Search Results (1,674)

Residual stresses and internal strains in 3D printing can lead to issues such as cracking, warping, and delamination—challenges that are amplified when using functional composite materials like magnetic PLA filaments. This study investigates the thermo-mechanical strain evolution during fused filament fabrication (FFF) of magnetite-filled PLA using an integrated methodology combining strain gauge sensors, high-resolution infrared thermal imaging, and synchrotron X-ray microtomography. Printing parameters, including nozzle temperature (190–220 °C), build platform temperature (30–100 °C), printing speed (30–60 mm/s), and cooling strategy (fan on/off) were systematically varied to evaluate their influence. Results reveal steep thermal gradients along the build direction (up to −1 °C/µm), residual strain magnitudes reaching 0.1 µε, and enhanced viscoelastic creep at elevated platform temperatures. The addition of magnetic particles modifies heat distribution and strain evolution, leading to strong sensitivity to process conditions. These findings provide valuable insight into the complex thermo-mechanical interactions governing the structural integrity of magnetically functionalized PLA composites in additive manufacturing. Full article

The main objective of this study was to investigate boiling heat transfer during refrigerant flow in a mini-channel heat sink. The test section consisted of multiple parallel mini-channels, each with a depth of 1 mm. The working fluid was heated by a thin layer of Haynes-230 alloy with a thickness of 0.1 mm. The outer surface temperature of the heater was measured using infrared thermography, while other thermal and flow-based parameters were recorded via a dedicated data acquisition system. The mini-channel heat sink was tested in seven different spatial orientations, with inclination angles relative to the horizontal plane of 45°, 60°, 75°, 90°, 105°, 120°, and 135°. The analysis focused on the early stage of the experiment, corresponding to the forced convection, boiling incipience, and subcooled boiling region. A time-dependent, two-dimensional model of heat transfer during flow boiling of a refrigerant in asymmetrically heated mini-channels was developed. The temperatures of both the heating foil and the working fluid (Fluorinert FC-770) were described using appropriate forms of the Fourier–Kirchhoff equation, subject to relevant boundary conditions. Two sets of time-dependent Trefftz functions were employed to solve the governing equations: one set corresponding to the two-dimensional Fourier equation and another, newly derived, for the energy equation in the fluid. The results include thermographic images of the heated surface, temperature distributions, fluid temperatures, local heat-transfer coefficients, and boiling curves. A comparison of the heat-transfer coefficients obtained using the Trefftz-based approach and those calculated using Fourier’s law demonstrated satisfactory agreement. Full article

In response to problems such as large target scale variations, strong background noise, and blurred features leading by low contrast in infrared target detection in near space environments, this paper proposes an efficient detection model, YOLO-MARS, which is based on YOLOv8. The model introduces a Space-to-Depth (SPD) convolution module into the backbone section, which retains the detailed features of smaller targets by downsampling operations without information loss, alleviating the loss of the target feature caused by traditional downsampling. The Grouped Multi-Head Self-Attention (GMHSA) module is added after the backbone’s SPPF module to improve cross-scale global modeling capabilities for target area feature responses while suppressing complex thermal noise background interference. In addition, a Light Adaptive Spatial Feature Fusion (LASFF) detector head is designed to mitigate the scale sensitivity issue of infrared targets (especially smaller targets) in the feature pyramid. It uses a shared weighting mechanism to achieve adaptive fusion of multi-scale features, reducing computational complexity while improving target localization and classification accuracy. To address the extreme scarcity of near space data, we integrated 284 near space images with the HIT-UAV dataset through physical equivalence analysis (atmospheric transmittance, contrast, and signal-to-noise ratio) to construct the NS-HIT dataset. The experimental results show that ${mAP}_{@0.5}$ increases by 5.4% and the number of parameters only increase 10% using YOLO-MARS compared to YOLOv8. YOLO-MARS improves the accuracy of detection significantly while considering the requirements of model complexity, which provides an efficient and reliable solution for applications in near space infrared target detection. Full article

