Diagnosis Of Damage Research Articles

GW-MUSIC focusing algorithm with high accuracy for composite damage diagnosis

The Guided Wave (GW)-based Multiple Signal Classification (MUSIC) method for damage imaging in composite structures offers high-resolution and flexible sensor array placement, demonstrating considerable potential for precise damage localization. However, the application to real aircraft composite structures, characterized by complex structural features, presents substantial challenges. These include complex boundary reflections, pronounced anisotropy, and diminished scattered signals from damage sites. This paper presents a novel single-peak anisotropy-compensated MUSIC method for damage imaging of complex composites. This method, designed for improved localization accuracy, combines a self-excited calibration strategy to mitigate anisotropy with single wave peak focusing to amplify signals and reduce boundary reflections. Experimental validation on a complex composite wing structure demonstrated significant enhancements in localization precision, affirming the method's efficacy.

High-Precision Monitoring Method for Bridge Deformation Measurement and Error Analysis Based on Terrestrial Laser Scanning

In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures.

Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements

This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structures.

Enhancement of PZT-based damage detection in real-scale post-tensioned anchorage under ambient conditions

Varying ambient temperature significantly affects electromechanical (EM) impedance responses of PZT sensors, leading to inaccurate damage detection results. In this study, we investigate temperature influences on the PZT's EM impedance response and introduce a filtering method to enhance PZT-based damage assessment of a real-scale post-tensioned anchorage. Firstly, the principles of the PZT-based damage identification technique are outlined. Next, the sensitivity of the PZT's EM impedance under simultaneous damage and temperature effects are extensively analyzed and quantified using finite element modelling. Thirdly, a 10-day long-term impedance monitoring of a real-scale anchorage specimen is conducted under ambient temperature conditions and different strand-looseness scenarios. The issues on damage detection performance of the PZT under varying ambient temperature are discussed. Finally, a temperature filtering technique based on principal component analysis is adopted to enhance the accuracy of strand looseness detection in the test anchorage. The numerical and experimental outcomes show that the impact of ambient temperature can overshadow the effect of strand looseness, leading to false damage diagnosis. By removing the influence of temperature on the EM impedance response, the strand looseness in the test anchorage was successfully diagnosed.

Damage diagnosis of plates and shells through modal parameters reconstruction using inverse finite-element method

Damage diagnosis of plates and shells through modal parameters reconstruction using inverse finite-element method

Frequency Recognition of a Free Damped Vibration Signal Based on Improved Elias Aboutanios Interpolation Method

The free damped vibration signal of engineering structures is an energy-limited signal, and the effective signal length is limited by the damped factor. The classical periodic graph method is affected by signal damped coefficient and noise, and it is difficult to obtain satisfactory recognition accuracy. This article proposes the IEAI method for frequency estimation of freely damped vibration signal, considering the influence of initial phase. By combining discrete Fourier transform with complex operations, the signal is iteratively interpolated to improve the accuracy of frequency estimation .The simulation results show that the mean error of the IEAI method is 1.3%~26.5% of the traditional Periodogram method, A&M method and Welch method, and the Root-mean-square deviation is 7.4%~40.3% of the latter three methods under the condition of high signal-to-noise ratio; In the case of low signal-to-noise ratio, the mean error of the IEAI method is 16.2%~43.0% of the latter three, and the Root-mean-square deviation is 20.8%~81.8% of the latter three. The experimental results of cantilever beam damage identification show that the IEAI method is more sensitive to small structural damages and can be used for the identification and diagnosis of small structural damages.

Random vibration response-based damage diagnosis for a population of composite aerostructures under varying operating conditions and uncertainty: experimental assessment

Random vibration response-based damage diagnosis for a population of composite aerostructures under varying operating conditions and uncertainty: experimental assessment

Management of Chondroblastoma in Pediatric Patients: 21 Years of Single-Center Experience.

