Search Results (3,466)

This study reports the structural, optical, and photocatalytic properties of Nd-doped Al₂O₃ nanoparticles synthesized via a high-temperature solid-state reaction method. The impact of varying Nd concentrations (1%, 2%, and 3%) on the host lattice was investigated through X-ray diffraction (XRD), which confirmed the successful integration of Nd³⁺ ions and revealed a concentration-dependent lattice expansion. Diffuse Reflectance Spectroscopy (DRS) demonstrated characteristic 4f-4f transitions of Nd³⁺, while Tauc plot analysis indicated a systematic blue shift in the optical bandgap from 4.5 eV to 4.63 eV with an increasing dopant content. The photocatalytic efficiency was evaluated through the degradation of Basic Fuchsin (BF) dye under UV irradiation. The Al₂O₃:3% Nd sample exhibited superior performance, achieving an 83% degradation efficiency within 160 min, following pseudo-first-order Langmuir–Hinshelwood kinetics (k_obs = 0.02428 min⁻¹). Photoluminescence (PL) studies further corroborated the structural integrity and defect dynamics, showing a significant enhancement in NIR emission (880–920 nm) at higher doping levels without reaching the concentration-quenching threshold. These results suggest that Nd-doped Al₂O₃ nanoparticles are highly effective for environmental remediation and optoelectronic applications. Full article

Efficient crop health monitoring is crucial for global food security. Supervised deep learning approaches are often impractical due to the scarcity of large, labeled datasets. To address this limitation, this study adapts EfficientAD, an unsupervised, label-free anomaly detection framework originally designed for industrial inspection, for agricultural imagery on small datasets. The method utilizes a Patch Description Network (PDN) for localized feature extraction, a student network for local anomalies, and an autoencoder for global structural constraints. Benchmarked against AnoGAN, Pix2Pix, InTra, and Teacher–Student models, the framework demonstrated superior performance on the MVTec AD, PlantVillage, Coffee Leaf, and a custom real-world Sweet Potato dataset. The model achieved perfect area under the receiver operating characteristic curve (AUROC) scores of up to 100% in categories like “Pongamia”, “Potato”, and “Coffee Leaf”. While image-level classification was exceptionally robust, pixel-level localization (AUPRO) proved sensitive to complex agricultural backgrounds. To overcome this, a background interference analysis was conducted using Background Removed (BGRM) and out-of-distribution Background Replaced-Green (BGRP-G) strategies on the custom dataset. Notably, the BGRP-G strategy remarkably improved the image-level AUROC from 88.9% to 99.5% and substantially boosted the pixel-level AUPRO from 47.1% to 61.9%, successfully preserving the boundary integrity of severe structural defects. Achieving millisecond-level latency without complex data augmentation, this adapted label-free framework offers a versatile, highly efficient solution for real-time crop health diagnostics on resource-constrained Edge AI devices. Full article

Soluble growth stimulation protein form of interleukin-1 receptor-like 1 (ST2) may signal myocardial stress, and elevated ST2 blood levels are associated with adverse outcomes in adult heart disease. Data on ST2 in children with congenital heart disease (CHD) is limited. This study explored ST2 in newborns and older children with atrial septal defect (ASD), as this represents a common CHD type that remains clinically challenging to recognize in childhood with slowly evolving symptoms. A case–control study was carried out in newborn ASD cases versus controls measuring ST2 on dried blood spot samples and additionally in pediatric ASD cases versus controls on venous blood together with cardiac magnetic resonance before and after treatment. ST2 was higher in newborns with ASD (n = 19) compared to controls (n = 93); (p < 0.01). Receiver operating characteristics to diagnose newborn ASD by ST2 showed an area under the curve of 0.848. Levels of ST2 decreased in pediatric ASD (n = 16) after treatment (p = 0.014). Lower left ventricular ejection fraction correlated with higher ST2 levels before (r = −0.348) and after treatment (r = −0.497). Elevated ST2 in newborns may aid early ASD diagnosis. Levels of ST2 in pediatric ASD decrease after treatment, and higher levels are associated with lower left ventricular ejection fraction, warranting further study. Full article

