Search Results (239)

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

The goal of this study was to develop an automated monitoring system based on infrared thermography (IRT) for the detection of group-housed pig ears temperature. The aim in the first part of the study was to recognize pigs’ ears by using neural network analysis (SwinStar-YOLO). In the second part of the study, the goal was to automatically extract the maximum and average values of the temperature in the ear region using morphological image processing and a temperature matrix. Our dataset (3600 pictures, 10,812 pig ears) was processed using 5-fold cross-validation before training the ear detection model. The model recognized pigs’ ears with a precision of 93.74% related to threshold intersection over union (IoU). Correlation analysis between manually extracted and algorithm-derived ear temperatures from 400 pig ear samples showed coefficients of determination ($R^2$) of 0.97 for maximum and 0.88 for average values. This demonstrates that our proposed method is feasible and reliable for automatic pig ear temperature monitoring, serving as a powerful tool for early health warning. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

Water treadmill (WT) exercise is used for horses’ rehabilitation and training. Given that each training needs to be individualized for each horse, the goal is to assess whether infrared thermography (IRT) can serve as a non-invasive tool for daily monitoring of individual training and rehabilitation progress in horses undergoing WT exercise. Fifteen Polish Warmblood school horses were subjected to five WT sessions: dry treadmill, fetlock-depth water, fetlock-depth water with artificial river (AR), carpal-depth water, and carpal-depth water with AR. IRT images, collected pre- and post-exercise, were analyzed for the mean temperature (Tmean) and maximal temperature (Tmax) across 14 regions of interest (ROIs) representing the body surface overlying specific superficial muscles. While on a dry treadmill, Tmean and Tmax increased post-exercise in all ROIs; wetting of the hair coat limited surface temperature analysis in ROIs annotated on limbs. Tmax over the m. brachiocephalicus, m. trapezius pars cervicalis, m. triceps brachii, and m. semitendinosus increased during walking in carpal-depth water, which therefore may be suggested as an indirect indicator of increased activity related to forelimb protraction and flexion–extension of the limb joints. Tmax over the m. latissimus dorsi and m. longissimus increased during carpal-depth WT exercise with active AR mode, which may be suggested as an indicator of increased workload including vertical displacement of the trunk. Full article

Background/Objectives: To investigate acute caffeine (CAF: 375 mg, ≈4.8 mg/kg body mass) effects on energy expenditure (EE) and substrate kinetics during high-intensity interval exercise in individuals with high (HBAT) versus low (LBAT) brown adipose tissue activity using time-trend polynomial modeling. Methods: This is a randomized, double-blind crossover study in which 35 highly-trained males [HBAT-CAF, HBAT-PLA (Placebo), LBAT-CAF, LBAT-PLA] performed 30-min treadmill HIIE. Infrared thermography (IRT) assessed BAT activity by measuring supraclavicular skin temperature (SST). Breath-by-breath ergospirometry measured EE (kcal/min) and carbohydrate (CHO), lipid (LIP), and protein (PTN) oxidation. We applied second- and third-order polynomial regression models to depict the temporal trajectories of metabolic responses. Results: HBAT groups showed 25% higher sustained EE versus LBAT (p < 0.001), amplified by CAF. CHO oxidation exhibited biphasic kinetics: HBAT had 40% higher initial rates (0.75 ± 0.05 vs. 0.45 ± 0.04 g/min; p < 0.001) with accelerated decline (k = −0.21 vs. −0.15/min; p = 0.01). LIP oxidation peaked later in LBAT (40 vs. 20 min in HBAT), with CAF increasing oxidation by 18% in LBAT (p = 0.01). HBAT-CAF uniquely showed transient PTN catabolism (peak: 0.045 g/min at 10 min; k = −0.0033/min; p < 0.001). Conclusions: BAT status determines EE magnitude and substrate-specific kinetic patterns, while CAF exerts divergent modulation, potentiating early glycogenolysis in HBAT and lipolysis in LBAT. The HBAT-CAF synergy triggers acute proteolysis, revealing BAT-mediated metabolic switching. Full article

Infrared thermography (IRT) is explored as a non-invasive method for indirectly measuring parameters related to animal performance and welfare. This study investigates a feature extraction method termed the “thermal signature” (TS), a descriptor vector derived from the temperature matrix of an animal’s body surface, representing the percentage distribution of temperatures within predefined ranges. The TS, combined with environmental data, serves as a predictor attribute for machine learning-based classifier models to assess heat stress levels. The methodology was applied to a dataset collected from two groups of five dairy calves housed in a climate-controlled chamber and exposed to two artificial heat waves over 13 days. Data, including IRT measurements, respiratory rate (RR), rectal temperature (RT), and environmental variables, were collected five times daily (from 6 a.m. to 10 p.m., every four hours). Classifier models were developed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) algorithms. The RF models based on RR achieved the highest accuracies, 94.1% for two heat stress levels and 80.3% for three heat stress levels, using TS configurations with six temperature ranges. The integration of TS with machine learning-based models demonstrates promising results for developing or enhancing classifiers of heat stress levels in dairy calves. Full article

