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A method is proposed to measure the in-plane thermal diffusivity of anisotropic solids along an arbitrary direction using laser-line lock-in thermography. In this method, the sample is heated by an intensity-modulated laser beam, which impinges on the sample surface with the shape of a narrow strip. The resulting temperature oscillations are recorded by a thermographic camera and a phase thermogram is obtained by lock-in processing the image sequence. The thermal diffusivity along the direction perpendicular to the laser line can be determined from the linear dependence of phase lag with the distance to the heating line using the slope method. Unlike laser-spot lock-in thermography, in which the slope method only provides the thermal diffusivity along the principal directions, in this configuration, the thermal diffusivity can be measured experimentally along any direction, just selecting the relative orientation of the sample with respect to the laser line. Moreover, this configuration allows averaging several parallel phase profiles leading to more reliable thermal diffusivity values. The experimental results are presented for a highly anisotropic material, namely, a unidirectional carbon fiber-reinforced polymer plate. [ABSTRACT FROM AUTHOR]

PURPOSE: We determined interblink corneal surface-temperature decline and tear-film evaporation rates of localized tear breakup cold regions (LCRs) and localized tear unbroken warm regions (LWRs) of the corneal surface, as well as that of the overall average corneal surface. METHODS: Each subject underwent 4 inter-day visits where the interblink corneal surface-temperature history of the right eye was measured using a FLIR A655sc infrared thermographer. Corneal surface temperature history was analyzed to determine the overall, LCR, and LWR temperature-decline rates. Evaporation rates of LCR and LWR regions were determined from the measured LCR and LWR temperature data using the physical model of Dursch et al. RESULTS: Twenty subjects completed the study. Mean (SD) difference of LCR temperature-decline rate was -0.08 (0.07)°C/s faster than LWR (P < 0.0001). Similarly, evaporation rates of LCR and LWR were statistically different (P < 0.0001). At ambient temperature, mean LCR and LWR evaporation rates were 76% and 27% of pure water evaporation flux, respectively. There was no statistically significant difference between the inter-day measured temperature-decline rates and the interblink starting temperature. CONCLUSIONS: Significant differences in corneal temperature-decline rate and evaporation rate between LCR and LWR were quantified using infrared thermography. In agreement with literature, LCRs and LWRs correlate directly with fluorescein break-up areas and unbroken tear areas, respectively. Because lipid-evaporation protection is diminished in breakup areas, higher local evaporation rates and faster local cooling rates occur in LCRs relative to LWRs. Our results confirm this phenomenon clinically for the first time.

Active thermography using pulsed heating is a fast and reliable method for detecting flaws in composite and metallic materials. This paper analyzes the temperature decay that occurs immediately after flash heating the front surface of stainless steel specimens as a function of time, based on a novel application of the equivalent circuit approach (ECA). The temperature decay from the front surface is equated to the discharge of a capacitor. The ECA is based on the charging (temperature rise due to flash heating) of a capacitor, followed by its discharge (temperature decay) through a series of resistors (which depends on the conductivity of the material) and capacitance (which depends on the thermal capacitance of the layers) through which the heat is dissipated. The proposed approach analyzes the sequences of temperature data obtained at each pixel location during cooling from a step wedge and a specimen with multiple flat-bottom holes. Time constant maps derived from the analysis are used to ascertain the thickness of the step wedge, detect the flaws, and evaluate the remnant thickness of the flaws. A correlation has been established between the thickness and the time constants. The above approach has been used to estimate the diameter of the flat-bottom holes. [ABSTRACT FROM AUTHOR]

Extremely intense blue upconversion luminescence (UCL) is got in Tm3+/Yb3+ co-doped BiTa 7 O 19 (BTO) phosphors, which are synthesized by solid phase sintering. The doping concentration of Tm3+ is fixed at 7 mol%, and the concentration of Yb3+ is adjusted. When the concentration of Yb3+ is 80 mol%, the strongest blue UCL can be obtained under 980 nm laser excitation. The blue UCL integral intensity under 808 + 980 nm laser co-excitation is 1.54 times of the sum of intensity under 808 and 980 nm single excitation, respectively. Blue UCL thermal enhancement behavior was found for all the samples under 808 or 785 nm laser excitation, and blue UCL thermal quenching happened under 660, 660 + 808, and 359 nm excitations. It is demonstrated that UCL thermal enhancement behavior is caused by oxygen defects. The possible electron transition pathways are proposed under excitation with different laser wavelengths. Using the luminescence intensity branching ratio (LIR) technique, the maximum relative temperature sensitivity range is calculated to be from 0.00610 to 0.00732 K−1 at 303 K. The experimental results show that BTO:Tm3+/Yb3+ has excellent blue UCL with thermal enhancement, which can have some applications in the field of UCL display and temperature sensing in high temperature environment. [ABSTRACT FROM AUTHOR]

Infrared thermal imaging (IRT) remotely and contactless maps the temperature on the examined surface, recording the distribution of infrared radiation emitted by each body whose temperature is higher than absolute zero. The aim of the study was to evaluate the usefulness of thermography in the assessment of asymptomatic infection foci in patients with high systemic infection. The 150 cases diagnosed based on roentgenograms, divided into 6 groups of diagnosed odontogenic lesions, along with a control group. Thermal imaging was performed with a FLIR Systems T1020 thermal camera. Thermal image analysis was performed using ThermaCAM Researcher Pro 2.10, MS Office Excel 2022 and Statistica 10. The periapical areas of selected dead teeth were selected as areas of interest. The Mann Whitney’s U test showed statistically significant (p < 0.001) differences in average temperature between each patient’s and healthy group. Depper’s analysis showed statistical significance also between the ZM and BZ groups (p = 0.004). Moreover, obtained results may also suggest that thermal imaging can be useful in identify odontogenic infection foci. The thermal asymmetry of periapical tissues of teeth differentiates dead from living teeth, as well as individual pathologies related to the process of gangrenous pulp decay. Thermographic mapping is a promising diagnostic technique that can detect asymptomatic inflammations that carry the risk of infection of the entire body. [ABSTRACT FROM AUTHOR]

