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In this paper, an infrared thermography technique is used to assess the fiber orientation on the surface of carbon fiber reinforced polymer (CFRP) moulded with randomly-oriented strands (ROS). Due to the randomness of the material, a point by point inspection would be very time consuming. In this paper it is proposed to use a flying laser spot technique to heat a line-region on the surface of the sample instead of a spot. During our experiments, a flying laser spot inspection was performed in 30 s while a point by point inspection of the same area would require about 25 min. An artificial neural network (ANN) was then used to estimate the fiber orientation over the heated line. The classification rate obtained with the network was 91.2% for the training stage and 71.6% for the testing stage.

Supplemental Digital Content is available in the text., Human adipose-derived mesenchymal stem cells (hADMSCs) are believed to be potential key factors for starting the regenerative process after tissue injury. However, an efficient method of delivering these regenerative cells to an external wound site is still lacking. Human amnion and pig skin have long been used as skin wound dressings for the treatment of burns and other skin lesions. Herein, we present the generation of two constructs using these two biomaterials as effective scaffolds for the culture of hADMSCs. It was found that hADMSCs seeded onto radiosterilized human amnion and pig skin are viable and proliferate. These cells are able to migrate over these scaffolds as demonstrated by using time-lapse microscopy. In addition, the scaffolds induce hADMSCs to secrete interleukin-10, an important negative regulator of inflammation, and interleukin-1β, a proinflammatory protein. The interplay between these two proteins has been proven to be vital for a balanced restoration of all necessary tissues. Thus, radiosterilized human amnion and pig skin are likely suitable scaffolds for delivery of hADMSCs transplants that could promote tissue regeneration in skin injuries like patients with burn injuries.

This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

This work presents a non-destructive analysis based on infrared thermography (IRT) and speckle inspection methods performed on a helicopter blade. In both cases, thermal stresses are needed in order to provoke the visualization of defects. The sample possess fabricated defects, and three techniques were selected, namely, long-pulse thermography, flash thermography, and digital speckle photography (DSP) to retrieve their positions. The first two techniques belong to the group of infrared imaging, that are inherent to the analysis of the infrared thermal patterns to detect internal anomalies in the material, whilst the last one corresponds to the optical imaging group that requires visible light to measure the material response under a thermal stimulus. The active approach, useful to produce a gradient in either, thermal and/or displacement field of the material, was used. In all cases, heat lamps were used to generate the required thermal stresses. Post-processing algorithms were applied to raw data in order to improve the defect detection. Results are finally compared to evaluate pros and cons of each method.

Application of hyperspectral infrared imagery for mineral grain identification suffers from a lack of prediction on the irregular grain’s surface along with the mineral aggregates. Here, we present an investigation to determine the reliability of automatic mineral identification in the longwave Infrared (LWIR, 7.7–11.8 μ m ) with an LWIR-macro lens having a spatial resolution of 100 μ m . We attempt to identify eleven different mineral grains (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). A machine learning-based algorithm (implemented by software) compares all of the pixel-spectra to the ASTER spectral library of JPL/NASA using spectral angle mapper (SAM) and normalized cross-correlation (NCC) to create false-color maps. Then a hue-saturation-value (HSV) principle component analysis (PCA) based K-means clustering approach groups the mineral regions in different categories. The results were compared to two different ground truths (GT) (i.e. rigid-GT and observed-GT) for quantitative calculation and as an integrated step for validating our approach. Observed-GT increased the accuracy up to 1.5 times higher than rigid-GT, from 45.67 % to 69.39 % . The samples were also examined by micro X-ray fluorescence ( μ XRF ) and scanning electron microscope (SEM) in order to retrieve information on the mineral aggregates and the grain’s surface. The results of μ XRF imagery (aggregate map) were compared to the results of automatic mineral identification techniques, using ArcGIS software, and the results represent a promising performance for automatic identification.

In addition to being a serious threat to aircraft safety, kiss-bonding is also difficult to detect using conventional non-destructive testing techniques. It is the objective of this study to develop an autonomous high resolution technique to detect kiss-bonds and disbonds between carbon nanotube (CNT) skins and the host substrate of large-scale nanocomposite structures. The newly developed automated dynamic line-scan thermography (LST) approach is capable of speedy detection (up to 50 mm/s) of contact kiss-bonds as well as disbonds in laminated CNT-reinforced nanocomposite.