In this article, a multidisciplinary methodological approach for studying a wooden panel painting is applied. The theoretical framework, within which this research has arisen, is the application of state-of-the-art non-destructive techniques for addressing issues concerning the constituting parts and composing materials of the artwork. Hereby, a post-Byzantine icon was studied, which was dated back to 1836. It is a painting executed on a wooden panel, with a decorated wooden frame attached. The artifact was thoroughly investigated through the application of infrared thermography (IRT), multispectral imaging (MSI), and macroscopic X-ray fluorescence spectrometry (MA-XRF). These analyses provided crucial information about the verso of the painting (i.e., the wooden panel and the frame) and allowed for the revelation of important details of the recto of the painting, which were not visible due to the presence of an old, decayed varnish. Additionally, through the detailed mapping of the distribution of various chemical elements on the recto of the painting and the frame, it was possible to identify the materials used and techniques employed. It is therefore shown that, when combined, the non-destructive methodologies in consideration can provide adequate information referring to the materiality and state of preservation of panel paintings, permitting the conservator to proceed to a tailored conservation treatment. Full article

The determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work. Full article

Non-destructive testing (NDT) methods are critical for evaluating the structural integrity of and detecting defects in composite materials across industries such as aerospace and renewable energy. This review examines the recent trends and successful implementations of NDT approaches for composite materials, focusing on articles published between 2015 and 2025. A systematic literature review identified 120 relevant articles, highlighting techniques such as ultrasonic testing (UT), acoustic emission testing (AET), thermography (TR), radiographic testing (RT), eddy current testing (ECT), infrared thermography (IRT), X-ray computed tomography (XCT), and digital radiography testing (DRT). These methods effectively detect defects such as debonding, delamination, and voids in fiber-reinforced polymer (FRP) composites. The selection of NDT approaches depends on the material properties, defect types, and testing conditions. Although each technique has advantages and limitations, combining multiple NDT methods enhances the quality assessment of composite materials. This review provides insights into the capabilities and limitations of various NDT techniques and suggests future research directions for combining NDT methods to improve quality control in composite material manufacturing. Future trends include adopting multimodal NDT systems, integrating digital twin and Industry 4.0 technologies, utilizing embedded and wireless structural health monitoring, and applying artificial intelligence for automated defect interpretation. These advancements are promising for transforming NDT into an intelligent, predictive, and integrated quality assurance system. Full article

Infrared thermography (IRT), a widely used nondestructive testing method, is commonly employed to identify moisture in historic walls. However, its reliance on manual interpretation by experts makes the process both time-consuming and costly. This study addresses the challenge of detecting wall moisture; this issue is closely linked to the deterioration of cultural heritage structures. This study focuses on the brick walls of the Tainan Confucian Temple, the oldest Confucian temple in Taiwan. The targeted are walls coated with lime plaster mixed with red mineral pigments, a traditional finish that gives the temple its distinctive red appearance. This study proposes a system to automatically identify wall areas, mark low-temperature zones, and determine the presence and distribution of moisture. Visible and infrared thermal images of these walls are captured and preprocessed to normalize the size and enhance the features. Finally, two convolutional neural network (CNN) models are trained in this study: one for identifying wall regions and the other for detecting low-temperature areas. The proposed method achieves an accuracy of 91.18% in detecting wall moisture, representing a 24.05% improvement over conventional object recognition techniques, the accuracy of which is 73.5%. In addition, this method requires only 3 s to detect the wall moisture, representing a 99.92% reduction in processing time compared to the conventional manual method. This method not only provides a fast and objective method for assessing moisture in lime-plastered heritage walls but also significantly enhances the efficiency of restoration efforts. This method can be applied to similar wall structures in other Confucian temples, offering broad potential for heritage conservation. Full article

Background/Objectives: Chronic low back pain (CLBP) is a prevalent condition that significantly impacts the aging population. Among non-invasive assessment tools, infrared thermography (IRT) has been highlighted as a radiation-free method to evaluate thermal variations in the lumbar region. However, its applicability in clinical practice and correlation with functional and pain-related parameters remain unclear. This study aimed to analyze the thermal profile of the lumbar region in elderly individuals with CLBP and explore potential correlations between lumbar temperature patterns and clinical factors such as pain intensity and functional capacity. Methods: A cross-sectional observational study was performed in an outpatient setting. The population included thirty-one elderly individuals diagnosed with CLBP. IRT was used to assess the lumbar temperature distribution, including participants who reported pain radiating to the lower limbs. Pain intensity was measured using a numerical rating scale (0–10). The functional assessments included spine mobility tests and validated questionnaires evaluating clinical characteristics. Results: No significant differences in lumbar temperature patterns were observed among the participants. Additionally, no correlation was found between pain intensity and functional capacity based on a thermographic analysis. Nonetheless, individuals reporting lower fatigue levels and those with a higher body mass index (BMI) were generally associated with cooler thermal readings on the lumbar region’s thermographic maps. Conclusions: These findings suggest that IRT may require methodological refinements, including optimized technical specifications and image acquisition protocols, to enhance its applicability in assessing CLBP. Indeed, IRT might not be the most effective tool for evaluating pain-related thermal changes in elderly populations. Further research is needed to clarify its role in clinical assessments. Full article

