Diagnosis Of Damage Research Articles

Detecting Changes in Boundary Conditions based on Sensitivity-based Statistical Tests

Structural health monitoring is a promising technology to automatically detect structural changes based on permanently installed sensors. Vibration-based methods that evaluate the global system response to ambient excitation are suited to diagnose changes in boundary conditions, i.e. changes in member prestress or imposed displacements. In this paper, these changes are evaluated based on sensitivity-based statistical tests, which are capable of detecting and localizing parametric structural changes. The main contribution is the analytical calculation of sensitivity vectors for changes in boundary conditions (i.e., changes in prestress or support conditions) based on stress stiffening, and the combination with a numerically efficient algorithm, i.e. Nelson’s method. One of the main advantages of the employed damage diagnosis algorithm is that, although it uses physical models for damage detection, it considers the uncertainty in the data-driven features, which enables a reliability-based approach to determine the probability of detection. Moreover, the algorithm can be trained and the probability of detecting future damages can be predicted based on data from the undamaged structure—in an unsupervised learning mode—making it particularly relevant for unique structures, where no data from the damaged state is available. For proof of concept, a numerical case study is presented. The study assesses the loss of prestress in a two-span reinforced concrete beam and showcases suitable validation approaches for the sensitivity calculation.

The dilemma of COVID-19 diagnosis in pregnancy

Coronavirus disease 2019 (COVID-19) is a new and rapidly developing health crisis. Ongoing research looks at the prevalence and consequences of COVID-19 in the obstetric community and postnatal period. In the COVID-19 era, pregnant mothers were prone to infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a higher risk of poor pregnancy outcomes. Therefore, an accurate and early diagnosis is necessary for this vulnerable group screening for asymptomatic carriers is a cornerstone to limiting the COVID-19 pandemic. It is vital to evaluate patients’ clinical symptoms and epidemiological history carefully. Although the serological test, reverse transcription polymerase chain reaction (RT-PCR), can confirm infection, it cannot determine the degree or severity of the illness. Moreover, it has false-negative results. Imaging tests allow an exact diagnosis of lung damage, the severity of the disease, and the classification of patients. Comprehensive analysis of serological and imaging data will assist in the formation of an appropriate clinical diagnosis. This review will discuss what is new and important in confirming COVID-19 infection in pregnant women and the pros and cons of each.

HierMUD: Hierarchical multi-task unsupervised domain adaptation between bridges for drive-by damage diagnosis

Monitoring bridges through vibration responses of drive-by vehicles enables efficient and low-cost bridge maintenance by allowing each vehicle to inspect multiple bridges and eliminating the needs for installing and maintaining sensors on every bridge. However, many existing drive-by monitoring approaches are based on supervised learning models that require massive labeled data from every bridge. It is expensive and time-consuming, if not impossible, to obtain these labeled data. Furthermore, directly applying a supervised learning model trained on one bridge to new bridges would result in low accuracy due to the shift between different bridges’ data distributions. Moreover, when we have multiple tasks (e.g., damage detection, localization, and quantification), the distribution shifts become more challenging than having only one task because different tasks have distinct distribution shifts and varying task difficulties. To this end, we introduce HierMUD, the first Hierarchical Multi-task Unsupervised Domain adaptation framework that transfers the damage diagnosis model learned from one bridge to a new bridge without requiring any labels from the new bridge. Specifically, our framework learns a hierarchical neural network model in an adversarial way to extract features that are informative to multiple tasks and invariant across multiple bridges. To match distributions over multiple tasks, we design a new loss function based on a newly derived generalization risk bound to adaptively assign higher weights to tasks with more shifted distributions. To learn multiple tasks with varying task difficulties, we split them into easy-to-learn and hard-to-learn tasks based on their distributions. Then, we formulate a feature hierarchy to utilize more learning resources to improve the hard-to-learn tasks’ performance. We evaluate our framework with experimental data from 2 bridges and 3 vehicles. We achieve up to 2X better performance than baseline methods, including average accuracy of 95% for damage detection, 93% for localization, and 0.38 lbs mean absolute error for quantification.