The life quality of patients with craniofacial malformations is severely affected by the physical disabilities caused by the malformation itself, but also by being subjected to bullying, which leads to a series of relevant psychological and societal effects that have an economic impact on the health sector. Orofacial clefts, notably cleft lip (CL), cleft palate, and microtia, are the most common craniofacial birth defects in humans and represent a substantial burden, both personal and societal. On the other hand, osteoarthritis is a widespread degenerative disease that is becoming more common due to the extension of the human lifespan and to an increase in injuries in young people as a result of their lifestyle. Advances in tissue engineering as a part of regenerative medicine offer new hope to patients that can benefit from new tissue engineering therapies based on the supportive action of tailored 3D biomaterials and the synergic action of stem cells that can be driven to the process of bone and cartilage regeneration. This review provides an update on recent considerations for stem cells and studies on the use of advanced biomaterials and cell therapies for the regeneration of craniofacial congenital malformations and articular degenerative diseases.

Composite materials are being successfully used in aeronautics for more than two decades. This great success is mainly because this kind of materials is typically lighter when compared to metals and more resistant to corrosion. Resistance and stiffness of composite materials are usually related to their fiber orientation. The development of non-destructive methods is required to assess this kind of property without damaging the sample. In this work, a non-destructive method based on infrared thermography is presented for fiber orientation assessment. More specifically, two approaches are presented and results are compared for fiber orientation assessment of randomly oriented strand parts. The first one is a point-by-point inspection based on pulsed thermal ellipsometry and the second one is a line approach based on flying laser spot inspection combined with neural artificial networks. Results showed that the second approach provides similar accuracy when compared to the first one; however, it is much faster which makes the second approach very interesting for industrial applications.

International audience; In this paper, two Non Destructive Testing approaches by active infrared thermography mounted on a 6-axis robot are presented and studied. Data acquisition and thermal excitation is carried out dynamically over various CFRP specimens with increasing geometry complexity, from planar, to convex and concave shapes. An automated procedure is proposed to reconstruct thermal image sequences issued from the two scanning procedure studied: Line Scan and Flying Line procedures. Defective area detection is performed by image processing and an inverse technique based on thermal quadrupole method is used to map the depth of flaws. Results obtained are discussed and perspectives are addressed.

Active thermography is a proven technology used in a wide variety of applications. In the most common approach using a static configuration, the elements involved in the inspection do not move. This presents serious drawbacks when it is applied to the inspection of large products and machines. An alternative approach is the dynamic inspection, which enables the inspection of large and complex products with better resolution, but it is also extremely challenging as data reconstruction is necessary. This work analyzes two methods for dynamic inspection using active infrared thermography: the thermal photocopier and the line scan. Automatic robust methods are proposed to calculate the temperature–time history, producing a pseudostatic sequence that can be further processed using advanced data processing algorithms to improve defect detection. Results demonstrate the robustness of the proposed methods and the ability to inspect large products with excellent results.

In this paper, eddy current pulsed thermography in transmission mode was used to detect the damages caused by low-velocity impacts in carbon fiber-reinforced polymer and basalt-carbon hybrid fiber-reinforced polymer laminates. In particular, different hybrid structures including intercalated stacking and sandwich-like structures were used. The impact energy of 12.5 was used for the evaluation of the impact damage level. Ultrasonic phased-array C-scan was performed for comparative purposes. In addition, the advantages and disadvantages of the two structures were identified and discussed.

The quantitative characterization of defects in images is commonly performed using the signal-to-noise ratio (SNR). However, there is a strong debate about this measure. First, because there is no single accepted definition of SNR. Second, because the SNR measurements are highly affected by the regions used to estimate the power of the signal and noise in the image. This work provides an overview of some of the most commonly used SNR measures. Images with different sources of noise, and defects with different contrasts, are used to evaluate and compare the ability of these measures to quantitatively characterize defects. The measures are also evaluated when the images are transformed using common image processing operations, including filtering and gamma correction. This work also proposes a methodology to define the regions used to estimate the power of the signal and noise in the images. Two alternative procedures are proposed weather prior information is available about the inspected specimen or not. The proposed methodology is applied on real data from infrared testing, where the considered SNR measures are evaluated.

