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Context: Soft tissue mobilization is frequently employed for delayed onset muscle soreness (DOMS) management. Foam roller and percussive massage are 2 popular soft tissue mobilization methods preferred by various professionals. However, their effects on DOMS symptoms are controversial and there are no studies comparing these 2 methods. The aim of the present study was to compare the acute effects of soft tissue mobilization with a foam roller or a percussive massage device on DOMS in young male recreational athletes. Design: A parallel, single-blinded, randomized controlled trial. Methods: Thirty-six participants (median [interquartile range 25/75]; age: 20.0 [19.3/21.0] y) were randomly allocated to percussive massage group (n = 12), foam roller group (n = 12), and control group (n = 12). First, a fatigue protocol targeting quadriceps femoris was performed. Then, participants received soft tissue mobilization with foam roller/percussive massage or rested for 10 minutes according to their groups. Pain and fatigue were evaluated by a visual analog scale, and the skin surface temperature of over the quadriceps femoris was measured with thermal camera imaging. Evaluations were performed at baseline, following fatigue protocol, at 24th hour, and at 48th hour. Changes from the baseline at 24th and 48th hours were compared between groups. Results: No significant between-group differences were observed at the assessments performed at 24th or 48th hour regarding the changes from baseline in pain (P value for 24th hour = .905, P value for 48th hour = .733), fatigue (P value for 24th hour = .895, P value for 48th hour = .606), or skin surface temperature measurements (P values for 24th hour = between .300 and .925, P values for 48th hour = between .311 and .750). Conclusions: Soft tissue mobilizations applied with foam roller or percussive massage device do not seem to be superior to passive resting in alleviating DOMS symptoms in recreational athletes. [ABSTRACT FROM AUTHOR]

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F 1 -Scores between 86.79% and 100%. [Display omitted] • Transfer learning is applied to automate the classification of thermal images of equipment in ETRs. • A thermal imaging dataset is set up and augmented to improve the generalization abilities of proposed models. • Establish standard comparisons among four transfer learning architectures. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes an induction heating element that addresses heat transfer and thermal homogenization while being fully submerged in a liquid, specifically focusing on heating of water as a case study. The design calculations, fabrication, and realization of a resonant induction heating system that heats the proposed heating element are presented. The geometric shape of proposed heating element is a hollow cylinder that maintains structural strength while delivering required thermal energy because of an external time‐varying magnetic field. Detailed mathematical modeling of the proposed heating element's equivalent electric circuit has been presented while considering physical parameters including diameter, thickness, height, material properties, magnetic field frequency, and skin effect. A laboratory scale prototype of the induction heating system including the proposed heating element has been built, and experiments have been demonstrated. The experimental results of the working induction heating resonant inverter and RLC resonant circuit inductance and capacitance with induction load are presented. Additionally, the results of thermographic images and temperature–time profile of the water sample, which was heated using the proposed heating element, are provided to demonstrate the heating element's reliability and practicality. This research contributes to the field of induction heating technology by investigating a heating element that is submerged in a liquid medium, resulting in a uniform temperature distribution throughout the liquid. The conventional method of heating water in a stainless steel cup‐shaped container is an inefficient heat transfer process. In stainless steel cup‐shaped containers, a portion of the energy input used to boil the water is lost to the environment through the surface area of the container that is not in direct contact with the water. This heat loss occurs due to the container's design, which exposes a large surface area to the surrounding environment, allowing thermal energy to dissipate through conduction, convection, and radiation. [ABSTRACT FROM AUTHOR]

BACKGROUND: To mitigate post‐harvest losses and inform harvesting decisions at the same time as ensuring fruit quality, precise ripeness determination is essential. The complexity arises in assessing guava ripeness as a result of subtle alterations in some varieties during the ripening process, making visual assessment less reliable. The present study proposes a non‐destructive method employing thermal imaging for guava ripeness assessment, involving obtaining thermal images of guava samples at different ripeness stages, followed by data pre‐processing. Five deep learning models (AlexNet, Inception‐v3, GoogLeNet, ResNet‐50 and VGGNet‐16) were applied, and their performances were systematically evaluated and compared. RESULTS: VGGNet‐16 demonstrated outstanding performance, achieving average precision of 0.92, average sensitivity of 0.93, average specificity of 0.96, average F1‐score of 0.92 and accuracy of 0.92 within a training duration of 484 s. CONCLUSION: The present study presents a scalable and non‐destructive approach for guava ripeness determination, contributing to waste reduction and enhancing efficiency in supply chains and fruit production. These initiatives align with environmentally friendly practices in agriculture. © 2024 Society of Chemical Industry. [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]