Deep learning applications have witnessed significant advancements across diverse domains, revolutionizing tasks such as image recognition, disease classification, cancer detection, natural language processing, and autonomous decision-making. However, striking a balance between performance and cost remains a fundamental challenge. Performance, which encompasses accuracy, efficiency, and effectiveness, is crucial for the real-world applicability of deep learning models. Attaining high performance often involves using resource-intensive models with intricate architectures, extensive datasets, and hyperparameter tuning. While these factors lead to improved performance, they also result in increased costs, including computational resources, time, and energy consumption. This research addresses this challenge by proposing a novel low-cost deep learning model based on Sharpened Cosine Similarity (LC-SCS) for breast cancer classification using thermal images. In our study on a DMR-IR dataset, we also utilized pre-trained models: ResNet-101, VGG-16, Inception-V3, ResNet-50, VGG-19, and Xception. The proposed LC-SCS model achieved an impressive accuracy of 94%, trailing just 4% behind the leading VGG-19 model, while maintaining a low computational cost. It achieved recall and precision scores of 0.95 each, with an F1-score of 0.94. The LC-SCS model excels in computational efficiency, demanding only 1.85 GFLOPs and 1.2 million parameters. The memory requirement is minimal at 865.35 MB, and it exhibits a latency of just 0.003 s. Additionally, the CPU execution time for prediction is 3.59 s. Comparatively, the best-performing pre-trained VGG-19 model achieved 98% accuracy but incurred significantly higher costs. The proposed LC-SCS model showcases a remarkable 298.63 times reduction in GFLOPs, a 116.31 times reduction in total parameters, a 4.57 times reduction in memory requirements for model weights, a 5 times reduction in latency, and a 3.33 times reduction in CPU execution time for prediction, thus making it as an exceptionally resource-efficient model, particularly in scenarios with limited computational resources. [ABSTRACT FROM AUTHOR]

In recent years, the estimation of biometric parameters from facial visuals, including images and videos, has emerged as a prominent area of research. However, the robustness of deep learning-based models is challenged, particularly in the presence of changing illumination conditions. To overcome these limitations and unlock new opportunities, thermal imagery has arisen as a viable alternative. Nevertheless, the limited availability of datasets containing thermal data and the small amount of annotations on them limits the exploration of this spectrum. Motivated by this gap, this paper introduces the Label-EURECOM Visible and Thermal (LVT) Face Dataset for face biometrics. This pioneering dataset includes paired visible and thermal images and videos from 52 subjects along with metadata of 22 soft biometrics and health parameters. Due to the reduced number of existing datasets in this domain, the LVT Face Dataset aims to facilitate further research and advancements in the utilization of thermal imagery for diverse eHealth applications and soft biometric estimation. Moreover, we present the first comparative study between visible and thermal spectra as input images for soft biometric estimation, namely gender age and weight, from face images on our collected dataset. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Methods Results Conclusion Hot flashes (HFs) are experienced as sudden sensations of heat. We hypothesized that brown adipose tissue (BAT) activation could increase the likelihood of HFs in winter. The aim of this study was to test whether women with more BAT activity were more likely to experience self‐reported or biometrically measured HFs.Women aged 45–55 years (n = 270) participated in face‐to‐face interviews and anthropometric and ambulatory measures. Level of BAT activity was estimated from the difference in supraclavicular skin temperature measured by infrared thermography before and after cooling. Logistic regressions were applied to examine whether bothersome HFs (yes/no) during the past 2 weeks were associated with BAT activity, adjusting for menopausal status, childhood exposure to cold, waist/hip ratio, and self‐reported health. Linear regressions were used to examine the frequency of self‐reported and biometrically measured HFs during the study period and BAT activity, adjusting for potential confounders.Menopausal status, childhood exposure to cold, waist‐to‐hip ratio (WHR), and self‐reported health were associated with both BAT activity and HFs. After adjusting for potential confounders, an increase in BAT activity almost tripled the likelihood of bothersome HFs (OR 2.84, 95% CI 1.26–6.43). In linear regressions, BAT activity was not associated with frequency of subjective or objective HFs during the study period, but childhood exposure to cold was associated with subjective HF report (β = 0.163, p = 0.010).To our knowledge, this is the first study of BAT activation and HFs. Our results support a role for BAT activity in HF experience. Therefore, we encourage further examination of the role of BAT, as well as childhood exposure to cold, in HFs. [ABSTRACT FROM AUTHOR]

Elastomeric interphases (liners) protect the residual limb and distribute mass bearing to the prosthetic socket. However, discomfort due to excessive sweat and heat in the residual limb is the most relevant factor that negatively affects the quality of life in transfemoral prosthetics users. Additionally, a warm and humid environment and the non-uniform mass distribution can compromise blood flow in the residual limb; these factors favor the development of skin lesions, the dropout rate of prosthetic use, and subsequent amputations. Previous mathematical models that predict skin temperature distribution based on Pennes' model and explain the Fourier and non-Fourier thermic response are available. However, these models require knowledge of difficult parameters to obtain in conditions that lead to amputation. Therefore, the current study aims to propose an experimentally based method to design a personalized liner with areas of different diffusivity rates by using perforations according to thermal needs, preserving the mechanical properties of the liner. After thermal and mechanical characterization, we elaborated a personalized plane liner according to the thermal expression of the anterior thigh using infrared analysis. We tested the liner to probe its ability to dissipate heat. [ABSTRACT FROM AUTHOR]

