Search Results (309)

Background/Objectives: The use of dentin grafts in bone regeneration has gained increasing attention as an alternative to conventional grafting materials. Mesenchymal stem cells (MSCs), known for their osteogenic potential, have been combined with various biomaterials to enhance regenerative outcomes. This study aimed to evaluate the regenerative potential of dentin grafts and bovine-derived xenografts, with or without MSCs, in experimentally created bone defects in a rat model. Methods: A total of 25 male rats were randomly assigned to five groups: control, dentin graft, dentin graft and MSC, xenograft, and xenograft and MSC. Standardized 2-mm cortical defects were created bilaterally in the femoral shafts. Histological and immunohistochemical analyses were performed after a 90-day healing period. Statistical evaluation was carried out using the Kruskal–Wallis H test and Bonferroni-adjusted pairwise comparisons. Results: Complete healing was achieved in all groups without evidence of complications or inflammatory reactions. Immunohistochemical staining demonstrated no positive vascular endothelial growth factor (VEGF), collagen type I (COL1), or osteopontin (OPN) reactions in defect areas, consistent with complete maturation, although collagen type 3 (COL3) positivity was observed in residual xenograft material. Quantitative analysis showed that the dentin graft and MSC group achieved the highest degree of new bone formation (M = 92.88%, SD = 6.09), significantly greater than the control (p = 0.002) and xenograft groups (p = 0.013). Conclusions: Both dentin grafts and xenografts demonstrated enhanced bone defect healing when combined with MSCs. Nevertheless, dentin grafts in conjunction with MSCs yielded the most favorable regenerative outcomes, suggesting their clinical superiority over conventional xenografts. Full article

Periodontal ligament stem cells (PDLSCs) represent a promising cell source for true periodontal regeneration due to their ability to form bone, cementum, and functional ligament. This review critically synthesised twelve in vivo studies (rats = 5, pigs = 2, dogs = 2, sheep = 2, one human trial) evaluating PDLSC transplantation for periodontal defects. A comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library (to May 2025) identified 358 records, of which 12 met predefined inclusion criteria. Data extraction encompassed cell source, scaffold, dose, follow-up, and quantitative regenerative outcomes. Nine studies reported cell doses (5 × 10⁵–2 × 10⁷ cells) and six PDLSC regeneration rates (33–100%). After normalisation for host mass, effective delivery ranged from 10⁵ to 10⁶ cells·kg⁻¹, with optimal outcomes typically above 10⁵ cells·kg⁻¹. PDLSC transplantation consistently enhanced alveolar bone, cementum, and periodontal-ligament regeneration compared with scaffold-alone or untreated controls, with the highest outcomes obtained using biocompatible scaffolds such as Hydroxyapatite/Tricalcium Phosphate (HA/TCP), Gelfoam, or amniotic membrane. Both autologous and allogeneic PDLSC achieved equivalent performance and excellent safety, while xenogeneic models confirmed immune tolerance. Despite encouraging results, the evidence remains preliminary—most studies were short-term and small-scale, and only one randomised human trial has been published. Standardisation of cell preparation, scaffold selection, dosing (absolute and mass-normalised), and follow-up is urgently needed. Future research should include Good Manufacturing Practice (GMP)-compliant clinical trials and mechanistic studies on PDLSC differentiation, paracrine signalling, and exosome-mediated effects to consolidate their translational potential for predictable periodontal regeneration. Full article

Aging water pipelines are increasingly prone to structural failure, leakage, and ground subsidence, creating critical risks to urban infrastructure. Closed-circuit television endoscopy is widely used for internal assessment, but it depends on manual interpretation and lacks reliable quantitative defect information. Traditional vanishing point detection techniques, such as the Hough Transform, often fail under practical conditions due to irregular lighting, debris, and deformed pipe surfaces, especially when pipes are water-filled. To overcome these challenges, this study introduces a deep learning-based method that estimates inverse projection parameters from monocular endoscopic images. The proposed approach reconstructs a spatially accurate two-dimensional projection of the pipe interior from a single frame, enabling defect quantification for cracks, scaling, and delamination. This eliminates the need for stereo cameras or additional sensors, providing a robust and cost-effective solution compatible with existing inspection systems. By integrating convolutional neural networks with geometric projection estimation, the framework advances computational intelligence applications in pipeline condition monitoring. Experimental validation demonstrates high accuracy in pose estimation and defect size recovery, confirming the potential of the system for automated, non-disruptive pipeline health evaluation. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