This study aimed to develop a novel biomaterial for neural tissue regeneration by combining chitosan (CS), a natural polymer, with graphene oxide (GO) at concentrations of 3%, 6%, and 9%. The homogeneity, conductivity, three-dimensional characteristics, and ability to support cell viability of the composite materials were systematically evaluated. Fourier-Transform Infrared (FTIR) spectroscopy confirmed the successful incorporation of GO into the CS matrix, while UV-Vis and photoluminescence (PL) spectrometry revealed modifications in the optical properties with increasing GO content. Thermogravimetric analysis (TG-DSC) demonstrated improved thermal stability of the composites, and swelling tests indicated enhanced water absorption capacity. Although some agglomerates were observed, the homogeneity was reasonable at both macroscopic and microscopic level (optical visualization–FTIR and electron microscopy). The composite films exhibited promising physical and electrochemical properties, highlighting their potential for neural tissue engineering applications. Their biological activity was assessed by culturing neuronal cells on the CS-GO scaffolds. Results from MTT, LDH, and LIVE/DEAD assays demonstrated excellent cell viability, moderate-to-good cell attachment, and the promotion of intercellular network formation. Among the tested formulations, the CS-GO 6% scaffold showed the most favorable biological response, with a significant increase in SH-SY5Y cell viability after 7 days (p < 0.05) compared to the CS control. LIVE/DEAD imaging confirmed enhanced cell attachment and elongated morphology, while the LDH assay indicated minimal cytotoxicity. Notably, a critical threshold was identified between 6% and 9% GO, where conductivity increased by approximately 52-fold. Future studies should focus on optimizing the composite parameters, loading them with specific biologically active agents and thus targeting specific neuronal applications. Full article

This study evaluates the capabilities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion remote sensing sensors for mapping ophiolitic sequences and identifying manganese mineralization in the Bela Ophiolite region, located along the axial fold–thrust belt northwest of Karachi, Pakistan. The study area comprises tholeiitic basalts, gabbros, mafic and ultramafic rocks, and sedimentary formations where manganese occurrences are associated with jasperitic chert and shale. To delineate lithological units and Mn mineralization, advanced image processing techniques were applied, including band ratio (BR), Principal Component Analysis (PCA), and Spectral Angle Mapper (SAM) on visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Using these methods, gabbros, basalts, and mafic-ultramafic rocks were effectively mapped, and previously unrecognized basaltic outcrops and gabbroic outcrops were also discovered. The ENVI Spectral Hourglass Wizard was used to analyze the hyperspectral data, integrating the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and N-Dimensional Visualizer to extract the spectra of end-members associated with Mn-bearing host rocks. In addition, the Hyperspectral Material Identification (HMI) tool was tested to recognize Mn minerals. The remote sensing results were validated by petrographic analysis and ground-truth data, confirming the effectiveness of these techniques in ophiolite mapping and mineral exploration. This study shows that ASTER band combinations (3-6-7, 3-7-9) and band ratios (1/4, 4/9, 9/1 and 3/4, 4/9, 9/1) provide optimal results for lithological discrimination. The results show that remote sensing-based image processing is a powerful tool for mapping ophiolites on a regional scale and can help geologists identify potential mineralization zones in ophiolitic sequences. Full article

Background: Timely and accurate identification of wound infections is essential for effective management, yet remains clinically challenging. This study evaluated the utility of a near-infrared autofluorescence imaging system (Fluobeam^®, Fluoptics, Grenoble, France) and a thermal imaging system (FLIR^®, Teledyne LLC, Thousand Oaks, CA, USA) for detecting bacterial and fungal infections in chronic wounds. Fluobeam^® enables real-time visualization of microbial autofluorescence without exogenous contrast agents, whereas FLIR^® detects localized thermal changes associated with infection-related inflammation. Methods: This retrospective clinical study included 33 patients with suspected wound infections. All patients underwent autofluorescence imaging using Fluobeam^® and concurrent thermal imaging with FLIR^®. Imaging findings were compared with microbiological culture results, clinical signs of infection, and semi-quantitative microbial burdens. Results: Fluobeam^® achieved a sensitivity of 78.3% and specificity of 80.0% in detecting culture-positive infections. Fluorescence signal intensity correlated strongly with microbial burden (r = 0.76, p < 0.01) and clinical indicators, such as exudate, swelling, and malodor. Pathogens with high metabolic fluorescence, including Pseudomonas aeruginosa and Candida spp., were consistently identified. Representative cases demonstrate the utility of fluorescence imaging in guiding targeted debridement and enhancing intraoperative decision-making. Conclusions: Near-infrared autofluorescence imaging with Fluobeam^® and thermal imaging with FLIR^® offer complementary, noninvasive diagnostic insights into microbial burden and host inflammatory response. The combined use of these modalities may improve infection detection, support clinical decision-making, and enhance wound care outcomes. Full article