Background Treatment of severe or chronic skin wounds is an important challenge facing medicine and a significant health care burden. Proper wound healing is often affected by bacterial infection; where biofilm formation is one of the main risks and particularly problematic because it confers protection to microorganisms against antibiotics. One avenue to prevent bacterial colonization of wounds is the use of silver nanoparticles (AgNPs); which have proved to be effective against non-multidrug-resistant and multidrug-resistant bacteria. In addition, the use of mesenchymal stem cells (MSC) is an excellent option to improve wound healing due to their capability for differentiation and release of relevant growth factors. Finally, radiosterilized pig skin (RPS) is a biomatrix successfully used as wound dressing to avoid massive water loss, which represents an excellent carrier to deliver MSC into wound beds. Together, AgNPs, RPS and MSC represent a potential dressing to control massive water loss, prevent bacterial infection and enhance skin regeneration; three essential processes for appropriate wound healing with minimum scaring. Results We synthesized stable 10 nm-diameter spherical AgNPs that showed 21- and 16-fold increase in bacteria growth inhibition (in comparison to antibiotics) against clinical strains Staphylococcus aureus and Stenotrophomonas maltophilia, respectively. RPS samples were impregnated with different AgNPs suspensions to develop RPS-AgNPs nanocomposites with different AgNPs concentrations. Nanocomposites showed inhibition zones, in Kirby–Bauer assay, against both clinical bacteria tested. Nanocomposites also displayed antibiofilm properties against S. aureus and S. maltophilia from RPS samples impregnated with 250 and 1000 ppm AgNPs suspensions, respectively. MSC were isolated from adipose tissue and seeded on nanocomposites; cells survived on nanocomposites impregnated with up to 250 ppm AgNPs suspensions, showing 35% reduction in cell viability, in comparison to cells on RPS. Cells on nanocomposites proliferated with culture days, although the number of MSC on nanocomposites at 24 h of culture was lower than that on RPS. Conclusions AgNPs with better bactericide activity than antibiotics were synthesized. RPS-AgNPs nanocomposites impregnated with 125 and 250 ppm AgNPs suspensions decreased bacterial growth, decreased biofilm formation and were permissive for survival and proliferation of MSC; constituting promising multi-functional dressings for successful treatment of skin wounds. Electronic supplementary material The online version of this article (10.1186/s12951-017-0331-0) contains supplementary material, which is available to authorized users.

Demands for Composite materials is increasing more and more because of their specific mechanical properties, especially in aerospace industry. Due to the porous structure of composite materials, there is the negligible probability of breaking up and defects in the internal structure. Detection of deep defects is a challenging subject in the field of Non-destructive testing. Due to the large size of composite components in the aerospace industry, line scanning thermography (LST) coupled with a robot arm is used to inspect large composite materials. In this paper, an innovative optimization procedure has been employed using analytical model, 3-D simulation using COMSOL Multiphysics, experimental setup and signal processing algorithms. The goal is to maximize the detection depth and signal to noise value as the criteria to evaluate the inspection quality and performance. the proposed algorithm starts searching to find the optimization variables of robotized LST such as scanning speed, source power and distance considering all technical and mechanical constraints. The optimal values are dependent on the material structure, thermal specifications of the composite, defect shape and infrared camera resolution. Using the proposed optimization algorithm, the detection depth was increased to 3.5 mm in the carbon fiber reinforced polymer and the signal to noise ratio was enhanced to 95%.

In this paper, eddy current pulsed thermography was used to evaluate ballistic impact damages in basalt-carbon hybrid fiber-reinforced polymer composite laminates for the first time, to our knowledge. In particular, different hybrid structures including intercalated stacking and sandwich-like sequences were used. Pulsed phase thermography, wavelet transform, principle component thermography, and partial least-squares thermography were used to process the thermographic data. Ultrasound C-scan testing and X-ray computed tomography were also performed for comparative purposes. Finite element analysis was used for validation. Finally, an analytical and comparative study was conducted based on signal-to-noise ratio analysis.

Recently, basalt-carbon hybrid composite structures have attracted increasing attention due to their comprehensive mechanical performance, if compared to carbon fiber reinforced polymer composites (CFRP). Low-velocity is considered as one of the most severe threats to composite products, since it is usually invisible and it occurs frequently in service. In this regard, non-destructive testing (NDT) techniques, especially emerging modalities, are expected to be an effective damage detection method. In this paper, eddy current pulsed thermography (ECPT), as an emerging NDT technique, was used to evaluate the damage induced by low-velocity impact loading in a CFRP laminate, as well as in two different-structured basalt-carbon hybrid composite laminates. In addition, ultrasonic C-scan and X-ray computed tomography were performed to validate the thermographic results. Pulsed phase thermography, principal component thermography and partial least squares thermography were used to process the thermal data and to retrieve the damage imagery. Then, a further analysis was performed on the imagery and temperature profile. As a result, it is concluded that ECPT is an effective technique for hybrid composite evaluation. The impact energy tends to create an interlaminar damage in a sandwich-like structure, while it tends to create an intralaminar damage in an intercalated stacking structure.

In this work, the potentialities of the infrared vision to explore sub-superficial defects in polychromatic statues were investigated. In particular, it was possible to understand how the reflector effect of the exterior golden layers could be minimized, applying advanced statistical algorithms to thermal images. Since this noble metal is present as external coating in both artworks, an in-depth discussion concerning its physicochemical properties is also added. In this context, the principal component thermography technique and, the more recent, partial least squares thermography technique were used on three different datasets recorded, providing long thermal stimuli. The main images were compared both to phasegrams and to the thermographic signal reconstruction results in order to have a clear outline of the situation to be debated. The effects of view factors on the radiation transfer linked to the specular reflections from the surface did not falsely highlight certain features inadvertently. Indeed, the raw thermograms were analyzed one by one. Reflectograms were used to pinpoint emissivity variations due to, e.g., possible repainting. The paper concludes that, as it is possible to understand from a physical point of view, the near-infrared reflectography technique is able to examine the state of conservation of the upper layers in cultural heritage objects, while the infrared thermography technique explores them more in-depth. The thesis statement is based on the thermal and nonthermal parts of the infrared region, therefore, indicating what can be detected by heating the surface and what can be visualized by illuminating the surface, bearing in mind the nature of the external coating.

