Search Results (353)

Synthetic aperture radar (SAR) provides all-weather imaging but faces challenges in visual interpretation due to low contrast and coherent speckle noise. To address contrast deficiency and edge blurring in SAR-to-optical translation, we propose a Dual-Scale Aligned Network (DSAN) built upon Pix2PixHD. First, an asymmetric dual-prior architecture is designed: the global generator ingests low-resolution SAR images enhanced by histogram equalization to capture macroscopic structures, while the local generator utilizes original high-resolution SAR images to preserve microscopic details, alleviating the trade-off between contrast and fine textures. Second, a Dual-Scale Fusion Module (DSFM) coupling Large Kernel Attention and Collaborative Attention breaks scale barriers, enabling bidirectional cross-scale alignment and deep fusion. Third, a continuous differentiable soft-edge loss is formulated using logarithmic dynamic range compression to prevent highlights from dominating gradients and enforce boundary consistency across urban areas, water bodies, and farmlands. Experiments on the Nanjing and public SEN1-2 datasets demonstrate that DSAN outperforms state-of-the-art models—including Pix2PixHD, CycleGAN, MSTMNet, and ICMA—in perceptual distribution realism (FID) with the sharpest geometric boundaries. Ablation studies confirm the effectiveness of the asymmetric dual-prior design, DSFM, and the refined edge loss. Full article

Fully enclosed noise barriers (FENBs) are widely used in high-speed railway systems to mitigate environmental noise; however, the transient aerodynamic loads generated by train passage can induce complex structural responses. The relationship between the spatial–temporal evolution of these aerodynamic loads and the dynamic response of the complete FENB structural system remains insufficiently understood. To address this issue, this study develops a sequential computational fluid dynamics–finite element analysis (CFD–FEA) framework that directly relates the transient pressure evolution during the complete train-passage process to the deformation and stress responses of the principal FENB components. The unsteady aerodynamic field generated by high-speed train passage is simulated using a moving-mesh CFD model, and the resulting time-dependent pressure loads are subsequently applied to a finite-element structural model. Train speeds ranging from 250 to 330 km/h are considered. The results reveal strongly transient and spatially non-uniform pressure distributions inside the FENB, characterized by nose-induced compression, a middle negative-pressure region, and wake-induced pressure fluctuations. Both structural deformation and equivalent stress increase with train speed, and the exit stage produces the most pronounced structural response because of the strong negative-pressure effect. Different structural components exhibit distinct response characteristics, with localized stress concentrations occurring in the glass panels and H-section steel columns. By establishing the correspondence between transient aerodynamic pressure evolution, train-passage stages, and component-level structural responses, this study provides a more comprehensive understanding of the aerodynamic load–structural response mechanism of FENBs and provides a basis for structural design and engineering assessment under increasing train speeds. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

Heavy metal deposition along high-traffic roadsides poses a persistent threat to agricultural safety, yet the partition barrier efficiency across rhizosphere–root–shoot interfaces in perennial nut crops remains poorly understood, representing a significant research gap. This study determined the concentrations of potentially toxic elements in the rhizosphere soils and distinct organs (leaves, pericarp, and edible seeds) of Siirt pistachio trees along a distance gradient (0, 50, and 100 m, plus a control site) in the Siirt and Tillo districts. To filter analytical baseline noise, all raw datasets were subjected to strict solid-matrix limit of detection (LOD) screening using a standardized dilution factor of 30 mL/g (DF = 15 mL final volume/0.5 g sample mass). Soil analysis revealed that the alkaline pH (6.90–7.27) and highly calcareous nature (21.97–65.75%) of the rhizosphere acted as a powerful edaphic barrier, immobilizing metals in the soil and limiting their translocation to aboveground tissues. Plant accumulation followed a leaf > pericarp > seed hierarchy, proving the canopy’s role as an effective vegetative filter. Crucially for food safety, highly toxic Cd (<1.74 µg/kg) and Bi remained entirely below detection limits in edible seeds. Cr peaked in leaves (730.42–795.00 µg/kg) but was highly restricted in seeds. Detected kernel concentrations of As, Co, Ni, Pb, and Sb were strictly below international toxic thresholds, while essential Cu physiologically concentrated in seeds and leaves. Consequently, the cumulative Hazard Index (HI) remained exceptionally below the 1.0 critical safety limit for both adults (<0.18) and children (<0.32). This confirms that roadside pistachios pose zero non-carcinogenic health hazards and are completely safe for human consumption. Full article

Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation. Full article

Hierarchical multi-UAV planning can produce scheduled paths that become unsafe during execution because tracking dynamics, actuator limits, sampling, and communication are not fully represented upstream. We introduce a certificate-guided safe-tracking framework that treats each planned path as a versioned execution contract with explicit tube, separation, timing, uncertainty, communication, and input bounds. A rigid-body-derived translational interface supports a command-producing control Lyapunov function–control barrier function quadratic program with hard safety constraints. When a candidate becomes infeasible, a separate minimum-slack program localizes the conflict without passing its command to the plant, and an explicit repair map converts the resulting witness into timing or vertical-spacing updates, with escalation when local repair fails. Randomized comparisons show that certificate feedback removes the observed tube and separation failures of a plain CBF-QP while maintaining reliable completion and competitive tracking relative to a tracking-error-bound comparator. Disturbance and sensor-noise sweeps characterize robustness, and separate indoor flights confirm single-reference trackability. The framework therefore turns execution infeasibility into actionable planning feedback rather than a terminal controller failure. Full article

This paper demonstrates how detailed acoustic micro-segmentation can serve as a vital decision-support tool, providing localized, evidence-based data required to effectively integrate environmental acoustics and foster community co-design in urban rail modernization planning. Furthermore, while physical noise barriers are widely praised as standard mitigation products in transport engineering, our analysis highlights their severe inherent limitations, including spatial constraints, high costs, and visual pollution, making them highly unsuitable for dense historical fabrics. As viable alternatives, we propose the implementation of active, source-targeted engineering technologies (e.g., rail absorbers, modernized track beds) alongside dynamic operational governance. To be effective, these alternative solutions must be sourced and embedded directly into the earliest stages of infrastructural project documentation. Ultimately, this paper demonstrates that evidence-based acoustic governance is an essential institutional pillar for long-term resilience, providing interdisciplinary perspectives that inform future sustainable transport policy and practice. Full article

Automatic fault interpretation is essential for seismic geological modeling; however, fault intersections, noise interference, and topological complexity frequently lead to over-segmentation or under-segmentation. To address these challenges, we propose multi-scale contour constraints Delaunay-based region growing (MC-DBRG), an instance segmentation method for three-dimensional (3D) fault point clouds. This approach effectively extracts individual fault instances from fault probability volumes. The pipeline begins with voxel downsampling and multi-scale feature extraction applied to the fault point cloud. A Gaussian Mixture Model then performs binary classification to delineate explicit and closed fault contour walls. These walls function as physical barriers during the subsequent Delaunay triangulation-based region growing, which initializes from non-contour smooth points. Implementing these constraints guides the growth process and prevents topological adhesion among intersecting faults. To correct over-segmentation caused by data discontinuities in the preliminary results, we introduce a global manifold secondary geometric merging strategy. A macroscopic planar trend constraint is subsequently applied to accurately reassign and backfill contour points and noise. Experimental results indicate that MC-DBRG resolves boundary ambiguity and spatial adhesion within complex intersecting faults. The generated fault models preserve high topological integrity and precise instance labels, establishing a reliable foundation for automated seismic geological modeling. Full article

The rapid expansion of next-generation sequencing technologies has generated unprecedented volumes of genomic data; however, translating these data into reliable and clinically actionable insights remains a major challenge in precision medicine. Artificial intelligence (AI) has emerged as a key enabling technology across the genomic medicine pipeline, supporting variant detection, variant interpretation, polygenic risk prediction, disease subtyping, biomarker discovery and treatment–response modelling. This review provides a clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine. Major computational paradigms, including machine learning, deep learning, ensemble methods, multimodal AI, explainable AI frameworks and emerging foundation models, are discussed in the context of their contribution to genomic analysis and clinical decision support. Particular emphasis is placed on the factors that determine model robustness and clinical utility, including dataset composition, class imbalance, label noise, calibration, ancestry representation, distributional shift and external validation. Evidence from rare genetic disorders, cardiovascular genetics and precision oncology is examined to illustrate both successful translational applications and persistent barriers to implementation. The review further analyses common sources of failure in real-world genomic AI systems, including overfitting, limited transportability across populations and sequencing environments, inadequate interpretability, and insufficient prospective validation. Ethical and regulatory challenges are discussed in relation to clinical accountability, genomic privacy, algorithmic bias and equitable implementation. Ultimately, the successful clinical translation of genomic AI will depend not only on methodological innovation, but also on rigorous validation, transparent reporting, continuous calibration, robust governance and sustained expert oversight. Full article