A Two-Stage Approach for Damage Diagnosis of Structures Based on a Fully Distributed Strain Mode under Multigain Feedback Control

The application of distributed fiber sensing technology in civil engineering has been developed to obtain more accurate and reliable information for structural health monitoring (SHM). With this sensing technique, high-density strain data are provided to benefit the stability and robustness in a closed-loop damage detection method which has not yet been investigated. To address this concern, a two-stage approach for structural damage detection combining a modal strain energy-based index (MSEBI) method with a hybrid artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. In this study, the fully distributed strain measurement is taken advantage of, and a strain-based, closed-loop system with multiple gains aggregated for damage sensitivity enhancement is established, by which high-precision damage location and quantification can be realized through the proposed two-stage method. For the first step, the closed-loop strain mode shapes are used to construct the MSEBI for damage localization. For the second step, we adopt the PSO algorithm to train the parameters (weights and biases) of the neural network in order to reduce the difference between the real and expected outputs and then use the trained network for quantifying the damage extent. Furthermore, validation is completed by contemplating a two-span, bridge-like structure.

Application of X-ray examinations in General practice for the diagnosis of lung damage in patients suspected of COVID-19 infection

General practitioners who have access to diagnostic investigations from primary health care and clear referral guidelines to follow can utilize these resources as efficiently as hospital physicians and doctors from secondary health care [1-3]. More complex imaging methodologies such as magnetic resonance imaging (MRI) and X-ray computed tomography (CT) cannot be used in Bulgarian General Practice yet and they are subject to restrictions by the National Health Insurance Fund. The most readily available imaging method that general physicians can apply in the diagnosis of COVID-19 patients is convectional chest radiography. This is a first-line test for detecting lung pathology and it is helpful for evaluation of patients with a high pre-test probability of overt COVID-19 pneumonia, clinical follow up, and for the evaluation of potential complications [4].

Risk factors and diagnosis of iatrogenic damage of periodontal structures and roots during orthodontic Mini-Implants implantation procedures - a systematic literature review

Introduction and purpose
 In recent years, Mini-Implants (MI) have gained popularity and new complications have emerged with them. The article assesses the current reports on the scale of the problem, factors and diagnostic possibilities related to iatrogenic injuries of periodontal tissues and roots.
 
 Description of the state of knowledge
 The incidence of complications was estimated to be 13.5%. Complications are more common in the mandible and are estimated at 20-27.1%. Periodontal and root damage are an important risk factor for treatment failure. The fact that the implant does not interfere with the alveolus structure gives the treatment effectiveness in 90%, and the contact with the alveolar “lamina dura” is 62.5%. Interference with the root structure is successful in only 31.2%.
 Risk factors for failure include: young age, poor hygiene, smoking, area of mandibular molars, contact with the root, thickness of the bone and lack of experience. The implant between the roots is a risk of root trauma, as the location close to the tooth cemento-enamel junction, no preparation for surgery, not using surgical templates, strenuous implantation or implant migration under the influence of orthodontic forces. Radiographs are the gold standard in the diagnosis of complications, but they do not allow for a full assessment of the three-dimensional structures of the implant. A promising method is the evaluation of the torque as the implant is placed in its final location. The patient's subjective feelings are questionable in diagnosis and require further research.
 Summary
 There are many complications in using MI and the most important of these is damage to the structures of the periodontium and the tooth root. Despite the low failure rate, failure should be prevented by pre-operative evaluation, in-operative monitoring, and post-operative diagnostics.

Multi‐Channel Domain Adaptation Deep Transfer Learning for Bridge Structure Damage Diagnosis

The successful application of deep learning in bridge damage diagnosis relies on the assumption that the training and test data sets obey the same distribution. However, it is difficult to obtain labeled data of damage status for a bridge in using. Otherwise, it is difficult to apply a model trained with bridge A (source domain) to diagnose bridge B (target domain) because of the distribution discrepancy of data from different working environments or bridges. In response to these problems, motivated by transfer learning, a new bridge damage diagnosis method, namely, the multichannel domain adaptation deep transfer learning based method (MDADTL), is proposed in this paper. First, a CNN based multichannel multi‐scale feature extractor is introduced to extract features. Second, a multichannel domain adaptation module based on maximum mean discrepancy (MMD) is proposed for transfer learning, so that the learned features are domain‐invariant. Through the above process, MDADTL trained with labeled data obtained in the laboratory or the testing bridge is expected to diagnose other bridges with unlabeled data. Experiments prove the effectiveness and advancement of the proposed method. This exploration will promote the practical application of deep learning in bridge damage diagnosis. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

Modeling generic size features of delamination, such as area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it holds important information for damage diagnosis and prognosis, including the delamination area, its center, and perimeter, useful for structural safety evaluation. In this context, this paper develops a novel particle filter (PF)-based framework for delamination shape prediction. To this end, the delamination image is discretized by a mesh, where control points are defined as intersections between the grid lines and the perimeter of the delamination. A parametric data-driven function maps each point position as a function of the load cycles and is initially fitted on a sample test. Then, a PF is independently implemented for each node whereby to predict their future positions along the mesh lines, thus allowing delamination shape progression estimates. The new framework is demonstrated with reference to experimental tests of fatigue delamination growth in composite panels with ultrasonics C-scan monitoring.

