Thermal Image Data Research Articles

Automated image-based fire detection and alarm system using edge computing and cloud-based platform

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Thermographic assessment of mastitis progression in sahiwal cattle: Insights into the patterns in the natural course of infection

Mastitis is a global concern in the dairy sector, demanding innovative solutions for effective management for quality lifetime milk production. In this study, infrared thermography (IRT) as a non-invasive technology was integrated into routine farm activities for continuous health monitoring of animals. For 30 days, we systematically monitored the udder health status in 40 Sahiwal cows (160 quarters), employing IRT along with the California Mastitis Test (CMT). We also assessed somatic cell count (SCC), microbial identification, and milk quality parameters of representative samples. The thermal imaging data was analyzed, considering both backward propagation from the 0th day to the −10th day and forward propagation from the 0th day to the +10th day. Our findings revealed that on the 0th day, the mean temperatures of the udder surface skin temperature (USST) and teat skin surface temperature (TSST) exhibited differences (p < 0.05) between the quarters affected by sub-clinical mastitis (SCM) and clinical mastitis (CM) in comparison to the healthy quarters, with the highest degree of difference observed. The observed temperature differences between CM and SCM quarters compared to healthy ranged from 1.8 to 3.62 °C and 0.98 to 3.23 °C for USST, and from 1.68 to 3.16 °C and 0.56 to 2.32 °C for TSST, respectively. Furthermore, our observations indicated that both udder and teat quarters responded differently to mastitis. A temperature rise of 1.37 °C in SCM quarters and 1.75 °C in CM quarters was observed between the −10th and -8th day relative to day 0, with the increase being more pronounced in the morning hours. Also, a notable temperature surge occurred during the -2nd and -1st days relative to the 0th day. The log10SCC values and milk quality parameters significantly differed (p < 0.05) between mastitis-affected and healthy samples. In addition, Staphylococcus spp. was identified as the predominant mastitis-causing pathogen in the bacteriological identification conducted in this study. Therefore, IRT efficiently assesses the initiation point of udder infection in Sahiwal cows, aiding in effective udder health management.

Gas Detection and Classification Using Multimodal Data Based on Federated Learning.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long-short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches.

Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems

The world’s energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are needed. Early detection of PV faults is vital for enhancing the efficiency, reliability, and safety of PV systems. Thermal imaging emerges as an efficient and effective technique for inspection. On the other hand, evidence indicates that monitoring inverters within a solar energy farm reduces maintenance expenses and boosts production. Optimizing the efficiency of solar energy farms necessitates comprehensive analytics and data on every inverter, encompassing voltage, current, temperature, and power. In this study, our objective was to perform two distinct fault analyses utilizing image processing techniques with thermal images and machine learning techniques using inverter and other physical data. The results show that hotspot and bypass failures on the panels can be detected successfully using these methods.

Consistency-constrained RGB-T crowd counting via mutual information maximization

The incorporation of thermal imaging data in RGB-T images has demonstrated its usefulness in cross-modal crowd counting by offering complementary information to RGB representations. Despite achieving satisfactory results in RGB-T crowd counting, many existing methods still face two significant limitations: (1) The oversight of the heterogeneous gap between modalities complicates the effective integration of multimodal features. (2) The absence of mining consistency hinders the full exploitation of the unique complementary strengths inherent in each modality. To this end, we present C4-MIM, a novel Consistency-constrained RGB-T Crowd Counting approach via Mutual Information Maximization. It effectively leverages multimodal information by learning the consistency between the RGB and thermal modalities, thereby enhancing the performance of cross-modal counting. Specifically, we first advocate extracting feature representations of different modalities in a shared encoder to moderate the heterogeneous gap since they obey the identical coding rules with shared parameters. Then, we intend to mine the consistent information of different modalities to better learn conducive information and improve the performance of feature representations. To this end, we formulate the complementarity of multimodality representations as a mutual information maximization regularizer to maximize the consistent information of different modalities, in which the consistency would be maximally attained before combining the multimodal information. Finally, we simply aggregate the feature representations of the different modalities and send them into a regressor to output the density maps. The proposed approach can be implemented by arbitrary backbone networks and is quite robust in the face of single modality unavailable or serious compromised. Extensively experiments have been conducted on the RGBT-CC and DroneRGBT benchmarks to evaluate the effectiveness and robustness of the proposed approach, demonstrating its superior performance compared to the SOTA approaches.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress.

