Search Results (1,195)

Wire Arc Additive Manufacturing (WAAM) is a cost-effective method for fabricating large aluminum components; however, it tends to suffer from heat accumulation and coarse anisotropic microstructures, which can limit the part’s performance. In this study, a wall is fabricated using a hybrid unified additive deformation manufacturing process (UAMFSP) method, which integrates friction stir processing (FSP) into WAAM, and is compared with a Metal Inert Gas (MIG)-based WAAM wall. Infrared (IR) thermography revealed progressive heat buildup in MIG walls, with peak layer temperatures of about 870 to 1000 °C. In contrast, in the UAMFSP process, heat was redistributed through mechanical stirring, maintaining more uniform sub-solidus profiles below approximately 400 °C. Also, optical microscopy and quantitative image analysis showed that MIG walls developed coarse, dendritic grains with a mean grain area of about 314 µm², whereas the UAMFSP produced refined, equiaxed grains with a mean grain area of about 10.9 µm². Microhardness measurement (Vickers HV0.2, 200 gf) confirmed that the UAMFSP process can improve the hardness by 45.8% compared to the MIG process (75.8 ± 7.7 HV vs. 52.0 ± 1.3 HV; p = 0.0027). In summary, the outcomes of this study introduce the UAMFSP process as a method for addressing the thermal and microstructural limitations of WAAM. These findings provide a framework for further extending hybrid additive–deformation strategies to thicker builds, alternative alloys, and service-relevant mechanical evaluations. Full article

Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their practical use is limited by invasiveness, discomfort, and restricted feasibility for continuous monitoring in daily-life or field environments. Consequently, extensive efforts have been devoted to developing non-invasive CBT measurement and estimation techniques. This review provides an application-oriented synthesis of invasive reference methods and representative non-invasive approaches, including in-ear sensors, infrared thermography, ingestible telemetric sensors, heat-flux-based techniques, and model-based estimation using wearable physiological signals. For each approach, measurement principles, accuracy, invasiveness, usability, and application domains are comparatively examined, with particular emphasis on trade-offs between measurement fidelity and real-world implementability. Rather than ranking methods by absolute performance, this review highlights their relative positioning across clinical, occupational, and daily-life contexts. While no single non-invasive technique can universally replace invasive gold standards, recent advances in wearable sensing, heat-flux modeling, and multimodal estimation demonstrate growing potential for practical CBT monitoring. Overall, the findings suggest that future CBT assessment will increasingly rely on hybrid and context-aware systems that integrate complementary methods to enable reliable monitoring under real-world conditions. This review is intended for researchers and practitioners who need to select or design CBT monitoring systems. Full article

This study presents a measurement–modeling pathway for diagnosing corrosion-driven contact degradation in power equipment using infrared thermography and color-channel analysis. Thermal data were acquired with a Fluke Ti450 (LWIR, 7.5–14 $\mathsf{\mu }\mathrm{m}$) under typical high-altitude, temperate conditions in Quito, Ecuador. Radiometric parameters (emissivity, distance, ambient/reflected temperature, and humidity) are reported explicitly, and images are processed with a reproducible pipeline that combines adaptive thresholding, morphology, and region-of-interest statistics, including $\Delta T$ relative to a reference region. A worked example links an observed hotspot to emissivity-corrected temperature and discusses qualitative implications for the effective contact resistance $R_{\mathrm{eff}}$. Uncertainty is summarized through a per-case template that propagates $u_{\Delta T}$ to $u\left(R_{\mathrm{eff}}\right)$ and Weibull characteristic life $\eta$. Environmental influences (solar load, wind, and emissivity variability) are acknowledged and mitigated. Two field cases illustrate the approach to substation assets. Because the dataset comprises single-visit inspections, formal parameter estimation (e.g., EIS-validated $R_{\mathrm{eff}}$ and full Weibull/Arrhenius fits) is reserved for longitudinal follow-up. By making radiometry, processing steps, and limitations explicit, the study reduces ambiguity in the transition from temperature contrast to physics-based interpretation and supports auditable maintenance decisions. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

