Search Results (1,336)

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

Pesticide application efficiency is fundamentally governed by the spatial distribution of droplet deposition. However, characterizing this dynamic process is challenging due to complex environmental and canopy variables. Consequently, conventional offline sampling methods lack the temporal and spatial resolution required for modern intelligent spraying systems. This review systematically examines recent progress in droplet deposition detection. We first revisit traditional methods like water-sensitive paper, addressing high-coverage quantification biases and fluorescence-based techniques. Next, we analyze real-time sensing technologies, including capacitive and optical sensors, highlighting their responsiveness and inherent physical constraints. Furthermore, deep learning approaches for droplet detection, overlap segmentation, and geometric-to-physical regression are discussed. While these methods substantially enhance feature extraction, they often struggle with cross-scenario generalization. Ultimately, current techniques face inherent trade-offs among real-time capability, quantification accuracy, and environmental adaptability, remaining insufficient for complex field conditions. To enable reliable closed-loop control in precision plant protection, future research must prioritize multi-modal sensor fusion, the integration of data-driven and physics-based models, and real-time deployment via edge computing. Full article

Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by layer, leading to degraded surface quality and impaired mechanical performance of as-built components—a critical bottleneck limiting their large-scale industrial adoption. Accurate and robust layer-wise melting quality recognition remains a challenge due to the complex surface morphologies induced by such melting anomalies. This study presents a machine learning-enabled in situ monitoring approach for layer-wise melting quality identification in L-PBF. By systematically varying laser power and scanning speed, 24 parameter combinations were designed to fabricate specimens with three distinct melting states: over-melting (OM), lack of fusion (LOF), and normal melting. A high-resolution complementary meta–oxide–semiconductor (CMOS) camera was used to capture layer-wise surface images of the specimens, and following abnormal layer filtering and manual validation, a high-quality dataset comprising 5110 layer-wise images was constructed. Two mainstream machine learning approaches were systematically evaluated and optimized for melting quality classification: a support vector machine (SVM) model leveraging handcrafted gray-level co-occurrence matrix (GLCM) texture features achieved a classification accuracy of 96.77%, while a convolutional neural network (CNN) model with end-to-end feature learning directly from raw images attained a superior accuracy of 98.14%. In terms of computational efficiency, the CNN model exhibited a faster inference speed with a per-layer inference time of just 0.036 s, nearly half that of the SVM model (0.068 s per layer). Most critically, the CNN model completely eliminated fatal cross-class misclassification between OM and LOF—an error mode common in the SVM model that would trigger erroneous process corrective actions in practical industrial applications. The findings demonstrate that image-based machine learning provides a reliable technical foundation for intelligent in situ monitoring of the L-PBF process. With its high accuracy, strong robustness, and superior computational efficiency, the CNN model can effectively support on-site operational decision-making, reduce material and time losses, and enhance process stability in industrial settings, thus exhibiting significant potential for practical engineering deployment. Full article

Alzheimer’s disease (AD) is a global health challenge requiring early and accurate diagnosis, yet current clinical methods struggle with early stages. Deep learning approaches for MRI-based diagnosis face persistent challenges related to image quality issues, limited model generalization, and subtle inter-class variations. To address these limitations, this paper proposes a robust, end-to-end brain MRI-based framework for multi-class classification of AD stages. Positioned within the broader research priority of artificial intelligence and intelligent healthcare technologies, the proposed methodology incorporates an attention-based ensemble of deep learning models alongside an enhanced image preprocessing that uses Real-ESRGAN to mitigate common compression and resolution degradations in 2-D MRI slices. The ensemble makes use of the superior capabilities of the Swin Transformer to capture global contextual dependencies and EfficientNet-B3/MobileNetV2 for effective multi-scale feature extraction, with feature fusion performed using a Squeeze-and-Excitation attention mechanism. The experiments were performed on a publicly available Alzheimer’s MRI dataset, resulting in classification accuracy of 94.47% and 92.28% for the two proposed frameworks. The robustness and clinical interpretability of the framework are emphasized through comprehensive metrics and qualitative analysis. This framework demonstrates promising benchmark performance on a standardized public dataset, highlighting the potential of cross-paradigm ensembles combined with super-resolution preprocessing. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

