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Assessing the feather condition of broilers is crucial for monitoring the animal welfare status and detecting the occurrence of feather pecking activities. Currently, the feather condition of individual broilers is manually scored by trained experts. To provide a more objective and efficient tool for feather condition scoring, a novel deep learning-based model, named Feather Condition Scoring Network (FCS-Net), was proposed based on RGB and thermal infrared images. The FCS-Net model combined the ResNet18 architecture with the proposed Dense Feature Fusion (DFF) module, which can effectively learn the feature mapping relationship between RGB and thermal infrared images. Before inputting the images into the network, an image registration process was conducted to align the RGB and thermal infrared images. The results showed that the FCS-Net model had a good performance for feather condition scoring, with the Accuracy of 97.02%, the Precision of 96.99%, the Recall of 97.04%, the F 1 of 97.01%, and the Inference speed of 15.34 fps. Compared to the ResNet18_RGB model, which only utilise RGB images, the FCS-Net model showed notable improvements in Accuracy by 4.02%, Precision by 3.90%, Recall by 4.08%, and F 1 by 4.01%. Moreover, it was observed that the FCS-Net model focused more on the back region of the broilers through heatmap visualization. Furthermore, the algorithm was compared with six typical image recognition algorithms including VGG16, ResNet18, SE-ResNet18, DenseNet121, Mobilenet_V2, and Shufflenet_V2_ x 1_0, as well as the state-of-the-art (SOTA) feather condition assessment methods. The results showed that the FCS-Net model achieved better performance than the six algorithms and the SOTA feather condition assessment methods. This study provided a valuable reference for automated monitoring of feather condition scoring of broilers in smart farming. • Proposing a Feather Condition Scoring Network (FCS-Net) for broilers. • Introducing Dense Feature Fusion module to integrate RGB and thermal images. • FCS-Net model surpassed both RGB or thermal image-based models. • FCS-Net provided a more accurate and efficient tool for farm automated monitoring. [ABSTRACT FROM AUTHOR]

A loop heat pipe (LHP) is an efficient method of conserving energy in data center cooling applications. In scenarios where the installation is constrained by height or distance limitations, pump driving is needed. This paper examines the performance changes induced by a gas pump both experimentally and theoretically. An adjustable, oil-free linear compressor is utilized as a gas pump. The evaporator is a finned-tube heat exchanger and the condenser is a water-cooled plate heat exchanger. When the filling ratio of the working fluid is insufficient, employing a gas pump can enhance the heat transfer performance. However, when the filling ratio of the working fluid is sufficient, while the gas pump can increase the flowrate of the working fluid, the heat transfer rate (HTR) does not change significantly. In fact, it may reduce the energy efficiency ratio of the heat pipe. Infrared thermography has proven to be an efficient tool for estimating the area ratio of different zones within the evaporator, which is crucial for the output regulation of the compressor. The area ratio of the two-phase zone is nearly linear to the HTR. Through the establishment of a physical model of a gas-pump-driven loop heat pipe (GPLHP), the impacts of the LHP size and gas pump operation on the heat transfer performance are analyzed. It is found that the gas pump can extend the application range of the LHP, although it has a minimal impact on the maximum HTR. How to select a gas pump for an LHP is discussed. [ABSTRACT FROM AUTHOR]

In this work, a hydrometallurgical process for the recycling of diamond core drilling crowns by means of aqua regia leaching and subsequent alkali leaching was investigated. This investigation continues a previous study in which nitric acid was used for the acid leaching phase. In the current study, higher tungsten recovery was achieved, reaching 98.2%, which is an improvement of about 1.5%. Another advancement of this study was the high Co recovery (97.21%) and the high purity of the tungsten trioxide obtained, comparable to the previously proposed technological process. Furthermore, a novel laboratory method for testing recycled diamond drilling crowns based on infrared thermography was introduced. Although this innovative approach is not the most accurate, it is fast and cost-effective and provides valuable results before the actual field test is conducted as a final evaluation. In addition, the infrared thermography method offers the advantage of non-destructive testing, ensuring that the diamond drilling crowns can be assessed without compromising their structural integrity. Other instrumental methods used to characterize the products and intermediates were X-ray diffraction (XRD), scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDS), and laser desorption ionization mass spectrometry (LDI-MS). The analytical method for the concentrations in all working solutions was ICP-AES. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the effect of using naturally shaded pastures on scrotal thermoregulatory capacity, testicular echotexture, and sperm morphology of Nelore (Bos indicus) and Canchim (5/8 Bos taurus x 3/8 Bos indicus) bulls in a tropical climate region. Sixty-four adult Nelore and Canchim bulls were used, equally allocated in Full Sun (FS, n = 32) or Crop-Livestock-Forestry (CLF, n = 32) pasture systems. During five consecutive climate seasons, the bulls underwent monthly breeding soundness evaluations and the biometeorological variables in the systems were continuously monitored. Microclimate was significantly different between systems. CLF system had lower BGHI than FS throughout the experimental period. No triple interaction (Season x Breed x Treatment, P > 0.05) was observed for any of the variables. Animals in CLF showed lower body temperature in Summer (FS:39.41 ± 0.05 vs. CLF:39.30 ± 0.05 °C; P = 0.005) and in Autumn (FS:39.54 ± 0.05 vs. CLF:39.35 ± 0.05 °C; P = 0.005). Access to shading did not determine differences in the evolution of scrotal biometry, temperatures, and scrotal thermal gradients (P > 0.05). Regardless of breed, animals in CLF showed greater right testicular volume (FS:247.5 ± 5.7 vs. CLF:259.0 ± 5.7 cm³; P < 0.05), more suitable parenchyma echotexture, and fewer microlithiasis spots in the Spring and Summer. Testosterone concentration was higher in FS (FS:2.6 ± 0.2 vs. CLF:2.1 ± 0.2 ng/mL; P = 0.035). Canchim bulls presented higher total sperm defects during the Autumn and Winter (P = 0.010), but the total defects levels for Canchim and Nelore bulls were in normal range for adult bulls. Thus, the natural shade in CLF system was effective in improving the microclimate of pastures and minimizing adverse environmental effects on some reproductive features of interest in beef cattle. [ABSTRACT FROM AUTHOR]

