Quantification Of Damage Research Articles

Deep Learning Enables Automatic Detection of Joint Damage Progression in Rheumatoid Arthritis-Model Development and External Validation.

Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in rheumatoid arthritis (RA), evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally validate our automated RA scoring algorithm (AuRA), and demonstrate its utility for monitoring radiographic progression in a real-world setting. The algorithm, originally developed during the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM) challenge, was trained to predict expert-curated Sharp-van der Heijde total scores in hand and foot radiographs from two previous clinical studies (n = 367). We externally validated AuRA against data (n = 205) from Turku University Hospital and compared the performance against two top-performing RA2-DREAM solutions. Finally, for 54 patients, we extracted additional radiograph sets from another control visit to the clinic (average time interval of 4.6 years). In the external validation cohort, with a root-mean-square-error (RMSE) of 23.6, AuRA outperformed both top-performing RA2-DREAM algorithms (RMSEs 35.0 and 35.6). The improved performance was explained mostly by lower errors at higher expert-assessed scores. The longitudinal changes predicted by our algorithm were significantly correlated with changes in expert-assessed scores (Pearson's R = 0.74, p< 0.001). AuRA had the best external validation performance and demonstrated potential for detecting longitudinal changes in joint damage. Available in https://hub.docker.com/r/elolab/aura, our algorithm can easily be applied for automatic detection of radiographic progression in the future, reducing the need for laborious manual scoring.

Fatigue life estimation of nickel-based single crystal superalloy with different inclined film cooling holes: Initial damage quantification and coupling of damage-fracture mechanics models

Quantitative assessment of the initial damage state of a structure and fatigue life prediction on this basis, especially when it is sensitive to manufacturing quality, is crucial for both engineering application and material science. Traditional solutions based on fracture or damage mechanics either require precise constitutive relationships and cumbersome physical mechanism models or simply ignore the initial damage state. This study developed a novel equivalent initial flaw size (EIFS) quantitative evaluation method for film cooling holes in Nickel-based single crystal superalloy turbine blades to address the strong correlation between the initial damage in and fatigue life of the material. The temperature and stress field differences at the edges of holes with different inclination angles were simulated using a solid-liquid-gas three-phase level-set model and considered to represent the same initial damage. Next, the coupled damage–fracture mechanics model was used to achieve equivalent crack insertion, crack propagation, and fatigue life prediction based on the EIFS. The results indicate that this method can provide a highly robust EIFS distribution interval and that the crack geometry correction factor and EIFS distribution range are weakly correlated with the loading conditions (with an error within 2 %). The EIFS-based fatigue life predictions demonstrated a notable degree of accuracy, with the majority of the predicted and experimental fatigue lives falling within a three-fold dispersion band and all predictions remaining confined within a five-fold dispersion band of the actual lifetime. These results represent a substantial advancement in accuracy compared to traditional damage mechanics predictions. Therefore, this study provides a powerful approach for evaluating the fatigue life of turbine blades considering the initial damage state and can be widely applied to guide drilling process optimization and blade fatigue analysis.

Abstract LB086: Measuring oral DNA damage to evaluate electronic cigarette use as a tobacco harmreduction strategy