This study investigates the influence of surface texturing on temperature distribution in lubricated sliding contacts using infrared thermography. The work addresses the broader challenge of understanding thermal effects in conformal hydrodynamic contacts, where localized heating and viscosity variations can significantly affect tribological performance. A pin-on-disc configuration was employed, featuring steel pins with laser-etched micro-dimples that slid against a sapphire disc, allowing for thermal imaging of the contact zone. A dual-bandpass filter infrared thermography technique was developed and rigorously calibrated to distinguish between the temperatures of the steel surface and the lubricant film. Friction measurements and laser-induced fluorescence were used in parallel to assess contact conditions and the behavior of the lubricant film. The results show that surface textures can alter local frictional heating and contribute to non-uniform temperature distributions, particularly in parallel contact geometries. Lubricant temperature was consistently higher than the surface temperature, highlighting the role of shear heating within the fluid film. However, within the tested parameter range, no unambiguous viscosity-wedge signature was identified beyond the dominant temperature-driven viscosity reduction captured by the in situ correction. The method provides a novel means of experimentally resolving temperature fields in sliding textured contacts, offering a valuable foundation for validating thermo-hydrodynamic models in lubricated tribological systems. Full article

Residential energy renovation remains a central pillar of climate mitigation and social sustainability strategies, yet renovation rates persistently lag behind policy targets, particularly in older urban neighborhoods. This study investigates the underlying causes of renovation inertia using a neighborhood-scale mixed-methods approach that combines door-to-door household surveys, façade infrared thermography, and expert focus groups. Using a post-industrial residential district in Liège, Belgium, as an exploratory case, the study jointly analyzes building conditions, household characteristics, and renovation contexts. The results reveal that renovation failure cannot be explained solely by technical deficiencies. Instead, three interacting socio-technical mechanisms emerge: adaptive occupant behaviors that mask poor building performance, a constrained renovation agency shaped by tenure and income asymmetries, and the stratification of energy awareness along social lines. Together, these mechanisms reinforce a form of renovation lock-in in which technical degradation, behavioral adaptation, and institutional fragmentation mutually sustain inaction. By integrating physical diagnostics with social and experiential data, the study explains why conventional incentive-based renovation policies systematically underperform in comparable urban contexts. Rather than treating energy renovation as a purely technical or economic decision, the findings highlight the need for policy instruments that explicitly address agency constraints, behavioral compensation, and unequal exposure to energy-related risks. The proposed mixed-method framework is transferable to other urban neighborhoods and offers a replicable approach for diagnosing renovation barriers, supporting more socially sustainable energy transition strategies. Full article

Landslides are complex geological phenomena that pose significant hazards to human life, infrastructure, and the environment. Understanding their mechanisms requires reliable data and advanced analytical methods. Thermal surveys offer valuable insights into surface temperature variations and moisture distribution, supporting the detection of precursory signs of slope instability. Numerical modeling, in turn, enables the simulation of physical processes that control landslide activation and propagation, as well as the prediction of potential landslide-affected zones. This study presents a bibliometric analysis of Scopus-indexed publications from January 2005 to March 2025, focusing on the integration of thermal surveys and numerical modeling in landslide research. The results highlight a steady increase in publications over the past two decades, reflecting growing interest in these innovative approaches. China and Italy are the leading contributors in terms of the number of publications, while Italy achieved the highest citation impact, with 445 total citations. These findings highlight the emerging research trends, showing the potential of combining thermal and thermo-numerical methods to enhance landslide monitoring and mitigation strategies. Full article