Rakı, a traditional distilled beverage produced from grapes, holds significant economic importance in Türkiye; however, comprehensive consumer-focused sensory research remains limited. This study aims to determine the aroma profile, sensory characteristics, and consumer preferences of commercial rakı to guide producers in aligning product characteristics with consumer expectations. Nine commercial rakı samples were evaluated. The aroma composition was analyzed using SBSE-GC-MS. Sensory attributes were assessed by a trained panel through descriptive analysis (DA) and by 100 consumers utilizing the Check-All-That-Apply (CATA) method alongside a liking test. Eighty-one aroma compounds were identified, predominantly the phenylpropanoids trans-anethole and estragole, with monoterpenes and sesquiterpenes dominating the secondary profile. Integrating instrumental data with DA evaluations suggests that anethole and sesquiterpenes likely contribute to the attributes related to visual coating, body, creamy, mastic, persistency, and complexity. Consumer profiling revealed two distinct preference groups. Older, frequent consumers preferred complex, high-alcohol profiles with trigeminal harshness and visual glass coating, whereas younger, casual consumers preferred smoother rakı with a traditional white appearance, reacting negatively to “boiled aniseed” flavors and the yellowish tint of oak-aged versions. The CATA technique effectively distinguished these profiles. To enhance overall product quality, producers should eliminate “boiled” defects and adjust sensory profiles: complex products for experienced consumers and visually traditional, smooth profiles for younger consumers. According to current knowledge, this is the first study to employ the CATA method alongside consumer profiling and preference mapping in the sensory evaluation of rakı. Full article

Electrochemical chlorine production is of considerable industrial importance in areas such as water treatment, chemical manufacturing, and disinfection. However, conventional precious metal-based dimensionally stable anodes (DSAs), such as RuO₂- and IrO₂-based systems, are limited by high cost and resource constraints, motivating the development of low-cost alternative catalysts. In this study, Mn₃O₄ electrodes with controllable defect characteristics were fabricated by electrochemical deposition under various processing conditions. The effects of defect modulation and surface modification on the structural, electronic, and electrochemical properties of the electrodes were systematically evaluated. X-ray diffraction analysis confirmed that all deposited films retained a stable tetragonal Mn₃O₄ crystal structure, indicating that the deposition parameters primarily influenced defect states rather than the bulk phase. Mott–Schottky measurements revealed that the Mn₃O₄ electrodes exhibited p-type semiconducting behavior, with charge carrier densities on the order of 10¹⁴ cm⁻³, suggesting that oxygen vacancy-related defect states may contribute to the observed electronic properties of the electrodes. To further enhance anodic performance, Pt was introduced onto the Mn₃O₄ surface via sputtering, resulting in significantly improved charge transfer characteristics. Electrochemical measurements demonstrated that the best performing Pt/Mn₃O₄ electrodes delivered a current density exceeding 100 mA cm⁻² at an applied potential of 1.5 V versus Ag/AgCl. More importantly, defect-enriched Pt/Mn₃O₄ electrodes exhibited markedly enhanced chlorine evolution activity, with the chlorine production rate increasing from approximately 14 µmol cm⁻² to 29 µmol cm⁻², corresponding to an enhancement of about 2.07-fold. Faradaic efficiency analysis further showed that sample (g) and sample (n) achieved chlorine evolution efficiencies of 59.2% and 74.6%, respectively, indicating a higher tendency toward chlorine evolution for the Pt-modified electrodes under the tested conditions. These findings suggest that the synergistic combination of defect engineering and surface modification effectively modulates the electronic structure of Mn₃O₄, providing a viable strategy for improving chlorine evolution performance. Full article

Fetal alcohol spectrum disorders (FASD) encompass a continuum of developmental abnormalities caused by prenatal alcohol exposure, resulting in persistent neurodevelopmental and structural defects. Accumulating evidence indicates that redox dysregulation plays a central role in the pathogenesis of FASD. Ethanol disrupts cellular redox homeostasis by promoting excessive reactive oxygen species production and depleting endogenous antioxidants, thereby perturbing key redox-sensitive molecular networks. Dysregulation of these pathways leads to mitochondrial dysfunction, endoplasmic reticulum stress, lysosome dysfunction, and disrupted cellular processes, including proliferation, differentiation, and migration, while also promoting apoptosis and neuroinflammation, ultimately leading to the developmental abnormalities characteristic of FASD. Recent studies demonstrate that antioxidant supplementation or targeted modulation of redox-sensitive signaling can mitigate these deleterious effects in preclinical models. This review synthesizes current knowledge of the molecular networks underlying redox dysregulation in FASD and discusses emerging antioxidant and dietary interventions with therapeutic potential. Elucidating these mechanisms provides critical insight into the pathogenesis of FASD and may inform the development of effective strategies for the prevention and treatment of FASD. Full article