Chondroblastoma (CB), a rare benign bone tumor that produces chondrocytes, often develops in the epiphysis or apophysis of children and young adults. The treatment of these rare tumors is complex. The standard treatment protocol involves curettage with local adjuvants and bone graft or cement application. The authors examined 38 CBs to determine risk factors for local recurrence, complications, and functional outcomes following epiphyseal curettage. Twenty-two girls and sixteen boys aged 10 to 17 years with histologically confirmed chondroblastoma who arrived at our hospital between January 2000 and June 2021 were reviewed retrospectively. Clinical data, radiographic images, histological results, treatment, functional outcomes, and the local recurrence rate were examined-surgical treatment involved total tumor curettage, followed by bone grafting and adjuvant techniques. Local recurrences have also been reported. The most frequently affected site was the proximal femur. Sites of involvement included the proximal femur in 10 (26.3%) cases, the proximal tibia in 8 (20.8%), the humerus in 5 cases (13.2%), the distal tibia in 4 cases (10.5%), the distal femur in 3 cases (7.9%), the supracetabular region in 3 cases (7.9%), the talus in 1 case (2.6%), the calcaneus in 1 case (2.6%), the scapula in 1 case (2.6%), the lumbar spine in 1 case (2.6%), and the iliac bone in 1 (2.6%) patient. The mean follow-up was 144.2 months (24 to 276). The local recurrence rate was 7.9%. The mean Musculoskeletal Tumor Society (MSTS) score was 28.3 points (17 to 30). The mean duration of symptoms at presentation was 5.8 (range, 1 to 28) months. Aggressive curettage and bone grafting resulted in local control and good outcomes in most pediatric patients. In a relatively small proportion of cases, long-term complications and recurrence can occur due to growth plate damage and late diagnosis. In patients admitted to the pediatric clinic with pain, which is often accompanied by localized edema and joint effusion, early detection via advanced radiological scans (X-ray, CT, or MRI) may prevent delays in diagnosis.

Pulsed DPOAEs in serial measurements : Combined analysis paradigm of simultaneously occurring changes in hearing thresholds and DPOAEs. German version

To date, there is no consensus on how to standardize the assessment of ototoxicity in serial measurements. For the diagnosis of damage to the cochlear amplifier, measurement methods are required that have the highest possible test-retest reliability and validity for detecting persistent damage. Estimated distortion-product thresholds (LEDPT) based on short-pulse distortion-product otoacoustic emission (DPOAE) level maps use individually optimal DPOAE stimulus levels and allow reliable quantitative estimation of cochlea-related hearing loss. Hearing thresholds were estimated objectively using LEDPT and subjectively using modified Békésy tracking audiometry (LTA). Recordings were performed seven times within threemonths at 14frequencies (f2 = 1-14 kHz) in 20ears (PTA4 (0.5-4 kHz) < 20 dB HL). Reconstruction of the DPOAE growth behavior as afunction of the stimulus levels L1, L2 was performed on the basis of 21DPOAE amplitudes. Anumerical fit of anonlinear mathematical function to the three-dimensional DPOAE growth function yielded LEDPT for each stimulus frequency. For the combined analysis, probability distributions of hearing thresholds (LTA, LEDPT), DPOAE levels (LDP), and combinations thereof were determined. LTA and LEDPT each exhibited atest-retest reliability with amedian of absolute differences (AD) of 3.2 dB and 3.3 dB, respectively. Combining LEDPT, LDP, and LTA into asingle parameter yielded asignificantly smaller median AD of 2.0 dB. It is expected that an analysis paradigm based on acombination of LEDPT, suprathreshold LDP, and fine-structure-reduced LTA would achieve higher test performance (sensitivity and specificity), allowing reliable detection of pathological or regenerative changes in the outer hair cells.

AlphaLISA-Based Immunoassay for Detection of Troponin T in Serum of Patients with Acute Myocardial Infarction.