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

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

Submarine groundwater discharge (SGD) is a recognized contributor to the hydrological and biogeochemical functioning of coral reef ecosystems located along coastlines. However, the distribution, size, and thermal properties of SGD remain poorly understood at most land–reef margins. We developed, deployed, and demonstrated an operational method for airborne detection and mapping of SGD using the 200 km coastline of western Hawai'i Island as a testing and analysis environment. Airborne high spatial resolution (1 m) thermal imaging produced relative sea surface temperature (SST) maps that aligned geospatially with boat-based transects of SGD presence–absence. Boat-based SST anomaly measurements were highly correlated with airborne SST anomaly measurements (R2 = 0.85; RMSE = 0.04 °C). Resulting maps of the relative difference in SST inside and outside of SGD plumes, called delta-SST, revealed 749 SGD plumes in 200 km of coastline, with nearly half of the SGD plumes smaller than 0.1 ha in size. Only 9% of SGD plumes were ≥1 ha in size, and just 1% were larger than 10 ha. Our findings indicate that small SGD is omnipresent in the nearshore environment. Furthermore, we found that the infrequent, large SGD plumes (>10 ha) displayed the weakest delta-SST values, suggesting that large discharge plumes are not likely to provide cooling refugia to warming coral reefs. Our operational approach can be applied frequently over time to generate SGD information relative to terrestrial substrate, topography, and pollutants. This operational approach will yield new insights into the role that land-to-reef interactions have on the composition and condition of coral reefs along coastlines. [ABSTRACT FROM AUTHOR]

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

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

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

We present the case of a 23-year-old woman who reported weakness in the left upper limb, decreased warmth, numbness in the fingers, pain in the clavicular region, and a severe cold sensation in the limb. A thermal imaging camera examination was performed for diagnostic purposes, which guided further diagnostic and therapeutic management towards arterial thoracic outlet syndrome (aTOS). Following surgery and rehabilitation procedures, significant remission of symptoms was achieved and the patient's condition improved. This is the first report on the diagnosis of aTOS using thermal imaging, paving the way for further clinical research into this effective, rapid, and radiation-free method of diagnostic imaging. Conclusion: Thermal imaging is one of the most effective, readily available, and patient-safe methods for diagnosing vascular disease associated with flow disruption. [ABSTRACT FROM AUTHOR]

This study explores the interfacial friction in ultrasonic micro-injection moulding by using different polymer feedstock shapes, characterisation of micromoulding melts through thermal imaging and assessing microneedle feature replication. Industry standard polypropylene pellets and discs with different thicknesses were used for varying the amount of interfacial friction during sonication. High-speed thermal imaging and tooling containing sapphire windows were used to visualise the melt characteristics. Moulded products were characterised using laser-scanning confocal microscopy to quantify microneedle replication. The study demonstrates that (i) the interfacial area for the different feedstock shapes affects the heating in ultrasonic micro-injection moulding significantly, (ii) disc-shaped feedstocks result in initially higher flow front velocities and exhibit dominance of viscoelastic heating over interfacial friction and (iii) industrial pellet feedstocks provide a good combination interfacial friction and viscoelastic heating and more viscosity reduction in overall leading to better microreplication efficiency. The results presented could have a significant impact on the process development of ultrasonic micro-injection moulding where process repeatability can be improved by controlling the interfacial friction. The research provides an essential contribution to the development of this process, where interfacial frictional heating can be tailored specifically for miniature functional components, offering improved precision and reduced energy use when compared with conventional methods. [ABSTRACT FROM AUTHOR]

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

Pediatric hemangiomas are benign vascular malformations or angiodysplasias characterized by the proliferation of endothelial cells in blood vessels. Their incidence is 4%–10% in children under 1 year of age, and they most frequently occur on the head and neck [ 1 , 2 ]. In addition to causing facial deformities, facial hemangiomas can lead to both functional and aesthetic issues, often resulting in significant complications. These complications negatively impact the quality of life for patients and can also affect the psychological well-being and self-esteem of their families [ 3 ]. With the expected global smartphone penetration reaching 70% [ 4 ], this case report explores the use of smartphones integrated with thermographic cameras to monitor the involution of hemangiomas during sclerosing treatment. This approach represents a promising advancement in managing and assessing hemangioma treatment through accessible, real-time monitoring technology. [ABSTRACT FROM AUTHOR]

Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, with global warming and climate change, there have been many cases of successful hornet invasions in some areas. The successful invasion of hornets not only has an impact on local human security and economic activities, but also seriously threatens the bee ecosystem, which has attracted the attention of scientists. Therefore, taking some measures to suppress the foraging and expansion of hornets becomes an important subject. Compared with attractant trapping and poison baits, hornet-tracking technology, which tracks hornets to find their nests, is a more efficient and feasible method. This review paper discusses several common hornet-tracking methods, analyzes the advantages and disadvantages of different methods, and provides a theoretical basis for the development of hornet-tracking technology. Hornet is a general term for insects of the genus Vespa (Hymenoptera: Vespidae). Hornets are predatory insects distributed worldwide. They often appear at apiaries in groups to prey on honey bees, and cause incalculable losses in the honey bee industry. In the face of hornet intrusion, tracking a homing hornet to find its nest is the most efficient way to discover and eliminate the hornets around an apiary. Here, five hornet-tracking methods (hornet tag tracking, triangulation, thermal imaging technology, harmonic radar, and radio telemetry) are reviewed. The advantages, disadvantages and feasibility of each method are discussed to improve the strategies for tracking hornets. Therefore, this review provides ideas for the development of hornet-tracking technology and for improving honey bee protection. [ABSTRACT FROM AUTHOR]