Non-destructive testing of cooling passages has attracted a considerable interest due to the critical rule of these segments in the performance and endurance of different parts. In this research, the performance of active infrared thermography in the non-destructive inspection of cooling passages located inside the metallic bases has been investigated. Water vapour was selected as an excitation fluid so as to create the thermal contrast. Specimen material, hole depth, hole diameter and injection pressure were selected as the input parameters effecting on the thermal contrast. The results obtained via statistical analysis revealed that the hole depth and injection pressure have the noticeable influence on the dimensionless output temperature recorded with thermal camera. A 50% decrease in dimensionless temperature contrast has been revealed when the hole depth has become four times. Besides, a similar increase in injection pressure, elevated the above-mentioned parameter almost 20%. In contrast, the parameters of specimen material and hole diameter specified no perceptible effects. Moreover, infrared thermography with water-vapour excitation proved proper capability in detection of 60% and 80% blockage located inside the holes. [ABSTRACT FROM AUTHOR]

Condition assessment of concrete bridge decks is critical for ensuring structural integrity and public safety, particularly as infrastructure ages. Traditionally, such assessments are made using destructive techniques, such as coring through concrete to collect samples for analysis. However, these invasive methods create weak points within the structure and risk damaging essential components, such as cutting through rebars. This paper explores the use of three non-destructive testing (NDT) methods—electrical resistivity (ER), impact echo (IE), and infrared thermography (IRT)—to evaluate the structural health of concrete bridge decks and overlays. These techniques are tested individually and in combination through a mock-up experiment to detect defects such as delamination and corrosion. The findings demonstrate that while each NDT method has specific strengths—surface ER with a 46.67% detection rate, IE with 40%, and IRT with 53.33%—the combined detection rate increased to 60%. This combined approach provides a more comprehensive assessment and is expected to help establish better maintenance strategies for aging infrastructure. The study highlights the importance of optimizing NDT methods for real-world applications, addressing current limitations such as environmental sensitivity and scanning speed, to improve the early detection and prevention of structural failures. [ABSTRACT FROM AUTHOR]

This paper presents the experimental analysis of a multi-antagonistic actuation system involving nickel-titanium (NiTi) shape memory alloy (SMA) wires that can be thermally activated individually, one or more at a time. In particular, the study focuses on the hysteretic effects that occur during the thermal activation sequences. A planar structure involving six NiTi SMA wires has been designed as a first step. Particular attention was paid to the tightening at the ends of the wires. A solution to reduce slippage has been proposed by using a specific thermal cycle during the tightening operation. Then, an experimental setup involving infrared and visible range cameras was developed to measure the movement of the mobile platform and the SMA wire temperatures, respectively. Finally, important points of the actuation have been studied through different tests, particularly the "attraction" effect of any heating step on the following steps. The study also highlights system initialization challenges (resetting the reference position of the mobile platform and initial prestressing), leading to shifts with respect to the positions obtained by finite element simulations. The paper shows that the multi-antagonistic and hysteretic character of actuation systems involving more than two NiTi SMA wires can be successfully managed. [ABSTRACT FROM AUTHOR]

Heat stress is a condition that impairs the animal’s productive and reproductive performance, and can be monitored by physiological and environmental variables, including body surface temperature, through infrared thermography. The objective of this work is to develop computational models for classification of heat stress from respiratory rate variable in dairy cattle using infrared thermography. The database used for the construction of the models was obtained from 10 weaned heifers, housed in a climate chamber with temperature control, and submitted to thermal comfort and heat wave treatments. Physiological and environmental data were collected, as well as thermographic images. The machine learning modeling environment used was IBM Watson, IBM’s cognitive computing services platform, which has several data processing and mining tools. Classifier models for heat stress were evaluated using the confusion matrix metrics and compared to the traditional method based on Temperature and Humidity Index. The best accuracy obtained for classification of the heat stress level was 86.8%, which is comparable to previous works. The authors conclude that it was possible to develop accurate and practical models for real-time monitoring of dairy cattle heat stress. [ABSTRACT FROM AUTHOR]

Quantitatively evaluating the debris cloud-induced pitting damage in the AL-Whipple structure of spacecraft, is still a challenge, let alone microstructural plastic deformation usually accompanied by geometric cratering defects, which may decrease material stress-carrying capacity and structural life. With this motivation, a dedicated modelling technique is proposed to gain an insight into various modalities (i.e., geometric and plastic defects) of pitting damage-induced thermal modulation for infrared inspection. Then, a sequence of infrared images is obtained and analysed using the gap smoothing algorithm and naive Bayes classification methods. On this basis, a quantitative characterization framework is proposed to characterize the damage modalities and their morphologies. This method is validated using a 3D numerical model which contains pitting damage acquired from the real-world structure. Modality classification and morphology evaluation results are well consistent with the artificial defects, in terms of the modality, diameter, and depth. Then they‘re experimentally corroborated, in which the severity and distribution of pitting damage are precisely characterized, in terms of the crater number and morphologies, as well as the size of plastic regions around these craters, which are consistent with the actual observations. This work proposes a non-destructive strategy for pitting damage evaluation, which is engineering-friendly. [ABSTRACT FROM AUTHOR]