Quantitative Infrared Thermography Conference (14th, 2018, Berlin, Germany)

In this paper, shortwave infrared (SWIR), mid-wave infrared (MWIR) thermography, air-coupled ultrasound (ACU), and X-ray radiography results were combined to retrieve the differences between a painting on the canvas sample constructed following the art master rules and an imitation far from it. Advanced image processing techniques including pulsed phase thermography (PPT), principal component thermography (PCT), and partial least square thermography (PLST) were applied to the MWIR images in order to highlight the shapes of subsurface features. Interestingly, additional defects were also discovered since the samples were handmade. Finally, a comprehensive and comparative study of these techniques was conducted to summarize their advantages and disadvantages.

Carbon fibres have become the natural choice as reinforcements for polymer composite materials (PMCs). The non-destructive inspection of dry carbon fibre preforms has the potential to increase the reproducibility and reduce the cost of PMC manufacturing, by identifying defects in dry multilayer preforms prior to resin injection. However, use of optical excitation thermography for inspecting dry carbon fibre preforms that constitute the structural reinforcement precursor in the manufacturing of PMCs is poorly documented in the open literature. In this work, optical excitation thermography was used for inspecting six dry multilayer carbon fibre preforms featuring different textile structures, thicknesses and defects, for the first time. Advanced image processing techniques were used in processing the thermographic data for comparative purposes. In particular, partial least square thermography, as a recently proposed technique, was studied in detail. Finally, the performance of different thermography techniques was analysed in terms of: (1) summarizing the capabilities of image diagnosis/processing techniques by signal-to-noise ratio analysis, and (2) identifying the monitoring modalities most suitable to industrial manufacturing.

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was given.

Infrared Radiation (IR) artwork inspection is typically performed through active thermography and reflectography with different setups and cameras. While Infrared Radiation Reflectography (IRR) is an established technique in the museum field, exploiting mainly the IR-A (0.7–1.4 µm) band to probe for hidden layers and modifications within the paint stratigraphy system, active thermography operating in the IR-C range (3–5 μ m) is less frequently employed with the aim to visualize structural defects and features deeper inside the build-up. In this work, we assess to which extent the less investigated IR-B band (1.5–3 μ m) can combine the information obtained from both setups. The application of IR-B systems is relatively rare as there are only a limited amount of commercial systems available due to the technical complexity of the lens coating. This is mainly added as a so-called broadband option on regular Mid-wave infrared radiation (MWIR) (IR-C’/3–5 μ m) cameras to increase sensitivity for high temperature applications in industry. In particular, four objects were studied in both reflectographic and thermographic mode in the IR-B spectral range and their results benchmarked with IR-A and IR-C images. For multispectral application, a single benchmark is made with macroscopic reflection mode Fourier transform infrared (MA-rFTIR) results. IR-B proved valuable for visualisation of underdrawings, pencil marks, canvas fibres and wooden grain structures and potential pathways for additional applications such as pigment identification in multispectral mode or characterization of the support (panels, canvas) are indicated.

The interest toward lightweight composites in the aeronautical industry grows year by year. The challenge is the identification and characterization of defects by using an integration of different techniques. The use of the infrared thermography (IRT) method for the inspection of lightweight composites is poorly documented in the open literature, due to the low heat diffusion through the honeycomb cores. In this study, IRT was used to retrieve the unknown positions of internal inserted objects in three different lightweight composite structures, by using three new thermal image processing integration methods. The corresponding post-processed results were compared against the traditional principal component thermography and pulsed phase thermography image processing techniques. The comparison showed that the integrated signal smoothing processing enhanced the image quality compared to the established techniques. Then, a thermal-physical analysis corresponding to the experimental results was conducted, in order to explain the experimental results. Finally, the advantages and disadvantages of the different presented methods when applied on the different lightweight composite structures were summarized.

In this paper, an infrared pre-processing modality is presented. Different from a signal smoothing modality which only uses a polynomial fitting as the pre-processing method, the presented modality instead takes into account the low-order derivatives to pre-process the raw thermal data prior to applying the advanced post-processing techniques such as principal component thermography and pulsed phase thermography. Different cases were studied involving several defects in CFRPs and GFRPs for pulsed thermography and vibrothermography. Ultrasonic testing and signal-to-noise ratio analysis are used for the validation of the thermographic results. Finally, a verification that the presented modality can enhance the thermal image performance effectively is provided.