This paper proposes an automated system integrating infrared thermography (IRT) and computer vision for air leak detection and localization in end-of-line (EOL) testing stations. This system consists of (1) a leak tester for detection and quantification of leaks, (2) an infrared camera for real-time thermal image acquisition; and (3) an algorithm for automatic leak localization. The python-based algorithm acquires thermal frames from the camera’s streaming video, identifies potential leak regions by selecting a region of interest, mitigates environmental interferences via image processing, and pinpoints leaks by employing pixel intensity thresholding. A closed circuit with an embedded leak system simulated relevant leakage scenarios, varying leak apertures (ranging from 0.25 to 3 mm), and camera–leak system distances (0.2 and 1 m). Results confirmed that (1) the leak tester effectively detected and quantified leaks, with larger apertures generating higher leak rates; (2) the IRT performance was highly dependent on leak aperture and camera–leak system distance, confirming that shorter distances improve localization accuracy; and (3) the algorithm localized all leaks in both lab and industrial environments, regardless of the camera–leak system distance, mostly achieving accuracies higher than 0.7. Overall, the combined system demonstrated great potential for long-term implementation in EOL leakage stations in the manufacturing sector, offering an effective and cost-effective alternative for manual inspections. Full article

The growing application of Fiber-Reinforced Polymer (FRP) composites in rehabilitating deteriorating concrete infrastructure underscores the need for reliable, cost-effective, and automated nondestructive testing (NDT) methods. This review provides a comprehensive analysis of existing and emerging NDT techniques used to assess externally bonded FRP (EB-FRP) systems, emphasizing their accuracy, limitations, and practicality. Various NDT methods, including Ground-Penetrating Radar (GPR), Phased Array Ultrasonic Testing (PAUT), Infrared Thermography (IRT), Acoustic Emission (AE), and Impact–Echo (IE), are critically evaluated in terms of their effectiveness in detecting debonding, voids, delaminations, and other defects. Recent technological advancements, particularly the integration of artificial intelligence (AI) and machine learning (ML) in NDT applications, have significantly improved defect characterization, automated inspections, and real-time data analysis. This review highlights AI-driven NDT approaches such as automated crack detection, hybrid NDT frameworks, and drone-assisted thermographic inspections, which enhance accuracy and efficiency in large-scale infrastructure assessments. Additionally, economic considerations and cost–performance trade-offs are analyzed, addressing the feasibility of different NDT methods in real-world FRP-strengthened structures. Finally, the review identifies key research gaps, including the need for standardization in FRP-NDT applications, AI-enhanced defect quantification, and hybrid inspection techniques. By consolidating state-of-the-art research and emerging innovations, this paper serves as a valuable resource for engineers, researchers, and practitioners involved in the assessment, monitoring, and maintenance of FRP-strengthened concrete structures. Full article

The current study investigates the thermal, metallurgical, and mechanical results in similar and dissimilar weldments of Monel 400 and AISI 316L. Infrared thermography (IRT) was employed to record thermal cycles, while X-ray diffraction (XRD) was used to analyze the residual stresses post-welding. Mechanical properties were assessed through tensile and microhardness tests, and microstructural evolution was examined using energy-dispersive spectroscopy (EDS) and scanning electron microscopy (SEM). IRT results showed peak temperatures of 1788 °C for Monel 400 and 1750 °C for AISI 316L. Residual stress analysis revealed compressive stresses of 293 MPa in dissimilar welds, compared to 235 MPa in Monel 400 and tensile stresses of 57 MPa in AISI 316L. Ultimate tensile strength (UTS) values were 543 MPa for dissimilar welds, 533 MPa for Monel 400, and 556 MPa for AISI 316L, with corresponding microhardness values of 207 HV, 203 HV, and 168 HV, respectively. Microstructural analysis identified coarse Ni-Cu phases in the Monel 400 heat-affected zone (HAZ), austenitic structures in AISI 316L, and intermetallic compounds in dissimilar welds. The findings highlight the impact of thermal distribution, residual stress, and microstructural evolution on weld performance, providing insights into optimized welding parameters for improved joint integrity and mechanical properties. Full article

Cycling comfort significantly impacts both enjoyment and performance, with discomfort potentially leading to injuries. Traditional bike-fitting methods, while effective for biomechanical adjustments, often overlook physiological responses such as pressure distribution and asymmetry. Infrared thermography (IRT), a non-invasive technique analyzing surface temperature variations, offers the potential to enhance bike fitting by identifying pressure points, asymmetries, and discomfort zones. This review evaluates the application of IRT in bike fitting, highlighting its ability to provide objective, real-time insights into cyclist comfort and injury prevention. However, limitations such as variability in thermographic protocols and the lack of standardized methodologies necessitate further research. By addressing these gaps, IRT could transform bike fitting into a more precise, personalized, and effective practice for cyclists across all levels. Full article