Assessment Of Defects Research Articles

Non-destructive assessment of the internal defects of FRED® pear by a low radio-frequency capacitive technique

Postharvest pear fruit internal browning damages were assessed non-destructively using a capacitive instrumental chain in the super low (SLF) – low (LF) radiofrequency region combined with Image analysis. A rapid, inexpensive, and non-destructive instrumental chain was set up: an LCR meter interfaced with a PC receives and transmits signals through a parallel plate capacitor in the frequency range from 100 Hz to 10 kHz. The capacitance data were used to classify the FRED® pear fruit into two classes, “with defects” and “healthy”, using the unsupervised fuzzy C-means clustering analysis. The main results show good classification rates: 91 % of the samples are correctly classified as damaged. Further samples should be implemented to understand how the model performs with samples from different ripening stages. Despite this, the technique appears promising for non-destructive internal quality assessment in pear fruit.

Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning

Detecting defects on aerospace surfaces is critical to ensure safety and maintain the integrity of aircraft structures. Traditional methods often need more precision and efficiency for effective defect detection. This paper proposes an innovative approach that leverages deep learning and infrared imaging techniques to detect defects with high precision. The core contribution of our work lies in accurately detecting the size and depth of defects. Our method involves segmenting the size of the defect and calculating its centre to determine its depth. We achieve a more comprehensive and precise assessment of defects by integrating deep learning with infrared imaging based on the U-net model for segmentation and the CNN model for classification. The proposed model was rigorously tested on both a simulation dataset and an experimental dataset, demonstrating its robustness and effectiveness in accurately identifying and assessing defects on aerospace surfaces. The results indicate significant improvements in detection accuracy and computational efficiency, showing advancements over state-of-the-art methods and paving the way for enhanced maintenance protocols in the aerospace industry.

Integration of cone beam computed tomography for interdisciplinary diagnosis and treatment planning

Cone-beam computed tomography (CBCT) has revolutionized dental imaging by providing precise three-dimensional visualization of hard tissue structures with minimal radiation exposure compared to conventional CT. This review explores the integration of CBCT in interdisciplinary diagnosis and treatment planning across various fields of dentistry. CBCT offers versatile applications including assessment of impacted teeth, airway analysis, temporomandibular joint evaluation, detection of jaw pathologies, and precise diagnosis of dento-maxillofacial fractures. Its ability to generate static and rotational 3D images aids in surgical planning and treatment outcome prediction. Furthermore, CBCT plays a crucial role in orthodontics for localization of impacted teeth and evaluation of craniofacial anomalies such as cleft lip and palate. In periodontics and implantology, CBCT facilitates detailed assessment of periodontal structures, bone defects, and precise implant placement. Additionally, CBCT assists in maxillofacial prosthetics by enabling virtual surgical planning and custom prosthetic fabrication. This paper underscores CBCT as an indispensable tool in modern dental practice, enhancing diagnostic accuracy, treatment precision, and patient outcomes across diverse clinical scenarios.

Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections.

Improving the Classification of Defect Levels in Main Electrical Equipment Through Advanced Prompting on a Large Language Model

The stable operation of the power system is closely related to the national economy and people’s livelihoods. Therefore, the timely detection, qualitative assessment, and handling of major equipment defects are crucial. The classification of defect levels in main electrical equipment is a fundamental task in this process, which is often manually completed, supplemented by knowledge bases or expert systems. However, this approach is time-consuming, labor-intensive, involves challenging human–machine interaction, and relies on expert experience. Conversational large language models, such as ChatGPT, ERNIE Bot, ChatGLM, have garnered widespread recognition in various domains. However, these models may have errors in the reasoning process, resulting in biased or even erroneous outputs, which is referred to as the “hallucinations”. The hallucinations’ problem of large language models poses challenges in specific fields. To mitigate the hallucinations in large language models, researchers often seek to incorporate domain-specific knowledge into these models through methods like fine-tuning or prompt learning. In order to enhance model performance while minimizing computational costs, this study adopts the prompt learning approach. Specifically, we propose a large language model prompt learning framework based on knowledge graphs, aiming to provide the large language model with reasoning support by leveraging specific information stored within the knowledge graph and receive explainable reasoning result. Experimental results demonstrate that our module achieved superior results on the power defect dataset compared to the non-prompt method.