Material extrusion (ME) is a widely used additive manufacturing (AM) technique, known for its versatility, cost-effectiveness, and ability to produce complex parts on-demand with greater customization and reduced waste. However, the process is impeded by unpredictable factors causing defects such as voids, overextrusions, and underextrusions, which smart manufacturing in Industry 4.0 aims to mitigate. In this study, we report a novel infrared (IR) thermography–based continuous data acquisition and processing framework that can differentiate various levels of in situ underextrusions. While existing underextrusion detection techniques require mid-print interruptions, our framework detects defects without any interruption. The methodology includes integrating an IR camera into a commercially available extrusion-based 3D printer for continuous in-printing data acquisition. The G-code for printing a rectangular block is intentionally modified to induce various levels of known underextrusions. Additionally, a novel signal processing algorithm is developed to automate real-time data processing and analysis, including signal normalization, artifact removal, and feature extraction. Results are obtained by developing a correlation matrix to compare the correlation coefficients of time series thermal data from the printed samples. Time-domain thermal features are also extracted to identify extrusion levels of 25%, 50%, 75%, and 100%. This study demonstrates that by utilizing the proposed framework, thermal data can identify various extrusion levels without mid-print interruption and determine the severity of process deviations within 5 s. This framework paves the way for integrating a thermal data-driven closed-loop monitoring and adjustment system capable of producing first-time-ready parts. [ABSTRACT FROM AUTHOR]

This study evaluates the thermal impact of a one-drill protocol for osteotomy preparation in dental implant surgery. Our findings demonstrate a significant reduction in heat generation compared to traditional sequential drilling, suggesting potential benefits for implant osseointegration and patient comfort. Specifically, the one-drill protocol was associated with lower peak temperatures and a reduced duration of elevated temperatures. These findings suggest that the one-drill protocol may contribute to improved implant stability and reduce the risk of thermal-induced bone damage. While further research is needed to confirm these findings in clinical settings, the results of this study provide promising evidence for the potential advantages of the one-drill protocol in dental implant surgery. Additionally, the one-drill protocol may offer simplified surgical workflows and reduced instrument management, potentially leading to improved efficiency and cost-effectiveness in dental implant procedures. [ABSTRACT FROM AUTHOR]

Thermographic techniques are widely used to characterise material damage, porosity and moisture in structures. Due to the thermographic signal reconstruction (TSR) technique, active thermography has made significant progress. In this study, the TSR technique was applied to identify a rubber layer that mimics an internal defect, which is attached to the back of the steel plate, after a 1.0 min thermal stimulation. The peaks of the first and second derivative logarithmic curves correspond to the important time point revealing the position of the attached flaw. Furthermore, a new post-processing technique for infrared thermographic data was proposed by exploiting polynomial orders of the fitting curves to produce a synthetic image, which can highlight the flaw with distinctive contrast. Finally, the upgraded TSR technique was applied to image a natural shelling defect in a rail sample with an excellent contrast. [ABSTRACT FROM AUTHOR]

Simple Summary: Cattle are increasingly exposed to high temperatures with accelerating climate change, presenting the risks of heat stress, poor welfare, and even death. In Australia, the sudden onset of extreme conditions means that deaths are not uncommon. Understanding the impact of high environmental temperatures on cattle behaviour and physiology is critical. We aimed to investigate changes in infrared eye temperature and their association with behavioural responses, such as changes in body postures, alterations in feeding behaviour, and body self-care. Twenty-four Angus steers were individually housed to assess heat load responses when they were fed either a standard finisher diet based on cereal grains or an alternative diet with 8% of the grains replaced by an isoenergetic amount of lucerne hay. No differences were observed between different dietary cohorts. The infrared eye temperature (IRT-Eye), of the steers exposed to high environmental temperatures was increased, and this had a strong association with behaviour and physiology. Cattle with increased IRT-Eye temperature showed stress-related behavioural responses, including a downward-facing head, ears directed backwards, and other indicators of heat stress such as increased panting, standing, and increased rumen temperature. The strong association of IRT-Eye with behaviour and physiology suggests that it can be used to detect heat stress in cattle. Cattle in regions with high ambient temperatures are at risk of heat stress. Early detection is important to allow action to be taken to minimise the risks to cattle exposed to thermal stress. This study aimed to investigate the impact of heat stress on IRT-Eye temperature and its association with the behavioural and physiological responses of heat-stressed Angus steers (n = 24) on finisher and or substituted diets. Overall, 2 cohorts of 12 Angus steers were individually housed in a climate-controlled facility to examine responses to heat stress when fed on a standard finisher diet, based on a high percentage of cereal grains, and on a substituted diet in which 8% of the grains were replaced by an isoenergetic amount of lucerne hay. Exposing feedlot cattle to hot environmental conditions increased IRT-Eye temperature, which had a strong association with behaviour and physiology. There was no evidence of differences between the different dietary cohorts. The cattle with increased IRT-Eye temperature showed stress-related responses, including a downward-facing head, ears directed backwards, and other indicators of heat stress such as increased panting, standing, and increased rumen temperature. The strong association of IRT-Eye temperature with stress-related behaviours, as well as with rumen temperature and panting behaviour, highlights the potential for IRT-Eye to be utilised as a non-invasive tool to assess cattle responses in hot conditions. [ABSTRACT FROM AUTHOR]