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, and simplistic feature fusion strategies. In response to the above challenges, this paper proposed the Multi-Scale Frequency-Enhanced YOLO (MSFE-YOLO) algorithm that integrates multi-scale frequency domain enhancement with defect-aware attention mechanisms. First, a Multi-Scale Frequency-Enhanced Convolution (MSFC) module was constructed, which extracted multi-scale spatial features in parallel through depth-adaptive dilated convolutions, explicitly modeled high-frequency edge information using the Laplacian operator, and achieved adaptive fusion of multi-branch features via learnable weights. Second, a Cross-Stage Partial with Multi-Scale Defect-Aware Attention (C2MSDA) module was designed, integrating Sobel operator-based edge perception, multi-scale spatial attention, and adaptive channel attention to collaboratively enhance features across spatial, channel, and edge domains through a gated fusion strategy. Finally, an Adaptive Feature Fusion Enhancement (AFFE) module was proposed to achieve adaptive aggregation of multi-level features through a data-driven weight generation network and cross-scale feature interaction mechanism. Experimental results on the NEU-DET and GC10-DET datasets demonstrated that MSFE-YOLO achieved the mAP@0.5 of 79.8% and 66.7%, respectively, which were 1.7% and 2.1% higher than the benchmark model YOLOv11s respectively, while maintaining an inference speed of 89.3 FPS, which satisfied the real-time detection requirements in industrial scenarios. Full article

The challenge of substituting bone defects necessitates the search for effective biomaterials based on biopolymer composites with biocompatible fillers. A promising approach in bone tissue engineering is the use of regenerative scaffolds based on polyhydroxyalkanoates (PHAs), specifically poly(3-hydroxybutyrate)—P(3HB), which are characterized by high biocompatibility and osteoinductive potential. In this study, we evaluate the changes in the mechanical, thermal, and morphological properties of P(3HB) within P(3HB)-copolymers/HA, P(3HB)/CS, P(3HB)/PLA/CS, and P(3HB)/PLA/HA composites. These materials, containing various filler contents (up to 70 wt.% of HA–hydroxyapatite or CS–chitosan), were obtained using melt extrusion compounding. It is shown that the modification of biopolymer matrices promotes a decrease in melting temperature, improvement of mechanical characteristics, and an increase in material elasticity. At high filler concentrations, nanoparticle agglomeration and a deterioration of physical-mechanical properties were observed. It was established that a content of 10–20 wt.% of nano-hydroxyapatite and chitosan is optimal, as these composites most closely match the mechanical properties of bone tissue. The results obtained indicate the high potential of the developed nanocomposites for the creation of biodegradable implants in reconstructive orthopedics. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

To address the issues of uneven grayscale distribution, weak defect features, and small target scales on the coating surface of LCD polarizers during manufacturing, an improved YOLOv10-N-based method is proposed for surface defect detection. First, a polarizer coating defect dataset is constructed based on the LCD polarizer coating process and the characteristics of coating defects. Adaptive median filtering is then employed for image denoising, while a particle-swarm-optimization-based improved histogram equalization method is adopted for image enhancement. Next, the Scale-aware Pyramid Pooling (SCPP) module is introduced into the C2f module of the backbone network to construct the C2f_SCPP feature extraction module, thereby improving the model’s ability to detect coating defects with different morphologies through multi-scale semantic feature fusion. In addition, rotation-equivariant convolution PreCM is incorporated into the SPPF module of the backbone network to build the SPPF_PreCM module, which effectively suppresses feature redundancy and scale conflicts while strengthening the representation of tiny defects. Finally, while retaining the original Distribution Focal Loss (DFL) branch of YOLOv10, WIoU is used to replace CIoU as the IoU loss term in bounding box regression, thereby improving localization accuracy and accelerating model convergence during training. Experimental results show that, compared with YOLOv10-N, the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 1.8 and 2.8 percentage points, respectively, demonstrating its effectiveness for polarizer coating defect detection. However, its generalization capability under diverse production environments, varying illumination conditions, and complex noise scenarios still requires further investigation. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

