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The key idea in this paper is to propose multi-labels classification algorithms to handle benchmark thermal datasets that are practically associated with different data characteristics and have only one health condition (damaged composite materials). A suggested alternative approach for extracting the statistical contents from the thermal images, is also employed. This approach offers comparable advantages for classifying multi-labelled datasets over more complex methods. Overall scored accuracy of different methods utilised in this approach showed that Random Forest algorithm has a clear higher performance over the others. This investigation is very unique as there has been no similar work published so far. Finally, the results demonstrated in this work provide a new perspective on the inspection of composite materials using Infrared Pulsed Thermography.

Background: Few randomized controlled trials with a midterm follow-up have compared matrix-assisted autologous chondrocyte transplantation (MACT) with microfracture (MFx) for knee cartilage lesions. Purpose: To compare the structural, clinical, and safety outcomes at midterm follow-up of MACT versus MFx for treating symptomatic knee cartilage lesions. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 48 patients aged between 18 and 50 years, with 1- to 4-cm2 International Cartilage Repair Society (ICRS) grade III to IV knee chondral lesions, were randomized in a 1:1 ratio to the MACT and MFx treatment groups. A sequential prospective evaluation was performed using magnetic resonance imaging (MRI) T2 mapping, the MOCART (magnetic resonance observation of cartilage repair tissue) score, second-look arthroscopic surgery, patient-reported outcome measures, the responder rate (based on achieving the minimal clinically important difference for the Knee injury and Osteoarthritis Outcome Score [KOOS] pain and KOOS Sport/Recreation), adverse events, and treatment failure (defined as a reoperation because of symptoms caused by the primary defect and the detachment or absence of >50% of the repaired tissue during revision surgery). Results: Overall, 35 patients (18 MACT and 17 MFx) with a mean chondral lesion size of 1.8 ± 0.8 cm2 (range, 1-4 cm2) were followed up to a mean of 6 years postoperatively (range, 4-9 years). MACT demonstrated significantly better structural outcomes than MFx at 1 to 6 years postoperatively. At final follow-up, the MRI T2 mapping values of the repaired tissue were 37.7 ± 8.5 ms for MACT versus 46.4 ± 8.5 ms for MFx ( P = .003), while the MOCART scores were 59.4 ± 17.3 and 42.4 ± 16.3, respectively ( P = .006). More than 50% defect filling was seen in 95% of patients at 2 years and 82% at 6 years in the MACT group and in 67% at 2 years and 53% at 6 years in the MFx group. The second-look ICRS scores at 1 year were 10.7 ± 1.3 for MACT and 9.0 ± 1.8 for MFx ( P = .001). Both groups showed significant clinical improvements at 6 years postoperatively compared with their preoperative status. Significant differences favoring the MACT group were observed at 2 years on the KOOS Activities of Daily Living ( P = .043), at 4 years on all KOOS subscales (except Symptoms; P < .05) and the Tegner scale ( P = .008), and at 6 years on the Tegner scale ( P = .010). The responder rates at 6 years were 53% and 77% for MFx and MACT, respectively. There were no reported treatment failures after MACT; the failure rate was 8.3% in the MFx group. Neither group had serious adverse events related to treatment. Conclusion: Patients who underwent MACT had better structural outcomes than those who underwent MFx at 1 to 6 years postoperatively. Both groups of patients showed significant clinical improvements at final follow-up compared with their preoperative status. MACT showed superiority at 4 years for the majority of the KOOS subscales and for the Tegner scale at 4 to 6 years. The MACT group also had a higher responder rate and lower failure rate at final follow-up. Registration: NCT01947374 ( ClinicalTrials.gov identifier).

