Pulsed thermography was exploited to identify the presence of glass defects in order to get an indication of the conservation status of archaeological glass. Indeed, the process of degradation in artifacts subjected to centuries of burial can be of great relevance. More specifically, we evaluated the potential of pulsed thermography to map the presence of flakes in archaeological glass. This was achieved by comparing different heating setups and signal-processing algorithms. Tests were carried out previously on glass mockups with surface defects and then on archaeological artifacts.In the modern world, one-third or more of breast cancer patients still undergo uni- or bilateral mastectomy. Breast cancer patients, in general, have a good prognosis and long-term survival. Therefore, the treatment must not only focus on survival but also on the quality of life. Breast reconstruction with an autologous free deep inferior epigastric artery perforator (DIEP) flap is one of the preferred options after mastectomy. A challenging step in this procedure is the selection of a suitable perforator that provides sufficient blood supply for the flap to prevent necrosis after anastomosis. In this pilot study, the possibilities for dynamic infrared thermography (DIRT) are investigated to select the best suitable perforator. The measurements are done with external cooling in the preoperative stage to accurately predict the location of the dominant perforators. During the surgery, in the peroperative stage, measurements are done for mapping the influence of a specific perforator on the perfused areas of the abdominal flap. Perforators are sequentially closed and opened again to map the influence of that perforator on the vascularization of the flap, visualized with the help of the thermographic camera. The acquired steady-state thermal images could help decide which parts of the abdominal flap to use for the reconstruction so that the chance of (partial) necrosis is reduced. In the postoperative stage, DIRT could visualize the arterial and or venous thrombosis before they become clinically obvious as (partial) necrosis. At present DIRT seems to be a valuable investigation for the pre-, per-, and postoperative phases of DIEP-flap reconstructions. Large, high-quality clinical studies are needed to determine its definitive role.The paper presents electrical and optical properties of interband cascade infrared photodetectors with InAs/GaSb type-II superlattice absorbers. We compare the detection parameters of detectors grown on the native GaSb substrate and lattice-mismatched GaAs substrate and seek solutions to enhance device performance, specifically with using an optical immersion. The detectors grown on GaAs have better detection parameters at room temperature, but, at lower temperatures, the misfit dislocations become more important, and detectors grown on GaSb become better.Bio-fluids are the source of a large number of metabolites. Identification and quantification of them can be an efficient step for understanding the internal chemistry of the body as well as for developing objective diagnostics of diseases. Several techniques have been developed so far; however, their metabolite identification and/or quantification are not reliable enough for acceptance by clinicians. As another promising step in this direction, we push infrared spectroscopy of bio-fluids in gas phase. Here we discuss features of breath and urine headspace realized with Fourier transform infrared spectroscopy. Molecular identification procedures based on component analysis of gas samples are proposed. In this paper, we show that aggregate data from different bio-fluids in gas phase can strengthen the diagnostics of the body state and disease.Thermographic testing is an inspection method that primarily indicates the presence of discontinuities in a tested sample. Its application to coatings can indicate a presence of local thickness variations; however, it mostly does not bring any quantitative information about the thickness of the coatings. This contribution is focused on a quantification of the thermographic inspection, which would make possible an evaluation of coating thickness differences. Flash-pulse thermographic testing was applied to thermally sprayed coatings. The importance of a precise synchronization of flash source and thermographic recording was determined. Different evaluation methods were analyzed, and their comparison showed that a time-power transformation method is the most suitable for quantification of the inspection results.Breast cancer accounts for the highest number of female deaths worldwide. https://www.selleckchem.com/products/baxdrostat.html Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis of the disease due to its low cost and that it does not emit harmful radiation, and it gives good results when applied in young women. This work uses convolutional neural networks in a database of 440 infrared images of 88 patients, classifying them into two classes normal and pathology. During the training of the networks, we use transfer learning of the following convolutional neural network architectures AlexNet, GoogLeNet, ResNet-18, VGG-16, and VGG-19. Our results show the great potential of using deep learning techniques combined with infrared images in the aid of breast cancer diagnosis.We present the design of single-mode fibers for two-stage higher-order soliton compression at 2 µm wavelength and achieve high-degree pulse compression in cascaded single-mode fibers. The compression performance for the initial input pulse width from 1 to 50 ps is also investigated. For the initial third-order soliton of 10 ps, a compression factor of 75.7 has been achieved, and the pedestal energy is only 46.66%.Infrared spectrum analysis technology can perform fast and nondestructive detection of gas and has been widely used in many fields. This work studies the quantitative analysis technology of the infrared spectrum based on deep learning. The experimental results show that the quantitative analysis model of logging gas established here can reach 100% recognition accuracy for elemental gas; further, the accuracy rate of spectral of mixed gas recognition reached 98%, indicating that the infrared spectrum logging gas detection model based on deep learning can quickly and accurately perform quantitative analysis of logging gas.