Validation of assessment methods for creep crack growth rates in irradiated components from Nimonic PE16 Tie-Bar tests

Creep crack growth is a life-limiting failure mechanism in high-temperature metallic components. The creep crack growth response is linked to the underlying creep deformation and failure properties, such as creep ductility. Irradiation effects such as helium (He) embrittlement in high-temperature reactor components trigger decreases in creep ductility. Creep crack growth (CCG) data obtained from unirradiated and irradiated Nimonic PE16 tie-bars confirm the accelerated crack growth rates under neutron irradiated conditions. The exact same data were also used to validate the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2, developed from unirradiated material data. To do so, the mechanics-based creep crack growth law of Nikbin-Smith-Webster (NSW) is adopted to estimate the crack growth rates in unirradiated PE16, and then adjusted through changes in bulk mechanical properties in the irradiated case to estimate the changes in crack growth rates. The estimated ȧ-C∗ trend was employed as part of the analytical defect assessment procedure for transient creep crack growth in Code Case N-934 of ASME BPVC Section XI Division 2. Evaluation of results via the procedure and the estimated trends confirm the validity of the approach to predict increases in creep crack growth in neutron irradiated components, as observed in experimental data. The approach yields moderately conservative predictions for crack growth rates, and accurately captures the relative increase in creep crack growth rates for the irradiated case. The predictions were independently validated by predicting the C*-values expected in the loading configuration for a given recorded crack growth rate ȧ. The validation of the defect assessment method introduced in Code Case N-934 against both unirradiated and irradiated PE16 CCG data provides a practical path to its implementation in metallic components across high-temperature reactor designs. This is particularly useful where crack growth data in irradiated material may not be readily available. The technique also allows for propagating uncertainties in crack growth predictions which stems from compounding effects such as variability in unirradiated properties, degree of irradiation and corresponding embrittling effects, crack size and load perturbations. The relevance of mechanical properties accurately depicting aspects of the He embrittlement process for practical applications in plant and the importance of creep crack growth testing are discussed.

Drainage Pipeline Defect Detection System Based on Semantic Segmentation

Since drainage pipes are widely used in daily life and are necessary for cities to function normally, intelligent fault detection in these pipes has emerged as both an urgent necessity and a major area of development. Compared to traditional target detection methods, the semantic segmentation-based pipeline defect detection method has some special qualities. It can effectively detect a variety of pipeline defect types and segment the defects using precise geometrical attributes to support subsequent defect assessment. This study replaces the backbone network and fuses bar pooling and cascade feature fusion modules in the encoding and decoding phases, respectively, to obtain richer semantic information. This approach strikes a balance between segmentation accuracy and model complexity, aiming to address the problems of the large computational volume of semantic segmentation and the loss of detailed semantic information of pipeline defects. In the pipe defect dataset, the proposed approach is compared with the baseline algorithm and the experimental findings indicate that it has a greater segmentation accuracy than the conventional algorithm. Additionally, the lightweight design of the algorithm lowers the model’s complexity.

Caesarean Scar Defect Assessment by Saline Infusion Sonography After Primary Caesarean Section

Caesarean Scar Defect Assessment by Saline Infusion Sonography After Primary Caesarean Section

Assessment of defects and performance in bio-based ceramic composites: a focus on capillary properties and non-destructive evaluation

Assessment of defects and performance in bio-based ceramic composites: a focus on capillary properties and non-destructive evaluation

Time‐domain spectra of ultrasonic wave transmitted through granite and gypsum samples containing artificial defects

AbstractThe internal defects in rock masses can significantly impact the quality and safety of geotechnical projects. Mechanical waves, as a common nondestructive testing (NDT) method, can reflect the external and internal structures of rock or rock masses. Analyses on the reflected and transmitted waves enable nondestructive identification and assessment of potential defects within rocks. Previous studies mainly focused on the variation of single or limited wave features like main frequency, amplitude and energy between the intact and non‐intact samples. In fact, most information contained in the waveforms is neglected. Techniques of data mining can provide a powerful tool to reveal this information and therefore a more accurate determination of the internal structures. In this study, 995,412 NDT data from 14 types of granite and gypsum samples with different cross‐section shapes and different types of defects are recorded by an ultrasonic wave generation and collection system. This dataset can be used not only as the training data for defect classification in NDT but also as a good reference for conventional NDT analyses. Besides, time‐series data analysis is an opportunity and challenging issue, this dataset holds great potential for broader application in general time‐series classification analysis.

Spatial distribution of dark heartwood and wood rot in sugar maple at the northern edge of its range

The wood of sugar maple trees (Acer saccharum Marsh.) is generally perceived as containing more defects, such as dark heartwood and wood rot, at the northern limit of its range compared to southern locations; however, assessments of these internal defects have primarily relied on direct observation, limiting the scope of analysis. To enable large-scale assessments, models are needed to estimate the internal quality of individual trees based on external defects. Accordingly, this study aimed to investigate the relationships between external and internal defects within sugar maple stems. Then, large-scale estimates of the importance of such defects were produced to understand the effects of site conditions and climate. Increment cores were sampled from 1,108 trees across 108 plots. Results revealed that the probability of wood rot and the proportion of internal defects were associated with the presence of cracks and fungal fruiting bodies. Also, colder sites increased the probability of wood rot as well as the proportion of the stem affected by internal defects. Moreover, the western Balsam fir-Yellow birch subdomain had both the highest probability of wood rot and the greatest proportion of internal defects. Future studies should examine how this spatial distribution could evolve with climate change.