Long-term implantable neural electrodes underpin brain–machine interfaces, deep brain stimulation, epilepsy monitoring, and closed-loop neuromodulation. Following chronic implantation, however, the foreign body response (FBR) at the electrode–tissue interface remains a major constraint on long-term performance, as reflected by increased interfacial impedance, lower signal-to-noise ratios, fewer resolvable units, and higher stimulation thresholds. This deterioration arises from interrelated events that include implantation injury, protein adsorption, blood–brain barrier disruption, complement activation, glial reactivity, oxidative stress, glial scar formation, and neuronal loss. It cannot be attributed solely to material ageing or encapsulation failure. This review examines the molecular mechanisms of neural-electrode FBR and relates them to surface-biofunctionalization strategies, including antifouling coatings, bioactive ligands, immobilized neurotrophic factors, drug-eluting electrodes, and emerging immunomodulatory interfaces. Establishing mechanistic links among molecular events, material interfaces, and functionalization strategies may guide the rational design of durable neural electrodes. Full article

Glaucoma is a leading cause of blindness, and early detection is critical. Convolutional neural networks (CNNs) have shown impressive performance in glaucoma diagnosis, but their black-box nature remains a barrier to clinical use. Existing explainable AI (XAI) methods such as Grad-CAM have limitations in identifying and quantifying subtle regional features. In this study, we propose a method to clarify what CNNs focus on by analyzing how model performance changes under localized adversarial noise. Using VGG16 for glaucoma classification, we applied noise generated by the Fast Gradient Sign Method (FGSM) to the whole fundus image and to specific subregions, then compared the impact on classification performance. Results showed that perturbations to the optic disc, especially its outer margin, had the greatest effect on model performance. This suggests that the CNN captures fine anatomical features such as optic disc cupping and neuroretinal rim thinning, which aligns with what ophthalmologists typically look for. At the same time, perturbations in the macula and perivascular regions also affected performance, indicating gaps between current clinical diagnostic criteria and the CNN’s decision-making process. This approach can help establish the clinical reliability of CNNs and may also reveal features that have not been recognized in conventional clinical practice. Full article

Direct antibody labeling is essential for immunoassays, multiplexed imaging, and biosensing; however, current methods are often time-consuming, restricted by antibody source, risk compromising protein performance, or vary with multiple steps. We introduce multiplex in situ tagging (MIST) Linker, a rapid and Fc-site-specific labeling tool that conjugates fluorophores or DNA oligonucleotides to antibodies from diverse commercial sources in as fast as 10 min using minimal starting material. MIST Linker achieves >90% cleavage upon UV exposure, facilitating rapid cyclic imaging on a single specimen. Validated across multiple species and sources, the platform outperforms conventional two-step immunofluorescence and immunohistochemistry in various tissues and cell lines. By enabling the rapid, cost-effective customization of antibody panels, MIST Linker significantly lowers the barrier to accessing antibody–DNA conjugates for spatial biology. When integrated with the spatial MIST platform and MIST-Explorer, it enables high-plex, single-cell spatial proteomics at high signal-to-noise ratios in human clinical biopsies, mouse specimens and cell lines. This toolkit provides an efficient, accessible solution for high-resolution spatial mapping, allowing for the in-depth analysis of cell subpopulations, biomarker distributions, and signaling events in complex biological specimens. Thus, MIST Linker offers a versatile, accessible, and scalable solution for antibody-labeling-based research and clinical diagnosis. Full article