Accurately estimating the absolute distance and height of objects in open areas is quite challenging, especially when based solely on single images. In this paper, we tackle these issues and propose a new method that blends traditional computer vision techniques with advanced neural network-based solutions. Our approach combines object detection and segmentation, monocular depth estimation, and homography-based mapping to provide precise and efficient measurements of absolute height and distance. This solution is implemented on an edge device, allowing for real-time data processing using both visual and thermal data sources. Experimental tests on a height estimation dataset we created show an accuracy of 98.86%, confirming the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Larix kaempferi is in high demand in Korea due to its value in timber and afforestation. However, it faces challenges in terms of propagation and the collection of physiological information for seedling production. In particular, moisture supply is crucial in seedling production. Therefore, establishing efficient irrigation regimes based on optical measurements is essential. Optical measurement methods are expected to be non-destructive, rapid, and reduce labor consumption in nursery systems. This study applied optical measurements using vegetation indices (VIs), chlorophyll fluorescence (FL) imaging, and thermal (TH) imaging to explore the efficient irrigation period for one-year-old Larix kaempferi seedlings in greenhouse conditions under drought stress and perform rehydration experiments. It was observed that all the seedlings survived without irrigation until day 4 (D4) (soil moisture content: 5.3%). Upon rehydration on D6, 83.33% of the seedlings survived until D14. According to the optical measurement results, the TH parameters, PRI (photochemical reflectance index), and Fm (maximum fluorescence in a dark-adapted state) showed sensitive stress responses in all drought treatment pots on D6. Among them, thermal imaging was found to have the highest potential for addressing limitations and being utilized in the greenhouse. The results of this study are expected to provide foundational data for the development of smart nursery systems for efficient irrigation in the future. [ABSTRACT FROM AUTHOR]

The air conditioning (A/C) of cabins allows for customized control, but manual adjustments may distract drivers, as well as result in energy inefficiency. Several existing thermal sensation models require complex inputs, which are challenging to gather whilst driving. To address this issue, this study developed a non-contact thermal sensation model for cabin occupants based on thermal imaging sensor. To collect actual data used for modeling, an outdoor subject experiment was conducted. In this study, initial training was conducted to compare the performance of six algorithms in building the model, with random forests algorithm showing the best performance. Besides, this study employed the recursive feature elimination (RFE) method with cross-validation algorithm for identifying the key features. In the end, the model was retrained using the selected features. The model that incorporated both environmental parameters and facial-temperature features demonstrated the best performance, with an R2 of 0.659 on the test set. Eliminating the hard-to-measure windshield surface temperature resulted in a slight reduction in accuracy, yielding an R2 of 0.651. To verify the generalizability of the model, this study further conducted independent validation experiments. The selected model, which exhibited a mean absolute error (MAE) of less than 0.4 in thermal sensation units, was proven to be highly applicable. The results can offer new solutions for automatic control of cabin A/C. [ABSTRACT FROM AUTHOR]

With the rapid increase in vehicle numbers, efficient traffic management has become a critical challenge for society. Traditional methods of vehicle detection and classification often struggle with the diverse characteristics of vehicles, such as varying shapes, colors, edges, shadows, and textures. To address this, we proposed an innovative ensemble method that combines two state-of-the-art deep learning models i.e., EfficientDet and YOLOv8. The proposed work leverages data from the Forward-Looking Infrared (FLIR) dataset, which provides both thermal and RGB images. To enhance the model performance and to address the class imbalances, we applied several data augmentation techniques. Experimental results demonstrate that the proposed ensemble model achieves a mean average precision (mAP) of 95.5% on thermal images, outperforming the individual performances of EfficientDet and YOLOv8, which achieved mAPs of 92.6% and 89.4% respectively. Additionally, the ensemble model attained an average recall (AR) of 0.93 and an optimal localization recall precision (oLRP) of 0.08 on thermal images. For RGB images, the ensemble model achieved mAP of 93.1%, AR of 0.91, and oLRP of 0.10, consistently surpassing the performance of its constituent models. These findings highlight the effectiveness of proposed ensemble approach in improving vehicle detection and classification. The integration of thermal imaging further enhances detection capabilities under various lighting conditions, making the system robust for real-world applications in intelligent traffic management. [ABSTRACT FROM AUTHOR]

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F1-Scores between 86.79% and 100%.

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F1-Scores between 83.11% and 98.34% in the pre-trained architectures.