On the explainability of convolutional neural networks processing ultrasonic guided waves for damage diagnosis

Among the maintenance policies adopted to guarantee the safety of structures throughout their service life, condition-based maintenance policies driven by structural health monitoring approaches have progressively gained importance over the last years. Within this field, among the several methods proposed in the literature to diagnose damage affecting thin-walled structures, satisfactory performances have been achieved by adopting tomographic algorithms to process ultrasonic guided waves, even though with some limitations. Recently, such limitations have been overcome by adopting machine learning-based algorithms, even though their implementation in real life applications is still limited because of the mistrust in neural networks determined by their black box-like nature. To date, however, several explainability algorithms have been proposed to interpret the behaviour of neural networks, in particular in the medical and in the military fields, where trust in the tools adopted must be guaranteed. Thus, exploiting the potentialities of explainability frameworks, in this work the layer-wise relevance propagation algorithm is employed to explain the predictions of convolutional neural networks for classification and for regression that characterise damage by processing ultrasonic guided waves excited and sensed by means of a 2-D network of piezoelectric devices. First, the explainability algorithm is applied to give a score to each sample of the acquired ultrasonic guided waves, then such scores are collected by means of a properly developed aggregation strategy to rank the most informative couples of piezoelectric devices. The capabilities of the explainable damage diagnosis framework are demonstrated by means of a numerical, yet realistic, case study involving a metal plate affected by crack-like damage. In particular, the focus is set on the explanation of the behaviour of the neural networks involved, with the aim of building trust in such algorithms and, possibly, revealing damage-related hidden features of ultrasonic guided waves.

Distribution Sub-Domain Adaptation Deep Transfer Learning Method for Bridge Structure Damage Diagnosis Using Unlabeled Data

Deep learning based bridge damage diagnosis methods can successfully use labeled data to detect bridge damage. These successful applications usually need that the training samples (source domain) and test samples (target domain) obey the same probability distribution. However, it is difficult to acquire a large amount of labeled data with damage information from actual bridges. It is also difficult to apply a model trained with bridge A to diagnose bridge B because of the distribution discrepancy of data from different bridges or environments. Therefore, transferring a well-trained damage diagnosis model to another bridge with unlabeled data remains a major challenge. Motivated by transfer learning, this paper proposes a new intelligent damage diagnosis method for bridges, namely, sub-domain adaptive deep transfer learning network (SADTLN), to solve the feature generalization problem in different bridges. In our method, a multi-kernel local maximum mean discrepancy (MK-LMMD) based sub-domain adaptation module, including a domain classifier for aligning the global distribution and a sub-domain multi-layer adaptation for aligning local distribution, is proposed for transfer learning, so that the learned features are domain-invariant. Experiments prove the effectiveness and advancement of the proposed method. This exploration will promote the practical application of intelligent bridge structural damage diagnosis.

Non-hepatotropic viral hepatitis and its causative pathogens: The ongoing need for monitoring in children with severe acute hepatitis of unknown etiology.

Non-hepatotropic viral hepatitis and its causative pathogens: The ongoing need for monitoring in children with severe acute hepatitis of unknown etiology.

Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats.

Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R > 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage.

Pelatihan Memperbaiki Peralatan Listrik Rumah Tangga Pada Masyarakat Di Pare Pare Sulawesi Selatan

This community service activity was carried out for the people of Pare Pare South Sulawesi. This service activity is carried out in the form of training in the maintenance and repair of household electrical appliances. The purpose of this community service activity is to provide community service by providing training on: how to maintain household electrical appliances in general, diagnose damage, and repair damage based on diagnosis. Materials provided in this training include: maintenance of household electrical appliances, repair of damage to household electrical appliances, operation of household electrical appliances, and installation of household electrical devices. The implementation of community service regarding household electrical appliances ran smoothly. This community service activity has yielded results; increasing participants' knowledge of the benefits of maintaining electrical appliances, increasing participants' knowledge of damage diagnosis, and increasing participants' knowledge of correct repairs of household electrical appliances

Role of cardiac MRI in the diagnosis of immune checkpoint inhibitor-associated myocarditis.

