Search Results (537)

Rapid urbanization and the widespread use of impervious materials have intensified the urban heat island (UHI) effect, raising surface temperatures and energy demands. Conventional concrete pavements contribute significantly due to their high thermal conductivity and low reflectivity. This study systematically investigates the development of thermally efficient concrete paver blocks using sustainable alternative fine aggregates to mitigate heat accumulation while retaining a minimum compressive strength of 35–45 MPa (recommended for medium traffic). Unlike prior isolated studies, this research offers a comprehensive comparative analysis of three sand replacements—Vermiculite powder (12.5–50%), Perlite powder (20–80%), and Crushed Glass (7.5–30%)—in M30-grade concrete. Fresh and hardened properties were evaluated through slump, density, and compressive strength tests at 7, 14, and 28 days, while infrared thermography quantified surface temperature variations under controlled heat exposure. Results showed significant thermal improvements, with optimal mixes Vermiculite 25% (VC-25), Perlite 40% (PR-40), and Crushed Glass 15% (CG-15) reducing surface temperatures by 25.1 °C, 22.2 °C, and 18.2 °C, respectively, while maintaining compressive strengths of 47.8 MPa, 38.8 MPa, and ~58 MPa. VC-25 proved superior, achieving the lowest surface temperature (26.3 °C) and 48.8% lower heat absorption than conventional concrete. The study establishes optimal replacement thresholds balancing insulation and strength, supporting SDGs 11, 12, and 13 through climate-responsive, resource-efficient construction materials. Full article

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

Background/Objectives: Non-ablative fractional lasers are widely used for scar remodeling, yet treatment parameters are often selected empirically, and thermal thresholds for consistent outcomes remain undefined. This study explored whether forward-looking infrared (FLIR) thermography can estimate laser-induced surface temperature changes during 1550 nm Er:Glass laser therapy and examined the association between post-treatment temperature elevation and early clinical improvement. Methods: A retrospective analysis was conducted on patients treated with fractional Er:Glass laser for post-surgical or traumatic scars. Skin temperature was recorded using FLIR C5 imaging at baseline (T₀), after topical anesthesia (T₁), and immediately post-treatment (T₂). The temperature change (ΔT₂) was calculated as T₂ − T₀. Clinical outcomes were assessed one month after treatment using standardized digital photographs and Vancouver Scar Scale (VSS) scores. Safety data were collected from post-procedure observations and patient reports. Results: Mean surface temperature increased from 32.4 ± 0.9 °C at T₀ to 33.7 ± 0.7 °C at T₂ (ΔT₂ = +1.3 ± 0.6 °C, p < 0.001). Hypertrophic scars showed higher ΔT₂ values than linear scars (p = 0.02). A moderate temperature elevation was modestly associated with early VSS improvement (r = 0.42, p = 0.003). Representative cases with ΔT₂ values around 1.5–2.5 °C exhibited favorable short-term changes in texture and pigmentation. No adverse events were observed during follow-up. Conclusions: Real-time FLIR thermography may provide a non-invasive method to indirectly assess surface thermal response during non-ablative fractional treatment. A moderate temperature increase may be associated with an exploratory thermal response range linked to early clinical improvement, but the findings are preliminary. Further prospective, controlled studies with standardized treatment parameters and longer follow-up are required to clarify whether ΔT₂ has clinical relevance as a physiologic parameter for temperature-based assessment in scar management. Full article