Abstract Background: Smokers increase their risk of numerous types of diseases, including lung and oral cancer. Despite this knowledge, approximately 18.7% of U.S. adults are smokers, and millions of smokers across the U.S. struggle to quit tobacco every day. Although difficult due to nicotine addiction, you can increase your chances of quitting with the tools available, and help is an important part of any quitting plan. The prevalence of electronic cigarette (e-cigarette) use has increased as users perceive them to be safer than traditional tobacco smoke. Studies have shown that e-cigarettes contain fewer chemicals than tobacco smoke. Therefore, addressing the effectiveness of e-cigarette use in reducing tobacco harm is essential and is currently a priority for the FDA. Aim: To determine whether combustible tobacco users who are provided with free e-cigarettes and free e-liquids reduce the levels of DNA damage in their oral epithelial cells. Methods: Adult smokers who smoked at least 5 cigarettes per day over the past year and did not plan to quit smoking in the next 3 months were enrolled in a randomized 3-arm clinical trial (NCT03113136): “Usual Brand Cigarettes” (UBC), Low Wattage e-cigarettes (LWe), and High Wattage e-cigarettes (HWe). Free combustible cigarettes or e-cigarettes plus e-liquid were provided during the first 12 weeks according to assigned group. Tobacco product use details, CO level, and oral mucosa samples were collected at 0, 4, and 12 weeks. DNA was extracted from oral cells and damage was quantified by q-PADDA, at three time points (Week 0, 4, 12). Data analysis was performed using ANOVA model. Results: Participants who provided samples at all time points were included in this DNA damage study. 201 complete data sets (UBC=77, LWe=61 and HWe=66) were available for DNA damage quantification at the time of this report. At enrolment (week 0), there were no significant differences in age, sex or tobacco product use across groups. At 12 weeks, combustible tobacco use was significantly reduced in smokers also using LWe or HWe (dual users). At 4 and 12 weeks, the CO levels were reduced in dual users of combustible tobacco and e-cigarettes (LWe or HWe), compared to exclusive combustible tobacco users. Compared to combustible tobacco users, dual users of combustible tobacco and LWe or HWe showed a significant reduction in the levels of oral mucosa DNA damage at 4 and 12 weeks (3 to 5 fold). Conclusion: Our study shows that when provided with free e-cigarette devices and e-liquids, smokers who were dual users or completely switched to LWe or HWe significantly reduced the levels of DNA damage in their oral mucosa compared with exclusive combustible tobacco users. These data suggest that LW and HW e-cigarette devices are an effective harm reduction tool and might help to reduce tobacco-associated health effects. Funding: NIH/NCI (R01CA204891, Wagener; R01CA242168, Queimado). Citation Format: Vengatesh Ganapathy, Mayilvanan Chinnaiyan, Daniel Brobst, Balaji Sadhasivam, Jean D. Nshimiyimana, Yan Zhao, Theodore Wagener, Lurdes Queimado. Measuring oral DNA damage to evaluate electronic cigarette use as a tobacco harmreduction strategy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB086.

(11) - Quantification of Myocardial DNA Damage Predicts Prognosis of Heart Failure from Various Underlying Diseases

(11) - Quantification of Myocardial DNA Damage Predicts Prognosis of Heart Failure from Various Underlying Diseases

LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.

Dynamic characteristics of stone masonry walls before and after damage: Experimental and numerical investigations

Seismic behavior of masonry walls has been heavily investigated, especially by means of laboratory experiments employing cyclic tests to determine the mechanical parameters and seismic capacity. Nevertheless, the dynamic properties of the tested walls often remain unknown, even though the nature of the seismic response is dynamic and profoundly affected by the structure's dynamic properties. This paper presents an investigation on the dynamic properties of three different masonry wall panels in healthy and damaged states, and examines if damage quantification via tracking the changes in dynamic properties is feasible. Ambient vibration and impact measurements are used for the dynamic identification of wall panels, before and after they are tested in reversed-cyclic in-plane shear-compression tests. The natural frequencies, damping ratios, and mode shapes of the walls are determined and compared to each other. Moreover, the damage progression and its effect on the dynamic features of the URM wall panel is investigated using a discrete element model of the benchmark wall that is validated in terms of the force-displacement response and damage pattern of the wall. The results of the study indicate that changes in natural frequencies and mode shapes are traceable, although it is difficult to infer damage quantification relationships from these changes. The outcomes of this study also highlight that numerical models verified with the nonlinear quasi-static behavior do not necessarily match the wall's dynamic behavior, and that more research is required to update nonlinear numerical models. Overall, the results contribute to the knowledge regarding the dynamic characteristics of masonry walls in healthy and damaged conditions, and to quantify the damage in masonry walls as well as the changes in their dynamic properties.