Immune checkpoint inhibitor (ICI)-induced cardiotoxicity is a rare immune-related adverse event (irAE) characterized by a high mortality rate. From a pathological point of view, this condition can result from a series of causes, including binding of ICIs to target molecules on nonlymphocytic cells, cross-reaction of T lymphocytes against tumor antigens with off-target tissues, generation of autoantibodies and production of proinflammatory cytokines. The diagnosis of ICI-induced cardiotoxicity can be challenging, and cardiac magnetic resonance (CMR) represents the diagnostic tool of choice in clinically stable patients with suspected myocarditis. CMR is gaining a central role in diagnosis and monitoring of cardiovascular damage in cancer patients, and it is entering international cardiology and oncology guidelines. In this narrative review, we summarized the clinical aspects of ICI-associated myocarditis, highlighting its radiological aspects and proposing a novel algorithm for the use of CMR.

Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania.

(1) Background: This study aims to evaluate the association of CRP, NLR, IL-6, and Procalcitonin with lung damage observed on CT scans; (2) Methods: A cross-sectional study was performed among 106 COVID-19 patients hospitalized in Timisoara Municipal Emergency Hospital. Chest CT and laboratory analysis were performed in all patients. The rank Spearmen correlation was used to assess the association between inflammatory markers and lung involvement. In addition, ROC curve analysis was used to determine the accuracy of inflammatory markers in the diagnosis of severe lung damage; (3) Results: CRP, NLR, and IL-6 were significantly positively correlated with lung damage. All inflammatory markers had good accuracy for diagnosis of severe lung involvement. Moreover, IL-6 has the highest AUC- ROC curve; (4) Conclusions: The inflammatory markers are associated with lung damage and can be used to evaluate COVID-19 severity.

Electrocardiography in Hypertensive Patients without Cardiovascular Events: A Valuable Predictor Tool?

Background Hypertension is an important risk factor of cardiovascular (CV) disease. An early diagnosis of target organ damage could prevent major CV events. Electrocardiography (ECG) is a valuable clinical technique, with wide availability and high specificity, used in evaluation of hypertensive patients. However, the use of ECG as a predictor tool is controversial given its low sensitivity. This study aims to characterise ECG features in a hypertensive population and identify ECG abnormalities that could predict CV events. Methods We studied 175 hypertensive patients without previous CV events during a follow-up mean of 4.0 ± 2.20 years. ECGs and pulse wave velocity were performed in all patients. Clinical characteristics and ECG abnormalities were evaluated and compared between the patients as they presented CV events. Results Considering the 175 patients (53.14% male), the median age was 62 years. Median systolic blood pressure was 140 mmHg and diastolic blood pressure was 78 mmHg. Median PWV was 9.8 m/s. Of the patients, 39.4% were diabetic, 78.3% had hyperlipidaemia, and 16.0% had smoking habits. ECG identified left ventricular (LV) hypertrophy in 29.71% of the patients, and a LV strain pattern was present in 9.7% of the patients. Twenty-nine patients (16.57%) had a CV event. Comparative analyses showed statistical significance for the presence of a LV strain pattern in patients with CV events (p=0.01). Univariate and multivariate analysis confirmed that a LV strain pattern was an independent predictor of CV event (HR 2.66, 95% IC 1.01–7.00). In the survival analysis, the Kaplan–Meier curve showed a worse prognosis for CV events in patients with a LV strain pattern (p=0.014). Conclusion ECG is a useful daily method to identify end-organ damage in hypertensive patients. In our study, we also observed that it may be a valuable tool for the prediction of CV events.

An Energy-Based Damage Diagnosis Under Changing Environmental Temperature for Online SHM

Structural health monitoring (SHM) for early diagnosis of damage in structures has become vital in improving the structural integrity and safety of the structure and thereby providing a smooth operation for most of the important in-service civil structures such as buildings and bridges. SHM also contributes to the substantial reduction in the maintenance cost of the structure. During serious structural failures, SHM can safeguard human life through an early warning system. This paper presents an output-only model-free technique by extracting damage features from the energy profiles derived from measured time-history responses. The proposed algorithm is organized into two phases i.e. the learning phase and the monitoring phase. While the learning phase can optionally be executed offline, the monitoring phase can preferably be online. The damage feature is extracted by carrying out a fractal dimension analysis of the continuous waveform of energy associated with the jolt (i.e. rate of change of acceleration) evaluated from the acceleration responses of the structure. A novel damage index is proposed, to localize the damage, using the damage features extracted from the fractal dimension, while handling operational and environmental variability (EoV). The proposed algorithm is evaluated through numerical simulations on a typical span of 220[Formula: see text]m RCC bridge across river Amaravathi in Tamil Nadu, India and also a 20-story frame structure. Experimental investigations are also carried out by testing a laboratory-scale 10-story frame structure and also an RCC bridge model of a 7.7[Formula: see text]m span to evaluate the practical amenability of the proposed algorithm. The experimental and numerical studies carried out in this paper, suggest that the proposed energy-based diagnostic technique can effectively localize the damage even in the presence of confounding factors like EoV. Conclusively, the proposed diagnostic technique can be an effective tool for online SHM, as it is model-free with less tedious computations and can perform well even in the presence of noise.