Introduction: Odontogenic infections may influence distant structures, including the eye. Their extension into the paranasal sinuses and orbital region can contribute to inflammatory and glaucomatous conditions. Case Report: A 46-year-old man was examined for a possible odontogenic source of chronic eye disease. The patient had an 18-year history of progressive vision loss in his left eye associated with Posner–Schlossmann syndrome, chronic uveitis, and episodic elevation of intraocular pressure (IOP). Imaging studies revealed the presence of a foreign body in the alveolar recess of the left maxillary sinus, as detected on panoramic radiography, cone-beam computed tomography (CBCT), and infrared thermography (IRT). Preliminary IRT examination showed marked thermal asymmetry (ΔT = 1.1 °C) between the left and right sides of the maxilla. Worsening of ocular symptoms and increased IOP despite steroid treatment prompted surgical treatment. The foreign body, identified as a root canal filling, was removed, and the chronically inflamed sinus mucosa was excised. During a follow-up visit two weeks later, the IRT examination showed a reduction in temperature difference (ΔT = 0.2 °C) and routine postoperative healing. After two months, no thermal asymmetry was found (ΔT = 0 °C), and an ophthalmological examination showed no active inflammation. Six months after the procedure, the patient remained asymptomatic, and the IRT examination revealed only minimal residual variability within the measurement tolerance (ΔT = 0.1 °C), consistent with the resolution of the sinus inflammation. Conclusions: This case highlights the value of interdisciplinary diagnostics in identifying odontogenic contributors to chronic ocular disease. Infrared thermography proved to be a helpful non-invasive adjunct for detecting and monitoring subclinical maxillary sinus inflammation. Full article

Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows. Full article

This study presents a modular diagnostic framework for evaluating thermal degradation in aging building envelopes by integrating infrared thermography, panoramic reconstruction, and deep learning-based semantic segmentation into a unified workflow. The methodology combines image registration, panoramic synthesis, façade component segmentation, and quantitative surface temperature analysis to provide scalable and reproducible diagnostics. By excluding fenestration zones—where infrared measurements are physically unreliable—the framework focuses on opaque wall regions and window surroundings to ensure physically meaningful evaluation. Field validation was conducted on a multi-story office building constructed in 1996. The diagnostic indicators revealed a mean wall surface temperature of 14.3 °C with a standard deviation of 5.6 °C, and a temperature factor ranging from 0.67 to 0.78 under measured conditions. The vulnerable area ratio reached 9.1% for walls, while window areas showed greater vulnerability at 12.74%, with anomalies concentrated at frame–glass interfaces and perimeter seals. These quantitative results confirmed the framework’s ability to detect thermal irregularities and visualize localized anomalies. More importantly, the contribution of this study lies in establishing a systematic and extensible diagnostic pipeline that advances building envelope analysis, supporting large-scale energy audits, retrofit prioritization, and sustainable building management. Full article

The present study aimed to apply machine learning algorithms to estimate respiratory rate (RR, breaths min⁻¹) and rectal temperature (RT, °C) as indicators of thermal stress in Dorper breeding rams, based on environmental and thermal variables obtained through infrared thermography. The algorithms Random Forest (RF) and Support Vector Regression (SVR) with radial kernel were employed, using ocular globe temperature (OGT), air temperature (AT), relative humidity (RH), and coat surface temperature (CST) as predictor variables, and rectal temperature (RT) and respiratory rate (RR) as response variables. Data were collected on a property located in Garanhuns, Pernambuco State, Brazil, under two environmental conditions (with and without climate control), totaling 20 monitored animals and 120 paired observations. Model performance was evaluated using the coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), complemented by cross-validation (k-fold = 10), and model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each predictor variable to model predictions. The results indicated that the RF model showed superior performance in predicting the physiological variables RR and RT, with higher coefficients (RR: R² = 0.858; RT: R² = 0.687) and lower error values. For RR, the RF model achieved RMSE = 16.38 and MAE = 13.33; while for RT, the errors were RMSE = 0.217 and MAE = 0.154. In contrast, the radial kernel SVR model showed lower performance, with R² values of 0.742 (RR) and 0.533 (RT), and RMSE and MAE values of 21.05 and 17.38 for RR, and 0.262 and 0.196 for RT, respectively. The application of machine learning-based models proved to be a viable and accurate alternative for estimating physiological indicators of thermal stress, contributing to the development of automated thermal management strategies for sheep in the Brazilian semi-arid region. The proposed data-driven approach demonstrates that low-cost thermal sensors combined with explainable artificial intelligence can support automatic decision-making for climate adaptation and animal welfare in semi-arid sheep production systems. Full article