Damage Detection and Quantification via Multiview DIC at Varying Scales

Damage Detection and Quantification via Multiview DIC at Varying Scales

Damage Quantification Method for Aircraft Structures Based on Multitask CNN-LSTM and Transfer Learning

Damage Quantification Method for Aircraft Structures Based on Multitask CNN-LSTM and Transfer Learning

Influence of changing loading directions on damage in sheet metal forming

The impact of the stress state on damage evolution, fracture behavior, and product performance is well understood for proportional loading. However, many complex sheet forming operations involve non-proportional loading, which affect the material's hardening and fracture characteristics. This study investigates the influence of a loading direction change on damage evolution in a dual phase steel DP800. Specimens are pre-strained by tensile tests and subsequently loaded in either the same or orthogonal direction to the initial pre-strain direction by additional tensile tests and bending tests. Damage quantification by scanning electron microscopy reveals lower damage evolution after an orthogonal change of loading direction in contrast to monotonic loading directions. The load paths, defined as a history of triaxiality and Lode parameter during loading, are identified numerically under consideration of kinematic hardening. Since kinematic hardening leads to higher triaxialities after orthogonal changes, the load path is not the dominant influence on damage. A possible explanation for the experimental results is the void characteristics after tensile load. After the pre-straining in tensile test, voids are oriented orthogonally to the tensile direction and located between hard martensitic phases. The influence of this morphology on subsequent void growth is illustrated by a simulation verifying that an orthogonal change of loading direction results in void shrinkage, while monotonic loading directions lead to further void growth.

Research on Delamination Damage Quantification Detection of CFRP Bending Plate Based on Lamb Wave Mode Control.

The carbon-fiber-reinforced polymer (CFRP) bending structure is widely used in aviation. The emergence and spread of delamination damage will decrease the safety of in-service bending structures. Lamb waves can effectively identify delamination damage as a high-damage-sensitivity detection tool. For this present study, the signal difference coefficient (SDC) was introduced to quantify delamination damage and evaluate the sensitivity of A0-mode and S0-mode Lamb waves to delamination damage. The simulation results show that compared with the S0-mode Lamb wave, the A0-mode Lamb wave exhibits higher delamination damage sensitivity. The delamination damage can be quantified based on the strong correlation between the SDC and the delamination damage size. The control effect of the linear array PZT phase time-delay method on the Lamb wave mode was investigated by simulation. The phase time-delay method realizes the generation of a single-mode Lamb wave, which can separately excite the A0-mode and S0-mode Lamb wave to identify delamination damage of different sizes. The A0-mode Lamb wave was excited by the developed one-dimensional miniaturized linear comb transducer (LCT), which was used to conduct the detection experiment on the CFRP bending plate with delamination damage sizes of Φ6.0 mm, Φ10.0 mm, and Φ15.0 mm. The experimental results verify the correctness of the simulation. According to the Hermite interpolation results of the finite-element simulation data, the relationship between the delamination damage size and the SDC was fitted by the Gaussian function and Rational function, which can accurately quantify the delamination damage. The absolute error of the delamination damage quantification with Gaussian and Rational fitting expression does not exceed 0.8 mm and 0.7 mm, and the percentage error is not more than 8% and 7%. The detection and signal processing methods employed in the present research are easy to operate and implement, and accurate delamination damage quantification results have been obtained.

Quantification of the crack propagation and global failure mechanism of single- and multi-ring masonry arch bridges

This paper presents the development of a novel crack-based damage quantification method to assess the damage accumulation in masonry arch bridges subjected to externally applied loading. Initially, a damage index was proposed, considering the initiation and extension of tensile and sliding cracks. Then, the proposed damage indexing was adopted into a two-dimensional discrete element code of analysis to evaluate single and multi-ring masonry arch bridges. The numerical model was validated by comparing the computational outputs against the experimental results obtained from the literature. Following the validation, masonry arch bridge models were developed, featuring the four most representative bond configurations in arch barrels. Also, different bond properties were assigned to investigate their impact on the global failure characteristics of single- and multi-ring masonry arch bridges. From the analyses of results of the multi-ring masonry arch bridges investigated in this study, it was shown that the rapid growth of tensile cracks in radial joints and the occurrence of shear slippage in circumferential joints between adjacent rings were observed to be early signs for multi-ring bridges before the ultimate load reached. By analysing the correlation between the normalised damage index with the normalised load, damage accumulation in different masonry arch bridges were identified. This study offers a novel perspective on quantitively evaluating the damage accumulation and failure mechanism of masonry arch bridges and the findings may provide valuable insights into the assessment and management of existing masonry arch bridges.