Context: The coat is the first defense layer protecting animals from direct sunlight, influencing radiant-heat absorption and loss. Dark coat colour may increase animal heat impact because of higher absorption of solar radiation. Aims: We investigated the impact of pigmentation intensity on the heads of animals and their thermal responses to solar radiation. Furthermore, we compared the thermal responses between dark and light brown-coloured animals, to determine whether the duration of sun and shade exposure (3 h each) is sufficient to differentiate the heat gain and losses between the two groups. Methods: The effect of coat colour on rectal and surface temperatures in Boer goats, classified according to their head pigmentation pattern as dark (DB, N = 11) or light brown (LB, N = 11), exposed to sun and shade, was evaluated. The animals' body and head surface temperatures were measured the same day before, during, and after sun exposure by using infrared thermography. Individual thermographic images were analysed by body regions (rump, leg, neck) and head areas (eye, ear, upper and lower part of the head, and the muzzle). Key results: During solar radiation, all measured variables increased. In the shade, the rump, leg, and neck surfaces remained elevated, whereas the head area cooled down faster, with the highest temperature changes measured for the ear of about 10%. Most surface cooling occurred within 30 min after returning to the shade. Coat colour groups differed only in higher overall muzzle temperature of DB than that of LB goats (P = 0.048). Correlations between rectal and surface temperatures were low to medium, ranging from −0.02 (eye) to 0.31 (muzzle). Conclusions: The head area is suggested as the important thermal window for heat dissipation and selective brain cooling. Light brown-headed animals were more efficient in eliminating the heat gained from solar radiation when returned to shade. Implications: The colouration of the animal's head may play an important role for its heat tolerance. As an easy-to-recognise characteristic, coat colour can serve as a selection trait for local goat populations. In view of global warming, selection for lighter coat colour in domestic ruminants may also become important even under temperate climates. Coat colour plays an important role in heat tolerance of livestock, because dark coat colour may increase animal heat stress as a result of higher absorption of solar radiation. We focused on the influence of head pigmentation of Boer goats and found light brown-headed animals to be more efficient in eliminating the solar heat gained during outdoor grazing when returned to the shade. In view of global warming, breeding for light coat colour in domestic ruminants could alleviate heat stress. [ABSTRACT FROM AUTHOR]

In the automotive industry, short glass fiber-reinforced thermoplastics are widely used under the hood and subjected to dynamic vibrations of the engine in multiple directions resulting in fatigue failure. Under fatigue loading, a significant portion of the strain energy is stored within the material, while the remaining portion is lost due to internal frictions and the damage occurrence. Internal friction results in heat generation, which in turn causes an increase in external temperature. This increase in temperature leads to thermal degradation of the polymer. Investigations on the cause of the stiffness drop are not widely available in the literature. Therefore, this study explores the source of the stiffness drop under biaxial fatigue loading of a polyamide 6,6 reinforced with 30 wt. % short glass fibers (PA66GF30) and distinguishes the contributions of thermal degradation and damage accumulation. The thermal evolution of the specimens was captured by means of thermography. In addition, the digital image correlation (DIC) technique was used to measure the in situ strain field during the fatigue. Despite the temperature stabilization being observed around the 10,000th cycle, the reduction in the stiffness continued until failure which was attributed to the mechanical damage accumulation and cyclic creep during the fatigue tests. Dynamic mechanical analyses (DMA) were carried out to quantify the stiffness drop with the varying temperature. From the results, it is seen that the damage accumulation and cyclic creep during the fatigue tests were responsible for the major part of the stiffness drop. Finally, scanning electron microscopy (SEM) inspection of the fracture surface was performed to identify fatigue damage mechanisms. Four unique features associated with the fatigue damage were identified: (1) debonding of fibers from the matrix, (2) polymer matrix crazing, and (3) cavitation and porosities, (4) pull out fiber ends and break on the fiber. [ABSTRACT FROM AUTHOR]

This article explores the use of machine learning models in infrared thermographic nondestructive testing (NDT) for defect detection. The study found that incorporating temperature contrast data improved the efficiency of the models, achieving sensitivity rates of over 98% across all test datasets. However, the study also emphasized the importance of carefully selecting training data parameters and implementing proper data processing to avoid negative outcomes. The article provides references for further research in the field and discusses the potential of deep learning and other techniques in infrared thermography. [Extracted from the article]

A sore neck and back are very common among cyclists and can be caused by a change in spinal curvature and forward head posture during cycling. In addition, elite cyclists were found to have a decreased infrared thermography measurement during incremental exercise, and increased measurements after exhaustion. This study aims to assess the benefits of corrected cycling posture, evaluated by the OpenPose evaluation system and physical therapists, on exercise performance as determined by infrared thermography. We adopt a case–control study design. Thirty participants were divided into the group riding with a natural posture (control group) and the group riding with a corrected posture (experimental group). While both groups had comparable back and neck temperatures before and 10 min after finishing the exercise, the corrected posture group had a statistically lower temperature compared to the control one after riding for 30 min (33.58 ± 0.69°C vs. 35.18 ± 0.89°C; p < 0.001). This observed decrease in skin temperature during exercise implies better heat dissipation mechanisms and hence fatigue avoidance among cyclists with corrected posture. Larger studies with various static and dynamic cycling positions are required to gain better insights about the correlation between those positions, infrared thermography measurements, enhanced athletic performance and injury prevention. [ABSTRACT FROM AUTHOR]

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Rail breakage can cause train derailment that leads to catastrophic consequences, prevention of which is worthy of a significant amount of investment in finance and time. Non-destructive evaluation can provide qualitative and quantitative inspection of rail tracks in-situ to detect dangerous faults, even though these defects are primarily invisible or hardly noticed from the surface of the rail. To make it efficient and convenient, infrared thermography technique offers a remote operation without interfering with rail transportation. The image processing for edge detection can further quantify the defect deterioration by depicting the contour of the defect. Moreover, the optimal parameters for various edge operators provide reliable detection for natural fracture defects. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe. [ABSTRACT FROM AUTHOR]

Integrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Infrared thermography is a rapid, contactless technology that can provide real-time temperature measurements, with various applications in livestock science within the framework of precision livestock farming. The aim of the current review paper is to present the updated state of knowledge regarding the detection of mastitis in dairy ruminants using infrared thermography. Therefore, it summarizes the technological advancements, the diagnostic capacity, and the operational and analytical challenges of the method when applied under real-world conditions as a diagnostic tool for mastitis, while factors associated with the performance of the method are critically described and discussed to justify recognized strengths, weaknesses, opportunities, and challenges of the method. Among the health issues of major concern in dairy ruminants, mastitis stands out as being associated with considerable losses in productivity and compromised animal health and welfare. Currently, the available methods for the early detection of mastitis are either inaccurate, requiring further validation, or expensive and labor intensive. Moreover, most of them cannot be applied at the point of care. Infrared thermography (IRT) is a rapid, non-invasive technology that can be used in situ to measure udder temperature and identify variations and inconsistencies thereof, serving as a benchmarking tool for the assessment of udders' physiological and/or health status. Despite the numerous applications in livestock farming, IRT is still underexploited due to the lack of standardized operation procedures and significant gaps regarding the optimum settings of the thermal cameras, which are currently exploited on a case-specific basis. Therefore, the objective of this review paper was twofold: first, to provide the state of knowledge on the applications of IRT for the assessment of udder health status in dairy ruminants, and second, to summarize and discuss the major strengths and weaknesses of IRT application at the point of care, as well as future challenges and opportunities of its extensive adoption for the diagnosis of udder health status and control of mastitis at the animal and herd levels. [ABSTRACT FROM AUTHOR]

In order to quickly and accurately detect the internal defects and diseases of asphalt pavement, so as to take maintenance or other treatment measures in time to ensure road driving safety. At the same time, in order to promote the development of nondestructive testing technology for hidden diseases and defects in asphalt pavement, the existing nondestructive testing technology for internal defects of asphalt pavement was reviewed. The basic principles of ground penetrating radar, its application in asphalt pavement detection, the intelligent identification of ground penetrating radar spectrum, the detection principle of ultrasonic detection technology, the study of the attenuation law of ultrasonic wave and its application and research in the detection of internal defects in pavement were reviewed, the detection principle of infrared thermal imaging technology, nuclear densitometer, nuclear-free densitometer and Rayleigh wave method and the application and research in the detection of internal defects in asphalt pavement were summarized. The types of detection defects, advantages and disadvantages were discussed, and the existing problems and future development directions of each detection technology were discussed, so as to provide a reference and basis for the further application and research of nondestructive testing technology for internal defects of asphalt pavement. [ABSTRACT FROM AUTHOR]

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness. [ABSTRACT FROM AUTHOR]

Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal's surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively. [ABSTRACT FROM AUTHOR]

Sequential drilling is a common practice in dental implant surgery aimed at minimizing thermal damage to bone. This study evaluates the thermal effects of sequential drilling and assesses modifications to drilling protocols to manage heat generation. We utilized a custom drill press and artificial bone models to test five drill bits under various protocols, including sequential drilling with different loads, spindle speeds, and peck drilling. Infrared thermography recorded temperature changes during the drilling process, with temperatures monitored at various depths around the osteotomy. The results reveal sequential drilling does not eliminate the thermal damage zone it creates (well over 70 °C). It creates harmful heat to surrounding bone that can spread up to 10 mm from the osteotomy. The first drill used in sequential drilling produces the highest temperatures (over 100 °C), and subsequent drill bits cannot remove the thermal trauma incurred; rather, they add to it. Modifying drill bit design and employing proper drilling techniques, such as reducing drilling RPM and load, can reduce thermal trauma by reducing friction. Inadequate management of heat can lead to prolonged recovery, increased patient discomfort, and potential long-term complications such as impaired bone-to-implant integration and chronic conditions like peri-implantitis. Ensuring healthy bone conditions is critical for successful implant outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Varroa destructor is a significant global honey bee parasite and the primary threat to bee health. Due to its latent lifestyle, detecting the mite in a brood requires invasive techniques. Enhancing detection methods is critical for advanced research on mite population dynamics, spread, selection efforts, and control methodologies. In this study, we employed infrared thermal imaging, a less-explored technique in apicultural studies, to detect parasitism in Apis mellifera broods. Our findings indicate that handheld infrared thermal cameras can generate adequately detailed heat maps of the hive. These maps distinctly separate cells containing honey, pollen, and brood, with stable, reproducible temperature measurements observable in late autumn. Notably, mite parasitism induces a sustained temperature increase in developing honey bee pupae, consistently detected regardless of mite numbers in the cell. This study reveals an advanced thermoregulatory behavior in the honey bee colony, manifesting as a social fever phenomenon. Further research is necessary to explore the health benefits of this behavior for bees and the negative effects on the mite. Our method, combined with the development of AI-based image evaluation software, could provide beekeepers and researchers with a valuable tool for Varroa research and bee biological studies. The Varroa destructor is the most significant bee parasite and the greatest threat to bee health all around the world. Due to its hidden lifestyle, detection within the brood cell is only possible through invasive techniques. Enhancing detection methods is essential for advancing research on population dynamics, spread, selection efforts, and control methodologies against the mite. In our study, we employed infrared imaging to measure the thermal differences in parasite and intact Apis mellifera worker broods. Experiments were conducted over two years at the MATE Kaposvár Campus in Hungary involving five beehives in 2022 and five beehives in 2023. A FLIR E5-XT WIFI handheld infrared camera was used to create a heat map of capped brood frames. Our results indicate that the resolution of these cameras is sufficient to provide detailed IR images of a bee colony, making them suitable to detect temperature differences in intact and Varroa parasitized capped brood cells. Mite parasitism causes a time-dependent and sustained temperature increase in developing bee pupae, observable regardless of mite number. Our work demonstrates two different heating patterns: hotspot heating and heating cells that are responsible for the elevated temperature of the Varroa-infested cells as a social fever response by the worker bees. Based on our results, future research combined with AI-based image evaluation software could offer beekeepers and researchers practical and valuable tools for high-throughput, non-invasive Varroa detection in the field. [ABSTRACT FROM AUTHOR]