Common peroneal nerve injury is present in 40% of knee dislocations, and foot drop is the principal complication. Posterior tibial tendon transfer is a viable solution to replace the function of the anterior tibial tendon (ATT) in the mid-foot. Several techniques for posterior tibial tendon transfer exist today, with variable results reported. However, adding augmentation with side-to-side tenorrhaphy of ATT to the transferred posterior tibial tendon (PTT) enhances anterior tissue balance and load sharing stress between native ATT enthesis and PTT tenodesis, allowing early rehabilitation and improving functional outcomes. Side-to-side tenorrhaphy is performed after PTT tenodesis in the lateral cuneiform to improve reliability in foot drop. This technique allows shorter immobilization time (from 6 to 2 weeks), earlier rehabilitation, sooner weight-bearing, and decreased risk of arthrofibrosis, scar formation, and muscle atrophy., Technique Video Video 1 Anterior tibial tendon side-to-side tenorrhaphy after posterior tibial tendon transfer: a technique to improve reliability in drop foot after common peroneal nerve injury.

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results.

This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection. Engineering and Physical Sciences Research Council (EPSRC): EP/N509450/1 and Innovate UK: 105625.

ActiveU and passive thermographies are two efficient techniques extensively used to measure heterogeneous thermal patterns, leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse-nonnegative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT and tackle negative bases in sparse PCT with nonnegative constraints and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening data set (accuracy of 74.1%, 75.9%, and 77.8%).

Cytokines like IL-6, TNF-α, and IL-1β are important mediators of inflammation in many inflammatory diseases, as well as in cellular processes like cell proliferation and cell adhesion. Finding new molecules that decrease cell proliferation, adhesion (inflammatory infiltrate), and pro-inflammatory cytokine release could help in the treatment of many inflammatory diseases. The naturally derived poly(gallic acid) (PGAL), produced enzymatically from gallic acid in aqueous medium, is a non-toxic, thermostable multiradical polyanion that is antioxidant and has potential biomedical uses. Experimental evidence has demonstrated that PGAL reduces pro-inflammatory cytokines, which are the target of some inflammatory diseases. PGAL decreased IL-6, TNF-α, and IL-1β production in human monocytes exposed to PMA without affecting cell viability. Additionally, PGAL reduced cell proliferation by affecting the transition from the S phase to the G2 phase of the cell cycle. Cell adhesion experiments showed that PMA-induced cell adhesion was diminished with the presence of PGAL, particularly at a concentration of 200 μg/mL. These properties of PGAL show a potential use for treating inflammatory diseases, such as psoriasis or arthritis.

Objective. To evaluate minimum biosecurity parameters (MBP) for arthroscopic matrix-encapsulated autologous chondrocyte implantation (AMECI) based on patients’ clinical outcomes, magnetic resonance imaging (MRI) T2-mapping, Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score, and International Cartilage Repair Society (ICRS) second-look arthroscopic evaluation, laying the basis for a future multicenter study. Design. Pilot clinical study. We analyzed the logistics to perform AMECI to treat focal chondral lesions in different hospitals following strict biosecurity parameters related to tissue and construct transportation, chondrocyte isolation, and cell expansion. Patient progress was analyzed with patient-reported outcome measures, MRI T2-mapping, MOCART, and ICRS arthroscopic second-look evaluation. Results. Thirty-five lesions in 30 patients treated in 7 different hospitals were evaluated. Cell viability before implantation was >90%. Cell viability in construct remnants was 87% ± 11% at 24 hours, 75% ± 17.1% at 48 hours, and 60% ± 8% at 72 hours after implantation. Mean final follow-up was 37 months (12-72 months). Patients showed statistically significant improvement in all clinical scores and MOCART evaluations. MRI T2-mapping evaluation showed significant decrease in relaxation time from 61.2 ± 14.3 to 42.9 ± 7.2 ms ( P < 0.05). Arthroscopic second-look evaluation showed grade II “near normal” tissue in 83% of patients. Two treatment failures were documented. Conclusions. It was feasible to perform AMECI in 7 different institutions in a large metropolitan area following our biosecurity measures without any implant-related complication. Treated patients showed improvement in clinical, MRI T2-mapping, and MOCART scores, as well as a low failure rate and a favorable ICRS arthroscopic evaluation at a mid-term follow-up. Level of Evidence. 2b.