Introduction: Infrared thermography (IRT) is a non-invasive, non-ionizing imaging modality capable of rapidly capturing surface temperature variation. In dentistry, particularly orthodontics and TMD evaluation, IRT may serve as a valuable complementary tool to be added in conventional diagnostic protocols. Objective: Correlate possible relationships between thermographic findings of orofacial structures and cephalometric landmarks. Methods: An infrared imaging camera, FLIR^® i7, was used to record the regions of interest, correspondent to the temporal, masseter and orbicular oris muscles, in adolescents (n = 22). Bilateral temperature differences were considered as thermal asymmetries with a conventional threshold of 0.3 °C to distinguish an eventual hyperactivity or hyperfunctions of detrimental structures. The Trevisi cephalometric parameters that were taken into consideration for the study were SNA, SNB, ANB, OccltoSn, Wits relation to base and Molar/canine classes. Results: Most of the participants showed a normal temperature difference ΔΤ for the upper and lower orbicular oris muscle, right vs. left, 96% and 92%, respectively. The other ROIs presented a mixed pattern of thermal asymmetries; however, no statistically significant differences were found when crossed with the cephalometric landmarks. Conclusions: Asymmetrical patterns of infrared thermography can aid on the diagnosis and treatment plan of an orthodontic appointment, since the actual stability of pos-orthodontic treatment is highly dependent on the muscular activity of the tongue and lips, in particular when the patient has atypical swallowing. Our findings suggest that this technique can be used to quantify anatomical landmarks relevant to craniofacial morphology in specific populations, particularly at ages where muscular functional activity is strongly correlated with dentoskeletal development. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

The aviation industry needs to develop sustainable, fire-safe cabin interior materials. Although wood is eco-friendly, its high flammability makes it challenging to meet flame retardant standards. Enhancing wood fire safety requires the creation of an environmentally friendly and flame retardant coating. In this study, a new type of intumescent flame retardant (IFR) coating was applied to the wood surface using the layer-by-layer (LBL) technique, with fully bio-based chitosan (CS), agar, and phytic acid (PA) as key components. The coated wood demonstrated improved durability, flame resistance, and thermal stability. Particularly, the Wood-2 sample achieved a vertical burning test (UL-94) V-0 rate and a limiting oxygen index (LOI) of 53.1%, which exceeded most previous reported flame retardant coatings. Cone calorimeter test and infrared thermography analysis confirmed that a thick layer of intumescent char formed when the coating was exposed to heat, effectively hindering heat transfer and oxygen supply. This flame retardant effect is attributed to a synergistic mechanism involving nitrogen/phosphorus (N/P) elements. This study offers an environmentally friendly solution for wood flame retardancy and lays an experimental and theoretical foundation for the development of green aviation interior materials. Full article

The reliability of electromechanical systems is a critical factor in modern Industry 4.0, as unexpected failures in induction motors or gearboxes can cause costly downtime, productivity losses, and increased maintenance demands. Infrared thermography offers a non-invasive and real-time means of monitoring thermal behavior, yet its effective use for fault diagnosis remains challenging due to sensitivity to noise, environmental variability, and the need for robust feature extraction. This work proposes a novel end-to-end convolutional neural network (CNN) methodology for detecting and classifying faults in electromechanical systems through the processing of infrared thermography images. The method integrates an automatic preprocessing stage that isolates the Relevant Heating Areas (RHAs), preserving their geometric and thermal descriptors while filtering irrelevant background information. A tailored data augmentation strategy, including controlled noise injection, was designed to improve robustness under realistic acquisition conditions. The CNN architecture combines 3 × 3 and 5 × 5 kernels to capture both fine-grained and global heating patterns. Experimental validation is carried out under nine different faulty conditions, achieving 99.7% accuracy and demonstrating strong resilience against Gaussian blur and additive Gaussian noise. The results suggest that the method provides a scalable, interpretable, and efficient approach for fault diagnosis in electromechanical systems within Industry 4.0 environments. Full article