BACKGROUND: Wound complications after lumbar spine surgery may result in prolonged hospitalization and increased morbidity. Early identification can trigger appropriate management. OBJECTIVE: The aim of this study was to investigate the efficacy of infrared-based wound assessment (FLIR) after lumbar spine surgery in the context of identifying wound healing disorders. METHODS: 62 individuals who underwent lumbar spine surgery were included. The immediate postoperative course was studied, and the patient's sex, age, body mass index (BMI), heart rate, blood pressure, body temperature, numeric rating scale for pain (NRS), C-reactive protein (CRP), leukocyte, and hemoglobin levels were noted and compared to thermographic measurement of local surface temperature in the wound area. RESULTS: Measurement of local surface temperature in the wound area showed a consistent temperature distribution while it was uneven in case of wound healing disorder. In this instance, the region of the wound where the wound healing disorder occured had a lower temperature than the surrounding tissue (p > 0.05). CONCLUSIONS: This study demonstrates the ongoing importance of clinical wound assessment for early detection of complications. While laboratory parameter measurement is crucial, FLIR may serve as a cost-effective supplemental tool in clinical wound evaluation. Patient safety risks appear minimal since local ST is measured without touch. [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]

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]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study demonstrates how convolutional neural networks (CNNs), supported by open-source software and guided by corporate social responsibility (CSR), can enhance photovoltaic (PV) panel maintenance. Connecting industrial informatics with sustainable practices underscores the potential for more efficient and responsible energy systems within Industry 4.0. The rapid expansion of solar power necessitates effective maintenance and inspection of PV panels to ensure optimal performance and longevity. CNNs have emerged as potent tools for detecting defects in PV panels through infrared thermography (IRT). The review aims to evaluate CNNs' effectiveness in detecting PV panel defects, align their capabilities with the IEC TS 62446-3:2017 standard, and assess their economic benefits. A systematic review of literature focused on studies using CNNs and IRT for PV panel defect detection. The analysis compared performance metrics, economic benefits, and alignment with industry standards. CNN models demonstrated high accuracy in defect detection, with most achieving above 90%. Integrating UAVs for image acquisition significantly reduced inspection times and costs. CNNs are highly effective in detecting PV panel defects, offering substantial economic benefits and potential for industry-wide standardisation. Further research is needed to enhance model robustness across diverse conditions and PV technologies. [ABSTRACT FROM AUTHOR]

In this work, the effects of the mixing water loss capacity of hydrated lime mortars with different dosages were analysed—type O (mix 1:2:9), type N (mix 1:1:6), and type M (mix 1:0.5:4.5), with additions of submerged arc welding (SAW) slag. Infrared thermography tests and optical and scanning electronic microscopy analyses of the mortars were also carried out. The experimental results showed that the mortar samples with additions of SAW slag type M, using low-cost materials, proved to be in economic and technical terms (adhesion strength) the best solution, even more so if a spatter dash layer is used, a fact that increases the adhesion strength even more. Also, the infrared thermographic results revealed that the ability of the mortar paste to yield water to the ceramic substrate in the interface regions is a relevant factor in the adhesion of these coatings. Finally, the analyses by scanning electron microscopy and optical microscopy revealed that the ability to release water to the substrate is related to the hydration of the mortar and its anchoring capacity. Furthermore, the analyses carried out demonstrated that the adhesion of the mortars is influenced and increased with the application of a layer of splashes, as the pores of the substrate become more refined and better filled with the applied mortar. [ABSTRACT FROM AUTHOR]

Abstract Excessive seepage significantly affects the stability of levees during flood seasons. Recently, infrared thermal imaging technology has been proposed to detect the seepage leakage of levees for its simplicity, high efficiency and non-contact. However, previous studies mainly focused on theoretical and laboratory tests to verify its effect, which involve many assumptions and simplification. This paper aims to study the practical application effects of infrared thermal imaging technology on levee leakage detection. A typical embankment in Xiangyin County, Hunan Province is selected as the studied area, where a specific point containing water is considered as the potential leakage point. Then, DJI M300RTK UAV equipped with Zenith H20T gimbal is adopted to collect the visible and infrared images of the embankment’s leaking point. The visible and infrared image data collected at different times and at different measurement heights are analyzed to investigate the detection accuracy of infrared thermal imaging technology. Results show that the infrared thermal imaging technology can accurately identify the potential seepage leakage point of levees. Compared with the visible light recognition method, the infrared thermal imaging technology can identify the location and scope of leakage hazards more clearly and accurately at night. During the daytime, infrared detection is less effective than visible light method. Moreover, the measurement altitude between the drone and the target leakage points significantly affects the detection accuracy of infrared thermal imaging technology.

Abstract In recent years, the preservation and conservation of ancient cultural heritage necessitate the advancement of sophisticated non-destructive testing methodologies to minimize potential damage to artworks. Therefore, this study aims to develop an advanced method for detecting defects in ancient polyptychs using infrared thermography. The test subjects are two polyptych samples replicating a 14th-century artwork by Pietro Lorenzetti (1280/85–1348) with varied pigments and artificially induced defects. To address these challenges, an automatic defect detection model is proposed, integrating numerical simulation and image processing within the Faster R-CNN architecture, utilizing VGG16 as the backbone network for feature extraction. Meanwhile, the model innovatively incorporates the efficient channel attention mechanism after the feature extraction stage, which significantly improves the feature characterization performance of the model in identifying small defects in ancient polyptychs. During training, numerical simulation is utilized to augment the infrared thermal image dataset, ensuring the accuracy of subsequent experimental sample testing. Empirical results demonstrate a substantial improvement in detection performance, compared with the original Faster R-CNN model, with the average precision at the intersection over union = 0.5 increasing to 87.3% and the average precision for small objects improving to 54.8%. These results highlight the practicality and effectiveness of the model, marking a significant progress in defect detection capability, providing a strong technical guarantee for the continuous conservation of cultural heritage, and offering directions for future studies.

Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively.

The thermal diffusivity value of a building wall can be calculated theoretically by using the thermophysical properties of its materials listed in standards and literature. In these lists, building materials in the same category can be defined by different thermal properties; therefore, the theoretically-calculated thermal diffusivity values can be misleading. In this study, an experimental setup was developed that can directly measure the thermal diffusivity (α, m2 /s) value of building walls by the combined use of infrared thermography and guarded hot box methods. A 19cm-thick wall sample made of lightweight concrete block with pumice aggregate and mortar was prepared; this wall is positioned between a controlled warm ambient on its one side and a cold ambient on the other side, where its surfaces were monitored by sequential IR imaging. The thermal diffusivity value of the sample wall was calculated by using the data showing temperature change versus time and the relevant theoretical equation. The reference thermal diffusivity value (αREF) of the sample wall, calculated by using thermal properties measured by standard laboratory tests, is 3.40x10-7 m²/s. The thermal diffusivity (αIRT) of the same wall, measured with the proposed IRT-GHB experimental setup, is 3.38x10-7 m²/s. The value measured with that new setup is similar to the reference thermal diffusivity value. The results show that the new experimental setup can directly measure the thermal diffusivity of a wall. Compared to the existing ones, the proposed experimental setup presents a measurement technique that saves time and sets the basis of a scientific, practical, and more economical analytical method. [ABSTRACT FROM AUTHOR]

Ning Luo,1,&ast; Yi-Chen Zeng,1,&ast; Bai-Tian Fu,1 Je Wie Low,2 Jian-Qiao Fang1 1The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 2Institute of International Education of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jian-Qiao Fang, The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China, Email fangjianqiao7532@163.comIntroduction: Diabetic peripheral neuropathy (DPN) affects patients’ quality of life significantly. To date, selecting the appropriate treatment remains challenging. While electroacupuncture (EA) has shown promise as an effective adjunct therapy for DPN, and infrared thermography (IRT) has been considered as a potential predictor of treatment efficacy, the evidence for both remains inconclusive. As such, the objectives of this trial are twofold: to ascertain the efficacy of EA for DPN, and to explore the feasibility of IRT as an adjunctive objective tool for efficacy assessment.Methods: The study was designed as a randomized, parallel, controlled trial. It spanned over 6 weeks of treatment and an additional 4 weeks of follow-up. 104 eligible participants will be stratified for severity of disease: mild with Toronto clinical scoring system(TCSS) score 6– 8, moderate (TCSS score 9– 11), and severe (TCSS score 12– 19), and each level will be randomised in a 1:1 ratio into a EA group and waiting-list group. The waiting-list group received only the current conventional medication, while the EA group received an additional 12 EA sessions on top of the conventional medication. The primary outcome indicators is nerve conduction velocity (NCV), which will be tested at the baseline and week 6. Total clinical efficiency, TCSS, Clinical symptoms score of Traditional Chinese Medicine (TCM), Patient global impression of change(PGIC), Temperature of regions of interest (ROIs), and Physico chemical examination will be used as secondary outcome indicators. In addition, safety assessment will be determined based on adverse events during the trial.Conclusion: The expected results of this study will determine whether EA improves efficacy in the treatment of DPN with an acceptable safety profile, and investigating variations in the efficacy of EA across different levels of DPN severity. Furthermore, it will explore the viability of IRT as an objective measure for evaluating treatment effectiveness for DPN.Clinical Trial Registration: ClinicalTrials.gov identifier, NCT06054087.Keywords: electroacupuncture, waiting-list, diabetic peripheral neuropathy, clinical trial, infrared thermography

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.

Abstract Objective Conical orthopedic drill bits may have the potential to improve the stabilization of orthopedic screws. During perforations, heat energy is released, and elevated temperatures could be related to thermal osteonecrosis. This study was designed to evaluate the thermal behavior of an experimental conical drill bit, when compared to the conventional cylindrical drill, using polyurethane blocks perforations. Results The sample was divided into two groups, according to the method of drilling, including 25 polyurethane blocks in each: In Group 1, perforations were performed with a conventional orthopedic cylindrical drill; while in Group 2, an experimental conical drill was used. No statistically significant difference was observed in relation to the maximum temperature (MT) during the entire drilling in the groups, however the perforation time (PT) was slightly longer in Group 2. Each drill bit perforated five times and number of perforations was not correlated with a temperature increase, when evaluated universally or isolated by groups. The PT had no correlation with an increase in temperature when evaluating the perforations universally (n = 50) and in Group 1 alone; however, Group 2 showed an inversely proportional correlation for these variables, indicating that, for the conical drill bit, drillings with longer PT had lower MT.

Abstract Background To identify the sensitivity, specificity, and overall diagnostic accuracy of infrared thermography in diagnosing lumbosacral radicular pain. Methods Patients sequentially presenting with lower extremity pain were enrolled. A clinical certainty score ranging from 0 to 10 was used to assess the likelihood of lumbosacral radicular pain, with higher scores indicating higher likelihood. Infrared Thermography scans were performed and the temperature difference (ΔT) was calculated as ΔT = T1 - T2, where T2 represents the skin temperature of the most painful area on the affected limb and T1 represents the skin temperature of the same area on the unaffected limb. Upon discharge from the hospital, two independent doctors diagnosed lumbosacral radicular pain based on intraoperative findings, surgical effectiveness, and medical records. Results A total of 162 patients were included in the study, with the adjudicated golden standard diagnosis revealing that 101 (62%) patients had lumbosacral radicular pain, while the lower extremity pain in 61 patients was attributed to other diseases. The optimal diagnostic value for ΔT was identified to fall between 0.8℃ and 2.2℃, with a corresponding diagnostic accuracy, sensitivity, and specificity of 80%, 89%, and 66% respectively. The diagnostic accuracy, sensitivity, and specificity for the clinical certainty score were reported as 69%, 62%, and 79% respectively. Combining the clinical certainty score with ΔT yielded a diagnostic accuracy, sensitivity, and specificity of 84%, 77%, and 88% respectively. Conclusion Infrared thermography proves to be a highly sensitive tool for diagnosing lumbosacral radicular pain. It offers additional diagnostic value in cases where general clinical evaluation may not provide conclusive results. Trial registration ChiCTR2300078786, 19/22/2023.