We report the synthesis and in vitro evaluation of dual-function chitosan-silver nanoparticles (CTS-AgNPs) films with potential applications as wound dressings. We attempted to formulate nanocomposite films with appropriate AgNPs concentrations to simultaneously display antibacterial activity and suitability for cell culture. Nanocomposites were obtained by CTS-mediated in situ chemical reduction of AgNO3. Circular-shape AgNPs (sizes ca. 7–50 nm) well distributed within the CTS matrices were obtained in concentrations from 0.018 to 0.573 wt%. Efficacy (bacteriostatic and bactericidal properties) of CTS-AgNPs films to decrease planktonic and biofilm bacterial growth was AgNPs concentration- and bacteria strain-dependent. Films showed significant antibacterial activity against Gram-negative E. coli and P. aeruginosa and Gram-positive S. aureus. Antibacterial activity against S. epidermidis was moderated. Films suitability for cell culture was characterized using primary human fibroblasts (HF). HF displayed cell viability higher than 90% and the characteristic fusiform morphology of adhered fibroblast upon culture on films with AgNPs concentration ≤ 0.036 wt%. HF cultured on these films also showed positive expression of tropoelastin, procollagen type I and Ki-67, characteristic proteins of extracellular matrix and proliferative cells, respectively. In vitro assays demonstrated that cytocompatibility/antibacterial properties decreased/increased as silver concentration increased, suggesting that CTS-AgNPS nanocomposite films with ≈0.04–0.20 wt% might be considered as potential temporary dual-function wound dressings.

Bicycle frames made of carbon fibre are extremely popular for high-performance cycling due to the stiffness-to-weight ratio, which enables greater power transfer. However, products manufactured using carbon fibre are sensitive to impact damage. Therefore, intelligent nondestructive evaluation is a required step to prevent failures and ensure a secure usage of the bicycle. This work proposes an inspection method based on active thermography, a proven technique successfully applied to other materials. Different configurations for the inspection are tested, including power and heating time. Moreover, experiments are applied to a real bicycle frame with generated impact damage of different energies. Tests show excellent results, detecting the generated damage during the inspection. When the results are combined with advanced image post-processing methods, the SNR is greatly increased, and the size and localization of the defects are clearly visible in the images.

This paper describes a methodology for the modeling and simulation of a pulsed thermography inspection of carbon fiber-reinforced composites. A thermal–numerical model based on the 3D transient heat conduction equation for heterogeneous media is proposed to model the thermal response of the composite medium when a thermal pulse is applied to its surface. The solution of the model is developed using the finite volume method, in which the approximated equations are obtained by performing energy balances for each elementary volume. The experimental validation of the thermal–numerical model consisted of a comparison of the thermal decay curves for defective areas obtained through numerical simulation and with a pulsed thermography inspection of a laminated composite specimen with simulated defects (Teflon inserts). A parametric study was carried out in order to analyze the influence of the irradiation power density, non-uniform heating and characteristics associated with the defects on quantitative variables such as the onset time and thermal contrast. Numerical results showed that the variable thermal contrast is highly sensitivity to changes in the defect thickness, aspect ratio and parameters related to the external stimulation. Furthermore, an increase in the detectability of defects – especially those with lower aspect ratios – can be achieved by increasing the irradiation power.

The use of composite materials such as fiber reinforced materials has grown in the recent years. This growth is mainly due to their advantageous properties. They are typically lighter when compared to metals and more resident to corrosion. The resistance and stiffness of this materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is used to assess the fiber orientation on laminates reinforced with continuous fibers and randomly-oriented strands parts. More specifically, a thremography method called pulsed thermal ellipsometry is combined with an artificial neural network in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be efficiently used to inspect potentially large areas, i.e., with accuracy and speed.