The increasing amount of paper waste can be enhanced in its utility by transforming it into composites to utilize it as a technology for coating metal roofs to absorb heat. Various paper types have been proven to reduce heat inside the room below when applied to Galvalume roofs. Therefore, multiple types of paper waste are highly suitable for metal roof coatings. This research investigates whether differences in the types of paper mixed with epoxy and applied to the surface of Galvalume roofs will affect the temperature reduction inside the room. These differences in paper types also affect the level of thermal conductivity when measured with the same instruments and dimensions. Data were obtained by mixing small-sized cut paper materials and layering them on Galvalume sheets with the help of epoxy as an adhesive. The temperature data collection process was conducted by preparing 6 boxes with 4 thermocouples supplemented with thermal imaging testing. To broaden the analysis, images of the roof were also taken and analyzed for color to assess the extent of sunlight absorption and reflection on the roof. Four thermocouples were placed 20 cm above the roof, above the composite, below the Galvalume, and 40 cm below the roof or inside the room. Thermal conductivity data were analyzed separately and integrated with roof color data, further corroborated with temperature data collected by the thermocouples. The four sensors can determine the temperature inside and around the room, focusing on the temperature around the installed composite roof. The analysis shows that adding paper and epoxy-based composites can reduce the thermal conductivity of Galvalume roofs and lower the temperature inside the room below it. Compared to a Galvalume roof without coating, the composite coating with cardboard paper is the most effective, with an estimated average room temperature reduction of 1.9 degrees Celsius. For reducing the air temperature above the roof, the newspaper coating is the best choice, with an estimated average temperature decrease of 2.6 degrees Celsius. [ABSTRACT FROM AUTHOR]

An increase in plantar pressure and skin temperature is commonly associated with an increased risk of diabetic foot ulcers. However, the effect of insoles in reducing plantar temperature has not been commonly studied. The aim was to assess the effect of walking in insoles with different features on plantar temperature. Twenty-six (F/M:18/8) participants—13 with diabetes and 13 healthy, aged 55.67 ± 9.58 years—participated in this study. Skin temperature at seven plantar regions was measured using a thermal camera and reported as the difference between the temperature after walking with an insole for 20 m versus the baseline temperature. The mixed analyses of variance indicated substantial main effects for the Insole Condition, for both the right [Wilks' Lambda = 0.790, F(14, 492) = 4.393, p < 0.01, partial eta squared = 0.111] and left feet [Wilks' Lambda = 0.890, F(14, 492) = 2.103, p < 0.011, partial eta squared = 0.056]. The 2.5 mm-tall dimple insole was shown to be significantly more effective at reducing the temperature in the hallux and third met head regions compared to the 4 mm-tall dimple insole. The insoles showed to be significantly more effective in the diabetes group versus the healthy group, with large effect size for the right [Wilks' Lambda = 0.662, F(14, 492) = 8.037, p < 0.000, Partial eta-squared = 0.186] and left feet [Wilks' Lambda = 0.739, F(14, 492) = 5.727, p < 0.000, Partial eta-squared = 0.140]. This can have important practical implications for designing insoles with a view to decrease foot complications in people with diabetes. [ABSTRACT FROM AUTHOR]

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of the buried texts. As shown in this paper, the good agreement between the experimental data obtained in the historical books and the theoretical analysis proved that the capability of the adopted PT method could be fruitfully applied, in real case studies, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability. [ABSTRACT FROM AUTHOR]

Stripe noise is a general phenomenon in original remote sensing images that both degrades image quality and severely limits its quantitative application. While the classical statistical method is effective in correcting common stripes caused by inaccurately calibrating relative gains and offsets between detectors, it falls short in correcting other nonlinear stripe noises originating from subtle nonlinear changes or random contamination within the same detector. Therefore, this paper proposes a novel trend repair method based on two normal columns directly adjacent to a defective column to rectify the trend by considering the geospatial structure of contaminated pixels, eliminating residual stripe noise evident in level 0 (L0) remote sensing images after histogram matching. GF5-02 VIMI (Gaofen5-02, visual and infrared multispectral imager) images and simulated Landsat 8 thermal infrared sensor (TIRS) images deliberately infused with stripe noise are selected to test the new method and two other existing methods, the piece-wise method and the iterated weighted least squares (WLS) method. The effectiveness of these three methods is reflected by streaking metrics (Streaking), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and improvement factor (IF) on the uniformity, structure, and information content of the corrected GF5-02 VIMI images and by the accuracy of the corrected simulated Landsat 8 TIRS images. The experimental results indicate that the trend repair method proposed in this paper removes nonlinear stripe noise effectively, making the results of IF > 20. The remaining indicators also show satisfactory results; in particular, the mean accuracy derived from the simulated image remains below a digital number (DN) of 15, which is far superior to the other two methods. [ABSTRACT FROM AUTHOR]

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images. [ABSTRACT FROM AUTHOR]

In this article, we present an analysis of the properties of polyaddition poly(dimethylsiloxanes) (PDMS) and their potential applications after modification. The focus is on understanding how different fillers and mixing speeds affect the mechanical and electrical properties of PDMS, as well as the benefits and challenges associated with these modifications. Additionally, the prospects for future development of PDMS-based technologies, which could bring significant innovations in various industrial fields, are discussed. [ABSTRACT FROM AUTHOR]

Purpose: This review explores mathematical models, blinking characterization, and non-invasive techniques to enhance understanding and refine clinical interventions for ocular conditions, particularly for contact lens wear. Methods: The review evaluates mathematical models in tear film dynamics and their limitations, discusses contact lens wear models, and highlights computational mechanical models. It also explores computational techniques, customization of models based on individual blinking dynamics, and non-invasive diagnostic tools like high-speed cameras and advanced imaging technologies. Results: Mathematical models provide insights into tear film dynamics but face challenges due to simplifications. Contact lens wear models reveal complex ocular physiology and design aspects, aiding in lens development. Computational mechanical models explore eye biomechanics, often integrating tear film dynamics into a Multiphysics framework. While different computational techniques have their advantages and disadvantages, non-invasive tools like OCT and thermal imaging play a crucial role in customizing these Multiphysics models, particularly for contact lens wearers. Conclusion: Recent advancements in mathematical modeling and non-invasive tools have revolutionized ocular health research, enabling personalized approaches. The review underscores the importance of interdisciplinary exploration in the Multiphysics approach involving tear film dynamics and biomechanics for contact lens wearers, promoting advancements in eye care and broader ocular health research. [ABSTRACT FROM AUTHOR]