Traditional centennial olive groves represent ecologically valuable agroecosystems that support both biodiversity and cultural heritage across Mediterranean landscapes. On Lesvos Island, Greece, which marks the westernmost limit of the Persian squirrel (Sciurus anomalus) distribution, these centennial olive trees serve as essential nesting resources for this regionally Vulnerable species. However, the tree-level mechanisms determining den-site suitability remain insufficiently understood. We examined 288 centennial olive trees, including 36 with confirmed dens, integrating structural, physiological, and thermal metrics to identify the attributes influencing den occupancy. Our results showed that squirrels consistently selected older and taller olives with broad crowns and high photosynthetic activity, indicating a preference for vigorous, architecturally complex trees that provide stable microclimatic conditions. Infrared thermography revealed that occupied trees exhibited lower trunk temperature asymmetries and stronger thermal buffering capacity, highlighting the role of microclimatic stability in den-site selection. Overall, our findings show that den-site selection in S. anomalus is shaped by the interplay of structural maturity, physiological performance, and thermal coherence. By linking tree function to den-site suitability, our work advances a mechanistic understanding of microhabitat selection and emphasizes the importance of centennial olive trees as biophysical refugia within traditional Mediterranean agroecosystems. Full article

Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This review consolidates recent advances in intelligent drilling, focusing on how sensors and artificial intelligence (AI) are integrated to enable process understanding, prediction, and control. In-process monitoring modalities (e.g., cutting forces/torque, vibration, acoustic emission, motor current/active power, infrared thermography, and vision) are examined with respect to signal characteristics, feature design, and modelling choices for real-time anomaly detection, tool condition monitoring, and phase/interface recognition. Predictive quality modelling of burr, delamination, roughness, and roundness is discussed across statistical learning, kernel methods, and neural and hybrid models. Offline parameter optimisation via surrogate-assisted and evolutionary algorithms is considered alongside adaptive control strategies. Practical aspects of robotic drilling and multifunctional end-effectors are highlighted as enablers of embedded sensing and feedback. Finally, cross-cutting challenges (e.g., limited, heterogeneous datasets and model generalisability across materials, tools, and geometries) are outlined, together with research directions including curated multi-sensor benchmarks, multi-source transfer learning, physics-informed machine learning, and explainable AI to support trustworthy deployment in aerospace manufacturing. Full article

Temperature is a key driver of microbial metabolism, yet non-invasive methods for quantifying microbially generated heat in complex environments remain limited. Here, we present a low-cost digital temperature sensing system integrated into an Arduino-controlled data acquisition setup to monitor microbial activity in stratified Winogradsky columns, which are self-contained sediment microcosms that reproduce natural oxygen and sulfide gradients. Localized temperature differences of up to 0.55 ± 0.04 °C were detected between aerobic and anaerobic layers, consistent with microbial heat generation in active sediment zones. Short-term insulation experiments further amplified these effects, demonstrating that microbial thermogenesis can serve as a reliable proxy for metabolic activity. Compared with infrared thermography or isothermal microcalorimetry, the proposed approach is simple, cost-effective, and compatible with aqueous and stratified systems. The method enables real-time, non-invasive observation of microbial metabolic dynamics and establishes a framework for continuous thermal monitoring in living environmental microcosms. Full article

This study investigates the Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy designed to enhance glass-forming ability. The alloy was synthesized by arc melting and examined using infrared thermography, differential scanning calorimetry (DSC), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Thermographic measurements revealed a temperature arrest at ~1007 K associated with eutectic crystallization, accompanied by contraction visible as a flattened ingot surface. DSC confirmed the dominant eutectic transformation (−170.7 J/g). Compared with the previously studied Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy, this composition showed a simplified transformation sequence and a larger eutectic fraction. DSC of melt-spun ribbons demonstrated a three-step crystallization (659 K, 699 K, 735–773 K, completion ~820 K) with a total enthalpy of 180.4 J/g. The broad crystallization interval (ΔTc ≈ 161 K) indicates enhanced thermal stability compared with simpler Ni–P or Fe–Ni–P–C alloys. SEM/EDS observations revealed eutectic colonies with predominantly rod-like morphology and chemical partitioning in inter-colony regions, favoring precipitation of transition metal phosphides. XRD confirmed four crystalline phases (Fe–Ni, CrCoP, Ni₃P, MnNiP) in ingots, while ribbons exhibited a fully amorphous structure. These findings demonstrate that Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ possesses good glass-forming ability but forms multiple phosphides under slower cooling. Precise cooling control is thus essential for tailoring its amorphous or crystalline state. Full article