This work presents the synthesis and application of magnetic Fe₃O₄ nanoparticles supported on baobab seed-derived biochar (Fe₃O₄/BSB) for removing Congo red (CR) dye from aqueous solutions through an oxidative process. The biochar support offered a porous structure with a surface area of 85.6 m²/g, facilitating uniform dispersion of Fe₃O₄ nanoparticles and efficient oxidative activity. Fourier-transform infrared (FT–IR) spectroscopy analysis confirmed surface fictionalization after Fe₃O₄ incorporation, while scanning electron microscopy (SEM) images revealed a rough, porous morphology with well-dispersed nanoparticles. Thermogravimetric analysis (TGA) demonstrated enhanced thermal stability, with Fe₃O₄/BSB retaining ~40% of its mass at 600 °C compared to ~15–20% for raw baobab seeds. Batch experiments indicated that operational factors such as pH, nanoparticles dosage, and initial dye concentration significantly affected removal efficiency. Optimal CR removal (94.2%) was achieved at pH 4, attributed to stronger electrostatic interactions, whereas efficiency declined from 94.1% to 82.8% as the initial dye concentration increased from 10 to 80 mg/L. Kinetic studies showed that the pseudo-second-order model accurately described the oxidative degradation process. Reusability tests confirmed good stability, with removal efficiency decreasing only from 92.6% to 80.7% after four consecutive cycles. Overall, Fe₃O₄/BSB proves to be a thermally stable, magnetically recoverable, and sustainable catalyst system for treating dye-contaminated wastewater. Full article

The design of cycloidal reducers requires a detailed knowledge of the intensity and character of load, as well as the maximum von Mises stresses on critical components. In the available literature, the load distribution and the stress–strain state of the cycloidal reducer elements are typically determined based on factors such as cycloidal disc tooth profile modifications, contact deformations, and internal clearances, whereas the influence of thermal stresses is most often neglected. To address this research gap, an innovative numerical–analytical methodology has been developed, which, for the first time, enables the prediction of the distribution of temperature fields and the quantification of the influence of temperature on the contact forces and the stress–strain state of key elements of the cycloidal reducer. Furthermore, the proposed methodology can be adapted for application within a broader context of mechanical engineering. From a practical perspective, it is expected to be beneficial to companies engaged in the design of power transmission gearboxes, as valuable practical guidelines for engineering applications are provided. This study also provides new insights into the dominant sources of heat generation and offers a clearer understanding of how thermal energy is transferred from internal heat sources to the outer surface of the housing. Full article

Microwave-assisted rock fragmentation has been considered as one of the most promising technologies in rock excavation, but due to the fact that excavation is usually carried out in water-rich environments, understanding the dynamic fracture properties of rocks with different water contents after microwave irradiation is thus desirable. This study employed an enhanced split Hopkinson pressure bar (SHPB) system to perform dynamic fracture tests on pre-cracked semi-circular bending (SCB) specimens. It systematically explores the changes in the mechanical properties of sandstone under both dry and saturated conditions after exposure to 700 W of microwave radiation for 10 min. Infrared thermal imaging was utilized to capture the temperature distribution across the specimens, while digital image correlation (DIC) and high-speed photography were used to simultaneously record the crack propagation process. Based on the principle of energy conservation, the analysis of energy dissipation during fracture was performed, and the micro-damage evolution mechanism of the material was revealed through scanning electron microscopy (SEM). The results demonstrated that saturated sandstone exhibited a more rapid heating response and significantly lower dynamic fracture toughness and fracture energy compared to dry samples after microwave irradiation. These findings indicate that water saturation amplifies the weakening effect induced by microwaves, making the rock more susceptible to low-stress fractures. The underlying damage mechanisms of microwave radiation on water-bearing sandstone were interpreted with the theory of pore water pressure and structural thermal stresses. Full article

Uncooled microbolometers play a pivotal role in infrared detection owing to their compactness, low power consumption, and cost-effectiveness. This review comprehensively summarizes recent progress in thermistor materials and focal plane arrays (FPAs), highlighting improvements in sensitivity and integration. Vanadium oxide (VO_x) remains predominant, with Al-doped films via atomic layer deposition (ALD) achieving a temperature coefficient of resistance (TCR) of −4.2%/K and significant 1/f noise reduction when combined with single-walled carbon nanotubes (SWCNTs). Silicon-based materials, such as phosphorus-doped hydrogenated amorphous silicon (α-Si:H), exhibit a TCR exceeding −5%/K, while titanium oxide (TiO_x) attains TCR values up to −7.2%/K through ALD and annealing. Emerging materials including GeSn alloys and semiconducting SWCNT networks show promise, with SWCNTs achieving a TCR of −6.5%/K and noise equivalent power (NEP) as low as 1.2 mW/√Hz. Advances in FPA technology feature pixel pitches reduced to 6 μm enabled by vertical nanotube thermal isolation, alongside the 3D heterogeneous integration of single-crystalline Si-based materials with readout circuits, yielding improved fill factors and responsivity. State-of-the-art VO_x-based FPAs demonstrate noise equivalent temperature differences (NETD) below 30 mK and specific detectivity (D*) near 2 × 10¹⁰ cm⋅Hz ^1/2/W. Future advancements will leverage materials-driven innovation (e.g., GeSn/SWCNT composites) and process optimization (e.g., plasma-enhanced ALD) to enable ultra-high-resolution imaging in both civil and military applications. This review underscores the central role of material innovation and system optimization in propelling microbolometer technology toward ultra-high resolution, high sensitivity, high reliability, and broad applicability. Full article

Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages a novel synergistic integration of the Convolutional Block Attention Module (CBAM) with YOLOv4. The model employs MobileNet-v3 as the backbone to reduce parameter count, applies depthwise separable convolution to decrease computational complexity, and incorporates the CBAM module to enhance the extraction of critical optical features under complex backgrounds. Furthermore, a feature-level fusion strategy is adopted to integrate visible and infrared image information effectively. Validation on public datasets demonstrates that the proposed model achieves an 18.05 frames per second increase in detection speed over the baseline, a 1.61% improvement in mean average precision (mAP), and a 2 MB reduction in model size, substantially improving both detection accuracy and efficiency through this optimized integration in anomaly inspection of electrical equipment. Validation on a representative edge device, the NVIDIA Jetson Nano, confirms the model’s practical applicability. After INT8 quantization, the model achieves a real-time inference speed of 40.8 FPS with a high mAP of 80.91%, while consuming only 5.2 W of power. Compared to the standard YOLOv4, our model demonstrates a significant improvement in both processing efficiency and detection accuracy, offering a uniquely balanced and deployable solution for mobile inspection platforms. Full article

Background/Objectives: This study aimed to examine the dynamic thermal variations that occur in the posterior body regions of pregnant women by employing thermal imaging techniques. Methods: The study involved the participation of 34 women in various stages of pregnancy. The skin temperature (Tsk) distribution in specific body areas, including the spinal region and lower limbs, was analyzed under standard conditions. Results: The most considerable increase in body temperature (Tsk) recorded in female volunteers was achieved during the second trimester of pregnancy in physiologically stressed areas, such as the upper back (0.4 °C), lower back (0.77 °C), thighs (0.94 °C) and calves (0.32 °C). Contrastingly, a decrease in Tsk of noteworthy magnitude was observed in all body regions during the third trimester, with an average decrease of 1.7 °C. The lower back’s most substantial decrease was observed (1.95 °C). Furthermore, a disparity was observed in the Tsk distribution of the calves, with the highest ∆T_mean value recorded at approximately 0.5 °C, and the thighs exhibiting a ∆T_mean value of 0.25 °C. Conclusions: Preliminary studies have demonstrated the potential of thermal imaging as a reliable and safe method to support prenatal diagnosis. Its application can facilitate the early detection of health complications, including inflammatory states or posture and circulatory system disorders, thereby enhancing the standard of prenatal care. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article

Durian is a major tropical crop in Southeast Asia, but its yield and quality are severely impacted by a range of pests and diseases. Manual inspection remains the dominant detection method but suffers from high labor intensity, low accuracy, and difficulty in scaling. To address these challenges, this paper proposes EN-YOLO, a novel enhanced YOLO-based deep learning model that integrates the EfficientNet backbone and multimodal attention mechanisms for precise detection of durian pests and diseases. The model removes redundant feature layers and introduces a large-span residual edge to preserve key spatial information. Furthermore, a multimodal input strategy—incorporating RGB, near-infrared and thermal imaging—is used to enhance robustness under variable lighting and occlusion. Experimental results on real orchard datasets demonstrate that EN-YOLO outperforms YOLOv8 (You Only Look Once version 8), YOLOv5-EB (You Only Look Once version 5—Efficient Backbone), and Fieldsentinel-YOLO in detection accuracy, generalization, and small-object recognition. It achieves a 95.3% counting accuracy and shows superior performance in ablation and cross-scene tests. The proposed system also supports real-time drone deployment and integrates an expert knowledge base for intelligent decision support. This work provides an efficient, interpretable, and scalable solution for automated pest and disease management in smart agriculture. Full article

This study applied geostatistics to analyze thermal images of the back surface of pigs’ ears (TSO) to understand how spatial temperature variability influences thermoregulation. The objective was to assess TSO variability in pigs housed under two climate control systems, namely, pens without cooling (BTEST) and with an evaporative cooling system (BECS), using infrared thermography and geostatistical tools. A total of 432 thermal images were obtained from 18 finishing pigs at 08:00, 12:00, and 16:00. Semivariograms were modeled and validated, and kriging maps were developed to visualize the spatial temperature distribution. The pens were thermally characterized using reclassified Temperature and Humidity Index (THI) values. The Gaussian model (R² > 0.9) showed strong spatial dependence in temperature data. Pigs in the BECS system exhibited lower average TSO temperatures (28.2–38.6 °C) than those in the BTEST system, where temperatures exceeded 34 °C, highlighting the role of cooling in mitigating heat stress. In both systems, higher THI values were associated with increased TSO, indicating thermal discomfort under elevated environmental temperatures. Geostatistical analysis effectively revealed spatial patterns and variability in surface temperatures, providing key insights into how environmental conditions impact pigs’ thermal responses. Full article