3D Model Simulation in Preoperative Planning of Complex and Revision Hip Replacement

Complex and revision hip arthroplasty has remained one of the most discussed orthopedic topics in recent years. With the advancement of printing technologies 3D preoperative planning and live model simulation provides us with “hands on” procedures without any risk for the patient. Modern computer technologies present the possibility for preoperative planning of Total Hip Arthroplasty (THA) in digital environment. 3D printing of anatomical models in real size allows accurate assessment of the pathological deviations of the anatomy, precise preoperative planning of the reconstruction and simulation of the operative intervention ex-vivo. We present a series of 3D simulations for planning hip replacement, illustrating the technique, discussing its advantages and disadvantages, in complex primary and revision hip arthroplasty. In the complex and revision surgery of the hip joint, a correct assessment of bone defects is mandatory. Reconstructing the biomechanical parameters – centre of rotation, femoral offset and leg length discrepancy, is also of great significance. The 3D reconstruction with biomodel simulations offers exceptional accuracy in this respect. The method can be an excellent tool for education of surgeons in training and residents.

Housing Defect Assessment through Household Scale and General Contractor Level

Housing Defect Assessment through Household Scale and General Contractor Level

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Usage of coil-shaped conductive polymer composite as intrinsically flexible flaw sensing probe

The assessment of buried flaws in the interlayer structure of curved metal is crucial to system safety. To improve the flexibility of eddy current sensor, an intrinsically flexible flaw sensing probe made of coil-shaped conductive polymer composite is proposed. The experimental data show that the response signal of the coil-shaped composite changes at the flaw and increases with the increases of flaw height/length, with a maximum sensitivity of 0.435 mV/mm for differential amplitude and 4.904 °/mm for differential phase. The results show that the height of the characteristic signals in the response signals reflects the change in flaw height, and the length of the characteristic signals in the response signals reflects the change in flaw length. The relative change rate of the coil-shaped composite is higher than that of a metal coil of the same structure. The above results have laid a preliminary foundation for using the coil-shaped composite to detect flaws.

Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.

Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice. Establish an expeditious histopathological analysis of mutant mice testicular sections stained with commonly available hematoxylin and eosin (H&E) via enhanced deep learning model MATERIALS AND METHODS: Automated segmentation and cellular composition analysis on the testes of six mouse reproductive mutants of key reproductive gene family, DAZ and PUMILIO gene family via H&E-stained mouse testicular sections. We improved the deep learning model with human interaction to achieve better pixel accuracy and reduced annotation time for histologists; revealed distinctive cell composition features consistent with previously published phenotypes for four mutants and novel spermatogenic defects in two newly generated mutants; established a fast spermatogenic defect detection protocol for quantitative and qualitative assessment of testicular defects within 2.5-3h, requiring as few as 8 H&E-stained testis sections; uncovered novel defects in AcDKO and a meiotic arrest defect in HDBKO, supporting the synergistic interaction of Sertoli Pum1 and Pum2 as well as redundant meiotic function of Dazl and Boule. Our testicular compositional analysis not only could reveal spermatogenic defects from staged seminiferous tubules but also from unstaged seminiferous tubule sections. Our SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.

Maternal Medication Use in Pregnancy: A Narrative Review on Assessing and Communicating the “Risk” of Birth Defects to the Patient

The state of knowledge regarding the teratogenic effects of maternal use of medications during pregnancy is constantly evolving and is often uncertain. Timely access to high-quality information may reduce prolonged harmful exposures, decrease the number of preventable birth defects, empower patients with accurate information about the risks of exposure, and prevent unnecessary patient anxiety and pregnancy termination. In this narrative review, we describe the process by which the teratogenic risk of medications is assessed by experts in medicine, genetics, and epidemiology and how identifiable risks can be effectively communicated to patients. Risk assessment of birth defects in human pregnancy involves collecting and synthesizing available data through a proper and rule-driven evaluation of scientific literature. Expert consensus is a practical approach to determine whether a given exposure produces damage after careful consideration of gestational timing, dose and route of the exposure, maternal and fetal genetic susceptibility, as well as evidence for biological plausibility. The provision of teratogen risk counseling through appropriate interpretation of information and effective knowledge translation to the patient is critical for the prevention of birth defects and maximizing healthy pregnancies.

Extended Trochanteric Osteotomy Does Not Compromise Functional and Radiographic Outcomes of Femoral Stem Revisions with the Use of an Uncemented Modular Conical Stem.