Thin liquid film evaporation leverages latent heat and low thermal resistance to achieve superior heat transfer capabilities, making it pivotal for next-generation high-heat-flux thermal management systems. This paper presents a systematic review of the fundamental mechanisms, interfacial transport behaviors, and experimental techniques associated with static thin films and falling liquid films. This work elucidates the complex coupling of Marangoni convection, van der Waals disjoining pressure, and contact line dynamics. These mechanisms collectively govern film stability and the intensity of non-equilibrium phase change in the micro-region. The influence of surface wettability and dynamic contact angle hysteresis on hydraulic replenishment and dry spot formation is critically analyzed, offering insights into optimizing surface engineering strategies. In addition, the review categorizes advanced non-intrusive diagnostics, including optical interferometry, laser-induced fluorescence (LIF), and infrared thermography, evaluating their capacity to resolve spatiotemporal variations in film thickness (ranging from 10 nm to several μm) and temperature under complex boundary conditions. Special attention is directed toward falling film evaporation over horizontal tubes, addressing flow regime transitions and the impact of interfacial shear from external airflow. The work concludes by identifying key challenges in multi-physics coupling and proposing future directions for synchronized diagnostics and adaptive surface design. Full article

Background/Objectives: Cellulite is a common aesthetic condition in women, traditionally assessed using visual inspection and palpation-based scales that are inherently subjective. Therefore, image-based methods that may support standardized severity grading are of growing interest. To evaluate infrared thermography as an imaging-based method for grading cellulite severity and to perform methodological validation of a newly developed thermographic classification scale by comparing it with clinical palpation and anthropometric parameters. Methods: This retrospective, non-interventional study analyzed anonymized clinical and thermographic data from 81 women with clinically assessed cellulite. Cellulite severity was evaluated using the Nürnberger–Müller palpation scale and a newly developed five-point thermographic scale based on skin surface temperature differentials and histogram pattern analysis. The associations between the assessment methods were evaluated using ordinal statistical measures, and agreement was assessed using weighted Cohen’s kappa statistics. Results: Thermographic grading demonstrated high agreement with palpation-based assessment, with a percentage agreement of 93.8% and an almost perfect agreement based on weighted Cohen’s κ. A strong ordinal association was observed between the methods. Thermography consistently classified a subset of cases as one grade higher compared with palpation. No statistically significant associations were observed between thermographic grade and body mass index or waist-to-hip ratio. Conclusions: Infrared thermography enables image-based grading of cellulite severity and shows a strong concordance with established palpation scales. The proposed thermographic classification provides preliminary methodological validation of an imaging-based grading approach. Further multicenter studies involving multiple assessors and diverse populations are required to assess reproducibility, specificity, and potential clinical applicability. Full article

High-rise building facades are prone to defects due to prolonged exposure to complex environments. Infrared detection, as a commonly employed method for facade defect inspection, often results in low accuracy owing to abundant interferences and blurred defect boundaries. In this work, an intelligent defect detection method for high-rise building facades is proposed. In the first stage of the proposed method, a segmentation model based on DeepLabV3+ is proposed to remove interferences in infrared images using masks. The model incorporates a Post-Decoder Dual-Branch Boundary Refinement Module, which is subdivided into a boundary feature optimization branch and a boundary-guided attention branch. Sub-pixel-level contour refinement and boundary-adaptive weighting are hence achieved to mitigate edge blurring induced by thermal diffusion and to enhance the perception of slender cracks and cavity edges. A triple constraint mechanism is also introduced, combining cross-entropy, multi-scale Dice, and boundary-aware losses to address class imbalance and enhance segmentation performance for small targets. Furthermore, superpixel linear iterative clustering (SLIC) is utilized to enforce regional consistency, hence improving the smoothness and robustness of predictions. In the second stage of the proposed method, a defect detection model based on YOLOV11 is proposed to process masked infrared images for detecting hollow, seepage, cracks and detachment. This work validates the proposed method using 180 infrared images collected via unmanned aerial vehicles. The experimental results demonstrate that the proposed method achieves a detection precision of 89.7%, an mAP@0.5 of 87.9%, and a 57.8 mAP@50-95. surpassing other algorithms and confirming its effectiveness and superiority. Full article

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (T_S) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution T_S collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR T_S averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR T_S strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR T_S explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning. Full article