The conservation of artworks is playing an increasingly important role in society today. Non-destructive investigation can provide the potential to identify deterioration as early as possible. In this research, transmission mid-wave infrared (MWIR) vision and air-coupled ultrasound (ACU) were used to investigate two paintings on canvas made from different textile support materials. An X-ray technique was used in the work for validation. It was found that the transmission mode can probe deeper and the differences in absorption due to the different textile support materials can be distinguished. This paper summarises advantages of the transmission inspection mode and compares and analyses images from the two techniques from a physical point of view.

The non-uniformity of non-planar object inspection data makes their analysis challenging. This paper reports a study of the use of recurrent neural network and artificial feed-forward neural network in pulsed thermography during the automated inspection of non-planar carbon fiber reinforced plastic samples. The time series, including the raw temperature-time series and sequenced signals obtained from the first derivative after thermographic signal reconstruction was used to train and test the models respectively. Quantitative comparisons of testing results showed that the long short-term memory recurrent neural network model was more accurate in handling time dependent information compared to the artificial feed-forward neural network model.

The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities.

Background:Carboplasty is a new minimally invasive technique for knee osteoarthritis (OA) that consists of injecting tibial marrow aspirate into the bone-cartilage interface as well as intra-articularly.Purpose:To compare the clinical and imaging outcomes, as well as the safety, of carboplasty for symptomatic knee OA in a placebo-controlled trial.Study Design:Randomized controlled trial; Level of evidence, 1.Methods:The authors conducted a randomized controlled trial to compare carboplasty with placebo for the treatment of symptomatic knee OA. Patients who had failed medical treatment and had bone edema on magnetic resonance imaging (MRI) were randomized in a 1:1 ratio to carboplasty or placebo. The primary outcome of the study was the Numeric Pain Rating Scale (NPRS) for the knee at 1 year (scores range from 0 to 10, with a higher score indicating worse pain). Secondary outcomes were the Knee injury and Osteoarthritis Outcome Score (KOOS), treatment responder rate (based on achieving the minimal clinically important difference of the NPRS), MRI bone edema reduction, and treatment safety.Results:In total, 50 patients (25 carboplasty vs 25 placebo) were enrolled and followed up with for an average of 18 months (range, 14-24 months). The average NPRS at baseline decreased from 7.1 ± 0.9 to 2.9 ± 2.1 ( P < .001) at 1 year in the carboplasty group and from 7.7 ± 0.9 to 4.9 ± 2.2 ( P < .001) in the placebo group. On average, patients after carboplasty improved 60% from their initial NPRS, and patients after placebo improved 37% ( P = .003). Patients had a statistically significantly greater improvement from baseline in all KOOS subscales in the carboplasty group compared with the placebo group ( P < .001). The responder rates were 96% for carboplasty and 76% for placebo ( P = .098). Bone edema was reduced in 72% of patients in the carboplasty group and 44% of patients in the placebo group ( P = .045). Neither group had adverse events related to treatment.Conclusion:Carboplasty resulted in greater pain reduction, a significantly greater improvement in all KOOS subscales, and a similar safety profile compared with placebo in patients with symptomatic knee OA and bone edema.Registration:ISRCTN69838191 (ISRCT Registry).

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique, however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

Background Mexico is one of the countries with the highest number of deaths from the COVID-19 pandemic. In spite of this high mortality, in Mexico the number of confirmed cases and diagnostic tests per million population are lower than for other comparable countries, which leads to uncertainty about the actual extent of the pandemic. In Mexico City, healthcare workers represent an important fraction of individuals with SARS-CoV-2 infection. We performed a cross-sectional study whose objective was to estimate the frequency of antibodies to SARS-CoV-2 and identify associated factors in healthcare workers at a large hospital in Mexico City. Methods We conducted a serological survey in a non-COVID national referral teaching hospital. The study population included all the personnel that works, in any capacity, in the hospital. From this population we selected a representative sample of 300 individuals. Blood samples were collected and questionnaires were applied between August 10th and September 9th, 2020. Results ELISA results indicate a serological prevalence of SARS-CoV-2 infection of 13.0%. Working in the janitorial and security groups, having an educational level below a university degree, and living with a larger number of people, were all identified as sociodemographic factors that increase the probability of having SARS-CoV-2 infection. Conclusions Less favored socioeconomic groups face significantly higher prospects of experiencing SARS-CoV-2 infection and in institutions such as ours, providing janitorial and security workgroups with additional testing and counseling could help to limit the spread of contagion. The rate from the official number of confirmed cases in Mexico City is substantially smaller than the seropositive rate identified in this work.