This paper investigates the formation mechanism and key influencing factors of freckle defects that arise during the directional solidification of a novel third-generation nickel-based single crystal superalloy turbine blade. A combined experimental and multi-physics numerical simulation approach was adopted. The results indicate that freckle formation primarily originates from solutal convection, which subsequently triggers a cascade of processes, including the development of convection-induced segregation channels, flow-driven dendrite fragmentation, and the migration and aggregation of dendrite fragments. The severity of freckling is closely dependent on both the casting’s position within the furnace and its local geometric characteristics. Castings located in regions with poorer heating conditions exhibit lower temperature gradients and slower solidification rates, significantly increasing their susceptibility to freckle formation. Similarly, on a given casting, the side subjected to less favorable heating is more prone to freckle initiation. The freckle number varies non-monotonically along the blade height, increasing from 3 to a maximum of 16, with a temporary decrease near the platform and a final reduction near the top. This trend is mainly attributed to thickness-dependent channel segregation, as well as freckle propagation into the interior and coalescence at higher positions. This study provides a crucial theoretical basis for understanding the formation mechanism of freckle defects in nickel-based single crystal superalloys and offers valuable guidance for optimizing blade manufacturing processes, reducing solidification defects, and enhancing blade quality and service performance. Full article

This review provides a comprehensive overview of high-wear-resistant metal–ceramic surface engineering technologies based on Directed Energy Deposition (DED) for high-temperature industrial applications. In high-temperature processes such as continuous hot-dip coating, critical components (e.g., rollers and sleeves) are exposed to severe wear and chemical reactions, leading to rapid degradation and frequent replacement, which results in significant economic losses. This review focuses on the fundamental characteristics of DED processes and their advantages over conventional surface modification techniques such as HVOF, PVD/CVD, and arc-based methods. Particular attention is given to the process–structure–property relationships governing coating performance, including coating thickness, bonding characteristics, and high-temperature stability. Representative material systems, particularly WC-based metal–ceramic composites (e.g., Co–WC), are systematically discussed in terms of their wear resistance and applicability under severe operating conditions. Quantitative tribological performance metrics, including wear rate and friction coefficient, are also reviewed to provide a more rigorous understanding of coating performance. The analysis highlights that DED offers unique advantages in achieving thick coatings with strong metallurgical bonding and high applicability to repair and remanufacturing of large-scale components. In addition, recent advances in DED technologies, such as closed-loop control, self-regulating effects, and data-driven process optimization, are examined to highlight emerging trends in the field. The review also identifies current technical limitations and outlines future research directions, emphasizing the need for improved process control, defect mitigation, and integration of advanced monitoring techniques. Full article

In water-scarce regions such as Xinjiang, China, agricultural development is constrained not only by limited water resources but also by a strong reliance on water-saving irrigation technologies. Drip irrigation is a key measure for improving irrigation efficiency and promoting the sustainable development of water-saving agriculture. However, defects arising during the manufacture of labyrinth Drip emitters—the core components of drip irrigation systems—can undermine system reliability, leading to channel blockage and non-uniform irrigation. To tackle this issue, a defect detection approach is developed by integrating Digital Twin technology with an enhanced YOLOv8 model for online inspection of labyrinth Drip emitters on drip irrigation tape production lines. In parallel, a self-built dataset covering six defect categories is established. Supported by the DT framework, the standard YOLOv8 network is refined to strengthen its capability in identifying complex micro-defects. Specifically, DySnakeConv is introduced to better represent the curved and slender characteristics of labyrinth channels; DySample is incorporated to improve the reconstruction and representation of fine-grained details; an Efficient Multi-Scale Attention module is adopted to capture richer contextual information while suppressing background noise; and Inner-SIoU is applied to optimize the bounding-box regression process. Experimental results show that the model achieves 89.6% precision, 90.9% recall, and 93.9% mAP50. Compared with the baseline YOLOv8, precision, recall, and mAP50 are improved by 7.3, 3.9, and 3.3 percentage points, respectively. Under the same training conditions, the proposed model outperforms YOLOv10 and YOLOv11 in accuracy-related metrics. Specifically, compared with YOLOv11, precision, recall, and mAP50 are improved by 4.8, 5.0, and 2.6 percentage points, respectively; compared with YOLOv10, they are improved by 10.0, 7.7, and 7.3 percentage points, respectively. Meanwhile, the model maintains a lightweight size of 3.7 M parameters and a real-time inference speed of 150.2 FPS, demonstrating a favorable accuracy–efficiency trade-off. By extending manufacturing-level quality control to agricultural applications, the approach helps ensure uniform irrigation and improve water-use efficiency, providing practical technical support for precision agriculture in arid regions. Full article