Summary: Flap necrosis continues to occur in skin free flap autologous breast reconstruction. Therefore, we investigated the benefits of indocyanine green angiography (ICGA) using quantitative parameters for the objective, perioperative evaluation of flap perfusion. In addition, we investigated the feasibility of hyperspectral (HSI) and thermal imaging (TI) for postoperative flap monitoring.A single-center, prospective observational study was performed on 15 patients who underwent deep inferior epigastric perforator (DIEP) flap breast reconstruction (n=21). DIEP-flap perfusion was evaluated using ICGA, HSI, and TI using a standardized imaging protocol. The ICGA perfusion curves and derived parameters, HSI extracted oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) values, and flap temperatures from TI were analyzed and correlated to the clinical outcomes.Post-hoc quantitative analysis of intraoperatively collected data of ICGA application accurately distinguished between adequately and insufficiently perfused DIEP flaps. ICG perfusion curves identified the lack of arterial inflow (n=2) and occlusion of the venous outflow (n=1). In addition, a postoperatively detected partial flap epidermolysis could have been predicted based on intraoperative quantitative ICGA data. During postoperative monitoring, HSI was used to identify impaired perfusion areas within the DIEP flap based on deoxyHb levels. The results of this study showed a limited added value of TI.Quantitative, post-hoc analysis of ICGA data produced objective and reproducible parameters that enabled the intraoperative detection of arterial and venous congested DIEP flaps. HSI appeared to be a promising technique for postoperative flap perfusion assessment. A diagnostic accuracy study is needed to investigate ICGA and HSI parameters in real-time and demonstrate their clinical benefit.

Submarine groundwater discharge (SGD) is a recognized contributor to the hydrological and biogeochemical functioning of coral reef ecosystems located along coastlines. However, the distribution, size, and thermal properties of SGD remain poorly understood at most land–reef margins. We developed, deployed, and demonstrated an operational method for airborne detection and mapping of SGD using the 200 km coastline of western Hawai‘i Island as a testing and analysis environment. Airborne high spatial resolution (1 m) thermal imaging produced relative sea surface temperature (SST) maps that aligned geospatially with boat-based transects of SGD presence–absence. Boat-based SST anomaly measurements were highly correlated with airborne SST anomaly measurements (R2 = 0.85; RMSE = 0.04 °C). Resulting maps of the relative difference in SST inside and outside of SGD plumes, called delta-SST, revealed 749 SGD plumes in 200 km of coastline, with nearly half of the SGD plumes smaller than 0.1 ha in size. Only 9% of SGD plumes were ≥1 ha in size, and just 1% were larger than 10 ha. Our findings indicate that small SGD is omnipresent in the nearshore environment. Furthermore, we found that the infrequent, large SGD plumes (>10 ha) displayed the weakest delta-SST values, suggesting that large discharge plumes are not likely to provide cooling refugia to warming coral reefs. Our operational approach can be applied frequently over time to generate SGD information relative to terrestrial substrate, topography, and pollutants. This operational approach will yield new insights into the role that land-to-reef interactions have on the composition and condition of coral reefs along coastlines.

Significance: Leakage in the interfaces between restorative materials and tooth structure allows for fluid and bacterial acid infiltration, causing restoration failure due to secondary caries. Dentists spend more time replacing composite restorations than placing new ones. Previous in vitro and in vivo studies on enamel and root surfaces using shortwave-infrared (SWIR) and thermal imaging during dehydration with forced air have been promising for assessing lesion activity. Aim: We hypothesized that SWIR reflectance and thermal imaging methods can be used to monitor the activity of secondary caries lesions around composite restorations. The objective of this study was to employ these methods to measure the rate of fluid loss from lesions during dehydration with forced air to assess lesion activity. Approach: Sixty-three extracted human teeth with total of 109 suspected secondary lesions were examined using SWIR and thermal imaging during dehydration. The thickness of the highly mineralized transparent surface layer (TSL) at lesion interfaces indicative of lesion activity was measured by optical coherence tomography (OCT). Micro-computed tomography (MicroCT) was used to further confirm lesion severity and structure. OCT and MicroCT measurements of lesion structure, depth, and severity were correlated with fluid loss rates measured with SWIR reflectance and thermal imaging. Results: TSL thickness measured with OCT correlated with both SWIR reflectance and thermal measurements of rates of fluid loss ( p < 0.05 ). Increasing TSL thickness led to decreased permeability of lesions, potentially indicating full lesion arrest at TSL ≥ 70    μ m . SWIR performed better than thermal imaging for secondary lesion activity assessment, although both methods performed best on smooth surface lesions. Conclusions: Nondestructive SWIR reflectance and OCT imaging methods are promising for clinically monitoring the activity of secondary caries lesions.

Unmanned aerial vehicles are a synergistic technology that complements other new technologies and is in constant development. The paper focuses on using artificial intelligence (AI) in security surveillance. This article aims to develop an unmanned aerial system to monitor border areas and detect human silhouettes in challenging environmental conditions. For this purpose, thermal imaging technology was used for remote sensing in combination with artificial intelligence, particularly Yolo algorithms. After testing various Yolo versions, the target algorithm was implemented on an NVIDIA Jetson Xavier edge device. Prototyping of the AI-based thermal detection system was carried out on the DJI S900 multi-rotor aircraft. The final solution was implemented on a vertical take-off and landing aircraft. A summary containing observations and conclusions, as well as perspectives for the development of future work, are included at the end of the paper.