Cardiac Troponin Biosensor Designs: Current Developments and Remaining Challenges.

Acute myocardial infarction (AMI) is considered as one of the main causes of death, threating human lives for decades. Currently, its diagnosis relies on electrocardiography (ECG), which has been proven to be insufficient. In this context, the efficient detection of cardiac biomarkers was proposed to overcome the limitations of ECG. In particular, the measurement of troponins, specifically cardiac troponin I (cTnI) and cardiac troponin T (cTnT), has proven to be superior in terms of sensitivity and specificity in the diagnosis of myocardial damage. As one of the most life-threatening conditions, specific and sensitive investigation methods that are fast, universally available, and cost-efficient to allow for early initiation of evidence-based, living-saving treatment are desired. In this review, we aim to present and discuss the major breakthroughs made in the development of cTnI and cTnT specific biosensor designs and analytical tools, highlighting the achieved progress as well as the remaining challenges to reach the technological goal of simple, specific, cheap, and portable testing chips for the rapid and efficient on-site detection of cardiac cTnI/cTnT biomarkers in order to diagnose and treat cardiovascular diseases at an incipient stage.

Articular surface integrity assessed by ultrasound is associated with biological characteristics of articular cartilage in early-stage degeneration

Early diagnosis of articular cartilage damage and repeated evaluation of treatment efficacy are essential for osteoarthritis treatment. In this study, we established a simple ultrasound grading system for early degenerative articular cartilage and investigated its relationship with cartilage biological characteristics. The ultrasound grading system were based on surface integrity (S1a: continuous high-echo lines, S1b: discontinuous or weak high-echo lines, S2: surface irregular) and cartilage echogenicity (E1: with > 50%, E2: < 50% hypoechoic area of total cartilage layer) and verified by surface roughness (Ra; μm) and histological staining. Ra was lower in S1 than in S2, and the percentage of hypoechoic and safranin O-stained areas was positively correlated. Then we examined its relationship with histopathological evaluation (OARSI grade), gene expression, and protein production in responded to pro-inflammatory cytokine (IL-1ß) stimulation. OARSI grades were different among S grades. The superficial layer of S1 had higher expression of Collagen10, aggrecan, Sox9, and lower expression of Collagen1 and BMP2 than that of S2. S1 responded more pronouncedly to IL-1ß in IL-6, IL-8, and CCL2 production than S2. There was no difference among the E-grades. Taken together, our findings indicate that ultrasound assessment using surface integrity can reflect the biological characteristics of early degenerative articular cartilage.

Two-stage tendoplasty of the flexor digitorum profundus in a small child

BACKGROUND: Injuries to the flexor tendons of the fingers are uncommon in young children, and chronic injury is an extremely rare injury. Many issues of damage diagnosis, surgical intervention and rehabilitation techniques remain unresolved. In this regard, the treatment of chronic damage to the flexor tendons of the fingers in young children has practical and theoretical significances.&#x0D; CLINICAL CASE: A 16-month old underwent a two-stage treatment of chronic damage to the flexor tendons of the small finger. At the first stage, scars were removed from the fibro-synovial canal, and a silicone rod was inserted into the canal. At the second stage, the silicone spacer was replaced by a tendon autograft. The surgical treatment and postoperative management of the patient was described in detail. The full range (270 degrees) of active and passive motion was restored five months after the second stage of tendoplasty. Two years after the operation, good hand function.&#x0D; DISCUSSION: The analyzed publications mainly present the results of treatment of patients older than 1 year and 4 months. Only in the study by S.L. Piper et al. (2019) the youngest patient at the time of injury was 7 months old, at the time of first stage of tendoplasty 1 year and 3 months. The final volume of active motion was 160 degrees. In our case, the functional result was higher. Considering the undoubted rarity of damage uncharacteristic for this age, we intend to continue monitoring the patient.&#x0D; CONCLUSIONS: Comprehensive long-term treatment of a serious injury to the hand, considering the anatomical and physiological characteristics of a small child, made it possible to obtain a good functional result.