Aluminum alloys under long-term service or repetitive stress are prone to small fatigue cracks (FCs) with arbitrary orientations, necessitating eddy current probes with focused magnetic fields and directional selectivity for reliable detection. This study presents a flexible printed circuit board (FPCB) probe with a double-layer planar excitation coil and a double-layer differential receiving coil. The excitation coil employs a reverse-wound design to enhance magnetic field directionality and focusing, while the differential receiving coil improves sensitivity and suppresses common-mode noise. The probe is optimized by adjusting the excitation coil overlap and the excitation–receiving coil angles to maximize eddy current concentration and detection signals. Finite element simulations and experiments confirm the system’s effectiveness in detecting surface cracks of varying sizes and orientations. To further characterize these defects, two time-domain features are extracted: the peak-to-peak value ($\Delta$P), reflecting amplitude variations associated with defect size and orientation, and the signal width ($\Delta$W), primarily correlated with defect angle. However, substantial overlap in their value ranges for defects with different parameters means that these features alone cannot identify which specific parameter has changed, making prior defect classification using a Transformer-based approach necessary for accurate quantitative analysis. The proposed method demonstrates reliable performance and clear interpretability for defect evaluation in aluminum components. Full article

Background: Necrotizing enterocolitis (NEC) is a life-threatening condition characterized by necrosis of the gastrointestinal tract caused by hypoperfusion and hypoxia-induced inflammation. Surgical treatment often requires resection, with high morbidity and mortality. Intestinal tissue engineering using absorbable biomaterials represents a potential alternative. Small intestinal submucosa (SIS) is a biodegradable extracellular matrix (ECM) scaffold that may facilitate regeneration of the native tissue. Objectives: The aim of our study is to investigate the regenerative potential of SIS in a rat model with multiple gastrointestinal defects. Methods: In rats, after a midline laparotomy, an approximately 1 cm full-thickness incision was performed on the anterior gastric wall, on the antimesenteric side of the small and large intestine, each covered with an SIS patch. After three weeks, the graft sites and adjacent fragments were harvested and fixed in 10% neutral buffered formalin. Cross-sections of the grafted area were processed and stained with hematoxylin and eosin for histologic analysis. Results: Among the fifteen Wistar rats used in the study, the survival rate was 80% (12/15). Macroscopic examination of the abdominal cavity after the second surgery showed no complications. Adhesions were present in 92% (11/12). Histological examination demonstrated complete mucosal coverage in all stomach samples, nine of the small intestine, and ten of the large intestine. Mild fibrosis with minimal inflammatory infiltrates predominated. Ulceration with granulation tissue replacement was observed in three small intestine samples. Foreign body reactions were restricted to suture sites. Conclusions: In this multifocal injury model, SIS integrated effectively and supported early regenerative healing across gastric, small-intestinal, and colonic sites at 3 weeks. These data support further studies with longer follow-up, quantitative histology and functional assessment, and evaluation in neonatal-relevant large animal models to determine translational potential for NEC surgery. Full article