The temperature of melt blowing (MB) and melt spun fibers is frequently measured using infrared thermography (IRT). However, measurements are affected by the size and curvature of the fiber and by reflected radiation from the nozzle. A procedure was developed to obtain accurate measurements of fiber temperature, which corrects fiber size, fiber curvature, and reflected radiation. This procedure is more general than the ones used so far to correct fiber temperature because it corrects the three abovementioned factors using a single correction formula, this being the first time that fiber temperature correction for reflected radiation has been accounted for. It is also more accurate because it uses the local fiber diameter to correct fiber curvature, instead of mean fiber diameter. The procedure was used to measure the temperature of MB adhesive fibers produced by two multi-hole nozzle designs, the results indicating that, in melt blowing applications, the distortion caused by fiber curvature and reflected radiation on the measurement proves to be far more significant than that caused by fiber size, while correction generally leads to an increase in fiber temperature. Considerations as to the effect of different parameters on temperature correction, as well as the quality of thermographic images, are also provided throughout the text. The conclusions emerging from this study allow more accurate temperature measurements of melt blown and melt spun fibers to be obtained. [ABSTRACT FROM AUTHOR]

Due to the influence of illumination, noise, distortion and other factors on monocular vision images, the image quality is reduced, the difficulty of image information extraction is high, and there are often errors and uncertainties in background segmentation, which affect the effect of monocular vision image background virtualization. Therefore, a new depth information extraction monocular vision image automatic hierarchical background virtualization method is studied to improve the effect of background virtualization. The depth information map is extracted by anisotropic thermal diffusion equation. The morphology is used to fill the tiny holes in the depth information map, and its smoothing process is used to determine the image depth range, automatically layer the depth information map, and obtain the foreground layer and background layer. The background layer is virtualized by Gaussian blur operation. Pyramid image fusion method is used to fuse the foreground layer and the blurred background layer to complete the background virtualization of monocular vision image. Experimental results have shown that this method can effectively improve the clarity of depth information map edges, preserve a large amount of image edge information, and have high structural similarity, with an average value of 0.96. The efficiency is high, and the background virtualization time is only 15 ms. [ABSTRACT FROM AUTHOR]

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant–environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal‐based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing‐based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields. Summary statement: Using both trait and flux measurements, we have designed a new vegetation index: the combined nitrogen and drought index. This index describes changes in leaf optical properties during nitrogen and or drought stress that can be used to improve transpiration modelling during abiotic stress. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) is an important composite material widely used in aerospace and other industries. However, long-term service in harsh environments can lead to various defects such as debonding, delamination, water, cracks, etc. Therefore, it becomes imperative to conduct non-destructive testing (NDT) on CFRP to ensure its structural integrity and safety. Infrared thermography was employed for defect classification in CFRP laminate and CFRP honeycomb sandwich composites (HSC) by applied a convolutional neural networks (CNN). The proposed automatic defect classification method based on CNN is one of the goals of NDE 4.0 to apply advanced technologies (such as deep learning and AI) to improve NDT efficiency and accuracy. The infrared detection dataset consisted of five classes: debonding, water, delamination, crack, and health. To effectively expand the dataset, offline data augmentation technique were employed. A deep learning technique of Dense convolutional neural network (DCNN) were proposed to defect classification. AlexNet, VGG-16, ResNet-50 and DenseNet-121 based on transfer learning fine-tuning model was applied to classify debonding, water, delamination, crack and health. The classification results were analyzed by using a confusion matrix. The results shown that the accuracy of AlexNet, VGG-16, ResNet-50 and DenseNet-121 were 92.34%, 82.86%, 88.30%, 98.48%, respectively. DenseNet-121 demonstrates good performance in defect detection and recognition with an accuracy of 98.48%, and DenseNet-121 has high application potential in accurately classify and recognize defects in deep learning technique. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess, through a systematic review, the status of infrared thermography (IRT) as a diagnostic tool for skin neoplasms of the head and neck region and in order to validate its effectiveness in differentiating benign and malignant lesions. A search was carried out in the LILACS, PubMed/MEDLINE, SCOPUS, Web of Science and EMBASE databases including studies published between 2004 and 2024, written in the Latin-Roman alphabet. Accuracy studies with patients aged 18 years or over presenting benign and malignant lesions in the head and neck region that evaluated the performance of IRT in differentiating these lesions were included. Lesions of mesenchymal origin and studies that did not mention histopathological diagnosis were excluded. The systematic review protocol was registered in the PROSPERO database (CRD42023416079). Reviewers independently analyzed titles, abstracts, and full-texts. After extracting data, the risk of bias of the selected studies was assessed using the QUADAS − 2 tool. Results were narratively synthesized and the certainty of evidence was measured using the GRADE approach. The search resulted in 1,587 records and three studies were included. Only one of the assessed studies used static IRT, while the other two studies used cold thermal stress. All studies had an uncertain risk of bias. In general, studies have shown wide variation in the accuracy of IRT for differentiating between malignant and benign lesions, with a low level of certainty in the evidence for both specificity and sensitivity. [ABSTRACT FROM AUTHOR]

Al 2 O 3 reticulated porous ceramics (ARPC) are widely selected for high-temperature porous media burners due to their excellent thermal stability. However, the relatively low mechanical property and infrared emissivity of ARPC led to a low lifespan and radiation efficiency of the burner, limiting its application in high-temperature porous burners. In this work, a novel strategy was proposed to synergistically improve mechanical and radiation properties of ARPC and recycle of coal gangue. ARPC with three-layered struts, including microporous alumina-mullite coating, alumina skeleton and filling layer, were prepared by polymer replica technique and vacuum infiltration method. The addition of coal gangue could obviously strengthen ARPC, because coal gangue was attributed to form mullite, as well as the generation of residual compressive stress in the coating of three-layered struts. The decomposition of organic compounds within coal gangue enhanced the degree of microporosity on the coating, thereby increasing infrared emissivity by 21.8 %, which increased the surface temperature of porous burner from 857.4°C to 905.7°C. [ABSTRACT FROM AUTHOR]