Today, regenerative medicine requires new sources of multipotent stem cells for their differentiation to chondrocytes using the mediums of differentiation available in the market. This study aimed to determine whether the Mesenchymal Stem Cells (MSCs) isolated from Mobilized Peripheral Blood (MPB) in sheep using the Granulocyte Colony-Stimulating Factor (G-CSF), have the ability of first acquire a fibroblast-like morphology after being forced out of the bone marrow niche by G-CSF and second, if the cells have the capacity to express collagen type-II α I in primary culture using a human commercial media of differentiation. Six Suffolk male sheep with age of 2 years were mobilized using G-CSF. One subcutaneous injection of 10 mcg per kilogram of bodyweight were administered every 24 h during three consecutive days. At day four, a sample of 20 mL of peripheral blood was harvested, afterwards, monocytes cells were separated by ficoll gradient. The mobilized MSCs were expanded in primary culture in DMEM medium supplemented with 10% adult sheep serum for three weeks and characterized by an antibody panel for surface markers: CD105, CD90, CD73, CD34, and CD45, before and after primary culture. Subsequently, an aliquot of cells in the first pass were cultured in a commercial human chondrogenic medium for three weeks. As a result, the percentage of surface markers for MSCs (CD105, CD90, CD73) in expanded cells in primary culture significantly increased, at the same time a decrease in the markers for hematopoietic cells (CD34 and CD45) was observed and the cells morphology was fibroblast-like. After three weeks of differentiation culture, the immunofluorescence analysis evidenced the expression of collagen-type-II. It was concluded that Mesenchymal Stem Cells isolated from mobilized peripheral blood in sheep have the ability to pre-differentiate into chondral like cells and express collagen type-II when are stimulated with a human commercial chondrogenic medium in monolayer culture.

Autologous skin transplantation is today's "gold standard" treatment for full-thickness burns. However, when 30% of total body surface area is damaged, there is an important shortage of autologous donor sites for skin grafting; then, treatment alternatives become crucial. Such alternatives can be based on polymeric scaffolds capable of functioning as protective covers and cells/factors carriers. Chitosan (CTS) is a natural-derived polymer with relevant biological-related properties but poor mechanical performance. Improved mechanical properties can be achieved through lactic acid grafting (LA-g); nevertheless, LA-g affects the biological response towards the CTS-based materials. In this work, CTS-LA scaffolds with different LA-g percentages were synthesized and evaluated to determine appropriate LA-g degrees for full-thickness burns treatment. In vitro results indicated that the higher the LA-g percentage, the lower the capability of the scaffolds to sustain fibroblasts culture. Scaffolds with LA-g around 28% (CTS-LA28) sustained cell culture and allowed normal cell functionality. Further evaluation of CTS-LA28 as acellular and cellular grafts in a full-thickness burn mouse model showed that at 28 days post-burn, macroscopic characteristic of the reparation tissue were closer to healthy skin when cellular grafts were used for treatment; histological evaluation also showed that dermis cellularity and collagenous fibers structure were similar to those in healthy skin when cellular grafts were used for burns treatment. © 2017 Wiley Periodicals Inc. J Biomed Mater Res Part A: 105A: 2875-2891, 2017.

The applications of hyperspectral infrared imagery in the different fields of research are significant and growing. It is mainly used in remote sensing for target detection, vegetation detection, urban area categorization, astronomy and geological applications. The geological applications of this technology mainly consist in mineral identification using in airborne or satellite imagery. We address a quantitative and qualitative assessment of mineral identification in the laboratory conditions. We strive to identify nine different mineral grains (Biotite, Diopside, Epidote, Goethite, Kyanite, Scheelite, Smithsonite, Tourmaline, Quartz). A hyperspectral camera in the Long Wave Infrared (LWIR, 7.7-11.8 ) with a LW-macro lens providing a spatial resolution of 100 μm , an infragold plate, and a heating source are the instruments used in the experiment. The proposed algorithm clusters all the pixel-spectra in different categories. Then the best representatives of each cluster are chosen and compared with the ASTER spectral library of JPL/NASA through spectral comparison techniques, such as Spectral angle mapper (SAM) and Normalized Cross Correlation (NCC). The results of the algorithm indicate significant computational efficiency (more than 20 times faster) as compared to previous algorithms and have shown a promising performance for mineral identification.

Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral’s fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral’s surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals’ surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23± 2.66%. In conclusion, based on CWT’s ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

The emergence of composite materials has started a revolution in the aerospace industry. When using composite materials, it is possible to design larger and lighter components. However, due to their anisotropy, composite materials are usually difficult to inspect and detecting internal defects is a challenge. Line scan thermography (LST) is a dynamic thermography technique, which is used to inspect large components of metallic surfaces and composites, commonly used in the aerospace industry. In this paper, the robotized LST technique has been investigated on a large composite component which contains different types of internal defects located at a variety of depths. For theoretical analysis, the LST inspection was simulated using a mathematical formulation based on the 3D heat conduction equation in the transient regime in order to determine the optimum parameters. The solution of the model was performed using the finite element method. The LST parameters were adjusted to detect the deepest defects in the specimen. In order to validate the numerical results with experimental data, a robotized system in which the infrared camera and the heating source move in tandem, has been employed. From the experimental tests, it was noted that there are three sources of noise (non-uniform heating, unsynchronized frame rate with scanning speed and robot arm vibration) which affect the performance of the test. In this work, image processing techniques that were initially developed to be applied on pulse thermography have been successfully implemented. Finally, the performance of each technique was evaluated using the probability of detection approach.