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies. Full article

Fuzzing has established itself as a cornerstone technique for uncovering defects in both stand-alone executables and software libraries. In the domain of library testing, prior research has predominantly concentrated on the automated generation of fuzz drivers-code harnesses that invoke individual Application Programming Interfaces (APIs) under test. While these approaches successfully orchestrate API calls in the correct sequence, they often neglect a critical factor: the semantic relevance and structural validity of the input data supplied to each API parameter. Unlike monolithic programs, where inputs are typically drawn from well-defined file or network formats, API parameters may span a broad spectrum of primitive and composite data types-ranging from integers and floating-point values to strings, containers, and user-defined aggregates—each of which demands tailored mutation strategies to exercise deep code paths and trigger latent faults. To address this gap, we introduce PointFuzz, a novel fuzzing framework that integrates type-aware input generation into existing harness generation pipelines. PointFuzz begins by statically analyzing the API’s function signatures and associated type definitions to accurately identify the data type of every parameter. It then applies a suite of specialized mutation operators. This data-type-guided mutation maximizes the likelihood of traversing previously untested execution branches. Moreover, PointFuzz incorporates an innovative feedback mechanism that dynamically adjusts mutation priorities based on real-time coverage gains. By assigning quantitative scores to parameter-specific operators, our system continuously learns which strategies yield the most valuable inputs, and reallocates computational effort accordingly. Empirical evaluation across multiple widely used C/C++ libraries demonstrates that PointFuzz achieves superior API coverage compared to generic, agnostic-type fuzzers. These results validate the efficacy of combining type-aware mutation with adaptive feedback to advance the state of library API fuzzing. Full article

In additive manufacturing (AM), particularly with AlSi10Mg aluminum alloy produced via Laser Powder Bed Fusion (L-PBF), understanding and detecting defects is crucial for ensuring mechanical integrity. This study evaluates the effectiveness of active thermography as a fast, non-destructive testing (NDT) method for identifying typical L-PBF defects. Artificial defects (cubes, spheres, cylinders with unfused powder) were introduced by varying printing parameters. Their real geometry was assessed via micro-computed tomography (μ-CT), revealing deviations from nominal shapes. Thermographic tests using a laser heat source (≈40 W/cm²) were conducted to examine the detectability of these defects in the highly diffusive material AlSi10Mg. Results highlight both the limitations and potential of thermography as a cost- and time-effective alternative to μ-CT for quantitative inspection. Full article

ZnIn₂S₄, a visible-light-responsive layered sulfide photocatalyst with a suitable bandgap (~2.4 eV), exhibits considerable potential for the photocatalytic hydrogen evolution reaction (PHER) due to its low toxicity, excellent stability, and appropriate band alignment. Nevertheless, its practical deployment is limited by inherent issues such as rapid charge carrier recombination, scarce surface-active sites, and slow oxidation kinetics. Defect engineering strategies—including sulfur, zinc, and indium vacancies, as well as heteroatom doping—have been developed to mitigate these shortcomings. This review not only summarizes recent advances in these strategies but also elucidates the fundamental physicochemical mechanisms behind the enhanced photocatalytic performance. A systematic quantitative evaluation is presented, highlighting improvements in critical performance metrics such as hydrogen evolution rate, light absorption range, apparent quantum yield (AQY), and charge separation efficiency. Furthermore, the review offers a critical perspective on the current state of defect-engineered ZnIn₂S₄ systems. Promising future research pathways are outlined, with emphasis on atomic-precision synthesis and operando characterization techniques. Finally, we discuss persistent challenges in the field, including reproducibility in synthesis, long-term operational stability, and scalability toward industrial hydrogen production. Full article

In the present study, we evaluated the usefulness of a porous sintered calcium carbonate body with CaSiO₃ by comparing its osteogenic capacity with that of calcium carbonate without CaSiO₃ and that of β-tricalcium phosphate (TP). A cranial defect model of eight-week-old male Wistar rats was divided into three groups: calcium carbonate (CC), calcium carbonate-CaSiO₃ composite (CC+CS), and TP. Micro-computed tomography (CT) and histological analysis were performed at four and eight weeks postoperatively. Upon quantitative evaluation of newly formed bone volume by radiography, the CC+CS group demonstrated the highest value at eight weeks postoperatively and exhibited significantly more new bone formations than the CC group (p < 0.05). Upon histological evaluation, the CC+CS group demonstrated significantly higher new bone formation than the CC group (p < 0.05). Furthermore, in terms of residual graft material ratio, at eight weeks postoperatively, the amount of residual graft material in the CC+CS group was significantly higher than that in the TP group (p < 0.05). Therefore, the addition of CaSiO₃ enhances the functional regulation of calcium carbonate-based artificial bone and can be incorporated in bone graft materials. Full article