Survival and prognosis of patients with acute myocardial infarction (AMI) are highly dependent on rapid and accurate diagnosis of myocardial damage. Troponin T is the primary diagnostic biomarker and is widely used in clinical practice. Amplified luminescent proximity homogeneous assay (AlphaLISA) may provide a solution to rapidly detect a small amount of analyte through molecular interactions between special luminescent donor beads and acceptor bead. Here, a double-antibody sandwich assay was introduced into AlphaLISA for rapid detection for early diagnosis of AMI and disease staging evaluation. The performance of the assay was evaluated. The study found that the cTnT assay has a linear range of 48.66 to 20,000 ng/L with a limit of detection of 48.66 ng/L. In addition, the assay showed no cross-reactivity with other classic biomarkers of myocardial infarction and was highly reproducible with intra- and inter-batch coefficients of variation of less than 10%, notably, only 3min was taken, which is particularly suitable for clinical diagnosis. These results suggest that our method can be conveniently applied in the clinic to determine the severity of the patient's condition.

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

Zero-shot knowledge transfer for seismic damage diagnosis through multi-channel 1D CNN integrated with autoencoder-based domain adaptation

Accurate and timely structural damage diagnosis is crucial to efficient disaster response and city renovation in post-earthquake events. The scarcity of labeled data hinders the powerful deep learning techniques from in-domain damage detection on target structures. Cross-domain transfer learning has emerged as a captivating strategy through digging knowledge from the abundant source domain to detect the damage in the target domain. However, the heterogeneity among multi-domain structures poses the challenge in seismic damage diagnosis. This study proposes a novel zero-shot knowledge transfer approach for seismic damage diagnosis through multi-channel one-dimensional convolutional neural networks (1D CNN) integrated with deep autoencoder (DAE)-based domain adaptation (DA). The framework consists of three modules, namely, data preprocessor adaptive to seismic vibration signals, DAE-based DA module, and damage diagnosis via multi-channel 1D CNN. The DA module is customized to seamlessly translate the unseen target-domain data to mimic latent representation via a DAE pretrained on the source data, thus realizing rigorous zero-shot learning. Imbalanced data distribution is also considered during the network training and testing. Two representative phases of knowledge transfer are performed to substantiate the knowledge transferability of the proposed method. The first phase involves multi-class damage quantification on two ASCE benchmark models from the simplified model to the refined one, and the second phase conducts binary damage detection on a three-story reinforced frame structure from the finite element numerical model to the laboratory-tested physical model. Both examples show that the proposed method exhibits high prediction accuracy and a lower false negative rate in achieving zero-shot knowledge transfer for cross-domain structural damage diagnosis. With a delicate network design for diverse data, the proposed knowledge transfer framework can be further extended from the present zero-shot approach to the few-shot learning paradigm, thus suggesting a feasible algorithm adaptability and promising engineering applicability.

Pathobiochemical mechanisms of development of idiopathic inflammatory polyneuropathy

The article discusses the pathobiochemical mechanisms of the development of idiopathic inflammatory polyneuropathy. The authors’ analysis of the literature data showed. that the infection is not the direct cause of the disease, but plays the role of a provoking agent that triggers autoim mune mechanisms. Triggering factors may also include blood transfusions, surgery, bone marrow transplantation, and traumatic injuries to peripheral nerves. understanding the etiology, clinical manifestations and diagnosis of peripheral nerve damage is an important problem in the modern world of medicine.

Assessing joint health in haemophilia patients: The combined value of physical examination and ultrasound imaging.