Early diagnosis of heat load in beef cattle remains a challenge due to the limited understanding of behaviour-based indicators. This preliminary longitudinal study aimed to validate behavioural and physiological responses previously identified as heat load indicators. Black Angus steers were exposed to high environmental temperatures expected to cause heat load in the following sequence: an initial thermoneutral period, a hot period, and a recovery period. Changes in the positioning of key body parts, feeding behaviour, body maintenance, respiratory dynamics, and eye temperature were monitored. In the hot period, cattle increased their respiration rate, panting, and infrared eye temperature. Increased stepping by their left limbs suggested involvement of the right brain hemisphere in a stress response to high environmental temperatures. Cattle also held their heads more downward, ears backward, and their tail vertical, and reduced eating, grooming, and scratching during the hot period. Cattle responses to hot conditions were persistent in the recovery period, reflecting diagnostic relevance of the head, ear, and tail movements, stepping, especially by left limbs, and infrared eye temperature as non-invasive tools to identify heat load condition in cattle. The study reinforces our understanding of the specific behavioural and physiological responses to heat load condition, especially those involving left-limb stepping, ear and tail posture, and infrared eye temperature, are reliable indicators for identifying cattle experiencing high environmental temperature. Full article

Background: Prolonged sitting has been associated with adverse cardiovascular and neuromuscular responses; however, the temporal onset of these acute physiological changes remains unclear. This study aimed to determine the acute effects of prolonged sitting on blood flow, blood pressure, and muscle activity. Methods: A non-controlled clinical trial was conducted with 21 healthy adults (22.5 ± 1.60 years), both male and female. Participants remained seated continuously for three hours, with data collected every 20 min, including infrared thermography, blood pressure, and electromyographic activity. Skin temperature was measured using infrared thermography on the calf region of both legs, and the mean temperature was analyzed. Systolic and diastolic blood pressure were measured using an oscillometric device, and mean arterial pressure was subsequently calculated. Muscle activity was assessed through surface electromyography, using median frequency and root mean square values. Statistical analysis was performed using the Friedman test and the Durbin–Conover post hoc test, along with a subjective trend analysis of each variable over time. Results: A significant reduction was observed in both calf skin temperature and median frequency after 60 min of uninterrupted sitting (p < 0.05). Mean and systolic blood pressure exhibited an increasing trend after 160 min (p < 0.05). Conclusions: The exposure–response data from this study may contribute to the planning of future interventions aimed at refining recommendations for breaking up prolonged sitting periods. Full article

In this study, a three-dimensional Inverse Conjugate Heat Transfer Problem (ICHTP) is numerically and experimentally investigated to estimate the heat-source strength of multiple chips mounted on a printed circuit board (PCB) using the Conjugate Gradient Method (CGM) and infrared thermography. The interfaces between the PCB and the surrounding air domain are assumed to exhibit perfect thermal contact, establishing a fully coupled conjugate heat transfer framework for the inverse analysis. Unlike the conventional Inverse Heat Conduction Problem (IHCP), which typically only accounts for conduction within solid domains, the present ICHTP formulation requires the simultaneous solution of the governing continuity, momentum, and energy equations in the air domain, along with the heat conduction equation in the chips and PCB. This coupling introduces substantial computational complexity due to the nonlinear interaction between convective and conductive heat transfer mechanisms, as well as the sensitivity of the inverse solution to measurement uncertainties. The numerical simulations are conducted first with error-free measurement data and an inlet velocity of u_in = 4 m/s; the recovered heat-sources exhibit excellent agreement with the true values. The computed average errors for the estimated temperatures ERR1 and estimated heat sources ERR2 are as low as 0.0031% and 1.87%, respectively. The accuracy of the estimated heat sources is then experimentally validated under various prescribed inlet air velocities. During experimental verification at an inlet velocity of 4 m/s, the corresponding ERR1 and ERR2 values are obtained as 0.91% and 3.34%, while at 6 m/s, the values are 0.86% and 2.81%, respectively. Compared with the numerical results, the accuracy of the experimental estimations decreases noticeably. This discrepancy arises because the numerical simulations are free from measurement noise, whereas experimental data inherently include uncertainties due to thermal picture resolutions, environmental fluctuations, and other uncontrollable factors. These results highlight the inherent challenges associated with inverse problems and underscore the critical importance of obtaining precise and reliable temperature measurements to ensure accurate heat source estimation. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article