class distribution. The study supports the feasibility of real-time, accurate sleep staging from single-channel EEG using parameter-efficient deep models suitable for portable systems. Full article

This study aims to address the research gap in the digital analysis of traditional patterns by proposing an image-processing-driven parametric modeling method that combines graphic primitive function modeling with topological reconstruction. The image is processed using a dual-channel image processing algorithm (Canny edge detection and grayscale mapping) to extract and vectorize graphic primitives. These primitives are uniformly represented using B-spline curves, with variations generated through parametric control. A topological reconstruction approach is introduced, incorporating mapped geometric parameters, topological combination rules, and geometric adjustments to output topological configurations. The generated patterns are evaluated using fractal dimension analysis for complexity quantification and applied in cultural heritage imaging practice. The proposed image processing pipeline enables flexible parametric control and continuous structural integration of the graphic primitives and demonstrates high reproducibility and expandability. This study establishes a novel computational framework for traditional patterns, offering a replicable technical pathway that integrates image processing, parametric modeling, and topological reconstruction for digital expression, stylistic innovation, and heritage conservation. Full article