Search Results (1,749)

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Continuous unidirectional (UD) thermoplastic composite tapes are increasingly used in aerospace, automotive, and energy applications because of their high specific strength, low weight, recyclability, and compatibility with automated manufacturing. Since final component performance strongly depends on tape quality, reliable defect characterization and detection are essential. In this study, manufacturing-induced defects in polypropylene-based UD tapes reinforced with carbon and glass fibers were investigated using real images acquired directly from laboratory-scale production without synthetic data. Defects related to interfacial integrity, matrix distribution, fiber architecture, and surface irregularities were systematically analyzed, and a practical four-class defect taxonomy was established. To enable automated inspection under limited-data conditions, lightweight YOLOv8, YOLOv11, and the new YOLO26 models were comparatively evaluated using a UD tape-specific augmentation strategy combining physically constrained Albumentations and on-the-fly augmentation. Among the tested models, YOLO26-s achieved the best overall performance, reaching a mean mAP@0.5 of 0.87 ± 0.03, outperforming YOLOv11 (0.83) and YOLOv8 (0.78), with 0.90 precision and 0.85 recall. Interfacial (0.92 mAP) and matrix-related (0.90 mAP) defects were detected most reliably, whereas fiber-related (0.89 mAP) and surface defects (0.79 mAP) remained more challenging, particularly in glass-fiber-reinforced tapes due to transparency-masking effects. The results demonstrate the potential of compact deep learning models for computationally efficient and manufacturing-relevant in-line quality monitoring of UD tape production. Full article

We investigated the dynamics of molecular reorientation in freely suspended smectic liquid–crystal films (FSLCFs) under the influence of heat flux. We also examined how external thermal gradients affect molecular alignment in these ultra-thin films. FSLCFs were fabricated in a temperature-controlled chamber in this study. When heat flux was applied perpendicular to the film plane, rotation of line defects, known as 2π walls, was observed. This rotation resulted from thermomechanical torque acting on the molecular director, a phenomenon referred to as the Lehmann effect. By analyzing the changes in defect evolution, how heat flux drives the self-organization of liquid–crystal structures can be understood. In this study, we combined experimental observations and computational simulations to model and interpret the results. The results enhance the understanding of the underlying mechanisms governing molecular reorientation and defect dynamics in FSLCFs, particularly in non-equilibrium conditions, to study this mechanism in the microgravity environment. The results also contribute to the development of advanced liquid–crystal technologies, with potential applications in energy-efficient devices, adaptive materials, and space technology systems. Full article

This paper addresses the issues of blurred details, low contrast, and feature degradation in insulator images under harsh meteorological conditions, as well as the challenges of high computational complexity and insufficient real-time performance when deploying existing deep learning models on edge devices. It proposes a lightweight insulator defect detection method that integrates an improved image enhancement algorithm. The method introduces Mahalanobis distance-based modulation weight optimization for scene depth estimation and improves the color decay prior model to effectively enhance foggy insulator images. It further designs a lightweight detection network integrating region-aware routing attention mechanisms, utilizing multi-scale feature fusion strategies to achieve precise insulator identification and localization. Experimental results demonstrate that the proposed method significantly enhances inference speed while maintaining detection accuracy, effectively adapting to edge computing devices. This provides a viable technical solution for real-time deployment in intelligent transmission line inspection systems. Full article