Coplanar capacitive technique is a relatively novel electro-magnetic Non-Destructive Testing (NDT) method that could be applied to the evaluation of materials by moving a set of electrodes on the surface of the specimen. In addition to the design-related parameters such as electrode shape, size, and the separation distance between the main electrodes, the material of the specimen affects the coplanar capacitive probe performance. In this paper, a 3D Finite Element Modeling (FEM) was employed to assess and identify the electric field behaviour as a function of material under test for non-conducting and conducting specimens with/without defect. Physical experiments were carried out by a pair of rectangular coplanar electrodes on an aluminium specimen with surface defects covered by a 5 mm thick plexiglass insulation layer to verify the simulation results and evaluate the performance of the probe. A good qualitative agreement was observed between the numerical simulations and experimental results.

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

Background Complex meniscal lesions often require meniscectomy with favorable results in the short term but a high risk of early osteoarthritis subsequently. Partial meniscectomy treated with meniscal substitutes may delay articular cartilage degeneration. Purpose To evaluate the status of articular cartilage by T2 mapping after meniscal substitution with polyurethane scaffolds enriched with mesenchymal stem cells (MSC) and comparison with acellular scaffolds at 12 months. Methods Seventeen patients (18-50 years) with past meniscectomies were enrolled in 2 groups: (1) acellular polyurethane scaffold (APS) or (2) polyurethane scaffold enriched with MSC (MPS). Patients in the MPS group received filgrastim to stimulate MSC production, and CD90+ cells were obtained and cultured in the polyurethane scaffold. The scaffolds were implanted arthroscopically into partial meniscus defects. Concomitant injuries (articular cartilage lesions or cartilage lesions) were treated during the same procedure. Changes in the quality of articular cartilage were evaluated with T2 mapping in femur and tibia at 12 months. Results In tibial T2 mapping, values for the MPS group increased slightly at 9 months but returned to initial values at 12 months ( P > 0.05). In the APS group, a clear decrease from 3 months to 12 months was observed ( P > 0.05). This difference tended to be significantly lower in the APS group compared with the MPS group at the final time point ( P = 0.18). In the femur, a slight increase in the MPS group (47.8 ± 3.4) compared with the APS group (45.3 ± 4.9) was observed ( P > 0.05). Conclusion Meniscal substitution with polyurethane scaffold maintains normal T2 mapping values in adjacent cartilage at 12 months. The addition of MSC did not show any advantage in the protection of articular cartilage over acellular scaffolds ( P > 0.05).