In order to determine the possibility of using infrared thermography (IRT) in preventing lameness in dairy cows, the aim of this study was to evaluate temperature variations depending on the lesion presence, measurement points and leg position. The study used about 3 000 IRT records from 60 Holstein cows housed in a free-stall barn. Surface temperature measurements were taken at two regions of the hooves: the region of the coronary band, and the region of the skin up to 2 cm above the coronary band. A highly significant (P = 0.004; 0.006, P

Abstract Evaluating physical properties and mechanical parameters of rock slopes and their spatial variability is challenging, particularly at locations inaccessible for fieldwork. This obstacle can be bypassed by acquiring spatially-distributed field data indirectly. InfraRed Thermography (IRT) has emerged as a promising technology to statistically infer rock properties and inform slope stability models. Here, we explore the use of Cooling Rate Indices (CRIs) to quantify the thermal response of a granodiorite rock wall within the recently established Požáry Test Site in Czechia. We observe distinct cooling patterns across different segments of the wall, compatible with the different degrees of weathering evaluated in the laboratory and suggested by IRT observations of cored samples. Our findings support previous examinations of the efficacy of this method and unveil correlations between cooling phases in the field and in the laboratory. We discuss the scale-dependency of the Informative Time Window (ITW) of the CRIs, noting that it may serve as a reference for conducting systematic IRT field surveys. We contend that our approach not only represents a viable and scientifically robust strategy for characterising rock slopes but also holds the potential for identifying unstable areas.

Abstract Background Acupuncture is a method for treating tic disorder. However, there is a lack of sufficient clinical objective basis in regards of its treatment efficacy. Indeed, there are structural abnormalities present in energy metabolism and infrared thermography in children with tic disorder. Therefore, this study proposes a clinical trial scheme to explore the possible mechanism of acupuncture in treating tic disorder. Methods This randomized controlled trial will recruit a total of 90 children, in which they will be divided into non-intervention group and intervention group. The non-intervention group consists of 30 healthy children while the intervention group consists of 60 children with tic disorder. The intervention group will be randomly allocated into either the treatment group or the control group, with 30 children randomly assigned in each group. Children either received acupuncture treatment and behavioral therapy (treatment group) or sham acupuncture treatment and behavioral therapy (control group), 3 treatment sessions per week for a period of 12 weeks, with a total of 36 treatment sessions. Outcome measures include YGTSS, urinary and fecal metabolomics, infrared thermography of body surface including governor vessel. For the intervention group, these outcome measures will be collected at the baseline and 90th day prior to intervention. Whereas for the non-intervention group, outcome measures (excluding YGTSS) will be collected at the baseline. Discussion The main outcome will be to observe the changes of the severity of tic condition, the secondary outcome will be to observe the changes of structural characteristic of infrared thermography of body surface/acupoints along the governor vessel and to evaluate the changes of urinary and fecal metabolomics at the end of the treatment, so as to analyze the relationship between them and to provide further knowledge in understanding the possible mechanism of acupuncture in improving the clinical symptoms via regulating and restoring the body metabolomics network, which in future it can develop as a set of clinical guideline (diagnosis, treatment, assessment, prognosis) in treating tic disorder. ChiCTR2300075188(Chinese Clinical Trial Registry, http://www.chictr.org.cn , registered on 29 August 2023).

Background Early detection of free flap compromise is critical for salvage of the flap. Various methods of free flap monitoring have been described, but clinical assessment is the standard method for among all. In this study, role of infrared thermography is evaluated for free flap monitoring.

Induction heating is a clear, cheap, and highly effective technology used for many industrial and commercial applications. Generally, a time-varying magnetic field produces the required heat in the workpiece with a specially designed coil. The efficiency of the heating process depends highly on the coil design and the geometrical arrangement. A detailed and accurate finite element analysis of the induction heating process usually needs to resolve a coupled thermoelastic–magnetic problem, whose parameters values depend on the solution of another field. The paper deals with a shrink-fitting process design problem: a gear should be assembled with an axe. The interesting part of this case study is given the prescribed low limits for critical stress, the temperature of the gear material, and the heat-treated wearing surfaces. A coupled finite-element-based model and a genetic algorithm-based parameter determination methodology were presented. A thermal imaging-based measurement validated the presented numerical model and parameter determination task. The results show that the proposed methodology can be used to calibrate and validate the numerical model and optimize an induction heating process. [ABSTRACT FROM AUTHOR]

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications. [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]

A new method for detecting subsurface solid objects buried in farmlands, such as plastic bottles, wasted cans, etc., has been proposed by applying the technique of infrared (IR) thermography to monitor the temperature of soil surface subjected to solar irradiation. Through both experimentation and simulation, this study parameterizes the influence of environmental factors on IR images and validates the detection capabilities of the method. To verify the feasibility of IR thermography testing, the experimental section of the work is devoted to monitoring aluminum and polyethylene terephthalate cans buried in sand with varying grades of moisture. The dependencies between the efficiency of foreign object detection and their depth are derived. A restoring pseudothermal flux algorithm was used to reduce the impact of lateral diffusion on IR thermographic detection of foreign objects buried in soil. Variations of soil temperature caused by varying solar radiation during multiple day-night cycles are used to improve the detectable diameter-depth ratio. The described technique is efficient and provides no harm to human beings. [ABSTRACT FROM AUTHOR]