Background: While hydroxyapatite (HA) is considered stable and non-resorbable, other calcium phosphate phases such as Tricalcium Phosphate (TCP), Brushite, and Monetite are characterized by higher solubility and biodegradation rates. This review aims to map the clinical evidence of these resorbable phases. Objective: The aim of this scoping review was to map and synthesize the available clinical evidence on resorbable calcium phosphate phases, focusing on TCP-, brushite-, and monetite-based biomaterials in alveolar bone regeneration. The review evaluates clinical indications, surgical protocols, reported outcomes, and existing knowledge gaps. Methods: This scoping review was conducted in accordance with the PRISMA-ScR guidelines. A comprehensive literature search was performed in PubMed, MEDLINE, Scopus, and SCI Clarivate databases without language or time restrictions (from June 2025 to August 2025) using terms related to brushite, monetite, dicalcium phosphate anhydrous, ridge augmentation, bone regeneration, and dental implants. Clinical studies involving brushite- or monetite-based biomaterials used for alveolar bone regeneration were eligible, including randomized controlled trials, prospective cohort studies, and case series. Data were charted descriptively with respect to study design, patient characteristics, clinical scenario, biomaterials used, surgical approach, healing time, outcome measures, and reported complications. No meta-analysis or formal assessment of comparative clinical effectiveness was undertaken, in line with scoping review methodology. Results: Seven clinical studies were included. The identified evidence encompassed heterogeneous clinical scenarios, including post-extraction alveolar ridge preservation, localized ridge augmentation, and periodontal or intraosseous defects with relevance to future implant placement. Study designs, defect characteristics, biomaterial formulations, and outcome measures varied substantially. Across studies, brushite- and monetite-based materials were associated with new bone formation and progressive graft resorption, as assessed by clinical, radiographic, and histological outcomes. Direct comparisons between studies were not feasible due to methodological and clinical heterogeneity. Conclusions: The available literature on brushite- and monetite-based biomaterials in alveolar bone regeneration is limited and heterogeneous. Current evidence supports their biocompatibility and resorbable nature across different clinical contexts, but does not allow conclusions regarding comparative clinical effectiveness. This scoping review highlights important gaps in the literature, particularly the need for well-designed randomized clinical trials with standardized indications and outcome measures. Full article

Wafer map defect pattern recognition is a key task for ensuring yield in integrated circuit manufacturing. However, in real production lines it commonly suffers from scarce labeled data, long-tailed class distributions, and limited feature representations, which cause existing deep learning models to degrade in performance, particularly for minority defect classes and complex defect morphologies. To address these challenges, we propose a semi-supervised classification method for wafer maps based on a multimodal Wasserstein autoencoder (MM-WAE). The framework constructs three parallel feature branches in the spatial, frequency, and texture domains, using a multi-head attention mechanism and gating mechanism for adaptive multimodal fusion. This allows defect patterns to be comprehensively characterized by macroscopic geometric distributions, spectral periodic structures, and microscopic texture details. The Wasserstein autoencoder is introduced, with the latent space distribution regularized by a maximum mean discrepancy (MMD) loss using an inverse multiquadratic kernel. Additionally, an inverse class-frequency weighted cross-entropy loss and a modality consistency loss between the encoder and classifier jointly optimize the reconstruction and classification paths while leveraging large amounts of unlabeled wafer maps for semi-supervised learning. Experimental results show that MM-WAE mitigates performance limitations caused by insufficient labels and class imbalance, significantly improving the accuracy and robustness of wafer defect classification, with promising potential for industrial application and further development. Full article

Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios. Full article

Background: Compensatory mutations offer clues in deciphering the role of a particular protein in cellular processes. Here, we investigate an unknown compensatory mutation, present in the BEAF^NP6377 fly line, that provides sufficient rescue of the defective ovary phenotype caused by null BEAF alleles to allow the maintenance of fly stocks lacking the chromatin domain insulator proteins Boundary Element-Associated Factors BEAF-32A and BEAF-32B. We call this dominant mutation Tofu. Methods: We employ both classical genetics and genomic sequencing to attempt to identify the mutation. Results: We find evidence that points to a mutation in a predicted Polycomb response element (PRE) upstream of the ribbon transcription factor gene. This may lead to aberrant rib expression, which is otherwise not expressed in adult ovaries. BEAF and Rib colocalize to a set of promoters, suggesting overlap in gene regulation. Conclusions: Tofu could be a PRE mutation leading to the aberrant activation of rib in the ovaries. This could allow Rib to compensate for a lack of BEAF to activate one or more coregulated genes necessary for egg production in flies. Full article