The transport behavior of multiphase flow in porous media is governed by the cross-scale coupling between fluid properties and pore structure, and serves as the theoretical foundation for core processes in fields such as energy development, underground carbon storage, and environmental remediation. Accurately characterizing the intrinsic relationship between physical properties and seepage responses is crucial for enhancing engineering prediction capabilities and optimizing operational strategies. However, the inherent heterogeneity and multiscale nature of natural reservoirs, coupled with the limitations of traditional experimental methods in terms of optical opacity and spatiotemporal resolution, severely hinder a deep understanding of the mechanisms of multiphase flow at the pore-scale. This paper systematically reviews the methodological framework for characterizing physical properties and seepage mechanisms in multiphase flow systems, with a focus on cutting-edge breakthroughs in experimental measurement and visualization technologies over the past decade. Starting with classical and emerging testing methods for key physical properties such as saturation, relative permeability, capillary pressure, and interfacial tension, the paper evaluates the applicability, accuracy advantages, and inherent limitations of different techniques. The paper focuses on the latest advancements in pore-scale visualization technologies, covering microfluidic models, high-resolution X-ray CT, synchrotron rapid dynamic imaging, and multimodal, multiscale imaging fusion strategies; it also explores AI-enabled image processing and data analysis methods, as well as the application potential of cross-scale numerical coupling models in revealing transient seepage mechanisms and correlating them with macroscopic responses. Based on this, an integrated analytical framework of “physical property measurement—visualization characterization—theoretical modeling—engineering application” is established, and the core challenges and future pathways for advancing multiphase flow and seepage research toward “quantification of mechanisms, cross-scale correlation, and adaptation to in situ real-world conditions” are identified. Full article

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region. Full article

There is a growing demand for extreme miniaturization and enhanced sensitivity in next-generation sensing systems, including wearable devices and bioelectronics. Such advanced platforms require highly conductive, biocompatible, and mechanically robust architectures capable of conforming to dynamic surfaces. Conventional metallic thin-film fabrication techniques have reached their fundamental physicochemical limits, often suffering from suboptimal mechanical strength, complex multi-step processing, and high costs. In contrast, additive manufacturing methodologies offer streamlined microfabrication, yet traditional printing methods frequently struggle with low-viscosity constraints, insufficient metal loading, and significant material losses. This paper covers the morphological fidelity, mechanical resilience, and electrical performance of rheologically tailored, high-concentration (above 90%) gold nanoparticle paste deposited via Ultra-Precise Dispensing (UPD) technology. The capability of the UPD system to print complex, high-density fractal geometries with linewidths down to 5 μm is evaluated on both rigid and flexible substrates, glass and polyimide, respectively. The mechanical structural integrity of these conductive traces is characterized under initial 360-degree bending tests. Finally, the electrical stability and thermal response of a printed proof-of-concept temperature sensor are evaluated. The printed fractal microstructures exhibit good resolution and the fabricated sensor demonstrates good stability, displaying a linear thermal response with a temperature coefficient of resistance of 1.98·10⁻³ °C⁻¹, validating this combined material-deposition approach for microelectronics. Full article

With the rapid advancement of Earth observation technologies and the growing demand for intelligent remote sensing applications, high-resolution remote sensing imagery provides critical data support for a range of downstream applications, including land monitoring and disaster assessment. In this context, multi-label remote sensing image classification has become an important research task, because a single image may contain multiple ground-object categories with complex spatial distributions and semantic co-occurrence relationships. However, challenges such as the coexistence of multiscale objects, complex semantic dependencies, and long-tail category distributions impose significant limitations on existing methods in terms of feature representation capacity and class-balanced modeling. To address these challenges, a Multiscale Dynamic Reasoning Network (MSDR-Net) is proposed. Different from methods that focus on localized optimization for a single challenge, MSDR-Net establishes a task-driven modeling framework that jointly integrates multiscale feature extraction, label-aware semantic reasoning, and long-tail category optimization within an end-to-end architecture. The proposed network consists of three core modules. The Multiscale Feature Enhancement (MSFE) module incorporates a Feature Pyramid Network-based fusion mechanism, integrating deep semantic information with shallow, detailed features to effectively enhance the representation of multiscale objects. The Dynamic Semantic Reasoning (DSR) module introduces a Transformer-based global attention mechanism that models long-range dependencies among image features, enabling the capture of complex global semantic relationships. In the loss optimization stage, a Difficulty-Weighted Loss (DW-Loss) is introduced, which jointly incorporates category frequency weights and prior difficulty coefficients to dynamically regulate the contributions of rare classes and hard samples during training, thereby mitigating bias induced by class imbalance. Experiments conducted on the large-scale Detection in Optical Remote Sensing Images dataset demonstrate that the proposed method achieves superior performance. Ablation studies validate the effectiveness of each component, while comparative experiments indicate that MSDR-Net achieves a mean Average Precision of 95.88%, outperforming existing state-of-the-art methods. An improvement of approximately 1.74% is observed over the strongest baseline, MSCA, with consistent advantages demonstrated across Overall F1 and Class-wise F1 metrics. By unifying multiscale feature extraction, global semantic reasoning, and balanced loss optimization within a single framework, MSDR-Net provides a robust and efficient solution for multi-label classification in complex remote sensing scenarios. Full article