Background The versatile musculocutaneous latissimus dorsi flap and the thoracodorsal artery (TDA) perforator flap have developed into indispensable approaches in reconstructive surgery. While the anatomy of the TDA is consistent, the skin perforators vary in location and course. Dynamic infrared thermography (DIRT) recently gained popularity for perforator identification; however, its use and accuracy in thoracodorsal artery perforator (TDAP) mapping is yet to be determined. Methods TDAPs were visualized in 50 cases by DIRT. Based on the thermographic hotspots, the corresponding perforators were then identified by color duplex ultrasound (CDU) and handheld Doppler in a blinded fashion by two separate examiners. Results The midpoint of all perforator fascia passages was localized 99.7 mm caudal and 13.5 mm medial of the posterior axillary fold. The positive predictive value of perforator identification by dynamic infrared thermography was 86.5% and the correlating perforator fascia passage was 9.9 ± 5.8 mm from the hotspot midpoint, with a maximum of 29 mm. The positive predictive value of perforator identification by handheld Doppler was 95% and the signal was 7.2 ± 5.1 mm from the perforator fascia passage. Conclusion DIRT precisely localizes TDAPs. The fusion with CDU combines both modalities' advantages. The combination with handheld Doppler is a fast way of perforator imaging, decreasing the handheld Dopplers' high false positive rate. [ABSTRACT FROM AUTHOR]

As worries about climate change and energy security grow, the need for clean and sustainable energy sources becomes increasingly critical. Among the possible alternatives, solar thermal technology provides a dependable and adaptable method for harnessing the sun's energy as heat. At the core of this technique is the solar thermal absorber, a critical component that converts sunlight into useful thermal energy. The Graphene Based Disk-Square Integration Resonator Solar Absorber (GBDSIRSA) structure is examined in this work between 200 and 2500 nm in wavelength. The GBDSIRSA has remarkable performance throughout a wide variety of spectral ranges, underscoring its adaptability and effectiveness. GBDSIRSA has exceptional efficiency in absorbing light over the full spectrum, from UV to MIR wavelengths, with an average absorptance of 97.73%. The absorptance rates are particularly high, achieving 94.02% in the UV, 96.36% in the VIS, 97.12% in the NIR, and an astounding 98.94% in the mid-infrared (MIR) region. The GBDSIRSA is independent of polarization effect and also an incident angle independent up to 80 degrees. The machine learning (Local Regression) approach used with test size of 0.2 and 1.8780 ×10-5 mean squared error for absorptance prediction has good prediction accuracy R2 of more than 95% which is applied to simulation resource reduction. Because of its wide absorptance spectrum, polarization and incident angle independence, the GBDSIRSA is a very promising technology for a variety of applications, including thermal imaging and renewable energy. It can efficiently harvest solar energy. All things considered, GBDSIRSA's remarkable absorptance properties highlight its promise as a top choice for cutting-edge optical and solar energy applications. [ABSTRACT FROM AUTHOR]

Pressure ulcers (PU) are a globally recognised healthcare concern, with their largely preventable development prompting the implementation of targeted preventive strategies. Risk assessment is the first step to planning individualised preventive measures. However, despite the long use of risk assessment, and the >70 risk assessment tools currently available, PUs remain a significant concern. Various technological advancements, including artificial intelligence, subepidermal moisture measurement, cytokine measurement, thermography and ultrasound are emerging as promising tools for PU detection, and subsequent prevention of more serious PU damage. Given the rise in availability of these technologies, this advances the question of whether our current approaches to PU prevention can be enhanced with the use of technology. This article delves into these technologies, suggesting that they could lead healthcare in the right direction, toward optimal assessment and adoption of focused prevention strategies. [ABSTRACT FROM AUTHOR]

Objectives To investigate the hypotheses that in patients with SSc, the temperature gradient between the dorsum of the foot and toes (distal–dorsal difference [DDD]) is 'more negative' (toes cooler) than in healthy controls, is greatest along the first (great) toe and that the severities of thermographic abnormalities in the feet and hands are correlated. Methods Thermographic images of the dorsum of each hand and foot were captured using a thermal camera attached to an iPhone in 40 patients with SSc and 20 healthy controls. DDDs along the fingers (index, middle, ring and little) and toes (great toe and 'others') were measured. Results There was a non-significant trend for the great toes to be colder in patients with SSc than in controls. The mean great toe DDD was more negative in patients (right: −2.89°C, left: −2.91°C, mean: −2.90°C) than in controls (right: −2.36°C, left: −2.42°C, mean: −2.39°C) (P  = 0.37 for mean values). Patients' great toes were colder than 'other' (lesser) toes (right: −2.58°C, left: −2.63°C), although not significantly. In patients with SSc, finger and great toe temperature gradients were correlated (r  = 0.406, ρ = 0.01). Conclusion Our findings suggest that the great toe is the coldest in patients with SSc and that patients with the coldest fingers tend to have the coldest toes. Severe RP symptoms in the hands should prompt podiatry assessment and foot care education. Mobile phone thermography is a convenient tool for assessing the digital vasculature but first requires validation in larger studies with a longitudinal component. [ABSTRACT FROM AUTHOR]

Purpose: Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery. Dynamic infrared thermography (DIRT) can offer a solution for these disadvantages. Methods: The research presented couples thermographic examination during DIEP flap breast reconstruction with automatic segmentation approach using a convolutional neural network. Traditional segmentation techniques and annotations by surgeons are used to create automatic labels for the training. Results: The network used for image annotation is able to label in real-time on minimal hardware and the labels created can be used to locate and quantify perforator candidates for selection with a dice score accuracy of 0.8 after 2 min and 0.9 after 4 min. Conclusions: These results allow for a computational system that can be used in place during surgery to improve surgical success. The ability to track and measure perforators and their perfused area allows for less subjective results and helps the surgeon to select the most suitable perforator for DIEP flap breast reconstruction. [ABSTRACT FROM AUTHOR]