This study examines the distinct roles of the neural cell adhesion molecules Ncam1a and Ncam1b in zebrafish neuromasts during both homeostasis and hair cell regeneration. While both molecules contribute to the initial development of the lateral line system, previous work showed that a morpholino knockdown of ncam1b causes more severe developmental defects than ncam1a knockdown. However, in ncam1b mutants, only minor changes in FGF/Wnt signaling and cell proliferation are observed in the migrating primordium, which do not affect overall development of the lateral line development, suggesting compensation by Ncam1a. This work shows that after neomycin-induced hair cell loss, only Ncam1b is strongly re-expressed in regenerating hair and support cells. ncam1b mutants show delayed hair cell regeneration, with an increased number of proliferating support cells but impaired differentiation into hair cells. Notably, Ncam1a is not re-expressed during regeneration in ncam1b mutants. These regeneration defects likely arise from disrupted interactions of signaling pathways. Our data suggest that Ncam1b supports regeneration by sustaining the FGF pathway activity required for atoh1a induction. It also maintains balanced Notch signaling, which regulates support cell fate decisions. Together, these results highlight the crucial, non-redundant role of Ncam1b in coordinating signaling pathways to ensure proper hair cell regeneration in zebrafish neuromasts. Full article

Shop-floor weld inspection may appear to be a solved problem until a camera is deployed near a galvanized-sheet MIG welding line. The seam reflects light, the texture changes from frame to frame, and the defects of interest are often small and visually subtle. Additionally, the hardware near the line is rarely a data-center GPU. With those constraints in mind, this paper presents YOLO-MIG, a compact detector built on YOLOv10n for weld-seam inspection in practical production conditions. We make three focused changes to the baseline: a C2f-EMSCP backbone block to better preserve weak defect cues with modest parameter growth, a BiFPN neck to keep small-target information alive during feature fusion, and a C2fCIB head to clean up predictions that otherwise get distracted by seam edges and illumination artifacts. On a workshop-collected dataset containing 326 original images, with the training subset expanded through augmentation to 2608 labeled samples in total, YOLO-MIG achieves 98.4% mAP@0.5 and 56.29% mAP@0.5:0.95 on the test set while remaining lightweight (1.83 M parameters, 3.87 MB FP16 weights). Compared with YOLOv10n, the proposed model improves mAP@0.5 by 9.36 points and mAP@0.5:0.95 by 4.89 points, while reducing parameters, GFLOPs, and model size by 43.4%, 19.9%, and 29.9%, respectively. The results suggest that YOLO-MIG is not only accurate but also realistic to deploy at the edge for intelligent weld quality control. Full article

Oral squamous cell carcinoma (OSCC) therapy frequently produces acute and chronic injury to the oral mucosa, including surgical lining defects and radiochemotherapy-associated oral mucositis (OM). Beyond pain and ulceration, these injuries compromise nutrition, speech, oral hygiene, and feasibility of dental/implant rehabilitation, and may disrupt oncologic treatment delivery. The oral cavity imposes stringent constraints on regenerative biomaterials—continuous salivary flow, high microbial load, and repeated mechanical shear—such that clinical success depends on reliable mucoadhesion/wet adhesion, barrier function, mechanical compliance, and safe, spatially confined bioactivity. This PRISMA-informed evidence-mapped structured narrative review provides an evidence map and structured qualitative synthesis of hydrogel and scaffold platforms relevant to post-OSCC care, spanning clinically used mucoadhesive barrier formulations through emerging wet-adhesive multifunctional patches, acellular matrices, and tissue-engineered oral mucosa (TEOM) constructs. Clinically, the strongest evidence base remains barrier-forming gels and liquids that reduce OM pain and improve oral function during active therapy, establishing performance benchmarks for intraoral retention and patient-reported benefit. Preclinical studies are rapidly expanding toward multifunctional designs that integrate antimicrobial, anti-inflammatory, pro-epithelialization, and pro-angiogenic cues. However, a pervasive limitation is the inconsistent use of OSCC-relevant models (e.g., irradiated/xerostomic tissue beds), standardized functional endpoints (e.g., oral intake, durability under mastication, and neurosensory outcomes), and explicit oncologic safety evaluation, which severely compromises translational validity. For reconstructive applications, dermal matrices and early TEOM reports suggest feasibility for selected defects, but controlled comparative trials and scalable manufacturing pathways remain limited. Translational priorities include oncologic-by-design bioactivity (time-limited, locally confined cues), clinically anchored outcome reporting, and quality-by-design manufacturing aligned with device/combination/advanced-therapy regulatory requirements. Full article