Early diagnosis of joint damage is pivotal in haemophilia to prevent the occurrence and progression of haemophilic arthropathy thus providing optimal personalised management. The haemophilia joint health score version 2.1 (HJHS) is based on a physical examination of the mainly affected joints. Musculoskeletal ultrasound has demonstrated the capability to detect early changes in terms of synovitis and osteochondral damage. The haemophilia early detection with ultrasound (HEAD-US) score has been proposed as a simple and reliable evaluation tool. This study aims to investigate the correlation between the HJHS and the HEAD-US scores performed by two independent operators (physical therapist and musculoskeletal ultrasound expert) for the evaluation of the joint health status of patients with haemophilia. Consecutive adult patients independent of the severity degree were included. Elbows, knees and ankles were evaluated by a physical therapist by HJHS and by a musculoskeletal ultrasound expert following the HEAD-US protocol. We observed a good positive correlation between HJHS and HEAD-US (Spearman's rho 0.72). The main discrepancy in conceptually similar domains was found between the HJHS swelling and the HEAD-US synovitis (rho 0.17), as ultrasound was able to detect even mild synovitis when HJHS swelling was scored 0 in up to 40% of cases. The HJHS and HEAD-US correlate well even when performed by two independent operators. Musculoskeletal ultrasound is particularly useful for the early detection of synovitis. The routine assessment of both scores helps clinicians define the stage and extension of joint involvement and set up a personalised treatment.

A Mode Sensitivity Enhancement Method for Beam Bridge Using High-Density Strain Feedback

Abstract Closed-loop damage diagnosis method has attracted some attention in recent years; however, for lack of sufficient measuring points, it is difficult to achieve robust control of large structures cause the output dimension is not enough to meet the demand of system controllability and observability. On this basis, a mode sensitivity enhancement method for beam bridge using high-density strain feedback is proposed, in which high density dynamic strain measurements of the girder structure can be collected by distributed fiber sensor. Utilizing strain–displacement transformation relationship of the girder structure, the dynamic displacements can be obtained and used as output with high dimension to achieve the feedback control for eigenvalue sensitivity enhancement. To verify the proposed method, a series of numerical case studies of a beam bridge structure are performed, and it is demonstrated that the eigenvalue sensitivity can be enhanced effectively.

Targeted treatments for immune dysregulation in inborn errors of immunity

Primary immune regulatory disorders (PIRDs) constitute a subset of inborn errors of immunity and are characterized by lymphoproliferation, autoimmunity, malignancy, and infection. Unlike classical primary immune deficiencies, initial symptoms of PIRDs can manifest as autoimmunity such as cytopenias or enteropathy, which can often prove resistant to conventional treatments and occur years prior to the onset of infectious complications. Raising awareness about PIRDs among specialists and adopting a multidisciplinary approach is crucial for early diagnosis, intervention, and potential prevention of severe organ damage. Significant progress has been made in identifying several PIRDs, which has contributed to a more comprehensive comprehension of their underlying immunological mechanisms. This knowledge has paved the way for targeted therapies focusing on specific molecules, which tend to offer superior disease control compared to traditional immunosuppressants. This review, informed by the latest literature, explores prevalent PIRDs, detailing their clinical manifestations and recent advancements in treatment modalities.

A novel damage identification method based on acceleration response sensitivity and finite element model updating: Numerical and experimental

The utilization of time-domain data for structural damage diagnosis and finite element model updating has several benefits, such as direct implementation of the measured data with no requirement for domain changing and susceptibility to structural damages. This study introduces a straightforward sensitivity relation for the measured acceleration response, avoiding the requirement for computational techniques like direct differentiation methods. The proposed equation establishes a linear relationship between variations of the structural parameters and structural responses. The proposed sensitivity equation operates at the level of improved equations and exhibits greater accuracy compared to the current approaches. The numerical evaluation of the proposed sensitivity equation on two offshore jacket platforms reveals the accuracy of the proposed method. Additionally, an experimental case study to examine the efficacy and validation of the introduced sensitivity relation in real-world settings demonstrates the procedure’s viability. The numerical and experimental results reveal the accuracy of the proposed sensitivity equation.

Diagnosis of retinal damage using Resnet rescaling and support vector machine (Resnet-RS-SVM): a case study from an Indian hospital.