The administration of waste from constructing-demolition and the reuse of industrial waste materials are the main focuses of the sustainability initiatives. There are economic and environmental benefits to using recycled concrete aggregates (RCA). Nevertheless, the RCA mixes' poor performance necessitates the addition of more enhanced substances. This study investigated the moisture resistance of asphaltic mixes that included RCA and silica fume (SF). Coarse aggregates were replaced with RCA at three different percentages 15%, 30%, and 45%, which were pre-treated by immersing it in acid with a concentration of 0.1 mol. for a duration of 24 hours. Then the mixes containing RCA were incorporated with various amounts of SF, with 3%, 6%, and 9% of the binder's weight. These mixes were used to measure the Marshall characteristics and evaluate moisture resistance using indirect tensile strength, compresive strength tests. A thermal camera was employed to assess the modified asphalt's homogeneity. The thermal images demonstrated that after 30 minutes of mixing SF at 160 ℃, the asphalt cement has been uniformly distributed SF particles, as evident by the colour convergence. The findings revealed that the addition of RCA to the asphalt mix increased TSR and IRS levels by 6.68% and 8.93%, respectively, at RCA 30% compared to the original mixture. Additionally, the inclusion of silica fume led to a rise in TSR and IRS until 6%, after which there was a drop, but the levels remained above the original mix. The use of 30% RCA ratio with 6% silica fume resulted in the highest improvement in TSR and IRS, with a rise of 13.72% and 14.13%, respectively. [ABSTRACT FROM AUTHOR]

The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi's experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative autonomous survivor detection pipeline tailored for low‐cost micro aerial vehicles (MAVs) operating in post‐disaster indoor environments. This consists of three main components: (1) a novel pipeline for survivor geotagging, which includes autonomous navigation, mapping, and detection of survivors using thermal images; (2) a navigation strategy to ensure complete thermal scanning coverage for survivor detection using low‐cost commercial grade thermal camera; and (3) robust and accurate survivor detection using YOLOv8x and thermal imaging. To demonstrate the effectiveness of the proposed framework, first, the autonomous navigation algorithm is simulated in Robotic Operating System (ROS) and experimentally validated under different layouts. Second, the YOLOv8x algorithm is pretrained and achieves high accuracy. Finally, a real‐world implementation was conducted with partially covered survivors in a simulated post‐disaster environment. The results demonstrated the proposed pipeline can accurately map the layout of the environment and identify all survivors. This study demonstrates that affordable MAVs with basic thermal cameras can be effectively used to geotag survivors to support rescue missions during post‐disaster events. [ABSTRACT FROM AUTHOR]

Owing to their consistently emerging applications in modern photonics, highly sensitive and rapid mid‐infrared (MIR) photodetectors, operating at high temperatures, are of great significance. Herein, a novel PbTe/Ge heterostructure is introduced. Notably, the discovery of a 2D electron gas (2DEG) system on the surface of the PbTe layer is experimentally identified for the first time. A high‐performance PbTe/Ge heterostructure MIR photodetector utilizing a plasmonic photovoltaic effect is developed by employing asymmetric electrodes. The photodetecting device demonstrates an exceptionally high detectivity of 1.1 × 1011 Jones along with a short response time of 15 ns at room temperature. Additionally, the proposed plasmonic photovoltaic mechanism of the device is investigated and mainly ascribed to the propagating plasmons, generated by the coupling of the 2DEG with incident MIR photons. Moreover, a linear detector array (LDA) of PbTe/Ge is demonstrated for a thermal imaging application, which exhibits promising high‐quality real‐time imaging via the 2D photodetector arrays on Ge‐based integrated circuits. This work opens up a new way for the development of next‐generation, advanced MIR photonic devices and integrated photonic systems. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) focuses on distinguishing the most conspicuous objects in the scene. However, most related works are based on RGB images, which lose massive useful information. Accordingly, with the maturity of thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and more attention. Thermal infrared images carry important information which can be used to improve the accuracy of SOD prediction. To accomplish it, the methods to integrate multi-modal information and suppress noises are critical. In this paper, we propose a novel network called Interactive Context-Aware Network (ICANet). It contains three modules that can effectively perform the cross-modal and cross-scale fusions. We design a Hybrid Feature Fusion (HFF) module to integrate the features of two modalities, which utilizes two types of feature extraction. The Multi-Scale Attention Reinforcement (MSAR) and Upper Fusion (UF) blocks are responsible for the cross-scale fusion that converges different levels of features and generate the prediction maps. We also raise a novel Context-Aware Multi-Supervised Network (CAMSNet) to calculate the content loss between the prediction and the ground truth (GT). Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods. [ABSTRACT FROM AUTHOR]

Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion (IVF) aims to generate a fused image with important thermal target and texture information from infrared and visible images. However, the existing advanced fusion methods have the problem of insufficient extraction of visible image details, and the fused image is not natural and does not conform to human visual perception. To solve this problem, we propose an effective infrared and visible image fusion framework inspired by the idea of multi-exposure fusion. First, we design an adaptive visible light exposure adjustment module to enhance the low-brightness pixel area information in the visible image to obtain an adaptive exposure image. Secondly, three feature weight maps of the input infrared, visible light and adaptive exposure images are extracted through the multi-weight feature extraction module: DSIFT map, saliency map and saturation map, and then the feature weight maps are optimized through the Mutually Guided Image Filtering (MuGIF). Then, we use the Gaussian and Laplacian pyramids to decompose and reconstruct the feature weight map and input image to obtain the pre-fused image. Finally, to further enhance the contrast of the pre-fused image, we use a Fast Guided Filter to enhance the pre-fused image to obtain the final fusion result. Qualitative and quantitative experiments show that the proposed method exhibits better fusion performance on public datasets compared with 11 state-of-the-art methods. In addition, this method can retain more visible image details, and the fusion result is more natural. Our code is publicly available at https://github.com/VCMHE/MWF_VIF. [ABSTRACT FROM AUTHOR]

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study's training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection. [ABSTRACT FROM AUTHOR]