The thermal anomaly detection of building facades is critically important for the evaluation and upkeep of structures. While conventional methods are effective in numerous instances, their capability to discern flaws in complex environments is constrained. This research employs an optimized Vision Transformer (ViT) technology, enhanced by the Line Acquisition, Filtering, and Revision (LAFR) algorithm. By amalgamating the distinctive advantages of ViT’s attention-based mechanisms with the complementary data from thermal and visible imagery, this approach significantly bolsters the detection of thermal anomalies on building facades. The experimental outcomes reveal that our methodology, with an F1 score of 0.847, markedly surpasses traditional thermal imaging (F1 score of 0.466), showcasing its potential for precise anomaly detection in architectural contexts. This validates the method as a more robust and efficient solution for structural evaluation, promising to elevate the quality of architectural inspections and maintenance.

Wide-angle metalenses in the longwave infrared have shown great advantages over the traditional refractive doublets or triplets, due to light weight, CMOS compatibility, and low cost. However, previous endeavors have been plagued by challenges including a narrow waveband, large F-number, distortion, and spherical aberration. To address these problems, this study introduces two dispersive metasurfaces, placed near the front focal plane and upon the rear plane of a plano-convex lens, to correct optical aberrations. Utilizing this methodology, we propose and experimentally demonstrate an aberration-corrected hybrid metalens for thermal imaging in the 8–12 μm waveband, featuring an FOV of 24°, F-number of 1.2, and diameter of 12.2 mm. The developed hybrid metalens rigorously evaluated, exhibits Modulation Transfer Function (MTF) values exceeding 0.2 at 20 Lp/mm across the full FOV, and features an average transmission of 48.7 %, a relative focusing efficiencies of up to 42.1 %, polarization insensitivity and broadband imaging capacity. These results emphasize the potential applications of our system in diverse fields, such as camera lenses, autonomous driving, healthcare, and environmental monitoring.

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.

Abstract The lie-detection tests are traditionally carried out by well-trained experts using polygraph machines. However, it is time-consuming, invasive, and, overall, a cumbersome process, not admissible by the court of law. Trained individuals can easily flaunt these tests. On the other hand, facial thermal imaging could be more effective as it is noninvasive and could be a stealth method of tracking the facial blood flow patterns, which have been proven to detect deceit. This paper presents a method based on facial thermal imaging to detect deception in human subjects. The major issue in such research is the lack of proper real-life databases which emulate the crime scenes. In this paper, first, we have developed a database based on almost real-life theft incidents with due diligence using isolated subjects over a period of time at a government hospital under the plea of free health checkup. The experiment has been conducted at Midnapore Medical College and Hospital, West Bengal, India, with proper ethical committee approval. The participants are selected at the behest of the police department with habitual crime records. Most of them have been repeatedly charged with petty crimes of pick-pocketing and stealing. They are invited individually at different instances of time under the plea of medical checkup where they have been enticed to steal cash. It is followed by a two-stage process, a friendly interaction followed by a slightly tougher interrogation. Their forehead and periorbital region skin surface temperature are recorded by a hidden thermal camera. Upon analysis, conspicuous changes in the temperature profile and blood flow pattern have been observed in the individuals who stole money and those who did not.

Objective: To compare the thermographic pattern of regions of interest (ROI) of respiratory muscles in young asthmatics with and without bronchospasm induced by eucapnic voluntary hyperpnea (EVH). Materials and Methods: Cross‐sectional study carried out with 55 young (55% male and 45% females) aged 12.5 ± 3.3 years, divided in nine nonasthmatics, 22 asthmatics without exercise‐induced bronchospasm compatible response (EIB‐cr) and 24 asthmatics with EIB‐cr. The diagnosis of EIB was given to subjects with a fall in forced expiratory volume in the first second (FEV1) ≥ 10% compared to baseline. Thermographic recordings of respiratory muscles were delimited in ROI of the sternocleidomastoid (SCM), pectoral, and rectus abdominis intention area. Thermal captures and FEV1 were taken before and 5, 10, 15 and 30 min after EVH. Results: Twenty‐four (52.1%) of asthmatics had EIB‐cr. There was a decrease in temperature at 10 min after EVH test in the SCM, pectoral and rectus abdominis ROIs in all groups (both with p < 0.05). There was a decrease in temperature (% basal) in asthmatic with EIB‐cr compared to nonasthmatics in the rectus abdominis area (p < 0.05). Conclusion: There was a decrease in temperature in the ROIs of different muscle groups, especially in asthmatics. The greater drop in FEV1 observed in individuals with EIB‐cr was initially associated with a decrease in skin temperature, with a difference between the nonasthmatics in the abdominal muscle area. It is likely that this decrease in temperature occurred due to a temporary displacement of blood flow to the most used muscle groups, with a decrease in the region of the skin evaluated in the thermography. [ABSTRACT FROM AUTHOR]