Recent progress in Thermal and infrared Non-Destructive Testing (IRNDT) in different fields have provided interesting defect detection solutions. Principal Component Analysis (PCA) based K-means clustering have been successfully introduced and used in many clustering applications. However, PCA suffers from being relatively more sensitive to the noise due to having a linear transformation. On the other hand, Sparse Principal Component Analysis (SPCA) has a superior performance in relation to noise because of l 1 and l 2 norm additional terms which increase its robustness. As such, we propose SPCA based K-means clustering for defect segmentation. Principal Component Thermography (PCT) and Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) are also used as a pretreatment of data in order to reduce the number of components. Three types of specimens (CFRP, Plexiglass and Aluminum) have been used for comparative and quantitative benchmarking even in the case of adding Gaussian noise (0%– 35%). The results conclusively indicate the promising performance and confirmed the outlined properties.

In the last few years, composite materials have found an important niche of application in several industries, mainly because of their improved mechanical properties (higher stiffness, strength and resistance to fatigue). In this context, sandwich-composites, a special class of composite materials - are commonly used in the aerospace industry to manufacture lighter components. The increasing use of this type of materials in the aerospace sector has opened the necessity of inspection methods to evaluate its physical integrity and quality. Line Scan Thermography (LST) is one of the emerging technologies aimed to detect and evaluate subsurface defects present in the sandwiches composite structures. As a non-destructive testing and evaluation (NDT&E) technique, LST is a dynamic technique suited to inspect large and complex aerospace components. However, its performance to detect deeper and smaller defects is negatively affected due to the different sources of noise present in the collected thermal images. In this paper is studied the application of advanced signal processing techniques on LST data obtained from the inspection of a large composite component, which contains different types of internal defects located at a variety of depths. To evaluate the ability of each technique to reduce the noise, the signal-to-noise ratio (SNR) at the maximum signal contrast of each defect has been computed for further analysis.

A quantitative evaluation of delaminations in composite laminates encounters multiple difficulties due to the anisotropic behaviour of the laminate. Extensive calibrations for each structure are required and each depth needs certain manual modifications for optimal performance of the estimation routines. In this manuscript, a robust technique is developed using a numerical model to estimate the thermal diffusivity through the anisotropic material which improves defect depth estimation between each layer. Three different calibration depths are necessary to compute the diffusivity through a certain stacking sequence of a multi-layered composite laminate. The results are compared with the state-of-the-arts experimental evaluation techniques and with a regular numerical model. It is seen that especially for deeper defects, the optimised numerical model delivers more accurate results due to the considered anisotropic diffusivity.

Only select tissues and organs are able to spontaneously regenerate after disease or trauma, and this regenerative capacity diminishes over time. Human stem cell research explores therapeutic regenerative approaches to treat various conditions. Mesenchymal stem cells (MSCs) are derived from adult stem cells; they are multipotent and exert anti-inflammatory and immunomodulatory effects. They can differentiate into multiple cell types of the mesenchyme, for example, endothelial cells, osteoblasts, chondrocytes, fibroblasts, tenocytes, vascular smooth muscle cells, and sarcomere muscular cells. MSCs are easily obtained and can be cultivated and expanded in vitro; thus, they represent a promising and encouraging treatment approach in orthopedic surgery. Here, we review the application of MSCs to various orthopedic conditions, namely, orthopedic trauma; muscle injury; articular cartilage defects and osteoarthritis; meniscal injuries; bone disease; nerve, tendon, and ligament injuries; spinal cord injuries; intervertebral disc problems; pediatrics; and rotator cuff repair. The use of MSCs in orthopedics may transition the practice in the field from predominately surgical replacement and reconstruction to bioregeneration and prevention. However, additional research is necessary to explore the safety and effectiveness of MSC treatment in orthopedics, as well as applications in other medical specialties.

Nondestructive inspection of mosaic structures is not a novelty in the thermographic scene. Interesting works can be retrieved from scientific literature, some of them dedicated to the use of static active configurations and/or the passive approach for the inspection of plastered mosaics or the assessment of mosaic floors. In the present study, a mosaic made by synthetic tesserae of different colors depicting a dove was inspected by active thermography using a static configuration. The mosaic was manufactured with artificial defects positioned at several depths and locations, where some of them, due to their dynamic nature, enabled the monitoring of their thermal effects over time. In particular, the mosaic contains: a void into which compressed air can be injected, a sponge insert that can be soaked by a known quantity of water through an external tube, and a sub-superficial recirculation circuit from which a stream of cold or hot water can flow. The variability of the nature of these defects, simulating what happens in a real case, was conveniently modeled by numerical simulation approaches. The latter point was assessed through the aid of a simulation software, while the comparison of the results obtained by numerical analysis with those derived by thermographic testing was also performed.