Research on damage identification of jacket structure under wind and wave loads based on variational mode decomposition and chirplet transform

To conduct damage localization and damage quantification of jacket structure, a new damage identification approach based on variational mode decomposition (VMD) and chirplet transform (CT) is firstly introduced. Three kinds of environmental loads, namely, regular wave load, irregular wave load, and wind load, are set on the jacket structure to simulate the real environment of the jacket as much as possible. The acceleration response signals of each node of the damaged bar are extracted and analyzed by VMD and CT. The proposed method is used to analyze the time-frequency energy changes before and after damage for damage localization. Then, the time-frequency entropy of the damage location is further calculated for damage quantification. The feasibility of the introduced method is verified by setting different operating conditions, and the results show that the proposed method can locate and quantify the damage of the jacket under different environmental loads.

The Economic Burden of Epstein–Barr Virus Infection in the Russian Federation

Introduction: Epstein–Barr virus-related diseases are of great medical and social importance, which requires assessment of economic losses due to costs of medical care and sickness absenteeism. Objective: To propose a methodology for assessing economic damage and to use it for quantification of total damages caused by Epstein–Barr virus (EBV) infections in the Russian Federation in 2022. Materials and methods: The economic damage from EBV infections was determined using official statistics on cases of infectious mononucleosis (2022), upper respiratory tract infections, cardiac arrhythmias, atopic dermatitis, dorsopathy, chronic tonsillitis in the acute phase, cerebrovascular disease, psoriasis, and multisystem inflammatory syndrome associated with COVID-19. Results: Based on the methodology for assessing economic damages described in the article, we found that the total financial losses from EBV-associated diseases in the Russian Federation in 2022 amounted to 1,203.0 billion or 1.2 trillion rubles, which is significantly higher than similar values for all other registered infectious diseases with the exception of COVID-19. COVID-19-related multisystem inflammatory syndrome, upper respiratory tract infections, and cerebrovascular disease account for more than 90 % of all losses. The structure of total costs is dominated by indirect economic losses (1,096.75 billion rubles or 91.2 %) due to sickness absence from work. Conclusion: The findings allow us to outline directions of developing measures to reduce the economic damage from EBV-associated diseases.

G2019S selective LRRK2 kinase inhibitor abrogates mitochondrial DNA damage

Pathogenic mutations in LRRK2 cause Parkinson’s disease (PD). The G2019S variant is the most common, which results in abnormally high kinase activity. Compounds that target LRRK2 kinase activity are currently being developed and tested in clinical trials. We recently found that G2019S LRRK2 causes mitochondrial DNA (mtDNA) damage and treatment with multiple classes of LRRK2 kinase inhibitors at concentrations associated with dephosphorylation of LRRK2 reversed mtDNA damage to healthy control levels. Because maintaining the normal function of LRRK2 in heterozygous G2019S LRRK2 carriers while specifically targeting the G2019S LRRK2 activity could have an advantageous safety profile, we explored the efficacy of a G2019S mutant selective LRRK2 inhibitor to reverse mtDNA damage in G2019S LRRK2 models and patient cells relative to non-selective LRRK2 inhibitors. Potency of LRRK2 kinase inhibition by EB-42168, a G2019S mutant LRRK2 kinase inhibitor, and MLi-2, a non-selective inhibitor, was determined by measuring phosphorylation of LRRK2 at Ser935 and/or Ser1292 using quantitative western immunoblot analysis. The Mito DNADX assay, which allows for the accurate real-time quantification of mtDNA damage in a 96-well platform, was performed in parallel. We confirmed that EB-42168 selectively inhibits LRRK2 phosphorylation on G2019S LRRK2 relative to wild-type LRRK2. On the other hand, MLi-2 was equipotent for wild-type and G2019S LRRK2. Acute treatment with EB-42168 inhibited LRRK2 phosphorylation and also restored mtDNA damage to healthy control levels. We further investigated the relationship between LRRK2 kinase activity, mtDNA damage and mitophagy. Levels of mtDNA damage caused by G2019S LRRK2 were fully re-established within 2 h of a LRRK2 inhibitor wash out and recovery experiment, indicating the mtDNA damage phenotype is highly dynamic. G2019S LRRK2 mitophagy defects were not alleviated with LRRK2 kinase inhibition, suggesting that mitophagy is not mechanistically regulating LRRK2 kinase-mediated reversal of mtDNA damage in this acute timeframe. Abrogation of mtDNA damage with the mutant selective tool inhibitor EB-42168 demonstrates the potential of a precision medicine approach for LRRK2 G2019S PD. Levels of mtDNA damage may serve as a potential pharmacodynamic biomarker of altered kinase activity that could be useful for small molecule development and clinical trials.