Objective: Successful outcomes in head and neck surgery rely on maintaining perfusion in pedicled skin flaps. Thermal imaging offers a noninvasive means to assess tissue perfusion, potentially aiding in predicting flap viability. This pilot study explores the utility of SBTI (smartphone-based thermal imaging) for predicting flap vitality and monitoring during surgery. Methods: Thermal imaging was employed using the FLIR One System. An imaging protocol was established, defining points of interest (T1-T4) on pedicled skin flaps. Conducted over four months, the study integrated SBTI into reconstructive surgery for the face, head and neck defects post-tumor resections. SBTI's effectiveness was assessed with n = 11 pedicled flaps, capturing images at key stages and correlating them with clinical flap assessment. Thermal images were retrospectively graded by two surgeons, evaluating flap perfusion on a scale from 1 to 5, based on temperature differences (1 = ΔT < 2 °C, 2 = ΔT ≥ 2 °C, 3 = ΔT ≥ 4 °C, 4 = ΔT ≥ 6 °C, and 5 = ΔT ≥ 8 °C), with assessments averaged for consensus and compared with the clinical assessment control group. Results: The study encountered challenges during implementation, leading to the exclusion of six patients. Patient data included 11 cases with n = 44 SBTI images. Intraoperative assessments consistently showed good perfusion. One postoperative dehiscence was noted, which retrospectively coincided with intraoperative SBTI grading, but not with clinical assessment. Statistical analysis indicated consistent outcomes following clinical and SBTI assessments. Thermal imaging accurately predicted flap viability, although it had limitations with small flaps. Conclusion: SBTI proved effective, inexpensive, and noninvasive for assessing tissue perfusion, showing promise for predicting flap viability and intraoperative monitoring in head and neck surgery. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the demoulding characteristics of material-jetted rapid mould inserts having different surface textures for micro-injection moulding using in-line measurements and surface metrology. Design/methodology/approach: Material-jetted inserts with the negative cavity of a circular test product were fabricated using different surface finishes and printing configurations, including glossy, matte and vertical settings. In-line measurements included the recording of demoulding forces at 10 kHz, which was necessary to capture the highly-dynamic characteristics. A robust data processing algorithm was used to extract reliable demoulding energies per moulding run. Thermal imaging captured surface temperatures on the inserts after demoulding. Off-line measurements, including focus variation microscopy and scanning electron microscopy, compared surface textures after a total of 60 moulding runs. Findings: A framework for capturing demoulding energies from material-jetted rapid tools was demonstrated and compared to the literature. Glossy surfaces resulted in significantly reduced demoulding forces compared to the industry standard steel moulds in the literature and their material-jetted counterparts. Minimal changes in the surface textures of the material-jetted inserts were found, which could potentially permit their prolonged usage. Significant correlations between surface temperatures and demoulding energies were demonstrated. Originality/value: The research presented here addresses the very topical issue of demoulding characteristics of soft, rapid tools, which affect the quality of prototyped products and tool durability. This was done using state-of-the-art, high-speed sensing technologies in conjunction with surface metrology and their durability for the first time in the literature. [ABSTRACT FROM AUTHOR]

The thermal analysis of the equipment in this work paper, has an important role (for planning maintenance activities), because this machine ensures optimal loading with different material treatment solutions, intended for mattresses, according to customer requirements. In the present paper the authors conducted a research on the whole technological flow in order to plan predictive maintenance activities using online monitoring methods. The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. With one motor not functioning, the ventilation system may not be able to circulate enough air to properly cool the treated material. This can lead to overheating and potential damage to the material. Without all motors working together, the airflow and temperature within the treatment equipment may become uneven. The increased heat caused by a lack of proper ventilation can also harm the equipment itself, potentially causing mechanical failures or other issues. The failure of one ventilation motor can have significant negative impacts on the overall performance and effectiveness of the treatment process. It is important to promptly address and repair any malfunctioning motors to ensure the continued operation of the equipment and maintain the quality of the treated material. This paper presents only a part of the research carried out and in a future paper the rest of the analyses performed on the Squeezing Pader Machine will be presented. [ABSTRACT FROM AUTHOR]

Simple Summary: Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. This study aims to develop and evaluate an advanced algorithmic approach that integrates thermal imaging, statistical texture analysis, and t-distributed stochastic neighbor embedding (t-SNE) to accurately detect subclinical mastitis in dairy sheep. This approach focuses on improving the accuracy and reliability of non-invasive subclinical mastitis detection by using more sophisticated algorithmic procedures than traditional temperature differential methods, thereby enhancing livestock management and animal health monitoring. Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices. [ABSTRACT FROM AUTHOR]

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes. [ABSTRACT FROM AUTHOR]