A novel HIV-1 vaccine candidate under development targeting the highly conserved protease cleavage regions reduced viral acquisition and delayed disease progression in a macaque SIV-challenge model. Breakthrough virus isolated from vaccinees and control animals were sequenced in the regions surrounding the SIV protease cleavages. We identified unique viral mutations that were associated with alterations in viral load and maintenance of CD4+ T cell counts in vaccinees. To evaluate whether the vaccine-elicited mutations were detrimental to virus fitness, we produced 11 mutant constructs and transfection-derived viral stocks harboring mutations in both PCS2 (in CA/p2) and PCS12 (in Nef) that had emerged at high frequency during breakthrough viremia. Virus preparations harboring mutations displayed impaired proteolytic Gag processing, reduced viral RNA incorporation and p27-CA content. These mutants were also compromised in their ability to replicate in primary cells and cell lines. Interestingly, we observed only partial compensation of these PCS2 defects by downstream mutation at PCS12. In sum, we demonstrate that vaccine-elicited immunity directed to viral protease cleavage regions impair viral escape, and breakthrough virus cannot easily restore replicative fitness. Full article

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazardous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components, including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best performance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and an up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems enables a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure. Full article

Molybdenum (Mo) is currently considered as a potential diffusion barrier material for copper (Cu) interconnects, and these interconnect structures are generally processed using the technique of chemical mechanical planarization (CMP). While a limited number of publications on Mo CMP are presently available, the considerations for mitigating CMP-induced galvanic corrosion of Mo have remained largely underexplored. Using a model CMP system in pH-neutral slurries of citric acid with silica abrasives, the present work demonstrates how Mo barrier lines in contact with Cu wires in the CMP environment can develop CMP defects of galvanic corrosion. Including imidazole in the slurry considerably reduces the galvanic current of this corrosion process. The mechanisms of galvanic inhibition and material removal are examined by employing strategic tribo-electrochemical measurements. Open-circuit potential and potentiodynamic polarization measurements performed under surface abrasion aid the characterization of CMP-enabling surface reactions. The slurry’s surface chemistry initiates the primary modes of material wear for CMP, and corrosion-induced propagation of subsurface wear mostly governs the measured material removal rates for both Mo and Cu. Although the Cu:Mo selectivity of material removal is affected as the galvanic corrosion of Mo is suppressed, this effect can be controlled by varying the slurry content of imidazole. Full article

Irradiation creep of engineering alloys in nuclear reactor cores differs from the creep that is observed outside of the irradiation environment. It exhibits characteristics like high temperature thermal creep because it occurs in an environment of elevated vacancy point defect concentrations, but one must also consider the effect of interstitial point defects and the effect of both vacancy and interstitial concentrations, which are greater than the thermal equilibrium values, on an evolving microstructure. Irradiation creep is dependent on the point defect flux to different sinks and can be modelled using conventional rate theory. The net interstitial or vacancy point defect flux to different sinks determines the strain rate in a direction that can be considered perpendicular to the plane of the sink, which is the extra half plane of an edge dislocation or the plane of a grain boundary. There has been increasing evidence that, for complex alloys such as Zr-2.5Nb pressure tubing in CANDU reactors, the irradiation creep is largely dependent on the grain structure (size and shape). While the maximum amount of thermal creep by dislocation slip will be proportional to the distance a dislocation travels, i.e., proportional to the grain dimension in the direction of slip, observations indicate that the magnitude of irradiation creep is inversely proportional to the grain dimensions, indicating a creep mechanism dependent on diffusional mass transport. Mechanistic modelling of irradiation creep based on rate theory is described and used to account for high diametral creep rates observed for pressure tubes with unusual microstructures fabricated by non-standard fabrication routes. Full article