This study aims to use beeswax, a readily available and cost-effective organic material, as a novel phase change material (PCM) within blends of low-density polyethylene (LDPE) and styrene-b-(ethylene-co-butylene)-b-styrene (SEBS). LDPE and SEBS act as support materials to prevent beeswax leakage. The physicochemical properties of new blended phase change materials (B-PCM) were determined using an X-ray diffractometer and an infrared spectrometer, confirming the absence of a chemical reaction within the materials. A scanning electron microscope was used for microstructural analysis, indicating that the interconnection of the structure allowed better thermal conductivity. Thermal gravimetric analysis revealed enhanced thermal stability for the B-PCM when combined with SEBS, especially within its operating temperature range. Analysis of phase change temperature and latent heat with differential scanning calorimetry showed no major difference in the melting point of the various PCM blends created. During the melting/solidification process, the B-PCMs possess excellent performance as characterized by W70/P30 (112.45 J.g−1) > W70/P20/S10 (94.28 J.g−1) > W70/P10/S20 (96.21 J.g−1) of latent heat storage. Additionally, the blends tend to reduce supercooling compared to pure beeswax. During heating and cooling cycles, the B-PCM exhibited minimal leakage and degradation, especially in blends containing SEBS. In comparison to the rapid temperature drop observed during the cooling process of W70/P30, the temperature decline of W70/P30 was slower and longer, as demonstrated by infrared thermography. The addition of LDPE to the PCM reduced melting time, indicating an improvement in the thermal energy storage reaction time to the demand. According to the obtained findings, increasing the SEBS concentration in the composite increased the thermal stability of the resulting PCM blends significantly. Despite the challenges mentioned earlier, SEBS proved to be an effective encapsulating material for beeswax, whereas LDPE served well as a supporting material. Leak tests were performed to find the ideal mass ratio, and weight loss was analyzed after multiple cycles of cooling and heating at 70 °C. The morphology, thermal characteristics, and chemical composition of the beeswax/LDPE/SEBS composite were all examined. Beeswax proves to be a highly effective phase change material for storing thermal energy within LDPE/SEBS blends. [ABSTRACT FROM AUTHOR]

Thermal infrared imaging could detect hidden faults in various types of high-voltage power equipment, which is of great significance for power inspections. However, there are still certain issues with thermal-imaging-based abnormal heating detection methods due to varying appearances of abnormal regions and complex temperature interference from backgrounds. To solve these problems, a contour-based instance segmentation network is first proposed to utilize thermal (T) and visual (RGB) images, realizing high-accuracy segmentation against complex and changing environments. Specifically, modality-specific features are encoded via two-stream backbones and fused in spatial, channel, and frequency domains. In this way, modality differences are well handled, and effective complementary information is extracted for object detection and contour initialization. The transformer decoder is further utilized to explore the long-range relationships between contour points with background points, and to achieve the deformation of contour points. Then, the auto-encoder-based reconstruction network is developed to learn the distribution of power equipment using the proposed random argument strategy. Meanwhile, the UNet-like discriminative network directly explores the differences between the reconstructed and original image, capturing the deviation of poor reconstruction regions for abnormal heating detection. Many images are acquired in transformer substations with different weathers and day times to build the datasets with pixel-level annotation. Several extensive experiments are conducted for qualitative and quantitative evaluation, while the comparison results fully prove the effectiveness and robustness of the proposed instance segmentation method. The practicality and performance of the proposed abnormal heating detection method are evaluated on image patches with different kinds of insulators. [ABSTRACT FROM AUTHOR]

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU. [ABSTRACT FROM AUTHOR]

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites. [ABSTRACT FROM AUTHOR]

Textile-based thermoelectric (TE) devices are being investigated to power smart textiles autonomously. While previous research has focused on a solid system where the required junctions are fabricated into the device, there has been limited attention given to replacing these TE systems reliably. This work looks at a newer approach to the construction and demonstration of a wearable thermoelectric structure that employs three-dimensional knitted spacers to increase the temperature difference where the TE junctions are detachable and disposable. This system features positive and negative junctions which can be removed while maintaining its excellent voltage generation in low ΔT and good Seebeck coefficients. A mathematical model simulates the potential energy outputs and maximum power points generated, which can be used to increase the device's performance for future wearable sensing applications. [ABSTRACT FROM AUTHOR]

The Multisensor Multiresolution Technique (MMT) is applied to unmixed thermal images from ASTER (90 m), using 30 m resolution images from Landsat 8-9 reflective channels. The technique allows for the retrieval of thermal radiance values of the features identified in the high-resolution reflective images and the generation of a high-resolution radiance image. Different alternatives of application of MMT are evaluated in order to determine the optimal methodology design: performance of the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means classification algorithms, with different initiation numbers of clusters, and computation of contributions of each cluster using moving windows with different sizes and with and without weight coefficients. Results show the K-means classification algorithm with five clusters, without matrix weighting, and utilizing a 5 × 5 pixel window for synthetic high-resolution image reconstruction. This approach obtained a maximum R2 of 0.846 and an average R2 of 0.815 across all cases, calculated through the validation of the synthetic high-resolution TIR image generated against a real Landsat 8-9 TIR image from the same area, same date, and co-registered. These values imply a 0.89% improvement regarding the second-best methodology design (K-means with five starting clusters with 7 × 7 moving window) and a 410.25% improvement regarding the worst alternative (K-means with nine initial clusters, weighting, and 3 × 3 moving window). [ABSTRACT FROM AUTHOR]

Thermal camouflage technology can effectively hide an object by lowering its infrared emissivity and reducing its surface temperature. So far, it has been a challenge to realize thermal camouflage at high-temperatures with a single material. Here, we report a Ti 3 AlC 2 MAX phase material that exhibits excellent thermal camouflage properties from room temperature to 500 °C, and maintains a low emissivity of 0.25 at 500 °C, which is difficult to achieve for most metals due to the rapid increase in emissivity caused by oxidation. The thermal camouflage mechanism of Ti 3 AlC 2 was analyzed on the basis of experimental results and first-principles calculations, providing a feasible approach to select other MAX phases (such as Ti 3 SiC 2 , Cr 2 AlC, and Ti 2 AlC) with attractive thermal camouflage performance. Ti 3 AlC 2 and other MAX phases are promising candidates for thermal camouflage applications due to their low emissivity, high-temperature stability, good oxidation resistance and ease of fabrication. [ABSTRACT FROM AUTHOR]