An automatic method for estimating insect defoliation with visual highlights of consumed leaf tissue regions

As an essential component of the architecture of a plant, leaves are crucial to sustaining decision-making in cultivars and effectively support agricultural processes. When the leaf area is constantly monitored, a plant’s health and productive capacity can be assessed to foment proactive and reactive strategies. Because of that, one of the most critical tasks in agricultural processes is estimating foliar damage. In this sense, we present an automatic method to estimate leaf stress caused by insect herbivory, including damage in border regions. As a novelty, we present a method with well-defined processing steps suitable for numerical analysis and visual inspection of defoliation severity. We describe the proposed method and evaluate its performance concerning 12 different plant species. Experimental results show high assertiveness in estimating leaf area loss with a concordance correlation coefficient of 0.98 for grape, soybean, potato, and strawberry leaves. A classic pattern recognition approach, named template matching, is at the core of the method whose performance is compared to cutting-edge techniques. Results demonstrated that the method achieves foliar damage quantification with precision comparable to deep learning models. The code prepared by the authors is publicly available.

Vibration-based structural damage localization through a discriminant analysis strategy with cepstral coefficients

Over the past decades, Vibration-Based Methods (VBMs) have consistently exhibited exceptional effectiveness in the field of Structural Health Monitoring when it comes to assessing structural damage in both civil and mechanical structures. Recently, the progress made in data-driven strategies for localizing structural damage through the VBMs has resulted in substantial benefits. These advanced strategies not only enable an efficient decision-making process but also provide precise identification of repair locations for monitored structures. Importantly, they achieve this without the inherent complexity and computational burden typically associated with traditional model-based methods. In this study, an innovative data-driven method for localizing and quantifying structural damage is proposed. The method is developed on the basis of the principles of Linear Discriminant Analysis (LDA) and a newly devised modeling strategy that utilizes the power cepstral coefficients extracted from the structural acceleration response. The developed LDA model is able to highlight the local structural characteristics (i.e., mode shape-related information) embedded in the cepstral coefficients in the LDA latent space. Based on the highlighted local characteristics of the cepstral coefficients, a statistical pattern recognition strategy is proposed to effectively conduct the quantification and localization of structural damage. The proposed damage localization method is designed to function in a completely unsupervised-learning manner, which eliminates the requirement for the model to have access to any prior knowledge or information regarding structural damage during the training phase. The proposed method has been validated by both numerical simulations and experimental data.

Complex CFRP damage detection with dual multiscale 1DCNNs and self-selective fusion technique of guided waves

This article presents an innovative damage detection method, named DMS1DCNNs, based on new dual multiscale one-dimensional convolutional neural networks (MS1DCNNs) and a novel self-selective fusion technique for damage localization and quantification in complex carbon fiber reinforced plastic (CFRP) structures. Firstly, a new multiscale piecewise aggregate approximation algorithm (MIPA) is proposed to compress the guided wave (GW) signals and extract damage features at various time scales. Considering that signals from low-information sensing paths may interfere with the diagnostic performance of the model, a new method for self-selective fusion of GW sensing paths is proposed. This method can automatically fuse paths with more damage information and remove paths with less damage information. Then, a new DMS1DCNNs model is presented, capable of obtaining multiscale damage features in GW signals from both data and network levels. The model takes GW data as input and employs regression methods for direct prediction of the damage location and size. Finally, the method is validated using the GW testbed, and the mean relative error of location on complex CFRP is 8.32 % and the mean absolute error of quantification is 1.20 mm, superior to other state-of-the-art methods.