Terahertz (THz) imaging is increasingly used in the cultural heritage field. In particular, continuous wave (CW) and low frequency THz is attracting more attention. The first application of the THz technique inherent to the cultural heritage field dates back 10 years ago. Since 2006, tangible improvements have been conducted in the refinement of the technique, with the aim to produce clear maps useful for any art restorer. In this paper, a CW THz (0.1 THz) imaging system was used to inspect paintings on canvas both in reflection and in transmission modes. In particular, two paintings were analyzed: in the first one, similar materials and painting execution of the original artwork were used, while in the second one, the canvas layer is slightly different. Flash thermography was used herein together with the THz method in order to observe the differences in results for the textile support materials. A possible application of this method for the detection of artwork forgery requires some parameterization and analysis of various materials or thickness influence which will be addressed in a future study. In this work, advanced image processing techniques including principal component thermography (PCT) and partial least squares thermography (PLST) were used to process the infrared data. Finally, a comparison of CW THz and thermographic results was conducted.

This paper introduces and tests a statistical correlation method for the optimization of the pulsed thermography inspection. The method is based on partial least squares regression, which decomposes the thermographic PT data sequence obtained during the cooling regime into a set of latent variables. The regression method is applied to experimental PT data from a carbon fiber-reinforced composite with simulated defects. The performance of the regression technique is evaluated in terms of the signal-to-noise ratio. The results showed an increase in the SNRs for 96% of the defects after processing the original sequence with PLSR.

The enteric nervous system (ENS) of mammals is derived from neural crest (NC) cells during embryogenesis and at the beginning of postnatal life. However, neural progenitor cells from the ENS (or ENSPC) are also found in the adult intestine and can be used for neuronal regeneration in diseases that lead to a loss of cell population, such as Parkinson's disease (PD), in which there is a decrease of dopaminergic neurons. The objective of this study was to evaluate the capacity of ENSPC to restore damaged nervous tissue and to show that they are functional for a behavioral and neurochemical recovery. We found that animals with ENSPC implants exhibited a motor recovery of 35% vs. the lesion group. In addition, DA levels were partially restored in 34%, while Homovanillic acid (HVA) levels remained at 21% vs. the group with a 6-Hydroxydopamine (6-OHDA)-induced lesion, suggesting that ENSPC represent a possible alternative in the study of cell transplants and the preservation of functional dopaminergic neurons in PD.

The present study is focused on two topics. The former is a mathematical model useful to understand the deformation of paintings, which uses straining devices, adjustable and micrometrically controlled through a pin implanted in a hollow cylinder. Strains were analyzed by holographic interferometry (HI) technique using an appropriate frame. The latter concerns the need to improve the conservator’s knowledge about the defect’s detection and defect’s propagation in acrylic painting characterized by underdrawings and pentimenti. To accomplish this task, a sample was manufactured to clarify the several uncertainties inherent the influence of external factors on their conservation. Subsurface anomalies were also retrieved by near-infrared reflectography and transmittography techniques, using LED lamps and several narrow-band filters mounted on a CMOS camera, working at different wavelengths and in combination with UV imaging. In addition, a sponge glued on the rear side of the canvas was impregnated with a precise amount of water by means of a syringe to verify the stretcher effect by digital speckle photography (DSP) technique (using MatPIV). The same effect also affects the sharp transition of the canvas at the stretcher’s edge. In this case, the direct mechanical contact between stretcher and canvas was investigated by HI technique. Finally, advanced algorithms were successfully applied to the square pulse thermography data to detect three Mylar $$^{\textregistered }$$ inserts simulating different types of defects. These fabricated defects were also identified by optical techniques: DSP and laser speckle imaging.

Composite materials are widely used in the aeronautic industry. One of the reasons is because they have strength and stiffness comparable to metals, with the added advantage of significant weight reduction. Infrared thermography (IT) is a safe nondestructive testing technique that has a fast inspection rate. In active IT, an external heat source is used to stimulate the material being inspected in order to generate a thermal contrast between the feature of interest and the background. In this paper, carbon-fiber-reinforced polymers are inspected using IT. More specifically, carbon/PEEK (polyether ether ketone) laminates with square Kapton inserts of different sizes and at different depths are tested with three different IT techniques: pulsed thermography, vibrothermography, and line scan thermography. The finite element method is used to simulate the pulsed thermography experiment. Numerical results displayed a very good agreement with experimental results.

This paper reports on the use of a neural network in infrared thermography to classify defects, such as air, oil, and water, which can degrade material performance. A finite element method and experiment were adopted to simulate air, water, and oil ingress. Raw data, and thermographic signal reconstruction coefficients were used to train, and test the two multilayer, feed-forward NN models. Quantitative comparisons showed that the model using coefficients as features performed better than the one using raw data. It was more precise and had better test repeatability. This indicates the model is more generalizable.