Introduction: Infrared thermography is gaining attention in the field of sports medicine and performance. This study investigated the effects of static and dynamic warm-ups and a 90° change of direction (COD) exercise on the thermal response of the knee. Methods: Thermograms were collected using the FlIR E54 Imaging Camera from 85 healthy young adults, 46 men and 39 women, aged 20-31 years. The participants were divided in two groups, static and dynamic warm-up. Four thermograms were taken: baseline (T0), warm-up (T1), COD (T2), and rest (T3). Four regions of interest (ROIs) of the knee were analyzed: anterior upper half (AUH), anterior lower half (ALH), posterior upper half (PUH), and posterior lower half (PLH). Mixed ANOVA with the Bonferroni-Holm test and independent t-test were used for pairwise comparison and to spot differences between the right and left knees at T1 and T2 and at T0 between men and women, respectively. Results: The mixed ANOVA was significant for time points (p< 0.001) in all the ROIs and for the stretching/temperature interaction with different levels of significance. The t-test results for the right and left knees at T1 and T2 were not significant. The temperature in the static warm-up group followed a decrease at T1, a subsequent decrease at T2, and a recovery similar to the baseline at T3, in the ALH in men and women and in the PUH only in men. Conclusion: Static stretching was more suitable for preparing the knee for the COD exercise than the dynamic one in terms of the thermal response. [ABSTRACT FROM AUTHOR]

The main objective of this work was to investigate the influence of environmental factors, including air temperature (AT), relative humidity (RH) and temperature–humidity index (THI), on the difference between rectal temperature (RT) and eye temperature (ET) of dairy cows. The monitoring of these parameters is important for the further possible introduction of digitalization in animal welfare, especially in dairy cattle. The mean calculated difference between rectal temperature and eye temperature (RT–ET) was 1.5 °C. The average value of AT was 16.4 °C, and the average value of RH was 59.2%. The average value of THI was 60.4. The results of the study showed that, for the temperature difference ET-RT, a low degree of correlation was found both with temperature and with the temperature-humidity index THI (R = 0.22; R = 0.23). However, the observed temperature difference of the animal (ET-RT) showed a moderate degree of dependence on the relative humidity of air (R = −0.32). Although the positive correlation coefficient for AT and THI points to the higher criticality of summer measurements, the negative correlation coefficient for RH supports the use of infrared thermography for determining the temperature of animals even in a moister barn environment. [ABSTRACT FROM AUTHOR]

Infrared binocular cameras, leveraging their distinct thermal imaging capabilities, are well-suited for visual measurement and 3D reconstruction in challenging environments. The precision of camera calibration is essential for leveraging the full potential of these infrared cameras. To overcome the limitations of traditional calibration techniques, a novel method for calibrating infrared binocular cameras is introduced. By creating a virtual target plane that closely mimics the geometry of the real target plane, the method refines the feature point coordinates, leading to enhanced precision in infrared camera calibration. The virtual target plane is obtained by inverse projecting the centers of the imaging ellipses, which are estimated at sub-pixel edge, into three-dimensional space, and then optimized using the RANSAC least squares method. Subsequently, the imaging ellipses are inversely projected onto the virtual target plane, where its centers are identified. The corresponding world coordinates of the feature points are then refined through a linear optimization process. These coordinates are reprojected onto the imaging plane, yielding optimized pixel feature points. The calibration procedure is iteratively performed to determine the ultimate set of calibration parameters. The method has been validated through experiments, demonstrating an average reprojection error of less than 0.02 pixels and a significant 24.5% improvement in calibration accuracy over traditional methods. Furthermore, a comprehensive analysis has been conducted to identify the primary sources of calibration error. Ultimately, this achieves an error rate of less than 5% in infrared stereo ranging within a 55-m range. [ABSTRACT FROM AUTHOR]

The use of infrared thermography (IRT) for non-destructive testing (NDT) of large aerospace composites, such as carbon fibre reinforced polymer (CFRP), is often limited by the narrow field of view infrared cameras. This paper experimentally investigates the big-sized CFRP specimens with simulated debonding defects using a robot-based dynamic optics scanning infrared thermography (RDOS-IRT) method to meet these issues. Then, static pulse IRT and RDOS-IRT were performed, and the processing performance of different algorithms (pulsed phase thermography, PPT; total harmonic distortion, THD; matched filtering, MF; and supervised dimensionality reduction, SDR) on pulsed IRT was discussed. A combination of pseudo-static matrix reconstruction (FSMR) algorithm and static image sequence processing algorithm is innovatively adopted to improve the defect detection performance of RDOS-IRT results. The experiments show that the RDOS-IRT method with FSMR and static image sequence processing algorithm is effective; moreover, it has almost a similar detection ability compared to the traditional static pulse IRT method in practical applications and saves more time. Results show that the signal-to-noise ratio (SNR) of the repeated defects is not differentiated, and the detection effect is similar. Therefore, the RDOS-IRT method is a reliable tool for NDT tasks on big-sized CFRP specimens and can be practically applied in an industrial setting for high detection efficiency. [ABSTRACT FROM AUTHOR]

Organic electrochemical transistors (OECTs) underpin a range of emerging technologies, from bioelectronics to neuromorphic computing, owing to their unique coupling of electronic and ionic charge carriers. In this context, various OECT systems exhibit significant hysteresis in their transfer curve, which is frequently leveraged to achieve non-volatility. Meanwhile, a general understanding of its physical origin is missing. Here, we introduce a thermodynamic framework that readily explains the emergence of bistable OECT operation via the interplay of enthalpy and entropy. We validate this model through temperature-resolved characterizations, material manipulation, and thermal imaging. Further, we reveal deviations from Boltzmann statistics for the subthreshold swing and reinterpret existing literature. Capitalizing on these findings, we finally demonstrate a single-OECT Schmitt trigger, thus compacting a multi-component circuit into a single device. These insights provide a fundamental advance for OECT physics and its application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock new paradigms of information processing. Understanding the physical origin on hysteresis in organic electrochemical transistors is important for achieving non-volatility. Here, the authors report a thermodynamic framework that explains the emergence of bistable device operation via the interplay of enthalpy and entropy. [ABSTRACT FROM AUTHOR]