Tunnel linings play a vital role in underground infrastructure, yet their performance can be severely affected by pre-existing cracks. This study investigates the mechanical behavior and failure mechanisms of C30 concrete with artificial cracks under uniaxial compression, simulating various crack conditions observed in tunnel linings. Specimens were designed with varying crack lengths and orientations. Acoustic emission (AE) monitoring was employed to capture the evolution of internal damage and micro-cracking activity during loading. Fractal dimension analysis was performed on post-test crack patterns to quantitatively evaluate the complexity and branching characteristics of crack propagation. The AE results showed clear correlations between amplitude characteristics and macroscopic crack growth, while fractal analysis provided an effective metric for assessing the extent of damage. To complement the experiments, discrete element modeling (DEM) using PFC3D was applied to simulate crack initiation and propagation, with results compared against experimental data for validation. The study demonstrates the effectiveness of DEM in modeling cracked concrete and highlights the critical role of crack orientation and size in strength degradation. These findings provide a theoretical and numerical foundation for assessing tunnel lining defects and support the development of preventive and reinforcement strategies in tunnel engineering. Full article

Spigelian hernia (SH) is an infrequent aponeurotic defect in Spiegel’s semilunar line. The literature on pediatric SH is scarce. A comprehensive review of the previous literature was conducted. Eligible studies were identified by searching primary medical bibliography databases, and a pooled analysis of published case-level data was performed. Medians and interquartile ranges were used to describe the quantitative variables and proportions for categorical variables. The Kruskal–Wallis, Mann–Whitney U, and Fisher’s exact tests were used to compare group variables. Spearman’s and Pearson’s correlation analyses were used to assess the degree of correlation between variables, while Cramér’s V was applied to evaluate the degree of association among the variables. A p-value < 0.05 (two-tailed) was considered statistically significant. Our search identified 82 publications reporting on 123 patients (106 male, 86.2%), with an age range of 0–21 years. Forty-seven patients (38.2%) had a left-sided SH, fifty-six (45.5%) had a right-sided SH, and thirteen (10.6%) had a bilateral SH. Traumatic SH, mostly from bicycle injuries, accounted for 45 cases (36.6%), while 41 (33.3%) were associated with undescended testis (UDT). In this series of published cases, hernia incarceration/strangulation (I/S) was reported in 15 patients (12.2%), who were significantly younger (p = 0.02). Surgical correction was performed in 95 cases (77.2%), 14 of them laparoscopically, with a 35.7% conversion rate. Eight cases (6.5%) were managed conservatively. Overall, outcomes were favorable. SH is an infrequent pediatric condition that, based on the synthesized literature, predominantly affects males. The published cases suggest two main clinical phenotypes: a congenital form, often linked to ipsilateral UDT, and an acquired form, typically resulting from trauma. Analysis of the reported data indicates a higher risk of incarceration in early childhood. Surgical treatment is the most frequently reported approach with generally favorable outcomes, whereas the evidence for conservative management remains limited. This comprehensive review highlights the dual nature of pediatric SH and underscores the need for a high index of suspicion in relevant clinical scenarios. Full article

Three-layer polyethylene (3LPE) coated steel pipelines are currently the preferred solution for global oil and gas transmission. However, external corrosion beneath the 3LPE coating poses a serious threat to pipeline operations. The pressing concern for pipeline safety and integrity involves non-destructive evaluation techniques for the non-invasive and quantitative interrogation of such defects. This study therefore explores linear frequency-sweeping microwave near-field non-destructive testing (NDT) techniques for imaging and evaluating the pitting corrosion beneath 3LPE coating. An improved branch-cut method is proposed for the high-precision phase unwrapping of the microwave phase image sequence, and its superiority over traditional methods in terms of accuracy and robustness is validated. A background subtraction method based on kernel density estimation (KDE) is presented to suppress the lift-off effect on the pipeline geometry. In addition, the principal-component-analysis-wavelet-based principal component extraction and fusion enhance the detection signal-to-noise ratio (SNR) and image contrast, while mitigating the annular artifacts around the corrosion. The experimental results demonstrate the feasibility of the proposed approach for the detection, imaging, and characterization of external corrosion beneath the 3LPE coating of pipelines. Full article