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.

A Two-Stage Registration Strategy for Thermal–Visible Images in Substations

The analysis of infrared video images is becoming one of the methods used to detect thermal hazards in many large-scale engineering sites. The fusion of infrared thermal imaging and visible image data in the target area can help people to identify and locate the fault points of thermal hazards. Among them, a very important step is the registration of thermally visible images. However, the direct registration of images with large-scale differences may lead to large registration errors or even failure. This paper presents a novel two-stage thermal–visible-image registration strategy specifically designed for exceptional scenes, such as a substation. Firstly, the original image pairs that occur after binarization are quickly and roughly registered. Secondly, the adaptive downsampling unit partial-intensity invariant feature descriptor (ADU-PIIFD) algorithm is proposed to correct the small-scale differences in details and achieve finer registration. Experiments are conducted on 30 data sets containing complex power station scenes and compared with several other methods. The results show that the proposed method exhibits an excellent and stable performance in thermal–visible-image registration, and the registration error on the entire data set is within five pixels. Especially for multimodal images with poor image quality and many detailed features, the robustness of the proposed method is far better than that of other methods, which provides a more reliable image registration scheme for the field of fire safety.

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

A Study on Defect Detection of Dissimilar Joints in Cu-STS Tubes Using Infrared Thermal Imaging of Induction Heating Brazing

We proposed a novel detection method for identifying joint defects in the brazing process between copper tubes and stainless steel using a convolutional neural network (CNN) model. The brazing joints were created using high-frequency induction heating equipment, and infrared thermal imaging cameras were employed to capture the thermal data generated during the jointing process. The experiments involved 15.88 mm diameter copper tubes commonly used in plate heat exchangers, stainless-steel tubes, and filler metal containing 20% Ag. The thermal data were obtained with a resolution of 80 × 80 pixels per frame, resulting in 4796 normal joint data and 5437 defective joint data collected over 100 high-frequency induction-heating brazing experiments. A total of 10,233 thermal imaging data were categorized into 6548 training data, 1638 validation data, and 2047 test data for the development of the predictive model. We designed CNN models with varying hyperparameters, specifically the number of kernel filters and nodes, to evaluate their impact on detection performance. A comparative analysis revealed that a CNN model structure, exhibiting 98.53% accuracy and 99.82% recall on test data, was the most effective. The selected CNN-based defect prediction model demonstrated the potential of using CNN models to discern joint defects in tube configurations that are challenging to identify visually. This study opens avenues for applying CNN-based models for detecting imperfections in complex tube structures.

Learning from data to improve asphalt road construction processes

Asphalt road construction projects pose a lot of challenges in different disciplines, from the choice of construction methods, type and quantity of machines and personnel, pre-planning of all processes, ordering material from asphalt mixing plants, real-time logistics of handling material transport, and the asphalt paving and compaction process itself. Depending on the complexity and size of the construction site, preparation and execution can be influenced by a lot of unknown variables, like weather impact, material properties of the delivered asphalt or delays in the deliveries themselves. These circumstances might lead to irregularities in the asphalt material temperatures and thus suboptimal conditions for compaction or even construction stoppage - in any case, they lead to a reduction in quality of the built asphalt road. In recent years, a lot of mobile devices, sensors and software have been emerging as viable solutions in the construction industry to digitize and support these processes. Those real-time applications allow deeper insight into logistics or material properties on site and acquire data for downstream analysis. These endeavours in process optimization now benefit construction companies to increase efficiency, and for work safety and environmental aspects, it is crucial to optimize energy and CO2 consumption. Improved logistics and building roads with low-temperature asphalt will have to become a standard in road construction. The present study gives an overview of the digitalized processes in asphalt road construction with market-available solutions focusing on logistics data of the material delivery chain from mixing plant to paver, thermal imaging data of the built asphalt to validate asphalt temperatures, as well as machine and location data from asphalt paver and compaction rollers.

Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach.

Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).

CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

AbstractChronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease.

The use of Thermography to Determine the Boundary Conditions of Heat Exchange on The Inner Surfaces of Translucent Enclosing Structures

The paper presents an approach to determining the boundary conditions of heat transfer (heat transfer coefficients) on the inner surfaces of translucent enclosing structures using thermal imaging data. To implement this approach, it is necessary to use additional equipment in the form of sheets of paper. A sheet of paper is installed close to and perpendicular to the investigated surface of the structure. At the same time, the profile of the sheet of paper exactly repeats the geometry of the surface under study. Based on the results of thermal imaging, thermograms and temperature graphs are constructed on characteristic areas of the inner surface of the translucent enclosing structure. They are used to determine the thickness of the boundary layer of air adjacent to the inner surface of the translucent enclosing structure. These data are used later to calculate the heat transfer coefficient at the inner surface of these structures. The presented approach was implemented in the course of field studies of heat transfer conditions at the internal surfaces of four PVC windows.

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input into a Mask Region-based Convolutional Neural Network for training. The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries. Moreover, the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network.

Multi-modal Affect Detection Using Thermal and Optical Imaging in a Gamified Robotic Exercise

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.

Performance assessment and resilience of solar mini-grids for sustainable energy access in Ghana

Mini-grids are increasingly adopted as a solution to extending electricity to remote communities in Africa. However, the system's resilience to meet increasing demand amidst degrading performance over time continues to be challenging. This study assessed mini-grid performance and explored factors that should be considered in scaling up mini-grids for sustainability in five island communities in Ghana's Volta Lake. Power generation and load data, visual inspection and thermal imaging data, and electrical systems performance characteristics data were collected and analysed using data loggers installed on site, the IEA PVPS Task 13 guidelines, and the IEC-TS 61724-3: 2016 guidelines, respectively. The study's findings indicate that the solar power conversion efficiency of the mini-grids was generally low and had a 25% - 45% performance ratio. This low efficiency was due to inefficiencies in the battery bank, distribution losses, and unused losses. The overall system efficiency of the mini-grids ranged from 4% to 7%. This is consistent with the literature, which reports that PV power systems in tropical climates have an efficiency of 5% – 15%. The research emphasises the importance of system maintenance, load management, and considering climate change's impact on mini-grid reliability and sustainability.

Mapping Tree Water Deficit with UAV Thermal Imaging and Meteorological Data

The mapping of forest stands and individual trees affected by drought stress is a crucial step in targeted forest management, aimed at fostering resilient and diverse forests. Unoccupied aerial vehicle (UAV)-based thermal sensing is a promising method for obtaining high-resolution thermal data. However, the reliability of typical low-cost sensors adapted for UAVs is compromised due to various factors, such as internal sensor dynamics and environmental variables, including solar radiation intensity, relative humidity, object emissivity and wind. Additionally, accurately assessing drought stress in trees is a complex task that usually requires laborious and cost-intensive methods, particularly in field settings. In this study, we investigated the feasibility of using the thermal band of the Micasense Altum multispectral sensor, while also assessing the potential for modelling tree water deficit (TWD) through point dendrometers and UAV-derived canopy temperature. Our indoor tests indicated that using a limited number of pixels (< 3) could result in temperature errors exceeding 1 K. However, enlarging the spot-size substantially reduced the mean difference to 0.02 K, validated against leaf temperature sensors. Interestingly, drought-treated (unwatered) leaves exhibited a higher root mean squared error (RMSE) (RMSE = 0.66 K and 0.73 K) than watered leaves (RMSE = 0.55 K and 0.53 K), likely due to lower emissivity of the dry leaves. Comparing field acquisition methods, the mean standard deviation (SD) for tree crown temperature obtained from typical gridded flights was 0.25 K with a maximum SD of 0.59 K (n = 12). In contrast, a close-range hovering method produced a mean SD of 0.09 K and a maximum SD of 0.1 K (n = 8). Modelling the TWD from meteorological and point dendrometer data for the 2021 growth season (n = 2928) yielded an R2 = 0.667 using a generalised additive model (GAM) with vapor pressure deficit (VPD), wind speed, and solar radiation as input features. A point dendrometer lag of one hour was also implemented. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity, and air temperature, an RMSE of 4.92 (μm) and R2 of 0.87 were achieved using a GAM. Implementing leaf-to-air pressure deficit (LVPD) as an input feature resulted in an RMSE of 6.87 (μm) and an R2 of 0.71. This novel single-shot approach demonstrates a promising method to acquire thermal data for the purpose of mapping TWD of beech trees on an individual basis. Further testing and development are imperative, and additional data from drought periods, point dendrometers, and high-resolution meteorological sources are required.