Background: Models depicting sexual desire as responsive to sexual arousal may be particularly apt for women experiencing arousal or desire difficulties, and the degree to which arousal triggers desire may depend on the relationship context and desire target and timing—yet, these associations have not been directly tested among women with and without sexual interest/arousal disorder (SIAD). Aim: To assess the role of SIAD status and relationship satisfaction in the associations between genital arousal and 4 types of responsive desire. Methods: One hundred women (n = 27 meeting diagnostic criteria for SIAD) in romantic relationships with men viewed a sexual film (pleasurable intimate depiction of oral sex and penile-vaginal intercourse) while their genital arousal was recorded via vaginal photoplethysmography (n = 63) or thermal imaging of the labia (n = 37). Partner and solitary desire was assessed immediately before and after the film (immediate desire) and 3 days later (delayed desire). Outcomes: Outcomes consisted of genital response (z scored by method) and associations between genital response and responsive sexual desire. Results: The key difference between women with and without SIAD was not in their ability to experience genital arousal but in how their genital responses translated to responsive sexual desire. Women with SIAD actually exhibited greater genital arousal than unaffected women. Associations between genital arousal and desire were significant only for women with SIAD and depended on relationship satisfaction and desire type. For women with SIAD with low relationship satisfaction, higher arousal predicted lower immediate desire for a partner; for those with high relationship satisfaction, arousal was either positively related (vaginal photoplethysmography) or unrelated (thermal imaging of the labia) to immediate desire for a partner. Associations with other desire types were not significant. Clinical Implications: Patterns of genital arousal and partner-specific responsive desire among women affected with SIAD were indicative of an avoidance model in response to heightened genital arousal, unless relationship satisfaction was high; attending to genital arousal sensations could be a means of triggering sexual desire for women with SIAD who are satisfied in their relationships. Strengths and Limitations: This is one of the first sexual psychophysiologic studies to connect relationship factors to patterns of sexual response. The differing arousal assessment procedures and lack of official diagnosis may have attenuated results. The homogeneous sample and in-person session requirement limit generalizability. Conclusion: When compared with unaffected women, women affected by SIAD may exhibit stronger arousal responses with sufficiently incentivized sexual stimuli, and the connection between their genital arousal and responsive desire for their partners may be stronger and more dependent on relationship context. [ABSTRACT FROM AUTHOR]

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. Materials and Methods In patients undergoing free flap surgery, monitoring was done using standard clinical parameters and infrared thermography as per our institutional protocol. Mean temperature difference (∆T) between the flap and the surrounding skin was calculated using the temperature readings from the thermal images intra- and postoperatively. The accuracy of infrared thermography in flap monitoring was assessed in comparison to the standard clinical protocol. Results Forty-one flaps were included in the analysis, out of which five flaps got compromised. It was observed that the mean temperature difference was higher (mean ∆T 0.20–0.59 vs. 2.38–3.32) when there was a flap compromise, and this temperature difference was evident even before the development of clinical signs. The temperature difference in venous thrombosis (mean ∆T 1.0–2.7) was found to be slightly lower than in arterial insufficiency (mean ∆T 2.1–4.4). For a ∆T cutoff value of 2°C, the thermal camera had a sensitivity of 88.6%, specificity of 98.9%, positive predictive value of 93.9%, and negative predictive value of 97.7%. Conclusion Infrared thermography is a valuable and noninvasive objective tool in free flap monitoring, which can detect flap compromise (increasing value of ∆T) even before it becomes clinically evident. [ABSTRACT FROM AUTHOR]

Temperature influences nearly all aspects of fitness. However, reproduction is often more thermally sensitive than survival. Thermoregulation must maintain performance in both components of fitness to buffer populations from environmental change.We assessed the fitness benefits of thermoregulation in Enchenopa binotata treehoppers. Under realistic mesocosm conditions, we quantified fine‐scale microclimates using 3D‐printed operative temperature models. We then compared operative temperatures to treehopper body temperatures and translated patterns of thermoregulation into variation in survival and reproduction. We also assessed two thermoregulatory mechanisms: precise microclimate choice and heat‐escape behaviours. Finally, we applied our results to evaluate if arthropod thermoregulation is accurately characterized by two theoretical models commonly used to simulate responses to environmental change.We found substantial thermal variation at fine spatial scales relevant to insects: at a single point in time, temperatures within 30‐cm‐tall plants spanned ranges up to 19°C (23–42°C).Lethal operative temperatures were common when air temperatures were high. However, heat escapes allowed treehoppers to almost entirely avoid lethal temperatures.By contrast, individuals thermoconformed in the absence of lethal operative temperatures. This finding suggests that precise microclimate choice imposes high costs due to thermal uncertainty at fine spatial scales. Furthermore, given the narrow range of temperatures in which reproduction occurs, thermoregulation is unlikely to maintain reproduction.Thermoregulation was most effective in the lowest‐quality and most spatially variable thermal habitats. Treehopper thermoregulation therefore more closely follows cost–benefit models of thermoregulation compared to models that account for inhibited movement at extreme temperatures.Overall, even if thermoregulation can prevent lethal heat stress, it may have limited capacity to buffer arthropods and other small ectotherms from environmental change if it cannot maintain reproductive performance. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed. [ABSTRACT FROM AUTHOR]

Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments. [ABSTRACT FROM AUTHOR]