Background: Stem revisions in revision total hip arthroplasty (THA) with proximal bone stock loss may be dealt with utilizing modular, uncemented conical stems. During stem extraction, surgeons may resort to extended trochanteric osteotomy (ETO). However, ETO is associated with extensive blood loss and infections. This study compared the clinical outcomes, radiographic results and complications in THA revisions utilizing conical modular stem with and without ETO. Methods: Patients who underwent revision THA with or without ETO were assessed retrospectively. The minimal follow-up was 3 years. The functional evaluation included Harris Hip Score (HHS) and Short Form 36 (SF-36) as well as Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Numerical Rating Scale for pain assessment. The radiographic evaluation comprised bone defect assessment, osteotomy healing, stem migration and position, presence of radiolucent lines and stress shielding. Results: In total, 73 patients (80 hips) were included in the final analysis. The ETO group comprised 48 hips, and the no-ETO group comprised 32 hips. In the ETO group, pre-operative WOMAC scores were lower than in the no-ETO group (p = 0.012). No significant differences were found in terms of post-operative HHS, WOMAC, and NRS scores between groups, except worse results were found in the case of claw plate implantation. Patients in the no-ETO group exhibited better results in SF-36 than in the ETO-group. Osteotomy non-union was observed in four hips (9.5%). Stam varus/valgus position was within ±1.5 degrees (85.9%). Conclusions: ETO does not adversely impact outcomes in patients undergoing femoral stem revisions with modular conical stems. The invasive nature of these procedures prompts careful consideration in each case individually.

Diagnostic Correlation between Saline Infused Sono Hysterography and Three-Dimensional Transvaginal Ultrasound in the Assessment of Cesarean Section Scar Defect. A Study of a Diagnostic Test Accuracy

Abstract Background The evolution of Sono hysterography over the past years has contributed significantly to the assessment of uterine cavity. Saline infused Sono hysterography (SIS) may be recommended as an alternative to Diagnostic Hysteroscopy for the assessment of the uterine cavity and uterine scar in women with previous Cesarean Section. Three-dimensional (3D) ultrasonography that enhances visualization of the lower uterine segment (LUS), myometrial thickness and the size of uterine diverticulum is a more recent tool used to diagnose post- cesarean section uterine diverticulum. Objective To compare accuracy of three-dimensional transvaginal ultrasound versus saline infused sonography for assessing the characteristics, frequency of caesarean scar defects in symptomatic patients with a history of cesarean section. Patients and Methods This study was conducted with 72 women. patients were recruited from the ultrasound unit underwent both three-dimensional transvaginal ultrasound and saline infused son hysterography. This study, conforming to the declaration of Helsinki, was approved by the ethical committee of Obstetrics and Gynecology department, Ain Shams University, ethical committee number FMASU M D 52 \2021. Results 3D transvaginal ultrasound and Sono hysterography showed almost good agreement regarding prevalence of niche with kappa (κ) 0.780 with p-value (p < 0.001). Both imaging modalities show statistically significant higher mean value of volume in SIS was 591.67±271.05 comparing to 3D TVUS was 444.36±219.42, with p-value (p = 0.004). Conclusion There is a substantial agreement between 3D TVUS and SIS in evaluating various niche characteristics, encompassing depth, length, width, volume, AMT and RMT. The data strongly supports the advantages of 3D TVUS over SIS for assessing Cesarean scar defects, commonly known as “Niche.” These benefits encompass cost-effectiveness, improved time efficiency, enhanced patient tolerability, and heightened patient satisfaction associated with the 3D TVUS procedure.

Pediatric Three-dimensional Transesophageal Echocardiography: A Game Changer in Congenital Heart Disease

Background: Despite its potential, pediatric three-dimensional (3D) transesophageal echocardiography (TEE) faced technical limitations due to the size and rigidity of early probes designed for adults. Aim: In this paper, we present our experience in using the new pediatric 3D TEE probe in complex congenital heart disease (CHD). Methods: Recent advancements in the field of miniaturization have led to the development of a new pediatric 3D TEE probe, specifically designed for young children (weight ≥4.5 kg). This probe features a smaller, flexible design and high-spatial and temporal resolution, enabling detailed views of intracardiac structures and real-time imaging crucial for complex diagnosis, planning interventional and surgical procedures. We used this new probe in pediatric cases (weight between 4.5 and 6 kg) with complex congenital heart disease. Results: In double-outlet right ventricle or in Taussig Bing anomaly the new 3D TEE probe enabled detailed assessment of ventricular septal defect and its relations with tricuspid valve apparatus, and provided details of mitral valve leaflets not detectable by the transthoracic approach, even in small children. Conclusions: These cases highlight the probe‘s ability to provide detailed anatomical information, enhancing surgical planning and outcomes. The pediatric 3D TEE probe has the potential to be a game changer in defining intracardiac anatomy in complex CHD.