Inflammation plays a crucial role in defending the body against harmful stimuli and maintaining physiological balance; however, when it becomes chronic, it contributes to the pathogenesis of several long-term diseases, including autoimmune conditions, cardiovascular and neurodegenerative disorders, and various cancers. Although conventional anti-inflammatory drugs provide symptomatic relief, their long-term use is often associated with adverse side effects. This limitation has shifted scientific attention toward naturally occurring bioactive molecules with potent, safer anti-inflammatory activity. Dietary incorporation of phytopharmaceuticals, such as flavonoids, polyphenols, alkaloids, terpenoids, and fatty acids, has been shown to regulate immune and oxidative mechanisms and to modulate key inflammatory signaling cascades, including the NF-κB, mitogen-activated protein kinase (MAPK), and JAK/STAT pathways. These agents also influence cytokine secretion, NLRP3 inflammasome activation, and antioxidant defense mechanisms involving the Nrf2/HO-1 axis. The current review emphasizes the relevance of major natural plant products in therapy, like quercetin and rutin, resveratrol, glycyrrhizin, lycopene, and indole-3-carbinol. Moreover, recent progress in anti-inflammatory research has focused on novel resolution-based strategies that extend beyond inflammation and oxidative stress suppression. In addition, the review discusses innovations including nanoformulation-assisted targeted delivery, specialized pro-resolving lipid mediators such as resolvins and protectins, and microbiota-oriented therapeutic approaches. Additionally, the review highlights the integration of personalized medicine supported by multi-omics technologies to enhance treatment precision and clinical outcomes. By synthesizing findings from preclinical studies and clinical investigations, this work emphasizes the synergistic therapeutic potential of bioactive compounds from natural sources and resolution-enhancing techniques in restoring immune homeostasis and effectively mitigating chronic inflammation. Full article

As the role of remote sensing continues to grow, the fusion technology of low-spatial-resolution hyperspectral images and high-spatial-resolution multispectral images has become increasingly critical. Traditional methods rely on fixed rules and exhibit poor robustness, whereas deep learning methods struggle to establish efficient interactions between local and global information due to the complexity of their underlying networks. Therefore, we propose a deep learning fusion module that combines pixel-wise adaptive wavelet transform with a spectral–spatial dual-branch extraction. Firstly, by utilizing the unique properties of the wavelet transform, it is possible to effectively preserve spectral information and extract spatial edge features, thereby achieving preliminary fusion by leveraging both low-frequency and high-frequency components. To compensate for the lack of nonlinear expression capability in the wavelet transform, a dual-branch parallel extraction of spectral and spatial features is subsequently performed in the deep learning module. The Multi-Scale Group Convolution module (MSGC) is utilized to extract spectral information, while the Spectral Compression and Spatially Guided Gating Module (SCSGM) is employed to extract spatial information, thereby enhancing the data’s adaptive capability. A bidirectional attention mechanism is interspersed within the module to capture complementary information across different scales, ultimately reconstructing a high-resolution hyperspectral image. Finally, the proposed fusion strategy demonstrates superior performance in practical image reconstruction, outperforming more than ten state-of-the-art fusion methods. Full article

Environmental biofilms are persistent structural components of livestock production systems and represent under-recognized drivers of pathogen persistence and antimicrobial resistance (AMR). This review examines the engineering, ecological, and operational factors that promote biofilm formation in dairy, poultry, and swine environments, with emphasis on drinking water distribution systems, feeding infrastructure, housing surfaces, and waste channels. Biofilms develop preferentially in low-shear zones, dead ends, and aging materials, where they enhance microbial tolerance to sanitation and facilitate horizontal gene transfer. Conventional monitoring approaches, largely based on planktonic sampling and single-time-point testing, underestimate attached biomass and fail to capture spatial heterogeneity. Although molecular and sensor-based technologies provide improved resolution, their farm-level implementation remains limited by cost, standardization challenges, and the absence of validated operational thresholds. Current EU surveillance frameworks focus primarily on antimicrobial use and resistance prevalence in animal isolates, while environmental compartments are rarely incorporated as monitored system elements. This review proposes a proportionate, risk-based approach that integrates existing farm data streams such as antimicrobial use metrics and biosecurity scoring systems with targeted environmental assessment of high-risk infrastructure. Mitigation strategies emphasize mechanical disruption, combined chemical sanitation, hydraulic optimization, material selection, and infrastructure lifecycle management. Embedding environmental biofilm control within existing engineering and stewardship frameworks supports more resilient, systems-based management of infectious and AMR risks in livestock production. Full article