The lie-detection tests are traditionally carried out by well-trained experts using polygraph machines. However, it is time-consuming, invasive, and, overall, a cumbersome process, not admissible by the court of law. Trained individuals can easily flaunt these tests. On the other hand, facial thermal imaging could be more effective as it is noninvasive and could be a stealth method of tracking the facial blood flow patterns, which have been proven to detect deceit. This paper presents a method based on facial thermal imaging to detect deception in human subjects. The major issue in such research is the lack of proper real-life databases which emulate the crime scenes. In this paper, first, we have developed a database based on almost real-life theft incidents with due diligence using isolated subjects over a period of time at a government hospital under the plea of free health checkup. The experiment has been conducted at Midnapore Medical College and Hospital, West Bengal, India, with proper ethical committee approval. The participants are selected at the behest of the police department with habitual crime records. Most of them have been repeatedly charged with petty crimes of pick-pocketing and stealing. They are invited individually at different instances of time under the plea of medical checkup where they have been enticed to steal cash. It is followed by a two-stage process, a friendly interaction followed by a slightly tougher interrogation. Their forehead and periorbital region skin surface temperature are recorded by a hidden thermal camera. Upon analysis, conspicuous changes in the temperature profile and blood flow pattern have been observed in the individuals who stole money and those who did not. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to improve the accuracy and resolution of defect detection in glass fiber-reinforced plastic (GFRP) composites using pulse compression analysis for thermal wave imaging. GFRP materials, widely utilized in various industries owing to lightweight and durable properties, often present challenges in identifying subsurface defects. Traditional thermal wave imaging techniques face limitations in achieving high-resolution results. The study outlines the theoretical foundation of pulse compression and its application in thermal wave inspection. A comprehensive experimental setup was designed to validate the effectiveness of the proposed methodology. Results indicate a significant improvement in the localization and characterization of defects within GFRP composites. The findings of this research hold implications for nondestructive testing and quality control in industries relying on GFRP materials. The integration of pulse compression analysis into thermal wave imaging establishes a promising avenue for precise defect detection, contributing to the reliability and integrity of GFRP composite structures. Also, two key metrics, absolute thermal contrast and signal-to-background contrast, are used for quantitative analysis. [ABSTRACT FROM AUTHOR]

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Affect recognition, or the ability to detect and interpret emotional states, has the potential to be a valuable tool in the field of healthcare. In particular, it can be useful in gamified therapy, which involves using gaming techniques to motivate and keep the engagement of patients in therapeutic activities. This study aims to examine the accuracy of machine learning models using thermal imaging and action unit data for affect classification in a gamified robot therapy scenario. A self-report survey and three machine learning models were used to assess emotions including frustration, boredom, and enjoyment in participants during different phases of the game. The results showed that the multimodal approach with the combination of thermal imaging and action units with LSTM model had the highest accuracy of 77% for emotion classification over a 7-s sliding window, while thermal imaging had the lowest standard deviation among participants. The results suggest that thermal imaging and action units can be effective in detecting affective states and might have the potential to be used in healthcare applications, such as gamified therapy, as a promising non-intrusive method for recognizing internal states. [ABSTRACT FROM AUTHOR]

The underground water pipeline system is a crucial infrastructure that largely remains out of sight. However, it is the source of a clean and uninterrupted flow of water for our everyday lives. Various factors, including corrosion, material degradation, ground movement, and improper maintenance, cause pipe leaks, a silent crisis that causes an estimated 39 billion dollars of loss every year. Prompt leakage detection and localization can help reduce the loss. This research investigates the potential of two machine learning models as supporting tools for surveying extensive areas to identify and pinpoint the location of underground leaks. The presented combined approach ensures the speed and accuracy of the leakage survey. The first machine learning model is a hybrid ML model that employs thermal imaging to identify subterranean water leakage. It relies on detecting thermal anomalies and distinctive signatures associated with water leakage to identify and locate underground water leakage. The developed model can detect up to 750 mm underground leakage with 95.20 % accuracy. The second model uses binaural audio from geophones to localize the leakage position. The model utilizes interaural time difference and interaural phase difference for localization purposes, and the 1D-CNN network delivers an angle in twenty-degree increments with an accuracy of 88.19 %. Large-scale implementation of the proposed model could be a powerful catalyst to reduce water loss in the water supply system.

This article highlights the significance of parent-child interactions in shaping the neurobiological development of children and the mental health of parents. It emphasizes the impact of these interactions on various aspects of child development, such as brain development, emotional regulation, social information processing, and attachment formation. Positive interactions support secure attachment and adaptive stress responses, while negative interactions can disrupt these processes. The article calls for research that integrates social and neurobiological perspectives to enhance our understanding of these early connections and develop interventions that promote the well-being of both parents and children. The document also provides references to research articles and books that delve into the theoretical, methodological, and outcomes aspects of the mother-infant relationship, as well as the social interaction and developmental processes involved. It also touches upon the concept of early childhood trauma as a dynamic developmental process that arises from chronic and repeated experiences. This collection of resources offers valuable insights for researchers and library patrons interested in comprehending the complexities of mother-infant interactions and their impact on early childhood development. [Extracted from the article]