Keeping cool with poop: Urohidrosis lowers leg surface temperature by up to 6°C in breeding White storks

AbstractStorks (Ciconiidae) are renowned for defecating on their legs when exposed to high temperatures, a phenomenon known as “urohidrosis.” Previous work suggested that this behavior can cool down the blood supply to the legs and thus prevent hyperthermia in captive storks when overheated. However, no study has quantified the magnitude or duration of its evaporative cooling effect in free‐ranging birds. Here, we combine urohidrosis data with thermal imaging and microclimate data to investigate the thermoregulatory significance of urohidrosis in White storks Ciconia ciconia during the breeding season. We show that urohidrosis can reduce leg surface temperature by up to 6.7°C (4.40 ± 1.04°C). Yet its cooling effect was of short duration (lasting no more than 2.5 min) and decreased with time since defecation. Thus, for urohidrosis to significantly contribute to heat dissipation, storks must perform it repeatedly when overheated. Indeed, an individual can perform up to 11 urohidrosis events per hour; our estimates indicate that repeated urohidrosis could represent a significant amount of heat loss during short time span, equivalent to 4% of daily field metabolic rate for an adult stork. Our results point to an absence of differences in the cooling efficiency of urohidrosis between adults and nestlings, probably because all nestlings were recorded during the last phase of the ontogeny of thermoregulation. Besides, during the hottest days, adult storks delivered water to their nestlings, which might allow them to replenish body water reserves to sustain urohidrosis and other heat dissipation behaviors such as panting or gular fluttering. Our results indicate that urohidrosis might buffer the impact of high temperatures in breeding storks, preventing overheating and lethal hyperthermia. Gaining knowledge about behavioral thermoregulation in the heat is therefore crucial to better predict the future persistence and vulnerability of species under different climate warming scenarios.

Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool

This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. The proposed PINN method combines deep learning and physical principles to predict the temperature distributions in breast tissues and identify potential abnormal regions indicating the presence of tumors. The PINN model is normally trained by physics in terms of the residuals of the heat transfer equation, as well as boundary conditions with and without datasets of surface thermal imaging data concerning cancerous breast tissues, which can be used for future inverse thermal modeling to calculate tumor sizes and locations. The model is validated by comparing its predictions with those obtained by traditional Finite Element Analysis (FEA) for various cases. The comparison validates the PINN model as an accurate and fast method for thermal modeling and breast cancer diagnostic tool as the PINN simulation is found to be around 12 times faster than its FEM counterpart. The utilization of deep learning and physical principles in a diagnostic tool provides a non-invasive and safer alternative for breast self-examination compared to traditional methods such as mammography. These findings hold promise for the ongoing development of a new portable Artificial Intelligence (AI) tool for the early detection of breast cancer in breast self-examination as promoted by WHO, which is crucial for reducing mortality rates of breast cancer in the world.