In this article, an inverse mathematical model was developed to achieve fast and simultaneous estimation of thermophysical and geometric parameters for thermal barrier coating (TBC) systems in high-temperature environments with measurement errors. First, considering the convective and radiative heat transfer between the TBC system and the external environment, a one-dimensional unsteady conduction–radiation coupled heat transfer model was originally developed in high-temperature environments. Subsequently, this forward model was solved using the finite volume method (FVM), and the grid independence as well as the accuracy were validated. Thereafter, an accelerated inverse model, which adopts a principal component analysis (PCA)-optimized artificial neural network (ANN) to fit and substitute the forward model and employs the particle swarm optimization (PSO) algorithm for estimation, was developed based on the inverse method. Finally, under three different noise conditions, two situations were used to perform simultaneous estimation studies: a two-parameter case (top coat thermal conductivity and thermally grown oxide (TGO) layer thickness) and a three-parameter case (adding the thickness of an additional existing debonding defect layer). The results show that the PCA-optimized ANN-PSO-based accelerated inverse model is approximately 111–130 times faster than the traditional PSO-based inverse model, and the maximum relative errors of the arithmetic means of 20 estimations for two-parameter and three-parameter situations under three noise cases are on the order of 2.4% to 4.3%. Overall, the proposed accelerated inverse model achieves a favorable balance between speed and accuracy in multi-type parameter simultaneous estimation for TBC systems at high temperature, providing a methodological basis for addressing parameter estimation in more complex situations in future research. Full article

Physical access to robotic manipulators remains constrained by cost, safety requirements, and limited laboratory availability, creating barriers to both research and education. This paper presents a computational framework that combines stochastic error modeling with Digital Twin technology to characterize positioning uncertainty in a six-degree-of-freedom manipulator without requiring physical hardware. Four independent noise sources—joint encoder noise, thermal drift, elastic link deformation, and geometric parameter tolerances—are modeled as stochastic processes and propagated through the manipulator kinematics using Monte Carlo simulation with N = 10,000 trials across 50 workspace configurations. The results reveal that elastic deformation dominates the combined positioning error by a factor of 45.94 over encoder noise, contributing 99.97% of the total root-mean-square (RMS) uncertainty. A probabilistic workspace map constructed from 3000 sampled configurations quantifies accuracy and manipulability across the reachable space, exposing a counterintuitive trade-off: configurations with higher manipulability indices tend to exhibit larger positioning errors due to gravitational loading on extended links. Two control algorithms—a reverse process-based control law (RPBCL) and sliding mode control (SMC)—are evaluated under stochastic conditions over 200 trials. SMC achieves a mean steady-state error of 0.0029 mm, representing a 48.2% reduction compared to RPBCL (0.0056 mm), with the difference confirmed statistically significant by a two-sample t-test (t = 5.066, p = 0.000002). All results are visualized through a Unity3D Digital Twin interface that renders probabilistic workspace maps, three-dimensional error ellipsoids, and a real-time sliding surface monitor. The proposed framework provides a foundation for safe, hardware-free evaluation of manipulator control strategies in engineering education and research. Full article

This study investigates the sensitivity of Barkhausen noise emission against the stress state as well as microstructure alterations in the steel road barrier flange. The stress state is altered with respect to its residual state after the tensile test, as well as in the tensile and compression regions during the bending test. In order to provide deeper insight into the relationship between stress states, the Barkhausen noise and magnetostriction along two perpendicular directions is measured as well. The microstructure is expressed in terms of the dislocation density altered beyond the yield point. It was found that the increasing yielding during the tensile test reduces the bearing capacity of the flange as well as the notch toughness, and increases the dislocation density. The Barkhausen noise along the flange length exhibits a continuous descent with yielding due to the increased dislocation density, whereas this emission in the perpendicular direction increases due to the realignment of domain walls. The magnetostriction along the flange length is strong and positive, linked with the increasing Barkhausen noise along the flange length due to the tensile stresses, as contrasted against much the weaker magnetostriction along the perpendicular direction and the reversed evolution of the Barkhausen noise. However, the correlation between the MBN and magnetostriction is not straightforward; the systematic evolution of Barkhausen noise alterations as a function of plastic strain is fully missing. Full article