Accurate road segmentation is a core perceptual technology for autonomous driving, but faces two challenges: (1) ambiguous road boundaries caused by insufficient modeling of contextual information relationships in CNN-based networks and (2) inadequate LiDAR-camera fusion due to modality gaps between heterogeneous sensors. To mitigate these limitations, this paper proposes a novel approach, named LiDAR-Camera High-Resolution Feature Fusion Network (LC-HR2FNet), a multi-cross-stage fusion model designed for road segmentation. Firstly, a new type of pseudo-LiDAR-Image representation is generated via an early-level fusion strategy and data complementation. Sparse point clouds are transformed into dense LiDAR-Image data and then concatenated with RGB channel maps to form complementary multi-modal data inputs. Subsequently, a modified HRNet backbone integrated with cross-stage feature fusion is constructed to strengthen information interaction across different branches and enhance the modeling of contextual relationships. Additionally, a dilated feature collection model is designed to collect multi-scale confidence scores for pixel-wise class determination. Experiments on the KITTI road benchmark demonstrate that the proposed method achieves a MaxF of 97.39% on UMM_ROAD and an average of 96.28% across all urban scenarios, demonstrating superior performance and robustness. Full article

The rapid development of synthetic aperture radar (SAR) imaging technology has significantly enhanced maritime monitoring capabilities; however, SAR ship detection remains constrained by the limited scale and representation capacity of existing rotated bounding box datasets. Most publicly available datasets rely on horizontal annotations, which introduce redundancy and localization ambiguity in densely distributed and nearshore scenarios. Although rotated bounding boxes provide more precise geometric representation, large-scale multi-source rotated SAR datasets are still insufficient to support robust model training. To address this limitation, we construct a large-scale multi-source rotated SAR ship dataset (LMRD) consisting of 13,024 high-resolution image chips with over 38,000 annotated ship instances, covering multiple satellite sources, polarization modes, and diverse maritime environments, including offshore, nearshore, complex coastal, and densely distributed port scenes, thereby enhancing scene diversity and annotation precision. Furthermore, independent of the dataset construction, we propose a multi-domain feature fusion (MDF) framework built upon Oriented RCNN, which integrates high-frequency information and visual saliency cues to improve feature representation under complex backgrounds. Experimental results on the LMRD demonstrate that, compared with the baseline Oriented RCNN, the proposed MDF framework achieves a 2.7% improvement in average precision. Additional analysis indicates that the dataset characteristics and the multi-domain fusion strategy contribute to performance enhancement at different stages of the detection pipeline, validating the effectiveness of the proposed dataset for rotated ship detection while demonstrating the complementary role of multi-domain feature enhancement. Full article

Terahertz (THz) fingerprint detection is central to identifying characteristic absorption fingerprints of biomolecules derived from their intrinsic rotational and vibrational modes. The development of guided-mode resonance (GMR) technology together with pixelated design offers a new approach to enhance the recognition capability of such fingerprint spectra. Here, a novel secondary grating metasurface based on cycloolefin polymer (COP) is proposed, which adopts an ultra-minimalist dual-pixel complementary architecture to excite high-quality (Q)-factor GMR. Its spectral resolution does not exceed 50 GHz, enabling precise capture of target molecular characteristic information and meeting the requirements of broadband fingerprint sensing. More importantly, the design regulates the dual-pixel grating units through parameter gradient optimization and incorporates a dual regulation mode of static pixel-targeted coverage and dynamic angle fine tuning. By adjusting geometric parameters and incident angles, broadband coverage from 1.15 THz to 2.20 THz is achieved, which can accurately match the multi-fingerprint detection requirements of glutamic acid (Glu) and glutamine (Gln). This metasurface sensor, integrating the advantages of pixelation and high-Q-factor GMR characteristics, provides an effective strategy for enhanced broadband THz fingerprint sensing and shows broad application potential in the field of biochemical trace detection. Full article