Structural damage quantification using long short-term memory (LSTM) auto-encoder and impulse response functions

This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected “principal components” to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.

Nanobody-mediated neutralization of candidalysin prevents epithelial damage and inflammatory responses that drive vulvovaginal candidiasis pathogenesis.

Candida albicans can cause mucosal infections in humans. This includes oropharyngeal candidiasis, which is commonly observed in human immunodeficiency virus infected patients, and vulvovaginal candidiasis (VVC), which is the most frequent manifestation of candidiasis. Epithelial cell invasion by C. albicans hyphae is accompanied by the secretion of candidalysin, a peptide toxin that causes epithelial cell cytotoxicity. During vaginal infections, candidalysin-driven tissue damage triggers epithelial signaling pathways, leading to hyperinflammatory responses and immunopathology, a hallmark of VVC. Therefore, we proposed blocking candidalysin activity using nanobodies to reduce epithelial damage and inflammation as a therapeutic strategy for VVC. Anti-candidalysin nanobodies were confirmed to localize around epithelial-invading C. albicans hyphae, even within the invasion pocket where candidalysin is secreted. The nanobodies reduced candidalysin-induced damage to epithelial cells and downstream proinflammatory responses. Accordingly, the nanobodies also decreased neutrophil activation and recruitment. In silico mathematical modeling enabled the quantification of epithelial damage caused by candidalysin under various nanobody dosing strategies. Thus, nanobody-mediated neutralization of candidalysin offers a novel therapeutic approach to block immunopathogenic events during VVC and alleviate symptoms.IMPORTANCEWorldwide, vaginal infections caused by Candida albicans (VVC) annually affect millions of women, with symptoms significantly impacting quality of life. Current treatments are based on anti-fungals and probiotics that target the fungus. However, in some cases, infections are recurrent, called recurrent VVC, which often fails to respond to treatment. Vaginal mucosal tissue damage caused by the C. albicans peptide toxin candidalysin is a key driver in the induction of hyperinflammatory responses that fail to clear the infection and contribute to immunopathology and disease severity. In this pre-clinical evaluation, we show that nanobody-mediated candidalysin neutralization reduces tissue damage and thereby limits inflammation. Implementation of candidalysin-neutralizing nanobodies may prove an attractive strategy to alleviate symptoms in complicated VVC cases.

Probabilistic State Estimation Under Varying Loading States via the Integration of Time-Varying Autoregressive and Gaussian Process Models

In this work, probabilistic damage quantification under varying loading conditions in a non-stationary, guided-wave environment is being tackled via the synergistic integration between Time-varying Autoregressive (TAR) models and Gaussian Process regression models (GPRMs). Applying these TAR-GPRMs onto an aluminum coupon with simulated damage under different loading conditions fitted with piezoelectric sensors/actuators, the TAR models show the capability of capturing the non-stationarity in the ultrasonic guided-wave signals generated by the actuators under varying plate-loading conditions. They also provide insights to the maintainer by showing when in time the guided-wave time series deviate the most. In order to take advantage of that, a time instant selection algorithm was developed to allow flexibility in choosing the time instant(s) at which probabilistic damage quantification should be done. Finally, this quantification task is tackled by GPRMs, in which multiple GPRMs are trained using the TAR model parameters under varying conditions, and then used to predict damage size and/or loading state. While this framework is much more powerful in terms of tapping into the dynamics of how guided-waves change with varying conditions compared to simpler forms of GPRMs (such as damage index-trained GPRMs), training of TAR-GPRMs is far more complex. The advantages and challenges associated with the proposed TAR-GPRM approach is presented herein along with potential open areas for research in this regard.