Objectives: To evaluate the long-term clinical and magnetic resonance imaging outcomes of patients treated with an all-arthroscopic matrix-assisted autologous chondrocyte transplantation (MACT) technique for articular cartilage lesions of the knee. Methods: A total of 63 patients (21 women, 42 men) with a mean age of 35 ± 9.2 years, were treated with an all-arthroscopic MACT technique for articular cartilage lesions of the knee. Patients were prospectively evaluated preoperatively and after 2, 5, and 10 years of the surgery using multiple clinical scores: Lysholm, Tegner, International Knee Documentation Committee (IKDC), and the Knee injury and Osteoarthritis Outcome Score (KOOS); as well as with magnetic resonance imaging (MRI) T2-mapping at the same time points. Failure was defined as a reoperation because of symptoms caused by the primary repaired defect or a patient improvement of Results: Patients showed statistically significant clinical improvement in all PROM scores at 2, 5, and 10 years postoperative (PO) compared to their preoperative status. MRI T2-Mapping values of the repaired tissue decreased from 59.0 ± 14.6 ms preoperatively to 44.4 ± 15.6 ms at 10 years PO (P= 0.008), showing no statistical differences compared to the native cartilage values (39.1 ± 4.0 ms, P= 0.398). A surgical failure rate of 1.5% was documented, which was increased to 33% when clinical failures were also considered. Among all the analyzed factors linked to the likelihood of failure, lesions >4cm2 were the only ones to show statistical significance (P= 0.024). Conclusions: Patients treated with this arthroscopic MACT technique showed good and long-lasting clinical outcomes, as well as close to native cartilage characteristics on MRI T2-mapping. A limited number of failures were observed at 10 years PO, with most failures being in the first two years after treatment. Big chondral lesions (>4cm2) were found to have a greater likelihood of failing. UPLOAD- https://planion-client-files.s3.amazonaws.com/AOSSM/blobs/9e2d9f3a-b8a0-4921-9952-144a0de7acd7/1/Base_Px_AMECI_-_Clinica_2.pdf UPLOAD- https://planion-client-files.s3.amazonaws.com/AOSSM/blobs/e39a94d5-bbc5-4e27-8493-786a2237cef3/1/Patient-Reported_Outcome_Measures_1.pdf UPLOAD- https://planion-client-files.s3.amazonaws.com/AOSSM/blobs/aba360a2-961d-4b2e-a4fa-1049626e23d3/1/MRI_T2-Mapping_2.pdf UPLOAD- https://planion-client-files.s3.amazonaws.com/AOSSM/blobs/d9c78782-8a61-446f-b0e7-24873b631a5a/1/kaplan_meier.pdf

An assessment of mineral identification applying hyperspectral infrared imagery in laboratory conditions is presented here and strives to identify nine different minerals (biotite, diopside, epidot...

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. This paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of active thermography techniques in the aerospace industry. Furthermore, as the use of unmanned aerial vehicles (UAVs) for the remote inspection of large and/or difficult access areas has significantly grown in the last few years thanks to their flexibility of flight and to the possibility to carry one or several measuring sensors, the aim to use a UAV active thermography system for the inspection of large composite aeronautical structures in a continuous dynamic mode is proposed., Thermosense: Thermal Infrared Applications XLIII, April 12-16, 2021, Online Only, United States, Series: Proceedings of SPIE

Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available, however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

Coplanar capacitive sensors are employed in Non-destructive Testing (NDT) methods to measure the difference in dielectric properties of the materials. The most important design parameters for a coplanar capacitive sensor include the shape, size, and separation distance of the electrodes which affect the sensor performance. In addition, the impact of the shielding plate and guard electrode should be considered. In the framework of this paper, numerical simulations and physical experiments are studied for two shapes of electrodes, triangular and rectangular, by examining different sizes and different separation distances between electrodes to assess and analyze the important features of the coplanar capacitive electrodes, such as the penetration and strength of the electric field as a function of sensor geometrical properties. Therefore, a detailed analysis of numerical simulation using Finite Element Modelling (FEM) is provided to study these geometric parameters. In addition, the influence of the different frequencies, lift-off, and the presence or absence of a metal shielding plate and guard electrode on the output result is analyzed. Finally, sensors were manufactured and several experiments were carried out under different configurations. Comparison of the numerical simulation results and physical experiments illustrate that they are in good qualitative agreement.

Unmanned aerial vehicles are a modern day solution for reducing the time of inspections. This work aims to address the difficulties of using a UAV to inspect aircraft structures. Challenges such as non-uniform heating, low spatial resolution, and environmental noise cause some difficulties for defect detection and characterisation. Contrary to this, mounting sensors onto a UAV’s will further increase the noise, due to the motion, vibrations and sequence mismatching. Methods to tackle stabilisation and indoor localisation are used by utilising a Vicon system, this aims to increase the accuracy of the captured data when inspecting without GPS i.e. inspecting indoors. Other than active thermography, various methods were trialled to locate defects, passive thermography, photogrammetry and RGB image processing.