This study aims to address the challenge of identifying retinal damage in medical applications through a computer-aided diagnosis (CAD) approach. Data was collected from four prominent eye hospitals in India for analysis and model development. Data was collected from Silchar Medical College and Hospital (SMCH), Aravind Eye Hospital (Tamil Nadu), LV Prasad Eye Hospital (Hyderabad), and Medanta (Gurugram). A modified version of the ResNet-101 architecture, named ResNet-RS, was utilized for retinal damage identification. In this modified architecture, the last layer's softmax function was replaced with a support vector machine (SVM). The resulting model, termed ResNet-RS-SVM, was trained and evaluated on each hospital's dataset individually and collectively. The proposed ResNet-RS-SVM model achieved high accuracies across the datasets from the different hospitals: 99.17% for Aravind, 98.53% for LV Prasad, 98.33% for Medanta, and 100% for SMCH. When considering all hospitals collectively, the model attained an accuracy of 97.19%. The findings demonstrate the effectiveness of the ResNet-RS-SVM model in accurately identifying retinal damage in diverse datasets collected from multiple eye hospitals in India. This approach presents a promising advancement in computer-aided diagnosis for improving the detection and management of retinal diseases.

Applications of Exosomal miRNAs from Mesenchymal Stem Cells as Skin Boosters.

The skin is the outer layer of the human body, and it is crucial in defending against injuries and damage. The regenerative capacity of aging and damaged skin caused by exposure to external stimuli is significantly impaired. Currently, the rise in average life expectancy and the modern population's aesthetic standards have sparked a desire for stem-cell-based therapies that can address skin health conditions. In recent years, mesenchymal stem cells (MSCs) as therapeutic agents have provided a promising and effective alternative for managing skin regeneration and rejuvenation, attributing to their healing capacities that can be applied to damaged and aged skin. However, it has been established that the therapeutic effects of MSC may be primarily mediated by paracrine mechanisms, particularly the release of exosomes (Exos). Exosomes are nanoscale extracellular vesicles (EVs) that have lipid bilayer and membrane structures and can be naturally released by different types of cells. They influence the physiological and pathological processes of recipient cells by transferring a variety of bioactive molecules, including lipids, proteins, and nucleic acids such as messenger RNAs (mRNAs) and microRNAs (miRNAs) between cells, thus playing an important role in intercellular communication and activating signaling pathways in target cells. Among them, miRNAs, a type of endogenous regulatory non-coding RNA, are often incorporated into exosomes as important signaling molecules regulating protein biosynthesis. Emerging evidence suggests that exosomal miRNAs from MSC play a key role in skin regeneration and rejuvenation by targeting multiple genes and regulating various biological processes, such as participating in inflammatory responses, cell migration, proliferation, and apoptosis. In this review, we summarize the recent studies and observations on how MSC-derived exosomal miRNAs contribute to the regeneration and rejuvenation of skin tissue, with particular attention to the applications of bioengineering methods for manipulating the miRNA content of exosome cargo to improve their therapeutic potential. This review can provide new clues for the diagnosis and treatment of skin damage and aging, as well as assist investigators in exploring innovative therapeutic strategies for treating a multitude of skin problems with the aim of delaying skin aging, promoting skin regeneration, and maintaining healthy skin.

An Activatable Semiconducting Nanoprobe for Early and Accurate Diagnosis of Liver Injury.

Liver injury, caused by factors like viral hepatitis and drug overdose, poses a significant health risk, with current diagnostic methods lacking specificity, increasing the need for more precise molecular imaging techniques. Herein, we present an activatable semiconducting liver injury reporter (SLIR) for early and accurate diagnosis of liver injury. The SLIR, which is composed of semiconducting polymers with an electron-withdrawing quenching segment, remains nonfluorescent until it encounters biothiols such as cysteine in the liver. SLIR accumulates efficiently in the liver and respond rapidly to biothiols, allowing accurate and early detection of liver damage. The recovery of SLIR fluorescence negatively reflects the dynamics of oxidative stress in the liver and provides information on the severity of tissue damage. Thus, the specificity of SLIR, the fast response, and the efficient targeting of the liver make it a promising tool for the precise diagnosis of liver damage at an early stage.